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NIST Special Publication 970

Measuring the Performance and Intelligence

of Systems:

Proceedings of the 2000 PerMIS Workshop

August 14-16, 2000

Edited by

I

A.M. Meystel

E.R. Messina
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NIST Special Publication 970

Measuring the Performance and

Intelligence of Systems:

Proceedings of the 2000 PerMIS

Workshop

August 14-16, 2000

Edited by:

A. M. Meystel

E. R. Messina

Intelligent Systems Division

Manufacturing Engineering Laboratory

National Institute of Standards and Technology

Gaithersburg, MD 20899-8230

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Preface

This volume contains the materials of the Performance Metrics for Intelligent Systems Workshop, held at the

National Institute of Standards and Technology on August 14"^ through the 16**^, 2000. The central theme of the

meeting was Measuring the Performance and Intelligence of Intelligent Systems. The functioning of intelligent

systems is driven by evaluation of the "success" of assigning and achieving the goals. Both the adequacy of

assigning and the degree of achieving belong to the gray area of measuring performance. How well the system is

designed for achieving its goals, and how effective and efficient the efforts are that its control system produces —

these two issues belong to the domain of evaluating the degree of the intelligence of a system. Neither the system's

performance, nor its intelligence can currently be adequately measured by evaluation techniques other than those

generally used in control systems. Engineers and researchers are not satisfied with these approaches as they are

applied to intelligent systems.

The Workshop was the first formal gathering of the professionals actively working and/or interested in this area. The

problem is a multidisciplinary one in its essence. Therefore it should integrate both engineers and scientists actively

working in diverse areas such as economics, artificial intelligence, psychology, linguistics, biology, neurology, and

others. Unifying them for solving the problems of measuring performance and intelligence is a formidable problem:

their interaction is the only avenue that can bring to fruition this area of the science of intelligent systems.

This volume starts with the White Paper (Part 1) that initiated the process of communication among the

multidisciplinary group of engineers and researchers. The papers, submitted and accepted for presentation are

collected in Part II. Not all of them could be presented at the meeting because of the difficulties of traveling from all

over the world. They are grouped corresponding to the sub-area of the problem. Notes made by the participants of

the general Panel Discussions are collected in Part III. The decisions of the Advisory Board are presented in Part IV. Some results of the pre-workshop discussion are put together in the Appendix. We hope that this volume will help to

continue the process of consolidating the efforts and precipitating the results of research and design in this

innovative area of science. We will be grateful for the comments sent to us concerning the problem of measuring the

performance and intelligence of intelligent systems.

We wish to acknowledge the support of the Defense Advanced Research Projects Agency (DARPA) Mobile

Autonomous Robots Software Program. We are also very thankful to our partners and co-sponsors. The workshop

was co-sponsored by the National Aeronautics and Space Administration, the Institute of Electrical and Electronic

Engineers (IEEE), and DARPA, and organized in cooperation with the IEEE Neural Net Council. Our thanks go out

to our Plenary Speakers: H. Szu, G. Saridis, J. Albus, S. Grossberg, and W. Freeman. We are grateful to all the

participants and the very enthusiastic members of the Advisory Panel for their many and significant contributions.

A great debt is owed to Debbie Russell for helping produce the proceedings and Aveline Allen for logistics support.

Editors:

A. Meystel and E. Messina October 23, 2000

111

TABLE OF CONTENTS

PARTI

MEASURING PERFORMANCE AND INTELLIGENCE OF SYSTEMS

WITH AUTONOMY: White Paper 1

1 . Introduction 3

2. Intelligent Systems (or Agents) 4

3. The Problem of Measuring both Performance and Intelligence 7

4. A Case Study: Artificial CHmate System 10

5. The System Specifications and Vector of Performance (VP) 1

1

6. Intelligence, Goals Hierarchy, and Arbitration 13

7. What Constitutes the Vector of Intelligence (VI)? 16

8. The Tools of Computational Intelligence 19

9. The Architectures of Intelligence 21

10. Supervisory Control and Data Acquisition 23

11. Tests of Machine Intelligence Contemplated in the Past 23

12. Who Wins the Competition: the Real Intelligence or the Impostor? 25

13. Measuring the Intelligence Contemplated for the Future 26

14. Simulated Functioning and Scaled Hardware Testing of

Intelligent Systems 28

15. The Intelligence of Sensing and Sensory Processing 30

16. Questions To Be Answered 31

17. Glossary 32

18. Appendix: How is Testing of Intelligence Performed on Humans 33

PART II RESEARCH PAPERS 35

1. THE PHENOMENON OF INTELLIGENCE 37

1 . 1 Using the Metaphor of Intelligence

A. Wild, Motorola, USA 39

1.2. Technologies for Engineering Autonomy and Intelligence

T. Samad, Honeywell, USA 43

1.3 Theoretical Constructs for Measurement Performance and Intelligence in

Intelligent Systems

L. Reeker, NIST, USA 49

1 .4 Intelligence with Attitude

W. C. Stirling, R. L. Frost, Brigham Young University, UT, USA 60

V
1.5 What is the Value of Intelligence?

T. Whalen, Georgia State University, USA 68

1 .6 On the Computational Measurement of Intelligence Factors

J. Hemandez-Orallo, University of Val ncia, Spain 72

1 .7 On Definition of Task Oriented System Intelligence

M. Cotsaftis, LTME/ECE, France 80

1 .8 Minds, MIPS, and Structural Feedback

R. Sanz, I. Lopez, University of Madrid, Spain 86

1.9 Fast Frugal and Accurate - the Mark of Intelligence: Towards Model-Based

Design, Implementation, and Evaluation of Real-Time Systems

B. P. Zeigler, H. S. Sarjoughian, University of Arizona, USA 90

1.10 The Intelligence of an Entity

R.G. Garner, S.B. Henderson, Technology Applied Systems Corp., USA 95

2. METRICS FOR INTELLIGENT SYSTEMS 99

2.1 Performance Metrics for Intelligent Systems

J.M. Evans, E.R. Messina, NIST, USA 101

2.2 The Search for Metrics of Intelligence — A Critical View

L.A. Zadeh, University of California at Berkeley, USA 105

2.3 Measure of System Intelligence: An Engineering Perspective

S. Lee, SCS, SAIT, Korea, W.-C. Bang, and Z.Z. Bien, KAIST, Korea 1 12

2.4 Formal Specification of Performance Metrics for Intelligent Systems

Y. Zhang, Xerox, USA, A.K. Mackworth, University of BC, Canada 118

2.5 Metrics for Intelligence: the Perspective from Software Agents

PRELIMINARY NOTES

L. Pouchard, Oak Ridge National Lab, USA 123

2.6 Minimal Representation Size Metrics for Intelligent Robotic Systems

A. Sanderson, NSF, USA 127

2.7 Metrics for System Autonomy. Part I: Metrics Definition

A. Yavnai, RAFAEL, Israel 135

2.8 In Defense of the Additive Form for Evaluating the Multidimensional Vector

L.M. Polyakov, Globe Institute of Technology, USA 142

vi
2.9 A Method for Evaluating the IQ of Intelligent Systems

R. Finkelstein, Robotic Technology, Inc., USA 145

2.10 Machine Intelligence Ranking

P.P. Wang, Duke University, USA 160

2.11 Survivability and Competence as Measures of Intelligent Systems

R. Simmons, Carnegie-Mellon University, USA 162

2.12 Two measures for measuring the intelligence' of human-interactive

robots in contests and in the real world: expressiveness and perceptiveness

I. R. Nourbakhsh, Carnegie-Mellon University, USA 164

3. INTELLIGENCE OF DISTRIBUTED AGENTS 165

3.1 Goodness of Fit Measures for Intelligent Control of Interacting Machines

S. Phoha, D. Friedlander, The Pennsylvania State University, USA 167

3.2 Distributed Internet-Based Multi-Agent Intelligent Infrastructure System

X. Qin, A. E. Aktan, K. Grimmelsman, F.N. Catbas, R. Barrish, M. Pervizpour,

E. Kulcu, Drexel University, USA 172

3.3 Stigmergy — An Intelligence Metric for Emergent Distributed Behaviors

R.R. Brooks, The Pennsylvania State University, USA 186

3.4 Performance Self-Assessment by and for Regulation in Autonomous Agents

E.S. Tzafestas, National Technical University, Greece 193

3.5 On Measuring Intelligence in Multi-Agent Systems

S.P. Tolety, GTE Labs, USA and G. Uma 201

3.6 On the Development of Metrics for Multi-Robot Teams within the

ALLIANCE Architecture

L.E. Parker, Oak Ridge National Lab, USA 205

3.7 Shared Autonomy and Teaming: A Preliminary Report

H. Hexmoor, University at Buffalo, USA and H. Duchscherer, University of

ND, USA 211

3.8 Real Time Distributed Expert System for Automated Monitoring of Key

Monitors in Hubble Space Telescope

R. Fakory, M. Jahangiri, NASA, USA 218

3.9 Evaluating Performance of Distributed Intelligent Control Systems

W. J. Davis, University of Illinois, USA 225

vii
3.10 Hypothesis Testing for Complex Agents

J. Bryson, MIT, W. Lowe, Tufts University, and L.A. Stein, MIT, USA 233

4. MOBILITY AND BENCHMARKING 241

4. 1 Features of Intelligence Required by Unmanned Ground Vehicles

J.S. Albus, NIST, USA 243

4.2 A Standard Test Course for Urban Search and Rescue Robots

A.Jacoff, E. Messina, J. Evans, NIST, USA 253

4.3 Assessment of the NIST Standard Test Bed for Urban Search and Rescue

R. Murphy, J. Casper, M. Micire, J. Hyams, Univ. of South Florida, USA .... 260

4.4 Performance Metrics in the AAAI Mobile Robot Competitions

A.C. Schultz, Naval Research Laboratory, USA 267

4.5 Performance Evaluation of Robotic Systems: A Proposal for a Benchmark

Problem

S.K. Agrawal, A. M. Ferreira, S. Pledgie, University of Delaware, USA 271

4.6 A Multi-Sensor Cooperative Approach for the Mobile Robot Localization

Problem

A. Clerentin, L. DeLahoche, E. Brassart, CREA, France 273

4.7 Measuring Mobile Robots Performance: Approaches and Pitfalls

G.S. Sukhatme, University of Southern CA, USA 281

4.8 Search Graph Formation for Minimizing the Complexity of Planning

A. Lacaze, ECE, University of MD, USA, S. Balakirsky, NIST, USA 289

4.9 Metrics for Embedded Collaborative Intelligent Systems

M.E. Cleary, M. Abramson, M. B. Adams, S. KoHtz. Draper Lab, USA 295

4.10 Evolution of Mobile Agents

T. K. Shih, Tamkang University, Taiwan, R. O. C 302

5. MODELING THE BIOLOGICAL PROTOTYPES 309

5.1 Some Constraints on Intelligent Systems: Autonomous Computation in a Changing World

S. Grossberg, Boston University, USA 311

viii
5.2 The Neurodynamics of Intentionality in Animal Brains May Provide a Basis

for Constructing Devices that are Capable of Intelligent Behavior

W.J. Freeman, University of California at Berkeley, USA 314

5.3 Measuring Intelligence: a Neuromorphic Perspective

M.A. Jabri, University of Sydney, Australia 323

5.4 Grading Intelligence in Machines: Lessons from Animal Intelligence

S. Kak, Louisiana State University, USA 331

5.5 Biometric Techniques: The Fundamentals of Evaluation

T.A. Chmielewski, P.R. Kalata, Drexel University, USA 335

5.6 Measuring the Quality of Visual Learning

G. Bianco, University of Vernona, Italy 343

5.7 Autonomous Mental Development and Performance Metrics for Intelligent

Systems

J. Weng, Michigan State University, USA 349

6. LEARNING: TOWARD SELF-EVOLVING ARCHITECTURES 359

6.1 Evolution of Intelligent Systems Architectures: What Should Be Measured?

A. Meystel, Drexel University, USA 361

6.2 Machine IQ with Stable Cybernetic Learning With and Without Teacher

H. Szu, Digital Media Lab, Wash DC, USA 383

6.3 Properties of Learning Knowledge Based Controllers

E. Grant, G. Lee, NC State University, USA 392

6.4 Assessing the Run-Time Performance of Artificial Intelligence Architectures

S.A. Wallace, J.E. Laird, K.J. Coulter, University of Michigan, USA 399

6.5 A Metric for Monitoring and Retaining Flight Software Performance

C. Peterson, Computer Sciences Corp., USA 407

6.6 Flexible Robotic Assembly

D. P. Gravel, W. S. Newman, Ford, Case Western Reserve Univ., USA 412

ix
7. MULTIRESOLUTIONAL PHENOMENA IN INTELLIGENT SYSTEMS.. 419

7.1 Heterogeneous Computing

A. Wild, Motorola, USA 421

7.2 A Hierarchical Framework for Constructing Intelligent Systems Metrics

R.R. Yager, lona College, USA 423

7.3 Exploratory Analysis Enabled by Multiresolution, Multiperspective

Modeling

P.K. Davis, RAND CA, USA 431

7.4 Definition and Measurement of Machine Intelligence

G. Saridis, RPI, USA 441

7.5 Domain Independent Measures of Intelligent Control

D. Friedlander, S. Phoha, A. Ray, The Pennsylvania State University, USA.. 453

7.6 Choosing Knowledge Granularity for Efficient Functioning of an Intelligent

Agent

Y. Ye, IBM T. J. Watson Research Center, USA

J.K. Tsotsos, York University, Canada 459

8. EXPLORATORY ISSUES 465

8. 1 FIPER: An Intelligent System for the Optimal Design of Highly Engineered

Products

M.W. Bailey, GEAE, USA, and W.H. VerDuin, OAI, USA 467

8.2 Towards an Objective Comparison of Stochastic Optimization Approaches

J.C. Spall, S.D. Hill, and D.R. Stark, The John Hopkins University, USA 478

8.3 Some Measurable Characteristics of Intelligent Computing Systems

C. Landauer, K. Bellman, The Aerospace Corporation, USA 484

8.4 Generalizing Natural Language Representations for Measuring the

Intelligence of Systems

A. Meystel, Drexel University, USA 492

8.5 Toward Measures of Intelligence Based On Semiotic Control

C. Joslyn, Los Alamos National Laboratory, USA 499

8.6 Selected Comments on Defining and Measuring of Machine Intelligence

P.K. Davis, RAND, USA 505

X
8.7 Hierarchic Social Entropy and Behavioral Difference: New Measures of

Robot Group Diversity

T. Balch, Carnegie Mellon University, USA 511

8.8 The Development Approach to Evaluating Artificial Intelligence

A Proposal

A. Treister-Goren, J. Hutchens, AI NV, USA 519

8.9 General Scientific Premises of Measuring Complex Phenomena

H. M. Hubey, Montclair State University, USA 524

8. 10 Evaluation of System Intelligence via Pictorial Data Visualization

V. Grishin, View Trends, Ltd, USA, and A. Meystel, Drexel Univ., USA 533

PART III SUMMARY OF PLENARY DISCUSSIONS 541

PART IV

DECISIONS OF ADVISORY BOARD MEETING 557

APPENDICES 561

Appendix A: The Preliminary Discussions 563

Appendix B: Performance Metrics for Intelligent Systems. Workshop Schedule 643

Appendix C: Author Index 655

xi

PARTI

MEASURING PERFORMANCE AND

INTELLIGENCE

OF SYSTEMS WITH AUTONOMY

The White Paper

1

Measuring Performance and Intelligence

of Systems with Autonomy:

Metrics for Intelligence of Constructed Systems'

A White Paper Explaining Goals of the Workshop

1. Introduction

Thousands of person-years have been devoted to research and development in the various aspects of

artificially intelligent systems. There is no single field of study that contributes to the progress, but rather

several dozens, ranging from control to cognitive sciences. Much progress has been attained. However, there

has been no means of evaluating the progress of the field. How can we assess the current state of the science?

Some systems are beginning to be deployed commercially. How can a commercial buyer evaluate the

advantages and disadvantages of the intelligent candidates and decide which system will perform best for their

application? If constructing a system from existing components, how does one select the one that is most

appropriate within the desired system?

The ability to measure the capabilities of intelligent systems or components is more than an exercise

in satisfying intellectual or philosophical curiosity. Without measurements and subsequent quantitative

evaluation, it is difficult to gauge progress.

It can even be argued that researchers and developers perpetually re-invent the same components to

build their system, unable to reliably find existing components they could reuse. To paraphrase William, Lord

Kelvin: when you can measure something and put some numbers to it, then you know something about it, and

if you can't your understanding of it is of a 'hieager and unsatisfactory kind," although I am not sure that I

would be so adamant about the need for numbers.

It is both in a spirit of scientific enquiry and for pragmatic motivations that we embark on the quest

for metrics for intelligence of constructed systems.

This paper is a result of collective efforts to understand the problem, and the future publication based

on this paper will have multiple authors. The draft was written by A. Meystel. Initial editing was done by

J. Albus, E. Messina, J. Evans, D. Fogel, and W. Hargrove. These are the authors of multiple additions to

the initial draft: G. Bekey, H.-H. Bothe, B. Chandrasekaran, J. Cherniavsky, A. Clerentin, P. Davis, S.

3
2. Intelligent Systems (or Agents)

Intelligent systems (that are also frequently called "agents") can be introduced with different levels of

detail. The simplest possible and the most general model of intelligence is just a string of six consecutively

functioning elements forming a loop of closure: WORLD INTERFACE, SENSORS, PERCEPTION, WORLD

MODEL, BEHAVIOR GENERATOR, and ACTUATOR. The loop of closure consisting of these six modules

has a flow of knowledge circulating within this loop and changing its form within each of the modules. It is

possible to demonstrate that if one introduces the concept of intelligent agent in this simple form, a significant

degree of generality is achieved in talking about a single intelligent system as a part of the overall model of

functioning. Let us try to define this loop with knowledge circulation in it, as a scientific entity. The subsequent

description of an Intelligent Agent is relevant to our needs of analysis and design. This is the list of features

characteristic for an intelligent agent.

Feature 1. Intelligence is the faculty of an agent that allows to deal with knowledge and to achieve the

externally measurable success under a particular goal.

Feature 2. The knowledge of an agent is the collection and organization of information units.

Knowledge is presumed to appear as a result of the learning about the objects of the external world,

interconnections of the objects, and processes of changes produced by the agent within this external world.

These processes are characteristic for all intelligent systems.

Feature 3. The learning process is understood as recording the experiences encountered by an

intelligent system and deriving from these experiences a new set of rules that suggests how the intelligent

system should act under particular circumstances (in a particular situation and under particular goal). Feature

3A. Learning provides for a successful adaptation of agent (intelligent system) to changing environments, e.g.

different algorithms of new rules derivation can be utilized (i.e. algorithms of reinforcement, habituation,

Hebbian association, abstraction, generalization, etc.).

Learning^ invokes special metrics that affect the way of judging the performance and intelligence of

systems with learning. In the machine learning community there is a tendency to look at three metrics: the

ability to generalize, the performance level in the specific task being learned, and the speed of learning. From

the point of view of evaluating intelligence, the ability to generalize seems to be the most important one.

Systems can do rote learning, but without generalization, one cannot apply what has been learned to future

situations. Of course, if two systems were equivalent in their ability to generalize, with the same resulting level

of performance, then the one which could do this faster would be "better."

Feature 4. Experiences are understood and stored as triplets of the information units

"situation->action->new situation" that allow the behavior generation module of the agent to infer what is the

action that is required to improve the situation (evaluation is presumed).

Feature 5. A situation is understood and stored as a complete set of sensor inputs associated with a

particular moment of time in a form that allows for processing. A situation also includes the entire situational

Delaroche, L. Erasmus, D. Filev, L. Fogel, W. Freeman, S. Grossberg, S. Lee, P. Lima, L. Pouchard, A. Schultz,

C. Weisbin, A. Yavnai.

4
analysis, such as the operating goals, parameters, and hypotheses about external conditions, such as enemy

locations.

Feature 6. All artifacts of learning are evaluated for their desirability according to the criteria of

goodness existing in this particular agent.

Feature 7. Action of an agent results in a complete set of agent motion (or behaviors) that are

developed by actuators of the agent and are sensed by the agent as changes in the external world.

Feature 8. The intelligent system (or the agent) is presumed to be equipped with the relevant sets of

sensors and actuators, with the information storage, an inference system and a device for value judgment that

allows for ranking both the experiences and the rules and determining their preference for the goal of the

system.

One can see that no degree of sophistication is discussed in this setting. All processing is explained as

inference, and various versions of inference will entail different levels of sophistication. One of the important

mechanisms of inference is the mechanism of generalization: An agent is capable of inferring how to find an

appropriate group of objects, how to transform it into a single object, and how to derive the rules for the

generalized object from the rules that were known for its components.

So far, the described system looks very cozy and almost trivial in the very beginning of its existence.

However, as the amount of experiences grows, the complexity of computations grows exponentially and the

efficiency of goal-oriented functioning falls. No respectable agent would allow itself to be overburdened by

growing complexity. This is why the operator of generalization is introduced: agents cannot afford the

complexity of computations. This is the main reason for the emergence of mechanisms of generalization: they

create new objects by the virtue of merging similar objects delivered and utilized by the original set of sensors

and produced by actuators^.

These generalized objects form a new world of representation: the one belonging to a lower level of

resolution. As a result, we end up with a multiplicity of interrelated hierarchies of percepts, concepts,

commands and actions. Corresponding multiscale systems of objects form a storage of the World

Representation^. Any functioning actor has this system that provides its functioning.

Feature 9. The goal is the overall assignment to the system that determines the purpose of its

functioning and the preferences that system uses to choose the action, and eventually determines the structure of

its knowledge representation.

Contributed by A. Schultz

^ Generalization and abstraction occur on items resident in memory, in an indefinite amount of time. I

reflect on events from last year, yesterday, and this morning, and may detect a pattern I hadn't noted

before. This may be a higher-level generalization & abstraction than of the immediate kind applied to sensory inputs. ^ We are familiar with the fact that some researchers disagree with the need for World Representation. It could be argued that all architectures are equipped by some form of World Representation, albeit under a different label.

5
Feature 10. In the system with a multiscale knowledge representation the action determined at the

lower level of resolution becomes a goal for the higher level of resolution. Thus, we are used to the situation

that the goal arrives from the exterior of each level of resolution.

Feature X. The unknown feature.

What this feature is follows frO'm answering two questions that emerge immediately as soon as the first

nine features are introduced:

Question X.l Who creates the goal for the lowest level of resolution?

Question X.2 Can the goal be formulated internally (at a level of resolution)

The design of increasingly autonomous intelligent agents will also require an end-to-end approach, in

which all the aspects of perception, cognition, emotion, and action are realized in a single system^. Feedback

cycles of information processing need to be designed from perception through action and then back to

perception again, mediated by feedback through the environment. Such cycles of information processing can

evaluate the effects of system performance on the environment, and modify the system where needed to achieve

better environmental control. It has also become clear that, in addition to these externally mediated cycles of

information processing with the environment, internally mediated feedback is needed to achieve autonomous

system properties. Such internal feedback realizes properties of intentionality and attention that are

characteristic of biological intelligence.

Consciousness^ might be considered as a possible candidate for interpreting the Feature X. This is one

possible view on the contribution of consciousness as a feature (faculty) of intelligence. Only those creatures

that adequately forecast their environment survive, that is, recognize the dangers and opportunities in time for a

suitable reaction. Smce the real world is dynamic and uncertain, having a feature for discovering new ways to

solve new problems should be one of the key features of intelligence.

Consciousness provides a view of the self 'm the context of the immediate environment. As a capability

it did not arise suddenly, but rather, establishes itself at different levels and in different degree. The dog

understands his environment and his place within it with some degree of clarity. We know ourselves and our

environment in more precise terms and can even include unseen elements. I'm conscious of the time of day,

what happened yesterday, what might happen tomorrow, even what's happening in Serbia without having been

there. It is consciousness that allows manipulations of alternative models of the real world as we understand

them. Here is the basis for dealing with an enormous range of issues as they pertain to survival. The

mechanism of consciousness seems to be the "software" of human intelligence.

The primary problem with respect to consciousness is the underlying algorithmic mechanism. This

subject has received a lot of attention in recent years. The real challenge is to build a mechanism that is

conscious, not simply simulates the behavior of a conscious entity. There is no homunculus within us. The

question emerges, how does perception present itself to us as an integrated entity? How are we capable of

understanding our own consciousness?

These observations are taken from the abstract by S. Grossberg

Contributed by L. Fogel
A related problem is concerned with "binding." In what manner are the various modalities (vision,

hearing, and the other senses) combined when we now know that vision itself is compartmentalized with

separate perception centers for color, shape, texture, and so forth. How can all this be done in real time? There

are other intrinsic problems that are yet to be faced. An interesting question is, what will a higher level of

consciousness be like, above and beyond what we now have? What if our species grows into something even

more complex with greater intelligence? What would be the nature of self-awareness and understanding of the

world in which it operates? Could a machine facilitate consciousness through some symbiotic relationship?

There are more questions to be asked than answered. What are the links between survival and consciousness?

Consciousness is essential in an n-player game wherein survival depends upon the induced behavior of other

players and your relationship with them. Consciousness presumes a conscious ability. This too is an intrinsic

aspect of intelligence and we expect that it shall be addressed.

3. The Problem of Measuring both Performance and Intelligence

Both engineered and organized - that is, artificially produced - intelligent systems should demonstrate

qualities similar to those demonstrated by living creatures, and especially by humans: ability to work under a

hierarchy of goals, and subgoals ability to perceive the external world and recognize objects, actions and

situations, ability to reason, make decisions, plan, schedule and evaluate the results of actions and learn from

their experiences. These systems are actually Constructed Systems with Autonomy (CSA); we will call them

Intelligent Systems.

Intelligent Systems of interest have both their body and their mind designed by humans (engineers

and programmers); we have to recognize which part of the intelligence is incorporated in their "body" and

which is a faculty of their "mind" (i.e. its intelligent control system). The structure and the characteristics of

the "body" can relax the requirements of the intelligent control system if the results of past experience of

functioning or anticipated future situations are properly incorporated in the design. Proper distribution of

systems' intelligence between body and mind is a part of engineering design. Different degrees of autonomy

require different degrees of total intelligence, and a different distribution of total intelligence between the

"body" and the "intelligent controller".

Intelligent Control Systems are usually equipped with a system of Perception (Sensory Processing),

Knowledge Representation (where the world model is constructed, frequently in the form of the ontology), and

Behavior Generation (that creates task decomposition, plans and issues commands). As a rule, these systems

are multigranular (multiscale, multiresolutional), and they resolve their problems at various scales

simultaneously.

Multiple existing definitions of intelligence emphasize different facets of this complex phenomenon.

We will follow the definition of intelligence formulated by J. S. Albus in 1991: " intelligence will be defined

as an ability of a system to act appropriately in an uncertain environment, where appropriate action is that
which increases the probability of success, and success is the achievement of behavioral subgoals that support

the system's ultimate goal."^

Intelligent Systems differ in the depth and the breadth of the "appropriateness" of acting they

demonstrate in different situations. Subsequently, they differ in the degree of "success" they are capable of

achieving. The functioning could be made more appropriate and the level of success could be improved if we

understand how to measure their intelligence. Thus, the measure of intelligence can be frequently reduced to

measuring the "success" of functioning as provided by the ability to develop "appropriate" activities of the

constructed intelligent system. The problem is non-trivial as can be seen from the case study below. We

intentionally have chosen an exotic example since most of the readers can construct much more sophisticated

cases related to unmanned autonomous vehicles, cooperating multilink manipulators, space stations, robotcompanions, etc.

The Albus definition of intelligence is based upon understanding of the term success^. The success of

solving a given task depends on the system's faculties, plus on some influences, which might be of stochastic

nature or might not be measurable. One group of faculties can be called "the capacity to solve problems" or

intellect. Intelligence includes intellect and, in addition, a number of other faculties that together help to

facilitate the success. These additional faculties of intelligence include a) sensing abilities, b) skills of sensory

processing and image interpretation, c) the capacity to collect, store and organize knowledge, d) the ability to

use knowledge, i.e. via problem solving and decision making processes; the latter includes developing of the

alternatives of plans for future actions, evaluafing their preferability and choosing one of them, e) the ability to

transform the decision into actions that lead to a success. Thus, intelligence represents a 'potential ability' to

solve a given task in good time. A high intellect might compensate for the lack or deficiencies in other

components of intelligence, and vice versa. Many concepts of measuring intelligence exist. Many were proposed in communications during

preparation of this White Paper. This is what L. Fogel^ suggested:

1) Intelligence is measured in terms of the diversity of purposes that can be achieved under the

range of environments. This diversity is usually reflected in the number of dimensions in the

Space of Intelligence (see Section 6). The greater the diversity of purposes/situational constraints,

the greater the intelligence.

2) Measures of performance must be from the point of view of some social entity. Thus, the results

of measuring the degree of success are very relative. Accomplishing a certain task (or range of

tasks) may be of great value to Mr. A, and of little value to Mr. B. There can be no absolute

metric.

' J. Albus, "Outline for a Theory of Intelligence," IEEE Transactions on Systems, Man, and Cybernetics,

Vol. 21, No. 3, May/June, 1991, pp. 473-509

^ The subsequent consideration of the term success was proposed by H. -H. Bothe

^ From an e-mail message. May 30, 2000

8
3) The worth of performance must include the cost of the performing. In some cases, this is merely

operational cost, in others its R&D, T&E, acquisition and installation, as well as operations.

Rarely this cost may include removal minus salvage value.

One aspect of this integrated mechanism of intelligence as commonly understood is that the agent

who has it is often able to produce behavior that has a certain reasonableness to it"^. That is, if one knew the

goal of the agent, one might agree that the behavior was oriented to achieving the goal. A. Newell identified

this quality of intelligence as a kind of rationality. He then asked what made the agent successful in achieving

the goal. The answer was: the agent had knowledge and had some ways of using the knowledge for the goals.

The "way of using the knowledge" can be interpreted as and is embodied in the agent's architecture.

He then noted that sometimes an agents's knowledge is bound up for use for only certain types of goals. On

the other hand, for some agents, some of the knowledge is available for any goal for which it is potentially

applicable. Chandrasekaran gives an example of a visual system that has knowledge that elements of the

visual scene that have similar velocities probably belong to one object. However, while we "have" this

knowledge in some sense, it is typically not available for us to reason with in our deliberative problem solving.

It is simply hard-wired for use only for certain problems in vision".

On the other hand, we know many things explicitly. And as long as our memory doesn't fail us, we

are often able to use our explicit knowledge for many different goals for which the knowledge is relevant. In

the case of humans, we have a deliberative cognitive architecture that can often retrieve the relevant

knowledge and make it available for the explicit (conscious) problem solving.

A. Newell proposed that an idealized version of an intelligence (in the sense of rationality) would

always use knowledge K if it had it and if it was relevant for a goal. This is purely an architectural

characterization: it doesn't say anything about what kinds of knowledge are useful. If an agent has a certain

goal, if knowledge K is useful for it, and if it doesn't have it, the agent of course won't use the knowledge. But

the agent probably has some other knowledge K', which may be used to generate asubgoal of identifying the

knowledge needed and maybe acquiring it'^. With the appropriate ways of interacting with the world, the

agent would use knowledge K' first, and then acquire the knowledge K, and voila, the goal is achieved.

Focusing on the ability to use knowledge for any relevant goal characterized, for A. Newell, is an

extremely important aspect of intelligence. We would like to notice that one more faculty of intelligence is

involved: namely, focusing attention, which is frequently used by the agent in its search activities.

This discussion of the interpretation of the term intelligence was contributed by B. Chandrasekaran

'

' While this thought is powerful and probably correct, the example is not particularly persuasive. It is hard to say whether this knowledge is utilized by the subject that visualizes the scene. One might assume

that we group the adjacent points together into one object not because they have the same speed but, on

the contrary, we deduce that they have the same speed because they belong to the same object. The

grouping for declaring the fact of "belonging to the same object" might be done by the virtue of spatial

adjacency no matter what the speeds of the points actually are.

This formidable conjecture is based upon an assumption that the agent somehow fc^ows that by

achieving a subgoal, knowledge about how to achieve the main goal will be acquired. Given the current

state of practice, it would be more natural to assume that a problem solving intelligence should be

equipped by a faculty of searching, and in situations where knowledge is lacking, it develops a set of

searching activities.

9
This sense of intelligence goes against a common intuition in which intelligence is associated with

having the knowledge rather than the ability to use the knowledge you have to acquire the knowledge (i. e. to

focus attention and search). Later, we will discover that when we focus our attention and get engaged in

searching, usually we end up with finding groups of similarity and create clusters'^ — objects of the lower

resolution.

Newell's definition deserves our attention because it captures one sense of the term in a way to which

some sort of metric may be attached. Purely reactive machines ~ which map their perceptions directly into

actions, such as the thermostat — are on the low end of the scale. Further up are machines that can map from

perceptions to actions by considering a large but precompiled number of alternative paths that are constructed

by grouping, while groups are found by search and focusing attention.

4. A Case Study: Artificial Climate System

In an Artificial Climate System, it is required to maintain the temperature of the air in the controlled

rooms within some interval of temperature 0° (with some accuracy A0°), and provide the value of humidity

within some interval of h (with some accuracy Ah) for a particular moment of time t. In addition, the Artificial

Climate System should keep some function within some interval F,(0 °, A 0°, h, Ah, t)< AF experimentally

determined to be preferable for a human being. In this case, the goal pursued by the system is not a particular

state 5,(0 °A0° h, Ah. t) but is rather an unknown function F,(0°, A 0 ° h, Ah. t)<AF.

This problem is rather a nontrivial one. It can be compared with a problem of welding control where

the function of the seam quality is very complicated and typically unknown since it depends on many factors,

some of them hard to measure, or even evaluate. Generally, the problem is similar to the problem of optimum

control of all multivariable stochastic controllers with incomplete available information that do not pursue a

particular state but rather being within an interval of some cost-function. The explicit or implicit ability of a

system to generalize might be crucially important for providing a proper functioning of the system and maintain

the proper climate to the full satisfaction of the user.

Even more complicated functioning can be expected if this cost function is unknown, and the system

of Artificial Climate should learn it by observing the behavior of the human user. This would require observing

how many times the human user was turning "on" or "off the ventilator, how many times the user was turning

"on" or "off the cooling unit, the humidifier, and what were the measures of temperature and humidity at these

moments in the room. A simplistic automated system might be confused, but an intelligent system with

elements of learning will pursue a mutually satisfactory schedule of functioning for all interrelated subsystems.

The system will in fact learn the climate related "personalities" of the users and will learn to recognize who

demands what and when. Even more bold generalizations could be expected if the system can correlate the

user's behavior with the readings of temperature and humidity outside (not only inside!).

The goal of this learning process should be reduction of the amount of human intervention — that is,

increasing the autonomy of the system. If the human-user needs to tune the system less frequently, this would

' One of the elements of new knowledge generation.

10
mean that the system works better. An even more interesting situation might happen if there is more than one

user, and different users have different policies of tuning the system up, i.e. multiple users have different

propensities in intervening with the Artificial Climate System. The Artificial Climate System that would

minimize the total number of cases of human intervention would be considered a system for achieving

consensus in a particular multi-player game.

A further development of this system might be required if the owner of this particular hotel wants

actually to reduce the cost of energy required for keeping the customers satisfied. Then, the system can be

designed so that it will learn habits of the customers to keep their average number of complaints below some

particular level, while the energy consumed will be minimized. We can see that all these systems have a pretty

high degree of autonomy: they autonomously assign the schedule of subsystems functioning. On the other hand,

these systems are subserviently autonomous, i. e. they control their own behavior but the goals are totally

determined from the external user.

The solution of this problem might be different for the systems that have a sense of self. A system may

be considered to have a sense of self if it is equipped to take into consideration its own interests or advantage —

and generate goals and success criteria for itself Initially, we consider a set of regular obedient controllers that

are intelligent (to a degree) but do not have any self, yet. The system equipped with a self will try to keep all

sources of assignment satisfied (including customers and the hotel owner) while worrying primarily about

enhancement of its own life span (reducing aging, increasing reliability, and so on). In other words, a further

development of the system presumes its self-evolving and self-improving.

This Artificial Climate System with elements of autonomy can be qualified as an intelligent one. It

definitely should have elements of learning, should have an ability to recognize phenomena of the external

world that are required for its functioning, must use elements of deductive and inductive reasoning, and must

generalize upon the input information and the results of its own functioning. We can see that the "intelligence of

the system" can grow, as the goal of functioning grows in its dimensionality and levels of detail. We can judge

the degree of intelligence by the breadth and depth of the goals that are achieved and the performance measures

that are satisfied. We are not only interested in evaluating the correspondence between the goal and

performance criteria on one side and the degree of intelligence on another side. We are interested in tools that

allow for the growth of intelligence and more adequate satisfaction of the assignment.

5. The System Specifications and Vector of Performance (VP)

One specific property of intelligent systems is lack of knowledge about the future conditions of

functioning. The list of variables is incomplete, the intervals of future parameter changes are uncertain, the

goals to be pursued can be formulated only in general. Lack of clarity in design specifications calls for design

redundancy which amounts to the need for autonomously compensating for uncertain control specifications and

vaguely specified contingencies.

The system requirements identify the characteristics which the Intelligent System (e. g. unmanned

ground vehicle) must possess. The choice of the specific components from the Tools of Intelligence (see the

11
subsection on that topic) mandates which of the following capabilities are included to satisfy the specific system

requirements:

• to recognize objects, actions, situations

• to infer from the recognized elements of the scene

• to search for a required object within a scene

• to remember scenes and experiences

• to interpret situations

• to evaluate objects, actions, situations, and experiences

• to learn new skills from positive and negative experiences

• to generalize upon recorded similarities and acquire new concepts

• to detect an unfamiliar object, label it, and then learn about it

• to communicate with humans and other intelligent systems

• to collaborate with humans and other intelligent systems

• to interpret its own behavior

• to adapt to new environment

• to interpret behavior of other intelligent systems

• to properly generate a solution in an unexpected situation

• to perform task decomposition

• to plan and schedule in time planned activities

• to support all modes of planning/control required.

Other system requirements can be deemed pertinent to the general architectures of intelligent systems.

It seems practical to construct the Vector of Performance (VP) for each of the subsystems in full

correspondence with the subsystem's specifications. We always know quantitatively what the outputs of interest

are. The set of these outputs forms the target vector VPj. Within the space of performance there corresponds to

some particular area: the zone of performance determined by the set of specifications. After testing the real i-th

system or systems we receive a real vector or set of vectors { Vi} that are supposed to be compared with VPj.

The result of this comparison is the result of measuring a concrete V, by determining the degree of its

belonging to the zone of the performance space occupied by V?j. Note that this is not a standard singledimensional conventional measurement when a particular unit of measurement is introduced. Rather, this is

determining the membership function in a class.

The mathematics of comparison does exist. It is not frequently applied to the realistic cases because it

is not frequently requested by the professionals who are responsible for the evaluation and comparison of

complex systems. However, for some particular subsystems the comparison between {V,} and VPj is a common practice. We refer to the area of control systems where many comparison metrics have been

developed. Some additional effort would be required to apply a similar approach for more general and difficult

cases but this effort is within our reach.

In the area of intelligent systems, an additional difficulty is expected linked with the fact that a

concrete system is always a hierarchy of subsystems. For each particular subsystem chosen within a concrete

12
research and/or industrial domain, the comparison between {Vj} and VPt is well understood. However, not

much thought was given yet to the mathematics of integrating Vj and VPx of subsystems into V, and VPj of the

overall system. We are optimistic about development of the appropriate techniques. In many real situations, this

has been done in practice. It would be appropriate to expand the experience from real situations to the general

theory of (hierarchical) vector comparison since real situations affect the architectural issues in a more relevant

manner.

6. Intelligence, Goals Hierarchy, and Arbitration

A device with a very low level of "intelligence," can perform its duties and achieve the goals in an

excellent way within the boundaries of its "obtuseness." Yet, a very intelligent device with the ability to make

powerful generalizations of the available information, capable of performing a sophisticated processing of this

information and generating new concepts often cannot perform the task as well as a simple "obtuse" device, for

example, maintenance of the temperature in the room within a concrete interval. This very intelligent device

starts interfering with the level of humidity, looks for correlation links between recent commands of the human

operator, and doing other things that the user does not need. Thus the user response: what is the merit of

"intelligence" if the job has not been done or has not been performed in a timely manner (i. e. v. t'lin the

specified concrete interval)? Similar things happen with humans when an overeducated person is assigned for a

simplistic job.

Intelligent behavior is characterized by flexible and creative pursuit of endogenously defined goals'"^.

It has emerged in humans through the stages of evolution that are manifested in the brains and behaviors of

other animals. Intentionality is a key concept by which to link brain dynamics to goal-directed behavior. The

archetypal form of intentional behavior is an act of observation through time and space, by which information is

sought for the guidance of future action. Sequences of such acts constitute the key desired property of freeroving, semi-autonomous devices capable of exploring remote environments that are inhospitable for humans.

Intentionality consists of the neurodynamics by which images are created of future states as goals, of command

sequences by which to act in pursuit of goals, of predicted changes in sensory input resulting from intended

actions (reafference) by which to evaluate performance, and modification of the device by itself for learning

from the consequences of its intended actions. Imagination images, i. e. the images of the fuiure states produced

by the planner and/or the predictor, or the results of simulation can be produced in the form in which the SP

system would see if the actions were carried out, or in a symbolic form of topographical map representation (at

the lower resolution), or even in a descriptive form (at the lowest level of resolution).

Intelligent Systems are to be used in cases that are too complicated for using simple controllers;

otherwise simple programmed and/or automated devices should be used. A notion of closed vs. open systems

should be introduced that is relevant to the situations where programmed vs. intelligent devices can be utilized.

Closed systems can be characterized by having a clear assignment of the problem to be solved, and a crisp

From the abstract submitted by W. Freeman

13
ability to be characterized by a complete list of concrete user specifications in the terms of measurable

variables. These are the cases where using an intelligent system is excessive.

On the contrary, in an open system:

• the problem is not totally clear

• its parts are not concretized; decomposition is not obvious

• the variables are not listed in the beginning of design process

• many variables will emerge during the process of functioning

• the methods of their observations and registration are not known a priori

• many rules of action should be learned during the process of functioning.

So far, we can indicate two diametrically opposite strategies exercised by intelligent systems: one

strategy is characterized by a very long-term general goal, say, survival of a system, another by a set of short

term particular goals. The strategy of survival demands that intelligent systems be able to adapt to the

environment and all circumstances. The strategy of "adapting no matter what" determines particular laws of an

intelligent systems's functioning. The other strategy is "following particular goals" no matter what. The latter

strategy frequently leads to the destruction of the system at hand: it might perish while following its goals

persistently. Adaptation is not possible under the second alternative of intelligent systems since adaptation

demands a compromise of the particular short term goals that the system was assigned.

There is an intuitive feeling that the systems with the second strategy are somehow better, or preferable

than the systems that adapt no matter what. However, this intuitive feeling is difficult to rationalize and explain.

Obviously, these goals belong to different levels of granularity (scale, resolution) and they can be reconciled

only by considering the larger scope of the situation. Following the particular goal no matter what may lead to

the destruction of this particular system but will provide for survival of the rest of the team of intelligent

systems (e.g. a squad of unmanned autonomous vehicles; in other examples analogous situation takes place, i.e.

as the problem gets complicated, the solution moves to the domain of multiagent solutions).

Therefore, these two strategies can be compared with respect to some additional criterion that has a

higher priority than "just survival", or "just pursuing the goal." One of such external criteria is that of

"knowledge acquisition." Under this criterion, one should carefully analyze the very intention to survive while

abandoning the goal, or an intention to achieve the goal, even if the perspective of being destroyed is actually an

imminent one. Both intentions might turn out to be secondary issues if the rate of knowledge acquisition is at

stake, and in one case this rate was higher than in another. Indeed, one can adapt to the details of surrounding

environment even without knowing the broader world.

In the meantime, while the system is studying the world and ardently acquires knowledge of it, the

model of the world evolves so much that a simple adaptation is merely impossible'^, and the survival is

achieved for the system that has evolved. Negotiation is a powerful tool that allows for adjusting the intentions

(toward the goal achievement) to the rational evaluation of the losses that might occur if the goals are pursued

persistently and incessantly.

14
The possibility of negotiation and arbitration generates more complex scenarios Assume an agent

"wins" a particular negotiation at a given time. It would also allow for (but not require) that the tie-breaking

arbitration assures that all of the goals are brought to the attention of negotiating parties over time. The

arbitrator might want to make sure that a given agent wins "something," especially after losing out several

times. Or, in goal terms, the arbitrator might be concerned about maintaining a balance "in portfolio terms". If

there were goals associated with efficiency and discovery, the arbitrator might keep track of how cumulative

efficiency and discovery supplement the awards of goals achievement. If, as the result of a number of decisions

over time, efficiency was always winning out, and the locker of discovery items was empty, then the arbitrator

could adjust his tie-breaking rules. This means that autonomous intelligent agents should not always try to be

(locally) efficient, especially if they are equipped with learning.

On the other hand, we may be getting intelligences and goals of our agents mixed up. Suppose that

there is a set of goals (Gi,...Gn). Different agents might have different pure goals, or they might put different

weights on the various goals. Further, they might be better or poorer at pursuing those goals in differing

contexts. That is, they might have different components of intelligence (Ii,l2,..Is) and these would be more or

less important in the different contexts (Ci,...Cq). Indeed, a human may value beauty, order, material things,

family, and learning new things, just to mention a few items.

This human might be very good at aesthetic matters and family matters, but not so good at order and

material things. The agent might be good at trying and learning about new aesthetics-related things, but poor at

doing anything risky. It is typical for humans to have a portfolio of "intelligences" as well as "goals." It would

give some value to all the different goals, and would have some value to each dimension of intelligence.

Another human might be characterized as an explorer, although he would value family and wealth to some

degree—just not as much as new discoveries. Yet a third might be an explorer in search of tidiness (e.g. a

scientist). What do you think, which human will do better? It depends. An unequivocal answer might be

impossible at a single level of resolution because the true result depends on the distribution of the types of

agents and the contexts that the groups of agents find themselves in.

7. What Constitutes the Vector of Intelligence (VI)?

We are still in limbo about what we should measure to evaluate intelligence: the mysterious Vector of

Intelligence (VI), or the system's success as attributable to its intelligence. (The need to construct a VI emerges

in many areas.)

For example, the problem of the appropriate degrees of generalization, granularity, and gradations of

intelligence occurs in ontology development'^. What constitutes the appropriate scope and levels of detail in an

ontology is practically driven by the purpose of the ontology. The ability to dynamically assume one level of

Adaptation is understood as a mere parametric adjustment while the evolutionary changes in the

structure of a system are results of learning.

Contributed by P. Davis

1 7 Submitted by L. Pouchard

15
detail among many possible details is important for an intelligent system. It might depend on the purpose of a

system. In that sense the long term purpose of the system is different from its short term or middle term goals.

Clearly, the long term purpose and the multiple term goals are goals belonging to different levels of resolution

and should be treated in this way. This brings us back to the measures of intelligence through success: is

intelligence to be measured by the ability of a system to succeed in carrying out its goals?

The term "success" is a key word in the Albus definition, because it becomes a source of emerging

gradations in intelligence, the degrees of intelligence depend on the essence of the definition of the word

success. This means that if success is defined as producing a summary of the situation (a generalized

representation of it), the summary can be computed in a very non-intelligent manner especially if one is dealing

with a relatively simple situation. Indeed, in primitive cases, the user might be satisfied by composing a

summary defined as a "list of the objects and relationships among them" i.e. a subset of an entity-relational (ER)

network'^. On the other hand, the summary can be produced intelligently by generalizing the list of objects and

relationships to the required degree of quantitative compression with the required level of the context related

coherence. Thus, success characterizes intelligence if the notion of success is clearly defined.

The need in gradations of intelligence is obvious: we must understand why the probability of success

increases, because somebody is supposed to provide for this increase, and somebody is supposed to pay for it.

This is the primary goal of our effort in developing the metrics for intelligence. The problem is that we do not

yet know the basis for these gradations and are not too active in fighting this ignorance. What are these

gradations, how should they be organized, what are their parameters that should be taken into account? We can

introduce parameters such that each of the parameters affects the process of problem solving and serves to

characterize the faculty of intelligence at the same time.

The following list of 25 items should be considered an example of the set of coordinates for a possible

Vector of Intelligence:

(a) memory temporal depth

(b) number of objects that can be stored (number of information units that can be handled)

(c) number of levels of granularity in the system of representation

(d) the vicinity of associative links taken into account during reasoning of a situation, or

(e) the density of associative links that can be measured by the average number of EntityRelation (ER)-Iinks related to a particular object, or

(f) the vicinity of the object in which the linkages are assigned and stored (associative depth)

(g) the diameter of associations ball (circle)

See the summaries produced by the search-engines on the Web: to have it "quick and dirty" the first sentence, or the first 5 lines of an article is considered to be a summary, why not?

Summarizing an article (in unstructured natural language), if done properly, is a result of generalizing

the natural text description and transforming a narrative from one level of resolution by a narrative from

another level of resolution.

16
The association depth does not necessarily work positively, to the advantage of the system. It can be

detrimental for the system because if the number of associative links is excessively large the speed of problem

solving can be substantially reduced. Thus, a new parameter can be introduced

(h) the ability to assign the optimum depth of associations

(This is one more example of recognition that should be performed, in this case, within the knowledge

representation system).

Functioning of the behavior generation module evokes additional parameters, properties and features:

(i) the horizon of planning at each level of resolution

(j) the horizon of extrapolation at a level of resolution

(k) the response time

(This factor should not be confused with a horizon of prediction, or forecasting which should combine

both planning and extrapolation of recognized tendencies).

(1) the size of the spatial scope of attention

(This corresponds to the vicinity of the associative links pertinent to the situation in the system of knowledge

representation)

The following parameters of interest can be tentatively listed for the sensory processing module:

(m) the depth of details taken into account during the processes of recognition at a single level of

resolution

(n) the number of levels of resolution that should be taken into account during the processes of

recognition

(o) the ratio between the scales of adjacent and consecutive levels of resolution

(p) the size of the scope in the most rough scale and the minimum distinguishable unit in the

most accurate (highest resolution) scale

It might happen that recognition at a single level of resolution is more efficient computationally than if

several levels of resolution are involved. A finer system of inner multiple levels of resolution can be introduced

at a particular level of resolution assigned for the overall system (e.g. Burt's pyramid^'^). The latter case is

similar to the case of unnecessarily increasing the number of associative links during the organization of

knowledge.

Spatio-temporal horizons in knowledge organization as well as behavior generation are supposed to be

linked with spatio-temporal scopes admitted for running algorithms of generalization (e.g. clustering). Indeed,

we do not cluster the whole world but only the subset of it which falls within our scope. This joint dependence

of clustering on both spatial relations and the expectation of their temporal existence can lead to non-trivial

results.

One should not forget that generalization (the ability to come up with a "gestalt" concept) is conducted

by recognizing an object within the chaos of available spatio-temporal information, or a more general object

within the multiplicity of less general ones. The system has to recognize such a representative object, event, or

17
action if they are entities. If the scope of attention is too small, the system might not be able to recognize the

entity that has boundaries beyond the scope of attention. However, if the scope is excessively large, then the

system will perform a substantial and unnecessary job (of searching and tentatively grouping units of

information with weak links to the units of importance).

Thus, any system should choose the value of the horizon of generalization (that is the scope of the

procedure offocusing ofattention) at each level of resolution (granularity, or scale).

All of these parameters characterize the realities of the world and the mechanisms of modeling that we

apply to this world. These parameters do not affect the user's specifications of the problem to be solved in this

system. The problem is usually formulated in the terms of hereditary modeling that might not coincide with the

optimum modeling, or with the parameters of modeling accepted in the standard toolbox of a decision-maker.

The problem formulated by a user often presumes a particular history of the evolution of variables

available for the needs of the intelligent system. Simultaneously, the user requests a particular spatio-temporal

zone within which the solution of the problem is desirable. However, the input specifications often do not

require a particular decomposition of the system into resolution levels and the intelligent system is free to select

it in an "optimal" way. In other cases, the user comes up with an already existing decomposition of the system

that appeared historically and must not be changed (like the organizational hierarchy of a company and/or an Army unit). Sometimes, it is beneficial to combine both existing realistic resolution levels and the "optimal"

resolution levels implied by the optimum problem solving processes.

The discrepancy between these decompositions requires a new parameter of intelligence

(q) an ability of problem solving intelligence to adjust its multi-scale organization to the

hereditary hierarchy of the system, this property can be called "a flexibility of intelligence";

this property characterizes the ability of the system focus its resources around proper

domains of information.

In the list of specifications of the problem the important parameters are

(r) dimensionality of the problem (the number of variables to be taken into account)

(s) accuracy of the variables

(t) coherence of the representation constructed upon these variables

For the part of the problem related to maintenance of the symbolic system, it is important to watch the

(u) limit on the quantity of texts available for the problem solver for extracting description of the

system^'

and this is equally applicable for the cases where the problem is supposed to be solved either by a

system developer, or by the intelligent system during its functioning.

P. J. Burt, "Multiresolution Techniques for Image Representation, Analysis, and 'Smart' Transmission,"

SPIE Conference 1199: Visual Communications and Image Processing IV, Philadelphia, Nov. 1989.

^' Most of the input knowledge arrives in the form of stories about the situation. These stories are

organized as a narrative and can be considered texts. In engineering practice, the significance of the narrative is frequently (traditionally) discarded. Problem solvers use knowledge that has been already

extracted from the text. How? Typically, this issue is never addressed. Now, the existing tools of text

18
(v) frequency of sampling and the dimensionality of the vector of sampling

Finally, the user might have its vision of the cost-functions of his interest. This vision can be different

from the vision of the problem solver. Usually, the problem solver will add to the user's cost-function of the

system an additional cost-function that would characterize the time and/or complexity of computations, and

eventually the cost of solving the problem. Thus, additional parameters:

(w) cost-fiinctions (cost-functionals)

(x) constraints upon all parameters

(y) cost-flinction of solving the problem

This contains many structural measures. We need to trace back from an externally perceived measure

of "success" or intelligence to a structural requirement. E.g, the construction codes specify thickness of

structural members, but these dimensions are related to the amount of weight to support — the performance goal

is the lack of building collapse.

Important properties of the Intelligent Systems are their ability to learn from the available information

about the system to be analyzed. This ability is determined by the ability to recognize regularities and

irregularities within the available information. Both regularities and irregularities are transformed afterwards

into the new units of information. The spatio-temporal horizons of Intelligent Systems turn out to be critical for

these processes of recognition and learning.

Metrics for intelligence are expected to integrate all of these parameters of intelligence in a

comprehensive and quantitatively applicable form. Now, the set {Vlij} would allow us even to require a

particular target vector of intelligence {Vlj} and find the mapping {VIt}^ { V1,j} and eventually, to raise an

issue of design: how to construct an intelligent machine that will provide for a minimum cost (C) mapping

[{VPt}^ {VI,j}]^minC.

By the way, has this ever been done for the systems that are genuinely intelligent? Of course,

this question is not related to design, just to measurement.

8. The Tools of Computational Intelligence

Proper testing procedures should be associated with the model of intelligence presumed in the

particular case of intelligence evaluation. It seems to be meaningful to compare systems of intelligence that are

equipped with similar tools. In this section we introduce the list of the tools that are known from the common

industrial and research practice of running the systems with elements of autonomy and intelligence. It is also

expected that these tools can be used as components of the intelligent systems architectures. Thus, they might

help in developing and applying types of architectures that will be used for comparing intelligence of systems.

The following tools are known from the literature as proven theoretical and practical carriers of the

properties of intelligence:

• Using Automata as a Generalized Model for Analysis, Design, and Control

processing allow us to address this issue systematically and with a help of the computer tools of text processing.

19
• Applying Multiresolutional (Multiscale, Multigranular) Approach

1 . Resolution, Scale, Granularity: Methods of Interval Mathematics

2. Grouping: Classification, Clustering, Aggregation

3. Focusing of Attention

4. Combinatorial Search

5. Generalization

6. Instantiation

• Reducing Computational Complexity

• Dealing with Uncertainty by

• Implanted compensation at a level (feedback controller)

• Using Nested Fuzzy Models with multiscale error representation

• Equipping the System with Knowledge Representation

• Learning and Reasoning Upon Representation

• Using bio-neuro-morphic methodologies

• General Properties of Reasoning

Quantitative as well as qualitative reasoning

Generation of limited suggestions, as well as temporal reasoning

Construction both direct and indirect chaining tautologies (inferences)

Employing non-monotonic as well as monotonic reasoning

Inferencing both from direct experiences as well as by analogy, and

Utilizing both certain as well as plausible reasoning in the form of

1 . Qualitative Reasoning

2. Theorem Proving

3. Temporal Reasoning

4. Nonmonotonic Reasoning

5. Probabilistic Inference

6. Possibilistic Inference

7. Analogical Inference

8. Plausible Reasoning: Abduction, Evidential Reasoning

9. Neural, Fuzzy, and Neuro-Fuzzy Inferences

1 0. Embedded Functions of an Agent: Comparison and Selection

Each of the tools mentioned in the list allows for a number of comprehensive embodiments by using

standard or advanced software and hardware modules. Thus a possibility of constructing a language of

architectural modules can be considered for future efforts in this direction.

20
9. The Architectures of Intelligence

Listings of all tools of computational intelligence presently available and all properties of intelligence

measurable would not characterize the system exhaustively and would not suggest how to test the system. How

these tools are attached to each other — this is what matters! It turns out that the architecture of the system can

be decisive in providing active features of various intelligent systems.

Architectures of intelligent systems should support:

• Expected long-term mission planning (e.g. overall path planning and replanning for the

whole mission performance)

• Various principles of knowledge representation

• Navigation, guidance and motion control with self-orientation using a set of techniques

specified by the mission

• Auxiliary activities which require using additional intelligent control systems (e.g. for

manipulator arms installed at the mobile autonomous platform)

• Ability to acquire the data, which characterize and quantitatively measure mission

performance

• Perception capabilities: the character of the architecture will be strongly affected by the

characteristics of all the sensors to be installed on-board of the autonomous intelligent

system (for example, the unmanned ground vehicle); its intelligence will be affected by

the designer's decision regarding what particular vision and other off-the-shelf

perception systems are to be implemented, what is the level of human supervision^^

expected in the system (full autonomy, partial teleoperation, full teleoperation, etc.)

• Ability to handle sensing, data-processing, and decision making (including planning,

navigation, guidance, and control), dealing with uncertainties, especially while operating

in the uncertain environment

• Ability to respond to changes in the environment or its self-state without requiring

human intervention.

• Ability to optimize performance based upon some cost-function (e.g. minimum time of

task execution, minimum energy consumption, minimum final error of performance,

minimum risk of being detected and/or destroyed^^)

• Multi-robot (multi-vehicle, multi-system) coordination

• Robot-supervisor interaction (in a multi-robot case this may entail robots-supervisor

interaction, robots-supervisors interaction^"*, etc.)

A human supervisor will directly or indirectly assist the function of perception of the first group of

unmanned ground vehicles.

Often, all five of these factors are important: in this case weights must be assigned. However, some

theoretical difficulties should be overcome before using this case in practice.

In addition to the question: how should the interaction proceed among the members of the robotic

team. One can ask a similar question about the team of human operators supervising the robotic team.

21
• Ability to perform a variety of tasks (e.g. in the case of unmanned vehicles, the ability to

perform travel, reconnaissance operations, mine neutralizing, etc.)

• Fault-tolerant, reliable, and robust operation

• Measurable architecture performance both qualitatively and quantitatively^^

• Extensibility for improvements and adaptation to mission specifics

Other information processing functions will probably need to be supported but those listed above most

strongly affect the choice of architectural approaches. It is especially relevant in the cases where we are

explicitly talking about dealing with knowledge.

The first group of these implicit architectural matters^^ includes principles of knowledge

representation accepted in a particular intelligent system. A case could be made for semantic-based knowledge

representation, including tests for completeness and consistency. Although the theories for such tests exist (e.g.

Process Specification Language (PSL's) completeness and consistency can be proved within situation calculus).

The breadth and scope of knowledge represented in a knowledge representation system also determines and

conditions its possible re-use. Perhaps re-usable devices and software processes should be considered, since

such processes potentially decrease costs of further systems. One might expect that re-usability criteria could

be required for characterizing the intelligence.

Another group focuses more explicitly on ontologies that demonstrate the results of generalization

within the stored linguistic information. Ontology development aims at building a machine-readable semantic

layer within a (software) system. Ontologies formally express the knowledge contained in an application by

providing definitions for concepts, relations and functions, as well as rules for constraining the use of the terms.

Ontologies contain definitions for metadata and rules that constrain the interpretation and use of metadata.

Ontologies can represent relations of inheritance, aggregation and instantiation.

Ontology development supports system interoperability by solving problems related to semantic

ambiguities, and by enabling semantic communication between software agents. Software agents may refer to a common ontology to exchange messages. Actually, ontologies do not carry anything different in principle from

all hierarchical constructions within the knowledge base. However, they present it in a language form, for some

ontologies even in a natural language form. This opens an opportunity to communicate with large and

"interdisciplinary" knowledge bases in natural language.

Providing translation mechanisms for the interoperability of applications requires that applications

share a common ontology or that application concepts can be represented in a formal, declarative manner. Other

benefits of ontologies include reliable system specifications, accurate data and metadata descriptions, and

^^ This requirement should not be confused with the functional requirement of measurabiiity of

performing a particular function, and/or the overall mission such as time of arrival, or fuel consumed, or

percentage of mines neutralized. Here we are talking about performance of the architecture that should

be measured in terms of performing intelligent control operations (e.g. computations per alternative of

solution, goodness of solutions found, etc.).

Submitted by L. Pouchard

22
development of common data formats for collaborative analysis. Ontologies that exist for specific tasks or

domains permit knowledge sharing and re-use within the domain.

The scales and scalability criteria critical for intelligent systems are represented within ontologies, too.

10. Supervisory Control and Data Acquisition

Supervisory Control and Data Acquisition involves data collection, active communication with the

user, and display. This is a group of separate subsystems (actually, several levels of the architecture) within the

intelligent controller. These subsystems can be equipped by additional control loops and a separate knowledge

organization system required for communication. The purposes of these subsystems are:

• to prepare information relevant to the needs of corresponding levels of control and command

• to convey this information to the user or the supervisory controller

• to conduct the dialog with the corresponding level of control and command

• to display all the information in a user friendly form e.g. use of graphics, use of previously

negotiated modes of demonstration and protocol of explaining the ongoing activities

• to provide alarming, warning, notification both to other subsystems as well as for the

external levels of control and command

• to provide for security by allowing different levels of control and command with different

privileges.

• to facilitate printing and reporting functions, storage and display of historical data to

facilitate investigation of events, investigation, and other types of analysis.

11. Tests of Machine Intelligence Contemplated in the Past

1 . The Turing test, or imitation game was proposed by A.Turing in 1 950^^. In one version of this test

a human judge interrogates a program through an interface. If the program can fool the human into believing

that responses come from another human and not from a computer then the program should be considered

intelligent. Clearly, in this test we don't talk about intelligence as a phenomenon but rather about an ability of

pretending to be intelligent. At the present time, such an approach seems to be a naive one: it determines what

seems to be intelligent rather than what is intelligent.

Nevertheless, this approach has generated a lot of literature, in particular the famous problem of

Chinese room^^. J. Searle considers the following mental experiment. A person was given a set of formal rules

for manipulating Chinese hieroglyphs. This person does not speak or understand written Chinese, and he does

not know the meaning of these hieroglyphs, he just can distinguish them visually^^. The rules state that if a

symbol of a certain shape is given to him, he should write down another particular hieroglyph on a piece of

A. Turing, "Computing Machinery and Intelligence", Mind, Vol. 59, No. 236, October, 1950, pp. 433-

460

0 O° J. Searle, (1980) "Minds, Brains, and Programs", Behavioral and Brain Sciences,

23
paper. The rules prescribe how the groups of hieroglyphs should correspond one to another. When a set of

Chinese symbols enters from outside, the person applies the rules, writes down a set of other Chinese symbols

as specified by the rules, and returns the result to the external observer. The external observer perceives the

result as a grammatically correct answer in Chinese. However, the person inside does not understand Chinese.

(Note that the very possibility of conducting this experiment in reality is questionable: the list of required rules

would be prohibitively large if the scope of questions and required answers covers a broad domain and

demands for a high degree of sophistication).

Searle believes that the person in the Chinese room does exactly what a computer would be doing if it

used the same rules to engage in a grammatically correct conversation in Chinese. Both the computer and our

"inside" person are engaging in "mindless" symbol manipulation. This mental experiment leads J. Searle to the

following statements:

Axiom 1 : Computer programs are formal (syntactic) and manipulate symbols.

Axiom 2: Human minds have mental contents (semantics) and manipulate meanings.

Axiom 3: Syntax is not translated into semantics, therefore symbol manipulation does not contain any

understanding.

Searle's argument is intended to show that implementing a computational algorithm that is formally

isomorphic to human thought processes cannot be sufficient to reproduce the real process of thought. The last

decade of research in the area of intelligent systems demonstrated that this reasoning is too simplistic and is

not sufficient to adequately represent even existing constructed systems with autonomy (like unmanned

autonomous vehicles). Searle's schemes of analyzing processes of"thinking" are overly primitive and cannot

represent existing mechanisms of sensory processing, knowledge representation and behavior generation in

multiresolutional systems of motion control practiced in existing autonomous vehicles. Something more is

required. Researchers that develop intelligent systems challenge Searle's argument by creating new artifacts.

2. L. Zadeh's test can be formulated as follows: a paper is presented to the intelligent system, and it is

supposed to transform it into a summary^^. The quality of the summary can be judged by the ability of the

system to generalize and formulate the meaning of the paper in a sufficiently concise form. No doubt, any

system that can do it should be considered intelligent. Clearly, the system should be capable of generalizing.

Says L. Zadeh: " the ability to manipulate fuzzy sets and the consequent summarizing capability constitutes

one of the most important assets of the human mind as well as the fundamental characteristic that distinguishes

human intelligence from the type of machine intelligence that is embodied in present-day digital computers^'."

3. Various tests can be proposed based upon more mundane but more practical evaluations of

sophistication and rationality. For example, we can check a capability of a program to generate several

alternative decisions for a particular situation, and to select one of them properly; or its capabilities to analyze

^ A subtle detail: distinguishing and recognizing most of the hieroglyphs is a serious intellectual

problem by itself! 30 L. A. Zadeh, from his BISC letter of 1999

L. A. Zadeh, "Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,"

IEEE Trans, on Systems, Man and Cybernetics, Vol. SMC-3, 1973, pp. 28-44

24
the experimental data related to a particular physical system, and to compute a feedforward control, and to

introduce a law of feedback compensation. The key issue in the last case is the ability to use the experimental

data: different experimental data require different approaches to computing feedforward control, and different

laws of feedback compensation. The tradeoff "feedforward vs. feedback" is a real test of intelligence as a tool

for reaching successful balance under conditions of redundancy and uncertainty.

4. A. Newell has listed properties that intelligent system must have^^:

• recognize and make sense of a scene

• understand a sentence

• construct a correct response from the perceived situation

• form a sentence that is both comprehensible and carrying a meaning of the selected

response

• represent a situation internally

• be able to do tasks that require discovering relevant knowledge.

12. Who wins the competition: the Real Intelligence or the Impostor?

Using the Turing Test to evaluate intelligence has become commonplace, although as we have already

mentioned above, it does not evaluate intelligence but rather the ability of a system to pretend being intelligent.

Competitions are one of the straightforward primitive methods of judging the degree of intelligence. The

deficiencies of competition are clear from the following list:

• in a competition, a random set of circumstances can affect the results rather than a set of

capabilities of the competing systems; thus, only the results of multiple competitions can

be valid

• it is difficult, if not impossible, to separate the part of intelligence endowed in the body

design from the part of intelligence incorporated into the system of intelligent control;

thus, forjudging the intelligent control system, identical bodies are presumed

• competition in the natural environment cannot guarantee the equality of the problems to

be encountered by competing parties; in constructed (artificial) environments, the

difficulty of the problem drops drastically; it does not require that much "intelligence"

The latter feature is not necessarily always the case. The actual challenge is to provide a rich enough

environment within which the tests can be conducted. An example of this would be a completely instrumented

test course for evaluating autonomous robot mobility and mapping abilities, rather than the simple "box world"

that is frequently used. In fact, one of the keys to our efforts in performance metrics is to come up with these

sufficiently rich environments (test courses or very detailed, ground-truth simulation environments) which can

be used to evaluate the performance of different systems. It is not an easy task. We should encourage a broad

discussion on defining requirements for such environments.

^2 Newell, A. (1982). The knowledge level. Artificial Intelligence. 18(1), 87-127; Newell, A. and Simon, H. (1963), GPS: A program

that simulates human thought. In Computers and Thought , ed. Feigenbaum and Feldman. McGraw-Hill, New York.

25
Therefore, winning a competition, however exciting it might be, leads to the old pitfall of the Turing

test: winning requires no more than pretending to be intelligent rather than demonstrating real tools of

intelligence. Testing of intelligence is a must, but the way of testing is a matter of discussion. The challenge for

competitions is to overcome these obstacles. For example, developing an artificial environment that is dynamic

and challenging , yet reproducible.

13. Measuring the Intelligence Contemplated for the Future

Measuring Intellifactors. One can start analyzing the problem of measuring intelligence within the

domain of Albus's definition that assigns this faculty for control purposes^^. The factors of intelligence are the

factors of processes that contribute to intelligence (intellifactors). Logistically, they are dimensions of VI,

mathematically, we can express this as follows:

Xi ={x| X is all possible intellifactors}

and the set of intellifactors {Xjf}, is an element of the power set of Xi.

A measure of intelligence (IQ) is the measure that can assign a real number to the collective performance

of each element in the set Xj. The measure of intellifactor (IFQ) is a measure that assigns a real value to the

collective performance of each element in Xif.

Measuring the Power of Generalization. There exists a way to narrow the gap between building an

intelligent machine (with its ontogeny^^) and understanding the intelligence process by itself (with its

epistemology-'^). The way is to model the process in a biological system^^. How do brains do that? Brains avoid

catastrophic failure when the complexity of computations grows exponentially by use of the NN-dynamics for

generalization by creating "objects" (classes). It is experimentally confirmed that for the same operation of

generalization, computer elements need more computations than brain needs. One can judge on the comparative

productivity of computers during simple maps generalization^^ and instantaneous gestalt insights performed by the

brain during human processing of complex images.

Measuring the System's Intelligence by the Degree of Uncertainty. The latter observation is linked

with the entropy based considerations. Any measure of uncertainty (entropy in particular) is an acceptable

measure of intelligence. If one can measure our uncertainty in taking decisions among alternatives, one can

reduce this value of uncertainty (e.g., by learning), so our system is intelligent. But how do we measure the

value of each alternative? Again, by its uncertainty. A possible way is to measure the probability of success of

This concept of measuring intelligence was contributed by Louwrence Erasmus.

^^ or how it is done in a living organism

^"^ or how it is done in the theory of knowledge

This concept was proposed by W. Freeman. He refers to the A. Meystel's statement "the mechanism of

generalization to emerge: it creates new objects" quoted from his e-mail letters to Advisory Board

Members.

3^ J. D. McMahill, Interactive Generalization: User's Guide, CMU, Pittsburgh, PA 1998; G. L. Bundy, C. B.

Jones, E. Furse, "Holistic Generalization of Large Scale Cartographic Data," in J. Muller, J. Lagrange, R. Weibel (eds.), GIS and Generalization Methodology and Practice. Tay\or and Francis, London, 1995, pp.

106-1 19

26
meeting the specifications for each of the alternatives by successive applications or using a model (we might

call it Reliability, in this sense). The higher the success, the lower the uncertainty/entropy. We may counterbalance this with the cost (or complexity) of achieving very successful alternatives (typically, the higher the

reliability, the higher the cost)^^.

Constructing the Benchmarks. Judgment of the system's intelligence can be done by using indirect,

albeit easy to measure values. In constructing benchmarks, we use the fact that the fundamental attributes of

intelligence include:

• Ability to perform tasks in unstructured environments

• Ability to learn from experience

• Ability to transfer knowledge from one domain to another

• Ability to solve complex problems, requiring deductive and inductive reasoning

The following simple measures can be used as metrics for such abilities in machines^^:

• Size and complexity of programs required

• Memory requirement

• Solution time

Clearly, such measures are useful only if (a) they are applied to benchmark problems, (b) all

contestants use the same type and model of computer, and (c) all programs are written by comparably

competent programmers, so that the programs are optimal in some sense.

Given these constraints, we could test intelligent systems A and B on the same benchmarks. The one

that accomplishes the task more quickly, and does so with the least complex programs and least memory will be

declared "more intelligent". While evaluating the level of intelligence based on this definition (to avoid the

confusion of introducing a new one) we have to take into account'*^:

• type of uncertain environment

• strategy of achieving the goals

• capability of the system to automatically create and update its subgoals.

Most of the well-established methods for robust control design provide the capability to deal with

small parametric and structural uncertainties and therefore represent a basic level of intelligence in the control

system according to the definition of Albus. Situational uncertainty, e.g. drastic changes in the environment

that are due to completely different operating conditions, severe and unpredictable disturbances, etc.,

completely alter system dynamics, and therefore require control systems with a much higher level of

intelligence.

The latter considerations were suggested by P. A. Lima

Contributed by G. Bekey

From the abstract submitted by D. Filev

27
Measuring Autonomy vs. Intelligence. The following question can be considered a fundamental

one^': What is more important and meaningful to define and to measure with respect to the context of

Intelligent Autonomous Constructed System— Autonomy or Intelligence of a Constructed System? We are

looking for Autonomy, as the premier requirement of an Intelligent Autonomous System. From the designer or

the user point of view, Intelligence enables Autonomy, but it is not a system design objective or a system

requirement per se.

The definition of Autonomy is probably more precisely measurable and more meaningful and it is

easier to come to a consensus about what Autonomy or an Autonomous System is all about, rather than what is

Intelligence or an Intelligent System.

14. Simulated Functioning and Scaled Hardware Testing of Intelligent Systems

The hope is for a balanced combination of a) thorough simulation and b) scaled hardware testing.

Many researchers focus upon simulating systems with high autonomy*^, like B. Zeigler in USA, K.-H. Brassel

in Germany, I. Peters in Switzerland, J.-H. Kim and T.-G. Kim in Korea, and others. However, the challenge of

evaluating intelligence of these systems remains an active problem to be resolved in the upcoming decade.

The most intricate problems associated with the variability and combinatorics of realistic situations can

be resolved by simulating these situations. Thus even the predicament of absent hardware can be avoided by

simulating the problem-impregnated situations. Contests and competitions can be considered a part of this

paradigm. One cannot come even anywhere near covering in realistic testing the spectrum of philosophical*^

views of intelligence (just start to read the mind/body literature!) On the other hand, one might be inclined to

scale back the possible analogies to human intelligence and human involved testing to less convenient but more

pragmatic scenarios.

The Paradigm of Contest and Competitions

1. Symbolic systems. The a-y classification of measurable characteristics (see Section 7) can be made

very representative but is definitely too constrained by the existing general systems and ways of representing

information. Indeed, each of the 25 items on this list is a strong reduction of actual possibilities. Start with (a)

memory temporal depth: why it should be limited? or why should only one value of depth be considered? The

next item is (b) number of objects that can be stored: why should this number be limited? Then, we come to the

number of levels of granularity, definitely a limitation that should depend on the problem. Then, we face

limitation on the vicinity of associative links — the latter should not be limited as well! All 25 items on this list

limit the opportunity to find better (not to speak about "the best") solutions. In the meantime, the environment

From A. Yavnai's abstract

See in Ed. by H. Sarjoughian, F. Ceilier, M. Marefat, J. Rozenblit., 2000 AI. Simulation and Planning in High Autonomy Systems, Proc. of the SCS Conference in Tucson, AZ, March, 2000

From the e-mail letters by J. Cherniavsky

28
conducive for contests and competitions is by definition oriented toward a permanent atmosphere of

inventiveness and development of new signs and new phenomena to be encoded by these signs.

2. Systems with learning. Learning is never forgotten as a very important subsystem of intelligence.

However, there is not too much discussion related to the nature of learning as a substitute for real contests and

competitions. In the meantime, learning plays the role of rehearsing expected ("would be") situations of contest

and competition. Learning via prior experiences or via planning is a mechanism that prepares a system for

contingencies. Thus, learning serves as a critical characteristic of intelligence that solely determines both the

success and failure. It's there, but its primarily implicit and serves as a supportive system that serves rather for

improving functioning. Learning provides for a successful adaptation of the intelligent system to changing

environments, e.g. different algorithms for deriving new rules can be utilized for different cases (i.e. algorithms

of reinforcement, habituation, Hebbian association, abstraction, generalization, etc.). A multiplicity of situations

can be anticipated where, without learning, the central purpose of the system could not be achieved.

3. Application Focused Intelligence. In many cases, the intelligence might be defined relative to a

domain of application. Even in the human cases there are people who are "car intelligent" but "literature

ignorant" - different domains, different abilities. This generates a question: if in the human domain one might

distinguish different types of intelligence (Gardner's 7, Sternberg's 3, etc.) — should it be beneficial to try

something similar in the autonomous unmanned, or partially manned systems? Indeed, for a human, the need to

quickly move from one subject-oriented vocabulary to another might create a need to deal with using domain

oriented algorithms of generalization, or pattern recognition. Can it be beneficial m the unmanned cases?

All three of these questions can be resolved within the domain of contests and competitions. We can

create and focus on a specific domain where things like self-sustained, appropriate behavior, ability to quickly

act in an uncertain environment, etc. can be physically quantified by realistic measures of performance (units of

time, money, energy). The various contests (AAAI urban search and rescue, robotic soccer, the data-mining

contests, the information retrieval competitions, the speech understanding rallies, etc.) provide the plausible

level to measure and thus compare systems.

15. The Intelligence of Sensing and Sensory Processing

Available results have already suggested that the brain designs for sensory and cognitive processes

differ from, and are even computafionally complementary to, the designs for spatial navigation and action. This

complementarity can be noticed by observing that cognitive knowledge needs to accumulate in a stable way

over a period of years, with new knowledge not accidentally erasing previously learned, but still useful,

knowledge^.

The problem of data fusion (both heterogeneous or homogeneous) generated a demand that the

robustness of the fusion stage be closely linked to the number of significant criteria permitting to associate

information required for interpretation'*^. Both the uncertainty and the error of the input data, as well as

From the abstract by S. Grossberg

Contributed by A. Clerentin and L. Delahoche

29
uncertainties and errors of the available internal knowledge, jointly produce the uncertainty and the error of

interpretation. The uncertainty is meant to characterize the "degree of actual existence" of the data; the error

characterizes imprecision on the numerical evaluation of the data. The uncertainty and error estimation in

classical fusion processes are generally based on a probabilistic approach. As the number of factors to be

associated for interpretation grows, the need to work with multi-criteria techniques grows. The latter should

help to evaluate the performance of each stage of global fusion processing: for example, data fusion for

localization (generally allows for heterogeneous fusion) or data fusion for incremental map building (generally

demands for homogeneous fusion: the same kind of primitives must merge on different acquisitions). Here

again the use of tools like Dempster- Shafer theory of evidence might be promising.

In a number of applications, including the area of autonomous robotics, the problem of multi-sensor

fusion and joint interpretation determines the value of intelligence related to sensing and sensory processing. It

is clear that, in many situations, the use of multiple sensors is the only way of dealing with the richness of the

external world. Any given sensor takes information about only one of the many attributes of the environment.

But often the arriving information must be carefully gleaned for more than one attribute simultaneously. Only in

this case can the required depth of interpretation be achieved.

So, the problem is how to integrate the information, especially when the sensors are disparate and

when the viewpoints and even scales of incoming information are different. To overcome these problems,

several fusion methods are used. The majority use a probabilistic approach (Bayes rules). A significant portion

use a possibilistic approach that considers sensor evidence to be the value of belief (these rely on DempsterShafer theory). This theory is appropriately expressive, it explicitly represents ignorance, enabling the robot to

differentiate between ambiguous sensing results and not having sensed at all. Other approaches include fuzzy

logic or neural networks.

Information fusion is a growing research domain and of the numerous developed applications show

that it enhances the level of autonomy and intelligence of engineered systems, especially autonomous robots.

16. Questions To Be Answered

This is the list of questions that the Workshop will try to answef*^:

Question 1. What is the vector of intelligence (VI) that should be measured and possibly used as a

metric for systems comparison?

Question 2. Should VI be measured in addition to, or instead of, measuring the vector of performance

(VP) determined by the standard specifications?

Question 3. If two systems have the same VP, what is implied by the difference in their VI values?

Can this difference be represented in monetary (cost) units?

Question 4. Is it possible (and meaningful) to have different VI measures: a) goal-invariant, b)

resource-invariant, c) time-invariant?

Questions 4, 6, 7, 8 were contributed by S. Lee

30
Question 5. What should be recommended as a test of VI and how can VP be normalized so that

comparisons may be performed at the same normalized value of VP?

Question 6. Does a universal measure of system intelligence exist such that the intelligence of a

system can be compared independently of the given goals'*^? A goal-independent measure may be more

difficult to defme. A goal-dependent measure, however abstract the goal may be, can allow for a clear

comparison among the systems of different architecture but with the same goal. For instance, for the latter

case, an intelligence can be represented as how efficiently, and how optimally a system reaches the given goal

by itself i.e., the power of automatically solving problems defined as the discrepancy between the goal and the

current state.

Question 7. Should the intelligence measure of a system be solely based on problem-solving

capability at time "t" or should it contain the potential increase of problem-solving capability in the future

based on learning?

Question 8. Should the resources required for building systems and system operation play a role in

defining the measure of intelligence? As mentioned above, the efficiency in problem solving should be

included in the measure: for instance, the time and energy required to reach a solution should be taken into

consideration together with the optimality of the solution. But, it is not clear whether we should or should not

include the cost of building a system.

As a reminder, a set of other questions that are ingrained (directly, or indirectly) in the main

questions is formulated as follows:

Question 9. These are the less profound ("secondary") questions that should be addressed at the

workshop and possibly unequivocally answered:

a) how to form VI for various architectures?

b) should the questions 1 through 5 be related to intelligent systems, or autonomous systems,

or both?

c) what is the protocol for dealing with uncertainty when the uncertainty metric is to be

applied in the procedures of decision making? for example, how does the uncertainty of

planning affect the cost of goal achievement?

d) what are the guidelines in constructing the world model and determining its scope in the

variety of applications? how does the scope of "world model" affect the sophistication of

intelligent behavior?

e) how are the questions 1 through 5 related to (and the answers applied to) the systems that

are working under a hierarchy of goals?

0 should a competition between intelligent systems be considered a valid method ofjudging

VI value?

This seems to be hard to achieve for biological systems. This will be eventually addressed, but in the

short term run the concrete goal of particular cases seems to be more attainable. A single measure of

intelligence requires constructing a system of meta-knowledge.

31
17. Glossary

Autonomy — an ability to generate one's own purposes without any instruction from outside (L. Fog^l

Alternative definitions:

a) independence.

b) Self-government or the right of self-government; self determination.

c) Self-government with respect to local or internal affairs (AHD)

d) the right of self-government,

e) self-directing freedom (Merriam-Webster)

Autonomous System — a constructed system is autonomous if there is a likelihood that circumstances will

arise in which no-one can predict in advance what it will do. (T. Whalen)

Autonomous Intelligent System - an autonomous constructed system is intelligent if we can be reasonably

confident that whatever unpredictable thing it does do will be something that tends toward success in the goals

for which the system was constructed in the first place. (T. Whalen)

Agent (sometimes Autonomous, Intelligent) — a term that has been introduced to use the word system which

is regarded by many as a less desirable one when the software is involved, especially the one with properties of

intelligence. The term Agent has some anthropomorphic overtones. Agent is presumed to be a system that

probably can sense, reason and is intended to act. In other words. Agent should be understood as a system with

elements of intelligence and autonomy.

Intelligence - an ability of a system to act appropriately in an uncertain environment, where appropriate action

is that which increases the probability of success, and success is the achievement of behavioral subgoals that

support the system's ultimate goal (J. Albus)

Alternative definitions:

the ability to solve new problems in new ways (L. Fogel)

the capacity to acquire and apply knowledge (AHD).

the faculty of thought and reason (AHD).

the ability to adapt effectively to the environment, either by making a change in

oneself or by changing the environment or finding a new one (Britannica).

the ability to learn or understand or to deal with new or trying situations

(MWD)

the skilled use of reason (MWD)

the ability to apply knowledge to manipulate one's environment or to

think abstractly as measured by objective criteria (MWD)

32
18. Appendix

How is Testing of Intelligence Performed on Humans?

The most widely used intelligence tests include the Stanford-Binet (SB) Intelligence Scale and the

Wechsler Scales (WS). The Stanford-Binet test was first introduced in 1916 by Lewis Terman from Stanford

University. The individually administered test, revised in 1937, 1960, and 1972, evaluates persons two years of

age and older. It consists of an age-graded series of problems whose solution involves arithmetical, memory,

and vocabulary skills. WS-test gives both the overall IQ as well as separate IQs for verbal and performance

subtests. An example of a verbal subtest would be vocabulary breadth, while an example of a performance

subtest would be picture arrangement, so that they tell a comprehensible story.

IQ was originally computed as the ratio of mental age to chronological (physical) age, multiplied by

100. If a child of 10 performs the test at the level of an average 12-year-old, this 10-year-old is considered to

have a mental age of 12. In this case the child was assigned an IQ of (12/10)xl00, or 120. The concept of

mental age is not a persuasive one, and the computation of mental ages is not used frequently. The values of IQ

are more persuasive if they are computed on the basis of statistical distributions.

Intelligence tests created a controversy about what kinds of mental abilities constitute intelligence and

whether the IQ adequately represents these abilities. It turned out that intelligence tests give better results for

rich kids and are worse for less privileged racial, ethnic, or social groups. Consequently, psychologists have

attempted to develop culture-free tests that would more accurately reflect an individual's native ability. Johns

Hopkins Perceptual Test, developed in the early 1960s for measuring the intelligence of preschool children, has

a child try to match random forms (geometric forms, e.g. circles, squares, etc. are avoided because some

children may be more familiar with them). Another solution was to use test materials pertinent to a child's

living environment.

Psychometric tests are performed by observing and evaluating the performance of the Elementary

Cognitive Tasks (ECTs) with items of ECT based on past acquired knowledge, reasoning, and problem solving

requiring the concerted action of a number of relatively complex cognitive processes. A particular ECT is

intended to measure a few relatively simple cognitive processes, independent of specific knowledge or

information content.

Each ECT is devised to address a different set of cognitive processes, and performance on two or more

different ECTs yields data from which individual differences in distinct processes can be measured, such as

stimulus apprehension, discrimination, choice, visual search, scanning of short term memory (STM), and

retrieval of information from long term memory (LTM). ECTs typically do not depend on previously learned

information content, and in those that do, the content is so familiar that it should be common to all individuals

undergoing the test.

Most ECTs are so simple that every tested individual can perform them easily. The differences in

performance are measured in terms of response time (RT). The most interesting ECTs are those with RTs of

less than one second and with response error rates close to zero. The subject's median RT (over n number of

trials) and the subject's intraindividual variability of RTs (measured as the standard deviation of RT over n

33
trials) are of particular interest. Another type of ECT, known as Inspection Time (IT), measures sheer speed of

perceptual discrimination (visual or auditory) independently of RT.

Measures of RT and IT derived from the various ECTs are analyzed and their correlation is estimated.

For single ECTs, the correlations depend on the complexity or number of distinct processes involved in the

ECT. Some processes are more strongly correlated than others. Interpretation of these correlations depends on

the goal of testing and properties of intelligence that are tested. A similar approach to testing particular skills can be exercised in the area of intelligent systems. Our

ability to construct metrics should depend on the particular tools or facets of intelligence we will analyze as

related to the particular performance results.

However, all psychological tests of intelligence have one feature in common: they rely upon successful

performance of particular tasks, but they do not attempt to introduce any relatively comprehensive form of the

model of intelligence. It is understandable for measuring intelligence of such an object as a human being. It

would be unforgivable to impose similar detriment upon a researcher in cases where intelligent systems are

autonomous mobile vehicles, organizational systems, large computer based control systems like unmanned

power plants, structures of company management, stock market. If we succeed with these types of intelligent

systems, we might be encouraged to attribute some model to a human intelligence.

34
PART II

RESEARCH PAPERS

35

PART II

RESEARCH PAPERS

1. THE PHENOMENON OF INTELLIGENCE

1 . 1 Using the Metaphor of Intelligence A. Wild, Motorola, USA

1.2. Technologies for Engineering Autonomy and Intelligence

T. Samad, Honeywell, USA

1 .3 Theoretical Constructs for Measurement Performance and Intelligence in Intelligent Systems

L. Reeker, NIST, USA

1.4 Intelligence with Attitude

W. C. Stirling, R. L. Frost, Brigham Young University, UT, USA

1 .5 What is the Value of Intelligence and How Can It Be Measured?

T. Whalen, Georgia State University, USA

1 .6 On the Computational Measurement of Intelligence Factors

J. Hemandez-Orallo, University of Val ncia, Spain

1.7 On Definition of Task Oriented System Intelligence

M. Cotsaftis, LTME/ECE, France

1 .8 Minds, MlPs, and Structural Feedback

R. Sanz, 1. Lopez, University of Madrid, Spain

1 .9 Fast Frugal and Accurate — the Mark of Intelligence: Toward Model-Based

Design, Implementation, and Evaluation of Real-Time Systems

B. P. Zeigler, H, S. Sarjoughian, University of Arizona, USA

1.10 Applied Applications for Mimetic Synthesis: The

AAMS Project Summary (The Intelligence of an Entity)

R.G. Gamer, S.B. Henderson, R. N. Bishop, USA

37
/
Using the Metaphor of Intelligence

A. Wild

Motorola, Phoenix, AZ 85018

ABSTRACT

Constructed system with autonomy can be considered as possessing

intelligence, if intelligence is understood as a metaphor. It is useful to

be aware of that, when defining desirable features for constructed

systems, in areas such as reflecting the world (ontology), definition and pursuit of goals (teleology), or general human-like behavior

(anthropomorphism). Modeling and simulating integrated systems

exemplify the usage of multi-scale, multi-disciplinary representations,

as a basis for increasing the autonomy of some specific constructed

systems. Measuring the intelligence of constructed systems requires a Vector of Metrics for Intelligence. Its components will be defined by

different means, such as conducting existence tests for essential

capabilities, measuring the power to eliminate unnecessary

exploration, competitions of hardware-compatible systems, or vote by a jury.

KEYWORDS: constructed systems with autonomy, intelligence

1. INTRODUCTION

The intelligence of the constructed systems with autonomy

has to be understood as a useful metaphor, not to be stretched

too far [ 1 ]. As beneficiaries of such systems, we are actually

interested in their performance. The underlying assumption,

however, is that building intelligence into the system, whatever

its definition would be, would result in a generic and

systematic way to improve their performance.

While it is relatively easy to imagine ways to measure

performance, it is far less obvious how to measure intelligence,

as we lack a crisp, generally accepted definition, be that for

human beings, for other beings, or for artifacts. The casual observer perceive manifestations of

intelligence in multiple forms, and also will notice that somebody performing very intelligently in one situation may

show what appears to be a lack of intelligence in another

situation. This may suggest that intelligence is a local skill. On

the other hand, some researchers intuitively feel that intelligence is an intrinsic capability of an entity, and engage in exploring the commonalties between different entities considered intelligent.

Pragmatically, the latter seems the most promising

approach. If successful, it would provide the foundation for a methodology to construct systems with continuously

improved capabilities. To drive the progress, it is essential to

establish metrics, ranking systems according to their

intelligence. Note that for this purpose it is actually irrelevant

whether one considers intelligence as a generic or a local property. Depending on the viewpoint, the ranking would be

valid either within a specified sub-space or in general.

However, general methods, if possible, would have clearly a wider impact.

2. LIMITS OF THE METAPHOR

A multitude of aspects can be considered as elements or

capabilities necessary to support intelligent behavior. In some

versions, the Vector of Intelligence has 25 dimensions. It is supported by a set of computational tools, with a system

architecture counting 1 6 features, and is completed by a control and data acquisition system with supervisory

authority, also featurmg a number of capabilities. Many of

these elements do justice to the view adopted by the Italian Renaissance and illustrated famously by Leonardo da Vinci:

the man is the measure of all things. While this approach is quite effective, and may be often unavoidable, caution is in order to avoid excesses in at least three respects: our view of

the world, our goal setting capabilities and our own being.

2.1 Ontology

The dimensions of the vector of intelligence and the

supporting tools, architectural features and auxiliary

subsystem should not be excessively isomorphic with our contemporary perception of the world.

A few centuries ago, we might have asked an intelligent

system to recognize the four elements and their interactions,

we would have argued about the phlogiston, and hoped that eventually an intelligent system will extract the quintessence of

anything and everything. It should have recognized the

planets and the major stars, and have had the ability to synchronize actions with favorable skies. The Euclidean

geometry was a very pertinent model to simplify the

description of the world, by accepting that concepts like a

straight line do have a kind of existence. Likewise, all needed

knowledge about gravity was that there exists an attraction

force between two bodies, precisely equal to the Cavendish

constant multiplied by the two masses divided by the square

of the distance. This formula easily generated the laws derived

39
by Kepler from mountains of data and hundreds of years of

observations. The depth of our understanding was made

sensible (was measured ?) by this tremendous simplification.

Unfortunately, the space-time curvature of generalized

relativity eliminated the paradigm of the straight line, and

Newton's simple formula was unable to lead to a solution for three body interactions. Our present view is that the world

does not admit a simple description.

When facing complexity, we tend to rely upon hierarchy

to simplify interactions. Ideas about multi-resolution, multiscale views imply a hierarchy. We tend to require that an

intelligent system can do the same, being able to handle

several hierarchy levels. Their number and their adequate

utilization are candidates for intelligence metrics.

Computational tools of intelligence define rules and

procedures for crossing boundaries between hierarchy levels. However common and widely accepted, the hierarchical

representation of complexity is probably no more than the current model, and it seems reasonable to expect that it will be

eventually replaced by a different view. This would also induce an evolution of the intelligence metrics derived from a model of

the world, as it evolves historically.

As a matter of fact, the next paradigm may already take

shape under our eyes: can one speak about the Internet as about a constructed system with autonomy, exhibiting

intelligence ? And if yes, how would that intelligence be

measured ?

2.2 Teleology

We consider the ability to generate goals as a leadership

feature. Some philosophers consider this as the defining

feature of any living beings.

However, humans, and other living creatures, pursue

both explicit and implicit goals. They either conceptualized

themselves the explicit goals, or receive the goals form higher

authorities. In anyone of these situations, they may or may not

exhibit intelligent behavior. A simple positive example is young

James Watt, being given the goal to keep the pressure of a steam vessel constant. He did not conceive the goal himself,

actually, he was pursuing rather different interests. It was not a goal with any recognizable intellectual challenges. But Watt

generated a response that resonates until today, and will keep

resonating, being, among other things, largely responsible for

this workshop.

2.3 Anthropomorphism

A system scoring high on all dimensions of the Vector of

Intelligence and its auxiliaries will probably pass easily the

Turing test, it may do even more, it would be basically human,

at least to the extent of our current understanding of the way

humans are looking like. Some of the properties listed by

Neville address the ability to communicate like humans,

including such things as understanding a sentence and

developing knowledge. These ideas seem to relay on the

perception that the more a system is similar to a human being,

the more would it be perceived as intelligent.

Even if our current understanding of humans would be

definitive, this is approach may be an anthropomorphic trap.

Actually, there is no necessity for the constructed structures with autonomy to present any isomorphism with our ideas

about the human beings. Many of the most effective artifacts created by humankind are radically non-anthropomorphic, or non-biomorphic, for that matter. Starting with the wheel,

radically different from a leg, yet allowing better locomotion, one can easily follow with any number of examples. A jet

airplane is not a bird. A computer is not a brain. And a constructed automaton with autonomy is not a living being.

There is no recognizable necessity for these artifacts to be

indistinguishable from, or even similar to their closest living

relatives.

If one recalls the number of words in any language

describing non-intelligent behavior, one may conclude that copying too closely humans may be less than desirable.

3. PROGRESSING TOWARDS THE

METAPHOR

Building systems reflecting our view of the world, our purposes and our way of being, may prove productive. Multiscale representations are probably a useful way to handle the

complexity of the world in our minds, at this point in the

evolution of our understanding. We can legitimately expect

such representations to be useful in sciences and engineering.

The ultimate multi-discipline, multi-scale simulations are attempted by cosmologists, who hope to deduce the

characteristics of the universe, 1 0 to 15 billion years after the

Big Bang, from its characteristics when it was younger than one second.

Electronic engineers aiming to design integrated

microsystems, have simpler needs: to simulate, with some

quantitative accuracy, what happens on a silicon wafer within

a time span from a few nanoseconds to a few hours.

Microsystems are defined here as monolithic structures functionally equivalent to multi-chip systems. Increasing

integration levels drive the semiconductor industry towards

building system on a chip. To address this demand, design and

manufacturing must integrate heterogeneous elements with

traditional data processing circuits, encompassing multiple

disciplines, multiple scales in space and multiple scales in time,

within a coherent framework of computer aided design.

Adequate modeling and simulation enables closed loop

optimization and microsystem design automaton.

Microsystem design must handle multi-scale modeling in

time, to cope with the wide gap present in the temporal scales.

40
While atomistic calculations are useful for continuum

simulations, molecular dynamic simulations are limited to times on the order of nanoseconds. The gap can be bridged by a meso-scale calculation, for instance using the Lattice MonteCarlo (LMC) method to describe the hops between stable

states (nanoseconds) rather than the vibration frequencies of

the lattice (fractions of picoseconds). In space, multidiscipline, multi scale modeling is often required to link

macroscopic reactors to microsopic integrated elements. As an

example, a micromachined gear, 1 micrometer in diameter, can be analyzed using three hierarchical levels: continuum models

(finite element) for the body of the wheel, molecular dynamics

for gear teeth, and tight-binding for the contact between teeth. The connection is realized via a self-consistent overlap region,

while keeping the time discretization in both connected

domains in lock step, the whole system requiring massive

parallelization at Maui Supercomputer Center.

System

Molecular

Dynamics

Reactor Simulation archipelago

Circuit

Device

Process Topology

Currently, the multiple disciplines involved in microsystems are either unconnected, building an archipelago,

or put together by human programmers in an ad-hoc manner.

Active research, however, is aimed at systems able to build

bridges between the isolated domains, as a pre-requisite for

using optimizers in closed loop. This technique allows the

correlation between decisions at one manufacturing step and

the system level features and performance.

Using an optimizer at the meta-level to manage the design

process brings the system one step further. Many features

would be required to incorporate these or similar functions in a constructed system with autonomy, exhibiting some

intelligence.

This "bottom up" progression towards a development

system with autonomy increasingly adds features included

among the dimensions of the Vector of Intelligence. This seems a promising way towards the next challenges in engineering, believed to be nanosciences, biological systems,

and last but not least, robotics. Searching for their intelligent

features would surely provide underlying commonalties and

accelerate the progress.

Optimizer

evaluation request

evaluation

result

Framewodi

(VISTA)

simulation

device

request

device

description

simulation results

Device

Gotierator

Simulator

(MINIMOSOT)

4. MEASURING THE METAPHOR

As the Vector of Intelligence and its supporting structures are multi-dimensional, multi-faceted and quite heterogeneous, a set of metrics would probably be necessary, in the hope that if a unitary definition of intelligence would emerge, a composed

metric may by put forward. The four approaches presented

below are the beginning of the Vector of Metrics for

Intelligence.

4.1 Countingfeatures

Some features of the Vector of Intelligence and the supporting

structures can be tested by a go/no go test, they either exist

within a given system, or they do not. Furthermore, some of

them have clear numerical definitions and can be determined

by counting. The result of counting is final, as long as the structure does not evolve, or represent just an assessment at

that point in time, if the system can evolve. The only open

problem is how to of aggregate the different dimensions of the

Vector of Intelligence, so that ranking can be done.

4.2 Howfar awayfrom enumeration ?

Testing for functional correctness of a system poses serious

challenges even at the lowest levels. For example, testing the

hardware of a microprocessor, a finite state machine, is conceptually easy, yet unsolvable practically. Theoretically, a

test can run through all possible transitions between states, with all bit configurations at the external inputs, comparing at each step the outputs with the specification. The number of

states and transitions is finite, yet so large, that the test of a 32

bit processor running at 1 GHz would take a time longer that the

age of the Universe.

To reduce the number of tests, one can use additional

switching elements to reconfigure the structure to a finite state machine of lower complexity. If the logic gates and storage

elements in the finite state machine have been defined

algorithmically, one can safely accept that the functionality

would be correct, if no physical defects are present. In this

41
case, the simplified structure may be used to proof that all the

desired logic gates and storage elements (a few 1 0 or 1 00

million of them on contemporary chips) are present, functional,

and properly connected. These methods, currently used, are

still unable to provide satisfactory test coverage. At a more

abstract level, formal analysis of the structures is researched as the next opportunity to achieve it. If one adds to the testing

the requirement to proof that a system or a piece of software is providing optimum responses in all cases, the complexity of

the task is inhibiting.

In general, a measure of intelligence could be how much

of the space to be investigated is not explored through

enumeration.

This is almost isomorphic with some areas of scientific knowledge. For instance, the postulates of thermodynamics, to be accepted rather than demonstrated, point out what is impossible to achieve, saving us huge efforts, like trying to build all possible cases of perpetuum mobile of the first and

second species, in addition to trying to reach absolute zero. Obviously, the postulates are very effective in eliminating an

infinity of pointless attempts.

4.3 Contests

Intelligent systems are expected to perform well in uncertain

situations, and direct competition among systems might be an

appropriate way to generate uncertainty, providing means to rank them.

Examples of competitions are robot wars, fire-fighting

robot contests, or robot-soccer tournaments. It is necessary to define the contests such that they address either the body or

the mind of the systems in competition. Robot wars address

obviously both. Athletic capabilities, rather than intelligence,

also determined the outcome of the last World Cup for Robot

Soccer, at which one team had access to more powerful motors

than the other teams.

To dissociate the two components, an easy way would

be to organize games between robots mechanically identical,

but driven by different minds, a luxury seldom available with

human beings.

4.4 Vote

Capturing ail elements necessary for intelligent behavior is a complex and controversial endeavor. The Vector of Intelligence

and supporting features, even after unnecessary

anthropomorphic features have been eliminated, still has

dimensions judged by perception.

Contemplating the behavior of living beings, one would

readily identify some that would be spontaneously perceived

as non-intelligent (stupid), while a whole range would be rather

neutral, neither intelligent nor stupid. An alternative approach

to building intelligent systems, could be to address the topic of

building non-stupid systems, specifying what they should

NOT do.

For instance, they should not persist in error. A nonstupid system would recognize a hopeless situation, and

change its behavior or method. This distinguishes intelligence

from blind instinct: ants keep building their houses even after the eggs have been removed. Although methods have been

defined and implemented for quite some time to avoid stalling,

quite sophisticated autonomous systems on a remote Planet

still got stuck, as do soccer playing robots. When a player

manages to gets unstuck by spinning, the human observers

cheer. However, the opposite result is achieved, when players

start spinning without a recognizable reason. Given the subjective component in characterizing

behavior as being intelligent, one could also envision scoring

by the vote of a human jury. This would be similar to the

methods used in some sports such as skating, in which a jury

gives two notes: one for the technical merit, one for the artistic impression. After all, contests and games are entertainment,

and audiences are entitled to have some fun.

5. REFERENCES

[1] White Paper of the Workshop on Performance Metrics for

Intelligent Systems,

http://www.isd.mel.gov/conferences/performance_metrics

42
Technologies for Engineering Autonomy and Intelligence

Tariq Samad

Honeywell Technology Center

3660 Technology Drive

Minneapolis, MN 55418, U.S.A.

tariq.samad@honeywell.com

ABSTRACT

A critical need for a high performance autonomous system is the

ability to generate appropriate responses when faced with

conditions that were not explicitly considered during off-line design. This paper emphasizes three technical concepts as

essential for meeting this need: multimodels, anytime algorithms,

and dynamic resource allocation. An example from ongoing

research in the autonomous uninhabited aerial vehicle domain is used to illustrate the concepts. Some competing concepts are discussed, and connections with consciousness and metrics are

outlined.

Keywords: Autonomous systems, multimodels. anytime

algorithms, resource allocation, uninhabited air vehicles,

consciousness.

1. Introduction

Society, industry, and government are ail exhibiting

increasing interest in autonomous and semi-autonomous

systems—complex engineered artifacts that require minimal

or no human involvement for their operation. The

motivations for this interest range from cost-efficiency to environmental safety to national defense. Potential

applications are everywhere, especially where human

operation is infeasible or dangerous: warfare, deep space

missions, terrorism countermeasures, and toxic material

handling are examples that come readily to mind.

From one perspective, it could be argued that the history of

automation is the history of progress in engineering

autonomy. We have been successful in automating ever- higher levels of operation, from regulatory control to supervisory control on upward. The Wright Flyer required

the human pilot to perform the inner-loop control function.

Today's commercial aircraft can fly from point A to point

B, automatically closing the loop on not just the inner loop

but also outer loop, handling qualities, and waypoint

following functions.

But autonomy is much more than automation. Today's

engineered systems may be highly automated, but they are

brittle and capable of "hands-off' operation only under

more-or-less nominal conditions. As long as the system

only encounters situations that were explicitly considered

during the design of its operational logic, the human

element is dispensable. As soon as any abnormal situation

arises, control reverts to the human.

An autonomous agent must be capable of responding

appropriately to unforeseen situations—that is, situations

unforeseen by its designers. Some degree of

circumscription of a system's operating space will always

exist, since survival under every environmental extreme is inconceivable, but "precompiled" behaviors and strategies are not sufficient for effective autonomy.

Below, I first discuss some features and characteristics that I believe are necessary for engineering high-performing

autonomous systems. Next, in Section 3, an example from

work in progress—which is focusing on the development of

autonomous capabilities for uninhabited aerial vehicles—is presented. Section 4 discusses some alternative

perspectives on engineering autonomy, followed by a selective review of the consciousness controversy. I conclude with a measurement-related note.

Parts of this paper are adapted from (Samad and Weyrauch,

2000) wherein some further elaboration can be found.

2. Aspects OF Autonomy

What does it mean to be able to react appropriately to unforeseen situations? To be capable of exhibiting

behaviors that are not precompiled? I would like to emphasize three technical concepts: multimodels, anytime

algorithms, and dynamic resource allocation. These are discussed below, and a brief digression on the topic of

hierarchy is also included.

2. 1 Multimodels: Explicit representations of

heterogeneous knowledge

In the absence of a sufficiently rich built-in library of

canned responses to specific situations, an agent must be

able to rely on an explicit, algorithmically manipulable

knowledge base. Instead of reflexive responses being built

in, the knowledge base required to generate responses

deliberatively must be incorporated.

The knowledge base must capture relevant details about the

capabilities of the autonomous agent, its environment, other

agents it expects to be interacting with, its tasks or

objectives, etc. These "models" need not be perfect; they

represent what the agent believes, not objective truths. But,

almost regardless of their fidelity, they allow the agent to reason and to determine responses to a potentially hostile

world. The effectiveness of the responses will be a function

of the fidelity of the models (in part), but, I would maintain,

43
autonomy and effectiveness are separable. Stupid

intelligence is an oxymoron; stupid autonomy is not. (In

most of this paper, however, I do not make a careful

distinction between intelligence and autonomy.)

I use the term multimodels to refer to multiple,

heterogeneous knowledge representations. We later discuss

a domain-specific example, but here I would like to note one property of multimodels that is likely to be useful across domains. The degree of precision and accuracy of

knowledge that an autonomous agent must consider will vary with the situation it finds itself in. In some cases, disparate models may be used to capture different levels of

detail. However, a greatly preferable option is a unified

modeling framework that is capable of providing estimates

or predictions at multiple levels of resolution, the level in effect at any time being specifiable by a higher level function.

2.2 Dynamic resource allocation and anytime

algorithms

An autonomous agent must be able to dynamically manage

its processing and other (sensing, actuation, communication,

power) resources. In the face of multiple competing

demands and objectives, each of which requires individual

algorithmic attention, an agent cannot generally afford to examine any exhaustively. The world does not wait for

closure of contemplation.

Thus, tradeoffs must be made in real-time, to decide how

inevitably inadequate resources must be apportioned to the

multiple demands on them. This is an issue that generally

gets little attention from the intelligent systems community,

yet it is no less critical than the issue of designing

algorithms for information processing for autonomous

systems.

Different processing tasks have different criticalities,

deadlines, and other properties. Some tasks may need to be

executed on a fixed periodic basis, others may be eventdriven, others yet may be continually ongoing. This variety

is suggestive of the complexity me resource management for autonomous s_> Of particular interest for autonomous operation are "anytime" algorithms—algorithms that are able to flexibly

exploit available computational resources. Beyond a certain

minimum execution time that it may require to generate an

initial candidate solution, an anytime algorithm can

iteratively improve on this solution over time. Randomized

algorithms such as evolutionary computing are prototypical

examples.

Resource management in current control systems presents an illuminating contrast with the needs for autonomous

operation noted above. All control systems today have to address resource constraints. This is done by determining

ahead of time—during the design process—precisely which

tasks will need to be executed under what conditions. Task

execution schedules can then be precomputed and defined.

This static scheduling approach is infeasible for autonomous

systems.

2.3 Hierarchies, hut not strict ones

The sophisticated information processing systems we

currently engineer are almost always hierarchical. Further,

the design methodology that is proposed in today's technoculture emphasizes strict, hierarchically structured processes. Hierarchy as an engineering design heuristic has

much to recommend it, but I would assert that it is a mistake

to assume that all intelligent systems must be analyzable as

strictly hierarchical. One need only look at the central nervous system of any organism one thinks of as intelligent

(e.g., the human brain) as evidence. There is certainly

structure to the brain, but a formal, strict hierarchy is a counterfactual insistence. Bypass connections, reflex

reactions, affective conditioning, many intriguing

pathologies—these are all indicative of an organization that

is better thought of as a web than a tree, or at least as only

loosely hierarchical.

As an example, see Figure 1. Elements of the figure

resemble the typical multilayer hierarchical architectures

that attempts at engineering autonomous systems often

adopt (i.e., the organization as shown of the spinal column,

the brainstem, the thalamus, and the cerebrum). However,

additional pathways are also present, forming prominent

and crucial bypass structures and feedback loops.

^

Limbic System Cerebrum

( Motivation) (CognUive Processes)

Basal Ganglia

( Coordination)

Thalamus

{Data Concentrator)

A: Outer feedback loop

enabling and modifying

d>Tiamic response (coordination of iniention and sequential

activity)

Cerebellum (Prophoceplion

)

Spinal Column

B Inner feedback loop for sensorimotor system, tracking and modifying muscle response

errors (coordination of response)

Figure 1 . Simplified architecturefor primate central nervous system (figu"e courtesy ofBlaise Morton).

3. Example: Ioute Optimization for an

Uninhal. fED Autonomous Vehicle

We briefly dv uss here some ongoing research at Honeywel' ' vchnology Center, targeted toward the

developm \ i of algorithms and software mechanisms f

uninhabiti: • air vehicles (UAVs), with specific emphas'.

demandin iilitary applications. Multimodels, anytir.'

on

44
algorithms, and dynamic resource allocation feature

prominently in our research.

An example of a multimodel knowledge base for route and

trajectory optimization in a UAV is shown in Figure 2. The

figure shows a (wavelet-based) multiresolution

time/frequency model of a trajectory. By selectively setting

specific parameters—each associated with one of the boxes

in the top graphic—to zero, the space of trajectories can

automatically be constrained so that different segments of

the trajectory are defined in more or less detail as appropriate for a given situation. Trajectory optimization is then conducted over the enabled parameters, ensuring that computational resources are used efficiently. Under normal

conditions, we can expect that the resolution profile would

gradually decrease over the optimization horizon. The

figure also shows multiresolution models of aircraft dynamics and terrain; these and other models are necessary

to check various constraints on a hypothesized trajectory

and to calculate the cost function for optimization. (See

Godbole, Samad, and Gopal [2000] for more details.)

g. s

t

Figure 2. Multimodelsfor trajectory optimization for an autonomous aircraft.

This multimodel approach has been integrated with an anytime algorithm for route optimization, and a simulation

result is shown in Figure 3. A UAV is skirting a threat area when a target model (including the target's coordinates) is communicated to it. The original route (not shown in the

figure) was not overflying the target area but instead

adopting a low elevation radar-evading route over a ravine.

Once the target is detected, the online trajectory

optimization algorithm is executed. In this case, greater

resolution is desired over a medium horizon interval, and

minimizing the previous cost function for low flight is considered less important than rapidly generating an

alternative route that overflies the target area. As the UAV

continues its flight, incremental re-optimizations are performed at regular intervals, with the computational

resources expended on these optimizations varying

continuously depending on the particular objectives and

models under consideration at that time.

We currently use an evolutionary computing algorithm—an

extension of the algorithm outlined in (Samad and Su,

1996)—for optimizing the trajectory. The EC algorithm

searches over the space of nonzero coefficients in the

multiresolution wavelet-based representation noted earlier.

As I hope this example illustrates, the concepts of

muldmodels, anytime algorithms, and dynamic resource management are related in that effective autonomy requires

the integration of all of them. Given a particular situation

that requires an autonomous agent to react, it must be able:

• to access the knowledge it has that is relevant to the

situation in the context of its goals and abilities;

• to flexibly reason about its decision and control

options, adapting the level of scale and resolution in its processing to the situation and objectives;

• to tradeoff competing demands and requirements in the

face of resource limitations.

/

' Target detected:

higher resolution,

medium horizon,

rapid optimization

W; Dynamic Kinematic Turn Constraint Models Models Models IU>i

Fine Resolution Mid-Resolution Coarse Resolution

Tprrain MoHd Tpirain MnHpl Terrain Model @

Figure 3. A framefrom a simulation example ofactive

multimodel controlfor trajectory optimization.

4. Alternative Perspectives

There are, however, other reasonable solutions and

perspectives to engineering autonomy that are being

proposed, and a few are briefly noted in this section.

4. 1 Model-free autonomy

It seems reasonable to correlate the autonomy of a system

with the fidelity or scope of the models accessible to it, a connection 1 have made above. The richer the explicit

45
representations of its environment, itself, its collaborators,

etc., that a system contains (regardless of whether these

representations are acquired through learning or are hardwired by a designer) the more likely that an engineering

system can operate effectively without continuous human

supervision. So a model that can be symbolically

manipulated may be seen as a necessary condition for

autonomy.

But consider (as much research in intelligent systems is starting to do) an ant. There are certainly properties of ant behavior that we would be delighted to be able to incorporate within constructed systems with autonomy. An

artificial ant, if we were able to construct one, would be

considered to be a system with some non-trivial degree of

autonomy.

Or, if the capabilities of an ant do not warrant the

"autonomy" label, what about an ant colony? A million

ants no more make an explicit, manipulable model of the

world than an ant by itself

The most prominent exemplar of this line of research in autonomous systems is the "subsumption architecture" of

Brooks (1991), a central tenet of which is that the world can

be its own model. No representations are needed—in fact,

they are seen as harmful since in dynamic and ever- changing environments they can rapidly become outdated.

4.2 Is biology the only model?

Today, all the truly autonomous systems that exist are biological ones. It therefore seems appropriate to mimic

salient features of biological systems in the design of

engineered autonomy. However, an alternative viewpoint

may lead us to question such biomimicry. Most human

engineering, an endeavor that has enjoyed considerable

successes, has not drawn design inspiration from biological

principles—airplanes are an obvious example.

Architectural sketches of brain organization (as in Figure 1) may be dismissed as irrelevant by this argument.

Of course, until some non-biologically-inspired autonomous

artifact is produced, the study of existing autonomous

systems (i.e., biological ones) should be helpful. But it can

legitimately be argued that biology need only be a weak

model.

4.3 Autonomy need not be physically grounded

Our discussion above has exemplified autonomous systems

with UAVs, and most research in autonomy focuses on

vehicular systems (terrestrial, undersea, or in air or space).

While autonomous vehicles are a particularly exciting

prospect for future engineering systems, autonomy, as a property, should not be considered constrained to physically

mobile platforms.

In fact, it is important to consider autonomous systems that are not vehicles, since a broader understanding of autonomy

is contingent on an understanding of the full spectrum of the

topic. Different application areas will have specific

characteristics. For example, in the process industries there

is a continuing drive to increase the level of automation in plants, sometimes even quantified by a "number of loops

per operator" metric. An autonomous decision and control

system for an oil refinery will have to deal with issues

related to high dimensionality (a refinery can have 20,000

sensors and actuators), significant delays due to material

transport (dead times can be on the order of hours), and the

lack of full state feedback.

At an even greater remove from physicality, we can contemplate autonomous computer and communication

networks, which need operate only in the "virtual" realm.

5. Consciousness—Requirement or Red

Herring?

The notion of developing engineered sensors or actuators, or even low-level models of computation, that are based on

biologically gleaned principles is uncontroversial.

Embodying higher-level cognitive capabilities in computational systems, however, is another matter. Some

would argue that the sorts of phenomena found in the brains

of humans cannot even in principle be realized by the sorts of machines we are contemplating. The levels of autonomy,

intelligence, and adaptability exhibited by humans are thereby excluded (the argument goes) from realization in engineered systems.

The concept of consciousness lies at the center of this controversy. 1 take it as given that human-like performance

by a machine requires the machine to have something akin

to consciousness—an ability to reason about and reflect on

its own behavior, not just "blindly" follow preprogrammed

instructions.

There are two theoretical limitations of formal systems that

are driving much of the controversy—the issue under debate

is whether humans, and perhaps other animals, are not

subject to these limitations. First, we know that all digital

computing machines are "Turing-equivalent"—they differ

in processing speeds, implementation technology,

input/output media, etc., but they are all (given unlimited

memory and computing time) capable of exactly the same

calculations. More importantly, there are some problems

that no digital computer can solve. The best known

example is the halting problem—we know that it is impossible to realize a computer program that will take as input another, arbitrary, computer program and determine

whether or not the program is guaranteed to always

terminate.

Second, by Godel's proof, we know that in any

mathematical system of at least a minimal power there are

truths that cannot be proven and falsehoods that cannot be

disproved. The fact that we humans can demonstrate the

46
incompleteness of a mathematical system has led to claims

that Godel's proof does not apply to humans.

In analyzing the ongoing debate on this topic, it is clear that

a number of different critiques are being made of what we

can call the "computational consciousness" research

program. In order of increasing "difficulty," these include

the following:

Biological information processing is entirely analog,

and analog processing is qualitatively different from

digital. Thus sufficiently powerful analog computers

might be able to realize autonomous systems, but

digitally based computation cannot. Most researchers

do not believe that analog processing overcomes the

limitations of digital systems; the matter has not been

proven, but the Church-Turing hypothesis (roughly,

that anything computable is Turing-Machine [i.e.,

digitally/algorithmically] computable) is generally

taken as fact. A variation of this argument, directed

principally at elements of the artificial intelligence and

cognitive science communities, asserts that primarily

symbolic, rule-based processing cannot explain human

intelligent behavior.

Analog computers can of course be made from nonbiological material, so the above argument does not

rule out the possibility of engineered consciousness.

Assertions that the biological substrate itself is special

have also been proposed. Being constructed out of this material, neural cells can undertake some form of

processing that, for example, silicon-based systems

cannot. Beyond an ability to implement a level of self- reflection that, per Godel, is ruled out for Turing

machines, specifics of this "form of processing" are seldom proposed, although Penrose's hypothesis that the brain exploits quantum gravitational effects is a notable exception (Penrose, 1989). (It is worth noting

that no accepted model of biological processing relies on quantum-level phenomena.)

It has also been argued that intelligence, as exhibited by

animals, is essentially tied to embodiment.

Disembodied computer programs running on immobile

platforms and relying on keyboards, screens, and files for their inputs and outputs, are inherently incapable of

robustly managing the real world. According to this view, a necessary (not necessarily sufficient)

requirement for an autonomous system is that it undertakes a formative process where it is allowed to

interact with the real world.

Finally, the ultimate argument is a variation of the

vitalist one, that consciousness is something extramaterial. For current purposes this can be considered a

refrain of the Descartesian mind/body dualist position.

Modem variations on this theme include Chalmers

(1995)—an article that also includes a rebuttal by

Christof Koch and Francis Crick.

The issue of consciousness in machines has captured the

imagination of many as a result of the famous (or notorious)

Chinese room thought experiment suggested by John Searle

(1980). Searle imagines himself locked inside a room,

unable to communicate with anyone outside except through

slips of paper passed through a slot in the door. These slips

of paper are written in Chinese, a language Searle has no knowledge or understanding of However, Searle has been

given a voluminous "script" that details (in English) the

algorithmic manipulations that he should carry out upon

receipt of messages. Some of the messages can have

questions written on them, others may describe a story.

Searle allows that the script is perfect in that the

manipulations result in responses that Searle can transcribe

(the symbols that he reads, manipulates, and writes are meaningless squiggles to him) and pass back to his

interrogator. These responses are in fact appropriate in context; to the person outside, Searle must understand

Chinese. The point of the Chinese room (thought)

experiment is that knowing how the responses were

generated we would not say that Searle "understands"

Chinese. This is a critique of one school of thought that maintains that rule-based algorithmic processing is sufficient for understanding. Variations of the experiment

and the argument have since been directed at other types of

automated mechanisms.

Consciousness is a multifaceted phenomenon. 1 would

maintain that reflective, deliberative decision making is an important element, although admittedly not the only one. Thus the technical concepts discussed earlier—multimodels,

anytime algorithms, dynamic resource allocation—which, I argued, are essential for high-performance autonomous

behavior, are by the same token necessary correlates of

consciousness. (Our observations of) our own conscious

processing support(s) this contention—we dynamically

allocate cognitive resources as appropriate for an unforeseen

situation, scale the precision and resolution of our

processing accordingly, and rely on our knowledge of the

various systems and phenomena that constitute our

environment.

6. Toward Metrics

Even for humans, testing and quantifying intelligence is a controversial activity. The difficulty of compressing the

multifaceted nature of intelligence into one scalar quotient

has led to proposals to consider "intelligence" not as one

unitary quantity but as a collection of properties that are mutually incommensurable (e.g., Gardner, 1983).

But humans, as a species, have much in common. We all have the same sensory apparatus; the same physiology, more or less; the same innate drives; the same

communication apparatus; etc. If quantifying intelligence is so problematic for humans, one can wonder whether it is even sensible for artificial systems, which may have little or nothing in common. Comparing and contrasting the

47
intelligence of an intelligent search engine for the Web with

the intelligence of an autonomous vehicle is a challenge that

is not only huge but perhaps unaddressable. We will need

to decompose the notion of intelligence in this case too,

except that instead of a handful of separate factors we might

end up with a much larger number.

The technical concepts I have focused on in this paper can

all be considered dimensions along which autonomy and/or

intelligence can be measured. The extent to which an agent

has available explicit models of relevant phenomena and

systems, the scaling capabilities of the anytime algorithms

available to it, and the sophistication of its adaptive

computational resource allocation mechanisms, all bear on how well the agent will perform in a complex, dynamic

world. More research is needed before these connections

can be formalized or quantified—I have been concerned

here with just their identification.

Acknowledgement

The research discussed in this paper is supported in part by

the U.S. Defense Advanced Research Projects Agency

(DARPA) under contract number F33615-98-C-1340.

References

Brooks, R. (1991). Intelligence without representation, Artificial Intelligence, vol. 47, pp. 139-159.

Chalmers, D. (1995). The puzzle of conscious experience.

Scientific American, pp. 80-86, December.

Gardner, H. (1983). Frames ofMind. New York: Basic Books.

Godbole, D., T. Samad, and V. Gopal (2000). Active multi-model

control for dynamic maneuver optimization of unmanned air vehicles. Proc. Int. Conf. on Robotics and Automation, San

Francisco, CA.

Penrose, R. (1989). The Emperor's New Mind: Concerning

Computers, Minds, and the Laws ofPhysics, Oxford Univ. Press.

Samad, T. and T. Su (1996). On the optimization aspects of

parametrized neurocontrol design. IEEE Transactions on

Components, Packaging, and Manufacturing Technology, vol. 19, no. 1 , pp. 27-36.

Samad, T. and J. Weyrauch, eds. (2000). Automation, Control and

Complexity: An Integrated View. Chichester, U.K.: John Wiley

& Sons.

Searle, J. (1980). Minds, brains, and programs. Behavioral and

Brain Sciences, vol. 3, pp. 417-458.

48

t

.1
Theoretical Constructs and Measurement of Performance and Intelligence in

Intelligent Systems

Larry H. Reeker

National Institute of Standards and Technology

Gaithersburg, MD 20899

(Larry.Reeker @NIST. gov)

Abstract

This paper makes a distinction between

measurement at surface and deeper levels. At

the deep levels, the items measured are

theoretical constructs or their attributes in

scientific theories. The contention of the paper is

that measurement at deeper levels gives

predictions of behavior at the surface level of

artifacts, rather than just comparison between the

performance of artifacts, and that this predictive

power is needed to develop artificial intelligence.

Many theoretical constructs will overlap those in

cognitive science and others will overlap ones

used in different areas of computer science.

Examples of other "sciences of the artificial" are given, along with several examples of where

measurable constructs for intelligent systems are needed and proposals for some constructs.

Introduction

There are a number of apparent ways and

certainly many more not so apparent ways to measure aspects of performance of an intelligent

system. There are a variety of things to measure and metrics for doing so being proposed at this workshop, and it is important to discuss them.

To develop a measure of machine intelligence

that is supposed to correlate with the system's

future performance capability on a larger class of

tasks considered intelligent would be analogous

to human IQ. That would require agreement on

one or more definitions of machine intelligence

and finding a set of performance tasks that can

predict the abilities required by the definition(s),

and still might not say much about the nature of

machine intelligence or how to improve it.

One reason that metrics of performance

(and perhaps, of intelligence) are needed is that

they directly address the fact that it has been

difficult to compare intelligent systems with one

another, or to verify claims that are made for

their behaviors. Another reason is that having

measurements of qualities of any sort of entity

provides a concrete, operational way to define

the entity, grounding it in more than words

alone. All of these aspects - comparability,

verifiability, and operational grounding - were

undoubtedly at least part of what Lord Kelvin

meant about measurements providing a feeling

that one understood a concept in science. (See

the preamble to this workshop [Meystel et al 00]:

"When you can measure what you are speaking

about and express it in numbers, you know

something about it.")

The measurements that form the primary

topic of this paper are of a different type. They

are ones that look ahead to the future, when the

intelligent systems or artificial intelligence field

is more mature. The notion of mature field is defined here in terms of scientific theories that

predict the performance of the systems on the

basis of the underlying science. It is suggested

that really valuable measurements require

reliable predictions of this scientific sort, rather

than just ways to compare the technological

artifacts based on the science. To do this, it is necessary to develop theories containing

measurable theorefical constructs, as will be

discussed below.

The discussion of metrics for attributes of

theoretical constructs herein does not conflict in any way with the idea of overall system

measurements, comparisons, or benchmarks,

which are useful for the purposes mentioned

above. In fact, it is a philosophical problem to decide where theoretical constructs stop and

empirical constructs begin. Measurements of

artifacts will be referred to as surface

measurements, those of a more theoretical

nature as deep measurements, terms borrowed

from Noam Chomsky's [65] terms for levels of

syntactic description. The question of "how

deep" can be left open at this time. This paper

advocates looking for measurable theoretical constructs at the deeper level that will predict

surface behaviors at the level of the system or subsystem, or of an entire artifact.

* The latter term will be used herein because the

shortened form, "AI" is more common than "IS".

49
The remainder of the paper explains the

form that we will expect for AI theories in the

future if they are to qualify as scientific theories

and suggests theoretical constructs that may have

measurable properties. It will discuss existing constructs that are developing as candidates for

deep metrics and how they may relate to surface

measurement. It will compare them to constructs

in existing scientific theories at deep and surface

levels. It will suggest that they will naturally

relate to constructs from the artificial and natural

sciences, specifically from cognitive science and

computer science.

Computation Centered and Cognition

Centered Approaches to AI

At all levels, from surface to deep, the constructs to be measured may depend on the

approach taken to AI. There are two

distinguishable approaches that have been taken over the years, which we will call "computation

centered" and "cognition centered" . The

computation centered approach focuses on how

certain tasks can be accomplished by artificial systems, without any reference to how humans

might do similar tasks. We do not usually think

of numerical calculation as AI, but if we did, we

would have to think of the way it is done as computation centered. There is no particular reason to make it cognition centered.

In the cognition-centered approach to AI,

the tradition is to discover human ways of doing

cognitive tasks and see how these might be done

by intelligent systems. Sometimes the

motivation for this approach has been to try to find plausible models for human cognitive

processes (cognitive simulation), but for AI

purposes, it has often been a matter of using

human clues to try to accomplish the

computation centered approach. Some

researchers feel that developing the artifacts

using cognitive ideas may lead to more robust AI

systems (using "robust" in the sense that the

system is not narrow or "brittle" in its intelligent

capabilities). But it is a natural way to think

about the developing AI capabilities, since not

all areas related to intelligent activities have been

In the email exchange leading up to the

Workshop, a third approach, "Mimetic

Synthesis", whose prime concern is the "Turing

test" one of representing a computer to a human

user as if it were another human, was

distinguished from the two mentioned by Robby

Garner. It is a good distinction, though like the

others, the boundaries are not always clear.

explored and reduced to mathematical methods

to the extent of numerical calculations, or even

of mathematical logic, which might direcdy

facilitate a computation centered approach.

Mathematical logic makes an interesting case for pointing out that most AI researchers in practice blend the computation centered and

cognition centered approaches, since it is formalized, yet still can be approached in a cognition centered way. Computers actually

implement mathematical logic, which is essential

in control statements of programming languages.

However, actually proving theorems in logic

(beyond propositional logic, where truth-table

methods can be used), is a creative intelligent

activity. There, things become more complex, in

different ways. The first complexity is that is a

creative activity and we do not really understand even how people do it. Secondly, it is informationally complex: there are inherent

undecidability problems in logics of sufficient

richness for most interesting purposes.

In attempts to make it easier for humans to prove theorems, natural deduction methods were

invented by Gentzen [34] and developed by a number of people, notably Fitch [52]. In a sense,

natural deduction can be thought of as a computation-oriented version of theorem

proving, taking away some of the mental work of

creativity. But this does not change the inherent

informational complexity problems, which

provide inherent limits on computability.

Going beyond logic to general problem

solving one finds some empirical studies of

effective ways in which humans do it that antedate the computer. One of them, means-ends

analysis, was codified in the General Problem

Solver (GPS) program of Newell and Simon.

[63] (See also Ernst and Newell 65]. For

programs in the GPS era, it was in the spirit of

that work to attempt measurement of the extent

to which the program could mimic human

behavior. This was done by also studying verbal

protocols of people solving the problem. Any

way of comparing those to the performance of

the program was still pretty much a surface

measurement. Such surface measures of

cognitive performance, are also the heart of the

Turing test [Turing 50], but do not tell us much

about what is happening deeper in the system, as Joseph Weizenbaum showed with Eliza [66]

(and emphasized in an ironic letter [74]). In more recent times, case-based methods have

been advocated [Kolodner 88] as relating to way

some people solve problems and they do look

50
very promising. Some of the constructs from

these problem-solving methods will be

mentioned below.

Though computation centered and cognitive

centered approaches blend well, the

measurements that occur to the developers in the

two approaches will naturally differ, and this is particularly true as one tries to go to a deeper

level by using constructs that are based either on

cognition or on computation. In other words, AI

may have measurable constructs coming from at

least two different sources, the computation side

and the cognitive side. This fact has some

interesting implications as one looks at the

measurement of deeper constructs, which may

have to be reconciled with both approaches to be

meaningful.

The Structure of Scientific Theories

Today's views of scientific theory have

changed from those held in the 19"' Century,

Lord Kelvin's time. The bare-bones version of a

scientific theory today is that it consists of a model composed of abstract theoretical

constructs and a calculus that manipulates these

constructs in a way that can account for

observations and accurately predict the value of

experiments. The model is as central today as was the notion of measurement to Kelvin. The

theoretical constructs have a relation with

observed entities, properties and processes that

may be quite abstract, not necessarily readily

available to human senses, but following directly

from calculations based on the theory. There are a number of principles applied to a model that

give us increased confidence in the theory, but

the one most relevant here is that we can measure the observed entities to confirm the

predictions of the theories. So Kelvin's concern

has been preserved, but augmented, in today's

view of theories.

It is relevant to observe that the "calculus"

mentioned above is used in the dictionary sense "a method of computation or calculation in a

special notation (as of logic or symbolic logic)".

That means that it may be numerical or nonnumerical. In fact, as Herb Simon and Allen

Newell [65] pointed out, there is no reason that the calculus cannot be expressed in the notation

of a computer program, the better to speed its manipulation of the theoretical constructs.

For scientific theories in AI to be

respectable, there will be certain requirements on

them, and these affect whether they are accepted

or not and whether the theories in which they occur are accepted. The late Henry Margenau

had a pragmatic treatment of these requirements

in his book The Nature of Physical Reality

[Margenau 50]. A working Physicist as well as a philosopher, Margenau stressed that no amount

of empirical evidence was scientifically

convincing by itself, since it did not specify a unique model; and he also stressed the need for

the binding of theoretical constructs to one

another in a "fabric". This fabric was made up of

theory and of mappings to empirical data. The

theory was convincing to the degree that certain

criteria were met - not a "black and white"

situation, but one of degree. One of the criteria was the extent to which the models and

constructs were extensible to larger and larger areas of scientific endeavor. As the fabric of the

theory became larger and stronger, it became

more difficult to rip it asunder.

Perhaps our emphasis on finding metrics can

solidify the theoretical constructs of the field, as well as providing a means of measuring

progress. The key to doing this is not to think of

evaluation only as measurement of some

benchmarks or physical parameters

("behaviors") that are manifested in the

operation of the systems being evaluated. We

need to be thinking in terms of the inner

workings of the systems and how the parameters

within them relate to the measured externally

manifested behaviors.

One of Lord Kelvin's special interests was

temperature. Temperature is of course something that we experience, something not

wholly abstract. Certain physical properties are

related to temperature, and the most easily

observed is freezing and boiling of water. It took

some scientific discovery to realize that each of

these phenomena always take place at a particular (with a few reservations, like altitude

and purity of the water), but still, those are concrete embodiments. Temperature has been a subjective attribute during most of the history of

mankind, but the scientific notion of temperature

is a theoretical construct, even though it has a close correspondence to subjective experience.

The particular metrics chosen related to water

boiling (in both Fahrenheit and Celsius), to Freezing (in Celsius), and to the "coldest"

temperature that could be achieved with water,

ice and salt (in Fahrenheit). Lord Kelvin also

took the amazing step of developing a notion of

temperature that is really abstract. His zero point

of minus 273.15 degrees Celsius has never quite

51
been reached, and is far below what any person

could experience. Yet it is very real as a

scientific construct, one that is part of the fabric

of physical science and ties various aspects of

science together in that fabric.

Many other common terms in physical

theory, like mass and gravity, are theoretical

constructs, though they are related to human

senses. Only in relatively recent physics history

have mass and gravity been understood, and we

owe that understanding to bits of inspiration on

the part of Galileo and Newton. Having only

half a century of AI history to look back on, we

cannot really expect to have such a firm fabric of

theoretical constructs stitched together. But

some ideas are given below, after a comparison

of Sciences that study natural and the artificial systems.

Sciences of the Artiflcial and their relation to Natural Sciences

Herbert Simon came to the conclusion that

there was a place for what he called "Sciences of

the Artificial" in his important book [69]. He did

not invent the study of artifacts in a systematic

manner, but he realized accurately and acutely

that that artifacts could be subjects of "real

sciences", with deep theories of the sort that exist

in natural sciences. We will now consider some

of the implications of this idea.

The boundaries between sciences of the

artificial and the natural sciences are not clear- cut in practice because nature colors human

artifacts, determining their possibility and their

features. The "engineering sciences", the

portions of engineering that has been formalized

in the sense of that they can predict the behavior

of artifacts, including aspects such as stability

and strength can be considered sciences of the

artificial. The reason that this is not remarked

upon more often is that they have called upon

physical sciences more and more over the

centuries to aid the "ingenuity" that gives the

profession its name.

Linguistics is a science of the artificial. Human language is the artifact that it studies.

But of course, the properties of the artifact are shaped by the natural properties of human

learning and cognition, human hearing and

speech in many ways. In the domain of

phonetics, for example David Stampe's "natural

phonology" [Stampe 73, Donegan and Stampe

79] characterizes some of the interactions

between language as an artifact and as a natural

phenomenon. We do not understand even yet the extent of the interaction between linguistics and

human cognition. Is there an LAD (language

acquisition device) [Chomsky 75] innate in

humans that is specific to language, or is the

learning of language based on the same

principles as such other acquired systems as visual perception? Nobody knows for sure; but

whatever the case, the nature of the world and

the nature of learning processes must affect

language.

Computer Science is a science of the

artiflcial. Certainly, this is true insofar as it studies computers, which are artifacts; but also to the extent that it studies algorithms, which are human creations, too. The main subject studied

in much of Computer Science is not computers

but information, and the "state", which is all the

relevant information about a system at a given

time, is therefore a fundamental theoretical construct. Information is a theoretical construct

that is also fundamental in the natural sciences,

but whose significance as a theoretical construct has only become apparent in this century, as its relationship to entropy and its role in quantum

theory have been realized. So again. Computer

Science has both artificial and natural parts.

Economics, another science of the artificial,

studies a major artifact, the economy, and

looking at this science of the artificial can

provide some insight into the position of AI as a science of the artificial, and of the role of

measurable theoretical constructs.

Predictive Measurement in a Science of the

Artificial - An Example from Economics

Economics has struggled for longer than AI has

existed to find theoretical constructs that have

predictive power. Economics deals with large

amounts of aggregated data, so its empirical data

are statistical in nature, and its models are not as clear as physical models with respect to the

interrelationships among theoretical constructs, nor are they as widely accepted. Yet they do

allow some prediction of economic performance

and are used in control processes for the

purposes of economic stability, with a degree of

success.

As this paper is being written, the U.S.

Federal Reserve Bank is aggressively raising

interest rates because the employment rate

(inferred from job creation and unemployment

data) is high and economic growth (a function of

GDP change and other data) has been rapid. In

52
their models, this predicts increasing inflation (as

measured by the consumer and producer price

indices and other constructs). It has recently been

conjectured that there should be a role in these

models for productivity, the role of which is not

yet fully understood. So economic theory, as it develops, must relate all of these constructs and

others: average interest rates, supply and demand

for money and goods, savings rate, etc.

Economic theories and their constructs are

still complex and incomplete. Incorporated in

complex computer models, their predictions are not totally trustworthy, but the predictions are

testable. Economics provides an example from

another science of the artificial that AI should

follow in formulating and measuring constructs.

Surface Measures and Theoretical Constructs

in AI - Some Examples

The sort of predictive ability that economists

want, we would like to see in AI, too. If we have

theoretical constructs at some deeper level, we

can also use the theories of which they are a part

to simulate or predict mathematically what

happens if we increase or decrease parameters

related to those constructs. It is a thesis of this paper that there are theoretical constructs that can predict system performance measured in terms of surface measures. At this point in the

development of AI as science, it is hard to say

just exactly what they would be, but some ideas can be drawn from today's AI and related

subjects.

An Example Construct: Robustness

A surface measurement that could be very

valuable across a variety of systems is some

measure of robustness - the ability to exercise

intelligent behavior over a large number of tasks

and situations. From a computation-centered

standpoint, if systems become robust, AI

progress would be easier to see. From a cognition-centered standpoint, a system can

never really be intelligent if it is not robust. (One

way to think of a measure of intelligence in a

single system would be as a measure of

performance, robustness and autonomy.) The

surface way to determine the robustness of a system would be to try it on a number of tasks

and see how broad its methods are. But what

makes intelligent systems robust? Learning

ability, experience, and the ability to transfer that

experience to new situations are all things that

come to mind. A rough sketch of how

measuring theoretical constructs in those areas

might give us a predictive figure for developing

robust systems is given below.

Robustness: Learning?

If learning can make systems more robust, it should be interesting to measure the strength of

the system's learning component. How easily

does it adapt the system to a new situation?

Unsupervised learning has wide applicability,

but it can basically only determine clusters of

similar items. Supervised learning must be

presented with exemplars to learn relations,

which seems not to be enough for a machine to extend its own capabilities. Reinforcement

learning (RL) is a blend of both cognitive and

computational centered AI. It started out as a model of classical conditioning, but turned out to be applied dynamic programming. There are a number of different techniques within RL, all of

which have many possible applications. Neural

nets or other approaches may be used. The

theoretical constructs include the state space

chosen, the reinforcement function, and the

policy. The field is becoming quite

sophisticated, and there are known facts about

the relation of these to outcomes in particular cases [Mahadevan and Kaelbling 96]. Suppose

that a reinforcement learning system constitutes a part of the intelligence of an intelligent system.

There should be some way of predicting how

that system would do upon encountering

problems of a certain nature. By knowing how it chooses the concepts in its system and how they

react on problems of that type, one can provide a

partial evaluation of how effective the learning

system would be. By obtaining such figures for

all such subsystems, one could relate them to the

performance of the full intelligent system. There

is much work to be done in that direction.

Under certain circumstances, one can imagine learning extending robustness; but

having to learn each new variations of a problem, even by reinforcement, is unlikely to lead to robustness quickly. It is expected that reinforced

behaviors learned in one situation might be

identical to those needed in another system, so

this may lead to more rapid or better learning in the second situation. One approach to this is to condition behaviors that are not built into the

system initially, as explored by Touretzky and

Saksida [97]. But, still, one would like to have more general ways of reusing "big pieces" of

learned knowledge.

53
Robustness: Transfer of Learning?

Transfer of learning is a phenomenon that

we may be able abstract to theoretical constructs

that can help to predict robustness. It is still not a deep measure, so it will then be important to predict transfer of learning from deeper

constructs which will be mentioned below. At

present, it. is a research challenge to build

transfer of learning into systems. But it is possible to see how one could test for it.

As far as measurement, here is roughly how

transfer of learning might be measured:

1. Machine performance is measured on Task

1. The score is P(tl, Tl)) = performance at time tl on Task 1. P is some suitably broad

performance measure.

2. Performance is measured on Task 2 without

learning (this being an artifact where we can

control learning) to obtain P(tl, T2)

(keeping the time variable the same because

the same machine abilities are assumed

without learning even if the measurements

are not simultaneous).

3. Note that if the measure is to have a meaning, previous training that might affect

Tl or T2 must be controlled for, which

could be difficult.

4. The machine is now allowed to perform task

Tl in which it learns, achieving better

performance at some time t2, i.e. P (t2, Tl)

>P(tl,Tl).

5. It is then tested on T2, and the question is whether P (t2, T2) > P (tl, T2) without

having done additional learning on Task 2.

If indeed P (t2, T2) > P(tl, T2) in some

quantifiable way, the system has achieved (at

least locally) one of the goals of AI, the transfer

of learning from Tl to T2. The amount of

transfer can be measured by the amount of

improvement on task2 as a function of the

amount of training on task Tl. Let us assume

that we can describe this by some transfer

effectiveness function, E for the system being

tested. Let us say E(T1, T2, t) gives "the

effectiveness of training on Tl for time t in terms

of transfer toT2". We could describe this by a graph of performance on T2 as a function of

time being spent on Tl

Developing such a measure of transfer of

learning and getting it accepted is not simple. To

be useful, we would need a way of comparing Tl

and T2, to be sure that the second task is not just a subtask to the first. Difficult or not, defined

measurements such as these are steps toward

understands the construct "transfer of learning"

and achieving it in artifacts. The measurable

transfer construct would, in turn, help to provide

a measurement of robustness, since learning

transfer can make a system more robust. It is a

step toward measurement of intelligence, at least by some definitions of intelligence, and,

intuitively, at least, would have some predictive

power.

How might we go about defining the

similarity of Tl and T2, as suggested above? We

would have to decide what we mean by

similarity of task. An interesting essay in this area is "Ontology of Tasks and Methods"

[Chandrasekaran, Josephson and Benjamins

[98]].

Various candidates for potentially

measurable constructs that could be used to produce transfer but also to relate transfer to other phenomena are mentioned in a book edited

by Thrun and Pratt [98], who have both had a research interest in learning-transfer processes.

From the computation side comes the possibility

of changing inductive bias. From the cognitioncentered side, there is generalization from things

already learned; but overgeneralization can be a major problem in learning, so it needs to be

constrained. (Some simple constraints on

overgeneralization in language learning are discussed in [Reeker 76].)

Robustness: Case-Based Reasoning?

Case-based reasoning is an intuitively

appealing technique that was mentioned earlier

in this paper. The idea is that one learns an expanding set of cases and stores the essentials

of them away according to their conventional

features. They are then retrieved when a similar case arises and mapped into the current case. Potential theoretical constructs include indexing

and retrieval methods for the cases, case

evaluation and case adaptation to the new

situation. The cases could also be abstracted and

generalized to various degrees, to a model.

Case-based reasoning is important for

cognition centered AI. It is intuitively the way

many people often figure out how to do things,

and is thus embodied in the teaching methods of

many professional fields - law, business,

medicine, etc. It provides a launching pad for

creativity as well, as mappings take place from

one case to an entirely new one. Perhaps the

new case is not really concrete, but a vague new

54
idea. Then the mapping of an old case to it may

resuh in a creative act - what we usually call analogy. Analogy, metaphor in language, is a

rich source - absolutely ubiquitous - of new

meanings for words, and thus of new ways to describe concepts, objects, actions. Perhaps one

key to robustness is the ability to use analogy.

Four interesting papers by researcher in the area can be found in an issue of American

Psychologist [ Gentner et al91 ].

Existing Surface and Subsurface Performance

Measures

Researchers in text-based information

retrieval (IR) have traditionally considered

themselves not to be a part of the AI field, and

some have even considered that artificial intelligence was a rival technology to theirs; but

there is an overlap of interest. It is worth noting

that IR has had a useful surface measure of

system performance that has guided research and

allowed comparison of technologies. The

measure consists of two numbers, recall and

precision [Salton 71]. Recall measures the

completeness of the retrieval process (the

percentage of the relevant documents retrieved).

Precision measures the purity of the retrieval (the

percentage of retrieved documents judged

relevant by the people making the queries). If both numbers were 100%, all relevant documents

in a collection would be retrieved and none of

the irrelevant ones. Generally, techniques that

increase one of the measures decrease the other.

Real progress in the general case is achieved if one can be increased without decreasing the

other.

For the IR community, better recall and

precision numbers have both shown the progress

of the field. They also show that it is still falling

short, keeping up the challenge, especially as the

need to use it for very large information corpora

rises. In addition, they provide a standard within

the community for judging various alternative

schemes. Given a particular text corpus, one can

consider various weighting schemes, use of a

thesaurus, use of grammatical parsing that seeks

to label the corpus as to parts of speech, etc., to improve the retrieval process. The interesting

thing is to relate these methods and the

characteristics of the corpus to precision and

recall, but so far that has not been sharp enough

to quantify generally.

Related to information retrieval is automated

natural language information extraction, which

tries to find specified types of information in

bodies of text (often to create formatted

databases where extracted information can be

retrieved or mined more readily). A related but

different (cost-based) measure was defined

several years ago for a successful information

extraction project [Reeker, Zamora and Blower

83]. One measure was robustness (over the

texts, not different tasks as in the broader

intelligent systems usage discussed earlier). This was defined as the percentage of documents out

of a large collection that could be handled

automatically. The idea was that some

documents would be eliminated through

automated pre-screening (because those

documents were not described by the discourse

model the system used) and relegated to human

processing. Another measure was accuracy (the

percentage of documents not eliminated that were then correctly processed in their entirety,

by the system). Yet another was error rate (the

percentage of information items that were

erroneous - including omitted - in incorrectly

handled documents). From this more detailed

breakdown, estimates of the basic cost of

processing the documents, based on human and

machine processing costs and costs assigned to errors and omissions, was derived. The measure

could be used to drive improvements in

information extraction systems or decide whether

to use them, compared to human extraction

(which also has errors) or to improve the

discourse model to handle a larger portion.

For information extraction projects, it was

further suggested that the cost of erroneous

inputs might drive a built-in "safety factor" that could be varied for a given application [Reeker

85]. This safety factor was based on linguistic measures of the text (in addition to the discourse

model) that could cause problems for the system

being studied. The adjustable safety factor could

be built into the prescreening mentioned above.

In other words, the system would process

autonomously to a greater or lesser degree and

could invite human interaction in applications

where the cost of errors was especially high. It was suggested that the system would place

"warning flags" to help it make a decision on

screening out the document, and these could also

aid the human involved. Although this was a

tentative piece of work, the idea of tying a surface measure (robustness) into the underlying

properties of the system is exactly like tying

measurable surface properties into underlying

theoretical constructs. The theoretical constructs mentioned in this case were structural or semantic ones from linguistics.

55
From the area of software engineering comes another tradeoff measure that is worth

mention. The author did some work on ways of

providing metrics - surface metrics, initially - for

program readability (or understandability)

[Reeker, 79]. Briefly, studies of program

understanding had identified both go-to

statements and large numbers of identifiers

(including program labels) as problems. At the same time, the more localized loop statements

could result in deep embeddings that were also

difficult to understand for software repair or modification. The vague concept of readability

could be replaced by a measure of go-to

statements and maybe also one of the number of

different identifiers. This particular study

suggested depth of embedding as a problem and

also suggested a tradeoff between depth of

embedding a metric called identifier load.

Identifier load was a function of the number of

identifiers and the span of program statements over which they were used. Identifier load

tended to increase as depth of embedding was

reduced by the obvious methods.

There were a number of similar software

metrics studies in the 1970s, and they continue.

This approach, however, was part of an attempt

to look at natural language for constructs that might be of relevance in programming languages

and programming practice [Reeker 80]. The

depth measure was based on an idea of Victor

Yngve [60], which came out of his work in

linguistics - an idea that retains a germ of

intuitive truth. Yngve had in turn related his

natural language measure of embedding depth to measures of short-term memory from cognitive

psychology. Whether these relationships turn out to be true or lead to related ideas that are true or not, they illustrate how theoretical constructs can stitch AI, computer science, and other

artificial and natural sciences together. They

also illustrate the quest for metrics that can firm

up the foundations of the sciences.

More Constructs To Be Explored

There are many more existing theoretical constructs that have arisen within AI or been

imported from computer science or cognitive

science that beg to be better defined, quantified,

and related to other constructs, both deep and

surface.

Means-ends analysis and case based

reasoning have both been mentioned as forms of

problem solving. How do these cognitive

characterizations of problem solving relate to

one another? At a deeper level is the construct of short term memory mentioned in the previous

section in relationship to Yngve' s depth. How

does short-term or working memory relate to long term memory and how are the two used in problem solving? The details are not known.

The size of a short-term memory may not be as relevant in a machine, where memory is cheap

and fast. But we cannot be sure that it is not

relevant to various aspects of machine

performance because it is reflected at least in the

human artifacts that the machine may encounter.

For instance, in resolving anaphora in natural

language the problem may be complicated if possible referents are retrieved from arbitrarily

long distances.

A similar problem arises from long-term

memory if everything ever learned about a concept is retrieved each time the concept is searched for. This can lower retrieval precision

(to use the term discussed earlier for machine

retrieval) and cause processing difficulties on a given problem. It may be that Simon's notion of

bounded rationality is a virtue in employing

intelligence. Are we losing an important

parameter in intelligence if we try always to optimize rationality? For AI system, anytime

algorithms and similar constructs for

approximate, uncertain, and resource bounded

reasoning have been developed in recent years, and hold a good deal of promise [Zilberstein 96].

An interesting theoretical construct arising

out of AI knowledge representation and the

attempts to use it in expert systems and agents

and for other purposes is that of an ontology.

"Ontology" is an old word in philosophy

designating an area of study. In AI it has come

to designate a type of artifact in an intelligent

system: The way that that system characterizes

knowledge. In humans, ontologies are shared to a large degree, but certainly differ from every

person to every other, despite the fact that we

can understand each other. Are some ontologies

indicative of more intelligence than others in

ways that we can measure? One suggested

criterion for high intelligence is the ability to understand and use very fine distinctions (or to actually create new ones, as described in Godel's

memorandum cited by Chandrasekaran and

Reeker [74]). Is an ontology's size important, or

its organization, or both? Can one quantify a system's ability to add new distinctions?

A related issue is vocabulary. Many people

think that an extensive vocabulary, used

appropriately, is a sign of intelligence, or at least

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scholastic aptitude. In computer programs that

do human language processing, the vocabulary

consists of a lexicon that generally also has

structural (syntactic) information for parsing or generating utterances containing the lexical item

and meaning representations for the lexical item.

The lexicon can be much larger than any

human's vocabulary; but for the vocabulary to be

used appropriately for language production or understanding, it still falls far short of the human

vocabulary. For that to be improved better

techniques of semantic mapping are required,

including links to ontologies and methods of

inferring the ontological connections and of

idiosyncratic aspects of speakers with which a conversation is taking place. Is the vocabulary an

indication of the size of the ontology and the

distinctions it makes, or vice-versa? Nobody

knows; but better theories of how they link up

are needed for both understanding and fully

effective use of human language by intelligent

systems.

Another cognitive concept that is still a mystery is creativity, certainly a part of

intelligence, or at least of high intelligence.

Does the ability to add entirely new concepts,

not taught, constitute creativity? How does one

harness serendipity to develop creativity? Is creativity linked with sensory cognition, the

cognitive phenomena related to senses, such as

vision, including perception, visual reasoning,

etc. There is a need for deep theoretical constructs underlying notions like creativity, and

for measures of these constructs and their

attributes [Simon 95, Buchanan 00].

Turning to computational constructs, we

notice that much of the AI described above takes

place through various forms of search. Already

there exists a pretty good catalogue of variations on search and how to manage it, in which a good

deal of theory is latent. Some of the search is of

a state space, involving the ubiquitous state concept basic to theoretical computer science.

Search is also coupled with pattern matching,

which underlies many of the methods mentioned

earlier in this paper.

The potential constructs mentioned here are

just a sample of the ones already available in

Artificial Intelligence, and to them should be

added others found in some of the major works

of Newell and Simon on Problem Solving and

Cognition [Newell and Simon [65], Newell

[87]].

Summary and Author's Note

The development of a true science of

artificial intelligence is something that has

concerned the author for a long time. It has been

encouraging to see the development within the

field of interesting and non-obvious theoretical constructs. This paper has suggested that

theoretical constructs with attributes that we can measure are especially valuable and it has

suggested a number of such candidates. The

paper suggests that we enlist Lord Kelvin's

emphasis on measurement in choosing such

constructs. These same measurable theoretical constructs will in many cases relate (at least at deeper levels) to those of cognitive science,

computer science, and other sciences. They will

help predict measures at the surface that can be

used to provide metrics for the performance (and

through that, the intelligence) of intelligent

artifacts. We should have in mind the quest for

such measurable constructs as we move forward

in creating intelligent artifacts.

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59
Intelligence with Attitude

W. C. Stirling and R. L. Frost

Electrical and Computer Engineering Department

Brigham Young University

Provo, UT 84602

ABSTRACT

An essential feature of intelligence is the ability to make autonomous

choices. A new paradigm of satisficing decision making incorporates

two utilities for decision making, rather than the usual single utility

that is characteristic of optimal decision making. These two utilities may be used to define figures of merit for the intellectual power or

fitness of the decision maker as it functions in its environment. These

utilities may also be applied in group settings. In particular, societies

of negotiatory decision makers may undergo considerable tension as

they attempt to reach a compromise that is acceptable to the group as

a whole and to all members of the group. KEYWORDS: multi-agent decision theory, satisficing, attitude, negotiation

1. INTRODUCTION

There are three issues that must be addressed in the design of

an intelligent decision system: (a) defining the alternatives, (b)

defining the preferences, and (c) choosing between the alternatives as a function of the preferences. The first two issues are highly dynamic. Alternatives may appear and disappear and

preferences may change. Much of the study of intelligent sys- tems is properly focused on these dynamics. At the moment of

truth when a decision must be made, however, we must assume

that the alternatives and preferences have been defined, and all

that remains is to make the choice. This paper focuses on this

last, consummate step.

The ability to make decisions is essential to intelligent behavior. Indeed, the word intelligent comes from the Latin roots

inter (between) + legere (to choose). We thus assume that there

is only one essential characteristic of intelligence in man or machine—an ability to choose between alternatives.

Choices between alternatives, or decisions, are usually justified by the maximization of expected utility, an approach Simon calls substantive rationality [8]. We argue that for multiple agents, especially those in dynamic environments, the re- quirement for substantive rationality is too demanding. First,

although a solution may exist, the information or computing

power necessary to find it may be unavailable. We will often be

forced to fall back on what Simon terms procedural rationality, or the reliance on heuristic or ad hoc procedures defined by an

authority. Second, and more serious, is that the existence of an optimal solution may be in doubt. Von Neumann-Morgenstem

game theory shows that for many games a solution that is si- multaneously best for the group and for each individual in the

group simply does not exist. This seems to imply that a theory

of group decisions satisfactory for the synthesis of coordinating

agents cannot be obtained by a straightforward maximization of

utility.

We are thus motivated to consider definitions of rationality upon which we can build a more robust theory of intelligent

multi-agent decision making. We hold that the fundamental

obligation of a rational decision maker is to make decisions that

are, in some well-defined sense, good enough. Historically, the study of good enough decisions was first formalized by Simon,

when he introduced the term satisficing to characterize decisions that achieve the decision maker's aspiration level [6,7].

This notion of satisficing defines quality according to the criteria used for substantive rationality, but evaluates quality against

a standard that is chosen more or less arbitrarily. It essentially

blends substantive and procedural rationality, and is a species

of what is often termed bounded rationality.

Rather than blend the two extremes of substantive and pro- cedural rationality a la Simon, our work explores an alternative

which leads naturally to a set of satisficing solutions that is consistent with Simon's intent. It also guarantees the existence of

jointly rational decisions, and seems to be a natural vehicle for

the design and synthesis of intelligent decision systems.

We start by assuming that the most primitive way to make

decisions is to make intra-option comparisons in the form of

dichotomies. We define two distinct (and perhaps conflicting)

sets of attributes for each option and to either select or reject

the option on the basis of comparing these attributes. Such di- chotomous comparisons are intrinsic, since the evaluation of an option's merits is not referenced to anything not directly related to the option, including other options. They are also local comparisons; it is not possible to form a global ordering the

options on the basis of such comparisons. An intrinsically rational choice is one for which the decision maker's benefits are

at least as great as its costs. We define a satisficing decision

60
as one that is intrinsically rational, because these options are good enough, in the sense that their attributes have been favorably compared with a standard. We differ from Simon only

in the standard used for comparison: the positive and negative

attributes of each option, versus externally supplied aspiration

levels.

Intrinsic rationality appears to be a weaker notion than substantive rationality. Although it identifies all options that are,

in the sense we have defined, good enough, it does not insist on a unique solution. At the moment of truth, the decision

maker may choose any of the satisficing options with the as- surance that it will at least get its "money's worth." In practice,

however, the advantage of a theory founded on substantive rationality may be more illusory than real. Objective functions

themselves are often created by an ad hoc combination of pref- erences into a single performance index, and this combination

can be, and usually is, manipulated until satisfactory behavior

is achieved. Thus, even optimization approaches rely in their

application on satisficing notions, however informally.

As mentioned earlier, our approach to intrinsic rationality requires the definition of two preference functions, one to characterize the desirable attributes, and one the undesirable

attributes, of each option. An option is desirable to the degree

that it achieves the goal. It is undesirable to the degree to which

its adoption consumes the decision maker's resources, such as energy, safety, or other costs. Separate preference functions

permit the development of metrics to evaluate how suited the

decision maker is to function in its environment. Intuitively,

if a decision maker has options available to it that achieve its goal with low cost, it is well-suited for its environment. On

the other hand, if it must incur great cost or undergo great risk

to achieve its goal, it is clearly not as well suited. Although

the goal may be achieved equally well in either case, there is

a fundamental difference in the ability of the agent under the

two scenarios. This difference may not be easily discernible

under the substantive or procedural rationality paradigms, but

it is clearly discernible under the intrinsic rationality paradigm.

In the following we first summarize the mathematical development of satisficing decision theory. We next introduce a concept of attitude, or disposition, for the agents, and develop

figures of merit for evaluating the equivocation experienced by

the decision maker or decision making system. We then present

a basic negotiation theorem and describe a simple negotiatory

process to converge to a rational compromise. We then finish with an example and draw conclusions.

2. SATISFICING

Von Neumann-Morgenstem game theory is based on a very so- phisticated paradigm—global optimization. There are a number of basic problems, however, with optimization-based ap-

' Other researchers have appropriated this term to describe various notions of constrained optimization. In this paper, we restrict our usage to be consistent with Simon's original concept.

proaches. First, since it is well known that humans are not good

optimizers [ 1 , 2, 5], a decision-making system that seeks to ap- proximate human behavior may be unnecessarily constrained

by insisting on, and only on, optimal performance. Second, optimization is a fixed, or absolute concept, in the sense that if an

option is not the best, then it is unacceptable. There cannot be

degrees of optimization. Third, optimization is, fundamentally,

a notion of exclusive self interest, and does not easily generalize to settings where it is important to accommodate both group

and individual interests [4]. It is usually impossible to arrive at a joint solution that is simultaneously best for the group as a whole and for each member of the group.

Our notion of satisficing, on the other hand, does not insist upon optimal performance, and in return for this concession it logically permits degrees of satisficing and the accommodation

of both group and individual interests. By adjusting the tradeoff

standards between cost and benefit, it may be possible to find a

joint solution that is simultaneously good enough for the group

and good enough for each member of the group. This is the

fundamental goal of negotiation.

Our approach is to employ the mathematics, but not the

usual semantics, of probability theory. As discussed in [9, 10]

we may encode the preference relationships via mass functions,

which we term the selectability and rejectability functions. By

so doing, we are able to account for conditional preferences

(analogous to conditional probabilities) and to express both

joint (group) and marginal (individual) preferences.

We formalize this procedure as follows. Let Ui denote the

option set for the ith agent (we will assume Ui is of finite cardinality), i = 1, . . . , A^, let U = C/i X • • X Un denote the product space of joint options, and let u = [ui, . . . ,un}, where

Ui € Uj, denote an option vector. Let ps(u) indicate the degree to which the joint option u is successful in achieving a group goal. We require that X]uguPs(u) = 1 andps(u) > 0, so ps is a mass function, which we term the joint selectability massfunction. Also, letpR(u) indicate the degree to which the

joint option u consumes resources, and require this to also be a mass function, which we will term the joint rejectability mass

function. Next, let p5, : Ui [0,1] rndpur. Ui [0,1] be

marginal selectability and rejectability mass functions, respectively, derived fromps and j9r. by appropriate summation. For

a discussion of how these joint and marginal mass functions

may be practically constructed, see [9, 10].

These mass functions define a dichotomy for each option,

that is, they partition the attributes of the option into two cat- egories and provide a measure of support for each class of attributes. We evaluate each dichotomy by comparing the selectability (benefit) to the rejectability (cost) of each option. By

so doing, we define the jointly satisficing set

S6 = {ueU:ps(u) >6pr(u)},

and define the individually satisficing sets

S^ = {uet/,::p5.(n) >6pH.(u)},

61
i — 1,. . . ,N. The boldness parameter, b, is a constant in the interval [0, 1], which is nominally set to unity, but may be

decreased under special circumstances to be discussed below.

Sft is the set of all joint options that are good enough for the

group, and each is the set of all individual options that are good enough for the ith agent.

These sets provide the agent or group of agents with the

ability to make individual or group decisions. If the ith individual agent is empowered to make its own decision, it may choose

any member of E^. If the group as a whole is to make a collective decision, it may choose any member of Sf,. These choices

may be random, or they may be made according to some tie- breaking procedure.

3. EQUIVOCATION

Human decision makers often make qualitative assessments of

the difficulty, in terms of stress or tension, encountered in making decisions. Even if such knowledge does not have a direct

bearing on their immediate decisions, an appreciation of the

difficulty involved in forming the decision is an important as- pect of the decision-making experience. A decision maker need

not possess anthropomorphic qualities, however, to assess the

difficulty of making decisions, and we do not propose to endow

an artificial decision maker with some sort of ersatz anthropomorphic capability. Under our satisficing approach, however,

it is possible to evaluate attributes of the decision problem that correspond more to its functionality and fitness than to its success.Are decisions easily made and implemented, or do they

tax the capabilities of the decision maker? Such assessments

are not a typical undertaking of classical decision theory. Maximizing expectations has no need to concern itself with issues

such as "difficulty." Nevertheless, choices are not all of equal

difficulty.

By employing two utilities, rather than only one, we may

analyze them to ascertain the compatibility of the attributes of

the preferences. If they are compatible, in that options that conserve resources also achieve the goal, then the decision maker

is in a fortunate situation of being content. If the preferences

are incompatible, in that options that achieve the goal also are highly consuming of resources, then the decision maker is fundamentally conflicted. These attributes constitute attitudes, or

dispositions, of the decision maker.

The optimization literature is devoid of discussions concerning the attitude or disposition of the decision maker who,

like the paradigm it employs, is assumed to be dispassionate. It

is simply doing what should be done under the auspices of indi- vidual rationality, and attitudes or feelings, should they even exist (and they need not), are completely irrelevant. Furthermore,

to attribute anthropomorphic characteristics to a decision maker

would be seen by many as nothing more than a concocted story

line that is of marginal value if not completely misleading.

3.1. Attitude

It is fortunate if an option that conserves resources (low re- jectability) also achieves the goal (high selectability)—in this environment, a decision maker is content. Many interesting decision problems, however, are such that actions taken in the

interest of achieving the goal are expensive, hazardous, or have

other undesirable side effects. A decision maker in this sit- uation is conflicted. Contentment and conflict are basic dis- positional states that serve as guides to the decision maker's

functionality. A situation requiring frequent high-conflict decisions indicates that the tasks are difficult for the decision maker.

Making high-conflict decisions, however, is not a measure of

how well the decision maker is performing—it may, in fact, be

making good, but costly, decisions. It is also true, however, that a high-conflict environment may result in poor performance be- cause the decision maker is simply not powerful enough to deal

adequately with its environment. Such a situation might serve

as a trigger to prompt changes, such as activating additional

sensors, or otherwise seeking more information about the environment. It may also trigger a learning mechanism to prompt

the decision maker to adapt itself better to the environment.

Since selectability and rejectability are probabilities, it may be useful to appropriate some of the mathematical machinery of probability theory to aid in interpreting these quantities.

One way to gain some insight is to examine the entropy of selectability and rejectability.

Definition 1 The entropy of a mass function p is

Hip) = - ^p(u)log2p(u).

Entropy is usually employed in Shannon information the- ory as a measure of how much uncertainty (randomness or dis- order) is reduced, on average, as a result of conducting an ex- periment governed by the mass function [3]. In our context,

however, we wish to provide entropic interpretations for selectability and rejectability that are distinct from the usual probabilistic interpretation.

In assessing selectability, we consider expediency as anal- ogous to uncertainty. To motivate this interpretation, suppose

u' is implemented. If psiu') « 1, then log^psiu') ~ 0 which

is consistent with the notion that little reduction in expediency

occurs if an option with high selectability is implemented. Conversely, suppose ps{u') ~ 0, but is nevertheless implemented.

Then — logoPsiu') is large, indicating a great loss in expediency. The entropy of selectability is the average reduction in expediency that obtains as result of making choices according

to ps.

To interpret the entropy of pn, we consider expense as analogous to uncertainty. Suppose u' is implemented. If Pr{u') « 1, then log2Pi?(u') 0 which is consistent with

the notion that little reduction in expense occurs if a highly rejectable option is nevertheless implemented. On the other hand,

62
if Pr(w') ^ 0 and u' is implemented, then — log.2P/?(w') is

large, indicating a great reduction in expense. The entropy of

rejectabiiity is the average reduction in expense that obtains as

a result of making choices according topn.

Entropy is maximized by the uniform distribution; that is,

if p*{u) = ^ for all u e U, then H{p*) > H{p) for all mass

functions p over [/, and has entropy H{p*) = log2 n. A uniform ps generates the highest possible average expediency, and

a uniform p/? would generate the highest possible average expense. Consequently, it is useful to take the uniform distribution as a baseline against which to assess the properties of arbitrary mass functions. Let n be the cardinality of the action

space, U (assumed to be finite for this discussion).

Definition 2 If ps{u) = ^ (that is, selectability under is equal to selectability under the uniform distribution), then the

option is success neutral. If the selectability mass function

is uniform, then the decision maker's attitude will be success

neutral.

Definition 3 If = ^ (that is, rejectabiiity under p/? is equal to rejectabiiity under the uniform distribution), then the

option is conservation neutral. If the rejectabiiity mass function is uniform, then the decision maker's attitude will be conservation neutral.

Definition 4 If ps{u) > ^ (that is, selectability under p5 is greater than selectability under the uniform distribution), then

the option is attractive with respect to performance relative to other options

u is expedient.

Definition 5 If pr(u) > ^ (that is, rejectabiiity under p/j is greater than rejectabiiity under the uniform distribution), then u is unattractive with respect to cost or other penalty-u is expensive.

The relationship between selectability and rejectabiiity

permits the definition of four dispositional modes of the decision maker with respect to each of its options. Let U be the

set of all possible options.

Definition 6 If u e {7 is both expedient and expensive, then

the decision maker will desire to reject, on the basis of cost, an option that is suitable in terms of performance—it will be

ambivalent with respect to u.

Definition 7 If u e is both inexpedient (ps(u) < ^) and

inexpensive (pr{u) < ^), then the decision maker will be desirous of accepting the option on the basis of cost, but will be

reluctant to do so because of poor performance. The decision

maker will be dubious with respect to u.

Definition 8 If u £ U is expedient and inexpensive, then

the decision maker is in the position of desiring to implement

an option that would yield good performance—a dispositional

mode of gratification with respect to u.

Definition 9 If u G [/ is inexpedient and expensive, then the

decision maker will desire to reject, on the basis of cost, an

option that also provides poor performance, and will thus be in a dispositional mode of relief with respect to u.

These four modes provide a qualitative measure of the way

the decision maker is matched to its task. Gratification and relief are modes of contentment, while dubiety and ambivalence

are modes of conflict. Figure 1 illustrates these regions.

Figure 1: Dispositional regions: G = gratification, A

lence, D = dubiety, R = relief.

: ambivaFigure 2 illustrates various cases for n = 2, a two-dimensional decision problem. In these plots, the diagonal line represents the unit simplex, and the ps and pr values are plotted as vectors that lie on the simplex.

W2

Ui

(b)

Figure 2: Attitude: (a) The decision maker is dubious with re- spect to Ui and ambivalent with respect to U2- (b) The decision

maker is gratified with respect to Ui and relieved with respect

to U2.

3.2. Figures ofMerit

It would be useful to obtain formal expressions to capture some

of the features of the qualitative analysis described in Section

63
3.1., where it is qualitatively indicated that as these distributions become more closely aligned, the decision maker be- comes more ambivalent and dubious. We propose two measures that are similar, but not identical.

Diversity One important feature of the selectability and rejectability functions, therefore, is their dissimilarity. To obtain

such a measure, we again appeal to the notion of entropy, and

apply the Kulback-Leibler distance measure.

Definition 10 The Kulback-Leibler (KL) distance measure

of two mass functions, say pi and p2, is given by

The KL distance measure is an indication of the relative entropy of two mass functions. D(- \\ •) is not a true metric; it

is not symmetric and does not obey the triangle inequality. It is, however, non-negative, and it is easily seen that D(pi \\ P2) =

0 if and only if pi (u) = P2{u) for all u € U.

We may apply the KL distance measure to the problem of

ascertaining dissimilarity of the selectability and rejectability

functions by computing the KL distance between selectability

and rejectability.

Definition 11 The diversity functional is:

D{ps\\pn) = YlPsi^)^og2^

or, equivalently,

D{ps\\pr) = - Y^psiu)\og2PR{u) - H{ps).

a

Small values occur when the selectability and rejectability

functions are similar, indicating a condition of potential conflict. If they are identical, then the decision maker is in a position of wishing to reject precisely the options that are in its best

interest—an unfortunate condition of total paralysis.

Diversity is infinite if there exist options with nonzero selectability and zero rejectability. Such options are free options,

since no cost independent of achieving the goal is incurred by

adopting them (analogy: coasting saves fuel, but may or may

not get you to your destination). Diversity is not a measure of

performance; that is, if one decision maker has a more diverse

selectability/rejectability pair than another, that is not an indication that it will perform better than the other. It does, however,

provide an assessment of the environment in which the decision

maker operates.

Tension Although the diversity functional provides insight

into the relationship between selectability and rejectability, it does not afford a convenient comparison in the case where the

decision maker is neutral with respect to either selectability or

rejectability. To develop such a measure, it is convenient to re-normalize the selectability and rejectabiHty functions. Consider first the case where ps and pn are mass functions and U

is finite. Let

Ps = \ps{ui),... ,Ps{Un)\

VR = \Pr{Ui),... ,PR{Un)]

be selectability and rejectability vectors, and let p =

. . . , ^] denote the uniform mass function vector, where n is the cardinality of U. Although these vectors are unit-length un- der the Li norm, they are not of unit length under the L 2 norm.

It will be convenient to normalize these vectors with respect to

L2. Let |ps| = y^pspl", with similar definitions for \pr \ and

\p\. The L-2 normalized mass function vectors will be denoted

by P5 = and similarly for p^ and n. We express the similarity between ps and pr through the

inner product of the corresponding unit vectors, yielding the

expression psP^- This quantity will be unity whenps = pn,

and will decrease as the two mass functions tend toward becoming orthogonal, and thus captures some of the properties

we desire to model. If we normalize by the product of the projections of p5 and p;^ onto the uniform distribution, we tend to scale up the inner product as the mass function vectors become

distanced from the uniform distribution.

Definition 12 The tension functional is

T{ps\\PR) =

which simplifies into the convenient form:

n T{ps\\pr) = npspj = n^ps{ui)pR{ui).

i=l

Clearly, T{ps\\pr) is positive and bounded by the di- mension, n. If either the selectability or rejectability is uniform, then the tension function equals unity. If the rejectability

function is uniform, then the decision maker is rejectabilityneutral. If the selectability is uniform, then the decision maker

is selectability-neutral. \i T{ps\\pr) > 1, then the projection

of selectability onto rejectability is significant, and options that

are desirable are also costly. We may interpret this as a state of

conflict. On the other hand, f T{ps\\pr) < 1, then the projection of selectability onto rejectability is small, and the decision

maker is in a state of contentment.

A decision maker operating in a contented environment is well-tuned to its task—decisions that possess high rejectability

also possess low selectability. Such a decision maker should be

64
expected to achieve its goals with ease, and be adequate in most

situations. A conservation-neutral decision maker will function

much as would a conventional Bayesian decision-maker. If it is

success-neutral, it will function much like a minimax decisionmaker. If the decision maker is both conservation-neutral and

success-neutral, it is completely indifferent to the outcome, and

there is little point in even attempting to make a decision other

than a purely random guess.

4. NEGOTIATION

Negotiation under the individual rationality paradigm forbids

any individual participant, as well as any potential coalition,

from settling for a decision that is below its security, or minimax, level. This is a very strong restriction, which can lead to an empty core and the lack of a rational basis for negotiation.

There are many ways to modify this solution concept to justify solutions not in the core, such as accounting for bargaining

power based on what a participant calculates it contributes to

a coalition by joining it (e.g., the Shapley value), or forming

coalitions on the basis of no player having a justified objection

against any other member of the coalition (e.g., the bargaining

set). Also, it is certainly possible to invoke various voting or auctioning protocols to address this problem. We do not criticize the rationale behind these refinements to the basic theory, or the various extra-game-theoretical considerations that may

govern the formation of coalitions, such as friendship, habits,

fairness, etc. We simply point out that to achieve a reasonable

solution it may be necessary to go beyond the strict notion of

maximizing individual expectations and employ ancillary as- sumptions that temper the attitude and behavior of the decision

makers

Satisficing negotiation, however, permits controlled degrees of altruism. If agents are willing to lower their standards,

as defined by the boldness, b, they may obtain a satisficing compromise, where a joint decision is obtained that is good enough

for the group as a whole and good enough for each member of

the group. This potential result is guaranteed by the following

theorem.

Theorem 1 (The negotiation theorem.) Ifui is individually

satisficing for the ith agent, that is, Ui G Ej, then it must be the

ith element ofsome jointly satisficing vector u G Sf,.

Proof We will establish the contrapositive, namely, that if Ui

is not the ith element of any u e S;,, then Uj 0 S^,. Without loss of generality, let i = 1. By hypothesis, Psiui,^) <

bpR{ui,v) for all V e C/2 x • • x Un, so psi(^^i) =

EvPs(wi,v) < bY,^pn.{uuv) = bpR,{ui), hence ui ^ Hi

The content of the negotiation theorem is that, under intrinsic satisficing, no one is ever completely frozen out of a deal

every decision maker has, from its own perspective, a seat at the

negotiating table. This is perhaps the weakest condition under

which negotiations are possible.

A decision maker possessing a modest degree of altruism

would be willing to undergo some degree of self-sacrifice in the

interest of others. Such a decision maker may be viewed as an enlightened liberal; that is, one who is intent upon pursuing

its own self interest but gives some deference to the interests of

the group in general. Such a decision maker would be willing to lower its standards, at least somewhat and in a controlled way,

if doing so would be of great benefit to others or to the group in general.

The natural way for a decision maker to express a lowering of its standards is to decrease its boldness. Nominally, we

may set bi, the boldness of the ith agent, to unity, which reflects equal weighting of the desire for success and the desire to conserve resources. By decreasing bi, the agent lowers its standard

for success relative to resource consumption, and thereby in- creases the size of its satisficing set. As 6j —> 0 the standard is lowered to nothing, and eventually every option is satisficing.

Consequently, if all decision makers are willing to reduce their

standards sufficiently, a compromise can be achieved.

Figure 3 illustrates this negotiatory process. The amount

by which 6, must be reduced below unity is a measure of the

degree of compromising needed to reach a mutually acceptable

solution. As with tension and diversity, however, this degree

of compromising is not a measure of performance, but it is a useful figure of merit for assessing the degree of difficulty that

is associated with the negotiatory process.

Figure 3: The Enlightened Liberals negotiation algorithm.

This leads to a theory of social behavior than is very differ- ent from standard A'^-person von Neumann-Morgenstem game

theory. Whereas, under conventional theory, additional criteria may be required to foster successful negotiations, the satStep 1 : Agent i forms and i = 1, . . . ,N; initial- ize with bi = 1, 6i = min{fci, . . .

, 6;v}.

Step 2: Agent i forms its compromise set by eliminating

all option vectors for which its component is not

individually satisficing, resulting in Ci = {u e

Step 3: Broadcast and 6, to all other participants, re- ceiving similar information from them.

Step 4: Form the satisficing imputation set, N =

njLiCj. If N = 0, then decrement bj, j =

1, . . . ,N, and repeat previous steps until N 0.

Step 5: Agent i implements the ith component of the ra- tional compromise

P5j-Sjv(u) u = arg max . • ' '"€N PR,...R^{U)

65
isficing concept builds controlled degrees of compromise into

the decision-making procedure. If an agent reaches its limit

of compromise before negotiations are successful, it may be

forced to declare an impasse, rather than to sacrifice its stan- dards any further.

5. RESOURCE SHARING

The following simple example illustrates the fundamental dif- ferences between substantive and intrinsic rationality. Suppose

a factory operates A'' processing stations that function independently of each other, except that, if their power requirements

exceed a fixed threshold, they must draw auxiliary power from

a common source. Unfortunately, there are only A'^ — 1 taps to

this auxiliary source, so one of the stations must operate with- out that extra benefit. Although each station is interested in its individual welfare, it is also interested in the overall welfare of

the factory and is not opposed to making a reasonable compromise in the interest of overall corporate success. Let U denote the set of auxiliary power levels that are feasible for each Xi to tap, and let ff.U ^ [0, oo) be an objective

function for A",; that is, the larger the more effectively Xi

achieves its goal. Xj's choice is tempered, however, by the

total cost of power, as governed by an anti-objective function,

Qj'-U [0, oo), such that the smaller gi, the less the cost. Work

cannot begin until all players agree on a way to apportion the

auxiliary power. Table 1 displays these quantities for a situation

involving three decision makers.

u /i 9i /2 92 h 93

0.0 0.50 I.O 0.10 1.0 0.25 1.0

1.0 2.00 2.0 2.00 3.0 0.50 5.0

2.0 3.00 4.0 3.00 6.0 1.00 5.0

3.0 4.00 5.0 4.00 9.0 2.00 5.0

Table 1: The objective functions for the Resource Sharing

game.

A standard approach under substantive rationality is to view this as a cooperative game. The payoffs may be obtained

by combining the two objective functions, yielding individual

payoff functions of, say, the form

TTi{Ui,U2,U3]

-1 if Uj > 0 Vj

otherwise

i = 1,2,3, where Oj, and jj. are chosen to ensure compatible

units. To achieve this compatibility, we normalize fi and gi to unity by setting a, = ^^^^^^ and A =

^l^Jg.iu)

- The Pareto solution is up = {0, 1,3}, but, with an attitude

governed by expected utility maximization, Xi has no incentive

to agree to this apportionment. Thus, to solve this problem, a negotiation protocol must be invoked. Of the various protocols

that are possible, the only one that does not require assumptions

additional to that of self-interested expectations maximization

is the core. Unfortunately, the core is empty for this game. Essentially, this is because only two decision makers can share

in the auxiliary power source, effectively disenfranchising the

third decision maker. This situation potentially leads to an un- ending round of recontracting, where participants continually

make offers and counter offers in a fruitless attempt for all to maximize their expectations.

Let us now view the decision makers in their true character as enlightened liberals who are willing to accept solutions

that are serviceably good enough for both the group and the in- dividuals. From the point of view of the group, an option is

satisficing the joint selectability exceeds the joint rejectability

scaled by boldness. We define joint rejectability as the normalized product of the individual costs functions, namely,

PRiR2R3iui^U2,U3) OC 9l{ui)g2{u2)g3{U3),

where "oc" means the function has been normalized to sum to

unity. To compute the joint selectability, we note that, under

the constraints of the problem, only two of the agents may use

the auxiliary power source. We may express this constraint by

defining the joint selectability function as

Ps,S2sAUuU2,U3) oc I

Q otherwise

where IT is the set of all triples u = {u 1,1*2,^3} such that exactly one of the entries is zero. The individual rejectability

and selectability marginal mass functions are obtained by sum- ming over these joint mass functions according to the rules of

probability theory.

The enlightened liberals algorithm yields, for h >

0.8, an empty satisficing imputation set. But, when b is decremented to 0.8, the satisficing imputation set is N =

{{0, 1, 3}, {0, 2, 3}, {0, 3, 3}} and the rational compromise is u* = {0, 1, 3} which, coincidentally, is the Pareto optimal solution. It is not surprising that, at unity boldness, there are no

options that are simultaneously jointly and individually satisfic- ing for all participants, since there is a conflict of interest (recall

that the core is empty). But, if each individual adopts the point

of view offered by intrinsic rationality, it gradually lowers its personal standards to a point where it is willing to be content with reduced benefit, provided its costs are reduced commensurately, in the interest of the group achieving a collective goal.

The amount b must be reduced to reach a jointly satisficing solution is an indication of the difficulty experienced by the participants as they attempt to resolve their conflicts. Reducing

boldness is a gradual mechanism for decision makers to subordinate individual interest to group interest. This mechanism is very natural in the regime of making acceptable tradeoffs, but is

quite foreign to the concept of maximizing expectations ("you

get what you pay for" versus "nothing but the best").

The diversity and tension values for this decision problem

are given in Table 2. We interpret these values as follows.

66
Agent Diversity Tension

0.55 0.93

0.03 1.30

1.21 0.73

Group 2.85 0.51

Table 2: Diversity and Tension for Resource Sharing Game.

Group diversity is high and group tension is low, indicating

that, as a group, the system is fairly well suited for its environment, and that the system is powerful enough to make good

decisions. Individually, A'2 has the lowest diversity and the

highest tension. This situation is reflected in the structure of N,

where we see that .Y^ has several choices that are good enough,

but is either dubious or ambivalent about all of them. Thus, X2

experiences the most conflict in making decisions. X3 is quite content with its decision and so is A'l . The fact that Xi is not

conflicted as measured by diversity and tenseness may appear

somewhat contradictory, since it is Xi who ends up sacrificing

for the benefit of the group. But these figures of merit are not intended to be metrics of performance, only of the intellectual

power of the decision maker, in terms of its conflict between

selectability and rejectability.

[5] A. Rapoport and C. Orwant. Experimental games: a re- view. Behavorial Science, 7:\-36, 1962.

[6] H. A. Simon. A behavioral model of rational choice.

Quart. J. Econ., 59:99-1 18, 1955.

[7] H. A. Simon. Rational choice and the structure of the en- vironment. Psychological Review, 63(2): 129-138, 1956.

[8] H. A. Simon. Rationality in psychology and economics.

In R. M. Hogarth and M. W. Reder, editors, Rational

Choice. Univ. Chicago Press, Chicago, 1986.

[9] W. C. Stirling and M. A. Goodrich. Satisficing games.

Information Sciences, 1 14:255-280, March 1999.

[10] W. C. Stiding, M. A. Goodrich, and D. J. Packard. Satisficing equilibria: A non-classical approach to games and

decisions. Autonomous Agents and Multi-Agent Systems

Journal, 2000. To appear.

6. CONCLUSION

An intelligent agent is, first and foremost, a decision maker,

regardless of the problem context, the way knowledge is represented, or the criteria used to define performance. One way

to assess the functionality of the agent is to provide it with a means to evaluate introspectively its own fitness, or suitability,

to function in its environment. Satisficing decision theory pro- vides this capability. Although the figures of merit associated

with these fitness evaluations are not measures of performance,

they are useful measures of the innate intellectual (decisionmaking) power of the agent.

References

[ 1] M. Bazerman. A cridcal look at the rationality of negotiator judgement. Behavorial Science, 27:21 1-228, 1983.

[2] M. H. Bazerman and M. A. Neale. Negotiator rationality and negotiator cognition: The interactive roles of prescriptive and descriptive research. In P. H. Young, editor,

Negotation Analysis, pages 109-129. Univ. of Michigan

Press, Ann Arbor, MI, 1992.

[3] T. M. Cover and J. A. Thomas. Elements of Information

Theory. John Wiley, New York, 1991.

[4] R. D. Luce and H. Raiffa. Games and Decisions. John

Wiley, New York, 1957.

67
What is the Value of Intelligence?

Thomas Whalen

Professor of Decision Sciences, Department of Management

Robinson College of Business, Georgia State University

whalen@gsu.edu

Abstract

Probably the most widespread and significant existing

"performance metric for intelligent systems" is the dollar pre- miums that employers are willing to pay to recruit and retain more intelligent human employees compared to less intelli- gent ones. This paper examines some of the aspects driving

this economic metric in the search for analogies that may be

useful in establishing performance metrics for constructed

intelligent systems. Aspects considered include Language

Understanding & Capacity to Act, Goal-Directedness, Autonomy and Unpredictability, Information, Uncertainty, World

Models, and Self-Models and Self Awareness. The paper

concludes with a discussion of performance metrics for

human intelligence and a brief prospectus for the role of eco- nomic considerations in assessing the Vector of Intelligence

Keywords! economic value, intelligence

1. Introduction

Much of the discussion leading up to the conference on

"PerfoiTTiance Metrics for Intelligent Systems" focuses on an

"inner" view of intelligent performance, or rather of intelli- gence itself This inner view takes two very different fonns:

components like memory or MIPS that must be present inside an intelligent system, and metaphysical questions about the

"inner life" of an intelligent system, such as questions of

consciousness.

Rather than try directly to add to this interesting and

valuable train of thought, this paper approaches the subject of

performance metrics for intelligent systems from an external

perspective. The question under consideration hers is "What

is the economic value of intelligence?" Most of the discussion will concern the market value of human intelligence, in order to look for useful analogies for understanding and

measuring the economic value of intelligence in constructed

systems.

Individuals treasure intelligence in themselves and their

friends and family for a variety of reasons, most of which lead

rapidly into the spiritual or metaphysical realm, or, if you

prefer, into the most complex challenges of sociobiology.

Either way, creating a "performance metric" for intelligence

in this context seems neither feasible nor especially desirable.

On the other hand, consider the owners of a mediumsized business, who need to hire a number of employees to perform various tasks in the fiim. Why should the owners pay a higher salary and go through a more difficult and

expensive recruitment process to hire a more intelligent

employee when they can get a less intelligent employee with

the same training and experience more cheaply? To the extent we can give a quantitative answer to this question, the

dollar premium a business is willing to pay for intelligence is

a financial "performance metric for intelligent employees"

within the context of the job at hand. Understanding how

these dollar premiums arise in a variety of employment situa- tions can give important clues on how to put a value or "metric" on the performance of intelligent machines.

There are three distinguishable ways in which a smarter employee can be worth more money to a business than a stu- pid one with equivalent training and experience. These are: doing what I say, doing what I want, and doing what I need.

2. Language Understanding & Capacity to

Act

At the most fundamental level, "do what I say," an intelligent laborer can follow instructions better than a stupid

laborer. Smart employees can follow instructions that are more complex, less detailed, and require less time and effort

(in other words, less money) to prepare. Since they are less apt to misunderstand instructions, they require less money to be spent on supervising them than is the case for less intelli- gent employees with equal motivation. For constructed sys- tems, the equivalent is an expressive command language; one

that is the "natural language" for describing the task at hand,

whether it resembles a spoken human language, a specialized

technical language, or a graphical interface. Allied with this,

of course, is the capacity to actually carry out the instructions,

which some have referred to as the "body" as opposed to the

"mind" of the intelligent constructed system.

3. Goal-Directedness

It is possible to view the next level, "do what I want," as simply an elaboration of the ability of smarter employees to follow instructions that are less detailed. However, busi- nesses look hard for intelligent skilled craftsmen who can be

told what goals to accomplish without needing to be told how

to do so, and reward them with higher wages and better

68
treatment. A major topic of discussion has been the role of

goal - directedness in intelligent systems. In the world of

human employment, a laborer (first level) is given instructions about how to do a job; the goal may be implicit in the

instructions but is not an integral part of them from the

laborer's point of view. A craftsman (second level), on the

other hand, takes the goals provided by the employer and carries them out without further instruction. To do this, the

craftsman needs experience and training, but also puts more

intelligence into the work than the laborer does. '

Over time, a job may become more routinized, so that what originally required highly intelligent goal-seeking

behavior later requires only the following of rote instructions.

This can occur at either the structural level as the instructions

are written down for others, or within an individual as long

experience with a job eventually allows it to be done "without

thinking." The equivalent to this process in the area of constructed systems would be the replacement of complex, "intelligent" processes of sophisticated search and behavior

generation with stereotyped program modules or hardware

gadgets, reducing the "intelligence" used by a constructed

system while maintaining or even enhancing its performance.

4. Autonomy and Unpredictability

At both of the first two levels, management wants behavior of the employee to be predictable. Intelligence means

autonomy in the sense that, given equivalent training and

motivation, the intelligent employee does what is expected of

him or her without close supervision while the stupider

employee in the same job needs to be watched all the time.

However, autonomy in this context is almost the opposite of

creativity, spontaneity, or unpredictability; it is the stupid

employee, not the smart one, who comes up with the most

surprises.

It is only at the highest level, "do what I need," that businesses value unpredictability in their employees and consultants. Even here, there are two degrees of unpredictability.

Most of the time a person or company seeks advice on matters of law, engineering, medicine, or other fields, the advice has no "information" value if the one requesting it already knew

the answer; nevertheless, routine advice needs to be in line with professional standards. For example, though I do not want to be able to predict what my personal physician is going to tell me, I want it to be essentially the same as what

any competent physician would say given the same knowledge about me; in other words, I want my physician's behavior to be essentially predictable by other physicians. It is only

if I am suffering from an extremely serious disease, or if I am

knowingly participating in a clinical experiment, that I want

my physician to do something that will surprise the medical

profession!

5. Information

Some of the discussion about performance metrics for

intelligent systems has debated the applicability of entropy or

other aspects of information theory to measuring intelligence.

Fundamentally, "Information" implies informing somebody

about something they didn't already know. From this point

of view, an employer wants a laborer's work to provide no new information output at all, but a more intelligent laborer

requires less information input that an unintelligent one. A

craftsman working at the second level of "doing what I want"

takes compact information about goals rather than lengthy

information about procedures; the craftsman's work in sense

generates "information" to the employer about the methods

used, but this is information that normally is of no great

interest to the employer. It is only at the highest level, that of

the professional employee, that the employer is concerned

about receiving information output from the employee.

Information Information

Input Output

Laborer Do what I say High, procedural Ideally none

Crafts- Do what I want Low, Uninteresting

man goal-oriented

Profes- Do what I need Various Essential

sional

6. Uncertainty

The more uncertain the job environment is, the more

valuable an intelligent employee becomes. Procedural

instructions about an uncertain job environment must become

a complex collection of "ifs" and branches, compared to a more linear set of instructions for a job in a less uncertain

environment. Businesses have to pay more for employees

intelligent enough to follow such complex instructions than

they do for employees whose jobs do not contain much

uncertainty.

For sufficiently high levels of uncertainty in the job environment, management finds it unprofitable to prepare procedural instructions in a form that even the smartest laborer can

follow. Instead, it is more economical to hire craftsmen who

only need to be told the employer's goals and essentially left

to implement those goals according to their own skills and

' Note that myfocus here is on the degree ofintelligence demanded by the job, not on the intelligence possessed by the human

being doing it. Job demands place only a lower bound on the worker's intelligence. Nevertheless, the more intelligence the job

demands, the more the performance ofan intelligent employee will overshadow that ofa less intelligent one.

69
intelligence. The fundamental problem with the "Chinese

Room" thought experiment is that, while it might in principle

be possible to prepare and index a set of stimulus-response

instructions so extensive as to allow the occupant of the room

to carry on a conversation in Chinese without any knowledge

of the language, it is in fact such an immense task that it would be far cheaper and easier to build a machine that actually understood Chinese (and easier still to hire a human who

understands Chinese to sit in the room!).

At the highest levels of uncertainty (or extreme complexity, which as Zadeh points out has many of the same effects)

management can no longer be sure what goals are feasible or

profitable, and so seeks expensive and potentially surprising

guidance from professionals, and perhaps some day from constructed systems that produce "useful surprises" at a professional level.

7. World Models

It is very rare for an employer to ask about an employee's

internal model of the world or to pay a higher salary on

account of it. Laborers are paid to follow instructions intelli- gently in the real world, and craftsmen are paid to ply their

trades intelligently in the real world. Whether or not they use an internal model of the world to do so is of no economic

importance except as it is reflected, at one or more removes,

in their performance.

Professionals are paid to give "useful surprises" to their

employers or clients. This information (and actions informed

by it) generally have to do with the real world, though at times professionals may be asked for opinions about hypothetical situations. Even then, usually it is irrelevant whether

the answer comes from stored knowledge, experimentation,

or the exercise of a simulation-like model in the professional

expert's head. The exception is when the professional is

explicitly asked to provide a model, but in that case the model

is no longer an internal one, but an external analogy, flowchart, or computer simulation.

8. Self-Models and Self Awareness

Certainly, all of a firm's (human) employees have a self- model, a self-awareness, a consciousness. But only in a few

"helping professions" such as psychiatry or the clergy is an abov-average endowment in this area considered an advantage to job performance. Employers value some limited facets

related to self-awareness such as taking pride in one's work

and being safety-conscious, but outstanding self- consciousness and self-absorption are not considered signs of

outstandingly valuable intelligence by employers. Thus, with

regard to constructed systems, it might be an economically

important goal to build machines that "care" about doing a good job and know how to take care of themselves and those

around them. But we should not insist on a robotic Mother

Teresa; it would be a magnificent achievement to create a working system that was as caring and careful as a seeing-eye

dog.

9. Performance Metrics

Unlike constructed systems, human employees cannot be

opened up to inspect their components. Thus, employers in search of intelligent employees rely on a variety of benchmark tasks. Occasionally, they may use a benchmark task

that tries to screen out the effects of knowledge to focus on

pure intelligence — examples include IQ tests and program- mer aptitude tests. However, since job performance is more

important than what mix of knowledge, intelligence, and

other endowments it arises from, most benchmark tasks measure performance without much concern about the mix.

The most common benchmark task is performance on similar

jobs in the past.

Another interesting benchmark is formal education.

Completing any program of study implies an ensemble of

intelligence, knowledge, and skills for learning, writing, and

simply sticking to a task. The education most valued by

employers adds to this a body of knowledge relevant to the

job. However, for complex and unpredictable environments,

it may not be possible to specify in advance what body of

knowledge will be required. In such a case, a broad "general

education" demonstrates that a person has an advanced ability, refined by varied practice, to learn whatever is required in a new situation. With respect to constructed systems, a design team that hones and demonstrates their product's ability to learn and excel in a wide variety of problem environments, including artificial ones as well as real ones, can command a higher price for their machines than a design

team that only trains their system on what is "relevant" to its expected tasks, at least from customers whose jobs are at the

high end of uncertainty or complexity.

Performance metrics for intelligent systems based on

board games like chess and backgammon or parlour games

like the Turing test can be very useful in addressing philosophical questions about what it means to be intelligent, and

technological questions about how to implement it, but they

are of little direct economic interest. In particular, to pass the

Turing test in a job application context, an intelligent system

would have to refrain from showing any levels of ability not common among humans, and also to demand the same levels of salary and benefits as a human. What is needed, instead, is

a set of benchmark tasks, probably job-specific, with one or more of the following characteristics:

• Instructions are so complicated that it is more profitable to seek an intelligent

laborer system that understands them, than

to seek an unintelligent "Chinese room"

70
type system to follow the instructions with- out understanding.

• The environment is so complicated and

uncertain that it is more profitable to seek

an intelligent craftsman system that accepts

exogenous goals and carries them out according to its own skills and intelligence,

rather than to seek an unintelligent system

that simply follows instructions.

• The situation is so fuzzy that it is more

profitable to seek an intelligent professional

system to determine what goals are appropriate (presumably given exogenous metagoals) and do surprising things for the

benefit of the organization, rather than to seek an unintelligent system that simply

and predictably carries out exogenous goals

To be useful, an intelligent constructed system must provide a better cost/benefit ratio than any combination of

human bemg(s) and unintelligent constructed system(s). If more than one intelligent constructed system meets this test.

then the one with the best cost/benefit ratio, not necessarily

the smartest one, will be chosen.

10. Economics and the Vector of

Intelligence

The "white paper" for the 2000 Conference on Perform- ance Metrics for Intelligent Systems lists 25 potential coordinates for a possible Vector of Intelligence. A major challenge

is to find ways to systemafically quantify or otherwise specify

the values of these "coordinates." Without detracting from

the usefulness of methods oriented toward philosophy of

mind, toward control engineering, or toward academic computer science, let me propose an economic approach to measuring each of the 25 coordinates summarized in the following

table. In this economic approach, the challenge would be to estimate the derivatives of system cost/benefit ratio in a benchmark problem to "memory temporal depth," "number of

objects that can be stored," ... et cetera. The second derivative is as important as the first since most or all of these coordinates are subject to diminishing or even negative returns.

Twenty-Five Potential Coordinates for the Vector of Intelligence (from the White Paper)

(a) memory temporal depth

(b) number of objects that can be stored

(c) number of levels of granularity in the system of representation

(d) the vicinity of associative links taken in account during reasoning of a situation, or

(e) the density of associative links

(f) the vicinity of the object in which the linkages are assigned and stored (associative depth)

(g) the diameter of associations ball (circle)

(h) the ability to assign the optimum depth of associations

(i) the horizon of planning at each level of resolution

(j) the horizon of extrapolation at a level of resolution

(k) the response time

(1) the size of the spatial scope of attention

(m) the depth of details taken in account during the processes of recognition at a single level of resolution

(n) the number of levels of resolution that should be taken into account during the processes of recognition

(o) the ratio between the scales of adjacent and consecutive levels of resolution

(p) the size of the scope in the most rough scale

and the minimum distinguishable unit in the most accurate (high resolution) scale

(q) an ability of problem solving intelligence to adjust its multi-scale organization to the hereditary

hierarchy of the system,

(r) dimensionality of the problem (the number of variables to be taken in account)

(s) accuracy of the variables

(t) coherence of the representation constructed upon these variables

(u) limit on the quantity of texts available for the problem solver for extracting description of the system 20

(v) frequency of sampling and the dimensionality of the vector of sampling

(w) cost-functions (cost-functionals)

(x) constraints upon all parameters

(v) cost-function of solving the problem

71
On the Computational Measurement of Intelligence Factors

Jose Hern^ndez-Orallo

Departament de Sistemcs Informatics i Computacio

Univcrsitat Politecnica de Valencia, Camf de Vera s/n, 46022 Valencia, Spain

E-mail: jorallo@dsic.upv.es.

Abstract. In this paper we develop a computational framework for the measurement of

different factors or abilities usually found in intelligent behaviours. For this, we first develop

a scale for measuring the complexity of an instance of a problem, depending on the descriptional complexity (Levin LT variant) of the 'explanation' of the answer to the problem. We

centre on the establishment of either deductive and inductive abilities, and we show that their evaluation settings are special cases of the general framework. Some classical dependencies

between them are shown and a way to separate these dependencies is developed. Finally, some variants of the previous factors and other possible ones to be taken into account are

discussed. In the end, the application of these measurements for the evaluation of AI progress

is discussed.

1 Introduction

Are AI systems of today more intelligent than those of 40 years ago? Probably the answer is a clear

yes, at least for some of the current systems. However, another different question is 'How much

more intelligent?', and, even more, in which aspects are they more intelligent? In this paper we investigate a framework for the evaluation of such a progress in different

factors, extending in a natural way the work endeavoured in [12] and [11], specific for only some

inductive factors. For such an extension, the main aim should be to develop the less number of

factors as possible, by proposing general factors instead of specific ones. Moreover, the framework

would allow to studying their theoretical correlations, and reducing, when possible, a factor to another. This leads finally to a group of tests that can be adapted and implemented for measuring

different abilities of AI systems.

First of all, we must ascertain three problems for any evaluation of the ability of solving a

problem: to give a general scale of a complexity of the problem, to settle the unquestionability of

the solution to the problem and to establish a way to know whether the subject has arrived to the

solution.

Computational complexity scales problems according to the time different kinds of machines

require to solve them in the general case by using the optimal algorithm possible. However, most

problems of interest in AI are NP-complete. But, remarkably, some instances of NP-complete problems are easier than instances of polynomial problems. This assertion seems to be contradictory,

since any instance has an algorithm to solve that instance in linear or even constant time (the

program "if the input is x print the solution y"), so there is apparently no reason for stating that an instance can be easier than another. This has been shown to be false up to an extent, because

for some problems it is better (shorter) to give a more general solution than the specific solution

for an instance of the problem. This has been formalised under the notion of "instance complexity"

(see e.g. [16]), which gives the shortest solution to an instance of a problem provided it does not

give a contradictory solution for other instances of the same problem.

However, instance complexity is only of interest for large instances of a considerable descriptional complexity (or for sets of instances). Moreover, the difficulty of the problem is not usually

related to the descriptional complexity of the solution. For instance, the descriptional complexity

of the answers given by a theorem prover (an accepter) are very short, namely one bit to say

72
'yes' or 'no'. In the same way, the hardness of a prediction problem cannot be measured by the

descriptional complexity of the element predicted, but rather by the complexity of the reason why

the element has been predicted. The idea is then to measure the descriptional complexity of the

'justification' or 'explanation' of the solution. Consequently, any cognitive skill can be measured

within this framework provided that problem and solution can be formalised computationally.

The paper is organised as follows. After Section 2, where some notation is introduced. Section 3

gives a general formula of the hardness of the instance of a problem, by clarifying how to generalise

the concept of 'explanation' of a solution to a problem. Section 4 addresses the issue of specialising it

for deductive abilities and discusses their measurement. Section 5 does the same thing for inductive

abilities, but recognising that it is necessary to solve the unquestionability problem. Section 6 deals

with their dependencies and the possibihty of taking other factors into account. Section 7 discusses

the applications of these measurements, especially for the evaluation of automated reasoning and

machine learning systems. Section 8 closes the paper with the results and open problems.

2 PrelimiriEiries

Let us choose any finite alphabet E composed of symbols (if not specified, E = {0, 1}). A string or object is any element from E* , with o being the composition operator, usually omitted. By

(a, b) we denote a standard recursive bijective encoding of a and h, such that there is a one-to-one

correspondence between (a, b) and each pair (a, b). Note that this usually takes more bits than ao6.

The empty string is denoted by e. The term l{x) denotes the length or size of x in bits and logn

will always denote the binary logarithm of n. The complexity of an object can be measured in many ways, one of them being its degree

of randomness [14], which turns out to be equal to the shortest description of it. Descriptional

Complexity, Algorithmic Complexity or Kolmogorov Complexity was independently introduced by

Solomonoff, Kolmogorov and Chaitin to formalise this idea, and it has been gradually recognised

as a key issue in statistics, computer science, AI and cognitive science [16] [6]. The Kolmogorov Complexity of an object, defined as the shortest description for it, usually

denoted by C (plain complexity) or K (prefix-free complexity) turns out to be not computable in

general, due to the halting problem. One solution for this is to incorporate time in the definition

of Kolmogorov Complexity. The most appropriate way to weight space and time execution of a program, the formula LT3{px) -— l{px) + logTa(px), where r is the number of steps the machine /? has taken until x is printed by py, was introduced by Levin in the seventies (see e.g. [15]). Intuitively,

every algorithm must invest some effort either in time or demanding/essaying new information,

in a relation which approximates the function LT. The corresponding complexity, denoted by Kt

(see e.g. [16]) is a very practical alternative to K.

3 Problem Complexity by Its Explanation Complexity

Consider a problem instance tt as a tuple (5, C, /, A, 4>) where S is the context or working system

where the problem can be established, C is a Boolean function which represents a (syntactical)

validity criterion, / is the presentation of the instance, Ai is the answer and (j) \s & (semantical)

verifier^. The general problem is denoted by 7r( ) as the tuple (5, C, 4>). We say that E is an explanation for the problem instance tt iff £ is valid, i.e. C{{S, I ,E)) = true,

and £ is a means to obtain the solution, i.e., </>((5, 1 , E)) = Ai.

From here, it is easy to adapt the definition of Kt to measure the hardness of a problem.

Namely, the hardness of a problem instance 7r(5, C, /, A, 4>) is then defined as:

F(7r) = min{Lr(E]{5, C, /)) : £; is an explanation for tt} (1)

' Both C and </> could be joined in one function. We have preferred to separate them, because later it will be useful to distinguish between both parts of a correct solution, in order to establish purer factors.

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For instance, the hardness of a search problem is usually estimated by the size of the search

space. If the search problem is complex, it is necessary to say which branches have been selected in order to arrive to the solution, or either a long time is necessary to explore (and make backtracking)

to the misleading ones. It is the function LT which finds a compromise between the information

which is needed to guide the search and the logarithm of the time that is also needed to essay all the branches. On the other hand, if the search problem is linear (one possible branch), it is very

easier to describe the problem (just follow the rules in the only possible way). However, for very

long derivations, the inclusion of time can make hardness high too. For the evaluation of a subject's ability of solving a kind of problem 7r(-) it is necessary to generate a set of instances of that problem of different hardness. In order to scale the instances more properly, we introduce the concept of /c-solvability. An instance of a problem tt — {S, C, I, A, </>)

is k-solvable iff k is the least positive integer number such that:

The use of log /(/) is justified by the fact that, once the general problem is known, each instance

must be 'read' an this takes at least /(/) steps.

Once given a general scale of a complexity of the problem, it is then easy to make a test from the previous definition, provided that the unquestionability of the solution to the problem

is clear. Unquestionability can only be addressed depending on the kind of problem (we will see

this for deductive abilities and especially for inductive abilities in the following sections). Finally,

there is no way to know whether the subject has arrived to the solution if the explanation is not

given (and usually the explanation is difficult to check or the subject may not be able to express

the explanation in a comprehensible form). For instance, the subject may have given the right

solution but maybe due to wrong derivations. Fortunately, in the case of multiple solutions, this

situation will be discardable in the global reckoning of the test. In the case of few solutions, such as

'yes'/'no', it is then necessary to penalise the errors by using some formula that takes into account

the possibility of guessing the right answer 'by error'. Another question is the time limit for making the test. This would highly depend on the factor

to be measured, and whether there is a special interest on evaluating the ability to solve a given

problem or the ability to solve it quickly. The selection of the time limit and the evaluation of the

score according to it could be very interesting for evaluating resource-bounded rational systems.

Finally, we have not considered the possibility of multiple correct explanations for the same

solution, which would suggest a modification of (1). Consider the situation of the best explanation

with LT — n, but several other explanations of LT = n + l. Intuitively, the existence of these other

explanations also affects the easiness of the solution. However, this is very difficult to evaluate

in practice because there are always infinite slight variations of the best explanation (void steps,

redundancies, etc.), so the previous situation is extremely frequent (if not inevitable). It is then

assumed that for every k:

In other words, we assume that the proportion of valid and correct explanations wrt. valid

explanations is very small.

Once a general framework is established, let us study which deductive and inductive abilities are feasible and interesting to be measured within it.

4 Deductive Abilities

Apparently, deductive abilities are much easier to measure, because there is no possible subjectivity

in the correct answer; given the premises and the way to operate with them, only one answer is possible.

H{Tr) < k- log (2)

card{ E : LT{E) ^ k and C((5, /, E)) = true and (f){{S, I, E)) = At } «

card{ E : LT{E) = k and C((5, 1,E))^ true } (3)

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An instance of a deductive problem tt — {S,C,I ,A,(I)) can be defined in terms of the previous

framework in the following way: S corresponds to the set of axioms or axiomatic system, C is a Boolean function which says what is a \'alid application of the axioms, / is the instance of the

deductive problem, Ai the answer and 0 is a verifier, i.e., (p{{S,I,E)) = At, in this case, a verifier that checks whether Ai is a result of applying a solution to / in S. In this case the explanation E is represented by a proof in S stating that Ai is a the result of

/ or, in other words, a derivation from / to Ai.

Example: Consider for instance an accepter that tells whether a proposition is a theorem or not. Let S be the axioms of arithmetic. Let C a function that tells that a derivation is valid according to the rules

of application of the axioms, and let I be the instance "Is Fermat's famous conjecture true?" (recently a theorem). Which is the hardness of the solution A = 'yes'? The descriptional complexity of A (which is

just yes) would say that the instance is very easy, however its hardness given by H turns out to be the LT

of the proof with less LT. Consider instead the instance "solve 2+3" which, also with a low complexity of A = 5, turns out to be simple, because the derivation is describable easily and shortly from (S,C,I). In

general, any calculation is shortly describable, so its hardness will depend solely on its temporal cost. According to this example, we can distinguish some classical deductive problems that can be

measured. In particular, the following factors are distinguished:

— Calculus Ability: in this special case, C only allows a specific and deterministic application of

the rules or axioms of S. In this case the search space is hnear. As it has been said before, its complexity is exclusively given by the logarithm of the time which is needed from the input

/ to the output Ai. This ability is not of much interest to be measured nowadays, since it is better done by computers than humans, and it would finally measure the computational power

of the subject / machine. — Derivational Ability: in this case, C only allows a varied application of the rules or axioms

of S. Consequently, the search space is open. The complexity is then given by a compromise

between the logarithm of the time which is needed to know that a branch leads to no solution,

and some information that may say which branches to take (and which ones not to take). — Accepter Ability (proving ability): It is a special case of the previous ability, with the special

feature that / can only be 'yes' or 'no'. Theoretically, there is no reason for expecting that a

subject has a different result in this problem that in the previous one.

The way to implement a concrete test for the previous ability is not complicated. For calculus

ability, it is just necessary to generate some derivations. Their length will determine the time

which is needed to follow them. On the contrary, for the other two abilities, it is necessary to generate a possible derivation, and look that there are no shorter equivalent derivations. This,

in general, will be extremely costly, growing exponentially according to the value of fc-solvability.

Fortunately, there is no need for efficiency here. A hard test can be generated during days, even

weeks, and then passed to several subjects.

5 Inductive Abilities

A sequential inductive problem tf = (5, C, /, A, (/>) can also be defined in terms of the previous

framework in the following way: S corresponds to the background knowledge, / is a sequential

evidence (with /(/) = n), C is a Boolean function which represents the hypothesis selection criterion

(e.g. simplicity), Ai is the prediction of the (n + l)th element of the sequence and cf) is sl verifier,

i.e., (j){{S, I,E)) = Ai, in this case, a verifier that checks whether Ai is the (n + l)th element given

by the hypothesis with the background knowledge S and also checks whether both cover /. In this case the explanation E is represented by a 'hypothesis' wrt. S that affirms that A, is 'what follows' / or, in other words, a prediction from /. Example: Consider for instance a prediction problem. Let 5 be a background knowledge, containing,

among other things, the order of the Latin alphabet. Let C a function that tells that a hypothesis is

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good according to a selection criterion, and let / the instance "aaabbbcccdddeeef f fgggh" . Which is the hardness of the solution A, = 'h'? The descriptional complexity (in LT terms) of the hypothesis is again

what is taken into account. The main question of evaluation of induction is that of inquestionabihty. Even if the selection

criterion is given, two plausible explanations may differ slightly, and the selection criterion would

give that one is slightly better than the other, but this would depend highly on the descriptional

mechanism used. In [12] and [11] this difficult problem is addressed, according to a comprehensive

criterion, a variant of the simplicity criterion based on Kolmogorov Complexity in the style of

SolomonofT [19], but ensuring that the data is covered comprehensively, i.e. without exceptions.

Accordingly, the simplest explanatory description, denoted by SED{x\y), is defined in [11] as the

simplest (in LT terms) description which is comprehensive wrt. the data x given the background

knowledge y. To ensure unquestionability, the examples are selected such that there are no al- ternative descriptions of similar complexity that give a different description. Finally, there is a

small possibility that a good prediction is given by a 'wrong' explanation. This probability may be

neglected in the tests or corrected by a penahsing factor in the score of wrong results. From here, partially independent factors can be measured by using extensions of the previous

framework. For instance, inductive abilities, such as sequential prediction ability, knowledge applicability, contextualisation and knowledge construction ability can be measured in the following

way: — Sequential Prediction Ability: several unquestionable sequences of different /c-solvability are generated. A test for this ability has been generated in [12] and passed to humans, jointly with

a typical psychometrical test of intelligence. The correlation showed that this is one of the

fundamental factors of intelligence, although more experimentation is to be done.

- Inductive Knowledge Applicability (or 'crystallized intelligence'): a background knowledge B

and a set of unquestionable (with or without B, denoted by H{xi\B) and H{xi) respectively)

sequences Xi are provided such that H{xi\B) — H(xi) — u but still SED{xi\B) = SED{xi).

The difference of performance between cases with B and without B is recorded. This test would

actually measure the application of the background knowledge depending on two parameters:

the complexity of B and the usefulness of 5, measured by u. — Inductive Contextualisation: it is measured similarly as knowledge applicability but supplying

different contexts Bi, B2, Br with different sequences Xi^t such that H{xi^t\Bt) = H{xi^t) — u- This multiplicity of background knowledge (a new parameter T) distinguishes this factor from

the previous one. - Inductive Knowledge Construction (or learning from precedents): a set of sequences Xi is pro- vided such that there exists a common knowledge or context B and a constant u such that for H{xi\B) < H{xi) — u. A significant increase of performance must take place between the first sequence and the later sequences. The parameters are the same as the first case, the complexity

of B and the constant u.

It is obvious that these four factors should correlate, especially with the first one, which constitutes a necessary condition for haviiag a minimal score in the other factors.

6 Dependencies and Other Factors

Although there is a common (but argueable) view of induction and deduction as inverse processes, they are not inverse in the way they use computational resources. In fact, any inductive process

requires deduction to check the hypotheses, thus, obviously, inductive ability is influenced by

deductive ability. This has been usually recognised by IQ tests, where deductive and inductive

abilities usually correlate. Due to this fact, inductive factors usually are the main part of intelligence

tests, because deductive abilities are implicitly evaluated.

However, if we are looking for 'pure' factors the question is whether there is a way to separate

this deductive 'contamination' in inductive factors.

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The idea is to provide 'external' deductive abilities when measuring inductive factors, in order

to 'discount' the deductive effort than otherwise should be done. For this, given a problem tt =

(5, C, (f)) it is only necessary to provide an 'oracle' which computes 4> in constant time. The subject

must only guess models (hypotheses) and check them in the oracle, by providing the hypothesis

to it and comparing the results with the evidence I. This would measure the 'creative' part of

induction. In the following, let us denote by 'purely' inductive the corresponding factors to those

highlighted in the previous section which result from providing the oracle. This resembles a 'trial and error' problem considering reality acting as the oracle. The issue is how to implement this in a feasible way, especially for evaluating complex agents or even human

beings. The best way, in our opinion, is the construction of a 'virtual' world where the subject to be evaluated can interact and essay its hypotheses with no effort. In a similar way as the oracle for (/>, some difference could be estimated if the syntactical machine

C is (also) given. Although this would not be much representative for deduction, for induction it would discount the ability of working with the selection criterion, which is an important trait of

induction.

Nonetheless, deductive ability is also influenced by inductive ability as long as the problems

become harder. Some lemmata or rules can be generated by an intelligent subject in order to help to shorten the proof from the premises to the conclusion. This may explain why artificial problem solvers without inductive abilities have not been able to solve complex problems, and

this is especially clear in Automatic Theorem Proving. Consequently, recent systems are beginning

to use ML techniques for improving performance. Background knowledge could also be examined

in deduction, provided S includes the axioms but also some useful properties. This finally gives

similar factors as those given for induction:

— Deductive Knowledge Applicability: how lemmata or properties are used for a deductive problem. — Deductive Contextualisation: the ability of using different contexts for different problems. — Deductive Knowledge Construction: this will measure the increase of performance between first instances and last ones.

Finally, we have given a measurement for sequential uiduction, and it seems interesting to evaluate

non-sequential induction as well, where an unordered set of elements is given as e\-idence from

an unknown function that maps whether an element belongs to a set. In this case, the test could

give some possible values which might be members of the set,, although only one of tlieiu is really

in it. Solomonoff formalised deterministic (sequential) prediction [19] and recently, has formalised

non-sequential prediction [21]. This problem is similar to the inductive problem of learning a Boolean classifier and can be extended to the case of a general classifier. To eliminate the deductive

contamination of the measurement of non-sequential induction, the 'oracle' (f) should be a classifier,

telling, given a hypothesis, to which class the element belongs. The essay of an 'oracle' that accepts

several elements at a time should be considered as well. Once the basic deductive and inductive factors have been recognised, the question is whether

there are many other factors which are relevant to be measured. For instance, memory or 'memoisation ability' is a factor that is knowledge-independent and it can be easily measured. However,

this factor is not very interesting for AI nowadays.

Other factors, such as analogical and abductive abilities can be shown to be closely connected

to inductive and deductive abilities both theoretically and experimentally. A first approach for measuring them has been attempted in [12], and the test applied to human beings has shown the

correlation with inductive abilities. However, not every factor is meaningful. Factors like "playing chess well" are much too specific to be robust to the subject's background knowledge. However, it cannot be discarded that some

game-playing factor would measure competitivity and interactivity abilities aside from deductive

and inductive abilities.

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Finally, we have considered individual tests which measure one factor. For measuring several

factors at a time, the exercises should be given one by one and, after each guess, the subject should

be given the correct answer (rewards and penalties can be used instead). This has two advantages:

there is no need for the subject to understand natural language (or any language) to order to be

explained the purpose of the test, and there is no need to tell which factor or purpose is to be

measured in each part of the test. There is also one disadvantage, deductive problems should be

posed in terms of 'learn to solve', and this may devirtualise them.

7 Applications

Modern AI systems are much more functional than systems from the sixties or the seventies. They

solve problems in an automated way that before required human intervention. However, these

complex problems are solved because a methodical solution is found by the system's designers,

not because most current systems are more intelligent than preceding ones. Fortunately, the initial aim of being more general is still represented by some subfields of AI: automated reasoning and

machine learning.

Automated reasoning (more properly called Automatic Theorem Proving) is addressing more

complex problems by the use of inductive techniques, while maintaing their general deductive techniques. These systems, in fact, have been used as the 'rational core' of many systems: knowledgebased systems, expert systems, deductive databases, ... But, remarkably, the evaluation of the

growth of automated reasoning has not been established from the success of these applications

but from the increasingly better results on libraries of problems, such as the TPTP library [22]. However, there is no theoretical measurement about the complexity of the problems which compose

these libraries. Instead, some approximations, such as the number of clauses, use of some lemmatas,

etc., have been used. Following the approach presented in this paper it would be interesting to give a v-alue of k — solvability of each of the instances of these libraries. In a similar way, machine learning has recently taken a more experimental character and systems

are evaluated wrt. sets of problems. Except from general problems (classes), where their complexity

(or learnability) has been established, there is no formal framework for giving a scale for concrete

instances. In this new and beneficial interest in measurement, Bien et al. [1] have defined a 'Machine In- telligence Quotient' (MIQ), or, more precisely, two MIQs, from ontological and phenomenological

(comparative) views. Any comparison needs a reference, and the only reference of intelligence is, for the moment, the human being. This makes the approach very anthropocentric, like the Turing Test. The ontological approach, however, is not based on computational principles but on a

series of characteristics of intelligence that are defined on linguistical terms rather than computational/mathematical ones, such as long-term learning, adaptation, recognition, optimization, etc. Moreover, the evaluation is generally measured on performance on some specific problem, contrary

to the claim that "it is time to begin to distinguish between general, intelligent programs and

the special performance systems" [18]. Although this can be very appropriate for specific systems

where functionality is clear, in general this would not allow for the comparison of intelligence skills of different systems devised for quite different goals. How to define general and absolute characteristics of intelligence computationally is more difficult and new problems present themselves, but

the progress in the 'intelligence' of AI systems can only be measured in this way.

8 Conclusions and Future Work

Among the problems for making these measurement reliable there is the selection of a reference

machine. The evaluation of abilities with instances is dangerous because it depends on constants. Since there is no apparent preference for any descriptional mechanism, we plan to adapt these

notions for logic programming, because it is a paradigm that has been used both for automated

78
deduction and machine learning (ILP) as well as other uses (abduction, theory revision, ...), and,

in our opinion, is not biased.

For the moment, the framework which has been presented allow for the measurement of dif- ferent factors and clarifies the distinction between evolutionary-acquired knowledge, life-acquiredknowledge and 'liquid intelligence' (or individual adaptability). Several tests for different subfields

of AI could be devised following this paradigm, and the increasing scores for solving more and more complex (A;-solvable) problems may be a way to know how much intelligent AI systems are

wrt. previous generations systems.

References

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Two Methods and Applications", World Automation Congress (WAC), TSI Press, Albuquerque, NM.

2. Blum L. and Blum M., 1975, "Towards a Mathematical Theory of Inductive Inference". Inf. and

Control, 28:125-155.

3. Bradford P. G. and Wollowski, M., 1995, "A Formalization of the Turing Test (The Turing Test as an

Interactive Proof System)". SIGART Bulletin, 6(4), p. 10.

4. Chaitin, G. J., 1982, "Godel's Theorem and Information". Int. J. Theo. Phys., 21, 941-54.

5. Eysenck, H. J., 1979, The Structure and Measurement of Intelligence, Springer- Verlag.

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Journal, 42(4).

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9. Herken, R., 1994, The universal Turing machine: a half-century survey, Oxford Univ. Press, 1988, 2nd

Ed., 1994.

10. Hernandez-Orallo, J., 2000, "Computational Gain and Inference", Collegium Logicum, 4, Springer, in press.

11. Hernandez-Orallo, J., 2000, "Beyond the Turing Test", to appear in Journal of Logic, Language and

Information, to appear in Vol. 9 no. 4.

12. Hernandez-Orallo, J. and Minaya-Collado, N., 1998, "A Formal Definition of Intelligence Based on

an Intensional Variant of Kolmogorov Complexity" In Proc. of the Intl. Symp. of Engin. of Intelligent

Systems (EIS'98), ICSC Press, 146-163. 1998.

13. Johnson, W. L., 1992, "Needed: A New Test of Intelligence", SIGART Bulletin, 3(4), 7-9.

14. Kolmogorov, A. N., 1965, "Three Approaches to the Quantitative Definition of Information" , Problems

Inform. Transmission, l(l):l-7.

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16. Li, M. and Vitanyi, P., 1997, An Introduction to Kolmogorov Complexity and its Applications, 2nd

Ed., Springer- Verlag.

17. Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci, S. J. Halpem, D. F., Lochlin, J. C, Perloff, R., Sternberg, R. J. and Urbina, S., 1996, "Intelligence: Knowns and Unknowns",

American Psychologist, 51, 77-101.

18. N. J. Nilsson, Eye on the Prize. AI Magazine, July 1995.

19. Solomonoff, R. J., 1964, "A formal theory of inductive inference". Inf. Control, 7, 1-22, March,

224-254, June.

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IEEE Trans. Inform. Theory, IT- 24, 422-438.

21. Solomonoff, R. J., 1999, "Two Kinds of Probabilistic Induction", in the 'Special Issue on Kolmogorov

Complexity', The Computer Journal, 42(4), 256-259.

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of Automated Reasoning, 21(2), 177-203.

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Proc. London Math. Soc, series 2, 42, 230-65, 1936. Cor., Ibid, 43, 544-6, 1937.

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79
ON DEFINITION OF TASK ORIENTED

SYSTEM INTELLIGENCE

Michel Cotsaftis

LTME/ECE 53 rue de Crenelle 75007 Paris France

fax:33-(0) 1-42-22-59-02; email:mcot@ece.fr

ABSTRACT

With development of system complexity and performances, it is important to evaluate its ability to perform

tasks, especially in the case of opposing outer effects. This amounts to affect "intelligence" coefficient to the

system, which basically requires to transfer usual geometric space calculations to more global and qualitative

task space, the only one where this coefficient can have

a meaning irrespective of system structure. The problem is discussed here by defining the useful information

by its analytical expression explicit in terms of system

elements. By application to the class of deformable Lagrangian systems, adapted controlled structure is constructed. Intelligence measured by minimization of a di- stance between demand and result mainly appears as a compromise between information ball and robustness

ball reduction for fixed system complexity.

Keywords : System complexity, Functional Asymptotic Control, Useful Information and Entropy, Intelligence, Task Space Control.

1-INTRODUCTION

As technical systems required for real life task accomplishment are becoming very complex both in their (hard

ware) physical realization and in the related (software)

organization of their command-control structure, an emerging question is in the possible existence of a limit in

improving these systems. Supposing everything can be

continuously extended on hardware side, a direct conse- quence on soft side is the research of a quantitative way

to scale system capability, ie in short to meeisure their

"intelligence" [1]. One should first make sure that the

question has a well defined meaning as for human the definition of intelligence is multiform and depends on the

emphasized "qualities" in the tests. Also, a difficulty

is the domain on which this "intelligence" is applied,

as there exists different kinds of human "intelligence"

ranging from high abstraction to very applied domains.

To avoid these problems the angle of approach will be

modified and, as a system is generally designed for accomplishing a prescribed set of tasks, its "intelligence"

compared to another system will be evaluated in terms

of its "efficiency" to collect the relevant information for these tasks and to use it in its accomplishment. A companion question is system adaptation to different or even

adverse working conditions, which also amounts to evaluate the size of robustness ball corresponding to the

selected tasks. A difficulty however reappears with the

word "selected" as concerns "who" is chosing the tasks,

and this stresses the huge difference between dedicated

and self-deciding system structures. In first case, "intelligence" measurement is limited to evaluation of simple

faithfullness in design and organization, and to robustness to parameter change, whereas in second one, a new

dimension in system evaluation capability is added, showing that the problem cannot be handled in an universal

and unique framework.

Another strong restriction is coming from hardware. Example of lightweight robot arms[2] shows that for high

enough power there exists a breakpoint where internal

material structure generates excitation of internal deformation modes impairing initially researched perfor- mances. One may speculate that this could be cured by

adequate controller design using vision system, most adapted to detect working environment and to give more

flexibility to adapt to task change. As mounting vision sensor on robot arm is no longer possible with deformations, exterior more rigid fixture should be used. If environment is then correctly observed, robot arm vi- brations still remain, forbidding fast enough approach to

target. So including robot end effector in visual observation may appear as a natural solution, but the reality

is that this is not possible as actuator frequency range is significantly smaller than typical perturbation frequency

range. Active control robustification, a constant trend in control research over the last decades, becomes inefficient

beyond some today crossed limit because of the inavoidable spillover from low frequency actuator range to high

frequency internal system range which severely limits the

performances. This internal contradiction (more controlled active power for nominally better performance

80
leading to secondary internal phenomena downgrading

more this performance) also makes the "intelligence" as- sessment somewhat questionnable in the present context,

and bounds even more the domain where the problem has

a well defined meaning. Interpretation is that usefulness

of collected information from sensors is strongly system

depending, including human operator, raising the problem of its adequate selection for a given system and a

prescribed task.

Escaping from these difficulties is however possible by

observing that this limitation comes from unability of

computation-control system to reconstitute, as it classically does, actuator command from trajectory observation for its efficient control. Two different elements are implied in this statement. One is the impossibility past

some level of complexity to distinguish between two close

enough trajectories. Even with perfect end effector location in time and space, decomposition of this observation on base representation functions becomes unrealizable

when flexion and torsion effects are mixing up in a very

complicated motion. Control action becomes inefficient

if one-to-one relation between control and trajectory is no longer maintained. Even if it were maintained, the

power would have to be delivered, owing to speed and

torque requirements, in a too high frequency range for

present actuators, and this would be technically non realizable. The second element is also of fundamental nature

in that there is no direct action on deformation modes

from actuators, as they are receiving their power input

from rigid motion mode, leading to a mismatch between

internal natural power cascade and external one imposed

by feedback loop with usually spillover effect impairing

again system performance.

As there is inadequation between basic physics understanding and new bifurcated situation, classical point of

view should be changed. With trajectory non distinguishability the base ingredient for trajectory control, ie

its time dependence in usual state space representation,

should be abandonned. Only trajectories as a whole have

now a meaning, and global enough information is rele- vant. Reducing the complete non controllable system

dynamics to smaller initially driving rigid ones, time dependent system trajectory is embedded into a selected

class by application of fixed point theorem. The resul- ting control, explicitely expressed in terms of global sy- stem quantities, still gives asymptotic stability toward

desired trajectory, and exhibits the interesting property

to be at its level naturally organized toward task orientation. So in progressing toward higher quality perfor- mances with higher designed and more complex systems,

use of better components is not sufficient and control

structure has also to fit with system properties, implying

mainly application of subsidiarity principle guaranteeing

minimization of internal information flux. This restores adjustment of system hardware structure to possible

task assignment, as it gives again the system the way to have appropriate internal information exchange compatible with power flux. Resulting internal coherence thus

appears as an extremely important element in the possibility of measuring system "intelligence".

To illustrate the previous concepts developed at system

level, «sc/«/ information is defined in next paragraph and

task oriented control for general Lagrangian system dynamical equations is considered. Application to actuated

one-link robot arm with flexion and torsion deformations

carrying of-center massive object is discussed with EulerBernouilli approximation. When compared to usual control based on vision system which in present case cannot

insure trajectory stability, "local" deformation effects are

internally taken care of by proposed control. As much

lower information flux circulation is implied, vision sy- stem is freed for higher level task of driving the approach

to desired target, and for much more modest computing

requirement. In this sense, actual system may appear as more "intelligent".

2-SYSTEM REPRESENTATION AND

USEFUL INFORMATION

For global system improvement, system parts have themselves to be improved in their various components. Basically three hardware parts always exist in a system, 1)- a mechanical-physical part, 2)- a sensor-computing part,

and 3)- a power-actuation part, sec Fig.l. There also

exists a fourth software control law part, which should

enable the system to correctly perform in targetted range

within its new physical conditions, manifested by the

creation of a (possibly infinite) number of internal modes, thus increasing its number of degrees of freedom, and

making previous classical controls inadaptcd. The control based on the new physical conditions theoretically

exists[2] and still makes system trajectories asymptotically stable, ic it guarantees again tracking performance

requirements.

Due to larger excited frequency band when mode number increases, the problem now rests upon 1)- sensing

and treating this new added information, and 2)- generating the corresponding power inputs as needed for in- creasing system performances. The first point belongs

to sensor-computing part, and is handled within existing technology covering a large frequency band with

a wide set of technical solutions and corresponding to broad range of accuracy. For the second point, despite

the large size domain ([10~^?n, lO^m]) without going into

more specific microsystems, there still exists a frequency

gap between classical actuators low frequency domain

{[0,iOHz]), and high frequency domain corresponding

81
to "smart" material systems {[Z.10'^Hz,^.lO^Hz]). Any

new information is directly usable only if it belongs to the intersection of both sensors and actuators frequency

ranges. A very striking case is vision sensor giving an

over-detailed amount of informations not directly useful

for system control improvement. Consequently to give

the system adapted capability, the problem is not in getting more information as believable from the increase

of system internal degrees of freedom, but on the contrary to reduce the extra-information from state space

in frequency range outside actuator's one, and in order

to maintain robust asymptotic stabilization by adapted

control within the uncertainty ball corresponding to the

unpreciseness produced by this reduction. As shown on

Fig. 1 , it is after collection of rough information from sensors that there should exist a reduction process to filter the only relevant information needed for reaching system

targets. This leads to the definition of useful information

determined from task orientation rather than lower level unexploitable trajectory orientation. It is based on observation that occurrence of events rests upon removal of a double uncertainty : the usual quantitative one related

to occurence probability and the qualitative one related

to event utility for goal accomplishment. So events may

have same probability but very different utility, and this

explains why some extra informations on top of existing ones have no impact on reaching the goal. In present

case, it can be verified that, calling uj and pj the utility

and the probability of event Ej, and I{uj,pj) its associated information called useful information, the relation

(1)

holds for event E1UE2 with same utility u. On the other

hand, there is strict proportionality between utility and

corresponding information, so

IiXu,p) = \I{u,p) (2)

With eqns(l,2), there results that useful information is given by

I{u,p) = —ku.logp (3)

where k is Boltzmann constant. Usual entropy calculation is thus obtained by presupposing that all events

have same utility for goal accomplishment, which is cer- tainly true in Thermodynamics where all molecules are

totally interchangeable and thus indistinguishable. As

a consequence it is well known that only the invariant

corresponding to this equivalence class, here the energy

(or the temperature), allows to separate thermodynamical systems. Similarly internal system deformations (fle- xion and torsion) are undistinguishable events as they

are layered on invariant surfaces determined by the value of bending moment M at link's origin[3]. So using

their observation to improve system dynamical control

is not possible, in the same way as observing individual

molecule motion in a gas does not improve its global

control. As a result, raw sensor information has to be

filtered so that only useful information for desired goal

is selected. This is precisely the remarkable capability

of living systems to have evolved their internal structure

so that this property is harmoniously embedded at each

level of organisation corresponding to each level of development. In this sense they are remarkably intelligent.

An important element here is that the process has been

subsidiased into the hardware structure in order to free the upper levels. 3-LAGRANGIAN EQUATIONS FOR

DEFORMABLE SYSTEM

To proceed, advantage will be taken of the general la- grangian form of deformable system in order to exhibit directly on system equations the features discussed

above concerning information reduction. First there is a cascade effect of exterior forces onto rigid dynamics

feeding itself deformation modes, allowing reduction of

complete initial (infinite dimensional) system to (finite

dimensional) "core" rigid system, see Fig. 2. Then, and

as long as "natural" boundary conditions are considered for the system, only these intrinsic elements will be

really needed to control system dynamics. By "natural" are meant boundary conditions constructed with the

remaining terms coming from the various integrations by

part needed to transform system action variation into

Lagrange equations. More specifically, with Lagrangian

density

r r ( n^ / ^^ du^jx ,t) 0"' m-jx ,t)

duu{Sut) d^'ukjSut,

, Uk{^\,t), ^x,i

dt

(4)

depending on both discrete (rigid) variables qj{t) and

field (deformation) variables Uj {x, t

) up to their ^th space

derivatives, as well as their values on a part {S\) of total

system boundaries (5 = 5i x S2) of the space domain

D{x) in the additive form

1 V{x)

L

dt

(5)

of a rigid variable part Lr and a deformable one Co, and

where the arguments in the second part are the same as

in eqn(l). The variation of the action

-I"'to IJd{x)

Crdxdt (6)

82
inside the space domain D{x) and over the time interval

[<o,</] can finally be splitted into two parts, one under

the integral sign and another one expressed at the boundary (S) of D(x) and at the limits of the time interval (if

there are "transversality conditions"), and resulting from

integrations by part. Writting that system equations are deduced from the action J by a variational principle implies two elements :

- 1 - the Lagrange equations

dfjCx _ d dJjCr

dqj dt dqj

Ou.- h OixV

du at ou

are satisfied inside the space-time domain, with Uj the

control acting onto the system,

- 2 - the remaining boundary terms resulting from integration by parts are equated to the work done by exterior

force terms onto the system, ie.

'DCt

'OjCt

(8)

with

VCt/VZ = dCr/dZiSj) - d/ dt[dCT / dZ{Sj)\

DCr/Duf, = dCr/du^, - dydCTldu^j,v

and transversality conditions if any are satisfied. Boundary conditions are called "natural" when they are constructed from these quantities, and not from different ones.

For a 1-link system, the lagrangian writes in partitioned

form

C =Lr{qj{t),qj) + j i^d{qjAi,<l{x,t)A,(lfi^qnf]

+ l^s{qj,qj,qs,qs,q^iS,q^cs)

with rigid part

(9)

^'--'"[dt] ^"^""K dt

+ Km{0-Omf (10)

in terms of flexion and torsion variables u(t,x), 'y(t,x),

and interaction part

1 ,,0 , fd0 d-u{t,x)

\ ox

(12)

x = L

V IT , I , du(t,x) d-u(t,x) d-f(t,x) A = ( L -h // j — H h // ——— 1- It —^

dt dx dxdt dx

at links boundaries, out of which dynamical equations

and boundary conditions are easily obtained[4]. (If, It) are coordinates of tip mass m with respect to link's end,

and the various other coefficients characterize the beam

as usual within Euler-Bcrnouilli approximation. One can

verify that in link and actuator equations coupled by

compliance effect, are both acting the applied input tor- que r and the bending moment Ma = EI{d'^u{t, 0)/dx'^),

here the only term through which deformations are seen by system rigid part.

4-TASK ORIENTED CONTROL

In general, the system is assigned to perform an action,

and a control is set to give the system the ability to meet

the corresponding requirements. This is always expressed as satisfaction of Lyapounov theorem with adapted

Lyapounov function, writen in terms of system trajectory

parameters in state space. In other words, control is tra- jectory oriented, and all sensors are used in this view. In

particular, vision sensor if any will provide information on link tip motion. As seen above, this is misleading

as long as observed motion belongs to an indistiguishable class. Control has to be approached in task oriented

sense, and, for reaching the goal, is governed by a choice

of "good" informations depending of their utility defi- ned above. Starting from partial Hamiltonian density

associated to deformable part

= qj-^ + u——\-u(b) +u^(b)

dqj OU ou{b) OUf^ib)

(13) one will consider system Lyapounov function

r / 2

'2

(14)

in terms of rigid articular and actuator variables = d,

q2 — 6m, deformation part

, , dO duit.x

+

2 ( d'r(t,x)\

dt dt , , . , 2 0 (11)

dx dx

with positive parameter gains A'pj , Tyj . Its time derivative along system trajectories is

dt

Uj + F,

f OLr d OLr

\ dqj dt dqj

-f- Kpjqj + Tvj'ij

(15)

83
Substituting for d?qj/dt'^ from explicited Lagrange equations(7) and eliminating all other second order time derivatives, one will get an "inertia" term Faj which, on

physical grounds, is equal to forces other than exterior

forces Fj acting onto system of discrete variables qj , and

coming from the (back) effect of the field variables u(x,t)

onto discrete variables qj(t). As V is positive definite for

large enough definite positive gains [Kpj ,rvj), its derivative can be made definite negative by taking control Uj

so that the term between brackets is equal to —{Kyq)j,

where matrix A'v- is definite positive. The resulting form

of the control (supposing there is no exterior force)

Uj = -Kpjqj - Kvqj + KDiqjAjr 0 + ^^FjFaj (16)

and generalizes usual PD-control to full nonlinear case. In fact, it fits more generally the expression of dynamical

system control

U = Ucomp + UpDF+^U (17)

when writing the tracking condition for desired trajectory qj{t) = qjd{t) and splitting the various control components, with

I' PDF = V PD + ^^F (18)

Moreover, from argument above, the control law in eqn

(16) gives both asymptotic tracking of desired trajectory

for discrete variables and asymptotic stability for field variables as well as their first order time derivatives.

From eqn(15), equating the sum between brackets in its right hand side to -{Kvq)j amounts to take a controller of PDA type[5]. However, it should be observed that

the resulting invariant subset of dV/dt is the same as when Tj =0. So the same convergence property of the

solutions is expectable for any value of Tj . The reason

of introducing the new kinetic term with Tj ^ 0 is in the role of the direct acceleration term, or of the new

resulting term Faj after substitution, which is mainly to change the relative values of inertia-damping-stiffncss sy- stem parameters with respect to field modes, as already

observed and used for classical force control.

But after substitution from Lagrange equations this term

is an integral of a complicated function of field variables

and their space derivatives over the domain D(x). So

there is no advantage to use it in this form which requires

local knowledge of field variables inside the domain, unless Lagrangian structure is such that this integral trans- forms into explicit well identified and sometimes directly

measurable surface quantities. A very simple case occur when the Lagrangian is such that formally

dCD d dCD dcD d dcD

dqj dt dqj du dt dii (19)

Then from Lagrange eqns(7) there results

dip d dip

dqj di dqj - o^,- h du du

The "inertia" force term Fa2 is just equal to the boundary term in the first bracket of eqn(8) when Tj — 0,

and is expressible in terms of this quantity, and of rigid

variables qj and their first time derivatives when Fj > 0. This global expression contains all needed information to control the local action of (infinite dimensional) deformation effects, usually approached by decomposing this source term onto all projection space and cutting at a

finite mode number with spillover conscquences[6,7]

.

Much more than local control, more global task oriented

control will also be independent of (too) detailed information on link deformations. Typical task is to reach a preassigncd target under specific circumstances. Returning to eqn(3), this amounts to minimize the total

entropy production associated to any motion in the class of acceptable trajectories fixed by the local control defined in previous paragraph, so its expression depends

in general of all trajectory parameters. To this end, the

utility u will be taken as the gradient of a convenient

positive definite quantity such as a Lyapounov function

to define a steepest path and more importantly, to eliminate before data processing irrelevant task information,

saving enormous amount of time and data space. So with

{p) the set of all observed parameters one gets

dv

dp

(20)

and in cqn(3) only will remain terms for which this ex- pression is above a minimum threshold value corresponding to system sensitivity. So all collected information

from sensors is filtered in terms of its utility for the pre- scribed task. This explicit result is independent of the

dedicated or selfdcciding character of the system. With

eqn(14) for instance, the only dependence of V on trajectory parameters is through bending moment M, so when

taking the gradient with all sensor information, there

only remains a term dV/dAl, and more detailed trajectory information docs not appear. So adapted control

splits finally into a local one expressed in terms of global

(relative) invariants M, and a nonlocal one depending on utility of these quantities for reaching final target.

Though trajectory oriented the first one directly links to the task oriented second one and respects the very nature

of internal information provided by system structure. In

this respect, system intelligence is easily measured by in- formation flux from eqn(3) and by associated robustness

ball of the applied control corresponding to a distance

between demand and result.

84
5-CONCLUSION

Analysis of system structure shows that evaluation of its

intelligence is only meaningful in task space. This re- quires the satisfaction of internal coherence conditions

manifested by system ability to extract from its sensors the relevant information for these tasks. The problem

is studied here by defining the useful information which

precisely allow to pass from initial geometrical space to task space irrelevant of the way the system is designed

and organized. Application is made for Lagrangian sy- stems representing deformable bodies, for which equati- ons analysis shows that even if at first sight system nature is drastically changing with increase of state space

dimension to infinity, internal system organization also

changes in such a way that its local control still remains

fundamentally finite dimensional. Observation of new

deformation modes is not only useless, but also damaging in that it leads to control form interfering with natural internal feedback regulating power exchange bet- ween displacement and deformation. Sensors providing

too detailed information are not adapted as it has to be filtered for reconstitution of needed more global one. More efficient way is to use local control based on natural system invariants, directly linkable to more global

task oriented control based on useful information (rather than filtered one) expressed in terms of utility fac- tors constructed as the gradient of Lyapounov with re- spect to trajectory parameters. When they aggregate

into trajectory invariants, only their derivatives finally

appear, justifying again the choice of previous local control form. Moreover, the association of the two level form

presented here respects natural system organization and

minimizes information transfer between the two levels. System intelligence is directly measured by task adaptation expressed here as both circulating information flux

and robustness ball corresponding to local controller for a given distance between demand and result. References

[1] J. Khalfa, Ed. : What is intelligence, Cambridge

University Press, Cambridge, Mass., 1994

[2] W.J. Book, T.E. Alberts, G.G. Hastings : "Design

Strategies for High-speed Lightweight Robots", Computers m Mechanical Engineering, p. 26, 1986.

[3] M. Cotsaftis : Comportement et Controle des Syste- mes Complexes, Paris, Diderot Multimedia, 1997.

[4] M. Cotsaftis : "Global Control of Flexural and Torsional Deformations of One-link Mechanical Systems",

Kyherneiika, Vol. 33(1), 1997,pp. 75-86.

[5] P.T. Kotnik, S. Yurkovitch, U. Ozguner : "Acceleration Feedback Control for a Flexible Manipulator Arm"

,

Proc. 1988 IEEE Intern. Conf. on Robotics and Automation, Philadelphia, Penn.,Vol.l,1988,p.322

[6] M.J. Balas : "Modal Control of Certain Flexible Dynamic Systems", SIAM J. Control, Vol.l6,1978,p.450.

[7] Y. Sakawa : "Feedback Control of Second Order Evolution Equation with Unbounded Observation" , Int. J. of Control, Vol.41(3),1985,p.713.

Actuators

Power

Input

Displacement

Modes

Dcformation

Modes

Links

Position

Velocity

>-

Fig.2 : Complex System Structure of

Deformable Mechanical System

Useful

ifoormation SYSTEM CONTROL

Power

Input

Filtering

(Task

Oriented)

Power

Actuation

Part

Phjjsica

Part ^ ^

^Sensor

Computing

Part

Demand

RcsuIt of

Action

Error

( 1 rajcctor y Oriented)

Rough Information

Fig.l : System Structure with Main Component Parts

and Information Filtering for Task Orientation

Problem : Minimize distance(demand,result)

=f( system parameters)

85
Minds, MIPS and Structural Feedback

Ricardo Sanz and Ignacio Lopez

Universidad Politecnica de Madrid

{sanz , ilopez}@etsii . upm. es

Abstract

This paper tries to stress the need of having a clear understanding of the concept of intelligence before we can progress in the formulation of a measure for it. At the end it suggests a view of intelligence as structural feedback in model-based control systems.

Keywords: Intelligence, performance, behavior, mental models,

structural feedback.

1 Introduction

This paper tries to suggest the practical impossibility of finding

a single and useful^ measure of general intelligence for all types

of artificial systems performances unless we get some previous

result in the form of a sound theory of intelligence.

As was stated at the workshop website, its goal is to discuss

three challenges pertaining to intelligent system performance:

• how to measure performance;

• how to evaluate intelligence and

• how to put performance and intelligence into correspondence.

We will try to address the three points in order (see sections

4,5 and 6), but first we want to make a first comment. When

talking about intelligence a problem appears, and it is that "intel- ligence" is a moving target. Some centuries ago "a person able

to read" implied "a person very intelligent". Now we don't consider this ability as a symptom of intelligence in a person of our environment. But if we talk about an animal, forsone example

a dog, "able to read" is still considered a good manifestation of

intelligence.

So, what is that stuff that appears or disappears as you point at

different entities? Can intelligence be in the eye of the beholder?

We think that the term is used in two quite different ways: a) As a comparison between two entities that can be both explicit

or one implicit (a normal dog) and b) As an absolute measure of some core capability.

While we can mostly agree with Alex Meystel conception of

intelligence as a core concept underlying minds, perhaps all we

'From an engineering point of view, i.e. to build/analyze artificial systems.

are falling in the easy way of thinking mentioned by Bateson

[3, page 82] of using words that appear more concrete than they

are"

.

Before entering into main matter, let's start with a brief dis- cussion about the adequacy of ascribing mental properties like

intelligence to machines.

2 What is Intelligence?

It is common to address intelligence as a property inherent to something we call mind. The use of both terms, intelligence

and mind, is not that clear. In fact, each one of us appears to have his own notion of intelligence speaking in terms of everyday

life. Although deep thought and study about the topic can clarify

partial notions of intelligence, there is still no global perspective.

We want the following question to emerge: does intelligence

really exist? After what has been said and having in mind our

constant references to the concept, it really seems ridiculous to question it. But we would like to point out the fact that intel- ligence could well be one word hiding what can be considered

a too fuzzy concept \ By this we mean that the word does not have a fixed reference to something that can be pointed out, such

as a dog or a table (it lacks a true referent). It is in some sense a concept similar to a notion of a mathematical space, i.e.: everything which matches certain restrictions is part of intelligence.

The space of things that think. The concept has lost in this way the apparent rigidity; the

question, although, may be, in a more precise way: what are

the restrictions a feature has to match to form part of intelli- gence? And at this point the answers diverge because the number of possibilities is close to infinity. It would be an error to

put the question hke this. Perhaps it would be better to approach

the topic in another way: what is behind everything we seem to consider intelligent? Searching this instead of a particular set of characteristics would eventually lead to a rule with which the judgement of the existance of intelligence would be possible.

In any case, once it is clear if something is intelligent or not,

it would be tempting to determine how intelligent, that is, how

much intelligence it has. This question is too particular to be

-Bateson says about these words that they are too short and this shortness conveys an erroneous ascription of concreteness.

linguistic variable in its most pure sense: i.e. created by language.

'*This is to be thought in a sense of tuo broadfur understanding.

86
answered. The individual intelligent characteristics which constitute the intelligent set offeatures one self possesses are each

specialised, and in this way not comparable.-^ In this way, given a

set of intelligent characteristics, the only judgement that has any

sense needs to be put in terms of targets and adequation to those

targets: performance.

Returning to the rule which would enable discrimination between intelligent and not intelligent, it should not be focused on common aspects of features we usually consider intelligent, but on requirements which make them possible. For example parallel calculation, memory, etc. Having this in mind, the decision to consider something intelligent or not comes from the process of

analysis of the underlying capability, i.e.: learning what can be

expected from a being with such capability (eg. memory) when

in a particular environment and with a more or less elaborate set of targets. Apparently we end again with a certain notion of per- formance.

The last point we would like to focus on comes from looking

at the problem from a different angle. What if intelligence were

a concept only suitable -clear enough- for human minds? That

is, we call something intelligence, but it does not seem to have a bounded notion behind. So, supposing it is a collection of features we have grouped together, and not considering the fact that we could have done so in other ways, what makes us think that

intelligence is something (a table, a bus)? In other words, what

makes us think an alien would have a notion parallel to our intelligence as he would if he came to Earth and saw a table or a boat?

3 Human (species) chauvinism

Let's see what philosophers think about mental properties of machines. An example is what Crockett [5, p. 193] says about the use of human-like phrases to refer to machine thinking:

Our anthropomorphizing proclivity is to reify those abstractions and suppose that the computer program possesses something approximating the range of properties that we associate with similar abstractions in hu- man minds.

Even more amazing is his continuation:

This is harmless so long as we remember that such

characterizations can lead to considerable philosophic

misunderstanding.

What amazes me more in this text is that people like Crocket

strongly believe that we know what are the "abstractions in hu- man minds" but only suppose what the computer program possess. In our experience we know -most of the time- what are the abstractions -the representations- in mechanical minds but only

suppose what are those abstractions in biological minds.

'it would be like comparing -adding, subtracting, etc.- apples and dogs: impossible.

It is these days is when we are starting to get some direct insight into the inner working of human minds by means of PET

(positron emission tomography) or fMR (functional magnetic

ressonance [4]). As an example, fMR has confirmed what many

had long suspected -that men and women think differently. Yale

Medical School investigators did compare the brain operation of men and women while reading, discovering different activation

patterns in their brains while performing the reading task. Another example of the difficulties in matching human mental

concepts with machine mental concepts can be found in [2];

Indeed, if mechanical devices can distinguish wavelengths of light without having sensations, then why

do I experience any sensation at all?.

Most people tend to think that the human sensation is something more than the mere recognition of a input signal. Recognition at the simple level of signal capture, representation and

triggering of activity. "Sensation" is nothing more than the triggering of activity due to an input signal. The immediate implementation in a computer is as an interrupt handler. The only

difference is the high level of concurrence in biological computers that let them be truly concurrent in responses to sensations.

There are also human sensations that are so strong that they dis- able further sensations. This is, exactly, the type of behavior

found when a computer interrupt handler disables further inter- ruptions.

Computers provide minds for physical systems, and it is time

to clarify the true meaning of mental concepts.

4 Performance and MIPS in brains

A visible feature of biological intelligence is performance as Jim

Albus pointed in his definition of intelligence. This is related

to how we use the term for humans (remember the title of the book by Sternberg and Wagner, Practical Intelligence: Nature

and Origins of Competence in the Everyday World).

In our search for metrics for intelligence, we are exactly in the same situation as computer consumers and manufacturers were some decades ago in relation with client-requested performance

measures. As they both discovered, the old-basic measure of per- formance (MIPS: Million Instruction per Second) was useless to compare different architectures {e.g. CISC vs. RISC) or applications {e.g. data-bases vs. finite-element analysis). The only use- ful possibility they found was the evaluation of the performance

in specific tasks, and hence this was the origin of benchmarking. Unfortunately benchmarks are not single measures, and at- tempts to build weighted benchmarks only changed the focus of

the benchmark but not the final usability of them (they are always measures of niches of functionality).

Task-independent measures, like MIPS or bits/second or entropy, are too raw to be useful for most engineering purposes

because they are so far from the desired performance specifica87
tion that we lack a theory that can map one into another^. For

example, suppose that we want a distillation column controller

intelligent enough to minimize recirculation (a desired performance). Who can decide, based on a MlPS-like measure, if a fuzzy controller A can fulfill the task, or if model-based predictive controller B is better that A?.

This theory that maps a MlPS-like measure to performance

specified in useful terms is what we are seeking in our research on intelligent systems, because it is -in fact- The True Theory of

Intelligence. The theory will not only let us evaluate alternative designs, it will be a true explanatory discourse that will reduce

inteUigence to simpler, well grounded, terms. To follow Bateson suggestion of marking concepts that are not concrete enough and require further thinking, we can use

the term i-stujf to refer to the substance measured by True Intelligence Metrics. George Saridis probably will equate i-stuff

to negentropy and Jim Albus to performance. We will make a suggestion at the end of the paper.

5 Intelligence and bodily capabilities

In relation with what can we measure, we agree with Chris Landauer in the fact that "Success is not by itself the right crite- rion" because we have to split success into two contributions:

mind and body (and bodily intelligence is not what we are talk- ing about). As an example consider two implementations of a

future Mars rover whose main mission is going from point A to point B, one kilometer away, taking a sample of the ground each

50 meters:

Implementation H: 200 Ton. Caterpillar structure based on a combination of bulldozer, power shovel and truck. Control

of sample taking based on mechanical coupling of power

shovel to caterpillar (50 meters = sample). It lacks directional control because it is not necessary (it will advance

straight bulldozering any obstacle.)

Implementation T: 50 Kilograms. 10 Watt solar power panel.

Microrobotic arm. Who will attain success? If both are successful, who is more

intelligent? Is performance a manifestation of intelligence? The

two first questions are rhetoric. The answer for the last one is "not always".

There are some attempts to extend fundamental physical the- ory to include information at the same level as mass and energy.

In some sense we can analyze biological behavior as an exchange

of mass (feeding in / excreting out), energy (chemical in / ther- mal & mechanical out) or information (sensing in / speech out).

We can attach these interchanges to human subsystems, and in- formation will become associated to the mental system. This

division is, however, not very strict, because information is sup- ported by means of mass or energy, and some energy inputs are managed as mass inputs (specially in animals).

*This is, in fact, the third point mentioned in the introduction.

6 Conclusions

Our analysis of the Mars rover story is that if the T implementation is successful everybody will agree that it is more intelligent

than the H implementation. Even if both attain success. TO

achieve this result the T implementation needs some mental content and some algorithms to exploit this mental content. As we did say before we will propose a different interpretation

of i-stuff: it is focused on mental models. Following this idea, an intelligent being is a being that has models of his world in

his mind and achieves intelligent behavior using its models for

action. Intelligence is, from this perspective, a two sided concept: model-based mental content (static view of intelligence)

and model-based generation of behavior (dynamic view of intelligence).

Can the i-stuff be that collection models? Not so. Because all we know some knowledgeable people that are plain stupid.

What we consider the true core of intelligence is -plainlyfeedback. When feedback for action is done trough good models of the world it achieves incredible performance levels. When

feedback is used to tune parameter models it make systems adapt

to changing circumstances in the world. When feedback is used

to modify models of the world this is a pure learning process. When feedback is used to structurally modify the algorithms exploiting the models we arc talking of creativity^. Structuralfeedback is perhaps the highest manifestation of intelligence; when a system is able to create new control policies that will enhance its effectiveness.

Perhaps this proposal only muddles more the discussion be- cause model is even shorter than intelligence and it seems even more concrete; but we think that it is relatively easier to devise

metrics for model quality.

But even if we can measure quality of models and model evolution algorithms, we are still halfway lo the metric of intelligent

behavior, because we still lack a qualii\ measure of the use of

the model to generate the behavior (/.< . a metric of the architecture). Performance-based metrics, as suggested by Jim Albus

definition of intelligence, will fit this niche but still they will be

domain-dependent.

We strongly believe that, in the future, all these theories of

intelligence will consolidate in a Great Unification Theory (and

this structural feedback seems to us a good promising starting

point), that will let engineers build artificial intelligences with

the plasticity enough to adapt or tune to specific needs. Being

this the case, in our opinion the core foundation of it will be raw information processing with capability to autoorganize in the form of models of the world and model exploitators generating

behavior. The theory of intelligence can be viewed as a theory of

action, a theory of representation or both.

^Adaptation, learning, evolution, creativity, are facets -i.e. perceptions from

an external entity- of a system changing in response to interactions with the world.

88
References

[1] Christian Balkenius. Natural Intelligence in Artificial Creatures. PhD thesis, Lund University, Lund, Sweden, 1995.

[2] Stephen M. Barr. A mystery wrapped in an enigma. First Things: A Monthly Journal of Religion and Public Life,

(77), November 1997. A comment on The Conscious Mind:

In Search ofa Fundamental Theory. By David J. Chalmers.

Oxford University Press.

[3] Gregory Bateson. Steps to an Ecology of Mind. The University of Chicago Press, 1972.

[4] Neil R. Carlson. Physiology of Behavior. Allyn & Bacon,

sixth edition, 1998.

[5] Larry J. Crockett. The Turing Test and the Frame Problem. AI's Mistaken Understanding of Intelligence. Ablex

Publishing Corporation, Norwood, N.J., 1994.

[6] Kenneth M. Ford, Clark Glymour, and Patrick J. Hayes,

editors. Android Epistemology. MIT Press, Cambridge,

MA, 1995.

[7] Stanley P. Franklin. Artificial Minds. MIT Press, Cambridge, MA, 1995.

[8] Markus P.J. Fromherz, Vijay A. Saraswat, and Daniel G.

Bobrow. Model-based computing: Developing flexible machine control software. Artificial Intelligence, 114(1-

2): 157-202, October 1999.

[9] Donald Gillies. Artificial Intelligence and Scientific Method. Oxford University Press, Oxford-New York, 1996.

[10] John Haugeland, editor. Mind Design II. MIT Press, Cambridge, MA, 1997.

[11] Tariq Samad. Complexity management: Multidisciplinary

perspectives on automation and control. Technical Report

CON-R98-001, Honeywell Technology Center, Minneapolis, MI, January 1998.

[12] Ricardo Sanz, Fernando Matia, and Santos Galan. Fridges,

elephants and the meaning of autonomoys and intelligence.

In Proceedings of IEEE ISIC'2000, Patras, Greece, 2000.

[13] Luc Steels and Rodney Brooks. The Artificial Life Route

to Artificial Intelligence. Lawrence Erlbaum Associates,

Hillsdale, NJ, 1995.

[14] Kurt VanLehn, editor. Architectures for Intelligence. Lawrence Erlbaum Associates, Hillsdale, NJ, 1991. The 22nd

Carnegie Mellon Symposium on Cognition.

89
Fast Frugal and Accurate - the Mark of Intelligence:

Towards Model-Based Design, Implementation, and Evaluation of

Real-Time Systems

Bernard P. Zeigler and Hessam S. Sarjoughian

Arizona Center for Integrative Modeling & Simulation

Electrical & Computer Engineering Department

University of Arizona. Tucson, AZ 85721-0104, USA

Email: {zeigier|nessam}@ece.arizona.edu

URL: www.acims.arizona.edu

ABSTRACT

Engineered systems, whether called intelligent or not, principally

must rely on models to achieve their goals even in the simplest

situations. Therefore, a system's intelligence is a consequence of the

collective intelligence embodied in its models. In this paper, we

describe intelligence measurement grounded in the general concepts

of discrete event, model-based system design methodology. We

discuss the basic elements of the approach in view of their role in intelligence measurement. Computational resources in both

processing and communication forms are constraints on intelligence,

but they are not determinant The architecture which configures these resources plays a major role in the intelligence achieved. Further the

architecture must support fast and fhigal heuristics tuned to the environments in which the system is to operate. Real time processing

architectures built on discrete event modeling and simulation

principles are most suited to support "fast frugal and accurate"

intelligence. Such architectures must be designed with a software

engineering methodology that explicitly supports a system's control of its own computational resources and includes hooks for measuring

its intelligence in terms of the speed, frugality and accuracy of its responses.

1 INTRODUCTION

Unless we are talking about the affluent life known to many of

us in the recent past, the real world is a threatening

environment where knowledge is limited, computational

resources are bounded, and there is no time for sophisticated

reasoning. Unfortunately, traditional models in cognitive

science, economics, and animal behavior have used theoretical

frameworks that endow rational agents with full information

of their environments, unlimited powers of reasoning and

endless time to make decisions. Tacitly accepting this

paradigm - as seems the prevalent assumption - does not

provide a promising basis for measuring intelligence, the

theme of this conference.' Indeed, to measure intelligence

requires first an understanding of the essence of intelligence as a problem solving mechanism dedicated to the life and death

survival of organisms in the real world. Evidence and theory

from disparate sources have been accumulating that offer

alternatives to the traditional paradigm.

' NIST Workshop on Performance Metrics for Intelligent systems.

An important crystallization of the new thinking is the

"fast frugal and accurate" (FFA) perspective on real word

intelligence promoted by Todd and Gigerenzer [1]. FFA

heuristics are simple rules demanding realistic mental

resources that enable both living organisms and artificial systems to make smart choices quickly with a minimum of

information. They are accurate because they exploit the way

that information is structured in the particular environments in which they operate. Todd and Gigerenzer show how simple

building blocks that control attention to informative cues, terminate search processing, and make final decisions can be

put together to form classes of heuristics that have been shown

in many studies to perform at least as well as more complex

information-himgry algorithms. Moreover, such FFA

heuristics are more robust than others when generalizing to new data since they requu^e fewer parameters to identify.

It is important to note that FFAs are a different breed of

heuristics. They are not optimization algorithms that have

been modified to run under computational resource

consfraints, e.g., tree searches that are cut short when time or memory run out. Typical FFA schemes enable ignorancebased and one-reason decision making for choice, elimination

models for categorization, and satisfying heuristics for

sequential search. Leaving a full discussion of the differences

to [1], the critical distincfion is that FFA's are structured from

the start to exploit certain resfrictive assumptions, such as skewed frequency disfributions, about their input data. They

work well because these assumptions often happen to hold for

data from the real world. Thus FFAs are not generic inference

engines operating on specialized knowledge bases (the

paradigm of expert systems) nor other generalized processing

structures (e.g., [2]) operating under limited time and memory

consfraints. An organism's FFAs are essentially models of the

real environment in which it has found its niche and to which

it has (been) adapted.

New kinds of models for biological neurons provide

possible mechanisms for implementing intelligence that is characterized by fast, frugal and accurate heuristics. Work by

Gaufrais and Thorpe [3] has yielded a strong argument for

"one spike per neuron" processing in biological brains. "Onespike-per-neuron" refers to information transmission from

90
neuron to neuron by single pulses (spikes) rather than pulse

trains or firing frequencies. A face recognition multi-layered

neural architecture based on the one-spike, discrete event

principles has been demonstrated to better conform to the

known time response constraints of human processing and

also to execute computationally much faster than a comparable conventional artificial neural net [4]^. The

distinguishing feature of the one-spike neural architecture is

that it relies on a temporal, rather than a firing rate, code for

propagating information through neural processing layers.

This means that an intemeuron fires as soon as it has

accumulated sufficient input "evidence" and therefore the

elapsed time to its first output spike codes the sfrength of this

evidence. In contrast to conventional synchronously timed

nets, in fast neural architectures single spike information

pulses are able to traverse a multi-layered hierarchy

asynchronously and as fast as the evidential support allows.

Thorpe's research team has also shown that "act-as-soon-asevidence-permits" behavior can be implemented by "order-ofarrival" neurons which have plausible real world

implementations. Such processing is invariant with respect to input intensity because response latencies are uniformly

affected by such changes. Moreover, coding which exploits

firing order of neurons is much more efficient than a firingrate code, which is based on neuron counts [3,4].

Countering the evidence that intelligence is essentially

fast, frugal and accurate is Hans Moravec's prediction that by

2050 robot "brains" based on computers that execute 100

trillion instructions per second (IPS) will start rivaling human

intelligence [5]. Underlying this argument is that there is an

equivalence between numbers of neurons in biological brains

and IPS in artificial computers. It takes so many billions of

neurons to create an intelligent human and likewise so many

trillions of IPS to implement an intelligent robot. In sfrong

form this equivalence implies that pure brute force can produce intelligence and the structures, neural or artificial,

underlying fast and frugal processing are of little significance.

2 MODEL-BASED INTELLIGENCE AND

MEASUREMENT

In this section, we discuss intelligent systems from three

perspectives: knowledge representation, execution, and

measurement. Specifically, this paper makes the case that^

• computational resources in both processing and

communication forms are constraints on intelligence,

but they are not determinant

• the architecture which configures these resources plays

a major role in the intelligence achieved

• the architecture must support fast and frugal heuristics

tuned to the environments in which the system is to operate

• real time processing architectures built on discrete

event modeling and simulation principles are most

suited to support FFA intelligence

• such architectures must be designed with a software

engineering methodology that explicitly supports a system's control of its own computational resources and includes hooks for measuring its intelligence based on FFA standards.

2.1 Computational resources in both processing and

communicationforms are constraints on

intelligence, but they are not determinant

Morevac's claim that artificial intelligence will arise once the

processing power is there to support it can be the starting point

for a serious investigation to understand its merits. On the one

hand, we need yardsticks of intelligence and on the other,

yardsticks of computational resources (presuming that raw IPS

is not very discerning). We might have a diagram as shown in Figure 1. Let's assume for a moment that we have the framework in the form of a diagram as above, what can we do with it? We

can ask

• For a given level of resources, how smart can a system

be? This would prevent us from trying to build systems

that are infeasible with the resources at hand.

• For a given intelligence level, how much resources are needed? This would help provide cost estimates for

given intelligence requirements.

• How well does a system utilize its resources? Where

does its intelligence stand relative to the best achievable

in its resource league? Where does its level or resources

stand relative to the best in its intelligence class?

^ The face recognition layered net was executed by a discrete event

simulator and took between 1 and 6 seconds to recognize a face on a Pentium PC vs. several minutes for a conventional net on a SGI

Indigo. Recognition performance in both cases was very high. The

authors employed a training procedure which, while effective, is not

plausible as an in-situ learning mechanism.

We are not claiming that these are the only elements responsible for

intelligent behavior and by implication there are other means for

intelligence measurement.

91
Computation/Communication resources

Figure 1 : Intelligence measurement in terms of required resources

However, the yardsticks for resources and intelligence are not likely to be single dimensional linear orders but more

likely to be multidimensional, partial orders. Even more to measure FFA intelligence which is envu-onment-dependent,

we may need to condition measurement with respect to problem classes asking which kinds of problems are performable on which kmds of architectures.

2.2 The architecture which configures these resources plays a major role in the intelligence

achieved

This is a truism when applied to implementation of standard

fiinctionality - certain designs are better than others in implementing the same input/output behavior. However, in the

absence of a well-defined characterization of intelligence in terms of input/output behavior, the focus has so far been on

achieving intelligent behavior by whatever means possible,

not paying much attention to the critical nature of the

architectures that can support it. The results of Thorpe

mentioned above, however, suggest that FFA intelligence is only achieved with "single-spike" neuron architectures and

would be infeasible if the same neurons were employed in the manner assumed in conventional connectionist approaches.

2.3 The architecture must supportfast andfrugal

heuristics tuned to the environments in which

the system is to operate

Generalizing the idea that FFA heuristics embody models of

the environment, the ability to work with models of the

environment, one's self and others may be taken as key

component of intelligence. Model-based design was formally

introduced around 1980s as the basis to enable systems to reason about their own behavior in normal as well as abnormal

situations. Over the years, many architectures have been

proposed and implemented most of which typically suitable

for narrow well-defined domains. However, a generic

architecture based on simulation modeling concepts was

proposed by [6]. Briefly stated, generic model-based design

provides a generally applicable architecture in which

simulation and other engines execute models that embody

what the system employs about its environment - both

external and internal

2.4 Real time processing architectures built on

discrete event modeling and simulation

principles are most suited to support FFA

intelligence

Discrete event models can be distinguished along at least two

dimensions fi-om traditional dynamic system models - how

they treat passage of time (stepped vs. event-driven) and how

they treat coordination of component elements (synchronous

vs. asynchronous). Recent event-based approaches enable more realistic representation of loosely coordinated semiautonomous processes, while traditional models such as

differential equations and cellular automata tend to impose

strict global coordination on such components. Discrete event

concepts are also the basis for advanced distributed simulation

environments, such as the High Level Architecture (HLA) of

the Department of Defense, that employ multiple computers

exchanging data and synchronization signals through message

passing [7]. Event-based simulation is inherently efficient

since it concentrates processing attention on events -

significant changes in states that are relatively rare in space

and time - rather than continually processing every

component at every time step.

The DEVS (Discrete Event Systems Specification)

formalism [8] provides a way of expressing discrete event

models and a basis for an open distributed simulation

environment [9]. DEVS is universal for discrete event

dynamic systems and is capable of representing a wide class

of other dynamic systems. Universality for discrete event

systems is defined as the ability to represent the behavior of

any discrete event model where "represent" and "behavior"

are appropriately defined. Concerning other dynamic system

classes, DEVS can exactly simulate discrete time systems

such as cellular automata and approximate, as closely as

desired, differential equation systems. This theory is presented

in [8, 10]. It also supports hierarchical modular construction

and composifion methodology [11]. This bottom-up

methodology keeps incremental complexity bounded and

permits stage-wise verification since each coupled model

"build" can be independently tested. An abstraction is a formalism that attempts to capture the essence of a complex phenomenon relative to a set of

behaviors of interest to a modeler. A discrete event abstraction

represents dynamic systems through two basic elements:

discretely occurring events and the time intervals that separate

them (Figure 2). It is the information carried in events and

their temporal separations that DEVS employs to approximate

92
arbitrary systems. In the quantized systems [8], events are boundary crossings and the details of the trajectories from one

crossing to another are glossed over with only the time

between crossings preserved.

boundary

event

Time

interval

Discrete Event Time

Events and

spacing carry information Segment

Figure 2: Discrete event representation of continuous

trajectories

Recent results on discrete event neurons'* show that, using

a race analogy, a net of simple discrete event neurons can find

the shortest path in a graph in the shortest time possible. Here

is an instance where fast and frugal is provably optimal! In

confrast, finding the longest path (or a long path) is much

more difficult and requires much more sophisticated neurons.

It seems uncanny - indeed, counterintuitively so - that

minimizing performance measures such as distance, time, or

cost requires simple apparatus and can be done with fiill accuracy and without backtracking. As with FFA heuristics,

the mystery disolves when one recognizes that the discrete

event neural nets exploit the underlying nature of reality in which pulses compete in parallel, and where fast competitors

come first and lock out their slower countrparts from further

progress. In the real world, fast response is paramount^ and so minimizing time (or other meausers mapped into it) is

critically important to survival. So brains may have been

evolved to solve survival-critical problems with frugal means

(simple neurons) that embody race analogies. Finally we note

that discrete event neurons and one-spike-per-neuron

architecufres are necessary to embody the race analogy - other

models do-not work.

2.5 Such architectures must be designed with a

software engineering methodology that explicitly

supports a system's control ofits own

computational resources and includes hooksfor

We are currently writing these results for publication.

^ This is certainly a characteristic of e-commerce at internet speed.

measuring its intelligence based on FFA

standards

Based on a wealth of basic research in a variety of disciplines,

model-based design offers not only well-defined principles to design intelligent systems, but also can provide the means to assess a system from its inception to realization, operation,

and eventual retirement. For example, we can assess a system's correctness, performance, maintenance, and cost, all of which are reflections of a system's degree of intelligence.

We may also rank a system degree of intelligence in terms of,

for example, intelligence of embodied models and how

intelligently physical resources (computational and

communication resources) are used.

Model-based design suggests several ways to rank

intelligent systems based on their use of models:

• Distributed heterogeneous model-based architectures

rank higher than monolithic ones.

• Systems that employ models that are at a resolution

level compatible with the resources available to mterpret them rank above those that don't.

Model sets that include self-representation rank

above those that don't

Model sets that include representation of self and

others rank above those that include only self- representation.

• Other rankings may be based on Model abilities to handle both non-linguistic and

linguistic queries

System ability to maintain coherence in the

model base

System ability to inform meta-level models by

questioning lower level models

Recursive depth of the "models-of' relation.

Due to increasing complexity and size/scale of systems

(e.g., distributed agent-oreinted systems), it is becoming

imperative to follow well-defined software development

processes (e.g., waterfall, spiral, iterative, and/or incremental

process [12]). A typical software development process is composed of conceptualization, analysis, design,

implementation, and testing, and operation [13]. Indeed, the

development of many contemporary distributed,

heterogenouos systems must increasingly rely on such

development processes [14]. Furthermore, with the emergence

of archiecture-based paradigms, we can begin to devise

suitable architectures for intelligent systems [15]. The

archtiecture based apporach and software development

processes go hand in hand offering many invaluable

advantages such as incremental analysis, design, and testing.

We believe, with the adoption of a synergistic development

process (accounting for software, hardware, and bioware)

combined with an appropriate architectural paradigm, we can

incorporate, among other things, intelligence capabilities,

mefrics, and measurement methods in appropriate places.

93
3 ACKNOWLEDGEMENT

This research has been supported in part by NSF Next

Generation Software (NGS) grant #EIA-9975050.

4 REFERENCES

[1] Gigerenzer, G., P.M. Todd, (1999). Simple Heuristics

That Make Us Smart, Oxford University Press.

[2] Meystel, A.M., (2000). Simulation for Meaning

Generation: Multiscale Coalitions of Autonomous

Agents, in Discrete Event Modeling and Simulation

Technologies: A Tapestry of Systems and Al-based

Theories and Methodologies, Editors: H.S. Sarjoughian,

F.E. Cellier, Springer Verlag.

[3] Gautrais, J. T. Simon, (1998). Rate coding versus temporal order coding: a theoretical approach, Biosystems

(48)1-3, pp. 57-65

[4] Rufin, V.R., J. Gautrais, A. Delorme, T. Simon, (1998).

Face processing using one spike per neuron, Biosystems

(48)1-3 pp. 229-239

[5] Hans Moravec (1999). Rise of the Robots, Scientific

American, August 1999, pp. 124-132

[6] Zeigler, B.P., (1990). Object-Oriented Simulation with

Hierarchical, Modular Models: Intelligent Agents and

Endomorphic Systems., San Diego, CA: Academic Press

[7] Fujimoto, R. (1998). "Time Management in the HighLevel Architecture." Simulation 71(6): 388-400.

[8] Zeigler, B.P., H. Praehofer, and T.G. Kim, (2000). Theory

of Modeling and Simulation. 2ed, New York, NY:

Academic Press

[9] Zeigler, B.P., et al. (1998). The DEVS/HLA Distributed

Simulation Environment and its Support for Predictive

Filtering, ECE, The University of Arizona.

[10] Zeigler, B.P., et. al. (1997). "The DEVS Environment for

High-performance Modeling and Simulation." IEEE

CS&E 4(3)

[llJZeigler, B.P. and H.S. Sarjoughian (1999). Support for

Hierarchical Modular Component-based Model

Construction in DEVS/HLA. Simulation Interoperability

Workshop, Orlando, FL

[12] Pressman, R. (1997). Software Engineering: A

Practitioner's Approach, McGraw Hill

[13]Booch G. (1996). Object Solutions: Managing the ObjectOriented Project. Menlo Park, CA, Addison-Wesley

[14]0rfali, R., D. Harkey. (1997). The Essential Client/Server

Survival Guide, John Wiley & Sons

[15] Sarjoughian, H.S. and B.P. Zeigler (2001). "A Layered

Modeling and Simulation Architecture for Agent-based

System Development." IEEE Proceedings (to appear)

94
The Intelligence of an Entity

Robby Glen Garner

Steven Boyd Henderson

Preface

Mimetic Synthesis is a new terminology that more accurately describes a programming

methodology used to mimic human behavior in a computer such as a PC. Previous

work in this field has been incorrectly categorized under various aspects of Artificial

Intelligence (Al).

On Intelligence

Testing and quantifying intelligence is difficult at best, even if it's human intelligence. To

Quote Tariq Samad from "Notes on Measuring Intelligence in Constructed Systems",

"The difficulty of compressing the multifaceted nature of intelligence into one scalar

quotient has led to proposals to consider intelligence not as one unitary quantity but as

a collection of properties that are mutually incommensurable." Furthermore, one of the

many lessons from a century of work on human intelligence is that we still don't really

know what intelligence is.

Mimetic Entities

The early mimetic systems developed by Robby Garner are hierarchical in structure.

This allows the "Mimetic Entity" to synthesize the combined behavior of subsystems into

a unified presentation. This structure certainly suggests that one way to measure the

intelligence of such machines is to review the hierarchical concepts it uses and the

processes that contribute to the goals of the whole system.

One of the first hierarchical mimetic synthesizers was called Albert. This program

combined the behavior of several methods that shared the same goal of simulating

human conversation. Each method represents a separate strategy used to form the

response to a human stimulus phrase.

The first method is based on a simple model of behavior, where conversation is

represented by strings of (stimulus response) nodes. The goal of this particular

method is to find a match for the user's input stimulus in a database, and form the reply

with the corresponding "response" from the database. If the first method is not

successful, the program follows down the hierarchy from most specific method, to least

specific.

95
The second method looks in a table of Boolean rules and attempts to fit a rule to the

user's input. If a rule is satisfied, its corresponding response is used. The goal of this

method is to satisfy a Boolean expression based on the user's input phrase.

And so on, the third method attempts to find a generalization about the user's input

phrase using a "framed" template to determine a match. The goal of this method is to

find a generalization that applies to the user's input phrase.

Then finally, if none of the other methods has succeeded, a final method selects a "new

topic" from a pool of unused topics. The goal of this method is merely to make a

response. (To change the subject)

So, one can see that the overall goal of simulating conversation is attempted by using a

variety of strategies, all contributing to the main goal. The hierarchical structure ensures

that the best possible response may be used.

It must be obvious that the performance of the mimetic entity with regards to simulating

a conversation depends entirely on the performance of all of these various methods or subsystems. Yet it depends first and foremost on the person talking to it.

The Loebner Show

But what can we say about Albert's intelligence? None of the methods used are

intelligent, so their "unified" representation is not intelligent. Albert may be perceived as

intelligent by a human being as is evidenced by the 1998 Loebner Prize Contest, but the

program is not in fact intelligent, imp ://www . cs . flinders . edu.au/rcseaich/AI/LoebnerPrize/

Then if we can know what intelligence is not, does that tell us what intelligence is?

No, because none of the competitors in the Loebner contest have exhibited

intelligence. At best they exhibit a behavior which seems familiar to the

user (judge), and some of them have used very cleaver means to achieve this. But the

ingenuity of the programmer does not make the program intelligent.

One also has to agree that an imitation is not the same as the thing it imitates.

Furthermore, some may object to things that are artificial for no other reason except that

they are artificial. Yet if a thing works, does it matter why it works or what it is made

from? Some people would say that if a thing is not really "intelligent" then it is an

impostor, and therefore "dangerous." But if a tool performs a job according to

specification, why is that less intelligent than if a human being had performed the same

job?

By doing a job, there is at least one goal implied, and that is the completion of the

job. If a computer completes the same job as a human in a smaller amount of

time, we would say the computer has better performance, not better intelligence?

96
Human Intelligence

In dealing with other people, we assess their intelligence on a casual basis by observing

their behavior, the things they say, their solutions to problems, or other factors, many of

which are purely subjective.

Measuring machine intelligence would be much easier if people could agree on

how to measure human intelligence!

So I think there is always a disparity between "perceived intelligence" and "actual

intelligence", especially in evaluation of human intelligence. Intelligence is not solely

performance, but is it possible to measure intelligence without also measuring a

performance?

Sometimes a performance involves a great deal of preparation and training. If a man

repeats the same sequence of behavior, practices it over and over until it can be done

repetitively without thinking, is that intelligence?

Summary

The key to true intelligence is the ability of an entity to enlist strategy to accomplish its

mission, not preconceived knowledge, or rote behavior.

Military confrontation is a good example according to R. Neil Bishop. "Time and time

again, superior firepower and resources have been overcome by an inferior force with

an intuitive strategy, which gave them a monumental advantage."

Also strategy is the key element needed to develop successful research techniques

which, in pure science, may not even exist before the scientist begins. The strategy of

obtaining and integrating knowledge is the key to reaching beyond what is presently

known or understood.

The use of strategy applies not only to the highest level of abstraction, but is also

evident in the "rank and file" subsystems that perform even the most basic tasks

required by an entity as a whole. The strategy or algorithm employed by a programmer

may be akin to "instinct" in some systems. Is instinctive behavior intelligent?

97

PART II

RESEARCH PAPERS

2. METRICS FOR INTELLIGENT SYSTEMS

2.1 Performance Metrics for Intelligent Systems

J.M. Evans, E.R. Messina, NIST, USA

2.2. The Search for Metrics of Intelligence — A Critical View

L.A. Zadeh, University of California at Berkeley, USA

2.3 Measure of System Intelligence: An Engineering Perspective

S. Lee, SCS, SAIT, Korea, W.-C. Bang, and Z.Z. Bien, KAIST, Korea

2.4 Formal Specification of Performance Metrics for Intelligent Systems

Y. Zhang, Xerox, USA, A.K. Mackworth. University of BC, Canada

2.4. 1 Metrics for Intelligence: the Perspective from Software Agents

PRELIMINARY NOTES

L. Pouchard, Oak Ridge National Lab, USA

2.6 Minimal Representation Size Metrics for Intelligent Robotic Systems

A. Sanderson, NSF, USA

2.7 Metrics for System Autonomy. Part I: Metrics Definition A. Yavnai, RAFAEL, Israel

2.8 In Defense of the Additive Form for Evaluating the Multidimensional Vector L.M. Polyakov, Globe Institute of Technology, USA

2.9 A Method for Evaluating the IQ of Intelligent Systems,

R. Finkelstein, Robotic Technology, Inc., USA

2.10 Machine Intelligence Ranking

P.P. Wang, Duke University, USA

2. 1 1 Survivability and Competence as Measures of Intelligent Systems

R. Simmons, Carnegie-Mellon University, USA

2.12 Two measures for measuring the 'intelligence' of human-interactive

robots in contests and in the real world: expressiveness and perceptiveness

I.R. Nourbakhsh, Carnegie-Mellon University, USA

99

Performance Metrics for Intelligent Systems

John M. Evans and Elena R. Messina

Intelligent Systems Division

National Institute of Standards and Technology

Gaithersburg, MD 20899-8230

ABSTRACT

Research into intelligent systems and intelligent control is burgeoning. However, there is no consensus on how to define or measure an intelligent system. This lack of rigor hinders the

ability to measure progress in the field and to compare different

systems' capabilities. We discuss some of the challenges and

issues in defining performance metrics for intelligent systems and

issue a call to action to participants in the Performance Metrics for

Intelligent Systems Workshop to define practical metrics that will advance the state of the art and practice.

KEYWORDS: performance metrics, intelligent systems,

intelligent control

1. Introduction

Intelligent systems are increasingly being

identified as solutions to many advanced

applications in manufacturing, defense, and other

domains. Industry workshops [4] and roadmaps

[3] specifically call for intelligent control or

intelligent systems to address needs such as

• Adaptive, reconfigurable manufacturing

equipment and processes

• Self-optimizing, science-based control of

manufacturing unit processes

• "First part correct," that is, the ability to

design and manufacture a product correctly,

the first time and every time

• Self-diagnosing and self-maintaining systems

• Tool wear and breakage monitoring

Government agencies are basing major programs

on intelligent capabilities, for example,

• The Army Experimental Unmanned Ground

Vehicle Systems (Demo III)

• Defense Advanced Research Projects Agency

(DARPA)/Army Future Combat Systems

• DARPA Mobile Autonomous Robot Software

• DARPA Software for Distributed Robotics

• DARPA Tactical Mobile Robots

• National Aeronautics and Space

Administration (NASA) spacecraft and rovers

• Department of Energy (DOE) waste

remediation robot systems

• Department of Transportation (DOT)

Intelligent Vehicle Initiative

In addition to the examples above, there are myriad other efforts in academia, industry, and

government labs of work referred to as

"intelligent systems." Despite the common use

of "intelligent system" and "intelligent control,"

there is no uniform definition for either term.

Generally, they are characterized by having one or more of the following traits [1]:

• Adaptive

• Capable of learning

• "Does the right thing" or "acts appropriately"

• Non-linear

• Autonomous symbol interpretation

• Goal-oriented

• Knowledge-based

These terms are ambiguous and qualitative.

The Intelligent Systems Division of the National

Institute of Standards and Technology has

101
launched an initiative to better define what an

intelligent system is and how to measure its

performance. The mission of the Intelligent

Systems Division, one of five divisions in the

Manufacturing Engineering Laboratory, is "to

develop the measurements and standards

infrastructure needed for the application of

intelligent systems by manufacturing industries

and government agencies."

We are working with various industry groups

and government agencies to tackle the issue of

intelligent system performance. The

Performance Metrics for Intelligent Systems

Workshop is a foundational step, which brings

together a multi-disciplinary community to help

define the highest priority areas to concentrate on,

having the highest payoff.

2. The Challenge of Defining and

Measuring Machine Intelligence

Researchers have been pursuing forms of

machine intelHgence for several decades. There

have been many areas of focus, such as natural

language understanding, expert systems to aid

diagnoses, and decision-making tools for financial

systems. Closer to our domain of interest, much

effort has been focused on defining intelligent

control as a discipline, but even so, there are no

Figure 1: Intelligent Control as of 1985

quantitative measures.

Beginning with the efforts of Fu [1] and

Saridis [3] in the seventies, there have been

numerous conferences and workshops aimed at

the topic of intelligent control. Nevertheless,

the field remains fragmented due to its

multidisciplinary nature. As noted in the first

Symposium on Intelligent Control in 1985,

intelligent control was proclaimed a theoretical

domain, in which control theory, AI, and

operations research intersected (Fig. 1 from [6]).

The definition of an intelligent system may be

considered broader than that of intelligent control.

As a "system," there may be more constituent

parts, such as perception, world modeling, or value judgement. Yet more disciplines are brought into the picture. Examples of these

include data representation, image processing, and

decision theory.

Given the multi-disciplinary nature of the

systems we are concerned with, it is clear that

defining the scope and performance of these

systems is a challenge. Terminology is one of

the first hurdles that must be overcome. Different

disciplines ascribe different definitions to the

same words. For example, "complexity" may

refer to non-linear systems in one field and to

computational resources needed in another.

It is very difficult, if not impossible to

currently evaluate research into intelligent

systems. Since there are no quantitative metrics,

intercomparisons of results are not generally

possible. Sponsors are not able to adequately

judge whether research results meet their

requirements. Potential users have no impartial

evaluation reports, a la "Consumer Reports," of

intelligent systems, techniques, and tools. In

general, the lack of metrics slows progress.

There is a proliferation of data specific algorithms

and task-specific solutions.

One of the biggest costs paid is the duplication

of effort. New programs may be unable to have a firm definition of past accomplishments, hence

they may fund work that repeats previous

102
research. Research teams cannot leverage prior

existing work from other institutions and tend to

have to reinvent the wheel by building all of their

system's components from scratch. They are burdened with having to spend effort in building

components that are not part of their research

focus, instead of being able to leverage existing

"best of class" solutions and focussing on their

interests.

Another negative impact, from the sponsor's

viewpoint, is the lack of predictive ability in

assessing new applications. Without objective

performance evaluation metrics and an

understanding of capabilities and limitations, it is

difficult or impossible to assess claims of

competing approaches in formulating new

projects and programs. This leads to

inefficiencies and failures that could be avoided if

we had the measurement tools that we need.

3. Issues in Measuring Performance

Numerous questions must be answered when

considering how to define the performance of

these intelligent systems. We will present a few

questions. Many more will arise as we delve into

the matter more closely.

• Should we measure only the external behavior

of a system? Is that the only aspect that can

feasibly be measured? Or, is there value in

decomposing a system into components and

measuring their individual capabilities?

Examples would be measuring the path

planning algorithms in isolation from the

perception and other control subsystems.

• How generic does the measure of a system's

intelligence have to be? Should we strive for

general intelligence metrics that are domainindependent or are we better off focussing on

application and domain-specific metrics? Are

domain-independent metrics even

meaningful?

• How do we factor in "body intelligence," the

mechanical capabilities of a system as opposed to the control capabilities, when

assessing the performance of a system? If we

have a mobile robot, some of its abilities to

achieve its stated goal (e.g., traverse a rubble

pile to find survivors) can be attributed to its

mechanical properties rather than its software

intelligence.

• Are testbeds a viable measure of performance,

or do they invite "gaming," that is, encourage

solutions that are tailored to performing well

in the testbed? If we don't have testbeds, how

can we achieve reproducible measures of

performance?

4. Initial observations

One of the complicating factors in discussing

intelligent systems is the use of the word

"intelligence." It is freighted with significance

and analogies to human or biological intelligence

naturally arise. The quest for standard, uniform

measures of intelligence in biological systems

remains a subject of controversy. Therefore, we

would advocate avoiding the temptation to spend

too much time striving for performance measures

that are based on human or higher level biological

systems.

Observing that we are dealing with multidisciplinary technologies and multiple application

domains, we should expect that no single, unique

measure of performance is feasible. Therefore,

no single overarching and generic intelligence test will suffice. We need to strive for the right

granularity of metrics.

We must be prepared to attack the problem on

multiple fronts. It probably won't suffice to have

just a theoretical investigation or an experimental

one. Research must proceed on the theory as well as on gathering experimental data.

One of the key attributes of intelligent systems

is its multi-disciplinarity. This poses a challenge,

but also an opportunity. We can come together

from a variety of disciplines and form a new

103
community in which we share our expertise. We

must have dialog and information exchange

amongst ourselves in order to synthesize the best

results from the different fields that contribute

towards intelligent systems research.

That is the purpose of this workshop and the

reason for the diversity of the presentations that

you will hear.

5. Call to action

The challenge is thus to define performance

measures for new and evolving intelligent systems

technologies that can greatly improve industrial

productivity and advance government mission

objectives. We must work together to build a

technical foundation for measuring performance.

This includes agreeing on the domains to

investigate and a common set of terminology.

We must develop theoretical foundations,

methodologies, and supporting infrastructure for

achieving our goals. Ultimately, measures must

be developed that are practical, unambiguous,

easy to use and widely deployable. We must

simultaneously focus on attainable goals and

strategies for both near-term and long-term

measures of performance, as our understanding of

them and the capabilities of the systems

themselves evolve. Researchers, industry, and

government will benefit from practical solutions

they can readily apply, not from philosophical

ones.

6. References

[1] P. J. Antsaklis, "Defining Intelligent Control",

Report of the Task Force on Intelligent

Control, P.J Antsaklis, Chair, IEEE Control

Systems Magazine, pp. 4-5 & 58-66, June

1994.

[2] K.-S. Fu, "Learning Control Systems and

Intelligent Control Systems: An Intersection

of Artificial Intelligence and Automatic

Control," IEEE Trans, on Automatic Control,

Vol. AC- 16, 1971.

[3] Integrated Manufacturing Technology

Roadmapping Initiative, ""Manufacturing

Processes and Equipment Draft Roadmap,"

Oak Ridge, TN, December, 1998.

[4] Neal, R. and Messina, E., ed.. Proceedings of

the First Part Correct Workshop,

Gaithersburg, MD, April 2000.

[5] G. Saridis, Self-organizing Control of

Stochastic Systems, M. Dekker, NY, 1977.

[6] G. N. Saridis, "Foundations of the Theory of

Intelligent Controls," Proc. of the IEEE

Workshop on Intelligent Control, Eds. G.

Saridis, A. Meystel, Troy, NY 1985, pp. 23-

28.

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July 18, 2000

The Search for Metrics of Intelligence — A Critical View

Lotfi A. Zadeh*

Few issues in AI generate as much heated debate as those which in one way or another

relate to the questions: "What is intelligence?"; "Can machine think?"; and "How can intelligence

be measured?" One cannot but be greatly impressed by the incisive comments made by members

of the Intelligence Advisory Board. And yet, most of the basic issues relating to intelligence

remain unresolved — as they were half a century ago ~ when I moderated, at Columbia University, what I believe to have been the first debate on "Can machines think?" The debate involved

Claude Shannon, E.C. Berkeley, the author of Giant Brains, and Professor Francis J. Murray ~ a

prominent mathematician who as a consultant to IBM was active in the conception and design of

computer systems.

At that time ~ the dawn of the computer age ~ there was a great deal of interest in the ability or inability of computers to think as humans do. To a much greater degree than is the case now,

there were exaggerated expectations. In an article of mine entitled "Thinking machines ~ a new

field in electrical engineering," which appeared in the January, 1950, issue of the Columbia Engineering Quarterly (Zadeh 1950), I surveyed some of the articles which were published in the popular press at that time. The headline of one ofthe articles read "Electric brain capable of

translating foreign languages is being built." The problem of machine translation seemed to be

* Professor in the Graduate School and Director, Berkeley hiitiative in Soft Computing (BISC), Computer

Science Division and the Electronics Research Laboratory, Department of EECS, University of

California, Berkeley, CA 94720-1776; Telephone: 510-642-4959; Fax: 510-642-1712; E-Mail:

zadeh@cs.berkeley.edu; Webpage: http://www.cs.berkeley.edu/People/Faculty/Homepages/zadeh.html.

Research supported in part by ONR Grant FDN0014991035, NASA Grant NAC2-1 177, ONR Grant

N00014-96-1-0556, ARO Grant DAAH 04-961-0341 and the BISC Program of UC Berkeley.

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July 18, 2000

close to solution. Today, we know better. In 1 997, Martin Kay, one of the leading contributors to

machine translation had this to say: "Machine translation gave the initial inspiration to computational linguists and continues to motivate much of their work. That is surely fair enough since the

problem is clearly computational and obviously linguistic. But forty years of money and effort has

brought us hardly any closer to the answer. The world continues to pour money down the same

rathole with little discernible progress, with or without the linguists. The German government is

giving it a new twist: "Notice how we never seem to get anywhere on machine translation?"

The debates which raged in the past were largely of academic interest because there were

few, if any, systems that could be assessed as having a high level of intelligence. At this juncture,

this is no longer the case. Today, we can point with pride to Deep Blue, which beat Gary Kasparov. More importantly, we have a wide variety of systems which can perform highly non-trivial

tasks involving recognition, decision and control. We are, in fact, witnessing the beginning of

what may be described without exaggeration as the Intelligent Systems Revolution.

When AI was christened in 1956, it became the standard bearer of efforts to devise and

build machines that could exhibit human-like intelligence in performing various tasks. For some

time thereafter, the AI scene was one of unbridled enthusiasm and, as we now realize, unrealistic

expectations. In judging that period, however, what should be remembered is that - as Jules Verne

astutely observed at the turn of the century ~ scientific progress is driven by exaggerated expectations.

It took forty years for a computer to challenge and beat a chess champion. Why did it take

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July 18, 2000

SO long to achieve some of AI's objectives? In the first place, the basic difficulty of approximating

to what humans can do so easily without any measurements and any computations, e.g., understand speech, read handwriting, summarize a story and park a car, was greatly underestimated.

More important, however, is the fact that the needed technologies and methodologies were not in

place. In particular, we did not have the highly capable sensors and powerful computers which we

have today, and we did not employ such recently developed methodologies as neurocomputing,

evolutionary computing, probabilistic computing, machine learning and fuzzy logic.

In the past, what were called intelligent systems were for the most part symbol-manipulation oriented, e.g., machine translation systems, text understanding systems and game playing

systems, among others. Today, what we see is the rapidly growing visibility of systems which are

sensor-based and have embedded intelligence, e.g., smart washing machines, smart air conditioners, smart rice cookers and smart automobile transmissions. The counterpart of the concept of IQ

in such systems is what might be called Machine IQ, or simply MIQ (Zadeh 1994). However,

what is important to recognize is that MIQ ~ as a metric of machine intelligence ~ is product-specific and does not involve the same dimensions as human IQ. Furthermore, MIQ is relative. Thus,

the MIQ of, say, a camera made in 1990 would be a measure of its intelligence relative to cameras

made during the same period, and would be much lower than the MIQ of cameras made today.

Viewed in this perspective, the focus of activity in applications of machine intelligence is

shifting from writing computer programs that can prove difficult theorems, understand text, provide expert advice and beat a chess champion, to more mundane tasks devolving on the conception, design and construction of products and systems that have a high MIQ, making them

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July 18, 2000

reliable, capable, affordable and user-friendly. Among recent examples of systems of this kind are

programs which can detect the presence of known or new viruses in computer programs; checkout

scanners which can identify fruit and vegetables through the use of scent sensors; car navigation

systems which can guide a driver to a desired destination; password authentication systems

employing biometric typing information; ATM eyeprint machines for identity verification; and

molecular breath analyzers which are capable of diagnosing lung cancer, stomach ulcers and other

diseases.

If MIQ is accepted as a metric of machine intelligence, then a particular machine may be

said to be highly intelligent if has a high MIQ. But this beg the question ofhow the MIQ of a class

of machines could be measured. Comments made by members of the Intelligent Advisory Board

provide some guidelines. But a thesis that I should like to put on the table is that the existing conceptual framework of AI - which is based on first-order two-valued logic — is incapable of providing a suitable foundation for constructing realistic metrics of IQ and MIQ.

The problem with predicate-logic-based AI is that it embraces the principle of the

excluded middle, which asserts that every proposition is either true or false, with no shades of

gray allowed. But in the real world, as perceived by humans, it is partiality rather than categoricity

that is the norm. Thus, we generally deal with partial knowledge, partial order, partial truth, partial

certainty, partial causality and partial understanding. The essentiality of the role of partiality in

human cognition has been slow in gaining acceptance in AI. Without employing the notion of partiality, realistic metrics of IQ and MIQ cannot be constructed.

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July 18, 2000

Another concept that plays a basic role in human cognition is that of granularity, and,

more particularly, that of f-granularity. In essence, f-granularity is a concomitant of the bounded

ability of sensory organs and, ultimately, the brain, to resolve detail and store information. What

this means is that (a) the boundaries of perceived classes are not sharply defined; and (b) values of

perceived attributes are granulated, with a granule being a clump of values drawn together by

indistinguishability, similarity, proximity or functionality. For example, the granules ofAge might

be: very young, young, middle-aged, old and very old. Similarly, the granules of face may be:

nose, cheeks, chin, forehead, etc. F-granularity underlies the concept of a linguistic variable in

fuzzy logic.

The concepts of partiality and f-granularity play key roles in what may be called Precisiated Natural Language (PNL). What I should like to suggest is that PNL could play a central role

in formulation ofmetrics of intelligence. How these could be done is a complex task that will

require a major effort to yield concrete results. In what follows, I will confine myself to sketching

the basics of PNL and pointing to its use as a concept definition language.

Natural languages are expressive but imprecise. Mathematical languages are inexpressive

but precise. Basically, PNL draws on a natural language (NL) and a mathematical language (ML)

to provide a language which is precise and yet far more expressive than conventional meaningrepresentation and definition languages based on predicate logic.

In essence, PNL is a subset of NL which consists of propositions which are precisiable

through translation into a precisiation language GCL (Generalized Constraint Language). An

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July 18, 2000

example of a precisiable proposition is: It is very unlikely that there will be a significant increase

in the price of oil in the near future. The point of departure in PNL is the assumption that the

meaning of a precisiable proposition, p, is expressible as a generalized constraint on a variable.

Usually, the constrained variable and the constraining relation are implicit rather than explicit in

PA concept which has a position of centrality in GCL is that of a generalized constraint

expressed as X isr R, where X is the constrained variable, R is the constraining relation, and isr

(pronounced as ezar) is a variable copula in which r is a discrete-valued indexing variable whose

value defines the way in which R constrains X. Among the principal types of constraints are the

following: possibilistic constraint, r=blank, with R playing the role of the possibility distribution

of X; veristic constraint, r=v, in which case R is the verity (truth) distribution of X; probabilistic

constraint, r=p, in which case X is a random variable and R is its probability distribution; r=rs, in

which case X is a fuzzy-set-valued random variable (fuzzy random set) and R is its fuzzy-set-valued probability distribution; and fuzzy-graph constraint, r=fg, in which case X is a fuzzy-set-valued variable and R is its fuzzy-set-valued possibility distribution.

With these constraints serving as basic building blocks, which are analogous to terminal

symbols in a formal language, more complex (composite) constraints may be constructed through

the use of a grammar. Simple examples of composite constraints are: X isr R and X iss S; and, if X

isr R then Y iss S, or, equivalently, Y iss S if X isr R. The collection of composite constraints

forms the Generalized Constraint Language (GCL). The semantics of GCL is defined by the rules

that govern combination and propagation of generalized constraints. These rules coincide with the

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July 18, 2000

rules of inference in fuzzy logic (FL).

The capability of PNL to serve as a powerful definition language depends in large measure

on the fact that, by construction, GCL is maximally expressive. The conclusion that emerges from

this fact is that metrics of intelligence, if they can be defined, will necessarily have to be dc ;1 aed in

terms of PNL and have an algorithmic structure (Zadeh 1976). What this implies is that realistic

metrization of intelligence is not possible within the conceptual structure of existing methods of

definition and measurement. We cannot expect a concept as complex as that of intelligence to be

definable in traditional terms.

References

1. L.A. Zadeh, "Thinking machines - a new field in electrical engineering," Columbia Engineering

Quarterly 3, 12-13, 30, 31, 1950.

2. L.A. Zadeh, "A fuzzy-algorithmic approach to the definition of complex or imprecise concepts,"

Int. Jour. Man-Machine Studies 8, 249-291, 1976.

3. L.A. Zadeh, "Fuzzy logic, neural networks and soft computing," Communications of the ACM

37(3), 77-84, 1994.

Ill
Measure of System Intelligence: An Engineering Perspective

Sukhan Lee^, Won-Chul Bang^ and Z. Zenn Bierf

t System and Control Sector, SAIT, Korea

t Div. of EECS, Dept. of EE, KAIST, Korea

ABSTRACT

System intelligence can be measured experimentally either through

benchmark tests, or theoretically through the formal analysis of

system software architecture and hardware configurations. The latter approach is pursued here, since it serves directly as the criteria for designing and engineering intelligent systems in a directed manner,

rather than by trial and error. To this end, a structure of problem

solving and leaming of machine is proposed. Once a machine is represented with the structure, the intelligence can be measured via transforming it into an equivalent linguistic structure. A simple

example is also provided.

KEYWORDS: measure of system intelligence, measure by

linguistic equivalence, machine description language

1. INTRODUCTION

The intelligence of systems is emergent when the systems are able to accomplish loosely defined but complex tasks in an

unstructured and uncertain environment. The intelligence can be manifested by the capability of systems to autonomously

synthesize goal-oriented behaviors in adaption errors, faults,

and unexpected events through the real-time connection of

sensing and action. However, we still do not have a satisfactory quantitative way to characterize the "intelligence"

of systems. There are many kinds of intelligent systems in various fields. The adjective 'intelligent' is quite widely used

to describe their systems developed by many system engineers

and companies. One developer may say that his/her system is more intelligent than the others, but it can happen that another

claims the same thing. In this case, who can say one is more

intelligent than the others? One must have a kind of measure

of intelligence for systems or machines in order to answer this question. In this sense, it is worthwhile to provide a measure

oti how intelligent a machine is.

Many intelligent system techniques have been developed

and studied so far, but only a few studies have been done on 'Aow to measure intelligence of systems.' J. S. Albus

introduced the theory of intelligence in an engineering

viewpoint [1]. G. Zames initiated an effort for defining such an index as approximate a measure of the "task" and

"satisfactory" performances an "intelligent controllei" could

achieve versus those that a classical controller could achieve

[2]. The challenge involves characterization of performance in unknown environments, leaming, controller and task

complexity, and associated tradeoffs. E. C. Chalfant and S. Lee suggested an engineering perspective [3]. They thought

that one can represent all tasks of a machine in the form of

graphs and find an equivalent language for the graphs. Since a language consists of grammar and vocabulary, the descriptive

power of a machine can be represented by the grammar and

the vocabulary. Bien, et al. [4][5] proposed a couple of

methods to measure how much a machine is intelligent; they

considered the questions from the ontological (functional) and

phenomenological (behavioral) definitions on intelligent

machine.

Establishing the measure of system intelligence should

not only be able to turn the intelligent system into a formal

academic discipline but also provide a means of designing

better and more powerful intelligent systems in practice. The

measure of intelligence of a system or, more precisely, a constructed system with autonomy should take into

consideration various aspects of intelligence ranging from

perception, understanding, and problem solving to generalization and leaming from experience. A. Meystel

proposed a vector of system intelligence as a collection of

features representing intelligent functions of a system. The list of such features can be very comprehensive indeed. However,

formulating the measure of system intelligence based on such

a vector may not necessarily represent the essence of system

intelligence. The functional features describing the aspect of

intelligent behaviors may obscure the existing internal engine

by which intelligent behaviors are generated.

To begin with, the following questions are raised for answer prior to the definition of the metric of system

intelligence:

(a) Should the intelligence measure be goal-dependent or goal-independent?

(b) Should the intelligence measure be time-varying or time-invariant?

(c) Should the intelligence measure be resource- dependent or resource-independent?

For (a), it raises a question whether there exists a universal measure of system intelligence such that the

intelligence of systems can be compared independently of the

112
given goals. A goal-independent measure may be more

difficult to define, if not impossible, and more controversial. A

goal-dependent measure, however abstract the goal may be, can allow clear comparison among the systems of different

architecture but with the same goal. For instance, for the latter

case, intelligence can be represented as how efficiently, and

how optimally a system reaches the given goal by itself, i.e.,

the power of automatically solving problems defined as the

discrepancy between the goal and the current state.

For (b), it represents whether the intelligence measure of a system should solely be based on problem-solving capability

at time t or it should contain the potential increase of problemsolving capability in the future based on learning. Both are necessary. But, it is better to define the two separately before

integrating them together in one measure.

For (c), it raises an issue whether the resources required

for building systems and system operation should play a role for defining the measure of intelligence. As mentioned above,

the efficiency in problem solving should be included in the measure: for instance, the time and energy required to reach a solution should be taken into consideration together with the

optimality of the solution. But, it is not clear whether we

should or should not include the cost of building a system.

Section 2 provides definitions of engineering metric of

system intelligence based on the above three questions. In Section 3, machine intelligence structure is proposed, and an

equivalent linguistic structure follows in Section 4. Section 5 shows an example with a robotic arm. Finally, Section 6

concludes the paper.

2. DEFINITION OF ENGINEERING METRIC

OF SYSTEM INTELLIGENCE

System intelligence can be measured under considering

various points of views described in the previous section. An

approach in engineering perspective is pursued here with goaloriented, time-dependent, and resource-dependent definition

of engineering metric of system intelligence. We define

machine intelligence quotient (MIQ) in the following way.

The measure of system intelligence as problem-solving

capability at time t for the given goal set g, denoted by MIQ(g,

t), is defined by the capability of solving problems toward the

given goal set where the capability can be measured by the

scope of constraints (environmental variations), together with

the time and resources required, under which the system

succeeds in reaching the given goals.

The measure of self-improvement of system intelligence as learning capability with respect to time t, denoted by

dMIQ(g, t), can be defined by the rate of increasing MIQig, t) with respect to time based on learning from experience.

Capability of learning in the time duration of (t\, ti) is represented by the integration of dMIQ(g, t) between t\ and tiNow, the total measure of system intelligence, tMIQ, is defined by

tMIQ = max[MfQ(gJo) + ]dMIQ{g,t)dt] . ( 1

)

Let tmax be the time when the maximum of tMIQ is obtained. The learning rate is then defined by

max \dMIQ{g,t)dt tmax

Note that tfie universal measure of system intelligence,

uMIQ, may be defined in terms of integration of MIQ with

respect to goal, i.e.,

t uMIQ= \ meix{MIQ(g,t^)^ \dMIQ{g,t)dt\lg (2)

where G is the set of all goals.

As mentioned above, resources required for the machine

is combined into the machine intelligence, MIQ to resource

ratio, rMIQ, can be represented by

rMIQ - tMIQ/resources . (3

)

3. MACHINE INTELLIGENCE

As described in the previous section, machine intelligence can

be measured once MIQig, t) and dMIQ(g, t) are defined. We

now formulate the way of defining two quantities, MIQ

(problem-solving capability) and dMIQ (rate of increasing

MIQ based on learning capability).

The first step of problem solving is to understand the

situation and define what are the problems to solve. This

requires identifying the gap between the goal and current

states as well as recognizing the constraints and opportunities

imposed by the environment. Then follows the planning or decision-making to reduce the gap under constraints. The first step requires perception and understanding, whereas the

second step requires action and planning. Perception and

action can be represented as logical sensor and actuator systems, respectively, in a form of hierarchical graphs of

declarative knowledge components. Understanding can be

represented as the connection of what have been perceived to system internal knowledge. Planning can be represented as the

projection of what have been understood to the logical actuator system. The mechanism of these connections can be

rule-based. The overall structure of problem solving

mechanism is represented in Figure 1 with solid-line

connections.

Regarding the learning capability, a higher level of

consciousness that monitors these activities of understanding

and planning may exist in the form of thinking (a self-driven

function that monitors understanding and planning in the form

113
of questioning, virtual manipulation). In case that the machine

cannot understand an obtained data from logical sensors by

perception, the consciousness/emotion may adjust the

knowledge to allow the obtained data for understanding, i.e., identifying the gap between the goal and current states as well

as recognizing the constraints and opportunities imposed by

the environment. In addition, when an action already taken is decided to be further improved, the consciousness/emotion

may fix its knowledge to give a better plan later on. The

structure of learning mechanism is also shown in Figure 1 with dotted-line connection.

: Problem solving struc

: Learning structure

Figure 1. Structure of Machine Intelligence

The logical sensors and actuators as well as knowledge

and constraint can be represented by an equivalent linguistic

form. The same is true for representing the connection and

projection associated with understanding and planning. If the

functions of a system embedded in its hardware and software can be represented as a linguistic equivalent, based on the

above observation, the MIQ and dMIQ of the system may be

defined in the equivalent linguistic space. Thus, for a given

machine to measure its intelligence, transforming the machine

itself into this structure of problem solving and learning is first conducted, and then transforming it into the equivalent

linguistic structure is to be done, which is discussed in the next section.

4. MEASURE BY LINGUISTIC EQUIVALENCE

Transforming system architecture into an equivalent formal

language structure, a consistent measure of machine

intelligence associated with the corresponding formal

language can be obtained.

Any generic language used to build models representing

diverse architectures must contain mechanisms to implement

the features of all these architectures. For example, the parallel

structure of the subsumption model requires parallelism in the

language. At the other extreme, the functionality of a

centralized planner must also be representable. If the structure of the model differs, we must be prepared to clearly determine

equivalent operation.

4.1 The Machine Description Language

The basic unit of the Machine Description Language (MDL) is a behavior. The behavior nit is analogous to a sentence or statement constructed according to grammatical rules. There

statements are conglomerated to form a meaningful system.

The paper defines the grammatical rules of syntax of the

Machine Description Language. Generating the semantics of

an entire system is analogous to writing a program in a given

system.

An MDL model has a hierarchical layered architecture

composed of a number of various behaviors, some simple, and some complex. The simplest possible behavior is based on

direct triggering by a single binary sensor which elicits a simple actuator response. For example, an on/off contact switch can trigger a behavior called "bump" which causes a

short reverse movement combined with a turn.

Behavior modules are collected in groups which

implement a complete autonomous task, such as obstacle

detection. The collection of behaviors is called a wrapped

behavior. The linguistic analogy is a paragraph of subroutine

^hich encapsulates a single topic or function.

The composite wrapped behavior collectively implements some useful autonomous task. For example, a group of bump

behaviors based on different contact sensors can be wrapped

to form an obstacle rerouting wrapped behavior based on

direct contact. If ultrasonic range detectors are added, new

strands can be added to the composite object rerouting

behavior, and the improved behavior them before bumping

them. The old bump behaviors are kept as backups.

4.2 Analytical Measures with MDL

The performance of the system described here can be

measured using traditional back box empirical techniques. For

example, we can time its performance in executing a prescribed task. Alternatively, structural (linguistic) analyses

of the system can be used to determine theoretical bounds on

performance independent of implementational efficiency.

Structural analysis begins with identification of

measurable quantities and their effects on performance. Many

structural features can be measured; each contributes to the

emergent intelligence of the completed system in a different

way.

4.2.1 Behavior Attributes

We first consider measurable attributes of a behavior. Some of

the measurable structural features are:

Strand Count and Strand Segment Count : A behavior has some number of strands (i.e., sensor to actuator information path)

associated with it. Strands are regarded as instantaneous

communication links for the purpose of measurement. The

information packet propagation time between nodes, trigger.

114
and taps is zero. The number and thickness of strands in a

single behavior provides a measure of the resolution of

sensory information, trigger situation discemability, and the

dexterity or controllability of the actuator system. More

fundamentally, strand segment count and thickness together measure the information transport capacity of the behavior.

Node Count: Node count captures the complexity of the sensor and actuator trees of a behavior. The node count is taken as the sum of nodes and taps for both sensor and actuator trees.

Trigger Propagation Time: Each trigger has three measurable

attributes indicating the dimensionality of the input

(parameters of the sensed situation), the dimensionality of the

output (parameters of the desired response, based on the

sensed situation) and the propagation time of the information,

i.e., the delay between a sensed situation and the resultant

response.

Node Propagation Time: The delay an information packet

encounters between the time it enters a tap node, fusion node,

or arbitration node and the time it (or the effects of a change in the information) exits the node, is termed node propagation

time. It represents the processing time required to fuse

information, to arbitrate competing controls, or to extract or combine information.

Strand Propagation time: The strand propagation time id the

time for an information packet to travel from the sensor at the

beginning of the strand to the actuator at the end of the strand.

Behavior Response Time: The response time of a behavior is the sum of all information propagation timers along the

longest path between raw sensor input and raw actuator

output. The path may include nodes from other behaviors but

will include only one trigger propagation time. This differ

from the propagation time of the longest strand in that the

strand propagation time is measured from tap to tap, whereas

the behavior response time is measured from raw sensor input

to raw actuator output. Behavior response time is computed

as: 5 = max(Xa,+T) (4)

where

B : behavior response time

a, : node propagation time for node /

T : trigger propagation time

Behavior response time can also be measured empirically, as long as the response can be isolated from the response of all other behaviors.

4.2.2 System Attributes

Next we consider attributes of the combined system:

Trigger of Behavior Count: The number of separate triggers

(which is equivalent to the number of behavior modules)

indicates the number of separate situations and corresponding

responses, which the system can elicit, based on its sensory

information. The total number of triggers in the entire system

is and indication of complexity of the system and

sophistication of response (assuming a well-designed system).

Strand Distribution: Strands which rely on many lower level strands provide more abstract, goal-directed, and strategic

stimulus-response relationships, whereas the lower level strands provide greater reactivity and quicker response. The

distribution of the strands between these extremes indicates

the tendency for the system to generate behavior based on

reflexes or impulses vs. goal-seeking behavior. One measure

of this characteristic is the distribution of behavior

propagation times. Standard statistical measures such as mean

and median behavior propagation times, standard deviation,

minimum and maximum propagation, describe the

distribution. A median propagation time biased toward the

minimum indicates a more quickly responsive and reflexive

system whereas a bias toward the maximum indicated a

deliberative system.

Layering Depth: Another measure of deliberativeness is the

layering depth. The layering depth can be measured as the

number of trees belonging to different behaviors which an information packet must traverse to reach the raw motors from

the trigger. Because each group of wrapped behaviors

comprises an autonomous set of behaviors, the layering depth

or maximum depth of wrappers indicated the sophistication of

autonomy. A system, which is more deeply wrapped, may

indicate that it can perform more complex tasks autonomously.

Each behavior added to a wrapped behavior indicates that some environmental situation can arise which s not handled

optimally by the wrapped behavior by itself if a wrapped

behavior s itself wrapped along with new behaviors, the newly

wrapped set handles all the environmental stimuli of the

original wrapper plus all the situations detected by the new

behviors.

MIQ: The MIQ (Machine Intelligence Quotient) is then

defined as the product of the complexity of tasks the system

can handle and the performance in task execution. This measure embodies the tradeoff between reflexivity (speed) and

deliberativity (complexity). Task complexity is dependent both on the complexity and quantity of the tree structures. The

complexity of tasks can be measured using the system

attributes listed above, namely, trigger count, strand

distribution, layering depth, strand count, and node count. We

combine these as a weighted sum: T=Wyy + wg5 + w;^Pi+ w^a + w\.K (5)

where

T : Task complexity ability

y : Trigger count

5 : Average strand propagation time overall machine

115
A : Layering depth

a : Total strand count in machine

K : Total node count in machine

Wy,wg,w;^,Wff,w^ : Respective weights

Performance in task execution is derived from the collective

performance of behaviors. This can be computed as the

weighted sum of behavior response time and inverse average

strand propagation time (since speed increase as strand length

decreases ):

MIQ is then

E - WgB+ jS

MIQ= T E

(6)

(7)

Resource: Machine "resource" is a measure of implementation

requirements based on the architectural design of the machine.

The resource is defined as the product of cost and volume. We

compute the resource based on the number of processors and

communication links required to implement the system

directly in a parallel architecture. Processors are expensive

while communication links are cheap. However,

communication links can become numerous and occupy a

large part of the volume of a machine. These costs and

volumes are likely to change with new technology. The cost of

the system is the sum of the costs of the processors (trigger,

nodes, and taps) required. We assume one simple processor

per trigger, node, or tap. We denote this as C = CyY+C^n (8)

where

C : cost of machine

K : node count

Cy,C„: cost of trigger and node processors

The volume of the system is computed the same way:

v = Vyr+v,K (9)

Then resource is R = CV (10)

and the rMIQ is rMIQ = MIQ/R (11)

5. ENGINEERING CASE STUDY

A simple grasp controller based on the subsumption style of

robot control uses a gripper beam and finger contacts as sensors as shown in Figure 2.

Q

robot

body

gripper beamcontact

switc

Figure 2. A Simple Robot Arm

switch contact

stop closini ^ retract

closing qri pper

,

arm O

beam broken close gripper: (3

extend arm

gripper closure

arm extension

Figure 3. Subsumption Network

Figure 3 illustrates the simple subsumption network

which generates the behavior of the robot. The extend arm

behavior is always extends the arm (we ignore the condition of

a fully extended arm). As soon as the gripper beam is broken,

the sensor causes the "close grippers" behavior to trigger. The

white motor node simultaneously inhibits the arm from

extending with an inhibition node and activates the gripper

closure actuator, causing the gripper to begin closing. (The

gray nodes are taps - in this example they are motor taps or

arbitrators.) When the grippers contact the object, the contact switch is closed, causing the "stop closing gripper, retract arm" behavior to trigger. The white node on the output of this behavior is a sequential node - first the gripper closure motor

strand is inhibited, causing the gripper to first stop squeezing.

Finally, the behavior subsumes the output of the "extend arm"

behavior using a subsumption node, causing the arm to retract.

The MIQ and dMIQ of this system is easy to compute. All

weights are set to one to simplify the example. There are three

behaviors. The "extend arm" behavior is a trigger and a raw

motor node (the tap nodes belong to the /"close grippei^' and

"stop gripper..." behaviors). The behavior response time for "extend arm" is therefore 1 + 1=2. There is one strand in this behavior. The "close grippers" behavior has one raw sensor node, one motor node tree node, and either one raw motor

node or one motor tap; both of the two strands are the same

length, so we may use either. The response time is 3 + 1 = 4. The "stop closing..." behavior similarly has a response time of

4 and a strand count of two. The mean behavior response or propagation time is (2 + 4 + 4) / 3, or 3.333. Layering depth is two, and system strand count is 5. Average strand propagation

time over the entire system is (3 + 3 +3 + 3 + 1) / 5, or 2.6. There are nine nodes and nine strand segments in the entire system.

Based on these numbers, task complexity ability is 3 + 2.6 + 2 + 5+ 9 = 21. 6. Remember, this number means little except

as a comparative measure. Performance is 3.333 + 0.385 =

3.718. MIQ is then roughly 21.6 + 3.7 = 25.3. If we assume

116
costs and volume of one, then cost and volume are both 9 + 9 = 18. Resource is (18)(18) = 324, and the rMlQ is 21.6/324 =

0.0667

6. CONCLUSION

We have presented three important issues, which should be

considered when measuring machine intelligence, and

introduced the structure of machine intelligence, which shows

the internal mechanism of machine taking into account the

three issues. Any machine can be represented by the proposed

structure and the structure can be transformed into an

equivalent linguistic structure so that one may define the

metric of the machine intelligence in an analytical way.

In this paper, an equivalent linguistic structure has been

proposed. It needs to be further developed to present linguistic structure of machine intelligence for both MIQ and dMIQ with

respect to goals and time.

The formulation on MIQ, dMIQ, and rMIQ in Section 2

will be a good guide for defining machine intelligence since

its clearness in the sense of goal-dependency, timevaryingness, and resource-dependency.

REFERENCES

[1] Albus, J. S., "Outline for a Theory of Intelligence," IEEE

Tran. on Systems, Man, and Cybernetics, Vol. 21, No. 3, 1991,

pp. 473-509

[2] Antsaklis, P. J. "At the gates of the Millennium: Are we in control?," IEEE Control Systems Magazine, Vol. 20, No. I, February 2000, pp. 50-55

[3] Chalfant, E. C, and Lee, S., "Measuring the Intelligence of

Robotic Systems: An Engineering Perspective," Proceedings

of International Symposium on Intelligent Systems,

Gaithersburg, MD, October, 1999

[4] Bien, Z., Kim, Y.-T., and Yang, S., "How To Measure The

Machine Intelligence Quotient (MIQ): Two Methods And

Applications," Proceedings of World Automation Congress,

Alaska, May 9-14, 1998

[5] Bien, Z., Bang, W.-C, and Han, J.-S., "A Perspective of

Intelligent Machine and Measurement of Machine Intelligence

Quotient," Proceedings of the 4th Korea-Japan Joint

Workshop on Advanced Teleautomation and Intelligent

Mechatronics, Taejon, Korea, Nov. 4-6, 1998, pp. 64-67

[6] Lee, S., Schenker P. S., and Park, J., "Sensor-KnowledgeCommand Fusion Paradigm for Man/Machine Systems,"

Proceedings of SPIE International Symposium on Advanced

Intelligent Systems, Boston, MA, 1990

117
Formal Specification of Performance Metrics for Intelligent Systems

Ying Zhang

System and Practice Lab, Xerox Palo Alto Research Center

Palo Alto, CA 94304

Alan K. Mackworth

Department of Computer Science, University of British Columbia

Vancouver, B.C., Canada, V6T 1Z4

Email: yzhang@parc.xerox.com, mack@cs.ubc.ca

ABSTRACT

There are now so many architectures for intelligent systems:

deliberative planning vs. reactive acting, behavioral subsuming

vs. hierarchical structuring, machine learning vs. logic reasoning,

and symbolic representation vs. procedural knowledge. The

arguments from all schools are all based on how natural systems

(i.e., biologically inspired, from basic forms of life to high level intelligence) work by taking the parts that support their architectures. In this paper, we take an engineering point of view,

i.e., by using requirements specification and system verification

as the measurement tool. Since most intelligent systems are real- time dynamic systems (all lives are), requirements specification

should be able to represent timed properties. We have developed

timed V-automata that fit to this purpose. We will present this formal specification, examples for specifying requirements and a general procedure for verification.

KEYWORDS: formal specification, constraint-based

requirements, system verification

1. INTRODUCTION AND MOTIVATION

Over the last half a century, intelligent systems have

become more and more important to human society, from

everyday life to exploration adventures. However, unlike

most other engineering fields, there has been little effort towards developing sound and deep foundations for

quantitatively measurement and understanding such

systems. The lack of measurement and understanding leads

to unsatisfactory behavior or even potential danger for

customers. The systems may not achieve desired

performance in certain environments, or, the systems may

even result in catastrophe in life-critical circumstances.

Many researchers have suggested measures of

performance for intelligent systems, such as the Turing

Test [12], Newell's expanded list [9,10] and Albus's

definition of intelligence [4]. However, most of these measures are not based on formal quantitative metrics.

There are also efforts on comparing performance on pre- defined tasks, such as a soccer competition [II]. However,

these methods are domain specific therefore hard to apply

to general cases. We advocate formal methods for

specifying performance requirements of intelligent

systems. Much research has been done on formal methods

(http://archive.comlab.ox.ac.uk/formal-methods.html) over

the last twenty years. In this paper, we explore one of the

approaches, namely, using timed V-automata for

specifying performance requirements.

The timed V-automata model was developed in [13,

17] as an extension of discrete time V-automata [8] to continuous time, annotations with real-time. Timed Vautomata are simple yet able to represent many important

features of dynamic systems such as safety, stability,

reachability and real-time response. In the rest of this paper, we introduce the formal definition of timed Vautomata first, then present examples of timed V-automata

for representing performance metrics, and finally describe

a general verification procedure for this type of

requirements specification.

2. TIMED V-AUTOMATA

In general, there are two uses of automata: 1 . to describe

computations, such as input/output state automata, and 2.

to characterize a set of sequences, such as regular

grammars/languages. Examples of the first category are mosdy deterministic and examples of the second category

are mostly non-deterministic. However, all the original

automata work is based on discrete time steps/sequences.

Approaches to extending automata to continuous time have

been explored in hybrid systems community over the last decades [1,2,7]. The timed V-automata model that we

developed belongs to the second category, i.e., nondeterministic finite state automata specifying behaviors over continuous time. The discrete time version of Vautomata was originally proposed as formalism for the specification and verification of temporal properties of

concurrent programs [8].

118
2.1. Syntax

Syntactically, a timed V-automaton is defined as follows.

[Definition 1] A \f-automaton A is a quintuple (Q, R, S, e,

c) where Q is a finite set of automaton-states, R c Q is a

set of recurrent states and S c Q is a set of stable states. With each q e Q, we associate an assertion e(q), which

characterizes the entry condition under which the

automaton may start its activity in q. With each pair q, q'

6 Q, we associate an assertion c(q, q'), which

characterizes the transition condition under which the

automaton may move from q to q'. R and S are generalizations of accepting states. We denote by B = Q -

(R u S) the set of non-accepting (bad) states. Let be the

set of non-negative real numbers representing time

durations. A timed V-automaton is a triple (A, T, x) where

A is a V-automaton, T c Q is a set of timed automatonstates and t: T u {B} yj [°°} is a time function.

One of the engineering advantages of using automata

as a specification language is its graphical representation.

-,G

Figure 1. Examples of timed V-automata

2.2. Semantics

Semantically, each assertion denotes a constraint defined on a domain of interest. Let D be a domain of interest; D

can be finite, discrete, or continuous, or a cross product of

a finite number of domains. Physically, D can represent,

for example, speeds, distances, torques, sentences,

commands or a combination of the above. A constraint C

defined on D is a subset of D, C c D. Physically, a constraint represents certain relation on a domain, such as

a relation between external environment stimuli and an

agent's internal knowledge representation, or, a relation

between internal states and actions, or, the relation

between the current and next state. An element d in domain D satisfies constraint C, if and only if d e C.

The semantics of timed V-automaton is defined as follows. Let rbe a time domain, which can be continuous,

for example, R*. First, let us define runs of V-automata.

Let A - (Q, R, S, e, c) be a V-automaton and v: 7 ^ D be

a function of time. A run of A over v is a function r: T^Q

satisfying:

It is useful and illuminating to represent timed V-automata

by diagrams. A timed V-automaton can be depicted by a

labeled directed graph, where automaton-states are depicted by circle nodes and transition relations are by

directional arcs. In addition, each automaton-state may

have an entry arc pointing to it; each recurrent state is depicted by a diamond and each stable state is depicted by

a square, inscribed within a circle. Nodes and arcs are labeled by assertions as follows. A node or an arc that is

left unlabeled is considered to be labeled with true. Furthermore, (1) if an automaton-state q is labeled by V(/ and its entry arc is labeled by (p, the entry condition e(q) is given by e(q) = \|/ Acp; if there is no entry arc, e(q) = false,

and (2) if arcs from q to q' are labeled by (p,, i = 1 . . .n, and

q' is labeled by \\t, the transition condition c(q, q') is given

by c(q, q') = ((pi v...v(pn) AVj/; if there is no arc from q to

q', c(q, q') = false. A T-state is denoted by a nonnegative

real number indicating its time bound. Some examples of

timed V-automata are shown in Figure 1.

-.E Q

-E

1. lnitiality:v(0)e e(r{0)y,

2. Consecution:

a. Inductivity: Vt>0, 3qeQ, t'<t,Vt",

t'<t"<t, r(t")=q and v(t) e c(r(t"), r(t)) and

b. Continuity: Vt, 3qeQ, t'>t, Vt", t<t"<t',

r(t")=q and v(t") e c(r(t), r(t")). When T is discrete, the two conditions in Consecution reduce to one, i.e., Vt>0, v(t) e

c(r(pre(t)), r(t)) where pre(t) is the previous time

point of t.

If r is a run, let Inf(r) be the set of automaton-states

appearing infinitely many times in r, i.e., Inf(r) =

{q|Vt3t'>t, r(t')=q}. A run is called accepting if and only if

1. Inf(r) nR;!^:0, i.e., some of states appearing

infinitely many times in r belong to R, or

2. Inf(r) c S, i.e., all the states appearing infinitely

many times in r belong to S. For a timed V-automaton, in addition for a run to be

accepting, it has to satisfy time constraints. Let I c Tbe a time interval and |I| be the time measurement, and let r|I be

119
a segment of r over time interval I. A run satisfies time

constraints if and only if:

1. Local: For any q e T any time interval I, if r|I is a segment of consecutive states of q, then |I| <x(q);

2. Global: For any time interval I, if r|I is a segment

of consecutive states of BuS, then liXB(r(t))dt

<x(B), where Xb: Q—>{0,1 } is the characterization

function for the set B.

[Deflnition 2] A timed V-automaton TA = (A, T, t)

accepts a trace v, if and only if

1 . All runs are accepting for A;

2. All runs satisfy the time constraints.

With the semantics defined, we can infer that, for the

timed V-automata in Figure 1, (a) specifies the behavior of

reachability, i.e., eventually the system should satisfy

constraint G, (b) specifies the behavior of safety, i.e. constraint G is never satisfied, (c) specifies the behavior of

bounded response, i.e., whenever constraint E is satisfied,

constraint F will be satisfied within bounded time and (d)

specifies the behavior of real-time response, i.e., whenever

constraint E is satisfied, constraint F will be satisfied

within 5 time units.

3. EXAMPLES OF PERFORMANCE

SPECIFICATION

Timed V-automata are simple yet powerful for the

specification of behaviors of dynamic systems, since it integrates constraint specification with timed dynamic

behavior specification.

3.1. Examples of Constraint Specification

Constraint specification alone can specify many

performance metrics. Constraints specify relations between

external environment stimuli and an agent's internal

knowledge representation, or between internal states and

actions, or between the current and next states. Constraints can be finite, discrete or continuous, or any combination of

the above. Constraints can be linear, nonlinear, equalities or inequalities. Moreover, constraints can also specify

optimal conditions or optimality with extra constraints, or combinations of multiple optimal criteria and additional

constraints.

Considering the following examples for specifying

constraints:

1. Inequality: f(x) < 0 where x is a vector of

variables and f is a vector of functions.

2. Optimality: min |f(x)| where |x| is a norm for x.

3. Negation: \^ y.

4. Constrained Optimality: min|f(x)| given g(x)<0.

5. Robustness: Let f(x) be a set of output functions

with X as inputs. The robustness can be

represented by its Jacobian J = Af/Ax. There are many ways to state an optimal condition for

robustness. One method is to minimize |w| where

w is the diagonal elements of W in the singular

value decomposition of J = UWV^.

3.2. Examples of V-Automata

With automata, timed dynamic behaviors can be specified.

Here is a set of examples for specifying performance using

timed V-automata, as shown in Figure 1

:

1. Let G be a constraint that the distance between

the robot and its desired position is less than some

constant value. Then Figure 1(a) specifies that the

robot will eventually arrive its desired position.

2. Let G be a constraint that the error of a learning

algorithm is less than a desired tolerance. Then

Figure 1(a) specifies that the learning will eventually convergence. If let the state of —iG in Figure 1(a) as a timed state with time bound t, it further specifies that the learning will be done

within time t.

3. Let G be a constraint that the distance between

the robot and obstacles is less than some constant

value. Then Figure 1(b) specifies that the robot

will never hit any obstacle. If G denotes that the current memory usage is out of the limit, Figure

1(b) specifies that the memory usage at any time

is within its limit.

4. Let E be an external stimuli and F be a response.

Then Figure 1(c) specifies that there is a response

after stimuli within bounded time. Figure 1(d)

specifies that such a response is within 5 time

units. Even though timed V-automata are powerful, still they

are not able to represent all forms of performance metrics.

For example, optimal performance over time minjf(t)dt is not specifiable with timed V-automata. This form is mostly

used for characterizing energy, efficiency or overall errors. Furthermore, specification with probability behaviors are not included either. However, it is not hard to add

probability, for example, instead of "all runs" must be

accepting and satisfying time constraints, we can say "x%

runs" must be accepting and satisfying time constraints.

3.3 Performance Comparisons

Note that requirements specification defines what the

system should do, rather than defining how the system is organized, i.e., its architecture. For example, behaviorbased control [4,6] (which is arbitration based or a

horizontal hierarchy) has a different form of architecture

from function-based control [5] (which is abstractionbased or a vertical hierarchy); model-based systems have a

different form of architecture from learning-based systems.

120
event-driven systems have a different kind of architecture

from time-driven systems. Different systems with different

architectures can still be compared based on the behavioral

interface under the formal performance specification. For

example, given a set of requirements specification Rs and

system A satisfies a subset As c Rs and system B satisfies

a subset Bs c Rs. If As c Bs, system A is not better than

system B with respect to requirements Rs. Similarly, if system A satisfies requirement a and system B satisfies requirement P and if a implies p, system A is better than

system B with respect to the requirement.

However, this specification does not define metrics on

architectures. The measurement of performance should

come from the customer's point of view, but the

measurement of architecture should come from the

developer's point of view, i.e., design time, debug time,

upgrading time, modularity and the percentage of re-usable

components.

4. SYSTEM VERIFICATION

For most dynamic systems, stability or convergence is the

most important property that needs to be verified. For

example, we can verify that equation dx/dt = 0 satisfies the

property of V-automaton in Figure 1(a) with G as |x|<e for

any positive number e. The most commonly used method

for the verification of such properties is the use of

Liaponov functions. We developed a formal method based on model-checking, that generalizes Liaponov functions

[13,17]. This method is automatic if the domain of interest

is finite discrete and time is discrete [13].

The details of the model-checking method are out of

the scope of this paper. The basic principle is to first find a

set of invariants, each associated with an automaton-state

in the timed V-automaton. Then, find a set of Liaponov

functions, which are non-increasing in stable states and

decreasing in bad states. Finally, find a set of local and

global timing functions, where local timing functions are decreasing in timed states and global timing functions, like Liaponov functions, are non-increasing in stable states and

decreasing in bad states, in addition to be bounded in values.

5. RELATED WORK AND CONCLUSION

Much work has been done in formal approaches to system

specification and verification [1,2,7,8]. In general, there are two schools. One is to develop a uniform specification

for both systems and their requirements; the other is to use two different specifications, one for systems and one for

requirements. The advantage of the former is that the same

formal approach can apply to both system synthesis and

system verification. However, in most cases, if the

specification language is powerful for both systems and

requirements, the synthesis or verification tasks become

hard. We advocate the latter approach, i.e., using timed Vautomata for requirements specification and using

Constraint Nets [13,18,19] for system modeling. Control

synthesis [13,14] and verification [13,15,16,17,20] are also

studied in this framework.

In this paper, we have shown how to use formal

methods to specify the performance metrics of intelligent

systems, with timed V-automata as an example. The

advantage of formal methods over other methods lies in

their precision and generality. Timed V-automata, with its graphical depiction and constraint specification, is a simple

yet powerful formalism for specifying many properties of

dynamic systems.

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University of British Columbia, Canada, 1994.

[14] Zhang, Y. and A. K. Mackworth, "Synthesis of

Hybrid Constraint-Based Controllers," P. Antsaklis, W.

Kohn, A. Nerode, and S. Sastry, editors. Hybrid Systems

and Automatic Control, LNCS 999, Springer-Verlag,

1994, pp. 552-567.

[15] Zhang, Y. and A. K. Mackworth "Specification and

Verification of Constraint-Based Dynamic Systems," A.

Boming, editor. Principles and Practice of Constraint

Programming, LNCS 874, Springer- Verlag, 1994, pp. 229

-242.

[16] Zhang, Y. and A. K. Mackworth, "Will The Robot Do

The Right Thing," in Proc. Artificial Intelligence, 1994,

Banff, Alberta, pp. 255 - 262.

[17] Zhang, Y. and A. K. Mackworth, "Specification and

Verification of Hybrid Dynamic Systems Using Timed VAutomata," Verification and Control of Hybrid Systems,

LNCS 1066, Springer-Verlag, 1995.

[18] Zhang, Y. and A. K. Mackworth, "Constraint

Programming in Constraint Nets", V. Saraswat and P. Van

Hentenryck, editors. Principles and Practice of Constraint

Programming, MIT Press, Cambridge, MA, 1995, pp. 49 -

68.

[19] Zhang, Y. and A. K. Mackworth, "Constraint Nets: A

Semantic Model for Hybrid Dynamic Systems," Journal of

Theoretical Computer Science, Vol. 138, No. 1, pp. 211 -

239, 1995.

[20] Zhang, Y. and Alan K. Mackworth, "Modeling and

Analysis of Hybrid Systems: An Elevator Case Study,"

H.Levesque and F.Pirri, editors. Logic Foundations for

Cognitive Agents, Springer, Berlin, 1999, pp. 370-396.

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Metrics for Intelligence: the Perspective from Software Agents

PRELIMINARY NOTES

Line Pouchard

Collaborative Technologies Research Center

Computer Science and Mathematics Division

Oak Ridge National Laboratory

Oak Ridge, TN 37831-6414

pouchardlc@oml.gov

Each scientific development that claims to provide a "new way" for approaching existing problems

needs proper (i.e. formal and quantifiable) evaluation methods and consensus-based criteria for

measuring the validity of its claims. Taken together, these methods and criteria constitute the metrics by

which new developments are being measured against their claims. Various claims have been made in

the literature for the technology of intelligent software agents. Such claims include a new approach to

programming providing a breakthrough comparable to the one achieved through object-oriented

methods; an approach to programming that is more readily understood by non-programmers; an approach that lowers the costs of software inter-operability.

Software agents need proper metrics if the technology is to fulfill its promises and make a lasting

impact. One characteristic distinguishing software agents from software developed with object-oriented

and procedural methodologies is the anthropomorphic characteristics that agents exhibit. Various

taxonomies for software agents currently exist [1,2, 3]. Agents typically present one or several of the

following characteristics:

• Pro-activeness and goal-orientation

• Reactiveness (reactive agents)

• Autonomy (rational agents, and others)

• Mobility (mobile agents)

• Learning and reasoning ability (deliberative agents, and others)

• Social ability: communication and cooperation (multi-agent systems)

An agent is considered intelligent if it can learn from its environment and modify its behaviors and goals

to respond to environmental constraints that were uncertain and unforeseen at the time of development.

Agents are thus particularly adapted to model environments where software components act

autonomously on users' behalf and problem-solving environments where parameters of computation

dynamically change during processing. The ability to learn for an agent is coupled with the ability to

perform resource and knowledge discovery. This action may take the form of querying and updating

knowledge-based systems. Knowledge discovery and interpretation bring latency to the agent and may

impair the achievement of its overall goals. For instance, reactive agents that need a quick response

time may not embody much learning and reasoning because the overhead renders the agent useless.

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I

I

I Software agents present one or some capabilities that are affected by the choice of specific components '

described in the Tools of Intelligence (see White paper). For instance, searching for a required object

| within a scene is one area where software agents have successfully been implemented. If you take the | "scene" to be an information space like the Internet, information-gathering and retrieval agents display

this capability and have been successful at performing the task. Deliberative agents such as Belief-

\ Desire-Intention (BDI) agents exhibit the capability of remembering scenes and experiences as their '

Beliefs are based on this capability. These agents are also able to interpret and respond to unforeseen ; situations.

Agents' ability to autonomously execute processes on remote systems, given the appropriate

permissions, is also a characteristic some intelligent systems (but not all) need to efficiently and

effectively perform. This requires proper measures. This characteristic, known as mobility, has very

different meaning for physical agents.

Mobility requires intelligence for software agents because true mobility requires resource discovery.

For those agents designed as mobile agents the degree of mobility can constitute a measure of its intelligence. Mobile agents travel over networks such as the Internet and execute processes on remote

platforms. Mobile agents may start execute a process on a particular machine, be unexpectedly

interrupted, travel to another available platform, and continue the execution of the process from where it was interrupted. Such a mobile agent needs intelligence to interrupt and restart its execution

autonomously without resetting, and for determining which resources to use in a networked

environment. Network agents used for telecommunication applications (such as testing the reliability of

a network) exemplify these types of agents.

Social intelligence needs to be measured in multi-agent systems. The degree of social interaction and

the agents' ability to exhibit social behavior constitute an important criterion for multi-agent systems.

Not all agent-based systems need to exhibit this characteristic (mobile agents may never need to talk to

each other for instance). The type of social interaction between agents conditions knowledge

acquisition and interpretation. The social model affects the individual pursuit of goals and may

ultimately affect the survival of the system 14]. When one considers a multi-agent systems, there are at

least two models. Both types of multi-agent systems, collaborative and cooperative, display the

characteristics of open systems.

• Model 1: Each individual agent's goal is subservient to an over-arching goal of the system. We have

a cooperative system, where agents agree not to pursue goals detrimental to each other and the

whole system, even if these "careless" goals are in accordance with the individual agent's goal.

• Model 2: Each agent acts on its own behalf without recognizing a higher agent-entity with the ability

to regulate its goals (there is still a need for a kind of supervisor agent that regulates

communication). We have a collaborative system. This is the case for so-called rational agents,

used especially in e-commerce, where agents act in a market-like environment, with the ability to bid

for money on the goods and services each offers.

Agent-communication languages should theoretically let heterogeneous agents communicate, but none

currently do 15]. A significant part of the inter-operability issue is the lack of a shared content language

and ontology. An ontology expresses, for a particular domain, the set of terms, entities, objects, classes

124
and the relationships between them with formal definitions and axioms that constraint the interpretation

of these terms [6]. These definitions and axioms are written in a variety of logical languages (e.g. KIF

[7]), and provide a formal theoretical basis to domain taxonomy. They can serve to automatically infer

translation engines between software applications. By making explicit the implicit definitions and

relations of classes, objects, and entities, ontologies also contribute to knowledge sharing and re-use across systems. The use of ontologies in agent-based systems is proposed as a criterion for the metrics

of intelligent software agents. The degree of completeness and consistency of ontologies can be formally

proven and provide a quantifiable criterion.

Ontologies constitute an important criterion for the metrics of intelligent software agents, in particular

for agents exhibiting the social abilities of communication and cooperation. Software agents require the use of or a translation to a shared terminology and syntax in order to efficiently and effectively interoperate. Agent-communication languages such as KQML meet the challenges of inter-operability with

mitigated success [8]. Agent communication languages specify the possible use of ontologies in their

syntax but do not require it. FIPA ACL proposes an ontology service as a normative specification [9].

In conclusion, software agents exist either as standalone or in social systems. Agents are made of

components, and an agent-oriented architecture typically includes the agent application as well as an

environment in which agents execute. They may execute on a single machine, on several machines

connected locally or by wide-area network. These agents need a degree of mobility. They may be

developed by different developers on different platforms, and therefore need a common communication

language including protocol and ontologies (see [10] for an assessment of the state-of-the-art in this

area). In addition, since agents may exhibit any combination of the characteristics above, some

taxonomies of agents prefer a classification based on the domains in which software agents have been

successfully implemented [11], rather than on their inherent characteristics.

Software agents also exist as whole, where an agent-based system is made of the agent and the

underlying environment. The environment may include the knowledge repositories and ontologies

which are key to the agents' degree of intelligence. For this reason, the mind/body dichotomy, and the

proposition to measure the intelligence of the system based on the intelligence of the mind (controller),

do not hold for agent based systems.

In addition to characteristics applicable to Constructed Systems with Autonomy, the metrics of

intelligence for software agents need to include the following (not all these characteristics need apply for

the same system):

• be domain-specific

• measure the degree of mobility

• present an agent conmiunication language

• refer to ontologies.

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1 .Brenner, Walter; Zamekow, Rudiger, and Wittig, Hartmut. Intelligent Software Agents. Foundations

and Applications. Berlin: Springer Verlag; 1998; pp. 37-41.

2.Nwana, H. et. Al. "What is an agent?" Available at: http://wwvv.labs. bt.coni/proiects/agents/publish/papers/review2.htm#agent

3. Wooldridge, M. Intelligent Agents. In Multiagent Systems. A Modern Approach to Distributed

Artificial Intelligence. Weiss, Gehrard, ed. Cambridge, Mass.: MIT Press; 1999; pp.27-73.

4. Huhns, Michael N. and Stephens, Larry. Multiagent Systems and Societies of Agents. In Multiagent

Systems. A Modern Approach to Distributed Artificial Intelligence. Weiss, Gehrard, ed. Cambridge,

Mass.: MIT Press; 1999; pp. 79-120.

5. Singh, Munindar P. Agent communications languages: rethinking the principles. IEEE Computer.

1998;31(12):40-47.

6. Gomez-Perez, Asuncion. Knowledge Sharing and Reuse. In The Handbook ofExpert Systems. Boca

Raton, FL: CRC Press; 1998; pp. 10/1-10/36.

7. Knowledge Interchange Format. Available at http : //lo gic . stan ford . edu/ki F/dpans . html.

5. Labrou, Yannis; Finin, Tim, and Peng, Yun. Agent communication languages: the current landscape.

IEEE Intelligent Systems & Their Applications. 1999; 14(2):45-52.

9. Foundation for Physical Intelligent Agents (FIPA). Available at http://drogo.cselt.it/Fipa and

http://www.fipa.org .

10. Wooldridge, M. and Jennings, N.R. Applications of Intelligent Agents. In Agent Technology:

Foundations, Applications, and Markets. Berlin: Springer Verlag, 1998; pp. 3-28.

1 1. Nwana, Hyacinth S. and Ndumu, Divine T. A perspective on software agents research. The

Knowledge Engineering Review. 1999; 14(2): 125-142.

126
Minimal Representation Size Metrics for Intelligent Robotic Systems

Dr. Arthur C. Sanderson

Department of Electrical, Computer and Systems Engineering

Rensselaer Polytechnic Institute

Troy, NY 12180

sandea@rpi.edu

ABSTRACT

The minimal representation size criterion provides a metric for the configurational complexity of robotic tasks and may be used to evaluate alternative algorithms, strategies, and architectures for the accomplishment of specific tasks. The principles of explicit and

implict representation are used to define this complexity and the

resulting information measure derived may be considered as a measure of configurational intelligence of the system.

Specifically, these measures indicate the internal explicit

information required to specify the accessible states of the robotic system using its available perception and actuation capabilities.

The resulting approach may be used to evaluate and guide

applications tasks such as robotic assembly and multisensor

manipulation.

Keywords: minimal representation size, intelligent systems,

performance metrics, robotics

1. INTRODUCTION

intelligent robotic systems couple computational

intelligence to the physical world and such systems may be

considered as intelligent agents that perceive the

environment, and select an action or sequence of actions to

affect the environment. Such an intelligent agent constructs an internal "representation" of the environment, and uses reasoning to choose among alternative actions.

Specifically, we can define robots as "active, artificial,

intelligent agents whose environment is the physical

world". Such agents may be distinguished from software

agents, human agents, and others.

Such an intelligent robot is regarded as "rational" if the

agent makes decisions to choose actions that accomplish a known task goal, or increase a performance measure of the

task. It is important to distinguish the presence of

intelligence from the metric of performance. Intelligence

(reasoning), in itself, does not maximize overall

performance. However, intelligence may be used to choose

among a set of candidate actions that may improve

performance or achieve a goal.

An intelligent robot may also be characterized by its autonomy. In the context of these definitions, autonomy

refers to the capacity of the robot to define its own goals or sub goals, often based on its perception and internal

representation of the environment. Autonomy widens the

scope of tasks, which the same system can perform without

reprogramming, but in general, requires more sophistication

in the design and architecture of the system. The non- autonomous system may accomplish a smaller set of tasks

and may require efforts to constrain or redesign the

environment to conform to task assumptions.

The structure of an intelligent robot agent includes

perception, representation, reasoning, and representation.

The implementation of such an agent requires two major

components: (I) Algorithms that define the representation

structure and reasoning sequence, and (2) Architecture that defines the organization of the system to accomplish set goals and performance. In practice, the selection of the

architecture has been strongly intertwined with the nature of

the representation. For example, one simple intelligent

robot defines a perception-action pair such as "move hand

if you touch the hot stove!" Such a reflex action might be

expressed as a look-up table in which state representation is a simple binary element.

As the complexity of robots and tasks increases, a single

reflex action is inadequate to create required behaviors, and

architectural approaches have tended to evolve in two

directions. First, hierarchical architectures have been

based on the definition of a hierarchy of explicit

representation of the robot state. A hierarchy of perceptual

representation may involve image features, shapes, objects, scenes, etc., while a hierarchy of actions may involve joint

motion, arm motion, robot motion, sensor-based motion etc. The formal definition of such a hierarchical architecture [1]

has provided an important basis for building consistent,

predictable, and programmable robotic systems.

A second trend has been the development of behavioral

architectures [3] that expand upon simple reflexes by

creating a network of interdependent reflexes in order to increase the sophistication of the behaviors. One such

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behavioral approach is the subsumption architecture [5] that

utilizes finite state machines to impose a priority setting

logic on the reflex actions. The nature of such behavioral

architectures is to incorporate an implicit representation of

the environment in order to define a simplified state space

of perceptions and actions. From a systems perspective, the

behavioral architecture utilizes constraints or assumptions

about the environment to identify a subspace (manifold)

within the explicit state space. A refiex action, or set of

actions, may then be defined within the subspace with the

logical consistency to achieve goals and performance

metrics.

The distinction between explicit and implicit representations

is important to the interpretation of intelligence in systems.

A simple task example helps to illustrate these distinctions.

Consider a room with a single door containing a mobile

robot. The robot task goal is to exit the room, and it may

have a performance metric of minimum time to exit. Several different types of algorithms may be considered:

(1) . Random search (Figure 1 a)

The robot moves in random directions without using

perception, mechanically bouncing off the walls. Eventually, it is guaranteed to exit the room.

(2) . Wall following - simple reflex (Figure lb)

The robot uses a simple sensor to detect presence or absence of an adjacent wall. The algorithm:

IF ('wall-is-in-front') THEN (Turn-Right') ELSE

('Follow-wall-on-left')

is guaranteed to find the door, though the path may be

long.

(3) . Perception - Explicit state representation (Figure Ic)

The robot uses a sophisticated vision sensor to view the

door, acquire a perception, P, update the global internal

state representation, GS, and plan an explicit path to the door.

(4) . Perception - Implicit state representation (Figure Id)

The robot defines an implicit mapping of GS to local

state, LS, that is consistent with the desired goal state. By mapping perception into LS, rather the GS, the

resulting algorithm is often more efficient and simpler

to implement. In this case, consider a sensor that perceives only the width, W, of the door, but no other

attributes of the environment. We choose W to be the

local state representation, LS = W, and define a local refiex algorithm to choose an action. A: Choose A to increase W.

(a) . If robot, R, moves toward the door, W' > W.

(b) . If R moves perpendicular to the door, then W'>W.

The resulting local changes in W move the robot toward

and through the door, achieving the global goal. However,

LS is never sufficient to explicitly locate the robot in the

room, i.e. determine GS. This strategy is analogous to a potential field mapping related to the perceived door width

feature of the room. The same strategy may be used as a

feature-based method to guide a peg-in-hole or other

assembly problem using visual servoing of the area of the

target hole [26].

These examples illustrate several types of tradeoffs in the

design of intelligent systems, and also confirm that the most

intelligent system may not result in the optimal

performance on a given task, as illustrated in the

performance of the feature-based example. First, for this purely geometric task, we can define one component of the

intelligence of the system, the configurational complexity as the information required to represent the accessible states of the internal representation of the system. "Accessible

states" are defined as those states that may be achieved as goal states of the system through its perception-action

algorithms. In this sense, the representational intelligence

of the system is equated to the size of the internal

representation space.

For the examples in Figure (1), the configurational

complexity is found to be: (a). 1 bit, (b). 3 bits, (c). 30

bits, and (d). 10 bits, where a resolution of 10 bits has been

assumed for the vision sensor used in (c) and (d). By

considering the approximate number of steps required to achieve the result, on can similarly compute the cumulative

complexity for each of the tasks to be: (a). 100 bits, (b). 75 bits, (c). 60 bits, and (d). 20 bits. Therefore, the

minimal complexity approach to the task is given by

strategy (c) and may be regarded as a tradeoff between

explicit and implicit information needed for the task.

In addition, the time (number of steps) required for each

task is implicit in the cumulative information and reflects the inherent deficiencies in the worst case scenarios for (a)

and (b). Based on the viewpoint of encoded residuals

discussed in the next section, one can also calculate the

encoded implicit information for each strategy: (a). 20 bits,

(b). 18 bits, (c). Obits, (d). 12 bits.

Figures (e) and (f) emphasize the inherent assumptions that are often present in such systems. Strategies (a) and (b) are

not guaranteed to succeed for problems (e) and (f), where

the subspace manifold defined by the strategy is no longer

guaranteed to contain the goal. Strategies (c) and (d) may

still succeed but require more steps and a more

sophisticated algorithm.

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e. Room 'Afth inner walls

Figure (1). Examples of alternative strategies for the task of exiting a room through the door: (a). Random search, (b).

Wall-following, (c). Explicit representation and global planning, (d). Implicit representation and local reasoning. All four

strategies will accomplish the basic task. However, (a) and (b) are not general and will fail when the environment differs

from the basic assumptions, such as in (e) with inner walls, and in (f) with multiple doorways.

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2. MFNIIVIAL REPRESENTATION SIZE

The minimal representation size (MRS) methods

[6,18,19,23,24] used in this work are also called "minimum

description length" methods in the literature. The MRS

approach introduces an information measure of model

complexity and has been applied to a number of related

problems in attributed image matching [22], shape

matching [11], density estimation [4], and model based

sensor fusion [1 1-17]. The minimal representation criterion

defines the minimal overall data representation among a choice of alternative models and trades off between the size

of the model (e.g. number of parameters) and the

representation size of the encoded residuals. Intuitively, the

smaller, less complex, representation is chosen as the

preferred model for a given performance criterion. In terms

of the robotic systems we consider here, the representation

size combining state and model information serves as a measure of system intelligence, and the MRS criterion will select the minimal complexity system for a given task

performance, in practice, the MRS criterion has advantages

in the attainment of consistent metrics without the

introduction of problem specific heuristics or arbitrary

weighting factors. The MRS family of methods provides a type of "universal yardstick" for data and models from

disparate sources, and therefore has been successfully used

in multisensor fusion interpretation problems.

The MRS criterion has been proposed as a general criterion

for model inference by Rissanen [19] and by Segen and

Sanderson [23]. It is an expression of the ideas on

algorithmic information theory pioneered by Solomonoff

[24], Kolmogorov [18], and Chaitin [6]. The MRS

approach is based on the principle of building the shortest

length program that reconstructs observed data. The length

of this program or representation size depends on both the

statisHcs of the sensors and on the systems "knowledge" of

the environment, specified by a set of models and

constraints.

More formally, the representation size is the length of a program in bits that, when executed on a deterministic

Universal Turing Machine (UTM) [7] would reproduce the

observed data on the output tape. A model based encoding

scheme is used in which the data is thought to be arising

from one of the several available models in a model library,

Q. The models may differ in structure and number of

parameters. The observed data D is encoded by specifying an instantiated model q and the deviations or residuals of

the data D from the selected model q e Q. The resulting

representation size is

L[q,D|Q] = L[q|Q] + L[D|q,Q]

= L[q|Q] + L[A|q,Q] + L[D|Q,q,0]

where L[q,D] is the total representation size of data D when

explained using model q, given a model library Q.

L[d|A,q,Q] is the number of bits needed to encode the data

deviations or residuals from the model, given a coding

algorithm, A. L[A|q,Q] is the number of bits required to specify the coding algorithm itself, given an environment

model. L[q|Q] is the number of bits required to encode the

environment model (structure and parameters) given a model library, Q.

According to the minimal representation principle, the best

explanation of the observed data is the one with the smallest

representation size

Qop, = arg minqeQ L[q|Q] + L[A|q,Q] + L[D|A,q,Q].

This approach finds the simplest explanation of the data

that is most likely, and objectively trades off between

model size, algorithm complexity, and observation errors. Rissanen [19] showed that a finite set of random samples

from a class of probability distributions would be

complexity bounds as defined by Kolmogorov [18] and

others [6,24], and the representation size can be used to choose among alternative distribution models. Barron and

Cover [4] showed that such a minimal representation size

probability distribution is statistically accurate and the rate of convergence is comparable to other methods of

parametric and nonparametric estimation. In our previous

work [13-17], we have structured the model-based pose

estimation problem such that the pose transformation

parameters are isolated elements of the statistical model,

and may be estimated by the minimal representation

criterion.

3. PARTS ENTROPY AND INFORMATION

MEASURES FOR ASSEMBLY

Geometric task complexity is directly related to the

geometric state space and the precision of state definition or partidoning. In earlier work [20], we have defined the parts

entropy as a measure of configuration uncertainty in mechanical systems with particular application to assembly

analysis and assembly planning. In this formulation, the

entropy of a distribution of independent objects, or parts, is given by

H„ = H„( P,, ...,Pn) = -ZPk l0g2 Pk .

where uncertainty in position and orientation is described

by the joint probability distribution P(x,y,z,a,p,x) over the

joint ensemble. As an entropy measure [7], H may also be

interpreted as the information required to specify the

position of the objects in their geometric configuration

space.

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The part entropy of an object is defined with respect to the

mechanically distinguishable positions and orientations, and

the resolution, d, in each coordinate degree of freedom.

The symmetry of an object therefore strongly affects the

resulting orientational entropy and is defined by the set of

group operations that leave the object invariant. For

example, a sphere has 0 bits of orientational entropy, while

a cube with 10 bits of resolution would have 24 bits of

entropy.

The part entropy may be used as a basis for the

configurational representation size, and is directly related to the set of constraints or other geometric assumptions made

on the environment. For example, a flat surface reduces the

entropy of parts that sit on it. The entropy of a cube sitting on a table (with 10 bits of resolution) is 28 bits, while a

general rectangular solid will be 30.1 bits, and a cylinder

may vary from 20 to 30 bits depending on its proportions.

For an assembly task, we consider a set of parts {Qj}, I =

1,...,N, such that the part relationships are defined by join

probabilities P[Qi ... Qn], and the parts entropy is defined as the joint entropy H[Q] Qn]. If the parts are positioned

independently, for example, prior to assembly, then the

probabilities will be independent:

P[Qi...Qn] = P(Qi)P(Q2)...P(Qn),

and

H[Q, On] = IH(Qi).

As the assembly task proceeds, individual parts entropies

decrease as parts are positioned, and the entropy of the

ensemble decreases as part dependence is increased during

mating operations. In this sense, an overall goal of the

assembly task is to reduce the joint entropy of the ensemble

of parts. If we define the entropy of the final rigid

assembly to a reference frame with Hq = 0, then the relative

entropy of parts and subassemblies may be tracked as a function of time and the entropy fiow of the process

described in terms of bits per second, that is, information

fiow. Alternative systems choices and parts designs may be

compared in terms of the entropy flow and used to guide

decisions on assembly system design. An example

described in [20] tracks the parts entropy sequence for

sequential assembly for three different electronics assembly

strategies. Similar concepts of part probability distributions

may be linked to tolerance specifications of assemblies, and

have been used to evaluate assemblability based on maximum likelihood methods [21], and used to guide

assembly planning tasks [8-10].

4. MULTISENSOR FUSION MANIPULATION

EXAMPLE

Figure (2). Five fingered anthropomorphic robot hand

manipulating an object. The camera observes motions and

minimal representation metrics are used to determine object

configuration [16].

The MRS approach has been applied to the problem of

multisensor fusion for pose identification of objects using in manipulation by a robot hand. The setting of the task is shown in Figure (2). A five-fingered Anthrobot-3 [2] hand

is mounted on a six degree-of-freedom (DOF) articulate

PUMA-760 robot arm. The hand is provided with finger tip

tactile sensors that sense planar surface contact with the

grasped object. The hand is in the field of view of a calibrated camera with edge detection algorithms. A

polyhedral object is grasped by the hand and manipulated

within the camera view.

In this task scenario, the minimal representation criterion is used to integrate the perception and manipulation steps

through the use of consistent information-based criterion

for consistency of interpretation of the manipulation with

the viewed object pose from the camera. In this task, both

the camera information and the tactile sensing data is extremely noisy and uncertain.

The minimal representation formulation of this problem is described in detail in [16]. In this approach, the modelbased representation of the hand-eye coordination is described by a set of general constraint equations

h(y;z) = 0

where Y is a set of model features, and Z is a set of

observed data features. In general, such constraints may

131
themselves depend on other model features. Often

observed data features may not be related to actual events and identified as unmodeled data features.

The association between the observed data features and the

model features is defined by a correspondence w, and this correspondence is a part of the identified model. In addition, a model of the feature extractor, F, for vision and

tactile sensing is used to described the process. Application

of the MRS approach defines a representation size for each

candidate model and set of observations subject to the data

constraint manifold, DCM, defined by h(y;z). The

representation size of the model and encoded residuals is minimized within the measurement subspace locally

orthogonal to the DCM.

In general, the search over many candidate models and

correspondences is difficult and does not lend itself to linear continuous search techniques. In [16] we use a

differential evolutionary algorithm [25] to carry out this search and identify viable interpretations as minimal

representation size interpretations of manipulation and

sensing states of the system. Figure (3) shows an example

of the evolution of the configuration states of the system as

the differential evolutionary algorithm proceeds. The

system converges to a well-defined and consistent

interpretation of the current state (figure (4)).

5. DISCUSSION

The minimal representation size criterion provides a metric

for the configurational complexity of robotic tasks and may

be used to evaluate alternative algorithms, strategies, and

architectures for the accomplishment of specific tasks. The

principles of explicit and implict representation are used to define this complexity and the resulting information measures derived may be considered as a measure of

configurational intelligence of the system. Specifically,

these measures indicate the internal explicit information

required to specify the accessible states of the robotic

systems using its available perception and actuation

capabilities. The resulting approach may be used to evaluate and guide applications tasks such as robotic

assembly and multisensor manipulation.

As discussed here, the characterization of tasks is defined

with respect to geometric configurations. An important

extension of this work is to consider the application of such

a formulation to a more general task space involving, for example, force and dynamics of the system requirements.

Figure (3). Differential evolution algorithm utilizes representation size metric to search for consistent

interpretations of object pose in the hand of manipulator.

The minimal representation size pose requires the minimum

information to represent.

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Figure (4). Final minimal representation pose of the object

determined by the differential evolution search.

A second extension of this work is the consideration of

intelligent robotic systems with adaptation and learning

capabilities. As shown in the multisensor fusion

manipulation example, the representation size may be used

as a criterion for evolutionary learning of configuration

interpretations. In general, this approach might be used to guide learning of algorithmic structure and strategies

leading to more sophisticated behaviors.

REFERENCES

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Standard Reference Model for Teierobot Control System

Architecture (NASREM)", National Bureau of Standards

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Robots and Autonomous Agents), MIT Press, May, 1998.

[4]. Barron, A. R., and T. M. Cover, "Minimum complexity

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pp. 1034-1054, July, 1991.

[5]. Brooks, R. A., "A robust layered control system for a mobile robot", IEEE Journal of Robotics and Automation,

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[6]. Chaitin, G. J., "A theory of program size formally

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Correct and Complete Algorithm for the Generation of

Mechanical Assembly Sequences", IEEE Transactions on

Robotics and Automation, Vol. 7, No. 2, pp. 228-240, April

1991.

[9]. Homem de Mello, L. S. and A. C. Sanderson,

"Representation of Mechanical Assembly Sequences,"

IEEE Transactions on Robotics and Automation, Vol. 7, No. 2, pp. 211-227, April 1991.

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Criteria for the Selection of Assembly Plans: Maximizing

the Flexibility of Sequencing the Assembly Tasks and

Minimizing the Assembly Time Through Parallel Execution

of Assembly Tasks", IEEE Transactions on Robotics and

Automation, Vol. 7, No. 5, pp. 21 1-227, October 1991

[11]. Joshi, R., and A. C. Sanderson, "Model-based

Multisensor Data Fusion: A Minimal Representation

Approach", 1994 IEEE International Conference on

Robotics and Automation, San Diego, . CA, May, 1994.

[12]. Joshi, R., and A. C. Sanderson, "Multisensor Fusion

and Unknown Statistics using the Minimal Representation

Criterion", Proceedings 1995 IEEE International

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and Model Selection using a Minimal Re presentation Size

Framework", Proceedings lEEE/SICE/RSJ International

Conference on Multisensor Fusion and Integration for

Intelligent Systems, December 8-11, Washington, D. C,

USA, Dec. 1996.

[14]. Joshi, R., and A. C. Sanderson, "Experiments in Tactile/Visual Multisensor Fusion", 1997 IEEE

International Conference on Intelligent Robotics, Monterey,

CA, July, 1997.

[15]. Joshi, R., and A. C. Sanderson, "Minimal

Representation multisensor fusion using differential

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July, 1997

133
[16]. Joshi, R., and A. C. Sanderson, "Minimal

Representation Multisensor Fusion using Differential

Evolution", IEEE Transactions on Systems, Man, and

Cybernetics, January, 1999, Vol. 29, No. 1, pp. 63-76

[17]. Joshi, R., and A. C. Sanderson, Multisensor Fusion: A

Minimal Representation Framework, World Scientific

Publishers, December, 1999.

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theory and probability theory," IEEE Trans. Inform.

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Likelihood Configuration of Tolerances", IEEE

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[22]. Sanderson, A. C, and N. J. Foster, "Attributed Image

Matching using a Minimal Representation Criterion",

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Palo Alto, California, March 27-29, 1990.

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RA3, No. 5, pp. 404-417, October 1987.

134
Metrics for System Autonomy

Part I : Metrics Definition

Arie ("Arik") Yavnai

Head, Center for Autonomous Systems

RAFAEL, P.O.Box 2250 (39)

Haifa 31021, Israel

Tel: + 972-4-990-8801; Fax: + 972-4-990-8055

e-mail: a r i k va

(wra fae I . c o, i

ABSTRACT

In many real world applications, system Autonomy is the

most single significant and meaningful attribute of

Intelligent Autonomous Systems - IAS. This paper presents

performance metrics for !AS, which are related to

Autonomy. Metrics are presented and defined. These metrics

are currently being used in on-going research, development

and engineering work.

1. INTRODUCTION

From an engineering point of view, performance metrics for

IAS are needed for establishing and developing the

following system level processes: a) a sub-process within

the multi-phase system engineering process, e.g., system

requirements analysis; b) preliminary and detailed design

process; c) Concept-of- Operation development process; d)

comparative evaluation of alternative designs.

A fundamental question which is related to IAS performance

metrics is: Which entity is more meaningful and practical

to define and to measure with respect to IAS performance -

Autonomy or Intelligence! Our position is that from the user

point of view, as well as fi^om the system architect and

designer point of view. Autonomy is the premier

characteristic attribute of an IAS. Although Intelligence

enables Autonomy, it is not considered by us as either an

appropriate or a practical system design objective or a

system performance requirement per se.

The concept of Autonomy is probably more meaningful,

more communicatable, and more precisely measureable, and

it is easier to come to a consensus about what Autonomy or

what an Autonomous System is all about, rather than what is

Intelligence or what is an Intelligent System.

2. AUTONOMY

Currently, two distinguished approaches to define system

autonomy are used by researchers and groups within the

intelligent autonomous systems (including autonomous

agents) community. The first approach defines autonomy as

an entity which is assigned to the subject system or to the

subject agent by a higher level authority, e.g., a supervisor

agent. Within the context of this approach, autonomy is

defined with respect to the assigned responsibility of a

system or an agent. Within this context, autonomy reflects

the agent's decision-making capability and authority, and

the degree of self control the agent has over its own

decisions, see [1]. This approach is more commonly used

within the autonomous agents community. The other

approach defines system or agent autonomy with respect to

its self capability to accomplish its assigned mission goals

while operating under uncertain dynamic environment,

uncertain dynamic scenario and self faulty situations, and

without or with very little human or external agent

intervention, [2], [3]. We are using the later approach.

Definition : Autonomy is an attribute of a system which

characterized its ability to accomplish the system's assigned

mission goals without any or with only minimal external

intervention, while operating under constraints and under

uncertain dynamic environment and scenario conditions.

135
3. CRITERIA FOR METRICS 3.3 Generality

In the sequel, some guidelines for metrics selection are

proposed.

3.1 Scope

The proposed metrics should reflect system autonomy as

perceived by an external observer. Therefore, the autonomy

should be measured outside the system boundary, i.e., in the

interface of the system with external entities. Figure I, in

the sequel, illustrates the context of Autonomy Evaluation,

as perceived by an external observer. Four entities are

identified within the relevant context, namely: a) a Remote

user or supervisor; b) an External Agent; c) Environment &

Scenario; d) System Under Evaluation (SUE), which is the

Autonomous Intelligent System to be evaluated.

3.2 Autonomy Relevance

Meaningful, effective, and measurable metrics for system

autonomy should reflect the influence of the following

factors as related to system autonomy:

• Level of Abstraction of the commands and the data

provided to the autonomous system by the remote

user/ supervisor or by an external agent.

• Information bandwidth between a remote user/

supervisor or an external agent, and the system under

evaluation.

• The levels of complexity, dynamics and uncertainty

which are attributes to the environment under which

the system is operating and executing its mission.

• The levels of complexity, dynamics and uncertainty

which are attributes to the system operating scenario

while executing its mission.

Although the meaning of performance metrics is usually

domain and application specific, more general entities, such

as the principle of entropy can be used within the

framework of IAS performance evaluation. In our work,

entropy is used as a general measure of entity uncertainty,

and is applied to measure various parameters. Using entropy

as a general tool for representing uncertainty in the domain

of control and system engineering was proposed by Saridis

[4].

3.4 Structure Independence

The metrics for Autonomy should be independent of the

internal structure, e.g. : a) number of levels of the hierarchy;

b) the decomposition of IAS internal processes to resolution

scales; c) the computational paradigms, e.g. fuzzy vs. neural

networks, and d) other internal specific features. The

attempt to establish metrics which takes into account

internal specifics of the system will lead to an endless

confusing and unpractical effort, and to unstable

solution-depended metrics. System Autonomy is a system

attribute as perceived by an external observer. In analogy,

consider a consumer which want to buy a new car. His

decision will not depend on whether the fuel injection

control system uses a fuzzy logic based controller or a

differential geometry based non-linear controller. However,

his decision will probably be based on user-centered

parameters such as: fuel consumption (kilometers per liter),

number of passengers, safety measures, to name but a few.

In such evaluation, the internal specifics are irrelevant. So

are the internal specifics when one has to evaluate the

performance of an Autonomous Intelligent System.

136
4. METRICS 4.2 Entropy

In the following section, the metrics used for IAS

performance evaluation are defined. The nomenclature used

is described as follows:

4. 1 Nomenclature

(1)

We are using entropy as a measure of uncertainty of system

state, environment state, or scenario state. The uncertainty

associated with predicting the next entity state, given the

current entity state, is a measure of the entity irregularity or

'disorder'. The less is the entity regularity, the greater is the

next state prediction uncertainty and the greater is the

associated entropy. Thus, entropy can be used as a measure

of environment uncertainty as well as a measure of scenario

uncertainty. Entropy can also be used as a measure of

system uncertainty, which is directly related to system

performance. It can represents the uncertainty in selecting

the appropriate control from the set of all admissible

controls [4]. Entropy can also be used for representing

performance, e.g., system tracking error along a planned

trajectory in the system state space.

We define entropy as follows:

Nomenclature :

ChS - Channel Sensitivity

EnS - Environment Sensitivity

InS - Information Sensitivity

ScS - Scenario Sensitivity

H - Entropy

H{'^) - System. Entropy

H(V) - Environment Entropy

H(A)- Scenario Entropy

C Channel Capacity of Data Link

between Remote- User or External Agent

to System

^ - System Under Evaluation (SUE)

r - Environment

A - Scenario

I - Externally provided system Information

(global and mission related)

<^ - Remote User

Q. - Problem Context

n - Time step index

(2)

Entropy Definition

P{X,n,l) = Prob {X(n+l)=Xl \ X(n)} : x,e {X}

H (X,n) = -EP{X,nJ) • lnP{X,n,l)

I X - Entity State -

(e.g., best control action: Environment State;

Scenario State)

H - Entropy

//(vt*) - System Entropy

H(V) - Environment Entropy

H(A)

- Scenario Entropy

137
4.3 Channel Sensitivity

Channel Sensitivity- ChS, is defined as the differentia!

change of the system entropy which results after a

differential change in the channel capacity of the

information data link between a remote-user and the System

Under Evaluation - SUE, or between an external agent and

the SUE, has occurred.

(3)

Channel Sensitivity :

(ChS)

AC(n)/C(n)

II * = C=(-\,; r = r,; A=A,;*=*,

i

" ChS = - Y. (ChS)„

1 1, i

e {-p}; x<pe(^x;,- e {r};

G {A}; $d G {$}; Q= (T, A,

DtfniUiuiis:

If ChS -< 0 =^ SUE is Non-AulonmnoiLs w.T.l. C,

nndfv cuiilcxt Q

// ChS = 0 => SUE is Autonomous w.r.t. C,

uiidtr coTiitxl Q

// CiiS y 0 ^ SUE IS Non-Suptri'isuble w.r.t. C,

under context Q

(4)

Environment Sensitimty :

_

AH(^,n)/H('i>,n) - ^H{r,n)/H(r,n)

II *=1',„; C=C/;r=r„; A=Arf; $=#t

EnS = - E {EnS)„

nk=\

*„. Cje(cj;ra e {r};

Arf G {A}; *fe G 0= fA, $,C',/j

7/ ii'n^ )^ i SUE is Non-Autonomous w.r.t. V,

under context f)

If 0 -< EnS < 1 => SUE is Partly Autonomous w.r.t. i',

under context Q

If EnS = 0 SUE is Completely Autonomous w.r.t. V,

under context Q

4.5 Scenario Sensitivity

Scenario Sensitivity- ScS, is defined as the differential

change of the system entropy which results after a

differential change in the scenario entropy, or uncertainty,

has occurred.

4.4 Environment Sensitivity

Environment Sensitivity- EnS, is defined as the differential

change of the system entropy which results after a

differential change in the environment entropy, or

uncertainty, has occurred.

138
(5) (6)

Scenario Sensitivity :

_ AH('i,n)/H(<i,n)

' ^" AH{A,n)/H(A,n)

II C=C/; r=ra; A=Arf;*=*(,

ScS = - E (ScS)n n (=1

Arf 6 {A}; *^ € {*}; Q= (T. ^,C,I)

Definitions:

If ScS > 1 ^ SUE is Non-Autonomous w.r.t. A,

under context Q

If 0 ^ ScS < 1 ^ SUE is Partly Autonomous w.r.t. A,

under context Q.

If ScS = 0 => SUE is Completely Autonomous w.r.t. A,

under context Q

4.6 Information Sensitivity

Information Sensitivity- InS, is defined as the differentia!

change of the system entropy which results after a

differential change in the system global and mission related

externally provided information, has occurred. The

information includes the Mission Plan and the related Data

Bases which provided to the autonomous system by the

remote user/ supervisor or by an external agent, prior to

mission execution, or while the mission is executed.

Information Sensitivity :

AH('b,n))/H('S!,n)

(InSfn =

A///

II * = C=C,; r=r„; A=Aj,-$=l',

InS = - E (/n5)„ n 1 = 1

vi/,, e {vi/}; x^e{x}; G {r};

A, G {A}; 4>,, e {$}; n= (V, A, ^,C)

Definitions:

If hiS ^ J ^ SUE is Non-Autonomous

w.r.t. I, under c out trt

// 0 -< InS < J SUE is Partly Autonomous

w.r.t. I , under context {}

If InS = 0 => SUE is Completely A utonomous

w.r.t. I . under context Q

4. 7 Adaptation Rate Sensitivity

Adaptation Rate Sensitivity - ARS, is defined as the

differential change of the system entropy rate which results

after a differential change in the entropy of the subject

entity, e.g., environment or scenario, or uncertainty, has

occurred. Similarly, Adaptation Rate Sensitivity can be

defined in relation with differential changes of channel

capacity or information.

139
(7)

[3] Yavnai, A., (1991), "Entropy-based Criteria for

lintelligent Autonomous Systems", Proc. IEEE Intl'

Symp. Intelligent Control 1991, Arlington VA.,

August 1991, pp. 55-60

[4] Saridis G. N. (1995), "Stochastic Processes,

Estimation and Control - The Entropy Approach",

Wiley-Interscience, New-York, NY, 1995.

ARS = -Y: {AdR\

Arf e {A}; € {*}; n= ^r, *,c,/;

Definitions:

If ARS y 1 SUE is Non- Autonomous w.r.t. X,

under context Q.

If 0 < ARS < 1 SUE is Partly Autonomous w.r.t. X,

under context Q

If .ARS = 0 => SUE IS Completely Autonomous w.r.t. X.

under context Q

5. SUMMARY

Metrics for system autonomy has been defined and

presented. Following the metrics, a specific measure for a

certain application can be derived directly. Associated with

each definition, the broad classification of the SUE was

defined.

REFERENCES

[I] Barber, K.S., (1999), "Agent Autonomy:

Specification, Measurement, and Dynamic

Adjustment", 1999, pp. 1-8.

[2] Yavnai, A., (1989), "Criteria of System

Autonomability", Proc. Intl' 2nd Conference on

Intelligence Autonomous Systems, Amsterdam,

Dec. 1989, pp. 448-458.

140
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141
In Defense of the Additive Form for Evaluating the

Multidimensional Vector

Dr. Leonid M. Polyakov

Globe Institute of Technology

Leonid@globeinstitute.org

ABSTRACT

The topic of this discussion is an artificial (not a natural human) intelligence measurement. It would be better to call it an evaluation rather

than a measurement. The Additive Evaluation

Method is the only real method to make a evaluation of the vector value. KEYWORD: intelligence,

measurement, expert, additive.

ADDITIVE FORM.

Artificial Intelligence, like a human one, is

a composition of the different additive

abilities such as reasoning, learning,

decision-making, object recognition, and

so on. The multifunctional nature of

intelligence can be represented as a vector.

The intelligence measurement is not the same as a multiobjective optimization of the

intelligence systems. There are many

different methods of optimization

(Preference Structures, Compromise

Approach, Lexicographic Ordering

Approach, Genetic Approach, Pareto approach, etc.) [4,5, and other]. All of these

methods work with each function of the

intelligence separately and determine

preferences and a system's rank, but not an

intelligence value. The additive function is presented in the most of the research works

[2,3,6,7,9-14, and other].

The measurement is a process of assigning

numbers to the objects or events in accordance with certain rules of the system.

The number assignment is possible just on

the scalar scale. There are three types of

axioms related to a measurement process:

identity axioms, rank axioms, additivity

axioms. These axioms determine four scale

levels: scale of names, range scale, interval

scale and ratio scale. The analyses of these

scales are done in [2]. Only additivity

axioms can be applied to the real

measurement. These axioms can be applied

just to the scalar scale, as it was mentioned

above. A vector doesn't meet these

conditions. Just, the weighted-sum approach

and utility functions can be used in this case

[3,7] as the method of multivariable scales

aggregation and converts vector into a

sufficient scalar.

The last question is how to determine the

value of weight. The most known and

usable method is an expert method, but

there are several analytical methods to find out the value of this function [2,6].

Opponents of these methods of the

aggregation function complain against the

application of a human expertise as a source of information. They dispute an expert

ability to produce objective information.

Yes, a collective expertise has an element of

subjectivism but today we don't have a better way to measure a vector's values to make a comparison of two or more vectors'

values. Is this, a wonderful fact in that we

use an expert's intellectual ability in the

intelligence measurement? Certainly not, because the intelligence can be measured by

the scale of the intelligence. Only a human

being has the best sense of the value of the

intelligence functions. Each separate

intelligence function can be measured by

appropriate methods but, as an integrated

value, intelligence has to be presented as a

scalar.

There are many different methods to measure each separate intellectual ability.

For example, the value of the ability to learn can be presented as a ratio of an

142
increment of intelligence to an increment of

information. The number of iterations, or

the number of rules and trials (trial and

error method), or the entropy method, etc can determine the value of information. So,

the learning ability is:

L = d(I)/d(If). (1)

The amount of new information available to

the different systems can change the

intelligence value of these systems.

A values of a separate intellectual abilities (variables) don't give any ideas about

artificial intelligence integrated value.

Aggregation of the separate variables can be

done on the base of the utility theory. The

utility of intelligent alternative can be

presented as [2]:

Ua=I Uj (2)

i =1

where Ui is an utility of i- th basic

variable, « is a number of variables.

From (2) [2], we can get the quality index

of j-th alternative (domain specific by design)

in nondimensional units

n

Qj = Z Wi (F,)*( Fi/Fi max ) (3)

i= 1

Where Wi (Fi) is a weight function of

i-th variable (Fi).

A set of variables has to be named for

each problem separately.

Usually one of the variables is an

investment value of the j-th alternative

(Cj). In this case, equation (4) can be

rewritten as:

n - 1 Qj.(C max/ Wc) = I [ Wi (Fi)/Wc] * i= 1

Cn,ax*(Fi/Fimax )- Cj. (4)

This equation presents the evaluation ofjth alternative measured in cost units

(dollars). Now we can use money as a real universal scale of the measurement. Some

opponents can say, "it is immoral". A

measurement is not a moral category! can be added to the left and the right parts of

the equation (4). In this case we can get the

value of Qj*(C max/ Wc) presented in dollar

units. This value includes only intelligence

variables and can be called the intelligence

value of the j-th

alternative

n - 1

Ij= I [ Wi (Fi)/Wc] * i= 1

Qr,ax*( Fi/Fimax ). (5)

Where Wc is a weight function of

variable Cmax.

This is the direct way to calculate profit

(political factors are included). It is one more reason to use the Utility Method and

scalar scale. No other method permits us to get an intelligence evaluation in dollar

units. Each time in the shopping center,

when we are buying something we use ours preferences and convert a vector value into a scalar value presented in the dollar units.

The intelligence measurement is not a new

problem. The famous IQ and WAiS-3 [8] tests are the possible ways to make an

evaluation of the human intelligence. These

tests present an aggregated value of the

multifunctional intelligence and convert a vector value into a scalar value.

The opponents to these testes pointed out to the possible social problems bounded to these methodic, in case of artificial intelligence measurment this problem does

not make sense.

Conclusion.

The Additive Evaluation Method is the only

real method to make a evaluation of the vector value. It can't be write off from the

tools of intelligence value evaluation.

Artificial intelligence of the system should

be measured and presented as scalar.

143
This method is the only one, which can gives

financial evaluation of artificial intelligence

application.

Contemporary artificial intelligence systems

are design as a domain-oriented systems.

Only the expert can determine the

importance of each intellectual function with

regard to the certain domains.

References:

1. Pareto M. D'economic Politque.

Paris 1927, 695p.

2. Polyakov L.M., Kheruntsev P., Shkiovsky B., Elements of the

Automated design of the electrical

automated equipments of

Machine tools. Publ.

"Machinostrojenie", Moscow,

1974, 157p.

3. John Von Newmann. O.

Morgenstern, Economic Behavior

and Theory of Games, 1944,

650p.

9. Marino Y. P. Technological forecasting

for decision making, American Elsevior

Company inc. N.Y., 1972.

10. Fishbum P. C. A Study of Independence

in Multivariate Utility Theory.

Econometric, 37, No.l, 1969, p. 7-121

11. Fishbum P.C. independence in Utility

Theory with Whole Product Sets. Operations Research, V.13, 1965, p.28-

45.

12. Schziver A. Forecast Air Review, V. 16,

No.3, 1965,p.l2-23.

13. Larichev O. The Art and Science of

Decision Making, "Nauka", Moscow,

1979, 196p.

14. Hall A. Experience of Methodology for

Large Engineering Systems, "Soviet

Radio," M, 1975, 120p.

4. Dhar, V. Stein, R. Intelligent

Decision Support Methods

Prentice Hall, 1997, 244p.

4. Mitsuo Gen, Runwei Cheng.

Genetic Algorithms Engineering

& Optimization. A WileyInterscience Publication, 2000,

490p.

5. Sigford Y., Pazvin R., Project

PATTERN: a methodology for

determining relevance in complex

decision making. IEEE

Transaction. Eng. Manag. V.EM12, No.l, 1965, 210p.

6. Fishburn P. C, Additive Utilities with incomplete Product Set: Applications to priorities and

Assignments- Operations Research,

V. 15, 1967, No.3. p.537-542.

8. Charles G. Morris, Albert A. Maisto,

Psychology. Prentice Hall, 1999, 682p.

144
A Method For Evaluating the "IQ" Of Intelligent Systems

Robert Finkelstein

University OfManagement & Technology

1925 N. Lynn Street, 3"* Floor

Arlington, Virginia 22209

(703>5 16-0035 Ext. 24

And

Robotic Technology Incorporated

1 1424 Palatine Drive

Potomac, Maryland 20854-1451

(301)-983-4194

RobertFinkelstein@compiiserve.com

ABSTRACT

This paper describes a pragmatic process measuring the "IQ" of

individual intelligent robots and groups of intelligent robots. We offer

definitions for the characterizations "intelligence" and "IQ." We

define metrics and submetrics for individual robots and the collective, using the Analytic Hierarchy Process (AHP) to calculate weights for

the metrics and submetrics. They can then be used to evaluate

alternative technologies and systems for achieving individual and

collective inteUigent behavior in robots.

The defmed metrics and submetrics for individ ual robots include:

Intelligence (decomposed into the ability to make Correct Decisions and Right Decisions, and to Learn)

Effectiveness (decomposed into the ability to achieve Objectives,

Goals, and Priorities) Efficiency (decomposed into Accuracy, Precision, Time

efficiency. Energy Efficiency, and Side Effects)

The defined metrics and submetrics for groups of robots include: Q Command And Control (decomposed into Leadership,

Followership, and Efficiency)

Communications (decomposed into Message Initiation, Transmission, Understanding, and Efficiency) Effectiveness (decomposed into the ability to achieve Objectives,

Goals, and Priorities)

The values of the metrics are determined by experimenting with designed robotic systems, in the context of a scenario, in a simulation

or field experiment. The weighted metrics can be combined to obtained an "IQ" score for individuals or the collective.

Keywords: intelligent Robots, Metrics, IQ, Intelligent Systems.

AHP, Robot Groups

1. Introduction

A little learning is a dangerous thing;

Drink deep, or taste not the Pierian

spring;

There shallow draughts intoxicate the

brain

And drinking largely sobers us again. — Alexander Pope

The brain is the most overrated organ. —Woody Allen

There are no satisfactory definitions ofhuman intelligence,

so it is not surprising that are no generally accepted defmitions

of machine intelligence either. One implicit definition, "the

ability to cope with the unexpected, and the ability to bring

about the imexpected," is from a comment in the Economist

about the major attribute for a good U.S. president. Another

suitable defmition of intelligence, is "the ability to make an

appropriate choice or decision." The intelligence need not be

at the human level. The ability to make an appropriate choice

is common to all long-time survivors, including roaches and rats. Appropriate, for organisms, usually means enhancing the ability

to reproduce, die primal goal in life. A chicken is an egg's way

of making another egg. Appropriate intelligence for a robot

might mean the ability to accomplish its mission under a variety

of conditions.

It has been difficult to measure human intelligence in a

satisfactory way since the first "IQ" (Intelligence Quotient) tests were developed at the start of the 20* century. The tests, and

their interpretations, remain controversial. The measurement of

machine intelligence, however, is a somewhat easier task

145
primarily because ( 1 ) the functional domains ofinterest are more

narrowly defined than for people and (2) the underlying

mechanisms of machine intelligence are more accessible to experimentation than the means of human intelligence.

When intelligent machines are designed for a limited set of

fimctions - such as performing search and rescue of people

trapped in collapsed buildings - they can be tested within that small sphere ofendeavor regardless ofthe intended level oftheir

intelligence (e.g., whether they are as intelligent as insects or humans). In this sense, the robot's IQ test is analogous to a person's aptitude test for ajob or profession. More importantly:

for measuring the "IQ" of an intelligent machine, the tester has

access to the underlying intelligent control system. This allows

the intelligent control system to be connected to an avatar ofthe machine in a simulated environment. The tester can know the ground truth - have a "god's-eye view" - of everything in the environment, including every external manifestation ofbehavior

by the robot avatar as well as the control system's every internal

state (including learned and adaptive behavior). The intelligent

control system need not know - or care - that it is controlling an

avatar in a simulation and not a physical system in the real world. Of course, the validity of the simulation is only as good

as the ability of the underlying model to replicate the real-world

environment of interest.

Intelligence can be decomposed into the ability to make a

correct decision (the optimum decision given complete

knowledge), a right decision (the optimum decision given

limited knowledge), and learning (the ^ility to adapt to the environment, without necessarily making a decision which leads immediately to altered, observable external behavior). While

intelligence is an important metric (measure of merit) for an autonomous intelligent robot, there are two other key measures

(as per Peter Drucker): efficiency (a measure of how well the autonomous robot does things right) and effectiveness (a measure of how well the robot does the right thing). These

metrics take into account other system variables and

characteristics, including: energy expenditure, mobility,

reliability, stealthiness, etc. An intelligent robot with a failed engine or damaged servo motors cannot move to accomplish its mission no matter how well it has planned its path. Some

researchers are redefming human IQ to include a variety of

human talents, including physical skills. Indeed, some

anthropologists believe that human intelligence was quite

fragmented and narrowly focused task by task (as in Homo

neanderthalensis) until recently, when intelligence became

synthesized in Homo sapiens sapiens. Likewise, die "IQ" of an

intelligent robot might include the combined metrics of

cognitive and physical abilities: Intelligence, Effectiveness,

and Efficiency. Howeverthey are labeled or amalgamated, these metrics can be quantified and used to test die performance - the "IQ" - of any autonomous intelligent system.

1.1 Sundry Definitions Of Intelligence

"Civilization advances by extending the number of

important operations which we can perform without thinking.

— Alfred North Whitehead

For organisms, intelligence is a pragmatic mechanism of

survival; and all measures ofintelligence (whether for organism, man or machine; whether genetically encoded, pre-programmed,

or learned) involve an ability to make appropriate selections

[1,2], choices, or decisions. Human intelligence involves "the degree to which an individual can successfully respond to new

situations or problems. It is based on the individual's knowledge

level and the ability to appropriately manipulate and reformulate

that knowledge (and incoming data) as required by the situation or problem," [3]. Intelligence can be identified by an ability to cope with the unexpected and an ability to bring about die

unexpected, abilities against which to judge presidents, among

other notables, over history [4]. The subjective word

"appropriate," in relating intelligence to "appropriate" choice,

implies that a system can be intelligent only in relation to a

defined goal or environment.

Intelligence requires an ability to use information (where

information, according to Claude Shannon, is that which reduces

uncertainty) [2], and using information includes an ability to

detect new, non-chance associations [5]. Chen defines

intelligence (individual or organizational) "as the attainment of

relevant goals in specified contexts using appropriate means and

resulting in positive outcomes," [6], which is the same as saying,

as above, "intelligence is the ability to make an appropriate

choice."

A behaviorist would say, b the spirit of the Turing Test,

that if humans, machines, or organizations (collectives) behave

intelligently, then they are; if they manifest consciousness, then

they are conscious. Two parts of intelligence are: (1)

epistemological, in which the world is so represented that

solutions to problems follow from the facts expressed in the

representation; and (2) heuristic, in which there is the mechanisms that solves the problem and selects actions on the

basis of information (most work in artificial intelligence is devoted to the heuristic part) [7]. Entities can place different emphasis on these two kinds of mechanisms of intelligence,

depending on the context.

In one view [8], attributes of systems with higher

intelligence include:

mental attitude (beliefs, intentions, desires);

learning (ability to acquire new knowledge);

problem solving;

understanding (implications of knowledge);

planning and predicting consequences ofactions, comparing

146
alternative actions;

knowing limits of knowledge and abilities; drawing distinctions between similar situations; synthesizing new concepts and ideas, acquiring and

employing analogies, originality;

generalizing;

perceiving and modeling the external world;

understanding and using language and symbolic tools.

Some hold that the last attribute - language - is the prime

determinant of higher intelligence; that every representation of

knowledge is an interpretation, not decision-making or expertise

[9]. Symbolic manipulation [10] - communication - creates second order reality; an advanced, intelligent system must be

able to perceive second order reality (the meaning and values

attributed to first order reality) as well as first order reality (the

reality accessible to perceptual consensus, physical reality) [11].

A machine with higher intelligence should be able to: adapt

(changing itself or the environment) for survival; reason about

its own organization, reasoning ability, and external domains;

plan internal activities (database searches, decision-making) and

external activities (sending messages, physical actions); select among decision-making processes; make decisions using values

associated with possible actions; reason about its reasons for taking actions; value itself to avoid changing itself in a harmful

way. Ideally, the system should be self-conscious as well as self-adaptive [12].

The higher intelligent system should possess meta- knowledge, i.e., it should have knowledge about what it knows

without having to search exhaustively. For example, the system

should know whether it has knowledge about grapefruit if asked

the size of grapefruit [13]. Knowledge includes representations

of facts, generalizations, and concepts, organized for future use

[5]. Knowledge of general truths does not require a special

metaphysically distinct ingredient in humans [14]- machines can be designed to know such truths. "Knowledge is more than a

static encoding of facts; it also includes the ability to use those

facts in interacting with the world ... knowledge of something is the ability to form a mental model that accurately represents the thing as well as the actions that can be performed by it and on it. Then by testing actions on the model, a person (or robot) can

predict what is likely to happen in the real world," [15].

"The use or handling of knowledge" is cognition [16], "an

intellectual process by which knowledge is gained about

perceptions or ideas," [17]. An intelligent system can be

designed to learn ("any deliberate or directed change in the knowledge structure of a system that allows it to perform better on later repetitions" of a task [18]). But it would be difficult to give it common sense, which involves a larger variety of

different types of knowledge than expertise (a large amount of

knowledge of relatively few varieties) [19]. A robot is behaving consciously if it [20]:

receives information about its environment;

recalls and compares past experiences;

evaluates the quality of its experiences;

makes conceptual models of its environment;

projects consequences of alternative future actions;

chooses and implements actions which fiirther its goals.

By exhibiting purpose and intention, a machine would

behave as if it had free will and the ability to choose [21].

1.2 Group Intelligence

Organizations or collectives can become intelligent through

the emergent behavior of its organisms and machines.

"Emergent behavior involves the repetitive application of

seemingly simple rules that lead to complex overall behavior,"

[22]. The emergent behavior can be that of an ant and ant colony, a person and an organization, or a robotic vehicle and a combat platoon. Collective intelligence in insect societies,

especially for certain of the ants, bees, and termites, is reasonably understood. "Higher forms ofintelligence arise from

the synchronized interaction of simpler units of intelligence,"

[23]. This is true as well of "higher" forms of life, such as dolphins, wolves, apes, and humans. Social intelligence allows an individual organism to "analyze and respond correctly

(intelligently) to possible behavioral responses ofother members

of the group," [24]. Collective intelligence, an advanced form

ofintelligence, "involves group intelligence in which individuals

submerge their individual identity" [24] to the group's responses

to the threats and opportunities in the environment.

Communication among individuals is essential for collective

intelligence, whether by pheromone, vision, sound, touch, or

email. Information technology is now affecting the collective mtelligence and evolution ofthe human species, possible leading

to the emergence of a global intelligence, a system ofindividual

and collective humans and machines [25]. But, as always, the essence of intelligent behavior is control - at least self-control.

There have been a number of programs attempting to develop cooperative mobile robots, and over 200 papers have

been published concerning mobile cooperative robots [26]. Cao,

Fukunaga, and Kahng [27], on which much of the following

discussion of cooperative behavior is based, defme collective

behavior generically as "any behavior of agents in a system

having more than one agent," while cooperative behavior is defmed as "a subclass ofcollective behavior that is characterized

by cooperation." Cooperation should lead to the enhanced

performance ofthe collective over that ofthe simple aggregation

of individuals (i.e., the whole should be greater than the sum of

its parts). Cao et al. cite the following definitions ofcooperative

behavior (from various sources):

To associate with another or others for mutual, often

economic, benefit.

147
Joint collaborative behavior that is directed toward some

goal in which there is a common interest or reward.

A form of interaction, usually based on communication.

Joining together for doing something that creates a

progressive result, such as increased performance or saving

time.

Given some task specified by a designer, a multiple robot

system displays cooperative behavior if, due to some

underlying mechanism (i.e., the mechanism ofcooperation)

there is an increase in the total utility of the system.

As posed by Cao et al., the fundamental issue is: given a group of robots, an environment, and a task, how should

cooperative behavior arise?

The architecture of a computing system is that part which

remains unchanged unless an external agent changes it. The

group architecture of a cooperative system is the infrastructure on which collective behaviors are implemented and determines

the abilities and constraints of the system. The group

architecture for cooperative robots includes such considerations

as: robot heterogeneity and homogeneity, the ability of each

robot to recognize and model other robots, and communications.

Also, the architecture must be able to avoid conflicts among

robots for resources, such as paths through the environment,

goal objects in the environment, and communications

bandwidth.

In creating a group architecture, there are a number of

alternative design decisions. The architecture may be

centralized or decentralized. Centralized architectures are characterized by a single control agent. Decentralized

architectures, which are prevalent, may be either distributed or

hierarchical. In the former, all agents are equal with respect to control, while the latter are locally centralized. Decentralized

architectures may lead to emergent properties of systems, such

as intelligence or self-organization. Their inherent advantages over centralized architectures include fault tolerance, natural exploitation of parallel processes, reliability, and scalability.

Most robot architectures hybrid, where, for example, a central planner exerts high-level control over mostly autonomous

agents. A group of robots is homogeneous if the capabilities of

the individual robots are identical; otherwise they are heterogeneous (forming a more complex system).

Cooperation among robots can arise from eusocial behavior

(as opposed to explicit cooperative behavior) which results from

the behavior of individuals and not necessarily an a priori effort

at cooperation (e.g., ants and bees are eusocial). There are many

sorts of self-organizing systems (in which there has been much

research), but especially with respect to biological systems,

whether individual organisms (in which the individuals parts are self-organizing) or social groups (human or otherwise). The

aggregation of relatively limited individuals leads to the

collective's more capable intelligence (this is true of human

society as well). Individual robots that are selfish and utilitydriven, but must cooperate in order to survive, will display

emergent cooperative behavior. ExpUcit cooperation, as among

humans, can be driven by a desire to maximize individual utility, so tiiere are economic and game-theoretic approaches to examining cooperation.

It is difficult for human designers to account for the

multiplicity of control variables and contingencies to achieve

cooperative behavior in robots. It is easier to design the robots so that they learn to cooperated and adapt to their environment.

A number oftechniques are being developed for this approach,

including the use of neural networks and genetic algorithms.

The robotic group may employ various types of

communications processes for inter-agent interaction, including,

in one taxonomy: interaction by means of the environment;

interaction by sensing; interaction by explicit communications.

The simplest, most limited type of interaction occurs when the

environment itselfis the communications medium, providing the

equivalent of a shared memory among a group of robots. There

is no explicit communication or interaction among the

individuals.

Another form of group communications occurs when

individuals sense and perceive one another without engaging in

explicit communications. Using a suitable sensor (e.g., vision,

acoustic, chemical, touch), the individuals must be able to distinguish members of the group from other entities in the

environment. Resulting collective behavior includes flocking

and pattern formation relative to neighboring individuals.

Higher-order tactical group behavior generally requires

explicit communication among individuals, which can be

directed (to known recipients) or broadcast (to unknown

recipients). Architectures that enable this type of

communication resemble communications networics, and

communications protocols are necessary for inter-robot

communications. The message carrier can consist of various

portions ofthe electromagnetic spectrum (e.g., radio frequency,

microwave, optical, infrared) or other transmission mechanisms

(e.g., acoustic, chemical).

In order to fimction relative to others in a group, or with

respect to predators (threats) and prey (targets), individual

organisms (or robots) must be able to model the intentions,

beliefs, actions, capabilities, and states of those others. The

ability ofindividuals to model others in a group reduces the need

for communications; it encompasses implicit communications

via the environment and perception and includes representations

ofother individuals which can be used to make inferences about

the actions of those individuals.

There are many prospective means ofachieving cooperative

behavior among robots. The most direct is to explicitly program

148
the desired behavior. This is difficult and tedious in that the

programmer must a priori account for all possible contingencies.

Other methods are more promising, including biological (e..g,

social insects) behavioral approaches, task decomposition and

allocation approaches, game-theoretic approaches, machine

learning approaches, and approaches based on cooperation as an emergent property of complex group dynamics. Geometric

approaches include multi-agent path planning, moving to formation, and pattern generation.

For most military ^plications, explicit leader-follower

relationships are important, especially where robotic forces will be integrated with conventional forces. These roles and abilities may exist in all of the robots, where leaders are anointed - or emerge - based on circumstance (as is often the case for people). Or leaders may be specially trained as such.

For example, group behavior to achieve coordinated

movement in the world, such as path planning, can be

centralized (with a leader or universal path planner making

decisions) or distributed (with individual agents planning and

adjusting their paths). They may be hybrids, combining on-line,

ofF-line, centralized, and decentralized elements. Planning

systems may take into account all robots, or plan the path of

each one independently. Factors include dynamically-varying

global and individual priorities, environmental constraints and

obstacles, and the allocation of spsice-time resources. Conflicts

may be resolved by a central manager or negotiated among

individuals.

2.0 Evaluation Process

bi order to evaluate the performance ofan intelligent robot

(or group of robots) we can employ a pragmatic, behaviorallybased, teleological, and functional approach to measuring its "IQ" as follows:

Define the purpose or mission or objectives of the robot (or

group of robots)

Derive the worth criteria by which the robot's performance

may be assessed

Organize and integrate the worth criteria into a consistent

assessment structure The assessment structure employs suitable variables

(endogenous, status, and exogenous), metrics and

submetrics, a means of weighting or ordering the metrics

and submetrics, and a means of evaluating performance

against the ordered metrics and submetrics.

Measure tiie performance ofthe robot or group of robots, in the context of the desired scenario and environment, in a simulation or field exercise, calculating "IQ" from the

evaluated, weighted metrics and submetrics.

The evaluation process is illustrated In Figure 1.

Figure 1. Evaluation Process

worTM

CRITERIA

MEASURE

PERFORMANCE

ASSESSMENT

PROCESS

u—

The worth criteria are specified as metrics (e.g., worth

criteria which are measurable either objectively or subjectively),

which are commonly labeled as measures of merit, measures of

effectiveness, measures ofefficiency, measures ofperformance,

and so forth. Measures Of Merit (MOM) are often the worth

criteria associated with the system as a whole, while Measures

OfPerformance (MOP) are worth criteria often associated with

the system's subsystems (which may descend to the n*

subsystem level ofthe system). Using these labels, the MOP are below the MOM in a hierarchy ofworth criteria, with the MOM

comprising the MOP and being a function of the MOP values. For example, a robot's MOP may be "Time Efficiency," and

this, along with other MOP, then compose the MOM "Vehicle

Efficiency."

The objectives, for example, may be to demonstrate the

intelligent and cooperative behavior on the part of multiple

autonomous robots or robotic vehicles in the context of

scenarios relevant to a class of military missions. The primary

objectives of achieving (I) intelligent behavior, and (2)

cooperative behavior, lead to the definition of worth criteria focused on two system levels: ( 1 ) the individual robot or robotic

vehicle as a system, and (2) the group of robots or robotic

vehicles as a system (i.e., a system ofsystems).

149
2.1 Procedural Difficulties

The evaluation procedure is a formal procedure, as opposed

to an observer's using purely subjective judgment and intuition

to pronounce the performance of the system to be a success or

failure. However, formal decision-making procedures do not preclude (and often require) the use of subjective judgment.

Subjective judgment must be used in developing worth criteria and assigning them to the various performance consequences, as well as in deriving relative weights for the worth criteria (i.e., trading off worth among the various criteria). But if subjective

judgment is made explicit and logically consistent, then it can be

examined and questioned by all interested parties. The result is more likely to be free of incorrect or poorly formulated

assumptions.

Difficulties with the procedure include mapping from one

to many from the set of behaviors to the set of metrics, i.e.,

relating a single performance consequence to several worth

criteria. For example, if a robotic vehicle correctly senses and

notes an enemy mine, this event could be relevant to the vehicle

metrics for Intelligence and Effectiveness. Conversely, there can

be a mapping from many to one, i.e., many performance criteria may be related to a single worth criteria. For example, behavior

such as finding mines, avoiding rocks, and finding survivors,

among others, contribute to the vehicle's Effectiveness.

Other difficulties include the existence ofcomplex patterns

of interaction among various aspects of performance and

complex patterns of interdependence among subjective notions

of worth (such as distinguishing among Intelligence,

Effectiveness, and Efficiency; or among Command & Control,

Conmiunications, and Effectiveness. It can be difficult to distinguish between interactions among performance

consequences (i.e., system behavior), which is a resuh of

physical phenomena, and interdependence among worth criteria imposed by the analyst, which is a result of psychological

phenomena. Nevertheless, an evaluation process that combines

explicit subjectivity with objectivity is usually better than an

evaluation process employing only implicit subjectivity. But

"any assessment procedure, to generate comprehensible results, must stipulate very clearly whose point of view is being taken

and whose values are to prevail," [28].

2.2 Worth

Underlying the evaluation procedure is the concept of

worth, which may be defmed as the "conscious perceptions held

by an individual relating to his underlying feelings ofpreference,

aversion, and indifference. This includes not only direct awareness ofthe feelings themselves, but also the entire range of

cognitive elements supporting such feelings. Conscious

rationalizations, justifications, and explanations would all be

included in the meaning of worth," [28]. Worth is a fimction of

an object, the situation in which the object is placed, and the person evaluating the object. Notions of worth are formulated

by people observing external objects and they may be projected onto those objects; but worth remains in the subjective minds of

the observers. Worth judgements are neither true nor false; they

exist in-the minds of human beings.

Ideally, the metrics for intelligent systems should have

certain properties. They should be complete and exhaustive in

that all important performance objectives should be represented

by the list of measures. They should be mutually exclusive in

that no listed measure should encompass any other measure. The metrics should be restricted to performance objectives ofthe highest importance, derivable from lower criteria in a worth

hierarchy. They should be relatively independent in that decision-makers should be willing to obtain additional

satisfaction on one measure in exchange for reduced satisfaction on another measure at a rate relatively independent of the level of satisfaction aheady attained on each.

The example metrics selected herein for the intelligent

systems intersect somewhat and are therefore not completely

mutually exclusive, but their exclusivity is sufficient to provide

a reasonable evaluation of system performance.

The lowest level criteria in a worth hierarchy should be

represented by a simple performance measure. This connects the

criteria hierarchy, which emanates from the subjective minds of

the decision-makers, with the outer world of physical "reality." For example, the "Number of Targets Detected Per Unit Time"

would be a lowest level worth criterion for the higher level

criterion "Time Efficiency," which, in turn, would contribute to

the evaluation for the higher level worth criterion "Mission

Efficiency." The weighted worth scores may be aggregated to

calculate an overall index ofworth, i.e., an overall determination

of success or failure for the intelligent system.

2J Variables

The variables and their relationships symbolically represent

the operation of the intelligent system, in the context of the environment, in computer simulations or field exercises of

missions for the intelligent system. Figure 2 shows the

relationships among the variables and the metrics. Some

(although not all) of the system variables are relevant to the mission and group variables. Each of these sets of variables are

aggregated, through the application of various algorithms, into

metrics; these, in turn, are aggregated, through the application of more algorithms, into a scoring ofsuccess or failure. The values

of the metrics, i.e., their quantification as a result of simulation

or field exercises, determine the success or failure ofthe exercise

of the system (against a priori criteria). In each case, the expected values (e.g., the martini glass in the figure) are compared with the measured values (e.g., the coffee cup in the

150
figure) for the individual and group metrics and submetrics.

Success or failure (e.g., of the mission) can depend on the

individual, the group, or both.

Figure 2. Variables And Metrics

INDIVIDUAL- RELATED

VARIABLES

GROUPRELATED

VARIABLES

lb

1 EXPECTED

j

MEASURED

ffl

INDIVIDUAL METRICS

CROUP

METRICS

ALGORITHMS

4^

-4^

m 9

SUCCESS

OR FAILURE

A convenient taxonomy for the intelligent system variables

is illustrated in Figure 3. Exogenous variables are independent, or input, variables which are generally predetermined and

independent of the system. They act on the system but are not

acted on by the system. Exogenous variables may be either controllable or non-controllable. Controllable (or instrumental)

exogenous variables can be controlled or manipulated by the decision-makers of the system. Non-controllable exogenous

variables are generated by the environment in which the system

exists and behaves (and not by the system itself or its decision

makers). For example, the value of "Mission Timeliness" is a

controllable variable, while "Duration (Time) OfRain" is a noncontrollable variable. Non-controllable variables are associated

with the individual level; there are none at the mission/group

level.

The status variables describe the state ofthe system. They

interact with both exogenous and endogenous variables

according to the functional relationships of the system. The

value of the status variable may depend on an exogenous or endogenous variable in a preceding time period; when the input

Figure 3. Types Of Variables

EXOGENOUS VARIABLES

Example: Value Of

Missioa Timeliness

Example: Duratioo OfRain

CONTRC

(DECI

MAK

LLABLE

SIGN- ERS)

UNCONTROLLABLE

(ENVIRONMENT)

STATUS VARIABLES

(SYSTEM OPERATING

CHARACTERISTICS)

Example: RemaiDing

Mission Time

K7

ENDOGENOUS

VARIABLES

Example: Actual Time To Accomplish Mission

is from a portion of a variable's own output from a previous

period, a feedback loop exists. "Remaining Mission Time" and

"Number Of Objectives Achieved" are examples of status

variables.

Endogenous variables are dependent, or output, variables of

the system, generated from the interaction of the system's

exogenous and status variables according to the system's

operating characteristics. The "Actual Time To Accomplish A

Mission" is an example of an endogenous variable.

Whether a particular variable is an exogenous, status or endogenous variable depends on the purpose or nature of the

system's processes. For example, "Target Location" may be an exogenous variable if it is specified to the group a priori (as for a fixed target); it may be a status variable if, as a relative

location, it is periodically updated as the group moves; and it may be an endogenous variable if it is computed by the group on

the basis of sensor inputs.

An example of the use of the variables to derive metrics is given in Figure 4. Variables of different types are combined by

using an algorithm to obtain a measure of performance: the

exogenous variable "Interim Objective Type" (such as a rendezvous point); the status variable "Time Of Interim

Objective Accomplishment;" endogenous variable "New

Objective Selected" (by the leader vehicle); the status variable

"Number Of Objectives" (of this type accomplished); and the

151
status variable "Elapsed Mission Time." The MOPformed from

these variables is the "Number OfObjectives OfTypej (such as rendezvous points) Accomplished (by the group) Per Unit

Time." The algorithm in this example is simply the sum of the objectives accomplished divided by the mission time. This

MOP, along with others (such as "Energy Expended Per

Objective Accomplished"), might be combined into a top level metric called "Mission Efficiency."

Figure 4. Example: Variables Transfonned To Metrics

Figure 5. Metrics And Submetrics For

Individuals

iNT£LtlGEMCe EPFECTlVE«SS

INTERIM OBJECTIVE TYPE IT

Eiotnoiis VarUbIc: 0 ^, Where i = i* ObjectreeOfTypej

EFFICIENCY

TIME TO ACCOMPUSH

INTERIM OBJECTIVE I

Correct Objectives Decision Accomplished

Accuracy

r

Status Variible t Righl (kf ision Goals Accomplished

Precision

NEW

OBJECTIVE mfCTEO

Endogenous Variable O

.

Time

Efficiency

NUMBER OF

OBJECTIVES OF

TYPEj ACCOMPUSHED

Stalm

Variable

ELAPSED

MISSION TIME

<

Learning

Priorities Accomplished

Energy

Efficiency

Sutiu

variable

NUMBER OF

OBJECTIVES j ACCOMPLISHED

PER TIME

Algorithm For (MOP) , » /' "

Side Effects

MOE (Froa Several MOP)

2.4 Metrics For Individuals And The Collective

We define six metrics for intelligent systems for this example. The metrics are not completely mutually exclusive. But they do emphasize three behavioral aspects of an autonomous intelligent system and tiiree behavioral aspects ofa group ofsuch systems. Taken together, they provide a summary

quantification of how successfully the individual and tiie aggregate - the collective - perform in the context of their environment and mission.

2.4.1 Individual Metrics And MOP

The three selected top-level metrics are: Intelligence,

Efficiency, and Effectiveness, as shown in Figure 5.

Intelligence is defined here functionally as the ability ofthe system to make an appropriate choice - an appropriate decision. Because a value is the relative worA of a thing, the basis upon

one makes a choice, intelligence is related to values; what is

"appropriate" is situation-dependent. In the case of intelligent

systems formilitary-type missions, appropriate choices are those

that contribute to the success of the mission, or are perceived by

the system to contribute to die success of the mission in the

context of die information it possesses.

Information provided by sensors and processed by an

mtelligent control system can alter the mtelligent machine's

world model - and learning occurs. The ability to learn, based on experience, is one metric for intelligence. There are two

kinds of acceptable decisions diat the intelligent system can make: "correct" and "right." A correct decision is flie optimum

decision die system can make given a meta-view or complete

knowledge (ground truth or the "god's-eye" view). The right

decision is the optimum decision the vehicle can make given its

"real" and Umited knowledge. The intelligent machine (or a

person) may do well in making correct decisions despite limited

152
knowledge; this kind of decision-making is a metric that evaluates performance in an absolute frame of reference. It is

difficult or impossible for mortals to acquire the "god's-eye"

view in real life, but it is possible to have such a view in limited

scenarios and to evaluate the performance of men or machines

against such a standard.

In Figure 5 the metric "Intelligence" is decomposed into the submetrics (or MOP): "Correct Decision," "Right Decision,"

and "Learning." "Correct Decision" evaluates the machine's

intelligence against absolute performance standards. "Right

Decision" evaluates the machine's intelligence against a relative standard which discounts the limitations of the machine's

sensors and world model. The "Learning" MOP measures the

ability of the vehicle to adapt to its environment, without

necessarily making a decision that leads immediately to altered external behavior.

For example, an autonomous robotic vehicle might sense a

terrain feature it that doesn't appear in the terrain map stored in

its world model. Appropriate learning would occur if the vehicle were to alter its terrain map to include the feature; the

vehicle need not have altered its path or motion in order to

indicate learning - the change in the world model would be

sufficient to indicate learning. If the vehicle were to select a path to its destination that complied with all of its mission

criteria, but was then ambushed and destroyed by a hidden

enemy about which it could not have known, the vehicle would

have made a right decision in its path selection, but not a correct

decision.

The metric "Effectiveness" in Figure 5 is decomposed into the submetrics or MOP: "Objectives Accomplished," "Goals

Accomplished," and "Priorities Accomplished." "Effectiveness"

is the "bottom line" measure of merit, the measure of whether

the mission goal and its interim objectives were achieved by the

vehicle. Ordinarily, this might be the main metric, the one with

the greatest importance. However, developmental or prototype

systems may have, for example, various mechanical-type

subsystems that are not of operational quality. It is not

absolutely critical to the development of intelligent systems that prototype robotic vehicles accomplish its goals and objectives

with overwhehning panache. The display of mtelligence is more

important in a Phase I effort than the success or failure of the mission - which may depend on the success or failure of a

prosaic propulsion system. In the end, ofcourse, with a fielded

system, "Effectiveness" is a key metric. Ineffective intelligence

is barren, in machines or people.

The tactical "Objectives Accomplished" is an MOP based on the intermediate objectives the intelligent machine is assigned

to accomplish on its way to the ultimate mission goal, which

accomplishment is accounted for in the MOP "Goals

Accomplished." The final MOP for "Effectiveness" is the determination of the "Priorities Accomplished." The priorities

are those set in the value-driven logic of the robotic platform,

i.e., the relative importance of survival, energy conservation,

timeliness, etc. The robotic platform may be able to accompUsh

most of its intermediate objectives, yet fail at its ultimate goal

(just like people often do), or it may achieve its ultimate goal

while failing at its intermediate objectives (e.g., getting the lucky

break). Also, it may maintain or scramble its priorities while

succeeding or failing at accomplishing its objectives and goal.

The MOP for "Effectiveness" are thus sufficiently mutually

exclusive to highlight different aspects of the robotic vehicle's behavioral and mission performance.

The final metric, that of"Efficiency," is the least important

in a development program because a prototype platform's

mechanical performance is likely to be inferior to that required

for an operational platform. However, it is reasonable to account for this behavior in the testbed and include it in the final metric score. For an operational system, "Efficiency" becomes more important , but not usually as important as "Effectiveness."

"Efficiency" is a measure of how well the intelligent system

performs while attempting to accomplish its objectives and goal,

and how well it conserves resources. "Effectiveness" measures

the ability to accomplish the objectives and goal assigned by the

mission. The vehicle (like a person) may be extremely efficient and yet completely ineffective (such as working economically

toward the wrong goal); or it may be inefficient, yet able to accomplish its objectives and goal. "Effectiveness" and

"Efficiency" are not completely independent, but they are

sufficiently different to characterize different aspects of an

intelligent system.

There are four MOPs for the metric "Efficiency," as shown

in Figure 5: "Accuracy," "Precision," "Time Efficiency,"

"Energy Efficiency," and "Unexpected Adverse Side Effects."

"Accuracy" refers to the robot's ability to achieve its desired

states (position, speed, etc.) without significant systematic

errors. "Precision" refers to the vehicle's ability to achieve its desired states without significant random errors. "Time

Efficiency" measures the accomplishment ofobjectives and goal per unit time (such as the number oftargets detected per minute,

or the number of survivors retrieved per hour, the area searched

per hour, etc.). "Energy Efficiency" likewise measures the

accomplishment of objectives and goal per unit of energy

expended (such as the number ofmines detected perjoule, etc.). The "Unexpected Adverse Side Effects" refer to adverse

behavior displayed by the robot due to bugs, glitches, or errors

in the vehicle. Such behavior may not prevent the vehicle from

accomplishing its mission (or even detract much from its accuracy or precision), but it could reduce efficiency. For

example, every 100 meters the robot might inexplicably stop for

ten seconds; or it might mistake a wall for an entranceway and

try to enter.

153
Accuracy and Precision are basic to efficient performance

and should be weighted somewhat higher than Time and Energy

Efficiency. Side Effects, while disturbing and potentially

hannful to the success of the mission (or the continuation of a development program itself) is not ofhigh importance in a Phase

I development effort; the causes of eccentric behavior

presumably can be found and corrected. The existence of

peculiar vehicle bugs will become more worrisome as intelligent

machines become operational.

2.4.2 Group Metrics And MOP

The three metrics selected for the group or mission level

are: "Command and Control" (C^), "Communications," and

"Effectiveness," as shown in Figure 6.

Figure 6. Metrics And Submetrics For Groups

COMMAND

&

CONTROL COMMUNICATIONS

Leadenbip

iz

Message

Initiation

FoUowership

2E

Traasmission

Efficiency

IE

Understand 3E

Ellicleocy

Priorities

"Command and Control" (taken as a single measure) refer

to the ability ofthe robots to exhibit cooperative behavior widiin a leadership structure. One major system attribute - intelligence

- is measured from individual robot or platform behavior. The

other major system attribute - cooperation - requires more than one platform for measurement; it is measured from the

interactions of multiple intelligent systems. We assume an

explicit means of achieving robotic group behavior for the

applications ofinterest (e.g., leader-follower architecture), rather than implicit means (e.g., eusocial architecture).

The multiple systems can be designed to interact in many

different ways, just as people in various societies and institutions organize themselves in different ways. In particular, the organizational forms needed to achieve organizational goals can

range over a spectrum of types, from collegial to democratic to

autocratic to - and so on. The organizational form selected, for example, may be military-autocratic where some robots are

leaders and others subordinates, all in a hierarchy of authority

and power. (Authority is the right to act while power is the

ability to act).

In Figure 6 the metric "Command and Control" is decomposed into the MOP: "Leadership," "FoUowership," and

"C^ Efficiency." While cooperation may seem too weak a

characterization for the relationship between a military leader and his (its) subordinates, leadership always involves some form

of cooperation from followers - even from diose under duress.

The definition of"leadership," like that of"intelligence," is vague. Some of the definitions of leadership include [29]:

"Leadership is the exercise of authority and the making of

decisions," (Dubin, 1951);

"Leadership is the initiation ofacts that result in a consistent

pattern of group interaction directed toward the solution of

mutual problems," (Hemphill, 1954);

"The leader is one who succeeds in getting others to follow

him," (Crowly, 1928);

"Leadership is the process of influencing group activities toward goal setting and goal achievement," (Stogdill, 1 948);

Leadership is "the ability to handle men so as to achieve the most with the least friction and greatest cooperation,"

(Munson, 1921);

Leadership is "the process by which an agent induces a

subordinate to behave in a desired manner," (Bennis, 1 959);

Leadership is "the activity of persuading people to cooperate in the achievement of a common objective,"

(Koontz and O'Donnell, 1955).

An ideal form of leadership might be to motivate others such that they perceive themselves to be self-motivated, an

invisible, unobtrusive form of leadership. Then, there is the

eusocial leaderless leadership, a commonality ofpurpose arising

from the dynamics of group interactions, as exhibited by ants. Unlike human organizations, robotic systems might well be able

to accomplish invisible or leaderless leadership.

Effective leadership can be measured by how well the

leader's group performs its assigned fimctions in terms of group

productivity and group satisfaction, although in the case of the

robotic collective, group satisfaction is not a concern. In human

organizations, the effective leader possesses power which

originates from his position, from higher authority, and from his

traits, abilities and behaviors. The followers of the leader also have traits, abilities and behaviors which contribute to the

154
successful accomplishment of the mission. Between the leader and the followers are their relationships and the task structure. Technology impacts on the triad of leader, followers, and their

relationship in various ways; communications technology, for example, can alter the leader's power or facilitate orders to

subordinates.

For a robotic collective, there will also be leadership

potential (programmed algorithms), behavior (decisions based on value-driven logic), leader-follower relations (inter-vehicle

protocols), and task structure (the degree of control and the tradeoff of centralization versus decentralization). The leader robot will take the initiative in making decisions, select tactics and maneuvers, and issue appropriate commands to the follower

robot.

There can be no leader without a follower, and there can be no leadership without followership. So, for example, the followership of one robot vehicle will help define the leadership

ofanother robotic vehicle. The subordinate vehicle will respond

to commands appropriately, providing feedback to the command

vehicle and behaving with restraint (an aspect of control). The

subordinate vehicle will take command, transforming itself into the leader vehicle, when the latter cannot fimction properly

because it has been damaged or destroyed. (In the case of two

vehicles, the surviving follower becomes the "leader" in the nominal sense of performing the mission tasks of the leader vehicle without leading a subordinate).

The link between the leader and followers is achieved

through communication. Figure 7 illustrates the communication

process. One of at least two or more people or machines

perceive a need, problem, or situation that requires the transmission of information. The communication initiator - the sender - has an objective in sending die information. The sender

formulates a message that contains the information reflecting the

intentions of the sender. (Information, in a quantitative context,

is a measure of one's freedom of choice when one selects a message from an available set. In this entropic view, the message that water is wet, to one who knows this fact, would not

contain information. If information b related to choice, and

values are the bases of choice, and intelligence is the ability to make appropriate choices, then information, values, and

intelligence are related.) The sender selects a channel or medium over which to send the message, encodes the message

into the appropriate language and format, and transmits the message over the chatmel. The recipient ofthe message receives

and decodes it..

conforms to the objectives of the sender.

Figure 7. Communications

Two Or More M05t

People Or

Machines

'1 Tiir"^

Need

Problem

' Situation

Wf>i KOi!

Mssssage^

Sender's

Intentions ,

K0i5t

Transmission

* Signai

* Channel

HOiSt

Recipient

• Received

* Decoded

N0(5C

K0i5t

Feedt>ack:

' Confirm Receipt &

Understanding

Recipient Behavior Conforms To

Objectives Of

Sender

Understood •Same

Meaning As

Sender

Any step ofthe communication process can be disrupted by

noise. Noise may originate in the sender and disrupt the internal formulation or encoding of the message, or it may originate in the environment and disrupt the transmission of the message, or

it may arise in the recipient and discombobulate the decoding or

understanding of die message.

The commimication process, as outlined Figure 7, is true

for communication between people (using verbal, written, and

other means) or robotic vehicles (using radio frequency,

acoustic, optical, and other means).

The "Communication" metric is decomposed into four

MOP: "Message Initiation", "Message Transmission", "Message

Efficiency", and "Message Understanding". The MOP

correspond to the communications process as outlined.

For communication to have taken place, the recipient must

understand the message, i.e., must extract the information the sender intended. Ideally, the recipient provides feedback to the sender so that the latter knows that the information has been

received and understood. Sometimes the feedback consists ofthe

sender's observing subsequent behavior of the recipient that

The initiation and imderstanding of messages are more

important in a Phase I development effort than the performance

ofthe transmission mechanism (radio frequency or acoustic), or

the efficiency of the message protocol (lengdi and number of

messages needed to convey a quantity of information).

Acceptable performance might consist of appropriate messages

155
being initiated and a high percentage understood, given

reception (but many messages might not be received to due

noise or inadequacies in the transmission system).

The metric "Effectiveness" in Figure 6 is decomposed into the MOP: "Objectives Accomplished," "Goals Accomplished,"

and "Priorities Accomplished." Effectiveness, as noted

previously, is the bottom line metric, tiie measure ofwhether the mission goal and its interim objectives were achieved by the

robotic vehicles. Ordinarily, this might be the main metric, the one with the greatest importance. However, a developmental

robot may be a testbed with mechanical systems that are not of

operational quality. As we mentioned previously, it is not

absolutely critical to the success of a development program that the leader and follower vehicles accomplish their goals and

objectives with overwhehning panache. The display of

ability at the mission/group level (and intelligence at the

individual level) is a more important accomplishment during

development than the success or failure of the mission.

The tactical "Objectives Accomplished" is an MOP based on the intermediate objectives the robotic vehicles are assigned

to accomplish on their way to the ultimate mission goal, which

accomplishment is accounted for in tiie MOP "Goals

Accomplished." The fmal MOP for "Effectiveness" is the determination of the "Priorities Accomplished." The priorities

are those set in the value-driven logic ( e.g., the relative importance of survival, energy conservation, timeliness, etc.).

The robotic vehicles may be able to accomplish most of

their intermediate objectives, yet fail at their ultimate goal (just

like people), or they may achieve their ultimate goal while

failing at their intermediate objectives. Also, tfiey may maintain

or scramble their priorities while succeeding or failing at accomplishing their objectives and goal. The MOP for Effectiveness are thus sufficiently mutually exclusive to highlight different aspects of the leader-follower behavior and

mission performance.

2.5 Metrics And The Analytic Hierarchy Process

There are multi-criteria decision-making techniques which

can be used to define and weight metrics and evaluate akemative

systems and technology for prospective intelligent robots. One

such technique, the Analytic Hierarchy Process (AHP), is gaining popularity in the defense community (U.S. and Canada)

for aiding in the evaluation of weapons systems, and there are more than 600 papers and books describing the theory and

applications ofthe AHP. The mathematics underlying the AHP

is largely matrix algebra wherein one solves for certain eigenvalues [30, 31, 32].

Making decisions about complex problems involving

conflicting criteria and several alternatives is not a simple

process. Psychological research has demonstrated that the

human mind is limited in the number of items it can store in short-term memory. The AHP enables the decision-maker to transcend such limitations by visually structuring a complex

problem in the form of a hierarchy. Each factor and alternative can be identified and evaluated with respect to other related

factors. The AHP makes it possible to look at the elements of a problem in isolation: one element compared against anotherwidi

respect to a single criterion. The decision process reduced to its simplest terms - pairwise comparisons. This ability to structure a complex problem, and then focus attention on individual

components, improves decision-making. All judgements are synthesized into a unified whole in which the alternatives are

clearly prioritized from best to worst.

For example, one might look at two robots and note

(quantitatively) that the first weighs more than the second. In

addition to observing this, we have an ability (subjectively) to say that the first robot is much more flexible (i.e., has an ability

to perform more or varied functions) than the second, or just

moderately more flexible, or that the flexibility ofthe two robots

is the same. Or we might quantify the flexibility in terms of a measurable quantity (such as the number of defined fiinctions performed), for example. A multiplicity of such pairwise

comparisons of alternatives (or die use of objective data, where

available), against various criteria, build a metric that can be

used to makejudgments or decisions that are more objective and

rational than they would be otherwise.

We first performed this kind of analysis for determining

robotic "IQ" for autonomous underwater vehicles in 1985 [32].

This work was updated for robotic ground vehicles in 2000 [33].

The results of this analysis is summarized below.

2.6 Example Analysis

As an example from longer lists [33], exogenous variables

for individual robots include: coordinates (starting and final); maximum detection range (passive and active); terrain profile;

object (size, speed, acceleration, coordinates; rendezvous

coordinates; etc. Sample status variables include: vehicle speed

(linear and angular); vehicle position; vehicle bearing; sensor

status; power status; etc. Sample endogenous variables include:

probability of bring detected (actively and passively); risk of

known and unknown sensors along path; estimated path length;

computed position of object sensed actively; computed object

speed; etc.

Example exogenous variables for robot groups include: mission type; mission values; desired vehicle spacing;

designated group leader; primary mission objective types

(defenses, targets, vehicles, etc.); abort criteria; group clock

standard; etc. Sample group status variables include: groups

destroyed; vehicles per group destroyed; vehicles absent from

156
rendezvous; elapsed/remaining mission time; etc. Sample group

endogenous variables include: risk of active detection fo group;

risk of passive detection for group; number of objects of each

type sensed by group; best computed position of object sensed

actively by group; etc.

For the AHP Goal to "Evaluate Individual Robot IQ," the values of the weights for the metrics and submetrics, previously

described, were calculated with the following results:

* Intelligence = 0.54

Correct Decision = 0.10

Right Decision = 0.27

Learning = 0.17

* Effectiveness = 0.30

Objectives Accomplished = 0. 1

Goals Accomplished = 0.06

Priorities Accomplished = 0.09

* Efficiency = 0.16

Accuracy = 0.05

Precision = 0.05

Time Efficiency = 0.03

Energy Efficiency = 0.02

Side Effects = 0.01

For the AHP Goal to "Evaluate Group Robot IQ," the

values of the weights for the metrics and submetrics, previously

described, were calculated with the following results:

* Command & Control = 0.54

Leadership = 0.23

Followership = 0.23

Efficiency = 0.08

* Communications = 0.16

Message Initiation = 0.06

Message Transmission = 0.03

Message Understanding = 0.06

Efficiency = 0.01

* Effectiveness = 0.3 Goals Accomplished = 0.06

Objectives Accomplished = 0.15

Priorities Accomplished = 0.09

While there are many ways to evaluate the "IQ" of a robot

and groups of robots, a simple (vector) method is to add the products of the values obtained for the individual and group

metrics and their associated weights:

[1] Total Score = J WM

i Where W = i* Weight

M = i* Measure (Score)

The scores of each metric are obtained from measuring the

submetrics or MOPs in a series of experiments, in a simulation

or in the field. Each individual and group metric requires a

defined process for obtaining its score, which is then aggregated

into the Total Score (or "IQ"). There are many possible

approaches or algorithms, an examples are given in [33]. For

example, to evaluate the group Communications metric one

might define:

3

[2] SC = Y^WiRi + WiE

i=l

Where:

* SC = Score For Communications

* Ri = NMI/TMI = Message Initiation Ratio

* Rj = NESR/TMI = Transmission Ratio

* Rj = NMU/NESR = Understanding Ratio

* NMI = Number Of Right Messages Initiated

* TMI = Total Number Of Messages Initiated

* NESR = No. Messages Actually Encoded, Sent, And

Received

* NMU = No. Of Messages Rightly Understood By Recipient

* Wj = Weight of i* MOP (As Previously Calculated)

* E = Evaluation OfMessage Lengths And Quantity Compared

With What Would Be Right: (0 < £ < 1)

Example steps to measure the MOP associated with group

Communication include:

Step 1 : Store the time of initiation of messages (i.e., a new plan

of a robot to send a message to another robot), the contents of

die messages, the time oftransmission ofthe messages, the time

of reception of die messages, and the contents of the messages

as received by die receiving robot.

Step 2: The analyst, after the mission, calculates the Message

Initiation Ratio, Transmission Ratio, and Understanding Ratio.

The analystjudges the rightness ofthe message contents, as well

as the rightness ofthe understanding of the messages on the part

ofthe receiving robot, based on the robot's subsequent behavior.

The analyst also judges the righmess ofthe message lengths and

quantity (too much or too few) of messages and scores this as previously described.

Step 3: The analyst weights and combines the scores ofthe four

MOP associated with group Communications to calculate the

Communications Score, and weights and combines this score with the other weighted metric scores to obtain a final value for

157
the group "IQ."

Another example is a method for scoring the Effectiveness

metric for the individual robot. To score the accomplishment of

the tactical objectives and goal of a mission, the human

evaluator notes the number ofobjects (e.g., survivors in an urban

search and rescue operation) to be sensed or acted upon (e.g.,

located, given water or oxygen, carried to safety) by the robot

and divides by the total number of such objects in the scenario. A similar ratio is taken for the number of positions (rendezvous

locations, assigned reconnaissance positions, etc.) the vehicle

should have visited. The Priority MOP is evaluated by

determine whether the priorities in the value-driven logic were

followed as assigned, or modified according to the rules, through

the mission. For example, a score (e.g., 0 to 4) can be assigned

to each priority, then they are summed and averaged. For

Effectiveness we then have: m n

[3] /?(0,) = (ZZO,)/Or

J > m

[4] R{Pi) = (^Pi)IPr

i

[5] S(?r) = Y,VT./4

Where [0< Pr,< 4]

Where:

* R(Oij) = Object ratio (for goal or objectives)

* R(Pj) = Position Ratio (for goal or objectives)

* S(Pr) = Priority Sum Average

*

0|j = The i* Object of Type j (For example, j=l=survivor;

j=2=mine; j=3=areas to be avoided; etc.) * Pj = The i* position (goal or objective) Visited

* m = Total Types of Objects Sensed or Acted Upon (Or Total

Position Visited)

* n = Total Objects Of Each Type Sensed Or Acted Upon

* Ot = Total Number OfObjects Robot Should Interact With To

Achieve Goal Or Objectives

* Pj = Total Number Of Positions (Goal Or Objectives)

* Pr, = Stealtii

* Prj = Survival

* Prj = Timeliness

* Pr4 = Energy

The steps to measuring robot "Effectiveness" are:

Step 1: Specify the tactical plan for die mission. In an urban

search and rescue mission, for example, this might be to: Search

for a specified object or person; perform Reconnaissance (to search for entrances or signs of life); perform Surveillance (in a

specified region); Map (a specified region); Retrieve a person,

etc. If the mission goal for a group of two robots were to locate and retrieve survivors fi-om within a room on an upper floor, the

mission goal of one ofthe robots might be to locate a path to the upper floor by searching a lower floor. The mission-level goal

consists, for example, of a state-graph defining a sequence of

potential commands that the mission executor will issue to the group level planner. Store the robot's mission goal as specified

at the start of the mission, and any changes of the goal made

during the mission, with the time of the changes.

Step 2: Store the robot's input tactical commands for decomposed intermediate objectives (ifthey are changed during

the mission, store the changes along with the times of the changes), then store the changes in the state-gr^h which

indicate that a robot's input command has been accomplished by

the robot, and note the time of the accomplishment

Step 3: Determines whether the robot has substantially

accomplished its mission goal. Calculate the score quantitatively

e.g., using an Object Ratio or Position Ratio (for example, the

ratio of entrances to a collapsed building located to the total number of entrances in the building) or qualitatively (assign a

score to the mission).

Step 4: The Object Ratio and Position Ratio are used by the

analyst to calculate the Objective Score, siunming the number of

objects or positions that the robot interacted with in the accomplishment of its intermediate objectives and dividing by

the total number of such objects or positions with which it should have interacted (according to ground-truth).

Step 5: The values used in the value-driven route plaimer,

such as for stealth, survival, timeliness, and energy, should be

stored for retrieval by the analyst. At the conclusion of die

mission, the analyst calculates the Priority Sum Average by

evaluating the behavior the vehicle, assigning a scores, and

taking an average. Step 6: The analyst weights and combines the scores ofthe

three MOP associated with vehicle Effectiveness (i.e.. Goals,

Objectives, and Priorities) and calculates a total score for

"Effectiveness."

3.0 Acknowledgments

We gratefully acknowledge that this work was funded in

part by Contract 43NANB009812 from NIST.

4.0 References

[I] Conant, Roger, ed. Mechanisms Of Intelligence: Ross

Ashby's Writings On Cybernetics. Seaside, CA: Interscience

Publishing; 1981, p. 178.

[2] Glossary On Cybernetics And Systems Theory, American

Society For Cybernetics; 1984, pp. 21-22.

[3] Gevarter, William B. Intelligent Machines. Englewood

Cliffs, NJ: Prentice-Hall, Inc.; 1985, p. 229.

[4] TTie Economist, 17 October 1987, p. 13.

[5] Gregory, Richard L. The Oxford Companion To The Mind.

Oxford: Oxford University Press, 1987, p. 410.

[6] Chen, Minder, Yihwa Irene Liou, and E. Sue Weber.

158

I
Developing Intelligent Organizations: A Context-Based

Approach to Individual and Organizational Effectiveness.

Journal ofOrganizational Computing 2(2): pp. 1 8 1 -202, 1 992.

[7] McCarthy, J., and P. Hayes. Some Philosophical Problems

From The Standpoint OfArtificial Intelligence. In Readings In

Artificial Intelligence, Bonnie Lynn Webber, Nils J. Nilsson,

eds. Palo Alto, CA: Tioga Publishing Co 1981, p. 432.

[8] Fischler, Martin A., and Oscar Firschein. Intelligence: The

Eye, The Brain, And The Computer. Addison-Wesley

Publishing Co., Inc.; 1987, p. 4.

[9] Schrage, Michael. Book Renounces The Early Promise Of

Artificial Intelligence. Washington Post, 1 June 1987, p. 17.

[ 1 0] Wooldridge, Dean E. MechanicalMan: The Physical Basis

OfIntelligent Life. New York: McGraw-Hill Book Co.; 1968,

p. 128.

[11] Ashby, W. Ross. Design For A Brain. Chapman & Hall; 1976.

[12] Doyle, Jon. A Model For Deliberation, Action And

Introspection, MIT Artificial Intelligence Laboratory TR-581

May 1980, pp. 15-16.

[13] Barr, Avron, and Edward A. Feigenbaum. The Handbook

OfArtificial Intelligence. Stanford, CA: Heuristic Press; 1981,

p. 147.

[14] Rorty, Richard. Philosophy And The Mirror OfNature.

Princeton University Press; 1979, p. 125.

[15] Sowa, J.F. Conceptual Structures: Information Processing

In Mind And Machine. Addison-Wesley Publishing Co.; 1984,

p. 2.

[16] Gregory, op. cit, p. 149.

[17] Gevarter, op. cit., p. 225.

[18] Fischler, op. cit., p. 383.

[19] Minsky, Marvin, and Seymour Papert Perceptons. MIT

Press; 1969.

[20] Pugh, George E. The Biological Origin OfHuman Values. Basic Books Inc.; 1977.

[21] Gregory, op. cit., p. 383.

[22] Koza, John R, Genetic Programming. MIT Press; 1992,

p. 14.

[23] Stonier, Tom. BeyondInformation: The Natural History of

Intelligence. Springer-Verlag; 1992, p. 70.

[24] Stonier, op. cit., p. 72.

[25] Stonier, op. cit., p. 85-105.

[26] Arkin, Ronald and George Bekey, Robot Colonies, Kluwer

Academic Publishers, 1997, reprinted fi-om the special issue of

the Journal ofAutonomous Robots, Vol. 4, No. 1 , March 1997.

[27] Cao, Uny Y., Fukunaga, Alex S., and Andrew B. Kahng,

Cooperative Mobile Robotics: Antecedents And Direction, in Arkin, Ronald and George Bekey, Robot Colonies, Kluwer

Academic Publishers, 1997, pp. 7- 27.

[28] Miller, James R., Professional Decision-Making: A

Procedure For Evaluating Complex Alternatives, Praeger

Publishers Inc., 1970.

[29] Finkelstein, Robert, "Combat Robotics: Implications For

Leadership," Unmanned Systems, Winter 1987, pp. 9-10.

[30] Saaty, Thomas L., Decision Making For Leaders: The

Analytical Hierarchy Process for Decisions in a Complex

World, Wadsworth, Inc., 1982.

[31] Saaty, Thomas L., The Analytic Hierarchy Process:

Planning, Priority Setting, Resource Allocation, McGraw-Hill

Book Co. 1980.

[32] Saaty, Thomas L., A Scaling Method for Priorities m

Hierarchical Structures, Journal ofMathematical Psychology,

15, pp. 234-281 (1977).

[32] Finkelstein, Robert, Measures Of Performance And

Effectiveness For The MA UVESystem: MOEReport Number 5; Robotic Technology Inc., Item Number 0002AE, Contract No.

50SBNB7C4549, National Institute Of Standards And

Technology, June 1987.

[33] Finkelstein, Robert, Metrics: Evaluating The Performance

OfIntelligent Systems; Robotic Technology Inc., Contract No.

43NANB009812, National Institute Of Standards And

Technology, June 2000.

5.0 Author Biography

Dr. Robert Finkelstein is an Associate Professor and

Director of the MBA Program at the University ofManagement

and Technology and an Adjunct Associate Professor at the

University ofMaryland. He is President ofRobotic Technology

Inc. and Chairman of Decide-Now.Com, Inc. He received a DBA in Systems and Cybernetics fi-om the George Washington

University (GWU) in 1995; Ap.Sci. in Operations Research

from GWU in 1977; M.S. in Operations Research fi-om GWU in

1974; M.S. in Physics fi"om the University of Massachusetts in 1966; B.A. in Physics fi-om Temple University in 1964.

159
Machine Intelligence Ranking

Paul P. Wang

Duke University, BOX 90291

Department of Electrical and Computer Engineering

Durham, North Carolina, 27708-0291, USA

ABSTRACT

This talk addresses a number of issues which were

inspired by the draft of a document on Metric for

InteUigence of Constructed Systems. The

constructed systems here literally mean an autonomous control system. It is important to note

the opinions expressed in this talk reflect the

thoughts of the author and they do not reflect on

any institution, organization or professional

society.

There are six issues being raised in this talk. The

first issue deals with the discussion on the role

NIST should play. The institution of NIST was

chartered to serve American citizens to improve

their well benig and the noble goal of pursuing a

life of happiness. One of the most important tasks

is to measure, standardize, and rank the

engineering systems and the advancement of the

technology objectively. The autonomous

constructed systems were singled out with a high

profile to reflect their importance. Are there any

other man-made systems which are equally or more important?

Second issue has to do with measuring

intelligence. We are measuring intelligence

because technology embraces intelligence giving us a superior and high perfomiance system. On the

other hand, it is not NIST's mission to do all that

because it is there! The fundamental issue,

however, is to serve the citizens better via

improved technology which requires intelligence.

The definition of intelligence, however, is no

simple matter, as well as the definition of serving

citizens. Both cover a wide spectrum of needs and

desirable things other than autonomous systems of

which intelligence so happen needed to be put in

the center of the stage.

The third issue to be raised is the definition of

"machine intelligence" and how to measure it? Since the definition of human intelligence is complex and difficult, the definition of machine

intelligence is even more difficult!

The fourth issue has to do with the performance

evaluation of engineering systems. This issue

deals with value judgement. The debate by the

citizens among all walks of life and society as a whole must be carried out in order to establish

value judgement as a benchmark for measurement,

testing, and evaluations.

This brings us to the issue of testing and

measuring. The central issue is how are we to

conduct the machine intelligence test? It is not a simple matter because we have not yet settled the

definition of machine intelligence!

Equally important is the issue of understanding the crux of our present technology and forecasting of

fiiture technology. The reason is due to the fact

that there is absolutely no unique way to realize a high performance system. Here we are talking

about a federal institution to set the standard to

evaluate and rank a high performance system.

Generally speaking, the position this paper takes is that some of the issues raised in white papers are over simplified. Some of the long term frame

works have not been covered adequately. If one

believes in the basic assumptions, hypotheses set by the white paper and willing to live with all the

constraints already being laid out, then this paper

has no validity. The feeling of this author is that

the constraints dealing with intelligent machines

are overly constrained and a liberation effort

hence is needed.

160
The main concerns are : the basic charter of the

institution is unclear, the science on intelUgence is too complex, the need of application areas is too

complex, and the technologies available are too

uncertain to reach a consensus. With these constraints, I must say that the white

paper is truly an outstanding document flill of

creativity, imagination, and innovative ideas.

Congratulations to Alex Meystel and Jim Albus.

161

/
Survivability and Competence

as Measures of Intelligent Systems

Reid Simmons

Robotics Institute

Carnegie Mellon University

While the workshop is appropriately

named "Measuring the Performance of

Intelligent Systems", there may be come

confusion that the goal is actually about

measuring the intelligence of systems.

While measuring performance is a worthy,

albeit difficult goal, 1 believe that trying to measure intelUgence itself is misplaced.

To me, it seems pointless to debate

whether, for instance, playing chess

exhibits more "intelligence" than exploring

Mars, or whether using speech is

inherently more intelligent than doing

object recognition. From both pragmatic

and philosophical viewpoints, the more

that we can make it clear that we are

interested in performance, rather than

intelligence, per se, the better off we will

be.

So, what criteria are to be used for

measuring the performance of inteUigent

systems? I think that the two most

important characteristics are survivabiUty

and competence. By survivability, 1 mean the ability of a system to cope with

diversity in the environment, as well as

intemal faults (hardware and software).

By competence, I mean the ability of a system to successfiilly perform tasks.

Both survivability and competence can be

measured either empirically or formally.

Empirically, survivabiUty can be

measured by carefully controlling

environmental inputs and by modifying the

intemal state of the system (such as by

deliberately causing hardware faults).

Formally, with the right model one can

quantify the range of environmental

conditions and intemal states that can be

handled successfully. Similarly, one can measure competence either empirically or

formally by controlling for the range of

tasks and the environments under which

those tasks are to be performed.

This, of course, begs the question as to how to set up the experiments in an

unbiased and controlled fashion, and how

to model tasks and environments so that

formal evaluations are possible.

Unfortunately, I do not have good

answers for those questions, at this time

(although we are working on it!). The

problem is that most intelligent systems

exhibit chaotic behavior - small deviations

in input conditions lead to wide deviation

of behavior (of course, many inteUigent

systems are also chaotic in the colloquial

sense, but that is another matter...). Thus,

it is very difficult to set up "the same"

conditions to test different systems. One

can never be sure if the results are due to

actual differences between the systems

themselves, or due to smaU differences in

the environments. WhUe simulation can be used to perform standardized

experiments, simulators have the

disadvantage that they tend to be rather

simple models of reaUty, and so may not

capture the essence of what makes

survivabiUty and competence difficult.

What about things like robot competitions

and Turing tests? I am aU for them, but

not as quantitative measures of

162
performance, since they suffer from the

problem of variabiUty, as described

above. The reason that they are valuable

is that they come close to standardizing

tasks and environments in realistic

settings, and so can be used by

developers of intelligent systems to gauge

progress, in qualitative ways, against the

state of the art. While it is dangerous to

use the results of such competitions to conclude anything about one system vs.

another (especially one technology vs.

another, such as neural nets vs. expert

systems), competitions are usefiil as a

type of "bread-boarding" exercise.

Finally, an important aspect of intelligence

is adaptability. The question is whether

adaptability should (or can) be measured

independently from survivability and

competence. I would argue that

adaptability is merely one way of

increasing a system's survivabihty and

competence, and thus should not be

considered independently. While it may

tum out to be true that adaptable systems

are generally more survivable and

competent, it seems clear to me that this is

a hypothesis that needs to be

demonstrated empirically, or proved

formally, hi the absence of such proof, it seems to make little sense to measure

adaptabihty in isolation.

In summary, survivabihty and competence

are two critically important characteristics

of intelligent systems. While it is possible

to devise ways of measuring both, in a

rigorous fashion, it is difficult due to the

fact that autonomous systems interacting

with complex environments tend to be

chaotic. But, that fact should not lessen our resolve to try and measure

performance - it only serves to make us aware of the limitations and difficulties of

the enterprise.

163
Two measures for the "intelligence" of human-interactive

robots in contests and in the real world: expressiveness and

perceptiveness

lllah R. Nourbakhsh

The Robotics Institute

Carnegie Mellon University

Practical measures of intelligence are generally predicated on

a social-anthropocentric view of intelligence. This is hardly

surprising, but is undesirable because it results in intelligence

testing procedures that are uninformative when the subject is not human. For example, the classical Turing Test measures machine intelligence using the yardstick of human social

dialogue, in written form, as its gold standard. The problem is that such methodology is implicitly pass fail. Rather than

providing a relative measure for machines that are clearly

inferior to humans at social human interaction, this test simply

fails all such machines until and unless some superior machine

simply passes. In airness, it is possible to mitigate this to a small degree by narrowing the content area of the test.

Nevertheless, the Turing Test as applied to the mobile robot

system suffers generally the same fate. One can imagine, for

instance, a robot Turing Test in which the human teleoperated

robot is compared in performance to an autonomous robot in tasks such as navigation, manipulation and robot-human

interaction. But the robot will continue to suffer because its raw percepts and raw effectors are not comparable to that of a human. The solution, to force the teleoperating human to use

the same percepts as the robot itself uses, results in a robot

that whether teleoperated or not is disappointingly

unintelligent even when it successfully passes such a robot

Turing Test. The problem, then, is that a robot's potential for

interaction imposes an upper bound on its potential for

intelligence.

Based on this premise, I will propose in my talk that the form

of intelligence about which we care most in the case of

autonomous robots is interaction.

I will present a methodology for measuring the potential of a robot to engage in rich interaction, thereby establishing a behavioral and analytical way of measuring intelligence

without reverting to a direct anthropocentric pass fail test. I will define the concepts of expressiveness and perceptiveness,

which together place both upper bounds and lower bounds on

interactivity and thereby intelligence. Expressiveness is a measure of the output richness of an electromechanical

system. One can quantify expressiveness in terms of the

average effectory branching factor of an agent in its observable output space.

Perceptiveness is a measure of the fidelity of an

electromechanical system's effective mapping from

environmental change to output. This too can be quantified by

computing the set of possible output trajectories of an agent in

its perceptual workspace. These two measures prove to be

particularly useful because they contain no bias with respect to behavior-based and model-based robot architectures. After

defining expressiveness and perceptiveness, I provide some

quantitative results comparing the expressiveness and

perceptiveness of a simple unicellular organism, the

dinoflagellate, to that of several popular mobile robots. These

quantitative results demonstrate that from the perspective of

interactivity mobile robots have a long way to go before

challenging human intelligence.

164
PART II

RESEARCH PAPERS

3. INTELLIGENCE OF DISTRIBUTED AGENTS

3.1 Goodness of Fit Measures for Intelligent Control of Interacting Machines

S. Phoha, D. Friedlander, The Pennsylvania State University, USA

3.2 Distributed Internet-Based Multi-Agent Intelligent Infrastructure System

X. Qin, A. E. Aktan, K. Grimmelsman, F.N. Catbas, R. Barrish, M. Pervizpour,

E. Kulcu, Drexel University, USA

3.3 STIGMERGY An Intelligence Metric for Emergent Distributed Behaviors

R. R. Brooks, The Pennsylvania State University, USA

3.4 Performance Self-Assessment by and for Regulation in Autonomous Agents

E. S. Tzafestas, National Technical University, Greece

3.5 On Measuring Intelligence in Multi-Agent Systems

S. P. Tolety, GTE Labs, USA and G. Uma

3.6 On the Development of Metrics for Multi-Robot Teams within the ALLIANCE Architecture

L. E. Parker, Oak Ridge National Lab, USA

3.7 Shared Autonomy and Teaming: A Preliminary Report

H. Hexmoor, University at Buffalo, USA and H. Duchscherer, University of ND, USA

3.8 Real Time Distributed Expert System for Automated Monitoring of Key

Monitors in Hubble Space Telescope

R. Fakory, M. Jahangiri, NASA, USA

3.9 Evaluating Performance of Distributed Intelligent Control Systems

W. J. Davis, University of Illinois, USA

3.10 Hypothesis Testing for Complex Agents

J. Bryson, MIT, W. Lowe, Tufts University, and L.A. Stein, MIT, USA

165
i
Goodness Of Fit Measures For Intelligent

Control Of Interacting Machines

Shashi Phoha

David Friedlander

Applied Research Laboratory

The Pennsylvania State University

University Park, PA 16802

sxp26@psu.edu

Abstract

Technology developments in computing and communications have

enabled the development of intelligent machines which change

their intemal states in response to real time interactions with other machines or smart sensors in an ad-hoc network. Complex

systems such as these machines may be used to implement a

collaborative surveillance sensor network, a multi-robotic mine

hunting mobile network, or command and control of multiple

hostile or friendly aircraft in an air campaign.

This paper characterizes and evaluates cognitive response in distributed systems of interacting machines. We present a mathematical model of dynamic evolution of such systems and

characterize intelligent behaviors like self-organization,

identification of fHendly or hostile agents, collaboration for achieving common goals, defensive or offensive action against

hostile agents, etc. E.xploration of the concept of goodness of fit regarding intelligent behavior in interacting machines relates to (/) the contextual performance of the network in the presence of

expected perturbations in its operational environment, and f/) the

quality of adaptation it provides in order to deal with incorrect or incomplete information and unexpected changes in its operational

environment. The former relates to behavioral intelligence in executing assigned tasks in a dynamic environment. It can be

evaluated from the perspectives of various users by analyzing

system response. The latter relates to more intangible

characterizations of intelligence akin to creativity and adaptation. Measures of intelligent global behavior of these networks are formulated in terms of their ability to adapt to unexpected

perturbations in the environment and the robustness of their responses. This paper develops measures of fit as the ability of the network to adapt b variarions in the operational dynamics of the system. These measures assess the overall intelligence of the system in terms of its goodness of fit evaluation for dealing with

variations of the plant model for which the network was designed.

The quantification of these measures leads to constructive methods

of engineering distributed intelligent systems with specified levels of intelligence.

Key Words; distributed cognition, extrasensory network

intelligence, behavior based control, goodness offit measures, network adaptation, permissiveness, robustness.

1.0 Introduction

The inherent complexity of controlling a distributed

dynamic system implemented on an ad hoc network of

interacting machines stems fi-om the fact that an accurate plant

model based on physical laws cannot be easily formulated.

Concurrent dynamic processes embedded at each node of the

system interact in highly non-linear, time-varying and stochastic

ways and are subject to unpredictable environmental

disturbances. Hence model-based conventional control

techniques are inadequate. Alternate methods of designing

controllers whose structure and outputs are determined by

empirical evidence through observed input/output behavior,

rather than by reference to a plant model, are necessary. Several

techniques for such non-linear controller design have recently

been proposed in recent literature on Intelligent Control [Harris

94, Levis 93, Albus 93, Ramadge 87, Phoha 92, Phoha 98].

Albus [93] has developed the Real Time Control Architecture in which sensor and processing, value judgment, world modeling

and behavior generation subsystems interact to adaptively

generate appropriate response behaviors to sensor observations

and knowledge of mission goals. Meystel [93] has also

proposed a nested hierarchi cal control architecture for the design

of Intelligent Controllers. Brooks' subsumption architecture

[Brooks 86] for intelligent control is based on achieving

increasing pre-specified levels of competence in an intelligent

system by examining outputs of lower levels. In Phoha [92, 00]

and Ray [93, 95] we have modeled the essential dynamics of

these distributed systems as a network of interacting automata

that change their intemal states through interactions with other

nodes or the environment. Due to the well-known relationship

between automata and formal languages [Hopcraft 79], we have

thus introduced a hierarchical formal language structure for

multi-layered dynamic control [Peluso 96] for mission planning

and execution.

This paper develops constructive methods of formal

language based modeling and intelligent control of interacting

machine networks. Section 2.0 presents the mathematical

representation of the network as an interacting automata.

Section 3.0 formulates the control analysis and synthesis

problems. Section 4.0 develops goodness of fit measures for

intelligent network behavior. Section 5.0 discusses a method for

constructing intelligent machine networks with pre-specified

intelligence to adapt to unmodeled dynamics. Thus, a

167
constructive mechanism is formulated for designing

intelligent controllers for distributed networks. Applications

and open issues are discussed in Section 6.0.

2.0 Models OF Machine Interactions

In order to model the concurrent and interacting behaviors

of autonomous agents in the complex dynamic system, we

extend the automata representations of individual agents to form an interactive automata network, defined as a pair (G,

{a,}) consisting of a cellular space G, a potentially countably

infinite, locally-finite bi-directed graph, and an associated

family of interacting automata a,, allocated to each vertex

(cell) of a graph. In particular, Gq represents the operational

environment. Each automaton a, has a finite d, number of

neighbors to communicate with, and has the form

«, = ( Q„ ^i, r„ 6„ qoi, Qfi ) where

Qi is a finite set of control states representing

values of all variables,

Z| is a finite alphabet of input events and

I, = IoX^/, x...xZi^.,

r, is a finite alphabet of output events and

r,- = r„x r„ x--,< r,^/.,

5, is a local transition fiinction 5, . QxZ, -* Q x qm e Qj is an initial control state, and

Qf, e Qt is the set of terminal control states. The dynamic system operates locally as follows: an

interactive automaton a,- occupies each vertex (cell) / of G. Asynchronously, each a, looks up its inputs from neighbors

Xi,,...^ij., input from an environment x,,, and its own states,, and then changes its state and produces outputs for the

neighbors and the environment according to a local dynamics 6,. This atomic move is repeated any (possibly

very large) number of times.

The environment is modeled uniformly as an

interactive automaton, which can be nondeterministic or

stochastic, assuming incomplete knowledge about the

environment. A distributed environment is modeled as a subnetwork instead of a single node. Thus the local transitions 5j induce a global evolution from one system

configuration to another. This global evolution of the

system is viewed as a self-map

T: C ^C, where C = 11 , (fi ^ F,) are configurations

(total states) of the network, such that T(x), = 5,(x„x,Q,x,-,, ....,x,^).

TUeorbit ofx is the sequence ofconfigurations

{T'(x)},,o: =x,T'(A-),T-(.v),T^x),...

resulting from successive iterations of the global rule on x. As a dynamical system, the most basic question about a global map T is the effect of its repeated application in phase space to a random given configuration x.

3.0 Control Analysis and Synthesis Problems

The forward, or control analysis, problem is the following:

given local transition rules that determine the local interaction of

each automaton with its neighbors (namely the dynamics 5,), characterize the global effect of the rules on an arbitrary initial configuration x. In other words, determine a specific description

of the orbits of arbitrary configurations under T to identify their

long-term (asymptotic) behavior. The forward problem is also

known as the prediction of the emerging global behavior from

local rules. The synthesis problem is the inverse problem, which from

the point of view of constructing control laws, is even more

important. Given a desired global effect on configurations,

determine whether there exist, and if so, find the local rules 6,

whose induced global rule is precisely T. These local rules will

then yield a parallel algorithm for the underlying parallel

interacting automata to solve the problem of computing T(x) for

any configuration x in C. Assume, without loss of generality that E, = Z^V/ ^ j and

Z" is the n"^ cross product of Z. Then for / = 1,2, define

5, ={(cD,...,f7,,...,a)):a, eZ}. {S,} is a partifion of Z" under

the assumption that one event can occur at a given instant. Let

L{P) denote the open loop language and 0 the null event of the

plant generated by (G, a,) i.e. L{P)^'L"' . K c LiP) is called a

controller of (G, aj if K is prefix closed in Z"* and K=Y[Ki

where Kj is a controller for the subplant represented by a,

in the Ramadge and Wonham sense [Ramadge 87].

Assume that a weight \v > 0 is associated to Si such that Z M',=l. Following [Friedlander 00] we define a measure of a

given language L c Z" as follows: jU(L) = ^w^A,(L)

,

/=i where A, ( L) = 1

if 3S = {S„S^,...SjG L

such that O ?i S, G S, ; 0 otherwise.

In the next section we will use the notion of the measure of

a plant language to evaluate the intelligence inherent in a controlled plant.

4.0 Goodness of Fit Measures of Intelligent

Behavior

Exploration of the concept of goodness of fit regarding

intelligent behavior in interacting machines relates to (/) the

contextual performance of the ad hoc network in the presence of

expected perturbations in its operational environment, and (ii) the quality of adaptation it provides in order to deal with

incorrect or incomplete information and unexpected changes in

its operational environment. The former relates to behavioral

intelligence in executing assigned tasks in a dynamic

environment. It can be evaluated from the perspectives of

various users by analyzing system response. The latter relates to

168
th

more intangible characterizations of intelligence akin to

creativity and adaptation. We call this Extrasensory

Intelligence and proceed to define it as follows:

4.1 Extrasensory Intelligence

In order to characterize the extrasensory intelligence of a controller K, for a plant P modeled as an interacting

automata (G, a, , / = 1, ...,«), we study the set of plants which

are controllable by K.

A plant P' = (G , / = l,...,n) is said to be a k' generation variation of the plant ( G, ) iff k = ma\{ki : generating graphs ofa, and a' differ in

exactly kj edges (d-transitions)}.

For a definition of a generating graph of an automaton, see [Ramadge 87, Phoha 92].

An extrasensory intelligence function for a controller AT for a plant P represented by an interacting automata model

[G, Ui ,i=l,...,«] may be defined as the function 1:2^?

[0,1] such that /(A:) = proportion of plants P' controllable

by K which are k generations apart fi-om its nominal plant

model (G, a,- , / = \,...,n). The objective would be to characterize controllers, K, which achieve a specified value

of / {k) for a given k. We proceed to define other relevant measures of

extrasensory intelligence as follows:

Define

(/) IQ,{K,P) =

^^V,^if?^ Ei"[^(nO - L{P)\ D[L{Pl)] ,

where is any k"' generation variation of plant P, and

D\_L{Pl)]= 1 if a: controls , 0 otherwise; and

L{P/K) is the measure of the language of the closedloop, i.e. the controlled plant.

(») IQiK,P)=]imIQ,{K,P).

5.0 Constructing Systems with Prespecified

Intelligence

5.1 Building Brainsfor Interacting Bodies

In this section we examine how controllers can be iteratively

designed to achieve prespecified levels of extrasensory

intelligence in a complex plant represented by interacting

automata (G, a,, i=l,. ..,«). We first develop a controller

synthesis method which synthesizes a controller for two

interacting automata given the specifications for controlling each

of the plants represented by the automata. This method can then

be iteratively used to construct controllers for other interacting

nodes until a controller for the entire plant is designed. Then we

formulate the automata models for all the first generation

variations of the plant (G, a,\) and attempt to synthesize these

controllers using the synthesis automation process described

below in 5.2. If a first generation plant variation can be

synchronously synthesized, then we have increased the

extrasensory intelligence /(I) of the new controller

proportionately. Iterate this process until a prespecified value of

lik) is achieved. The entire process can be repeated fi-om here on to achieve prespecified levels of l{k). Note that desired levels

of l(k) may not be achievable.

5.2 Controller Synthesis Automation Tool

We have developed a Java-based tool, J-DES, as a graphical,

multiple-window, platform-independent software package for

automating the controller synthesis process in the setting of

finite automata representation of the plant. The process is outlined in Figure 1, and shown graphically in Figure 2. The major advantages of this synthesis tool, compared to TCT (available at http://odin.control.toronto.edu/cgibin-/dlctctcgi)

and Analyzer (available at httpTywww.o^.uky.

edu~kumar/CODE/-java.tar.gz), are the features of interactive

Synthesis of a Control

Language

Generate the specifications, i.e.. control objectives by hand:. Formulate the plant model G"(o.^.^.qn.o„,) byhand

Check accessibility of the plant model by code

Convert the specifications in Step 1 to an FSM by hand . S=(x.^a.xa.X„,)

Ensure trimness of the marked language K = L„(S) |jy code • Ensure is prefix closed by code

is controllable by code

the derived supervisor is the generate a su0remal controllabl hy code

ubianguage z <= 5 and is the

If Step 5 is not successful, repeat the process from Step 1 Figure 1. Controller Synthesis Process

Overview of Synthesis Procedure

'ControllCT,- Iteration- , ,1 TPs , u

GllZcoatroUafcle]"

Figure 2. Graphic View of Controller Synthesis

169
visualization and flexibility. The tool allows the user to

create finite automata graphically on a canvas within a window interface by positioning states with single or multisymbol transitions that always point to the destination state. The tool allows design for both modular control (i.e., horizontal task decomposition) problems and multi-level

hierarchical control (i.e., vertical task decomposition)

problems. The detailed design examples using this tool can be found in [Xi 00]. So far for two consecutive levels in the

hierarchy, the high-level virtual plant model, which is an

abstraction of the low-level closed-loop system in a mealy

machine representation, is constructed manually in the J- DES environment. It is this abstraction procedure (i.e., observer computation) that guarantees the hierarchical

control consistency.

We have developed an algorithm to extend this tool to optimize a controller for extra sensory intelligence. It is based on the fact that both the plant and controller are based on finite state automata.

Start with a controller, K, that controls a plant, P={ G,a,).

Let P =^y}P'k , where is any k"' generation variation of

/t=0 plant P, be a population of plants. The generation is

Jmxn defined as the original plant, P. Also, let = IJ^y where

K'j is any generation variation of controller K, be a

population of controllers. The controller with optimal

extrasensory intelligence, K"' e K is then defined by:

8.0 Acknowledgment

Some of the concepts of intelligence metrics presented in

this paper were initially developed on the JFACC project,

sponsored by the Defense Advanced Research Projects

Agency (DARPA) and Air Force Research Laboratory, Air

Force Materiel Command, USAF, under agreement number

F30602-99- 1-0547 (JFACC). The U.S. Government is authorized to reproduce and distribute reprints for

Government purposes notwithstanding any copyright

annotation thereon. The views and conclusions contained

herein are those of the authors and should not be interpreted

as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense

Advanced Research Projects Agency (DARPA), the Air

Force Research Laboratory, or the U.S. Govenmient.

7.0 Results and Open Issues

An experimental testbed implementing a hierarchical controller

architecture for a 3 -node aircraft command and control network

has been implemented under DARPA's JFACC program [Xi 00].

Preliminary results for evaluating extrasensory intelligence for

the discrete event controllers designed in this testbed are given

in Figure 3. The following inference may be drawn: l{k) decreases

exponentially as k increases and may be essentially presumed to be zero beyond k>l

.

Further experimentation is required to formulate control

mechanisms which possess extrasensory intelligence.

There are fundamental limits to intelligent adaptation in

artificial systems. Exploration of these limits, in terms of

adaptability of their generating grammars, are open issues. The

complexity of the adaptation process is another open issue.

Controllability of the Hierarchy

lit 1.21

•oo

Generation

Figure 3. Experimental Results for 3 -Node Aircraft

Command and Control Network

9.0 References

[Albus 93] Albus, J.S., "A reference model Architecture for

Intelligent Systems Design," in An Introduction to intelligent

and Autonomous Control, pp. 27-56, Kluwer Academic

Publishers, 1993.

[Brooks 86] Brooks, R.A., "A Robust Layered Control System

for a Mobile Robot, " IEEE Transactions on Robotics and

Automation, 2(3): pp. 14-23, 1986.

[Eberbach99] Eberbach, E., Brooks, R.R., and Phoha, S., "Flexible Optimization and Evolution of Underwater

Autonomous Agent, " Rough Sets Fuzzy Sets Data Mining and

Granular Soft Computer 1999, Yamaguchi, Japan, Springer

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[Friedlander 00] Friedlander, D., Phoha, S., and Ray, A.,

"Domain Independent Measures of Intelligent Control,"

170
Performance Metricsfor Intelligent Systems Workshop, to be held in Gaithersburg, MD, August 14-16, 2000.

[Harris 94] Harris, C.J., ed.. Advances in Intelligent

Control, Taylor & Francis, Bristol, PA 1 994.

[Hopcroft 79] Hopcroft, J. E. and Ullman, J.D., Introduction

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Addison- Wesley, 1979.

[Levis 93] Levis, A.H., "Modeling and Design of

Distributed Intelligence Systems," in An Introduction to Intelligent and Autonomous Control, pp. 109-128, Kluwer

Academic Publishers, Boston, MA, 1993.

[Meystel 93] Meystel, A., "Autonomous Mobile Robots:

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Scientific, Singapore, 1991.

[Peluso 96] Peluso, E., "A Hierarchical Structure of

Interacting Automata for Modeling Battlefield Dynamics:

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Dissertation. Department of Computer Science, The

Pennsylvania State University, 1996.

[Phoha 92] Phoha, S., Sircar, S., Ray, Al., Mayk, I., "Discrete Event Control of Warfare Dynamics, " The

Technical Proceedings of the 1992 Symposium on Command and Control Research and the 9"' Annual

Decision Aids Conference, Monterey, CA, 8-12 June 1992.

[Phoha 98] Phoha, S., Eberbach, E., Peluso, E., and

Kiraly, A., "Coordination of Engineering Design Agents for

High Assurance in Complex Dynamic System Design,"

Invited paper for Special Track on High Assurance in Intelligent System, i"' IEEE High Assurance Systems

Engineering Symposium, November 13-14, Washington,

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171
DISTRIBUTED INTERNET-BASED MULTI-AGENT INTELLIGENT INFRASTRUCTURE

SYSTEM

Xiaoli Qin' A.E.Aktan^ Kirk Grimmelsman^ F.N. Catbas'* Raymond Barrish^ M. Pervizpour^ E. Kulcu^

Drexel Intelligent Infrastructure And Transportation Safety Institute

Drexel University

3201 Arch Street, Suite 240

Philadelphia, PA 19104

ABSTRACT

The Commodore Barry Bridge (CBB) is a major long-span,

cantilever through truss bridge owned by the Delaware River Port Authority (DRPA). To evaluate the performance of this bridge, it

is necessary to implement an appropriate health monitoring sys- tem, conduct structural analysis, measure the operating and loading environment as well as the critical responses of the structure. The health monitoring system may be used in order to track operational anomalies, deterioration or damage indicators that may

impact service or safety reliability. The knowledge space re- quired to accomplish such complex engineering tasks is innite

and uncertain. This engineering domain itself is not well understood. To solve such engineering problems, not only is theoretic

knowledge required but also extensive heuristic experience. Organizing and formalizing the theoretical knowledge and heuristic

experiences of multidisciplinary human experts is the rst major

challenge. The building of an intelligent system that can rea- son and make rational decisions based on induction/deduction of

theoretic knowledge and analysis of heuristic experiences is the

second major challenge.

'Web: http://www.mcs.drexel.edu/ gxqin; Email: xq2 2 @drexel . edu.

^Web: http://www.di3.drexel.edu; Email: Aktan@drexel . edu.

'Web: http://www.di3.drexel.edu; Email: grimmeka@drexel . edu.

''Web: http://www.di3.drexel.edu; Email: fncatbasSdrexel . edu.

^Web: http://www.di3.drexel.edu; Email: barrisra@drexel . edu.

^Web: http://www.di3.drexel.edu; Email: Mesut . Pervi zpourSdrexel . edu.

^Web: http://www.di3.drexel.edu; Email: Eray . Kulcu@drexel . edu.

This paper presents the writers' progress towards the development of an intelligent infrastructure system that uses integrated

technologies of Case-Based Reasoning (CBR) and Rule-Based

Reasoning (RBR) to evaluate the performance of the CBB. The

system includes a case-base, a rule-base, a CBR agent, a RBR

agent and an inter-operational agent. The CBR agent and RBR

agent work with both case-base and rule-base. The case-base

and rule-base are inter-related through the index schemes. The

inter-operational agent evaluates the outputs of the CBR agent

and RBR agent to make decisions. This agent can be an alter- native human engineer. The CBR methodology is well suited

to formulate human experiences and phenomena that would not

lend themselves to organization and extraction in terms of rules. In contrast, the theoretical knowledge can be organized using

RBR technique. The combination of CBR and RBR technologies offers promise for developing a methodology for solving

complex real-life engineering operation problems. The CBR and

RBR agents are implemented and wrapped according to CORBA

(Common Object Request Broker Architecture) /DCOM (Distributed Component Object Model) standards in order to conmiunicate with each other and an external CORBA server or DCOM

server to acquire necessary knowledge.

INTRODUCTION

Case-Based Reasoning (CBR) techniques are a promising

for solving many engineering problems. CBR is a subeld of

Articial Intelligence (AI) that is premised on the idea that past

problem-solving experiences can be reused and learned from

in solving new problems. Rule-Based Reasoning (RBR) techniques are commonly used for developing expert systems in terms of building rules for solving generic or specie problems.

172
This paper discusses the use of combining case-based reasoning

and rule-based reasoning techniques to build a multi-reasoning

(multi-agent) system to solve a complex domain-specific problem —namely, evaluating bridge performance in civil engineering applications. This paper presents a three-phase approach to building such a system for this domain:

1. Knowledge Representation for Evaluating Bridge Perfor- mance: building a knowledge-base;

2. Case-Based Reasoning Engine and Rule-Based Reasoning

Engine: design of the CBR reasoner and intergration of the

existing RBR reasoner;

3. Implementation Issues: illustrations of how a multi-agent

system can be used during the phase of evaluating bridge

performance.

Foundation of Case-Based Reasoning and Rule-Based

Reasoning Tecliniques

The Case-Based Reasoning Cycle (1) precisely defines a methodology to build a CBR system for a given domain. A

case-based reasoning system can be viewed as a model which is a combination of a case-base and knowledge reasoning process

modules. These modules form a case-based reasoning shell, also

called a reasoner. They are the functions used to manipulate the

knowledge in the case-base and they act to process user inputs,

recall similar cases, retrieve the most similar case, evaluate and

adapt the retrieved case and update the case memory. The modules interact with the case-base during processing.

Normally, following problems are involved in a CBR sys- tem: knowledge acquisition, knowledge representation, case

retrieval, case adaptation and the learning mechanism.

1. Knowledge acquisition: How to acquire useful knowledge

from application problem domain.

2. Knowledge representation: How to use a formal language

to represent certain domain knowledge. The knowledge rep- resentation theory of case-based reasoning systems primarily concerns how to structure knowledge stored in the case- base to facilitate effective searching, matching, retrieving,

adapting and learning. One influential knowledge representation model is the dynamic memory model {11). It was developed by Schankand based on his theory. Memory Organization packet (MOP) theory.

3. Case retrieval: How to efficiently retrieve from the case- base the case most similar to the current problem. There

are two sub-processes involved in case retrieval: one is to

retrieve a set of similar cases from case-base, another is to find the most similar case in this set. The first sub-process is accomplished by designing appropriate index scheme for the

domain problem. The second task is done using the Nearest

Neighbor Matching Algorithm (NNM) (7).

82211 1 41111 411 1 822(1

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Figure 1 . Overview of tlie Commodore Barry Bridge

4. Case Adaptation Strategies: After a CBR system retrieves

the most similar case from the case-base, it normally needs

to perform adaptation on this retrieved case. There are several adaptation strategies which can be used in a CBR sys- tem. They are Simple Substitution, Parameter Adjustment

and Constraints Satisfaction (7).

5. Learning Mechanism: Learning is the last step in the CaseBased Reasoning system. In a CBR system, after a new

problem is solved, the case-base is changed by adding the

new case into it. By doing that, the system can retain more

and more knowledge along with problem-solving augmentation and achieve learning.

For a RBR system, following problems are involved: knowledge acquisition, knowledge representation, pattern matching definition and execution when pattern matching. The first two problems have the same characteristics as CBR system. For

the next two problems, brief explainations are given below:

1. Pattern matching definition: How to find patterns which

are stored in rulebase. This is accomplished by implementing Rete matching algorithm which is introduced extensively by Charles Forgy's PhD dissertation

2. Pattern matching definition: How to excute actions when

RBR inference finds applicable patterns. The inference en- gine loops through all matched rules and fires exhaustively

until no more applicable rules in the rulebase.

Domain Problems and Knowledge Representation

Commodore Barry Bridge. The Commodore Barry

Bridge (CBB) is owned by the Delaware River Port Authority (DRPA). It links Chester, Pennsylvania with Bridgeport,

New Jersey and was opened to traffic in 1974. The bridge

is the 3rd longest cantilever truss bridge in the world with a main span of 1,644 feet and a total bridge length of 13,912

feet. Figure 1 shows the principal structural system of the

CBB.

Presently, the Commodore Barry Bridge carries more than

6 million vehicles annually, much of it heavy truck traffic

173
seeking to avoid the traffic congestion of the busy Philadelphia metropolitan area (3). The bridge owner wished to objectively evaluate this aging and heavily loaded structure.

The Instrumented Monitoring of the Commodore Barry

Bridge. The Drexel Intelligent Infrastructure and Transportation Safety Institute (DIU), working in partnership with

the DRPA, has been investigating the application of various

health monitoring techniques to the CBB. Health monitoring, in the case of civil infrastructure systems, may be considered as measuring and tracking the operating and loading

environment of a structure and corresponding structural re- sponses in order to detect and evaluate operational anomalies and deterioration or damage that may impact service or

safety reliability. Designing a monitoring system for a longspan bridge was a major challenge for the DEI researchers. In the past two years. Din researchers have developed and

implemented a health monitoring system for the CBB. This

system was designed as a first-cut health monitoring sys- tem that would measure global and local responses of the

structure in critical members and regions of the bridge. The

system takes advantage of in excess of 100 data channels to continuously track the loading environment and numerous

structural responses of the bridge (3).

The Structural Identification of the Commodore Barry

Bridge. A primary step in implementing a successful

global health-monitoring system for a bridge is to accurately

conceptualize the structural systems. Long-span bridges

typically have numerous complex structural details, boundary, movement and continuity systems that require, at the

very minimum, identification and understanding fi-om a conceptual perspective in order to design an appropriate healthmonitoring system. These systems, when coupled with transient, non-stationary, nonlinear or unknown load effects and

responses, create a monitoring situation that is sufficiently

complex to justify a conceptualization effort (3).

Conceptualization of the structural systems is most effi- ciently accomplished through 3D CAD and solid modeling of the structure, site visits, photographs, and heuristics.

The Commodore Barry Bridge consists of sixty-three multigirder approach spans, eleven deck truss approach spans, and a three span cantilevered through truss. The total length

of the bridge from abutment to abutment is 13,912 ft (3).

Selecting the most appropriate bridge members to monitor was another major challenge for the DUI researchers.

The Din researchers have conducted extensive studies to identify critical bridge members. Correspondingly, sensors

are installed at those critical locations to monitor important

parameters. Figure 2 shows locations of critical members

and their instrumentation.

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Figure 2. Structural identification And Instrumentation

Why Is An Intelligent Reasoning System Necessary for

Health Monitoring? In health monitoring of large structural or infrastructure systems which have the probability of

brittle failure modes, engineers are very interested in "intel- ligent sentries". In the case ofthe CBB, the researchers have

developed sensor systems to monitor the local conditions at the critical regions that are susceptible to fatigue cracking.

If these systems sense an incipient cracking, they should in- form a human. In this type of effort, a false positive event is very dangerous while a false negative event is totally unacceptable. Therefore, the intelligent agent should be able to follow redundant reasoning and fusion of data from various sensors to rule against false positive while being cognizant

of false negative.

The second reason an intelligent agent is needed is for detecting and interpreting the initiation of conditions favorable

to deterioration. For example, analysis of internal humidity

and electro-chemical characteristics for concrete elements

could establish the onset of reinforcing steel corrosion.

Since a health monitor or supervisory control and data ac- quisition (SCADA) system for a major bridge or a major

infrastructure system must utilize many sensors distributed over a large geometric domain, it is impossible for humans

to continuously watch for continuously watch for incidents, events and complex phenomena pointing to out-of-ordinary

incidents, events and complex phenomena pointing to out- of-ordinary conditions with the structure. The only way a SCADA can become completely effective is if it has self- intelligence to alert human managers when needed.

Multi-Disciplinary Research. The monitor system for

CBB has been functioning since 1998, and additional data

has been obtained by many controlled tests. Data has been

interpreted by the researchers for characterizing the mechanical characteristics and the loading and response environment of the bridge structure in terms of a 3D finite-element

174
model. Researchers continue recording and viewing data

from continuous measurements in real-time, and from controlled load tests and ambient vibration tests that are conducted intermittently. The data is used for calibrating the

analytical model and validating its reliability for simulating

phenomena at the regional and element levels (2), (4), (3),

(8). Research at Din is conducted in three distinct areas. The

first research direction involves investigating, designing and

implementing health monitoring systems for civil infrastructure systems. This research is primarily conducted by civil and electrical engineers. The second research area involves

structural identification and analysis of instrumented civil

infrastructure systems. This requires a team of civil, mechanical, and electrical engineers. The third research area focuses on intergration. This research takes advantage of

computer science techniques to fiise distrubuted applications. In addition, knowledge engineering methodology is used to compile, structure and model human knowledge to solve complicated civil infrastructure problems. This re- search requires the efforts of a computer software engineer.

This paper discusses some of Din's efforts in the third re- search area.

Knowledge Representation of the CBB . Because of the

inherent complexity of the CBB bridge project, the knowledge space in this domain is incomplete and dynamic. It emcompasses civil engineering, electrical engineering and

computer science. It is not practical to fiilly compile and

model the knowledge in this project domain. However, ac- quiring and modeling the primary knowledge for major componts of the project from human engineer is approachable.

The major components of CBB project include health monitoring instrumentation and structural analysis. In this paper, a fragment of the knowledge for structural analysis of

the CBB and knowledge representation of it is presented.

Knowledge acquisition is achieved by specifying only the

important features of the problem. Features are only collected if they help solve the specific problem. Other knowledge that is not directly related to solving the problem is discarded. In this approach, a set of important features is predefined for the problem, and knowledge acquisition is done manually by a knowledge engineer. Because of restrictions mentioned above, the system will have some limitations. These limitations will be briefly discussed in the last section

The researchers have conducted extensive research on CaseBased Reasoning (CBR) and Rule-Based Reasoning (RBR)

methodologies. Both of these methods provide a very

promising way to organize, construct and program human

knowledge into a system. This system can contain human

experience, theroretical knowledge and respond to the real

world based on build-in reasoning mechanisms.

A language called CASL (5) is used to represent knowledge

pertaining to CBB project in this case-base. The structure of the case-base is based on Memory Organization Packet

(MOP) theory (11).

A language called CLIPS (9) is used to build the rule-base.

This language provides three paradigms to organize knowledge which are rule-based, object-oriented and procedurebased.

Case-Based Reasoning and Rule-Based Reasoning Engines

A reasoning engine is software agent which perceives

knowledge from the knowledge base, conducts logic infer- ence and reasoning and concludes results. In general, a rea- soning engine is used to reason on a specific kind of knowledge base. For example, CLIPS (9) is a tool which provides

language used to build a rule-base and a rule-based infer- ence engine is used to reason on the rule-base built by this language.

The case-based reasoning engine is the reasoning system

which allows a researcher to use archived cases to solve domain problems. Once domain knowledge has been used to build the case-base, organize memory, build indices, etc., the reasoning engine can execute searches based on the in- dex scheme. The engine can also perform other reasoning

processes, including case retrieval, adaptation and system

learning.

The rule-based reasoning engine is the reasoning system

which reasons on a rule-base. The domain knowledge

is compiled, modeled and structured in terms of a series of rules. The rule-based reasoning engine automatically

matches facts against patterns and determines which rules are applicable. If they are, the engine performs certain actions specified by knowledge base.

KNOWLEDGE REPRESENTATION FOR CBB

BRIDGE PERFORMANCE EVALUATION

Problem Formulation

The CBB project contains two major components. One of

them is structural identification and analysis of the bridge.

The other is health monitoring of the bridge. In this paper, only a small segment of the domain problem related to the

former will be presented as an example. The objective ofthe

structural identification approach is to characterize the as-is

structural condition and the loading environment of a bridge

through experimental information and analytical modeling.

Experimental data acquired from instrumentation is com175
• Dwbfpn and MsnaSPMWTt Afipllcsten*

Figure 3. Structural And Loading System Identification

Figure 4. Finite Element Model of the Commodore Barry Bridge

pared to results obtained from an analytical model of the

bridge. The results can be used to evaluate the correctness and reliability of the analytical model, to evaluate the per- formance critical bridge elements, and to issue notifications

regarding the safety of structures. Several 3D Finite Element (FE) models of the CBB were

developed to serve as tools for engineering decisions. The

analytical models incorporate the contribution of all force

resisting elements and mechanisms, particularly those associated with out-of-plane elements, into the analytical model.

In this manner, these elements and mechanisms can contribute to the behavior of the model as they do in the actual

structure, enabling more realistic and accurate simulations

ofretrofits, modifications, and loading scenarios (4). The FE

models were developed in several stages. First, the structural systems ofthe bridge were conceptualized by review the design calculations and drawings, shop drawings and site visit. Second, the structure was re-constructed using a 3D CAD

model. Finally, the CAD model was transformed into a FE

model using a commercial software program. Figure 3 summarizes the development stages and Figure 4 shows the complete 3D FE model of the through truss structure.

Din researchers conducted a controlled load on the bridge

to measure the critical responses. The measured responses

provide information necessary to verify analytical models

of the structure. The controlled load test on Commodore

Barry Bridge consisted of a static load test at pre-identified

locations and a crawl speed test. The Commodore Barry

Bridge was loaded statically by positioning two large cranes

in various configurations. The measured responses included

strain measurements for vertical truss members, lower chord

truss members, upper chord truss members, floor beams, and

for deck stringers near the hangers and midspan regions of

the through truss. The bridge member responses at several locations were measured under a 108 kip crane loading for several loading configurations. The loading configurations were also simulated

in the finite element model to obtain analytical responses, which were then compared with the experimental results. The finite element model was found to represent the measured response of the bridge quite well. Examples of model

validation and calibration are given in this section along with

examples of measurements that illustrate the complexities

associated with the response of the bridge. The maximum

incremental strain in one hanger due to the prescribed loading condition was measured to be 43.23 microstrain ( 1.25

ksi). After simulating the same loading condition in the

model, the strain value was obtained to be 45.5 microstain.

Some formula related above result are given as follows:

N

and

where

a: Stress (ksi)

8: Strain

A: Cross sectional area of hanger (m^)

A^: Axial load (kips)

E: Young's modulus (ksi)

In the following sections, the hanger analysis ofCommodore

Bridge will serve as an example to show how to represent the

knowledge related to this analysis problem. How to reason knowledge pertaining to it using multi-agent inference en- gines will also be discussed below.

176
The CBR Representation Schema

The knowledge pertaining to CBB project can be repre- sented in any kind of representing language. The reason that Case-Based Reasoning Language (5)(CASL) is chosen to

represent our domain knowledge is because the knowledge

associated problem domain is extremely dynamic and uncertain. Tremendous heuristic experiences are needed to solve

practical problems. CASL is a language specially good for

model and structure heuristic experiences. The contents of a case-base are described in a file known as a case file, using

the language CASL. The reasoner uses this case file to create a case-base in the computer's memory, which can then

be accessed and adapted in order to solve problems using

Case-Based Reasoning mechanism.

Like any other representing language, CASL has strict syntax, semantics, keywords and operators. The syntax of

CASL specifies the grammar rules of organizing knowledge,

and the semantics of CASL give the concise interpretation

of a sentence written in CASL with correct grammar. CASL

defines some basic types in the language: identifiers, strings,

numbers and operators, etc..

CASL normally divides a case-base into several modules,

each of which has its own syntax features and semantic explanations.

CASL semantics define the meaning of a sentence by specifying the interpretation ofthe keywords and basic types, and

specifying the meanings of operators. In the syntax blocks

of CASL, all keywords and literals are given in bold type.

A small example about hanger analysis is provided to show

how to use CASL to represent domain knowledge. When a problem is presented, certain conditions are specified. These

specifications are the input to the problem solver, or CBR

reasoner. The CASL structures the knowledge about in- put problems by defining the primary features of a problem.

Every primary feature has a weight associating to it. This

weight vaule indicates the importance of this feature. The brief explanations ofprimary modules and examples are given below:

1. Introduction. This module defines introductory text which is displayed when the reasoning process (reasoner)

is run. The purpose of the text is to help the user understand

the contents of the case-base or anything else of note.

2. The Case Definition. The purpose of this block is to define the problem features contained in a case. In the hanger analysis problem, the most important features are axial forces and bridge type. These features' weight val- ues are set to be 5 (reference weight). The cross sectional area ofhanger and Young's modulus etc., are not that important, comparatively speaking. Therefore, their weight values

are set to be 0 (reference weight). A sample case definition

using CASL is given below:

3. Index Definition. The purpose of this module is to define which fields are to be used as indices.

This part defines the fields which are used as indices when

searching for a matching case. The index scheme defines

the methods by which the reasoner should access the case memory. Indices are intended to streamline the matching

process. The index features are parts of the new problem

specification. For example, we use the features axial-force

and bridge-type as main indices to search the knowledgebase. The sample representation is given below:

index definition is index on axial-force;

index on bridge-type;

4. The Adaptation Rule Definition. The purpose of this block is to define rules used to modify a retrieved case from

the case-base to make it fit the current problem specifications. The global repair rule definition defined in this module allows adaptation rules to be applied on any modified

case. The rules defined here are derived from domain knowledge, formulae and constraints.

When the old hanger analysis whose "description of problem definition" part is the most "similar" to the current problem definition is retrieved from the case-base, its solution

part must be modified to fit the current problem definition.

The reasoner performs adaptations to an old solution according to certain rules defined by domain experts. The repair

rule definition is block of CASL can be used to define those

rules. In the hanger analysis problem, the following rules

(strategies) are defined:

(a) Perform simple parameter substitution: substitute parameters of old problem definition into new user input.

case definition is

field axial-force type is (number) weight is 5;

field bridge-type type is (Long-Span (Suspension, CableStayed, Truss, Arch), Short-Span, Culvert) weight is 5;

field axial-force type is (number) weight is 5; field cross-section type is (number) weight is 0;

field Youngs-modulus type is number weight is 0;

field Experimental Data type is number weight is 0; end;

177
(b) Perform old solution adjustment to make it fit substituted user input (current problem) according to domain

formulae.

(c) Check global constraints defined in the case-base to guarantee that no conflicts result.

In the sample given in Algorithm 1, the change yalue J is an

adaptation rule. It tests a certain condition (represented by

a formula) first; when the condition is satisfied, the action is

fired.

Algorithm 1: Adaptation knowledge representation:

(1) repair rule definition is

(2) repair rule change-valueA is

(3) when

(4) axial-force > radial-force

(5) then

(6) evaluate Stress OfHanger a to j

(7) evaluate Strain OfHanger e to f

(8) repair;

(9) end;

(10) end;

Algorithm 2: Case Instance Representation:

(1) case instance Hanger Analysis is

(2) bridge-type = Truss;

(3) axial-load = N;

(4) cross-section = A;

(5) Young-Modulus = E;

(6) Experimenta Data = D;

(7) solution is

(8) Stress = SI;

(9) Strain = S2;

(10) permissable capacity= C;

(11) local repair rule definition is

(12) repair rule rule.! is

(13) when

(14) bridge-type ^ Truss

(15) then

(16) pr 'Abandon your selection !

';

(17) pr 'This case can not be repaired to let you use!';

(18) reselect;

(19) repair;

(20) end;

(21) end;

5. Case Instance Definition. The purpose of this block

is to define the structure of a case instance. A case must

contain two parts: the problem part and the solution part.

The local repair rule definition defined in this module allows

adaptation rules to be associated with a case. These rules are invoked after the global adaptations have run their course. The past experiences of hanger analysis for applications are

stored in the case-base. Representation of these experiences

requires the design of certain structures which can represent cases properly. Normally, an experience (case) includes a problem statement part and a solution part. The case instance is block of CASL provides a kind of structure and

function. This block defines the same structure of problem

statement as the case definition is block defines.

A sample representation of a case is Algorithm 2:

The RBR Representation Schema

CLIPS (C Language Integrated Production System) is an

expert system tool developed by the Software Technology

Branch (STB), NASA/Johnson Space Center (9) . It is designed to facilitate the development of software to model

human knowledge or expertise. There are three ways to represent knowledge using CLIPS in a rulebase:

(a) Rule-Based Knowledge Representation: In this paradigm, knowledge is represented as a series rules. Rules are used to represent heuristics which specify a

set of actions to be performed for a given situation. A

rule is composed of a j/part and then part. The z/part

is a set ofpatterns which which specify the facts which

cause the rule to be applicable. The process of matching facts to patterns is called pattern matching (9). The

built-in inference engine matches facts against patterns

and determines which rules are applicable.

(b) Object-Oriented Knowledge Representation: In

this paradigm, knowledge is represented as a series modular components which inherit object-oriented

mechanism. For example, this mechanism makes hier- archy knowledge models possible.

(c) Procedural Knowledge representation: In this paradigm, knowledge is represented in terms of pro- cedural style like conventional language C, C++ and

Pasal etc. This capability is extremely usefijl when

knowledge can not be represented using rules or object-oriented mechanism.

Example of Rule-Based Knowledge Representation.

The following example shows how a rule-based representation is used for the CBB hanger analysis. The example

shows that when the RBR inference engine finds experimental data from instrumented hanger that is much larger than

178
analytical data from FE model, it fires and generates warning signal.

Algorithm 3: Rule-based representation by CLIPS:

(1) (defrule Warning-Signal)

(2) (experimental-data-isData)

(3) (analysis-data-isA-Data)

(4) assert(Something wrong on the CBB bridge!)

MULTI-AGENT REASONING SYSTEM

System Overview

The case-based reasoning engine, also called reasoner,

takes problem specifications and a case-base file as its inputs, performs reasoning about the problem, and returns an answer to the user automatically. The reasoning engine of

a case-based system consists of four process modules; each

of those modules performs certain fimctions. The modules

interact with the case-base and form a reasoning cycle. The

first module, Retrieved case, takes the current problem specifications as input and outputs a retrieved case. The second

module. Solved case, decides whether a retrieved case needs

to be adapted. This module either returns to the user a solution without fiirther modification or passes a solution to the next module which will perform adaptation on the case. The

third module, Repaired case, performs this adaptation and

returns an adapted case to the next module. The fourth module, Learned case, decides whether this new resolved case needs to be stored in the case-base. The kernel of CBR en- gine used in the problem domain was developed by Center

for Intelligent System. University of Wales (5). The primary

author has developed a wrapper for this engine, added extra

features for this engine and has added extra features including a Graphical User Interface (10).

The rule-based reasoning engine which is part ofthe CLIPS

system, also called inference engine, was developed by

NASA's Johnson Space Center (9). The CLIPS was a forward chaining rule language based on the Rete pattern

matching algorithm. The inference engine was implemented

as different modules. Every module has different fimctions

and piuposes. Detailed information about these modules is provided at the CLIPS website (9). Only basic ideas of this inference engine are introduced here. When the inference

engine is invoked (perceives input fi-om outside world), it automatically looks at the rulebase and matches facts against

patterns which are defined by the knowledge engineer. It then determines which rules are applicable. It selects a rule

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4

Rnkbaie

Figure 5. Multi-Agent Reasoning System Arcliitecture

and then the actions of the selected rule are executed. The

inference engine then selects another rule and executes its

actions. This process continues until no applicable rules re- main (9).

The following sections present how the writers will integrate

and implement these modules and the RBR inference engine. Only a detailed introduction of CBR reasoning engine

is presented here.

System Architechures

Main System Arcliitecture. Figure 5 shows the architechture of multi-agent reasoning system.

There are seven modules in the system. Each module per- forms specific tasks and fianctions.

(a) CBB Bridge Data Acquisition Module: This module

collects data fi-om instrumented sensors on the CBB

bridge and performs buffering and raw data storage

fimctions. The module consists of several different

data acquisition hardware and software systems ac- quired from a variety of vendors. Some of the vendors provide buih-in fimctions which can be integrated

Avith commercial data processing software. This makes

communication between the CBB Bridge Data Acquisition Module and the Data Preprocessing Component discussed below possible. For example, the OPTIM Electronics data acquisition system (6) provides

179
OLE interface that enables the system to communicate

with the commercial data processing software Lab-

\^ew which also supports the OLE standard.

(b) Data Preprocessing Component: This component

performs data preprocessing. It takes the CBB Bridge

Data Acquisition Module as input, checks data quality,

eliminates electrical signal errors and conducts preliminary data analysis and other related tasks. This module's kernel could be some data processing software

like LabView. The module outputs data either immediately to a display through some interface like web

browser or automatically to a database. For example, the former can be implemented through LabView

that can read data from OPTIM and send it to a web

browser for direct and immediate display to the user. The latter can be implemented by writing a customized

program which reads data from LabView's buffer or from its data storage disk. The program then routes the data to an archived database.

(c) Case-Based Reasoning Engine: This module per- forms GBR reasoning. It perceives knowledge from

casebase, extracts data from both the Data Preprocessing Component , an archived database and the Common Indice Component. It performs reasoning based on all the information mentioned above and returns reasoning results to the Decision Making Agent.

(d) Rule-Based Inference Engine: This module performs

RBR reasoning. It perceives knowledge from the rulebase, extracts data from both the Data Preprocessing

Component , an archived database and Common In- dice Component. It performs reasoning based on all the information mentioned above and returns reasoning results to Decision Making Agent which will be

introduced below. This module will be presented in more detail later. .

(e) Common Indice Component: This component serves

as hub to connect CBR system and RBR system together. It takes the user's problem as input, checks important features of domain problem and outputs these

features to the CBR engine and the RBR engine. It

triggers both engines through industry standard protocols such as COM(DCOM) which allows distributed

applications to communicate with each other. Since

the source code for the CBR and RBR engines are publicly available and they were created using C language,

it is not difficult to program a wrapper with a standard

interface for both engines.

(f) Decision Making Component: This component collects the output from the CBR reasoning engine and

the RBR inference engine. The component can be a

software entity or a human entity. The entity judges

the output from both engines and conducts reasonable

Reasoning

Engine

Functions

MatcWRcblevt

Figure 6. The primary functions of a CBR Reasoning engine

actions. For example, a software entity can issue warnings based on its judge from outputs of inference en- gines to warn the human manager that some members

of the bridge need to be reinforced or that the bridge

should be closed due to some catastrophic event.

Reasong Process of CBR Engine

The flow-chart in Figure 6 shows the main algorithm behind

the implementation of CBR reasoning engine. The two hollow arrows in the figure illustrate that the reasoning engine

must interact with the case-base.

The flow-chart shows that the requirements of a module can be broken into pieces or procedures called by the main fiinction. It also shows that a CBR engine forms a reasoning

loop. This reasoning loop begins with the procedure User

Specification and ends with the procedure Add Case. Pri- mary procedures used in the main algorithm are discussed

below separately.

Building the Index

The performance of a CBR system is determined by the

CBR reasoning engine whose efficiency is in turn deter- mined by the design of the index scheme and the case-base

memory organization. The index scheme design includes

how to specify index features and how to build them in computer memory. The index features are set by domain experts and are represented by the block index definition is of CASL. The procedure Indices takes the representations of index features as input and uses these to build the

index scheme. A linked-list data structure was chosen to hold the index feature input. The procedure Build Indices

places all the index features into the linked-list, and at the same time, builds the case-base memory organization. Fig- ure 7 illustrates these ideas.

180
Inda Bddini ud Memtt; OriuiatiM

CueQ) —p Caie(k) —^ CiM(li)

AxULoad v^ CMeaO —1> C«ie(j')

W —> CMeCk^ —1> CMe(h')

Figure 7. The index building and case-base memory organization

In this CBR system for the hanger analysis problem, two

features have been specified as index features: bridge-type

and axial-load. Each index feature is a node of the linkedlist and the data type for the nodes is the struct type in C.

The fields of the struct are used to hold attributes of the

index features. Figure 7 shows this data structure for the

index features and case-base memory. The procedure Build

Index first links the index features shaft diameter and load

direction. It then checks every attribute ofthe index features. For each attribute, BuildIndex searches for all the cases with

the same attribute value in the case-base file and links all of

these together.

Case Matching, Ranking and Retrieving

The purpose of building an index scheme is to speed up

searching. Here, searching means to find a set of cases fi-om

the case-base which are similar to the current input case. However, the goal is to find the case that has the maximum

similarity to the input case. Thus, a mechanism to rank the

similarity of cases is needed. In this section, the procedure

necessary to accomplish two goals (finding a similar case set and finding the most similar case in this set) is discussed.

First, a mathematical model is presented to demonstrate how

to find a set of similar cases in the case-base. What are similar cases'? Given an input case with certain index features

and their attributes, similar cases are those cases whose

index features and attributes are exactly the same as the

corresponding input case's. Figure 8 shows these ideas.

The top portion of the Figure 8 illustrates the mathematical

model for finding similar cases. The left and right circles

represent attributes F{A) and F{B) of index features A and B

ofan input case respectively. The C{n) represents a case n. If the left circle includes C{b) ,C{d) ,C{h) andC(a), which are the cases with attributeF(.4) offeature .4, and the right circle

Mathematical

Model for

Searching

Similar Cases

{C(«), C(h) } C= F(A) n F(B)

F(A) F(B)

Dmta Strnctim of CuclOrsaiilzation

Bridge-typci; H

va I H c<D I H

Axial-load H C(D I g>| c<«)

VH I $>| CQO I H C(n)

Figure 8. The mathematical model and an example for searching similar cases

includes C{i),CIJ),C{a) and C{h), which are the cases with

attribute F{B) of feature B, then their intersection contains cases C{a) and C{h), which have both attribute F{A) and

F{B). This can be represented in set theory as:

{C{a),C{h)]cF{A)r\F{B)

The bottom portion of Figure 8 provides a corresponding

example to illustrates how this process occurs in the casebase.

After all similar cases are found, a mechanism to find the

most similar case in this set is needed. In the system, the

Nearest Neighbor Matching algorithm (NNM) (7). Fig- ure 9 illustrates how this algorithm works in the CBR system

for the hanger analysis. To simplify the discussion, it is as- sumed that all ofthe component loads applied to the hangers

are at in the same direction.

The basic idea of the NNM algorithm is to compare the attribute value ofeach feature for each case in the set ofsimilar cases to every corresponding feature's attribute of the input

case, calculate the comparison values and then simi them for

each case to get a total comparison value. In the upper portion of Figure 9, the circles represent cases, the dots represent attribute values of features, index i rep- resents the input case, and index j represents cases in the

set of similar cases. The index k represents the features in a

case. The case A and case B in the figure are the cases fi-om

the similar cases' set. The fiinction d{k){ij) represents the

attribute's comparison value of one of the features (feature

k) between the input case and case A, which is equivalent to

181
D(IA)=S d(kKij)

D(IB)=2 «l'(k)(ij) k=l

caw

F(kXDj

Relevance Matrix

V(k)(Dl

V(k)(Dn

V(k)(R)l

d(kKll)=

1 * W(ll)

d(k)(nl)=

Siiii(F(R)i,,F(Dj) *W(iil)

V(k)(R)n

d(k)(lii)=

Siiii(F(R)i,F(Dj) * W(lii)

d(k)(im)=

1 * W(iui)

Sim(F(k)(R)i, F(k){I)j) = 0, %, (1- Ad / AT)

r: difference of feature value / difference range value between input and retrieved case

feature

Figure 9. The Nearest Neighbor Matching algorithm

the following formula (7):

W{ij)*SimiF{k){R)i,F{k)m

where:

k: a feature of a case. W{ij): the weight of a feature, defined in the case-base file. Sim{F{k){R)i,F{k){I)j): the degree of similarity between

one of the features in the input case and the corresponding

feature in a case from the similar case set. The total attributes' comparison value for a case is D(k)(IA),

which is equal to the niuneric function

2 W{ij)*Sim{F{k){R)i,Fik)m

to explain how to calculate every feature's attribute comparison value. In the matrix, F{k){R)i means "the feature k of a case fi-om the similar case set which has possible attribute

i, where the range of i can be fi-om 1 to some finite number". F{k){I)j has a similar meaning except in reference

to the input case. So, the first row of the matrix represents

all the possible attributes of feature kofa similar case, and

the first column represents all the possible attributes of fea- ture k of the input case. The intersection of row and column

is the comparison value of the feature k. The W{ij) is the

weight of a feature in a similar case. The degree of similarity has three possible values. First,

if two features match exactly, the degree of similarity equals

1. Second, if two abstract symbols are similar, its value is |.

Third, if two numbers are similar (i.e., both fall within the

range defined in the modification block), then a value is cal- culated which reflects how close they are in proportion to the range. Then, the Sim{F{k){R)i,F{k)iI)j) can be calculated

by:

Ad

Ar

where: Ad is the difference of the feature values between the input case and the retrieved case and Ar is the difference range value. For example, if the attribute value of feature axial load for the input case is 64 kips,

and the corresponding value for a similar case is 84 kips

Newtons, then Ad = 84-64=20. If the definition for the

range of similarity is fi-om 44 to 94, then Ar = 94-44=50.

Similarity between 64(input) and 84(a similar case) = 1 — i

= 0.6

Algorithm 4 defines the functions needed to find similar cases and the most similar case as mentioned above.

The procedure IndexXist^earchingf ) performs searching on the linked-list of index features. The procedure

CaseMst^earchingf ) searches out cases whose attribute value for certain features is the same as the input case's. The

procedure Computing.Weight-Cases ( ) performs calculates

the weight of a retrieved case and returns this value. The

procedure EvaluatingSimilar.Cases( ) ranks a case with

a weight. The procedure RetrievingJleaviestJOasef ) retrieves the case with the highest rank and returns this.

After finishing all calculations, the NNM algorithm selects the case which has the highest value of D{k){ij) to be the

most similar case. The key component of the NNM algorithm is the calculation of an attribute's comparison value for a feature between

a similar case and the input case. A matrix called the rele- vance matrix, shown in the lower part of Figure 9, is used

Adaptation of Cases

It is rare for a retrieved case to be exactly the same as the

newly defined problem. Most of the time the retrieved case

is only a similar situation, and so problem definitions and

corresponding solutions must be modified so that the modified case fiilly fits the current situation and its solution fully

182
Algorithm 4: Case matching, ranking and retrieving:

Input: User's input problem specification.

Output: The retrieved case with highest

weight.

MATCHING_RANKING-RETRIEVING(C/5'er/7i;JM0

(1) begin

(2) while true

(3) do

(4) IndexXistJSearching();

(5) CaseJ^ist.Searching();

(6) Computing-Weight.Cases( ); (7) if Case-MatchingSxact = True;

(8) rtturn RetrievingJOasei);

(9) else

(10) EvaluatingSimilai-Cases( ); (11) RetrievingJIeaviestjCase( ); (12) end

satisfies the current problem requirements. This procedure

as a whole is called the case adaptation (repair) process. A

series of rules are defined for adapting cases. These rules

are provided by domain experts or domain axioms and are applied to each case whenever it is necessary.

Adaptation rules are divided into global rules and local rules

The reasoner uses global rules to examine the problem fields and solution fields ofthe retrieved case. These rules are also

used to adapt the parameters ofthe retrieved case and check

constraint satisfaction conditions which are specified by the

knowledge-base. If there are any constraint conflicts, the

repair rules provide a new problem-solving proposal. Otherwise, they adapt the solution of the retrieved case to the

new problem. Sample adaptation rules for global repair are described in Algorithm 1. Figure 10 shows that a linked-list data structure is used to

store these adaptation rules. In the figure, every node has

two fields: one stores the condition ofa rule, the other stores the action. The procedure given in Algorithm 5 scans the

rule list repeatedly as it performs adaptation on a retrieved

case; if the condition part is true, it executes the corresponding actions on the case.

FUTURE WORK

This research touched upon both AI/CBR/RBR and bridge

engineering domains. The system discussed can also serve

as a template for other engineering domains. The following

areas are envisioned for future research.

(a) Implementation issues: Several challenges must be overcome in order to implement a muhi-agent sys- tem. The first chedlenge relates to knowledge engiRuleHcadPoifiter

1^ ^vj

Figure 1 0. The data structure of global and local rules

Algorithm 5: Algorithm for case adaptation:

Input: Retrieved case. Output: The modified case. CASE_ADAPTATlON(/?e^nevei/Case)

(1) b^n

(2) while true

(3) do

(4) if Global.Rules = True;

(5) FindingJGlobalJt.ule-Headpointer( ); (6) Searching-Global-Rules( ); (7) ApplyJ^odifyingRetrievedJOase();

(8) Parametric-Adaptation( ); (9) ConstraintS-Adaptation( ); (10) EvaluatingSolutions( ); (11) return ModifiedJSatisfied-Case;

(12) end

neering problems which are discussed below. Another challenge arises fi-om the fact that the system is composed of different modules, components and applications which are eventually distributed on different

computers and on a network. Developing efficient in- terfaces and a wrapper to permit them to effectively

communicate with each other is a major hurdle,

(b) Knowledge engineering issues: Because of the limitations of the CASL and CLIPS used to build our

system, there are still many limitations in expressing

problem solving intent. The case collection process is quite complicated and inefficient, and case-base maintenance is very unstructured. This makes debugging

the case-base very difficult. The rule scope defined for

the project domain is extensive because of the complexity of the CBB project. Improved methodologies

for case collection, rule collection and better protocols

183
to maintain the case-base and rule-base are needed.

(c) Knowledge acquisition issues: The authors built at- tribute (features) pairs during the initial design to allow

the user to interactively input knowledge. If the sys- tem is expanded (especially cross-domain), it would be

very difficult to enumerate all the features during design time to cover any and all possible problem specifications. Therefore, the development of an autonomous

knowledge acquisition system is a future challenge.

(d) Indexing issues: The authors built a fixed feature- based index scheme during the initial design to speed

up searching. However, as stated above, if the sys- tem is expanded, it would be impossible to optimize

this choice of index features, as the system is utilized,

many additional features may become important primary design factors. Since these features are not initially coded into the case-base or rule-base, the sys- tem will fail to find cases or match rules which have

these important features. Developing a dynamic index

scheme that will address this situation is an additional

research need.

(e) Graphical reasoning issues: In the Commodore

Barry Bridge project domain, many problems are solved by heuristic experience. Such experience is routinely relied on for interpreting processed images

or graphics; therefore, a graphical inference capability

becomes necessary. How to combine textual reasoning procedures with graphical reasoning procedures is another very important issue.

CONCLUSIONS

This paper discussed a system that uses Case-Based Reasoning and Rule-Based Reasoning as both a cognitive model

and problem solving methodology to deal with a bridge en- gineering problem for civil engineering applications. The

authors believe that this work will produce several insights

into how AI, CBR and RBR techniques can be better applied

to more realistic engineering problems:

(a) Knowledge Capture: Because the knowledge space

for the Commodore Barry Bridge domain is extremely

incomplete and dynamic, it is difficult to strictly rely on formalizing general, a priori, rules to help engi- neers to solve problems or automate the problem solving process. But using CBR techniques, the extensive

experience of many experts can be stored in a case

library. In contrast, rule-based techniques can compensate for the shortcomings of case-based techniques.

Through exploration of a usefijl rule-based tool like CLIPS, the knowledge can be modeled using objectoriented mechanism.

(b) Adaptability: CBR techniques can integrate knowledge acquisition, reasoning mechanisms, knowledge

storage and learning in one platform. Therefore, a sys- tem using CBR techniques can possibly grow and ex- pand to encompass a wider variety of assemblies with- out changing the fundamental system structure.

(c) Augmenting Intelligence: The proposed system,

rather than being completely autonomous, interacts with the user to obtain knowledge. It provides the flexibility to draw conclusions either from the system it- self automatically or by allowing the human engineer

to decide which actions he/she should take.

(d) Human-Guided Search: The system also provides

the flexibility to allow the engineer to loosen index

constraints to continue reasoning when an exact search

fails. In this manner, the engineer has the most opportunities to obtain a solution that is usefiil for his/her current problem.

Acknowledgements. Any opinions, findings, and conclusions or recommendations expressed in this paper are those

of the author(s) and do not necessarily reflect the views of

the supporting government and corporate organizations. The

authors gratefully acknowledge research support from the

following individuals: Mr. Robert Box (DRPA), Drs. Steven

Chase and Ghasemi (FHWA), and Drs. S.C. Liu and Ken

Chong (NSF).

REFERENCES

[1] Agnar Aamodt and Enric Plaza. Case-based reasoning:

Foundational issues, methodological variations, and sys- tem approaches. Artificial Intelligence Communications,

7:39-59, 1994.

[2] A. Emin Aktan, Kirk A. Grimmelsman, Raymond A.

Banish, and F.N. Catbas. Structural identification of a long

span bridge. Proceedings ofthe Fifth International Bridge

Engineering Conference, 1,2000.

[3] Raymond A. Banish, Kirk A. Grimmelsman, and

A. Emin Aktan. Instrumented monitoring of the com- modore barry bridge. Proceedings ofSPIE Fifth International Symposium on Nondestructive and Health Monitoring ofAging Infrastructure, 2000.

[4] F.N. Catbas, Kirk A. Grimmelsman, and A. Emin Aktan. Structural identification of commodore barry bridge.

Proceedings of SPIE Fifth International Symposium on

Nondestructive and Health Monitoring of Aging Infrastructure, 2000.

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[5] Center for Intelligent System. University of Wales. http://www.aber.ac.uk/ dcswww/Research/arg/cbrprojects/gettingxaspian.html.

Caspian.

[6] OPTIM Electronics http://www.optimelectronics.com,

2000.

[7] Janet Kolodner. Case-based Reasoning. Morgan Kaufmann Publishers, Inc., San mateo, CA 94403, 1993.

[8] Eray Kulcu, Xiaoli Qin, Raymond A. Barrish, and

A. Emin Aktan. Information technology and data management issues for health monitoring of commodore barry

bridge. Proceedings of SPIE Fifth International Symposium on Nondestructive and Health Monitoring ofAging

Infrastructure, 2000.

[9] NASA. Clips, http://www.ghg.net/clips/clips.html,

1999.

[10] Xiaoli Qin and William C. Regli. Applying case- based reasoning to mechanical bearing design. 2000 ASME

International Design Engineering Technical Conferences

and the Computers and Information in Engineering Conference.

[11] C.K. Riesbeck and R.S. Schank. Inside case-based

reasoning. Erlbaum, Northvale, NJ, 1989.

185
Stigmergy - An Intelligence Metric for Emergent Distributed Behaviors

Richard R. Brooks

Applied Research Laboratory

The Pennsylvania State University

P.O. Box 30

State College, PA 16803-0030

Email: rrb@acm.org

Abstract

Individual autonomous components can be constructed using simple

behaviors based entirely on locally available information. Simple

components aggregate to form complex systems with complex

behaviors. Artificial life research has proposed guidelines for constructing colonies of autonomous systems. Simulations

mimicking biological systems show these guidelines adequately

explain the behavior of many insect species. The complexity of

aggregated behavior often depends on stigmergy. Stigmergy occurs when behaviors by individuals modify the environment while being

regulated by the environment's state. Stigmergy has generally been

studied for the forward problem: predicting the consequences of local behaviors. It is also applicable to the backward problem: synthesizing

local behaviors to fulfill a global need. The concept provides an

objective measure of intelligence for natural and synthetic systems. A

system's intelligence is measured by its amount of effective stigmergy. It not only adapts to a changing environment, but also modifies the environment to suit the system's needs and goals.

Keywordst intelligence metrics, artificial life, stigmergy.

distributed intelligence

1. Introduction

"Self-centered - someone who does not think about me. " - Coluche (French Comedian

)

Egotistically, most people consider another person

intelligent when the other person agrees with them. The

Turing test is an egregious example of this tendency. A system

is intelligent, when its behavior resembles human behavior.

While flattering, this measure is not very objective.

Objectively, intelligence is a combination of many attributes. These include the ability to: • Achieve goals

• Compete with others

• Cooperate with others

• Develop new unexpected behaviors

• Adapt to a changing environment

These attributes are necessary but not sufficient for

describing intelligence. A truly intelligent system should also

interact with its environment, modifying the environment to

its advantage. This ability is based on what is called stigmergy

by Grasse [16].

Grasse coined the term stigmergy while studying highly

evolved societies of cooperating individuals. The societies

shared the following characteristics: • Construction of climate controlled communal housing

• Individuals altruistically sacrifice themselves for the

common good

• Equitable distribution of work among their members

• Division of tasks among castes of specialized workers

• Domestication of other species

• Creation of logistic networks to support cities and war

campaigns

These societies belong to the most universally successful

species on earth, controlling most of the air and ground space. They are distinguished by having six legs.

A collective view of intelligence is not limited to the

behaviors of insect societies. Cellular Automata research

shows how networks of extremely simple automata

collectively emulate general computation engines, such as Turing and von Neumann machines [27]. Connectionist

methods in artificial intelligence create complex behaviors in a network of extremely simple computation engines [10].

Minsky's Society ofMind describes human behavior emerging

from interactions among multiple simpler individual entities

[19].

Bonabeau's work [3, 4, 5] provides a starting point for an

objective definition and measure of intelligence. An

individual, or society of individuals, is intelligent when it exhibits a significant degree of stigmergy. It not only adapts to

its environment, it interacts with the environment, forcing the

environment to adapt to its needs and goals. This interaction is not purely deterministic but results in new behaviors that advance the system towards its goal.

The rest of this paper is organized as follows: Section 2

discusses relevant aspects of cellular automata, on appropriate

formalism for studying interactions of distributed systems.

Relevant studies of insect colonies are provided in section 3,

along with the original concept of stigmergy. Section 4

discusses applications of pheromones and stigmergy to synthetic systems. Some applications reproduce lifelike behaviors. Other applications create synthetic environments

using stigmergy-like control mechanisms. Section 5 describes

186
a distributed system where simple behaviors of local systems

combine to produce complex adaptive behavior for the

network. A possible stigmergy scale is further discussed in section 6, which concludes the paper.

2. Cellular Automata

A cellular automata (CA) is a synchronously interacting

set of elements (network nodes) defined as a synchronous

network of abstract machines [1]. A CA is defined by:

• d the dimension of the automata

• r the radius of an element of the automata

• d the transition rule of the automata

• s the set of states of an element of the automata

An element's (node's) behavior is a function of its internal

state and those of neighboring nodes as defined by d. The

simplest instance of a CA is uniform has a dimension of 1, a radius of 1 , and a binary set of states. In this simplest case for

each individual cell there are a total of 2^ possible

configurations of a node's neighborhood at any time step.

Each configuration can be expressed as an integer v;

V = i:^-,*2'^' (1)

where: / is the relative position of the cell in the neighborhood

(left=-l,current position =0, right=l), andy, is the binary value

of the state of cell /. Each transition rule can therefore be

expressed as a single integer r: 8

Iyv*2^ (2)

v=l

where y\, is the binary state value for the cell at the next time

step if the current configuration is v. This is the most widely

studied type of CA. It is a very simple many-to-one mapping

for each individual cell. The aggregated behaviors can be quite

complex [11]. Wolfram [27] has created four qualitative

complexity classes of CA's:

• Stable - Evolving into a homogeneous state.

• Repetitive - Evolving into a set of stable or periodic

structures. • Chaotic - Evolving into a chaotic pattern. • Interesting - Evolving into complex localized structures. Two further results show the computational abilities of

the CA. Simple CA's can be constructed that reproduce

themselves. This was one of the initial concepts von Neumann

had in mind when he originated the CA model [11]. CA

networks of sufficient size are capable of simulating general

computations [17]. Networks containing interactions of

extremely simple automata are therefore capable of producing

arbitrarily complex aggregated system behavior.

This is related to the ability of neural networks to produce

complex behaviors through network interactions among

simple threshold devices. Feed-forward and competition

networks can infer complex piecewise linear classification

functions from a set of examples [1 0, 22]. These abstractions

support the concept that intelligence is a property of

aggregated system interactions, rather than individual

components. It is worth noting that most connectionist

approaches rely on randomly choosing initial conditions in the

network.

3. Insect behaviors

Artificial life researchers seek new approaches to

intelligence, coordination, and self-organization among

distributed autonomous systems in insect colony behaviors

[24, 21]. Self-organization is very important in living systems.

The basic ingredients of self-organization are [3]:

• Positive feedback - includes recruitment and

reinforcement of behaviors.

• Negative feedback - counterbalances positive feedback to

stabilize the system.

• Amplification of fluctuations - randomness and

fluctuations are crucial to system adaptation.

• Multiple interactions - simple behaviors at the micro level aggregate into intelligent adaptations at the macro level.

In addition, arthropods have a number of broadcast signals

such as alarms [25]. These primitives are biologically inspired

and the basis of many complex animal behaviors such as swarming, flocking, etc. Insect colonies use pheromones to provide positive and

negafive feedback signals with these characteristics [14].

Pheromones are natural chemicals secreted by individual

animals, and received by other individuals using the sense of

smell. They influence the behavior and development of the

receivers. Pheromone interactions have been used to model

food collection, nest building, task allocation, and war in insect societies [3]. Computer simulations based on these

explanations have produced colony behaviors similar to those

found in nature [5]. Stigmergy is indirect communication between one or more agents through the environment using pheromone

interactions [16|. An individual interacts with its environment

depositing pheromones. The specific pheromone left depends

on the task being performed by the individual. Pheromones

degrade and diffuse over time. They also aggregate as shown

in figure 1 . In this way, multiple interactions can be combined

automatically to provide a single information source

describing the aggregate state of the environment. Sfigmergy

expresses the synergy that occurs when multiple agents form a feedback loop with their environment.

The presence of pheromones in the environment provides

dynamic information that regulates individual behaviors.

Individual actions aggregate into macro-behaviors and

pheromone signals aggregate into macro-information. In this way an agent modifies its environment, and the environment

adapts to the needs of the agent.

The best-known example of this is foraging for food by

ant colonies. Dorigo has expanded the basic concept into a general optimizafion methodology [12]. Each ant in a colony

187
Pheromone interactions

A PheiDinDiifi signal

Will each time step

T -2oBecome less intense and more difluse

Two positive pheionniie

signals combined

Become a more intense signal ofthe same type

A positive and a

Negative pheioitone

signal combine

To cancel eachother

Figure 1 - Pheromone primitive characteristics for information diffusion and reinforcement

performs two basic behaviors, regulated by two basic

pheromones:

• Look for food - wander in a stochastic manner

depositing a "searching for food" pheromone. If the "found

food" pheromone is detected, the stochastic movement is weighted to favor movement towards the "found food"

pheromone.

• Bringfood to the nest - when food is located, the ant picks it up. As long as the ant carries food it deposits the

"found food" pheromone. It moves in a stochastic manner

weighted towards the direction with the strongest "searching

for food" pheromone signal.

In [12] and [5] this behavior is analyzed in detail. By

heading towards the strongest concentrations of the

pheromones, ants tend to follow the direct path. By allowing

stochastic deviations, premature convergence to sub-optimal

solutions is averted. By aggregating behaviors of many

individuals, the system achieves a large degree of robustness.

Note that random decisions play a large role in this behavior. This resembles the use of random initial conditions

in neural networks. Many other meta-heuristic approaches,

such as genetic algorithms and simulated annealing [7], rely on stochastic, non-deterministic choices to find good quality

results.

Self-organizing systems of this type have several

appealing aspects, such as robustness and conservation of

resources. The existence of multiple possible solutions means

that if one solution becomes untenable another can be found.

Basing behavior on local decisions using purely local information reduces latency and bandwidth consumption. For

these reasons a number of artificial systems have been

designed using these principles.

Synthetic stigmergy has been applied to distributed route planning [5], military command and control [23], factory

workflow design [9], and telecommunications networks [4]. It provides a convenient formalism for expressing dynamic

interactions of multiple agents.

All of these approaches construct a synthetic environment

for cooperating agents. Agents change the environment,

adding information to it in the form of pheromones. Specific

attributes of the pheromone such as speed of dissipafion,

diffusion rate, and meaning are specific to the individual

application. Multiple simple agents then use the informafion

aggregated by the environment to steer their partially

stochastic behaviors. Note the similarity between this approach and the CA formalism. Macroscopic interacfions

between simple individual components provide complex

adaptive behaviors.

4. Synthetic Ecosystems 5. Autonomous Sensor Networks

Self-organizing systems are characterized in Bonabeau [3] by:

• Creation of spatio-temporal structures in initially

homogeneous media.

• Co-existence of many possible and reasonable solutions.

• The existence of bifurcations; common in non-linear

systems [2].

One application of this approach is in sensor networks.

Distributed sensor networks use mulfiple autonomous sensor nodes to provide a sensing system with greater precision and

dependability than any component sensor nodes [7]. When

multiple sensor nodes survey the same region, redundancy

reduces system sensifivity to single points of failure. At any

point in time, a single sensor provides a single data point.

188
Collaborative signal processing aggregates data points into a more reliable global estimate with dependability estimates.

This is similar to using multiple experiments to statistically

determine a parameter value and its variance [20]. A number

of military and commercial applications exist for this technology [6, 13].

Sensor nodes do local processing and relay information

among themselves. They self-organize into a coherent whole,

forming an ad hoc multi-hop network. Data is relayed from

one node to another. Routing choices can be made

dynamically using self-organization primitives such as pheromones. Master nodes determine a frequency-hopping

schedule that slave nodes follow. Data can be forwarded from

one cluster of nodes to the next, until a gateway to the Internet

is reached, at which point, a number of user workstations can

access the information simultaneously.

Sensor networks have a number of unique aspects.

Manual deployment and placement of a large network of

sensors would be time consuming and expensive. Ideally the

nodes could be deployed automatically. When the number of

nodes increases beyond a trivial number, manual network

organization becomes problematic as well. Figure 2 illustrates

many of the factors that influence network organization and

deployment.

When there are a large number of nodes, manual task

distribution becomes onerous and time consuming. If conflicts

exist in the needs of different user communities the process

becomes even more challenging. All of these reasons point to

the fact that the networks must be capable of self-organization

and autonomous tasking.

Nodes have a finite lifetime, which is shortened by

computation, sensing, data transmission, and data reception

because they are battery-powered. Most distributed

dependability theories are irrelevant to these networks [15].

Distributed dependability verifies the properties ofsafety (lack

of undesirable events in the network) and liveness (a networks

eventual return to a long-term steady state). Since batteries

will eventually be exhausted, the network will eventually fail. The property of liveness is impossible to attain. Instead, the

system must strive for adaptability. It should reconfigure and

tolerate multiple faults. Routing algorithms should avoid

creating "hot-spots" that frequently relay data through the

multi-hop network, since they will fail much more quickly

than the rest of the network. Traffic to relay system

housekeeping information should be kept to an absolute

minimum.

Traffic patterns for sensor networks differ from those in

more traditional ad hoc mobile communications networks,

such as cell-phones. In traditional ad hoc networks,

communications are desired between two specific nodes

(customers). The network routing protocol needs to find the

node no matter where it is located in the network. Sensor

networks have the opposite task. Information is required about

a specific location. The node identity is irrelevant. For this reason, routing is data-centric.

0.6

0.5

0.4

0.3

0 2

0.1

0 P Figure 2. When networks are manually or autonomously deployed and configured, a number of factors need

to be considered. These include sensor range and communications range r^. In the current implementation

rj > r^. In addition to this the nodes position is generally knovra from GPS units and have an associated

uncertainty

189
Figure 3 An example transient effect modeled for packet density in a simple sensor network.

In cell-phone networks, communications occur between

nodes in the network for a conversation of unknown length.

Conversation length is often modeled as an exponential

distribution. A reasonable goal is to find a path through the

network, which asymptotically approaches the least cost path over time. In sensor networks, queries tend to be punctual.

They are either to inform the user of the current state, or inform the user quickly when a given event occurs. It will not be unusual for a single packet to be sufficient. Asymptotic

optimality is irrelevant. Transient effects that can be ignored

in other systems become much more important. Figure 3

illustrates an example of the transient effects modeled for a simple network.

The Reactive Sensor Network project at the Pennsylvania

State University Applied Research Laboratory implements a

mobile code infrastructure that augments sensor network

adaptability [8]. This approach is inspired by the active

network paradigm [26].

This approach helps in implementing a self-tasking

network. Specific node work assignments need not be knovra

in advance. The software can be reconfigured and modified as needs arise. Similarly if the battery fails on anode performing

an essential task, another node can download the software

needed to replace it. Figure 4 provides a view of how the

individual nodes interact to form a single multicomputer.

Notice that it is a macroscopic multicomputer aggregating the

behaviors of its autonomous components.

Pheromone based control is also possible. One candidate

pheromone is remaining battery power. Another candidate

pheromone is distance to an Internet gateway. Combining the

Figure 4. The network is a large computing system formed of individual nodes and sensing devices. Task

distribution is determined based on current workloads.

190
two pheromones provides a self-organizing sensor data

network synthetic eco-system that avoids the creation of "hotspots." This extends the useful Hfetime of the network. The

CA formahsm is useful in exploring a distributed system like autonomous sensor networks (figure 4). Behaviors of

individual nodes can be simple and guided by local information. What is important is that the entire system

develops complex adaptive behavior from interactions among

the nodes.

6. Conclusion

Unfortunately, most intelligence metrics are inherently

subjective. They often translate into the Supreme Court's

metric for pornography: "I know it when I see it." Equally

unfortunately, most of us recognize intelligence mainly when

looking in the mirror. An example of this type of subjective

and narcissistic metric is the Turing test. For the concept of

intelligence to be useful, it needs an objective metric.

Can a purely deterministic system be considered

intelligent? If this is the case, arithmetic equations and

statements of fact are legitimate candidates for intelligence.

To the contrary, intelligence is beyond rote memorization and

execution of explicit recipes. Intelligence has a creative

aspect. An intelligent entity must provide unexpected,

creative, appropriate, results. This implies a nondeterministic,

random, or stochastic component. Distributed networks of

simple interacting automata are robust examples and are capable of performing general computations [27].

Two existing qualitative hierarchies provide objective

metrics of intelligence:

• Chomsky's language hierarchy: (1) regular grammars

recognized by finite state automata, (2) context free

grammars recognized by push-down automata, (3) context

sensitive languages recognized by linear bounded

automata, and (4) recursively enumerable sets recognized

by Turing machines [18]. • Wolfram's complexity classes of CA's: (1) stable, (2)

repetitive, (3) chaotic, and (4) interesting.

To measure the IQ of intelligent systems another qualitative

scale is needed that measures systems interactions with their

environment:

• Nonadaptive - most systems

• Adaptive - can regulate parameters to fit environmental

conditions. Most controllers would be in this class. • Self-Organizing - adapt to their environment and

autonomously reorganize as required. [12] and [8] are examples of this class.

• Full stigmergy - modify the environment to suit their

goals. Nest building termites, wasps, and humans are in

this category.

Discussing intelligent systems presupposes that intelligence is not a purely human attribute. It is an attribute in both living and artificial systems. For that reason, it is appropriate to use concepts from biological studies of nonhuman intelligence. In simulations of insect societies and

construction of artificial systems, stigmergy has been the key

to designing robust, creative, emergent behaviors. An

appropriate metric for comparing intelligence should be based on the system's stigmergy, stigmergy being the system's

ability to interact with and modify its environment to advance

the system's goals.

7. Acknowledgments

This effort is sponsored by the Defense Advance

Research Projects Agency (DARPA) and the Air Force

Research Laboratory, Air Force Materiel Command, USAF,

under agreement number F30602-99-2-0520 (Reactive Sensor

Network). The U.S. Government is authorized to reproduce

and distribute reprints for Governmental purposes

notwithstanding any copyright annotation thereon. The views

and conclusions contained herein are those of the author's and

should not be interpreted as necessarily represent the official

policies or endorsements, either expressed or implied, of the

Defense Advanced Research Projects Agency (DARPA), the

Air Force Research Laboratory, or the U.S. Government.

8. References

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introduction to dynamical systems. Springer Verlag, New

York. 1997.

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Fundamentals and Applications with Software. Prentice

Hall PTR, Upper Saddle River, NJ. 1 998.

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System: Optimization by a colony of cooperating agents,"

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Coordination in Sensor Networks. ACM MobiCom 99,

Seattle, WA, 1999.

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London, 1987.

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Computing in Asynchronous Environments. ACM

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inter-individuelles chez Bellicositermes Natalensis et Cubitermes sp. La theorie de la stigmergie: essai

d'interpretation du comportement des termites

constructeurs," Insect Sociology, vol. 6, pp. 41-84, 1959.

[ 1 7] H. Gutowitz, Cellular Automata: Theory and Experiment,

MIT Press, Cambridge, MA, 1991.

[18]Hopcroft, J.E., and UUman, J.D., Introduction to Automata Theory, Languages and Computation,Ad<^sonWesley, Reading, MA. 1979.

[19]Minsky, M., The Society of Mind. Simon and Schuster,

New York. 1986.

[20]Neter, J.,Wassennan, W. and Kutner, M.H., Applied

Linear Regression Models, Irwin, Burr Ridge, IL, 1989.

[21]Langton, C.G., ed. Artificial Life: An Overview. MIT

Press. Cambridge, MA. 1996.

[22]Pandya, A.S. and Macy, R.B., Pattern recognition with

Neural Networks in C++. CRC Press, Boca Raton, FL.

1996.

[23]Parunak, H. and Brueckner, S., "Synthetic Pheromones for

Distributed Motion Control," JFACC Symposium in Advances Enterprise Control. November 1999.

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[25]Skirkevicius, A., "Some Characteristics of Insect Pheromone Communication," Sensory Systems and

Communications in Arthropods. Pp. 55-61. Birkhaeuser

Verlag, Basel. 1990.

[26]Tennenhouse, D. L,. J. M. Smith, W. D. Sincoskie, D. J. Wetherall, and Minden, G.J., A Survey of Active

Network Research. IEEE Communications Magazine, vol. 35, no. I, 80-86, 1997.

[27] Wolfram, S., Cellular Automata and Complexity.

Addison-Wesley. Reading, MA. 1994.

192
Performance Self-Assessment by and for Regulation

in Autonomous Agents

Elpida S. Tzafestas

Institute of Communication and Computer Systems

Electrical and Computer Engineering Department

National Technical University of Athens

Zographou Campus, Athens 15773, GREECE,

brensham @softlab.ece.ntua.gr

ABSTRACT

We are studying the problem of connecting intelligence to performance

in the context of autonomous agents with very limited capabilities,

where performance is suspected to include very few parameters and be

easier to quantify than in more complex cases. We are reviewing and

comparing three behavioral models that solve three typical autonomous

agent problems, the explorer robot [1][2], the food-collector ant [3] and

the cooperative tit-for-tat agent [4]. In all three cases and despite the apparent differences between them, we have defined a single problemdependent performance measure and, on that basis, we have found that the most intelligent among several alternative models, i.e., the one that

to the eyes of an observer achieves better performance, is a self- regulatory mode! involving a two-level regulatory process and an

internal variable representing the state of the problem-solving process,

thus self-assessing recent performance. The power of the agent lies in the second level and regulation mechanism, that is problem-dependent,

and that has been shown to achieve the highest performance among

many alternative models in all three problems. The whole design thus allows the agent to assess its actual performance and correct its behavior by modifying accordingly the first-level regulation rates, or

equivalently by adapting the first level regulation law. From a symmetrical point of view, the agent may also be thought of as predicting the future state of the environment and adapting accordingly.

The self-regulatory process appears therefore as both the means to effective performance assessment and the low-level prerequisite to enhanced intelligence.

1. INTRODUCTION:

FUNDAMENTAL CONCEPTS

We are studying the problem of connecting intelligence to performance in the context of autonomous agents with very

limited capabilities, where performance is suspected to include

very few parameters and be easier to quantify than in more

complex cases. By definition, the bottom-up study of

intelligence relies on two axioms, evolution and interaction. The

axiom of evolution states that higher forms of intelligence

appear as a result of an evolutionary process that proceeds from

simpler to more complex forms. Complex intelligence thus

containts and requires antecedent simpler intelligence. On the

other hand, intelligence has no absolute value, but depends on

and is the result of dynamic interaction with a changing world.

From an evaluation point of view, intelligence is not a welldefined nor a well-specified property, but it depends on an

observer's point of view, or as Brooks [5] says "intelligence is in the eye of the observer". An agent demonstrating intelligence

through dynamic interaction with a changing world has to be

responsive to its environment and adaptive to a range of

unpredicted events and situations. For the sake of enhanced

stability, adaptivity methods should better be constructed or "controlled" by the agent itself. On the other hand, we, as designers of autonomous agents, are seeking universal design

laws that will make our job easier in the long term. To this end,

we are investigating a number of classical autonomous agents

problems in an attempt to identify common design solutions, that

is design solutions that share design principles. In what follows, we are reviewing and comparing three

behavioral models that solve three typical autonomous agent

problems, the explorer robot [1][2], the food-collector ant [3]

and the cooperative tit-for-tat agent [4]. In all three cases and

despite the apparent differences between them, we have defined

a single problem-dependent performance measure and, on that

basis, we have found that the most intelligent among several

alternative models, i.e., the one that to the eyes of an observer

achieves better performance, is a self-regulatory model

involving two regulatory processes and an internal variable

represendng the state of the problem-solving process, thus self- assessing recent performance.

The crucial internal agent variable has to be regulated

within bounds. The goal of the agent is to either bring it to a

limit (say 0) or prevent it from reaching the extremes. This

design step depends on the definition of a quantitative

environment or problem state, that will be next used as a metric

to evaluate different design alternatives.

Regulation occurs using positive feedback, so that the

agent's variable follows the tendency of the external world that it

193
tries to represent, although the value of the variable almost never

coincides with the truth, but rather it maintains a representational

distance from it. At a second or meta level, another regulation

process takes place that regulates the rates of the first level using

negative feedback. The power of the agent lies precisely in the

second level and regulation mechanism, that is problemdependent, and that has been shown to achieve the highest

performance among many alternative models (including the one

without meta-regulation) in all three problems.

The whole design thus allows the agent to assess its actual

performance and correct its behavior by modifying accordingly

the first-level regulation rates, or equivalently by adapting the

first level regulation law. From a symmetrical point of view, the

agent may also be thought of as predicting the future state of the

environment and adapting accordingly, because conceptually the

negative regulation law is the following:

If (the world diverges from the agent's

representation of it)

then (m the future) adapt so as to get

closer to the world,

else (in the future) adapt so as to amplify

differences from the world.

As a conclusion, the three case studies show that when an autonomous agent problem may be formulated as a regulation

problem, the most intelligent alternative model, i.e. the one

achieving highest performance, is one that continuously assesses

its own performance and regulates its internal parameters

accordingly. Therefore, in these cases intelligence appears as the

result of low level self-evaluation and regulation.

2, CASE STUDY I : EXPLORER AGENTS

l.X.The Problem

A typical problem encountered in the behavior-based robotics

literature is that of exploration : a set of agents (robots) lands on

a planet with the mission to explore its surface for samples of

minerals having certain properties. The robots arrive in a spaceship that serves as the planetary base in the course of the

mission. The mission is accomplished when the whole surface

contained within a certain distance from the base is explored,

i.e., when the agents have "swept" the whole area and exhausted

the sources of interest (cf for instance [6][7]). The agents are supposed to return to their base once their mission is accomplished.

The exploration problem has been traditionally tackled

from a "functional" point of view : "How does one or more

agents sweep a delimited area to exhaust the sources of

interest ?". The answer to this question is a control system, an

architecture, that allows an agent to navigate, perceive, detect

minerals etc., in order to sweep the area in question. A solution

such as those encountered in the literature (for instance [8]) that comprises a random component and even without spatial

reasoning or learning, statistically ensures the coverage of the

interest field and the exhaustion of the mineral sources. However, from a more "cognitive" point of view, this functionality alone does not respond to the crucial question : ''How do the agents know that they have swept the whole area, or that they have accomplished their mission In order to answer to that question, we have to reformulate the description

of the sweeping task, in a way so as to include an expression,

analytical or other, that represents the termination criterion, that

is the exhaustion of the mineral sources. To this end, it is sufficient to define an environmental variable, the density of

mineral sources, which characterizes the state of the explored

area at any moment. In what follows, this density will be

denoted as p^.. The explorer-sweeper agent's goal becomes

therefore to bring the value of that variable to 0. We will see that an agent having a representation of that variable constitutes a simple solution to this description problem.

Lastly, we seek an agent model that would "optimize"

performance, i.e. that would allow an agent to accomplish its mission as fast as possible.

In our simulations, the world under exploration is defined

as a square around the central base : the size of the world is therefore the length of the square's edge (the results reported

have been obtained in a 25x25 world). The agent's basic control

system, as well as the simulation details, is given in [1][2]. We

are analyzing next the single agent case, whereas the multiple

agents case is studied in [1][2].

2.2. The Solution : Reformulation of the Problem

We come now to the second question : "How does the agent

know it has swept the whole area in order to return to the

base ?". It needs a way to detect the degree of task completion

or else a termination criterion (sweeping completed). The only

parameter of the task that can be useful to the development of a termination criterion is the source density in the world pjt). If the agent knew in advance its inifial value pJO), we could define

as termination criterion a formula such as (pJO) * sqr(r)

samples have been collected} (where r is the size of the square's

edge, here 25). However, this criterion is not robust because if a sample is not detected, the agent will never terminate (on the

other hand, we could certainly allow ourselves to miss a couple

of samples).

A simple solution to this problem is to estimate

continuously the value of pj[t) and, given that it falls to 0 as a

side effect of the agent's activity, take as a termination criterion

pJO)=0. Estimation of the value of pJt) involves then a representational variable which is local to the agent (pJt)) and

194
may be done through a simple formula of proportional

adaptation : Represeatational variable : Pa(t)

Proportional adaptation (window w, rate r) : Pa(t) = Pa(t-w) + diff * r diff = Pcomp - Pa(t-W)

Pcomp = number of picked samples / number of

moves (during the adaptation window)

Termination criterion : Pa(t) < ep

where ep is a small threshold (here,

ep=0.001)

The Pcomp is the agent's estimate of p„, as computed during the

adaptation window and the proportional law ensures that the

estimate's update does not take place too quickly. This

representation/adaptation system shows the advantage of

robustness in front of perturbations/manipulations such as

reinitialization of pjt) during sweeping. Figure 1 illustrates the

coevolution of the two variables pJt) and pJt). As is shown in

the figure, the representational variable allows the agent to always solve its termination problem without ever taking the

real value of the variable it represents (except a crossing point).

Both variables fall progressively to 0 without ever taking the same value — we could say that pJt) "follows" pJt). Actually,

the rapid rise of pJt) in the beginning of the sweeping phase is due to the presence of a sensor of distant samples that makes the

agent head toward the mineral sources minimizing its erratic behavior in a way that most of the visited places contain

samples. The value of pJt) falls then because the value of pJt)

decreases as a side-effect of the agent's activity who finds less and less samples.

2.3. On Efficiency : Meta-Regulation

Next, we proceeded to study the relation between the adaptation

system's w and r parameters and the initial world value pJO).

The system has been simulated for several values of w and r in several initial world densities. The simulation results for three

sets of adaptation parameters (quick, medium or slow

adaptation) in a medium initial world density are given in fig. 1. The quick adaptation is more operational than the medium

one, which is in turn more operational than the slow one (always

according to the task duration criterion). However, the quicker

the adaptation, more fluctuations it shows, and the slower the

adaptation, more delays it shows. Furthermore, the same

parameter setting gives different results in different world

densities : the difference in the results is reflected on the shape

of the curves (for more curves, refer to [1][2]). More

particularly, the agent's response to different perturbations (the

shape of the curve of PaU)) differs according to the boundary

condition (pJO)) : for the same parameter setting, the agent

finishes its task more or less quickly according to the value of

pJO), that is the duration of the interval between the moment of

picking of the last sample and the definitive return of the agent

to the base is very variable. It seems therefore that to ensure the

agent's operationality in different worlds, we need to find a means to combine the operational advantages of quick

adaptation with the advantages of slow adaptation as far as curve

regularity is concerned.

tlH! .

Qki .

0 15

00

r% ZT^o 4140 5KI saw

Figure 1. Performance of the agent for different

parameter settings in a medium initial world density,

pJ0)=0.5 (pJ0)=0.l5), ti=1437, t2=3278, t3=6821.

First part (quick adaptation) : w-l5, r=0.3, dti=1437.

Second part (medium adaptation) : w=30, r=0.2,

dt2=1841.

Third part (slow adaptation) : w=45, r=0.1, dt3=3543.

More precisely, we need a quick adaptadon near the end (to

terminate quickly), but a slow adaptation during picking (to

avoid fluctuations). We have then to find a way to stabilize to the right parameter setting on-line. Otherwise stated, we need a

meta-adaptation system

Meta-adaptation has to affect the w and r parameters in a way that adaptation becomes quicker when p^^t is sufficiently

close to pa(t) and slower when it is far from it. This metaadaptation law translates the fact that the world is more reliable

when it is not much different from the agent's idea about it, otherwise it should not be taken too seriously.

Meta-adaptation : If Idiffj (= IPcomp-Pa(t-wj I

) < fp,

then quicker adaptation

r rr„ax, w -> (r = r + rr * (r^ax-r) , w = w + r^ * (Wmin-v)

)

otherwise slower adaptation

r -> r^in, W -> Wmax {r = r + rr * (rn,in-r), w = w + r„ * (Wn,ax-W) ) Figure 2 gives the results of applying the meta-adaptation

system in three initial world densities ; as is shown in the figure,

the agent's response (the shape of the curve) is the same for ail three exemplary densities, or else the residue of mission duration

after picking the last sample is approximately the same in all three cases. We have shown in [1][2] that the operationaUty of the agent

with the meta-regulation law does not depend qualitatively on

the values of w„i„, w^ax^ r„in, r^, r„ and r^. Furthermore, the

initial condition (pJO)) plays no role either.

195
t «

r

r ?

to

I 5.-)-|

Figure 2. Performance of the agent with a metaadaptation system for three initial world densities, low

(pJO)=0.l), medium (pJO)=OA) and high (pJ0)=0.9)

ipJ0)=0.l5). dti=897, dt2=1663, dt3=2211 (ti=897,

t2=2560, t3=477I). (fp=0.l, w^,„=15, w„^=40, /•„,„=0.15,

3. CASE STUDY II : TRAIL-MAKING ANTS

3.1. The Problem

In another variant of the previous problem ([8][9][10]) there are a few large sources distributed in the world. The solution in this case consists in allowing a robot to lay down trails or "crumbs"

while carrying a source sample to the home base, that another

robot or itself may follow to arrive to the source quickly. A

different version of the problem considers that trails laid down

by the robots evaporate slowly, in the same way as pheromone

quantities laid down by real ants in the physical world ([11]).

The first complete solution has been given in [10], where a number of increasingly complex and increasingly satisfactory

solutions have been analyzed. The Tom Thumb robot is able to successfully build, reinforce and correctly use trails from the

home base to the source, while the Docker robot [10] uses an

additional mechanism of sample "theft" from neighbors, which

allows robots to build chains resembling harbor Dockers. The

motivation for our work has been our feeling that the Tom

Thumb robot as defined is not stable because it assumes unbounded numbers of "crumbs", which is not physically

possible, and which would show in a real robotic

implementation. A detailed presentation of what follows may be

found in [3]. The Tom Thumb robot's behavioral diagram as described

in [10] is as follows : If (carrying samples)

If (back home) lay down samples

Else {go home, lay down 2 crumbs}

Else

If (found samples) pick up samples

Else

If (crumb or stimulus sensed) (*)

{follow stimulus, pick up 1 crumb}

Else move randomly

(*) In the Docker robot, the condition

(crumb or stimulus sensed) is replaced

by (crumb or stimulus or loaded robot

sensed)

.

The Tom Thumb robot lays down two crumbs while homing,

and picks up one crumb while following crumbs or stimuU.

Unless otherwise stated, all simulations reported below use a 30x30 grid world with a large source at one of the comers and a population of 10 robots starting with 50 crumbs each. Robots

may sense a sample or crumb from a distance of up to 3 grid

cells.We have simulated the behavior of the system as is, by

measuring the quantities of crumbs deposited in the world or owned by individual agents. As was expected, the quantities of

crumbs owned by robots generally fall below zero, while the

quantity of crumbs deposited in the world may rise without limit. The exact values of these quantities depend on the problem

parameters (distance from source to home base, number of

robots and source size) that define the expected number of robot

trips source-base necessary to complete the task.

3.2. The Solution : Reformulation of the Problem

An apparent quesdon arising at this point is, "what if we just

constrain robot behavior so as not to lay down crumbs when it does not have any ? aren't crumbs deposited so far enough ?" We have been able to see in several experiments that, first, depending on the problem parameters, the total quantity of

crumbs might not be sufficient, in which case the path to the source will be disconnected, and, second, when it is sufficient —

for instance if we start the above experiment with 1000 crumbs

per agent — the total number of crumbs deposited in the world

may rise tremendously. This last condition generates an

important problem : the robots will continue being attracted for a long time to an empty source, that is, the surplus crumbs will be

misleading. This observation brings us to the actual formulation

of the above trailing problem : We are seeking a laydown-pickup mechanism such that a

trail to a source is built quickly and reinforced while the source exists and vanishes shortly after the source is exhausted.

The problem of agent crumb exhausdon lends itself to a simple solution. Every time a robot needs to lay down or pick up

crumbs, it should do it in a way so as to preserve its own

quantity of crumbs within some desired bounds crumbs„i„ and

crumbs„ax< by using the following laws : For laydown crumbs (t+1) = crumbs (t) +

ri * ( crumbswin - crumbs (t)

)

For pickup crumbs ( t+1 ) = crumbs (t) + rp * ( crumbsmax - crumbs (t)

)

This simple regulation mechanism ensures that no agent will ever run out of crumbs completely. However, the absolute (real196
valued) quantity of crumbs deposited or collected at each cycle

will depend on the state of the agent : an agent with many

crumbs will lay down more and pick up less than an agent with

just a few crumbs remaining. This arrangement allows for trails

to be built rapidly (because agents in the beginning tend to lay

down large quantities of crumbs) and to vanish quickly (because

agents toward the end of the task have statistically only a few

crumbs, so they tend to pick up large quantities of crumbs). In what follows it will be assumed that crumbs,„i„=lO and

crumbsinax = 100, for all agents.

3.3. On efficiency : Meta-Regulation

A large laydown rate will be beneficial in the start and middle of

the task, when the agents would like to build and reinforce a trail quickly, while a large pickup rate would be beneficial toward the

end of the task, when the agents would like to destroy the trail to the exhausted source as quickly as possible. While a given

parameter setting would be more desirable than another one in a

particular context, our goal as designers should be to ensure the

better behavior globally, i.e., to ensure that the system will "discover" or identify the proper parameter setting in each

situation. Consequently, what we really want is not a particular

parameter setting, but a mechanism that will allow a robot to lay

down more and pick up less crumbs at the beginning of the task

(so as to build and reinforce the path) and vice versa toward the

end (so as to destroy it quickly). To this end, a measure of the

state of the task must be available. The only such measure that a robot may have is the number of the crumbs in the world.

However, since this quantity cannot be directly perceivable, we

have used an estimate of it, simply the number of crumbs at the current position of the robot. This estimate is used as follows : For laydown

If crumbs (t) >= world_crumbs_estimate

ri(t+l) = nit) + rri * (ri„,^x - ri(t))

else ri(t+l) = ri(t) + rri * (ri^in - ri(t))

For pickup

If crumbs (t) >= world_crumbs_estimate

rp(t+l) = rp(t) + rrp * ( rp^in - rp(t)

)

else rp(t + l) = rp(t) + rrp * (rp^ax - rp(t) ) As is obvious from the formulae, the rate of crumb laying

increases when the robot owns more crumbs than may be found

in its current position and decreases otherwise. Inversely, the

rate of crumb picking increases when the robot owns less crumbs than may be found in its current position and decreases

otherwise.

Figure 3 gives a typical result of the application of the

above model. Surprisingly enough, the self-regulation of the

laydown and pickup rates not just does change the qualitative

behavior of the agents (the quantity of crumbs in the world rises quickly to a fairly high value, stays close to it during the task,

and falls back quickly to zero when the source is exhausted,

while showing far less fluctuations than in the previous case),

but it improves results quantitatively as well : in all runs, including the one depicted, the duration of the task has been

shorter than with the non-regulated model.

100

30

60

40

20

0

160 320 480 640 800

Figure 3. Quantity of crumbs owned by a metaregulated agent in a typical run. It fluctuats between

the upper and lower limits.

4. CASE STUDY III

ADAPTIVE TIT FOR TAT AGENTS

4.1. The Problem

A major issue on the intersection of artificial life and theoretical

biology is cooperative behavior between selfish agents. The

cooperation problem states that each agent has a strong personal

incentive to defect, while the joint best behavior would be to cooperate. This problem is traditionally modeled as a special

two-party game, the Iterated Prisoner's Dilemma (IPD).

At each cycle of a long interaction process, the agents play

the Prisoner's Dilemma. Each of the two may either cooperate

(C) or defect (D) and is assigned a payoff defined by the

following table. Agent Opponent Payoff

C C 3 (= Reward)

c D 0 (= Sucker)

D C 5 (= Temptation)

D D 1 (= Punishment)

Usual experiments with IPD strategies are either tournaments or

ecological experiments. In tournaments, each strategy plays

against all others and scores are summed in the end. In ecological experiments, populations of IPD strategies play in

tournaments and successive generations retain the best strategies

in proportions analogous to their score sums. The first notable behavior for the IPD designed and studied

by Axelrod [12] is the Tit For Tat behavior (TFT, in short) : Start by cooperating,

From there on return the opponent's previous move.

197
This behavior has achieved the highest scores in early

tournaments and has been found to be fairly stable in ecological

settings.

The best designed behavior found so far in the literature is GRADUAL [13] which manages to achieve the highest scores

against virtually all other designed behaviors. This behavior

starts by cooperating and then plays Tit For Tat, except that it does not defect just once to an opponent's defection. Instead, it responds by playing blindly (nxD)CC, where n is the opponent's

number of past defections. That is, GRADUAL responds with

DCC to the first opponent's defection, DDCC to the second, etc. The justification given for the performance of this behavior is

that it punishes the opponent more and more, as necessary, and

then calms him down with two successive cooperations.

The motivation for our work has been our conviction that a behavior comparable to GRADUAL could be found, that has not permanent, irreversible memory. Instead, we are after a more

adaptive tit-for-tat based model that would demonstrate

behavioral gradualness and possess the potential for stability in front of changing worlds (opponent replacement etc.). Before proceeding, let us examine the high scores that GRADUAL obtains against other behaviors. Designed behaviors

found in the literature usually fall in one of three categories : • Behaviors that use feedback from the game, usually

cooperative behaviors unless the opponent defects, in which

case they use a retaliating policy (tft, grim, gradual, etc.).

• Behaviors that are essentially cooperative and retaliating,

but start suspiciously by playing a few times D in the

beginning, so as to probe their opponent's behavior and

decide on what they have to do next. For example,

suspicious tft (STFT) and the "prober" behavior of [13]. • Behaviors that are clearly irrational, because they don't use any feedback from the game. For example, the random

behavior and all blind periodic behaviors such as CCD,

DDC etc. A behavior will maximize its score, if it is able to converge to cooperation with all behaviors of the first two categories and

converge to defection against behaviors of the third category.

Steady defection against periodic behaviors is necessary in order

to achieve the highest possible score (see [4], for details).

The GRADUAL behavior fulfills both of the above

specifications, because it responds with two consecutive C's

after a series of defections, giving the chance to STFT or prober

behaviors to revert to cooperation, and converges to ALLD

against irrational behaviors. A solution to the permanent

memory problem has to demonstrate the same property.

between C and D. To this end, it should have an estimate of the

opponent's behavior, whether cooperative or defecting, and

react to it in a tit-for-tat manner. The estimate will be

continuously updated throughout the interaction with the

opponent. The above may be modeled with the aid of a continuous variable, the world's image, ranging from 0 (total

defection) to 1 (total cooperation). Intermediate values will represent degrees of cooperation and defection. The adaptive titfor-tat model can then be formulated as a simple linear model

:

Adaptive tit-for-tat

If (opponent played C in the last cycle)

then

world = world + r* (1-world) , r is the

adaptation rate

else

world = world + r* ( 0-world)

If (world >= 0.5) play C, else play D

The usual tit-for-tat model corresponds to the case of r=l

(immediate convergence to the opponent's current move).

Clearly, the use of fairly small r's will allow more gradual

behavior and will tend to be more robust to perturbations.

Now, let us simulate the behavior of the adaptive tit-for-tat agent against all three types of behaviors described earlier.

• For initially cooperative behaviors with feedback and a

retaliation policy, the model cooperates steadily and

converges quickly to total cooperation.

• For suspicious or prober behaviors, the model plays exactly

like tit-for-tat, while the value of the world variable

oscillates around the critical value of 0.5 (see figure 4

against suspicious tft). • For periodic behaviors, the value of the world variable

converges quickly to oscillations around the characteristic

value of "number_of_C's/number_of_D's" in the

opponent's period.

1

0.83

0.66

0.5

0.33

0.16

0

i World!

"1"

f" f"

1 i i

10 15 20 25 30

Figure 4. Interaction of adaptive tit-for-tat with

suspicious tit-for-tat (r=0.2, world(0)=0.5).

4.2. The solution : Reformulation of the Problem

The adaptive behavior that we are seeking should be essentially

tit-for-tat. Moreover, it should demonstrate fewer oscillations

4.3. On Efficiency : Meta-Regulation

It can be seen that the previous version of the model suffers from

manipulation of the world variable by the opponent. This shows

198
as stabilization of the agent to an oscillatory behavior (as is the case against stft) or a steady cooperative behavior against

irrational agents (as is the case against CCD). To bypass this problem, we exploited our observation that different rates for

cooperation and defection (r^ and rj, respectively) yield different

results. More specifically, we observed that the adaptive tit-fortat agent manages to get opponents such as stft or the prober to cooperate if rc>rd, while it manages to fall to steady defection

against periodic behaviors if rc<rd. Thus, what we need at this point is a method for the

adaptive tit-for-tat agent to discover whether the opponent uses a

retaliating behavior or is just irrational and to adopt accordingly

the proper rate setting. We have designed and examined several

such variants for estimating the opponent's irrationality and we

have finally found the following rule :

Throughout an observation window, record how

many times (n) the agent ' s move has

coincided with the opponent's move. At

regular intervals (every "window" steps)

adapt the rates as follows : If (n>threshold) then

rc — rmin/ — r^^x

else rc — rmax, — ^min

The rule may be translated as : If (the world is cooperative enough)* then

else rc ~ rmaxi — ^mln

(*) recall that "my move = opponent's move"

is the so-called pavlovian criterion of

cooperation ([14])

Note that the agent drops its cooperation rate when the world is assumed cooperative, and increases it otherwise, that is, it uses

negative feedback at the rate regulation level.

i

t ill.i j i i i 1

5 10 15 20 25 30

Figure 5. Interaction of the meta-regulated adaptive

tit-for-tat agent with suspicious tit-for-tat (rc(0)=0.2,

rd(0)=0.2, r„ax=0.3, r„^n=O.I, world(0)=0.5,

window=10, threshold=2). Compare with figure 4. We have shown in simulations that the adaptive tit-for-tat agent with the meta-regulation mechanism converges to the

proper behavior against both retaliating and irrational agents.

For example, figure 5 gives the behavior of the meta-regulated

adaptive tit-for-tat agent against STFT.

Finally, the adaptive agent manages to differentiate

between a retaliating agent and an irrational one that has initially

the same behavior. The agent first assumes that the opponent is

retaliating and becomes increasingly cooperative, but soon finds out that the opponent is actually irrational and reverts to defection.

5. DISCUSSION:

ELABORATING THE CONCEPTS

In all three case studies, we have shown that the agent's

behavior is based on a critical variable that drives its motivation

to act. This variable is coupled with the environment through the

agent's behavior. By regulating its own variable, an agent tries to regulate the corresponding world variable. Furthermore, this variable has cognitive value, since it represents the agent's idea

about the state of the environment. Seen this way, the agent may

be thought of as trying to approach or approximate the world

variable, i.e., as trying to adapt to its environment. The regulated

variables appear to be critical for an agent's survival or

operationality, and correspond to what Ashby [15] called

essential variables.

The operationality of the behavior is ensured through an

additional self-regulation mechanism acting on the adaptation

rates. This is an important observation, since it is compatible

with the dynamical approach to cognition [16], stating that the

most important factor in cognitive mechanisms is the nature of

dynamics involved. Mechanisms like the ones developed here

may be also regarded as a first step toward the realization of

autopoietic systems :

"... an autopoietic system is a homeostat ... the critical variable is the system's own organization ..." ([17], p. 66)

In sum, we have shown that self-assessment of performance by

an agent is done with the aid of a double regulatory process and

it allows it to become more operational in its work. This is in

line with classical control theory, where regulatory mechanisms

are used as the basis of behavior [18]. Inversely, similar

regulatory processes may be designed for other problems,

provided that the appropriate performance measure (or cognitive

variable) and its assessment model are given or may be

identified. In this sense, the long term perspective of this work is

to build a regulation theory for reactive autonomous agents. To

this end, a number of issues have to be investigated : • How do we identify the critical cognitive variable in each

case ? Equivalently, how do we formulate regulation in each

case ? • How many first level rates are necessary ? Equivalently,

how many independent regulation processes are there ?

199
Recall that the explorer agent has one such rate, whereas

both the trail-making agent and the adaptive tit-for-tat agent

have two of them.

• Which is the meta-regulation criterion ? Note that in all three cases studied this criterion is purely qualitative and

problem-dependent. Equivalently, this issue translates to "How can we observe and qualify a regulatory process ?".

• What is the nature of the meta-regulation dynamics ? A few

initial experiments show that most probably a "bang-bang"

dynamics (high-low value) is enough, because what counts

is the relation between two rates rather than their absolute

values.

• Finally, what is the role and value of "behavior in the

empty" (without perturbation) ? This behavior is purely

agent-specific and may differ among different agents, due to different parameter settings, defining thus the individual

"character" of an agent. Initial experiments have shown that the behavior in the empty allows some limited prediction to be made.

As a general conclusion, the answers to questions such as the

above could teach us a lesson on the power and potential of

regulation mechanisms for apparently qualitative problems.

They could also deepen our understanding of the scope and

limits of such mechanisms amd prompt us to problems of

immediately higher complexity, where regulation would not be

enough and why this is so.

6. REFERENCES

[1] Tzafestas, E., Vers une systemique des agents

autonomes : Des cellules, des motivations et des perturbations,

Ph.D. diss., Univ. Pierre et Marie Curie, Paris, 1995.

[2] Tzafestas, E., "Regulation Problems in Explorer Agents",

submitted.

[3] Tzafestas, E., "Tom Thumb Robots Revisited : Self- Regulation as the Basis of Behavior", Proceedings Artificial Life

VI, San Diego, CA, June 1998.

[4] Tzafestas, E., 'Toward Adapdve Cooperative Behavior",

Proceedings Simulation of Adaptive Behavior 2000, Paris, France.

[5] Brooks, R.A., "Elephants don't play chess", in P. Maes

(ed.), Designing Autonomous Agents, MIT Bradford Press,

1991.

[6] Brooks, R.A. and Flynn, A.M., "A robot being", Robots

and Biological Systems : Towards a new Bionics ? (P. Dario,

G. Sandini and P. Aebischer), 1989, pp. 679-701.

[7] Beckers, R., Holland O.E. and Deneubourg, J.-L., "From

local actions to global tasks: Stigmergy and collective robotics",

Proceedings Artificial Life FV (R. Brooks and P. Maes, Eds.),

MIT Press, Cambridge, MA, 1994, 181-189.

[8] Mataric, M., "Designing emergent behaviors : From local interactions to collective intelligence". Proceedings Simulation

ofAdaptive Behavior 1992, 432-441.

[9] Steels, L., "Towards a theory of emergent functionality",

Proceedings Simulation ofAdaptive Behavior 1990, 451-461.

[10] Drogoul, A. and Ferber, J., "From Tom Thumb to the

Dockers : Some experiments with foraging robots". Proceedings

Simulation ofAdaptive Behavior 1992, 451-459.

[1 1] Deneubourg, J.-L., Aron, S., Goss, S. and Pasteels, J.M.,

"The self-organizing exploratory pattern of the Argentine Ant",

Journal of Insect Behavior 3(1990):159-168.

[12] Axelrod, R., The evolution of cooperation. Basic Books,

1984.

[13] Beaufils, B., Delahaye, J.-P., and Mathieu, P., "Our

meefing with gradual: A good strategy for the iterated prisoner's

dilemma". Proceedings Artificial Life V, Nara, Japan, 1996.

[14] Nowak, M., and Sigmund, K., "A strategy of win-stay,

lose-shift that outperforms tit-for-tat in the prisoner's dilemma

game". Nature 364(1993):56-58.

[15] Ashby, W.R., Design for a brain - The origin of adaptive

behaviour. 2""^ revised edition. Chapman & Hall, 1960.

[16] van Gelder, T., and Port, R., "It's about time: An

overview of the dynamical approach to cognition", in Mind as Motion : Explorations in the dynamics of cognition, by T. van

Gelder and R. Port, Cambridge, Mass.: MIT Press, 1995.

[17] Maturana, H.R., and Varela, F. (1980). Autopoiesis and

cognition — The realization of the living, Dordrecht/ Boston, D.

Reidel Publishing.

[18] Slofine, J.-J.E. (1994). Stability in adaptation and

learning. From animals to animats 3, Proceedings of the 3rd

International Conference on Simulation of Adaptive Behavior,

MIT Press, pp. 30-34.

200
On Measuring Intelligence in Multi Agent Systems

Siva Perraju Tolety

GTE Laboratories

40 Sylvan Road,

Waltham MA 02451 USA

toletysgacrn . o rg

Garimella Uma

Abstract

Intelligent behavior means doing the right thing

[1]. Due to the bounded rationality of agents it is not always possible to do the right thing. Hence

intelligence implies doing the best possible given

the resources an agent or a multi agent system

has. So, a measure of intelligence should reflect an evaluation of the process by which the agent or the multi agent systems arrive at exhibiting

intelligent behavior. In multi agent systems,

intelligent behavior is emergent in nature rather

than additive. So, measures of intelligence

should attempt to estimate the net resultant behavior rather than the individual fine grained

reasoning processes of individual agents.

Intelligent quotient measures for the human mind

attempt to arrive at a single number based on a battery of tests. This number is a reflection of the

individual's standing in his or her group. In this paper we attempt to present our ideas about

arriving at such measures for multi agent

systems.

1. Introduction

Intelligence is expected to allow an agent to do the right thing. The level of intelligence

is reflected in the appropriateness of the

actions undertaken by an agent in the given

circumstances. Measures of intelligence

have been used in the human society for a

variety of purposes. These uses range from

efforts to identify deficiencies in individuals

and help them improve in these areas to efforts to rank people according to their

capabilities in a given area.

Research in agents and multi agent systems

is maturing and systems are being deployed

in real world settings. Consequently, users

of these systems would like to evaluate the

system, understand its advantages and

deficiencies and improve upon the same. Also if multiple intelligent systems purport

to accomplish identical or similar tasks, the users of these systems will have a natural

interest in making a comparison of the

different systems. Similar needs in the

human society gave birth to different

performance measures. The measures are usually referred using the generic term

Intelligence Quotient (IQ). These varied measures are based on differing views of

intelligence. In Section 2, we briefly outline

the differing views of human intelligence,

and how these views lead to different

perspectives of IQ measurements. In Section

3, we outline different multi agent

architectures and highlight various aspects

of these architectures that can be measured.

In Section 4, we outline a possible measure

for multi agent architecture developed for

problem solving activities in real time

domains. Section 5 presents the conclusions.

2 . Intelligence and its

Measurements in the Human

SocietyIntelligence is an abstract concept and

reflects in some way the competencies and

skills an individual possess. Some of the

questions about intelligence include [2]: • Is mental competence a single ability

applicable in a variety of settings? or

201
• Is competence produced by specialized

abilities, which a person may or may

not possess independently?

If we can resolve this question about

intelligence, the next question what are the

metrics by which you can measures these

competencies. Do these measures reflect in

the every day problem solving ability of the

individual? The answers to these questions

depend to a large extent on the perspective; one subscribes to, about intelligence. Two

popular views about intelligence are

• psychometric views and

• Cognitive-psychology view.

The psychometric view of intelligence

places emphasis on scores obtained in

carefully designed tests to evaluate specific

skills. This view gives rise to the popular

notion of intelligence quotient. Several

versions of intelligent quotients exist.

Usually these numbers are arrived by

performing factor analysis on scores achieved on tests about different skills. Thus IQ measures reflect the level of what

psychologists call crystallized intelligence

(Gc). Crystallized intelligence is the ability

to apply previously acquired problem

solving methods to the current problem.

Measures of crystallized intelligence

correlate strongly with another aspect of

intelligence viz. fluid intelligence (Gf).

Fluid intelligence is the ability to develop

techniques for solving problems that are new and unusual from the problem solver's

perspective.

The cognitive-psychology view is that

thinking is a process of creating mental

representation of the current problem,

retrieving information that appear relevant

and manipulating the representation in

order to obtain an answer [2]. This

definition encapsulates the concepts of Gc

and Gf. Forming a mental representation of

the problem is akin to fluid intelligence and

extraction of relevant information is similar

to crystallized intelligence. A variety of

tests based on the cognitive-psychology

view of intelligence are also available.

3 . Intelligence in Agents and

Agent Systems

Agent architectures reflect the underlying

problem solving processes. Agents can be

broadly classified as either reactive or

deliberative. Reactive agents are usually

preprogrammed to respond in particular

ways to various stimuli from the

environment. Agents with deliberative

architectures usually use some reasoning

process to arrive at a solution. Reflecting

upon the classification of intelligence

discussed in the earlier section, we can assume that reactive agents depend on

crystallized intelligence while deliberative

agents depend on fluid intelligence. So,

psychometric based measures are appropriate for reactive architectures, while

tests that try and evaluate the reasoning

processes are appropriate for deliberative

agents. Just as IQ tests target different

groups of the society, tests in the agent

world should also target specific agent sets. For example we might design a test suite

that tries to evaluate spidering skills of

Internet spider agents.

The power of multi agent systems is in their

property of emergent behavior. Apart from

the domain knowledge, multi agent systems

possess some special features, which make

them very attractive. These are

• communication and

• agent interaction

Agent interaction is achieved in a variety of

ways. Coordination is the generic agent

interaction mechanism that helps agents in a

multi agent setting achieve their individual

and common goals. Coordination can be

achieved either through cooperative or competitive mechanisms. Communication

protocols based on theories like speech acts enable agents to exchange small by

semantically rich messages in aid of their

problem solving. In this perspective of

multi agent systems, the communication and

202
agent interaction aspects of the systems

seem to determine the intelligence or performance' of the system. Hence we

propose that any performance measure for

multi agent systems should in some way be

able to rank different systems along these

two dimensions. So, an IQ measure for multi

agent systems is a function of three separate

factors. They are

• domain knowledge(DK)

• individual agent reasoning capabilities

(ARC)

• communication (COMM) abilities and

• efficacy of agent interaction AI).

MIQ =f(DK,IARC, COMM, AI)

Since we are interested in evaluating multi

agent settings, we can ignore the factors that can be attributed to individual agents viz.

DK and ARC. This implies we are assuming

that all agents are equally capable in a given

domain. This assumption though not suitable

for rigorous measurements, could however

be a good starting point. Hence

MIQ =f(COMM, AI)

4. Measuring intelligence /

performance in TRACE

TRACE (Task and Resource Allocation in a Computational Economy) is a system of

multi agent systems designed to operate

under time constraints and load variations

[3,4]. TRACE approach to problem solving

is based on an adaptive organizational

policy. The TRACE system is market based

multi agent system. Tasks and resources are allocated to different multi agent systems

based on their problem solving load and the

price they are willing to pay for the

resources. We assume that knowledge can

' We assume that higher level of intelligence

results in better performance. Consequently, a MAS which is better at a given set of tasks than

another MAS can be considered to be more

intelligent.

be transferred among agents and thus

domain knowledge plays no particular role

in the evaluation of the multi agent systems.

Intuitively, we know that the efficiency of a player in a market is determined by how

efficiently the multi agent system trades its funds for resources to aid in problem

solving activities. Different multi agents

systems in TRACE can adopt different

policies to decide on their problem solving

activities.

Now if we attempt to measure the

performance of multi agent systems with

different policies in the TRACE setting,

what are the attributes that can capture the essence of the equations in the previous

section? Multi agent systems sign up or commit for tasks and attempt to complete

them. In this process they undertake both

communication and agent interaction tasks.

These tasks are time bound and task

completion beyond a deadline is a wasted

problem solving activity. In order to achieve maximum returns for the problem

solving activity, multi agent systems will

drop some tasks (decommit). The lower the

number of decommitments the better the

performance. The number of

decommitments reflects how much a given

system has overreached. It in turn reflects on the shortcomings in its communication,

negotiation and problem solving abilities.

Thus in the case of the TRACE system we

feel that a normalized number of

decommitments is an accurate measure of

the performance of the multi agent systems.

We have implemented a prototype of the

TRACE system, in which it is possible to introduce multi agent systems with different

problem solving abilities and processes. We

intend to formulate a simple problem to be

solved by the multi agent systems. We then

intend to measure the number of

decommitments made and determine if this measure is a reasonably accurate measure

of the performance of the multi agent

system.

203
5. Conclusions

In this paper we made an attempt at trying

to understand the basis of intelhgent measures used in the human society.

Research in agent systems and muhi agent

systems led to the development of

architectures that in some way try to mimic

the problem solving skills in human beings.

Thus the science of intelligent quotient

measurements can be applied to the domain

of intelligent agents and multi agent

systems. In market based agent systems, we

propose that the number of decommitments

made by an agent along with the resources consumed is a measure of its ability. In more cooperative settings different but

appropriate measures need to be designed.

We conclude that the measures need to be

designed by considering a family of agent

architectures. For example we feel that a measure designed for a market based agent

system will be ill suited for a cooperative

multi agent system. We are currently

experimenting with a multi agent system

TRACE to understand the criteria for

measuring its performance.

References

1. S. Russel and E. Wefald, Do the Right

Thing, MIT Press, 1991

2. E Hunt, The Role of Intelligence in

Modem Society, American Scientist,

July- August 1995.

3. S Fatima, G Uma, T S Perraju, An

Adaptive Organizational Policy for

Multi Agent Systems, ICMAS 2000,

Boston USA. July 2000.

4. S Fatima, G Uma, "AASMAN" An

Adaptive Organizational Policy for a society of Market based agents,

Sadhana, Academy Proceedings of

Engineering Sciences, Indian Academy

of Sciences, Vol 23, No. 4, pages 377-

392.
On the Development of Metrics for Multi-Robot Teams

within the ALLIANCE Architecture

Lynne E. Parker

Center for Engineering Science Advanced Research

Computer Science and Mathematics Division

Oak Ridge National Laboratory, RO. Box 2008, Oak Ridge, TN 37831-6355

ABSTRACT

Quantitatively evaluating the effectiveness of software ar- chitectures for multi-robot control is a challenging task.

Exacerbating the problem is the fact that architectures are

typically constructed to address different design goals and

application domains. In the absence of benchmarks that

capture the variety of issues that arise in multi-robot co- ordination and cooperation, the system developer can only

evaluate an architecture for its own qualities. In this article,

we summarize the metrics of evaluation that we utilized in

applying our ALLIANCE architecture [17] to eight differ- ent application domains for multi-robot team control. We

explore the implications of the metrics we have chosen and

offer suggestions on future productive lines of research into

metrics for multi-robot control architectures.

Keywords: Multi-robot cooperation, metrics, ALLIANCE

1 Introduction

Research work in multi-robot systems has progressed significantly in recent years. Issues that have been studied are diverse, and include task planning and control

[1, 17, 12]; biological inspirations [6, 7, 13]; motion coor- dination [27, 2, 4]; localization, mapping, and exploration

[22, 21]; explicit and implicit communication [5, 9]; co- operative object transport and manipulation [23, 25]; re- configurable robotics [28, 24, 26]; and multi-robot learning

[11, 12, 10]. Demonstrations have been given of multi-robot

teams performing a variety of tasks, such object pushing,

foraging, cooperative tracking, traffic control, surveillance,

formation-keeping, and so forth.

However, most of this research is very specific and illus- trates only one or two basic concepts per project. Comparisons across different methodologies are difficult and quantitative evaluations of various multi-robot control algorithms

are scarce. While this is not unexpected for a field as new as cooperative robotics, enough progress has been made that

we believe it is time to begin determining how we identify

and quantify the fundamental advantages and characteristics of multi-robot systems. The characteristics most often

cited for motivating the use of multi-robot teams are as

follows:

• increased robustness and fault tolerance through re- dundancy,

• a potential for decreased mission completion time

through parallelism,

• a possibility for decreased individual robot complexity

through heterogeneous robot teams, and

• an increased scope of application due to tasks that are inherently distributed.

Other than direct measures of time, these characteristics are hard to quantify, yet vital to enabling the field to make objective comparisons and evaluations of competing

architectures. Thus, much research is needed in this area.

2 Background

Measuring the performance of intelligent systems in general, and multi-robot systems in particular, is a muchunderstudied topic. Some beginning work has been accomplished by Balch [3], who has developed metrics for measuring multi-robot team diversity. However, little research

has addressed the general issues of cooperation that provide

guidelines for the quantification and selection of the appropriate cooperative team for any given set of mission specifications. Such a characterization would be a significant step

towards the commercialization of cooperative systems, as it would facilitate the design of the appropriate cooperative

team for a given application. Issues of particular interest

in such a characterization include the following:

• Quantifying the overall system capability versus the

system complexity,

• Determining the appropriate distribution of capabilities across robot team members for a given application,

• Ascertaining the most appropriate control strategy for a given robot team applied to a given application so

as to maximize efficiency, fault tolerance, reliability,

and/or fiexibility, and

205
• Determining tradeoffs in control strategies in terms of

desirable traits, such as efficiency versus fault toler- ance.

Examples of this type of research include [8], which develops measures of effectiveness and system design considerations for the generic area coverage application, and [14],

which compares the power of local versus global control laws

for a "Keeping Formation" case study. However, much more

work remains to be accomplished towards the development

of quantitative comparisons of alternative approaches to co- operative team design. An understanding of the factors that

influence the relative performances of various approaches to cooperative control will enable not only an evaluation of existing methodologies, but will also aid in the design of new

cooperative control approaches.

Since addressing the issue of quantitative measurement

and system integration for the entire field of cooperative

robotics is extremely challenging, we have begun work in this area by focusing on our experiences with the ALLIANCE architecture. We developed the ALLIANCE ar- chitecture [17] to enable fault tolerant action selection in multi-robot teams. The focus was on an approach that operated successfully amidst a variety of uncertainties, such

as sensory and effector noise, robot failures, varying team

composition, and a dynamic environment. We have implemented ALLIANCE in eight different application domains

in the laboratory. This experience is the basis for our beginning work in the development of general metrics and system

integration as it applies to the use of ALLIANCE.

3 Brief Overview of ALLIANCE

We developed the ALLIANCE architecture to enable fault

tolerant action selection in multi-robot teams. The focus was on an approach that operated successfully amidst a variety of uncertainties, such as sensory and effector noise,

robot failures, varying team composition, and a dynamic

environment. The ALLIANCE architecture, shown in Fig- ure 1, is a behavior-based, distributed control technique.

Unlike typical behavior-based approaches, ALLIANCE delineates several behavior sets that are either active as a group or are hibernating. Each behavior set of a robot

corresponds to those levels of competence required to perform some high-level task-achieving function. Because of

the alternative goals that may be pursued by the robots, the

robots must have some means of selecting the appropriate

behavior set to activate. This action selection is controlled

through the use of motivational behaviors, each of which

controls the activation of one behavior set. Due to conflicting goals, only one behavior set is active at any point

in time (implemented via cross-inhibition of behavior sets).

However, other lower-level competencies such as collision

The ALLIANCE Architecture

Actuators

Sensors

Figure 1: The ALLIANCE architecture for multi-robot co- operation.

avoidance may be continually active regardless of the highlevel goal the robot is currently pursuing.

The motivational behavior mechanism is based upon the

use of two mathematically-modeled motivations within each

robot - impatience and acquiescence - to achieve adaptive

action selection. Using the current rates of impatience and

acquiescence, as well as sensory feedback and knowledge of

other team member activities, a motivational behavior computes a level of activation for its corresponding behavior set. Once the level of activation has crossed the threshold, the

corresponding behavior set is activated and the robot has

selected an action. The motivations of impatience and ac- quiescence allow robots to take over tasks from other team

members (i.e., become impatient) if those team members

do not demonstrate their ability - through their effect on

the world - to accomplish those tasks. Similarly, they allow a robot to give up its own current task (i.e., acquiesce) if

its sensory feedback indicates that adequate progress is not

being made to accomplish that task.

We have shown that this approach can guarantee, under

certain constraints, that the robot team will accomplish

their objectives [15]. We have implemented this approach

in a wide variety of applications in the laboratory on several different types of physical and simulated robot systems.

Figures 2 and 3 illustrate these different implementations.

The implementations include the "mock" hazardous waste

cleanup [17], box pushing [20], janitorial service [16], bounding overwatch [16], formation-keeping [14], cooperative manipulation [18], cooperative tracking of multiple moving tar- gets [19], and cooperative production dozing. These implementations and results now give us the basis for studying

issues of metrics within this framework.

206
4 Evaluation of Metrics in ALLIANCE Applications

In [16], the ALLIANCE architecture was demonstrated to

have the important qualities of robustness, fault tolerance,

reliability, flexibility, adaptivity, and coherence, which we

identified as critical design requirements for a cooperative

multi-robot team architecture. These broad characteristics,

however, were determined based upon qualitative evaluations of the various implementations we have performed.

Ideally, we would prefer to have more quantitative metrics

of evaluation for these higher-level team characteristics.

On a more application-specific level, we used several metrics to evaluate robot team performance within each of these

applications. Table 1 summarizes the metrics we used to

analyze the performance of multiple robot teams in eight

different ALLIANCE implementations. In these applications, concrete indicators of mission success were used, such

as numbers of objects moved, distance traveled, or number

of targets within view. Improved mission quality was based

upon the time taken to achieve these indicators. This is natural, since a primary benefit of multiple robot teams is using

parallelism to achieve mission speedup. In these implementations, no single metric was found to be most useful. The

need for a variety of metrics suggests that system performance measures are application-dependent. These examples also illustrate that, for typical applications, the most

important issues are whether and how well the robot team

completes its mission.

By focusing on application-specific metrics, however, the

broader-perspective qualities of robustness, fault tolerance,

adaptivity, etc., are not made explicit. Instead, these characteristics are hidden in the application-specific measures. Thus, any shortcomings in a robot team's ability to operate robustly or with a high degree of fault tolerance, for

example, would be measured by an increased time to complete the mission (or by never completing the mission at

all), a decreased distance traveled, fewer objects moved,

etc. It would be difficult, therefore, to determine the rela- tive levels of contribution of the various broader-perspective

qualities (e.g., fault tolerance vs. adaptivity) to changes

in the application-specific quantitative measures (e.g., distance traveled). Thus, if one wants to explicitly measure

fault tolerance across several control architectures, and/or

several application domains, these metrics are not suitable.

An important goal of research in the quantitative evaluation of robot control architectures is, therefore, the development of metrics that enable quantitative measurement

higher-level characteristics, including fault tolerance, reliability, fiexibility, adaptivity, and coherence. By aver- aging the results across multiple application domains, we

would then be able to explicitly compare alternative control architectures in terms of these important applicationindependent characteristics. Our continuing research is

Figure 2: Implementations of the ALLIANCE architecture

(on both simulated and physical robots). From top to bottom, these implementations are: "mock" hazardous waste

cleanup, bounding overwatch, janitorial service, and box

pushing.

207
Application domain # Robots Metric description Metric definition

1. "Mock" hazardous

w do l/v:; V/iccliiU.^

2-5 (P) a. Time of task

f*nrn nl pt i on

imax

b. Total energy

used where ei{t) is energy used by

robot i through time t (m robots)

2. Box pushing 1-2 (P) Perpendicular dist. pushed per unit time

d±{t)/t,

where dj_{t) is _L distance moved through time t

3. Janitorial service 3-5 (S) a. Time of task

completion

tmax

b. Total energy

used where ei(t) is energy used by

robot i through time t (m robots)

4. Bounding

overwatch

4-20 (S) Distance moved

per unit time

d{t)lt,

where d{t) is distance moved through time t

5. Formation-keeping 4 (P & S) Cumulative

formation error E(=0 Yli^leaderdii.i)jwhere di = distance robot i is misaligned at t

6. Simple multi-robot

manipulation

2-4 (P) Number of

objects moved

per unit time

j{t)/t,

where j{t) is number of objects at goal at time t

7. Cooperative

tracking

2-4 (P)

2-20 (S)

Avg. number of

targets observed

(collectively)

A _ V^<mai V->n g{B{t)J)

where B{t) = [h^j{t)\my.n-> {fn robots, n targets)

bjj{t) = 1 => robot i observing target j at t,

f 1 if exists i s.t. hijit) = 1 ^(^(^)'^) =

t 0 otherwise

8. Multi-vehicle

production dozing

2-4 (S) Quantity of earth

moved per unit time

q{t)lt,

where q{t) is quantity of earth moved through t

Table 1: Summary of metrics used in ALLIANCE implementations. (In the second column, "P" refers to physical robot

implementations; "S" refers to simulated robot implementations.)

208
Figure 3: Additional implementations of the ALLIANCE

architecture. From top to bottom, these implementations

are: cooperative manipulation, formation-keeping, cooperative tracking of multiple moving targets, and cooperative

production dozing.

aimed at developing these higher-level metrics for the evaluation of robot team performance.

Acknowledgments

This article has been authored by a contractor of the U.

S. Government under Contract No. DE-AC05-00OR22725.

Accordingly, the U.S. Government retains a nonexclusive,

royalty-free license to publish or reproduce the published

form of this contribution, or allow others to do so, for U. S. Government purposes. Research sponsored in part by the

Engineering Research Program of the Office of Basic Energy

Sciences, U. S. Department of Energy. Oak Ridge National

Laboratory is managed by UT-Battelle, LLC for the U.S.

Dept. of Energy under contract DE-AC05-00OR22725.

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210
Shared Autonomy and Teaming: A preliminary report

Henry Hexmoor"'^

" Computer Science & Computer

Engineering Department, Engineering Hall,

Room 313, Fayetteville, AR 72701

'^Center for Multisource Information Fusion

University at Buffalo, Buffalo, NY 14260

ABSTRACT

We outline how an agent's shared autonomy considerations affect

its interaction in a team. A unified model of acting and speaking

will be presented that includes teaming and autonomy. This model

is applied to the domain of satellite constellation. We introduce our

simulator and outline our application of autonomy and teaming

concepts.

KEYWORDS: Multi-agent Systems, Shared Autonomy,

Agent Teams

1. INTRODUCTION

We have presented Situated Autonomy as a moment-bymoment attitude of an agent toward a goal and have argued

that it is a useful notion in modeling social agents [6].

Harry Duchscherer^

^University of North Dakota

Grand Forks, North Dakota, 58202

with other agents, or (d) the agent has a relatively small and

undetermined responsibility toward the goal. Our focus in

this paper is when the agent perceives shared autonomy.

Situated autonomy is an important part of an agent's

action selection. Figure 1 shows a very simple action

selection in Belief Desire Intention (BDl) paradigm and the

role of situated autonomy. Along with goals and beliefs, we

believe situated autonomy is used in the process of

determining intentions. The process can be highly cognitive

as in planning or less cognitive as in reaction generation.

Enablers are the agent's perception of its own abilities,

social factors, tools, and resources.

There are many accounts of starting or joining a team [2,

4, 10]. We favor the ingredients of intentional cooperation

laid out by Tuomela: (a) collective goal or plan, (b) strong

correlation among member's interest or preferences, and (c)

having a cooperating and helping attitude.

Figure 1 Action Selection

We argued that a combination of the nature and the

strength of an agent's beliefs and motivations lead the agent

to perceive one of the following: (a) the agent chooses itself

to be the executor of the goal, (b) the agent delegates the

goal entirely to others, (c) the agent shares its autonomy

We believe that in common situations, an agent's

situated autonomy changes at a lot faster pace than its participation in a team. Once an agent perceives shared

autonomy toward a goal, it may be inclined to recruit one or more agents to form a team. After a team is formed, the

agent's degree of shared autonomy will change at the speed

of perceived changes to the cognitive ingredients of situated

autonomy. A recruited agent's degree of shared autonomy

will be smaller than the recruiter's shared autonomy but

after a team is formed will change to any level.

We are developing a model that unifies acting and

speaking [7]. This model uses production rules to encode a conversational policy. A conversational policy is a modeling system that is designed to encode a set of

conventions shared among a group of agents [5]. Such

systems are generally called Normatives [1,10]. A

prototypical agent follows the conventions of the group in communicating and sharing mental states. However,

situated autonomies of each agent will individualize its interactions and allow it to deviate from the expected

behavior.

* This work is supported by AFOSR grant F49620-00- 1-0302.

211
We will present a model of conversational model in generic, non-BDI format. Each agent will personalize parts

of conversational policy in its own BDl paradigm. A

conversation policy is two types of simple production-like

structures we will call transitions, shown below.

physical condition * spoken word/phrase * s ^ speak state

physical condition * speak state

spoken word/phrase

To model physical actions of an agent in reactive

behaviors, we introduce two other types transitions, shown

below.

physical condition * spoken word/phrase * act state

physical condition * act state

act

A number of agents may share a unified model. For

example, a group of agents may share a conversational

policy. The shared model becomes their Norm. By entering

a model and tracking the shared states, agents can synchronize their actions. Privately, each agent will consider transitions in terms of beliefs or goals, and

intentions.

In the general model, physical conditions arbitrate

among productions that provide alternative acts or words at a given state. However, each agent will have a personalized

perception and interpretation of the physical conditions in terms of beliefs. We consider agents' situated autonomy and

teaming consideration is determined by the agent's unique

perceptions of the common physical conditions. Below we

rewrite the transitions from an agent's perspective and add

situated autonomy. 'Physical conditions' and 'spoken

word/phrases it hears' are things about which an agent has

beliefs. The states are the agent goals (or interchangeably

desires). The 'physical act' or 'chosen word/phrase for communication' are the objects of an agent's intentions.

Belief (physical condition) * Belief (spoken word/phrase) * Goal (speak)

Goal ( speak)

Belief (physical condition) * Goal ( speak) * situated autonomy ^ Intention (spoken word/phrase)

Belief (physical condition) * Belief ( spoken word/phrase) * Goal (act

)

^ goal (act)

Belief (physical condition) * Goal (act) * situated autonomy ^ Intention (act)

The remainder of this paper is organized by working

through an example of a unified model and how agents can

personalize the physical conditions and consider teaming

and changes in their Shared Autonomy. We will present a simulation of a constellation of satellites that can be tasked

from ground. We will show our unified model and related

eak st^Bgs of learning autonomy level using this application

domain. We have not yet conducted experiments with

situated autonomy and hence we consider this report a preliminary report.

Figure 2. The Server's graphic screen

2. SIMULATION OF A CONSTELLATION

OF SATELLITES

We have developed our own satellite simulator to illustrate our research ideas outlined in this paper. The simulator

follows the principles of TechSat 21 [8]. SaVi is a similar

software created at the Geometry Center at the University of

Minnesota for the visualization and analysis of satellite constellations [3]. It has been used to simulate various

satellite constellations such as Globalstar, Iridium, and

Teledesic. SaVi differs from ours in that it is simply a simulator of orbital satellite constellations, and does not implement autonomy in its satellites.

Our simulator is composed of two primary modules; the

server, and the agent. The server module handles the

creation of all agent objects in the simulation and acts as a router to facilitate the passing of messages between various

agents. There are two types of agents that can be created

within this environment, satellite agents and ground station

agents. These agents are implemented as objects and have

similar capabilities, with the satellites having the additional

ability to change their location within the environment. The

server module is also responsible for the accurate

212
representation of all objects in the graphical environment

(Figure 2).

The agent module contains the functional components of

the agents. These components constitute the essence of the

agent's purpose and functionality. Behavioral functions and

autonomy states can be created and transitioned by

accessing these module components through the use of

behavior rules in the agent's behavior file. Behavior rules are comprised of conditional checks and assignment calls to

the functional components in the form of simple production

rules.

The satellite simulator was implemented using Mesa and

supported by the collision detection routines which are part

of the SOLID library package. The simulation is comprised

of a central solid sphere surrounded by a wire-frame sphere

to establish a latitude/longitude coordinate system. The

sphere is currently scaled to represent the earth, and

rotational velocity is approximately 120 times nominal.

Graphically, the satellites are represented as green spheres

with groundstations being yellow spheres on the planet's

surface. The entire simulation can be rotated on any of the

three axises. This allows for the simulation to be viewed

from various prespectives. Additionally, any agent can be

selected to be "tracked" in the simulation. This has the

affect of centering the agent at the origin, with all other

objects, including the planet, revolving around the agent.

Blue line segments are used to show connections between

satellites that have a line of sight communications

capability, or connections between a satellite and a ground

station (Figure 4). The SOLID library was used to make

this determination, since the Mesa libraries do not directly

support the detection of intersections between the

connecting line segments and the planetary bodies. All

satellites orbit at velocities which are appropriate for their

altitude, with respect to a planet such as the earth.

The satellites and ground stations that orbit and reside on

the planet are implemented as objects and have

communication capability to other agents via message

passing through the socket connection with the server. The

position of each of these agents is determined by the data

that is provided to the server in a text file. The text file contains only the most basic of information necessary to place the agent in the graphics environment of the server.

As each agent object is created, it reads a behavior file, which contains the rules that will govern its actions with

respect to communication policy and physical actions that may be needed to achieve a desired goal. The format and

examples of these rules is described in more detail in the

next section.

3. TAKING 3 SCANS OF AN AREA

Assume the ground station will need three independent

images of a given longitude and latitude from a given

altitude. Let's call the task 31mage. The ground station

issues the command to the nearest satellite and that satellite will be responsible to perform the task either by itself if no

satellites are available. The satellite will complete the

images itself taking one image in each orbit crossing the

given location. If the satellite so decides it recruits other

satellites to complete the task. Each of the recruited

satellites may recruit another satellite. After recruiting one

satellite, either satellite may decide to recruit a third

teammate.

Figure 4. Communication lines

Here we will present a conversational policy that will

govern interagent communication.

The following are the ?Lgent speak states: 0 - Start state

1 - Ground station has issued a command and a Satellite has

received this message.

2 - A satellite has received and accepted the command.

3 - A second satellite has been contacted.

4 - The second satellite has accepted the command.

5- A third satellite has been contacted.

6- The third satellite has accepted the command and we

now have a team.

7- Ground control has received the first image.

8- Ground control has received the second image.

9- Ground control has received the third image.

10- Success State

213
I

11- Failure State. This state occurs when any of the images

are not received in a reasonable amount of time. State 0 is the start of 3-imaging.

The following are the set of available words/phrases:

50 - Satellite agent says "Hello" to other agents to announce its presence, if it is currently idle. 51 - Ground station issues a command 3Image [Longitude]

[Latitude] [Altitude]

52 - A satellite accepts command. The satellite says "Roger

to 3Image"

53 - Ground states acknowledges that a team leader has

agreed to take the task and will now accept images by

speaking "Ready to receive images".

54 - A satellite recruits another satellites for 3 Image. The

satellite may say "Team 3 Image?"

55 - If a satellite accepts the request for being part of a team

for 3 Image, it may say "Willco".

56 - If a satellite rejects the request for being part of a team

for 31mage, it may say "Unable".

57 - "bye" is spoken when a team member is no longer able

to be part of the team. 58 - "Downloading Image #1" is spoken when image #1 is downloaded to the ground Station.

59 - "Downloading Image #2" is spoken when image #2 is downloaded to the ground Station.

SIO - "Downloading Image #3" is spoken when image #3

is downloaded to the ground Station.

SI 1 - "Received Image #1 " is spoken when image #1 is received by the ground Station.

S12 - "Received Image #2" is spoken when image #2 is received by the ground Station.

513- The ground station may say "Task Complete" when all three images are received.

514- With an excessive silence, the policy ends

unsuccessfully, "Task Aborted".

The following are the physical conditions. For each

condition we note the agent that perceives it.

PO - Start condition.

PI - There is a need for 3Imgaing and a satellite is chosen

for tasking. This condition is perceived by GROUND only.

P2 - Satellite is unable to participate in a team for one of

two reasons: It is in danger or it has not yet finished its previous task. This condition is perceived by the SAT that is contacted to perform the task. P3 - Satellite is able to take lead on a task and is available.

This condition is perceived by SAT only.

P4 - Another satellite is detected that can potentially be a team-mate. This condition is perceived by SAT.

P5- Satellite is able to be a team-player. This condition is perceived privately by the SAT. All SAT agents privately

perceive conditions P6-P10.

P6- An image has been collected.

i

I

I

I P7- An image has been successfully collected and

j transmitted to the ground station. I P8- Two images are successfully collected and transmitted j to the ground.

P9- Three images are successfully collected and transmitted

to the ground.

PIO- The chosen Satellite has received the command. j Ground station is now ready to receive images. This ! condition is perceived by GROUND only. I Pll- All the external conditions and instrumentation | conditions for taking a picture are met. I

In the following speak state transitions, each agent's

type is noted by a "GND" for ground station or "SAT" for

satellite. SATAVL, TIMEOUT, UNABLE, AND PICT are boolean conditions. SATAVL determines if a satellite agent

is available (free of prior tasks and capable of taking on new

a task) for the current agent. The availability is determined '

with respect to the satellite's current speak state and | physical conditions. TIMEOUT holds if an excessive | amount of time has elapsed since the last change in speak

j state. PICT indicates if the agent has any pictures that can I be downloaded to the ground station. UNABLE denotes the i satellite's propioception of being busy with a prior task or somehow being "out of service". PICT denotes the absence

[ of such a condition. "SPK:<(^e5^/naf/o«>" construct is used '

to specify to whom the spoken phrase is intended.

CUR_AGNT is the agent most recently identified as available by the SATAVL check. The default CUR_AGNT

is the speaking agent.

The following are the speak-state transitions.

i P0*1*GND*S1**0 i

I

I P3*0*SAT*Si*l

I P3*1*SAT*S3*2 I P4*2*SAT*S4>3 " ' '

[ P4*4*SAT*S'^5 I P4*3*SAT*S^2 i P4*3*SAT*S4^4

P4*5*SAT*S^6

P5*0*SAT*S3^2

P2*3*SAT*S'l*0

P2*4*SAT*S?>0 ' P2*5*SAT*S'?*0 ; P2*6*SAT*S'7*0 I P2*7*SAT*S'7*0 I P2*8*SAT*S'?*0 1 P4*4*SAT*S'?>2 ' P4*5*SAT*S?>3

P4*6*SAT*S'?*4 ;, P7*2*SAT*S11*7 !' P7*4*SAT*Sli*7 f P7*6*SAT*Sli>7

j P6*2*SAT*S13*7 [ P6*4*SAT*S13*7 ' P6*6*SAT*Sli*7

j P8*7*SAT*S12>8

214
P6*7*SAT*S1^8

P7*7*SAT*S1^8

P9*8*SAT*S1^9

P6*8*SAT*S1^9

P7*8*SAT*S1^9

P8*8*SAT*S13>9

P10*0*GND*S2-*1

P10*l*GND*S3->2

P10*2*GND*S8-*7

P10*7*GND*S9^8

P10*8*GND*SlO>9

P10*9*GND*S1?*10

1*SAT*S14*11

2*SAT*S14*11

3*SAT*S14*11

4*SAT*S14^11

5*SAT*S14*11

6*SAT*S14*11

7*SAT*S14*11

8*SAT*S14*11

P10*1*GND*TIMEOUT-*11

P10*7*GND*TIMEOUT-*11

P10*8*GND*TIMEOUT^11

The following are the speak transitions. SA denotes the

agent's level of situated autonomy.

P0*0*SAT*TIMEOU'P*SPK :ALL*SO

P1*0*GND*TIMEOUT->SPK : CUR_AGNT*S1

P0*0*SAT*S4^P5

P3*1*SAT*SPK :ALL*S2

P10*1*GND->SPK :ALL*S3

P4*2 *SAT*S^SPK : ALL* S4

P5*0 *SAT*S4>P2

P6*2 *SAT*S^SPK : ALL* S5

P7*2 *SAT*S^SPK : ALL* S8

P4*4 *SAT*S^*SPK : ALL* S4

P2*4 *SAT*S^SPK : ALL* S7

P7*4 *SAT*S?*SPK :ALL* S8

P2*6 *SAT*Si*SPK : ALL* S7

P7*6 *SAT*S^SPK : ALL* S8

P2*0*SA'P*SPK :ALL*S6

P2*3*SAT*SPK :ALL*S7

P2*5*SAT^SPK :ALL*S7

P2*7*SA'P*SPK :ALL*S7

P2*8*SAT*SPK :ALL*S7

P8*7*SA'P*SPK :ALL*S9

P9*8*SA'P*SPK :ALL*S10

P10*7*GND->SPK :ALL*S11

P10*8*GND->SPK :ALL*S12

P10*9*GND->SPK :ALL*S13

P10*11*GND-*SPK :ALL*S14

The following are the act transitions. "A" denotes an act, which in 3 Imaging is taking a picture.

P11*2*SAT*SA>A

P11*4*SAT*SJ*A

Pll*6*SAT*Si*A

In addition to the conversational policy and action rules

(above), we have designed rules for our agents to infer

physical conditions based on exiting physical conditions and

their current speak states and either (a) what they hear, (b)

propioception of time or success of their own task (taking a

picture), or (c) perception (availability of another satellite

for teaming). We will consider these rules to be more

domain oriented and intended for internal use of agents.

Collectively, we will refer to these rules as domain rules.

The following are mainly based on hearing.

Pl*0*GND*S2^P10

P2*0*SAT*Sf^P0

P0*0*SAT*Si*P3

P4*4*SAT*S^P3

P0*0*SAT*S3*P5

The following are mainly based on agent perception.

PO * 0 * GND*SATA'Tlt PI

P3 * 2 * SAT* SATAVD* P4

P3*4*SAT* SATAVL^ P 4

The following are mainly based on agent propioception.

P1*1*GND*S1*TIMEOUT*PO

P5*2*SAT^ICT»P6

P4*6*SAT£ICT^P6

P4*6*SAT*PIC'*P7

P6*2*SAT*PIC'*P7

P6*4*SAT*PIC'*P7

P6*7*SAT*PIC'*P8

P7*7*SAT*PIC'*P8

P6*8*SAT*PIC'*P9

P7*8*SAT*PIC^P9

P8*8*SAT*PIC'*P9

UNABLE -*P2

4. USING CONVERSATIONAL POLICY

Agents can use the conversational policy for forming their

beliefs, goals, and intentions. Each agent will apply the

policy, action, and domain rules to new messages it receives. The following is our highest-level loop pseudo

code for agent update.

For (agent; 1; numAgents)

While (new receive message)

{ 1. Determine SA

2. For (rule; 1; numRules)

If (rule applies)

a. Perform transitions

Use SA to resolve conflicts

b. Update beliefs and goals

3. Perform the intention for speaking or acting

within reaction constant

215
Given a goal and the prevailing physical conditions agents

constantly update their SA. SA is used in resolving conflicts

in rules and in final decision of intention to be formed.

Based on situated autonomy agents perform their picture

taking or recruit other agents as teammates. The GND agent

will note PO or PI (and form a belief) and will instantiate an

instance of 3Imaging conversational policy. GND will

maintain state 0 as its goal. Being in state 0 and having

perceived PI, GND will use a speak transition to intend and

then to issue SI. If the satellite (call it SATl) has received

the message SI the speak state transition is used to reach

state 1. GND and satellite SATl share the goal of being in state 1. SATl may perceive P3 and using a speak state transition to arrive at a desire to be in state 2 and also form

an internal goal in achieving the command. GND does not determine P3 so it has no access to this perception. It however has access to the state transition that allows it to desire state 2. In state 2, SATl privately considers P3, P4,

and Pll and arrives at a determination of situated

autonomy. In 3 Imaging, the lead agent once it reaches state

2, must consider exogenous physical conditions 3, 4, and 1

1

along with all agent endogenous factors to determine its autonomy. If it decides on shared autonomy, the agent must

begin recruiting other agents as teammates. Otherwise, it will either do the task itself or delegate it to others.

If SATl's decision favors a team formation, it uses a

state transition to arrive at state 3 and forms a desire in it. Due to space limitation, we will not discuss the details of

team formation. Since P4 is not shared with GND, it does

not have the same belief. Let's call the second Satellite SAT2. SATl and SAT2 now share the desire to be in state

3. If SAT2 perceives P5, it will use a state transition and moves to state 4 and forms a desire in state 4 and the goal to be a teammate in 3Imaging. If SAT2 perceives P2, it will

inform SATl and move back to state 2. SAT2 no longer has

to want state 3. SATl will desire State 2.

For an agent that is recruited to be a teammate in state 4

it has already decided to have shard autonomy. It must

consider its exogenous physical conditions 3 and 4 (P3 and

P4) along with all agent endogenous factors to determine its autonomy in order to decide whether yet another teammate

is needed. If it decides to recruit another agent it will move

through states to state 6.

5. AUTONOMY MEASURES

Situated Autonomy depends on time, and strengths of belief

and goal. [6]. Each agent reacts at different speeds. The

times between sensing and acting is an agent's reaction constant and the optimal values can be learned. This greatly

affects the agent's autonomy decision. Temporally, from the

shortest reaction time to the longest, an agent's autonomy is based on it's pre-disposition, disposition, and motivation.

Therefore, an agent's reaction constant is important. An

agent's beliefs used in autonomy decision vary from weak

to strong. An agent's goals are directed to self, other, or group. The goals vary in strength of motivation from weak

to strong.

In 3 Imaging, agents have different reaction constants and

we are experimenting with the effect of slow versus fast reacting satellites. An agent's beliefs are about the physical

conditions and the speak states and change in strength. The

goals are about taking images and they vary based on the

agent's prior commitment. If a Satellite agent has

committed to a 31maging task, it might commit to yet

another 3Imaging command if it senses that it can complete

the task. After the first command, the motivation level for the goal is set to be less than for the first command. A

combination of belief and goal degrees are used for determining SA.

As of this writing, our implemented system runs and images

are gathered. However, we do not yet have situated

autonomy experiments. We plan to compare runs of the

system with different reaction constants. The autonomy

levels in our agents will be learned as combinations of

beliefs and goals. The metrics we will use for feedback are timeliness of images collected.

6. SUMMARY AND CONCLUSION

We have developed a production-style representational

framework that unifies acting and speaking. Our

representation extends conversational policy scheme. It explains how agents can use the shared normative models of

conversational policy for forming private beliefs, goals, and

intentions. We outlined a scheme for flexible teaming that uses the notion of situated autonomy.

For an agent that is recruited to be a teammate in state 6

it has already agreed to have shard autonomy and since it is ^ave implemented our model in the domain of

the third member of the team no other teammates are constellation of satellites. Our system runs but we have not

needed. Conditions P6-P9 may be perceive by either yet completed experiments with how timely team formation

Satellite agent and all SAT agents share goals in state 7-11. improves our system performance.

In the next section we will briefly discuss how autonomy

will vary.

216
REFERENCES

[1] C.E. Alchourron and E. Bulygin, (1971). Normative

systems, Springer Verlag, Wien.

[2] P. Cohen, H. Levesque, I. Smith, (1997), On Team

Formation, In J. Hintika and R. Tuomela, Contemporary

Action Theory, Synthese.

[3] G. Bergen (1998), SOLID - Interference Detection

Library, Department of Mathematics and Computing

Science, Eindhoven University of Technology, P.O. Box

513, 5600 MB Eindhoven, The Netherlands.

(http://www.win.tue.nl/cs/tt/gino/solid/solid2_toc.html)

[4] F. Dignum, B. Dunin-Keplicz, and R. Verbrugge,

(2000), Agent Theory for Team Formation by Dialogue, In Proceedings of ATAL-2000, Boston.

[5] M. Greaves, H. Holmback, and J. Bradshaw, (1999). What

is Conversation Policy? In Autonomous Agents (Agents-99)

Workshop titled Specifying and Implementing Conversation

Policies, Seattle, WA.

[6] H. Hexmoor, (2000a). A Cognitive Model of Situated

Autonomy, In Proceedings of PRICAI-2000 Workshop on Teams with Adjustable Autonomy, Australia.

[7] H. Hexmoor, (2000b). Conversational Policy: A case study

in air traffic control, In Proceedings of International

Conference in AI, IC-AI-2000, Los Vegas.

[8] TechSat 21: Advanced Research and Technology

Enabling Distributed Satellite Systems, Overview Briefing

ofTecdhSat 21, http://www.vs.afrl.af mil/vsd/techsat2 1.

[9] R. Tuomela, (2000), Cooperation, Kluwer Pub.

[10] A. Valente and J. Breuker, (1994). A Commonsense

Formalization of Normative Systems, In Proceedings of the ECAI-94 Workshop on Artificial Normative Reasoning, J. Breuker (ed), Amsterdam, p. 56-67.

217
Real Time Distributed Expert System for Automated Monitoring of Key Monitors in Hubble Space Telescope

Reza Fakory, Ph.D. Majid Jahangiri, Ph.D.

Computer Sciences Corporation Computer Sciences Corporation

mfakory@csc.com miahangiri@v2pop.hst.nasa.gov

Abstract

A distributed expert system for monitoring the

critical telemetry (the Key Monitors) of Hubble

Space Telescope (HST) has been designed and

developed. The Key Monitors Expert System

(KMES) monitors the general health of the space

craft operation through analysis of the Key

Monitors data. KMES uses rule-based

approaches and notifies operators/system

engineers when it receives a limit violation from

Front End Processor subsystem (FEP). The

design of KMES is similar to the design of a previously reported system called "Expert

System for Automated Monitoring" (ESAM)

which was developed for HST [1]. However,

KMES uses an approach different from ESAM's

approach. ESAM was designed to monitor all telemetry mnemonics in a selected subsystem via

establishment of tight limits for mnemonics. On

the other hand, KMES has been designed to monitor the Key Monitors, providing

notifications for out-of-limit conditions in accordance with documented operational

procedures. Upon detection of an out of limit

conditions, KMES analyzes data for contingencies. It fires appropriate rules to request

associated engineering data from telemetry

repositories. Subsequently, KMES sends e-mails

and e-pages to notify the appropriate System

Engineers (SEs) and Operators. The duration of a

limit violation is monitored to eliminate transient

faults. KMES logs all out of bound (limits)

violations but only takes an action for each

persistent violation. In addition, the distributed

system design approach of KMES allows a pre

screening of data variations to reduce the number

of queued rules. Also, design of KMES was

modified to include only selected part (subdatabase) of a main database into KMES's

subprocesses. The sub-database contains data

associated with mnemonics that are used within

the associated subprocess. This approach

significantly reduced the required real-time

execution time and the memory usage for the

expert system.

KMES also allows the user to override any

activated miscompare. This feature permits

operators to adjust for known anomalies or changes in operational context. The system

generates event messages to override actions; the events include a user login ID and the reason for the override.

Currently, KMES includes rules to monitor

seven subsystems. KMES rules can be expanded

to include rules for other subsystems. This paper

describes the fundamental design and features of

KMES. The results for a simulated scenario

leading to failure of a Key Monitor and timely

detection of the failure by KMES and ESAM are presented.

1 Background

The Vision 2000 Command and Control System

(CCS) Product Development Team has been

formed to reengineer the HST ground system

[2,3]. The CCS ground system consists of

several systems including System Monitoring &

Analysis (SYM). Development of an expert

system for telemetry monitoring, fault detection

and recovery for the HST is one of the SYM's

responsibilities.

Prior to design of KMES, the SYM group

developed a real-time Expert System for Automated Monitoring (ESAM) [1]. The system was designed and developed to monitor the

general health of the spacecraft and to detect

faults within the Hubble Space Telescope (HST)

via monitoring all telemetry mnemonics within a selected subsystem. It employs modelbased/rule-based, hierarchical fault tree analysis

with forward-chaining rule propagation to compare expected state values with true states. The system uses a custom-built neural network

model and System Engineer (SE)-provided

algorithms to dynamically derive the expected

state values based on knowledge of real-time or

stored spacecraft commands. During operations,

real-time telemetry values (i.e., true states) are compared to the expected state values for

218
possible limit violations. The duration of a limit

violation is monitored to eliminate transient

faults. The system logs all miscomparisons but

only issues a system event message for each

persistent miscomparison. The persistence

implementation approach significantly reduces

the number of false miscompare messages.

Currently, ESAM only includes rules and models

associated with fault detection in Electrical

Power System (EPS) of HST. Further expansion

of ESAM for monitoring other subsystems of the

spacecraft encountered two problems. First, for

acquisition of telemetry data, from Information

Sharing Protocol (ISP) into the expert system, a shared memory technique was employed to overcome synchronization between RTserver,

the expert system server [1], and the ISP server. This design employed RTdaq, a COTS product

from Talarian Inc. [6], that acquired data from

shared memory and transferred data to RTserver.

Further tests and analysis of results revealed that occasionally data was dropped during

transmission from the shared memory to RTdaq.

In addition, RTdaq did not have provisions for

transmitting status flags that accompany the

telemetry data from ISP, which indicate the

general health and validation of the data. Second,

modeling and development of rules, for

incorporation of dynamic limits, required a

significant amount of time from experts and

system engineers with high level of expertise in the relevant subsystems of HST.

In order to overcome the first problem, it was

decided to develop new modules with direct

interface between RTserver and ISP via an

existing middleware. For the second problem, it was decided to monitor the critical telemetry (the

Key Monitors) and notify experts in accordance

with Key Monitors documentation [7]. In this design, the limit values are constants that are defined in the Project Reference Database

(PRD). The Front End Processor (FEP)

subsystem of CCS detects limit violations for all monitors. KMES receives the Key Monitor

values as well as the companion status flags from

FEP. The status flag indicates limit violated Key

Monitors. These new enhancements were

incorporated into the design of KMES, and

delivered as a part of a CCS Release delivery.

The following sections describe the design

features of the developed system.

2 Introduction

Expert systems are comer stones of knowledge

Management [4,5] foundations and as such are designed to reduce dependency on humans and

increase reliability of complex systems. For

many cases, expert systems are simply a way to codify the explicit and sometimes tacit knowledge of experts (operators and system

engineers) so it can be used to provide guidance

and solutions for known problems. The real time

Key Monitors Expert System (KMES) was

designed and developed to automate the experts

monitoring of the Key Monitors. In concept,

KMES has been developed to automatically

monitor the general health of spacecraft

operation and notify operators and system

engineers upon recognition of defined anomalies.

The Key Monitors are defined in the HST

Contingency Plan document [7]. This document

establishes a consistent and approved response to out of limit conditions or misconfigurations

throughout mission operations. The out-ofbound limits have constant values defined in the

Project Reference Database (PRD). In general,

PRD includes two sets of limits namely yellow

limit and red limit. For some Key Monitors

mnemonics, the yellow and red limits coincide.

In these cases, the response associated with red

limit violation has priority over the response

associated with the yellow limit violation. The

FEP subsystem determines violated telemetry

and sets a status fiag, which accompanies the

mnemonic value. For example, the FEP sets the

companion status fiag for a mnemonic to "L"

when the telemetry value drops below the lower

value of the red limit associated with the

mnemonic. The following sections describe the

developed system.

3 System Description

KMES is primarily a rule-based expert system.

KMES subscribes and receives the Key Monitors

mnemonic values and the companion status fiags

from ISP. Most of KMES rules are simple and

the hierarchy is shallow. However, the required

actions for some limit violations are contingent on configuration or statuses of other equipment.

Therefore, the rules associated with these limit

violations have hierarchical levels. Upon

detection of a violation, KMES looks for persistence of the violation. If the mnemonic's

value remains beyond limit boundaries, for a time greater than the persistence period, KMES

219
Front End

Processor

Telemetry DaU

User

Interface

CCS

Manager

Gondiiton

Deiection Message

SE

Notincation

Request

Event Manager

Analysis

Product

Generation Request

Figure- 1 KMES External Interfaces

fires the associated contingency rules. As a part

of actions within these rules, KMES sends

requests to the Analysis subsystem for historical

data products. Figure- 1 shows KMES external

Interfaces. KMES specifies the start time as well

as the stop time for the requested data. Format of

the requested data and type of the requested

historical data are stored in ASCII files that are accessible to the Analysis subsystem. The

generated historical data products are stored in a directory accessible to operators and system

engineers. The system engineers (SEs) may use

• RTD (Receive Telemetry Data) •

• MGS (Manage States) •

• REF (Respond to Events and Faults)

the data products to further analyze potential

problems use the products.

4 KMES Architecture

Figure-2 shows process architecture for KMES.

KMES consists of sub-processes for data

communication as well as subprocesses for

evaluation and reporting of the state of the

spacecraft. The main processes are:

PMD (Publish Monitoring Data)

RMD (Route Monitoring Data), the RTserver

Figure-2 KMES Distributed Architecture

220
Originally, KMES employed a single database

where all of KMES's processes included a copy

of the data-base. However, this approach caused

excess increase in the size of run time memory

usage when KMES was expanded to include all seven subsystems of HST. Therefore, the design

of KMES was modified to reduce the size of

memory usage. In this new approach, every

subprocess of KMES includes part of database

that contains data related to mnemonics which

are referenced or used within the subprocess.

The following sections briefly describe function

and features of each sub-process within KMES

4.1 Receive Telemetry Data (RTD)

The RTD process receives change-only data

from the ISP server. RTD sends this data to the

other KMES processes via the RMD process.

Originally this subprocess employed RTdaq (a

commercial product) and shared memory

approaches for synchronization between RMD

and ISP. However, it was found that occasionally

data was dropped out during transmission

between shared memory and RMD. In addition,

RTdaq did not have capabilities to transmit

status flags, which accompany the telemetry

data. ISP sends the status flags as a part of data

throughout the CCS subprocesses. These status flags indicate the status of data and they are set by the PEP subsystem within the CCS. A blank

status flag indicates that the data is valid. At this time, ISP sends telemetry data with status flags

that are set to nine possible values, one at a time.

The status flag values are prioritized, four of

these values indicate that the telemetry value is beyond pre-specified limits as defined in the

PRD. The remainders of the status-flag values

indicate if there has been a problem with data

conversion or data transmission. The RTD

subprocess was enhanced to eliminate the use of

RTdaq as well as the shared memory approach.

The enhanced version constructs custom

designed data-packets that are in RTsmartSockets format. The packets contain

changed only data and are sent to RTserver

(RMD) for distribution to subprocesses within

KMES.

4.2 Manage States (MGS)

The MGS process receives real-time telemetry

data from ISP and generates compare status associated with each received Key Monitors

mnemonic. The compare status indicates if there

is a miscomparison (corresponding to a limit

violation). This subprocess sets compare status

in accordance with values of status flags that accompany telemetry data received from ISP. A

compare status mnemonic may take four

different values for a miscomparison

corresponding to four possible ways of limit

violations:

a) Yellow Low;

b) Yellow High;

c) Red Low;

d) Red High.

Yellow limits are warnings as specified by

system engineers. Red limits are typically for

serious violations associated with hardware

limitations. MGS sends all compare status changes along with their status flags and time

stamp to REF subprocess via RMD.

4.3 Respond to Events and Faults (REF)

REF includes all rules associated with limit

violated Key Monitor mnemonics. Upon receipt

of a miscomparison associated with Key

Monitors from MGS, REF tracks the

miscomparison for a pre-specified period of time

(persistence time). If the limit violation persists,

then REF fires the appropriate rules and sends

appropriate historical data request with specified

start time and stop time to the Analysis

subsystem. REF also sends an event that indicates detection of the anomaly. The event

message also indicates how soon the requested

data product will be available for access by SEs

or operators. The Analysis subsystem receives

information for the data request from REF and

retrieves the historical data in accordance with

the format specified by SE(s) and Operators. The

list and format of the data request are stored in specially designed files called "Historical

Request Definition Files".

4.4 Publish Monitored Data (PMD)

The PMD process receives state data consisting

of mnemonic's name, value, and time stamp

from MGS (via RMD) while publishing this data

to ISP. Along with this data there is a status flag

indicating the override status of the mnemonic's

value. The status flag indicates whether the user

221
has overiden the mnemonic value within KMES

or that the mnemonic value is derived by KMES.

4.5 Route Monitoring Data (RMD)

The RMD process consists of a real time

RTserver. The process receives and routes data

and event messages within KMES's processes.

5 KMES Characteristic Features

KMES consists of a group of distributed

processes that communicate through a middleware layer. This modular design has many

advantages such as maintainability and

flexibility in where and how these processes are executed. If one process is overloading system

resources, it can be relocated to another host

machine. Among other advantages, KMES

employs a distributed system approach to

facilitate:

a) Change Data only executions

b) Maintenance simplification

This approach provides a capability to queue

only those rules that are affected by the status of

a mnemonic. In this way, only the rules that have

to send a historical data request and e-mail or e- page will be fired and the rest of the rules will not be examined until later times when a status change affects them.

6 Results

KMES has been developed with rules associated

with actions that are required when a Key

Monitor has violated its limits. Currently, Rules

associated with the following seven subsystems

of HST have been implemented:

• Data Management Subsystem (DMS)

• Electrical Power Subsystem (EPS)

• Instrumentation & Communication Subsystem

(l&C)

• Optical Telescope Assembly (OTA)

• Pointing Control Subsystem (PCS)

• Safmg Subsystem (Safmg)

The following section discusses the results obtained from operation of KMES during a simulated anomaly. For comparison, the results

of the previously designed system, ESAM, for the same simulated anomaly is also

demonstrated. As it was mentioned earlier,

ESAM detects anomalies by comparing the

engineering telemetry received from HST with some internally generated expected values.

ESAM analyzes the discrepancies between the

true and expected states to determine if an anomaly actually exists. Therefore, ESAM uses some tight and dynamically calculated limit

boundaries. In contrast, KMES depends on some

predefined and fixed limit boundaries.

The following section, compare the results from

ESAM and KMES for a simulated scenario

leading to failure of a sensor in the Electrical

Power System of the spacecraft.

7 Scenario

The test scenario was designed to examine the

rule executions resulted from an anomaly

associated with one of the HST batteries. The

spacecraft is equipped with six batteries. If only

four batteries are nominal then the entire battery

system is considered acceptable for normal

operation. Previously captured data from a routine spacecraft orbit was fed into the HST

simulator. The simulator was started in play back

mode with continuous data feed. Figure-3 shows

voltages for the first and the second batteries of

the spacecraft, respectively. Figure-4 shows the currents associated with the first and second

batteries. Figure-3 and Figure-4 also depict the

high-expected limit and the low-expected limit

calculated by ESAM. For comparison, Figure-3

also shows the constant limits used for Key

Monitors out of bound violations. As shown, the constant limits are normally wider than the limits

calculated by ESAM. The results for battery one

demonstrates that the system was in normal

operation until time 23:05, at this time (point A)

a ramp down sensor anomaly was simulated into

the telemetry data for voltage of the first battery.

Figure-3 shows that within about 4 minutes and

40 seconds (point B) into the incident, the

battery voltage fell below the low limit as calculated by ESAM. However, Figure-3 shows

that after 9 minutes and 30 seconds into the

anomaly, the battery voltage fell below the constant limit used by the FEP. At this time

KMES received an associated status flag from

FEP that indicated the limit violation. Therefore,

KMES queued the rules associated with the

battery anomaly and sent appropriate

222
notifications and historical data requests when

the limit violation persistence was satisfied. The

results show that ESAM detected anomaly

within about five minutes after initiation of the

anomaly. However, KMES sent anomaly

notifications within ten minutes after initiation of

the anomaly.

8 Future Work

A well-structured distributed expert system to monitor the Key Monitors of the Hubble Space

Telescope has been developed and delivered.

The results for the first release of this system are presented. The following highlights some of the

items sought for improvement and further

enhancements of the monitoring system:

a) a variable persistence time for each or subset

of the Key Monitor mnemonics;

b) retrieve appropriate operating procedure(s)

for response associated with an anomaly;

c) track violation changes from yellow limit

boundary into red limit violation boundary;

d) accept a user-defined periods for

Time update=1 0 min

22:54

Battery 1

^ ^1

1 A B

1

> —--

1 .^^L* ^ 1 1

23:04 23:14 23:24 23:34

Time upclate=1 0 min

Battery 2

i "^'""W

1

35.00

30.00

35.00

30.00

22:65 23:04 23:14 23:24 23:34

Telemetry Calculated Limit Constant Limit

Figure-3 Results For Battery Voltage

Time update^l 0 min

1 -r:^ Batteiy 1

_ -1

1

/

1

--—--

10.00

0.00

-1 o.oo

23:05 23:14 23:24 23:34

Time upclate=10 min

\ Battery

.[ L

=

fe.sr.!=..'!r...-=-.7r_.'i:.<i-..i:.^

i 1 1

'

h.r"' ^yX. ^^.....^..•^..sL..-i-„ j-..-!_..-!-..-!^»...>v.._e.>k,L,.Jk. _ f^'. .•~...t'... i^,,-:.-

10.00

o.oo

-10.00

53:05 23:14

Telennetry

Figure-4 Results For Battery Current

23:24 23:34

Calculated Limits

223
notifications frequency associated with each

of the limit violations;

e) GUI editor interface for visualization and

graphical editing of rules.

9 Conclusion

A distributed expert system, KMES, for notification of anomaly and initial response (i.e., request associated engineering data for analysis)

has been developed. The results of KMES for a simulated failure has been compared with similar

results obtained from a previously designed

expert system, ESAM. KMES uses the results of

anomaly detection with constant limit values

while ESAM calculates the expected limit boundaries. The results for detection of a sample

sensor failure by the two systems are demonstrated. It was found that when calculated

limits are employed then anomaly might be

detected earlier than when constant limits are used for detection of the anomaly. However,

notification of limit violations based on constant and established limits provides facilities for timely development of KB rules and execution

of approved notifications.

10 Nomenclature

CCS Command and Control System

ESAM Expert System for Automated

Monitoring

FEP Front End Processor

ISP Information Sharing Protocol

KMES Key Monitors Expert System

1 1 References

[1] "A Real-Time Expert System for Automated

Monitoring of the Hubble Space Telescope,"

R. Fakory and E. Ruberton, Intelligent

Systems Conference, Gaithersburg, MD,

September 1998.

[2] Consolidated HST Associated Mission

Products (CHAMP) Contract, NAS50000.

[3] "Re-engineering of the Hubble Space

Telescope (HST) Reduce Operational Costs

(Partll)," M. Garvis, K. Lethonen and W.

Burdick, internal report.

[4] "Knowledge Management Handbook," Jay

Liebowitz, CRC Press 1999.

[5] "Development and Deployment of a RuleBased Expert System for Autonomous Satellite Monitoring,"

L. Wong, F.Kronberg, A. Hopkins, F. Machi,

P. Eastham, Astronomical Data Analysis, Vol

101, 1996.

[6] Talarian Corporation, support@talarian.com.

[7] "Hubble Space Telescope (HST)

Contingency Plan document," Vol2, Lockheed

Martin Missiles &

Space (LMMS), report No. LMSC/P061924,

1997.

224
Evaluating Performance for Distributed Intelligent Control Systems

Wayne J. Davis

General Engineering, University of Illinois at Urbana-Champaign

Urbana,IL 61801, USA

Abstract

This paper provides a new framework for the distributed

intelligent control of complex systems. The behavior of a given subsystem as it interacts with other subsystems is

explored. The inherent limitations associated with distributed planning and control procedures are revealed. These

limitations further limits one ability to evaluate system

performance. Knowing these limitations, allows one to seek improved procedures for managing complex systems,

which should also lead to improved system performance.

1. Introduction

Measuring system performance inherently represents a

subjective task, beginning with the definition of the considered system. The decision of what system elements

will be included within the considered system is arbitrary.

Moreover, excluding elements from the considered system does not eliminate the potential for these elements to

interact with the considered elements. Rather, such interactions become inputs to the considered system, whose

values cannot be controlled. The definition of the considered system necessarily constrains the performance of the

system because one must relinquish control of these environmental inputs.

Any performance criteria employed to evaluate a system must be based upon system variables that can be

measured and controlled. Hence, the scope of the considered system inherently constrains the type of performance

evaluations that can occur. Often there are multiple criteria to be considered, which necessitates compromise among

the appropriate criteria. Compromise requires a subjective prioritization among the considered criteria, making

absolute performance evaluations nearly impossible to

achieve.

It becomes difficult to analyze and manage a complex system as a single monolithic entity. Complex systems are better represented as systems-of-systems. Again,

the definition of the included subsystems is arbitrary. Furthermore, each included subsystem will have its own state

and control variables. These variables again constrain

which performance criteria can be considered by each subsystem. What often emerges is a collection of subsystems

whose behaviors are characterized via different performance criteria.

Even if a monolithic specification for the system's

planning and control problems can be made, there are still shortcomings, expecially since the monolithic approach

ignores the system-of-systems nature. Monolithic specification do not capture the multi-resolutional nature of

complex systems where given subsystems address system

variables at different levels of detail and on different time

scales. Monolithic approaches do not scale well. For largescale systems, monolithic approaches become impossible

to implement.

On the other hand, distributed planning and control introduces other problems. Today's distributed planning and control technologies do not capture the true nature of the distributed planning and control requirements

for complex systems. Most decomposition procedures for

distributing planning still assume that a monolithic planning problem exists (see Lasdon [1] and Wismer [2]). In

general, this monolithic planning problem cannot be defined; and even if it could, its complexity would be well

beyond the scope of problems that can be addressed with

available decomposition procedures. Decomposition al- gorithms further seek an optimal solution to the monolithic planning problem. However, the relationship of optimal planning at the subsystem level toward the optimal

planning for overall system within which it resides is simply not understood. Today, we do not know how to coordinate the planning at a subsystem in order to insure global optimality for the overall system within which the subsystem resides.

Control is essential to implement plans. Again,

the current distributed control technologies are limited.

Perhaps the most common distributed control procedure

is the slow-fast decomposition (see Kokotovic et al. [3]).

Slow-fast decompositions certainly can address situations

where the desired response is known. They usually as- sume that an aggregated description for the overall response

is known over an extended horizon, which includes the

225
current time. Subsystems then manage the detailed description of this same trajectory over a shorter horizon

which again includes the current time. This process continues where each subsystem addresses more detail over an even shorter horizon beginning with the current time.

Implicitly, a monolithic control policy has been developed

in that one assumes that the desired aggregate response is known over the entire time horizon.

Distributed intelligent control (distributed planning

and control) approaches do not permit a monolithic description of the desired system trajectory. Rather, the system trajectory evolves as a collective response of several

subsystems considering different temporal horizons and

system elements. The planned response and the associated implementing actions evolve with time. Neither the

monolithic planning or control problems are ever stated or

solved. It is obviously difficult to manage such systems.

Even more difficult is projecting their performance.

I revisited the distributed planning and control

problem last year. My desire was to define what a distributed planning control system could accomplish. All the

basic principles, including optimality and controllability, were set aside. The goal was to determine how a subsystem could address its assigned planning and control responsibilities while effectively interacting with other subsystems. Subsequently, the coordination of the interactions among the entire ensemble of distributed planning

and control systems in order to provide an effective overall system response had to be addressed Testing effective- ness became a concern given the inherent inability to define the overall system problem as it continued to evolve

in time.

This paper provides a brief discussion of the basic

discoveries arising from this rapprochement. The fundamental principles of optimality and controllability have

been reexamined and mathematical proofs/arguments do

exist for the inherent limitations. Unfortunately, space limitations prevents me from providing these mathematical ar- guments. Instead, this paper will provide basic discoveries only. The paper first investigates how subsystems interact with each other. Next, the comprehensive nature of

the overall system response arising from these interactions

is addressed. Finally, the inherent limitations upon planning and control will be itemized. These limitations fundamentally impact one's ability to manage and project system performance. They must be addressed.

tPfM from Envkonmnt

|..

Aecfljited Asdgrwd

Goals Goal*

Assignors Subsystem Aawpum

Feedback Feedback

l/mtAssigm/n.tlmi Ifno Acceptors. then

mlwsti'iii is Ciealor. Output lifion Envhorvnent su/fmiem is I'rmess.

Figure 1 : Basic interactions for a subsystem

2. Fundamental Concepts

We begin our development with two basic assumptions:

• Most complex systems can be represented as a collection of subsystems that interact with each other. That

is, complex systems are actually systems of subsystems.

• Each subsystem has a purpose, which it fulfills by ex- ecuting tasks. Furthermore, the tasks that each subsystem can execute are related to the tasks that other

subsystems can execute.

Consider a single subsystem. Its associated control inputs include (see Figure 1):

• Endogenous control inputs that it generates in order to implement its planned response.

• Assigned goals from other subsystems.

• Feedback information from other subsystems.

Exogenous inputs from a subsystem's envirormient.

For a given subsystem, the assigned goals and

feedback information might also be considered as exog- enous inputs because these are generated by other subsystems. However, a given subsystem can determine which

goals it will accept. The goals that it assigns to other subsystems will also influence their behavior and subsequent

feedback information. Thus, only environmental inputs

cannot be influenced in any marmer by a given subsystem.

Should the subsystem have an option to accept or

reject a goal? We believe that such an option is essential

in order to insure that the recipient subsystem can feasibly

respond to the goal. If one subsystem carmot satisfy its assigned goals, then the subsystem cannot respond in a

feasible marmer and the ability to control the subsystem is diminished or eliminated.

The assignment ofgoals to another subsystem represents one type of output that can occur as a subsystem

responds to its control inputs. In addition, the given subsystem must provide feedback information to any other

subsystem fi-om which it has accepted a goal. Finally, the

226
/ \ /

^

Node Assigmw^ Type

1 C're.itor

2 (.'rwlor

1 CoordifMStor 4 1.2 Coordinator

5

1 CoordiniStor 3

Tr Process S

<; Pnvcss

.as

f

^

Type

C'rcator Crcator

("oontifsijior CiHirilmstor Coordinator

Process Process

Figure 2: Network representation of subsystem relationships.Figure 3. Hierarchical system(s) where each subsystem (node) has at most one Assignor.

subsystem may also generate outputs that act upon the

system's environment.

Typically, when one seeks to coordinate subsystems, one employs hierarchical based notions of supervisors (supremals) and subordinates (infimals). Hierarchies, right or wrongly, have been the subject of much re- cent criticism. In this paper, our desire is to provide a

neutral atmosphere for discussing such coordination concerns. We define the Assignors as the set of controllers

that can assign goals to a given subsystem. Acceptors are

the set of subsystems to which a subsystem can assign

goals. Figure 1 depicts the proposed relationships among

the subsystems.

There are two special situations. If the set ofAssignors for a given subsystem is empty, then the subsystem

receives only exogenous inputs from the its environment

and feedback information from its Acceptors. We refer to such a subsystem as a Creator because it generates goals

only, and does not accept any goals from any other subsystem. Every system model requires at least one creator. However, Creators are generally artificial constructs resulting from the modeling process. That is, the Assignors

for a Creator are assumed to be outside the scope of the

modeled system. Thus, goals coming from these external

subsystems, or implicit Assignors, are viewed as inputs to a Creator from the system's environment.

If the Acceptors set for a given subsystem is empty,

the subsystem is a Process. Processes can accept and process goals, but they cannot reassign their goals to any other

subsystem. Hence, Processors can only accept inputs from

their Assignors and the system's environment. In response

to these inputs, they generate outputs upon the environment and provide feedback information to their Assignors.

We can graphically represent the proposed system structure (see Figure 2). We first define a node for

each subsystem. We then employ directed arcs from a given

subsystem's node to each node within its Acceptors set. Finally, from each subsystem node within a given

subsystem's Assignors set, we draw a directed arc to the

node for the given subsystem. Using network terminology, the Creator(s) become the source(s) to the system

network while the Processes are the sinks. In general, there can be more than one path from a given Creator to a given Process. (In Figure 2, there are multiple paths from node 1 to node 7.) However, there

need not be a path from every Creator to every process.

(In Figure 2, there is no path from node 1 to node 8.) In

the special case where the number of elements in each

subsystem's Assignors set is less than or equal to one, the

representative system network becomes a tree and represents a conventional hierarchy (see Figure 3). If there is more than one Creator in the hierarchical case, then the

overall system must be represented as a set of disjoint hierarchies that do not interact with each other (see Figure

3).

Although the potential for loops can exist within

a system's network, loops should not exist from the conceptual point of view. Later we will show that the detail

considered by an Acceptor is greater than its Assignor. We

will also show that the planning horizon for any Acceptor

should be less than that of the Assignor. Loops could oc- cur when an Assignor for a given subsystem is also contained in the given subsystem's Acceptors set. However,

if a subsystem is simultaneously contained within the Acceptors and Assignors sets for another given subsystem,

then the simultaneous Acceptor/Assignor must be less detailed from the system to which it assigns goals and more

detailed than the same system fi^om which it accepts goals.

Obviously, the two implied relationships are contradictory.

227
Therefore, we may conclude that the network representation of the relationships among subsystems for all meaningful systems must be a directed acyclic network (containing no loops).

The reader should note that we have spoken of

goal assignments rather than tasks, as mentioned earlier.

We assume that a goal can contain a task. Moreover, a goal can also describe how an assigned task should be executed. For example, the Assignor might request that the

task be completed by a given time or completed at minimum cost.

3. Toward an Integrated Approach

No (sub)system can generate an optimal response when

acting as an independent agent. A given subsystem's re- sponse is dependent upon its goals and the subsequent re- sponse of the subsystems to which it has assigned goals.

Furthermore, one cannot demonstrate that the collective

response arising from the coordinated interaction among

all its subsystems is optimal because we have not (and

cannot) define the overall problem.

Because no subsystem can respond independently

from the other subsystems, it follows that each subsystem

must constantly interact with other subsystems: its Assignors and Acceptors. However, a given subsystem's in- teractions with an Assignor are fundamentally different

from its interactions with an Acceptor. Each subsystem

considers a time interval and a level of detail that differs

from those of its Assignors and Acceptors. Each subsystem

must move from its current state to a specified goal state while responding to any external inputs from the overall

system's environment and any peculiarities that arise in

its own dynamic evolution.

Let us consider the interaction of a given subsystem with its Assignors. A given subsystem can only

address its behavior over an interval. Nevertheless, the

way in which a subsystem responds within a time interval can affect the future behavior of the entire system beyond

the considered time interval. The problem is that the given

subsystem is incapable of assessing these future conse- quences beyond the time interval that it considers. The

subsystem must instead rely upon the subsystems contained

within its Assignors set to make such assessments. In performing this function, each Assignor considers the future

in order to specify goals for the given subsystem. The

subsystem receiving the goals employs those goals in or- der to define its desired final state at then end of its planning horizon.

Similarly, most subsystems are also limited by the

level of detail that they can consider. In order to affect the

more detailed responses that are required to meet its as- signed goals, each subsystem assigns goals to its Acceptors. Thus, as each Acceptor addresses an assigned goal, it provides a more detailed system response on behalf of the

subsystem that assigned the goal. The subsequent feedback information provided by the Acceptor during its ex- ecution of an assigned task assists the Assignor in assess- ing the beginning state for its planning horizon. (Later we

will demonstrate that this beginning state for a subsystem's

planning horizon cannot be the current system state. It must always be a projected future state from which the

given subsystem will attempt to a desired final state.) Two

extremes, or boundary conditions, for a given subsystem's

planning/control problem have now been specified. Its included planning and control (intelligent control) capabilities then guide the given subsystem from its projected

initial state toward the desired final state while responding to forecasted environmental inputs and other peculiarities of the system response.

Every element of the subsystem's planning/control problems changes with time. The subsystem's estimate of its initial state changes as its Acceptors execute

their assigned tasks. The subsystem's goal changes with

time as its Assignors respond to feedback information that

the subsystem provides. Finally, the forecasts for the

subsystem's future interactions with its environment must

be constantly updated.

We can now define three basic fiinctional requirements for each subsystem's intelligent controller. These

include:

Task Accepting: The intelligent controller must interact

with the intelligent controllers that manage each subsystem

within its Assignors set. The purpose of this interaction is

to define new goals and to update current goals. Each

assigned goal specifies at least one task to be addressed

along with a set of constraints. Because an Assignor addresses the system in a more aggregated sense than the

subsystem that accepts the task, the Task Accepting function must decompose the assigned tasks into subtasks. In

addition, the execution constraints accompanying each ac- cepted task must also be reformulated in order to specify

appropriate (or consistent) constraints for each defined

subtask.

The task decomposition and the associated constraint specification comprise a goal decomposition process. This goal decomposition must guarantee that the

228
accepted goals can be satisfied given the accepting

subsystem's current state. The Task Accepting function is

also responsible for continuously updating the projected

response of the subsystem as feedback information to each

Assignor. Remember, however, that an Assignor considers the system response in an aggregated manner. Thus,

the Task Accepting function must summarize its projected

response in order to provide the estimated performance

statistics that can be understood by its Assignor.

Task Assigning: After the assigned goals are decomposed,

the resulting subtasks and their associated execution constraints must be reassigned to the subsystem's Acceptors.

In making the subsequent goal assignments, the Task Assigning function will employ the selected control law that

implements the subsystem's current plan. The Task Assigning function also monitors feedback information from

each Acceptor to which it has assigned a goal. Using this

feedback information, it projects the future performance

of the subsystem as it continues to execute its assigned

goals under the selected control law. This projected re- sponse is then employed by the Task Accepting function

within the same intelligent controller in order to provide

feedback information to the subsystem's Assignors.

Performance Improvement: The system now has an es- timated current state as well as a projected response as it implements its current goals under the planned response

and enabling control law. The Performance Improvement

Function continuously seeks a better control law for implementing the subsystem's assigned goals. Remember, however, that every element of the control problem is dynamic

and uncertainties do exist. Given this reality, closed-loop

control laws inherently perform best because they can tailor their response to the system's current state. It is also

desirable to employ predictive control procedures whenever the current control action depends upon both the

system's current and predicted future state. Whenever a new control law is selected, it is forwarded to Task Assigning function for implementation.

4. The Fundamental Principles of a Coordinated

Response

This section addresses the basic system response. Figure

4 provides a primitive schematic for the multi-resolutional

behavior of these systems. Let t^ represent the current time

that advances with real-time. We then divide the fiiture

time axis into several intervals, including [t,, t^), [t^, t^), [t^,

t^) and so forth. Note that we have not yet included a time

interval between [t^, t,) or [t^, •) for reasons to be discussed

later. In Figure 4, the entire state vector has been projected as a single value upon the y-axis. This state trajectory is further divided into segments: one segment for each

time interval specified above. Let us assume that each

segment corresponds to the trajectory for a given subsystem

operating under the control of its intelligent controller.

Considering the subsystem associated with the state tra- jectory on the interval [t3, t^), its Assignors manage the

state trajectory beyond t^, while its Acceptors manage the

state trajectory on the interval [t^, t^). Suppose we view each component of the state tra- jectory as a sophisticated "Slinky." The multi-resolutional

nature of the systems implies that size and length of each

"Slinky's" spring gets smaller and shorter as its associated

time interval approaches t^. Now let us fiirther assume

that two adjacent "Slinkies" are attached to each other and

that the boundary conditions must match at each junction.

Time

Figure 4: Basic Schema for multi-resolutional system response.

229
The "Slinky" for interval [t^, t^) interfaces with a larger

"Slinky," with less resolution, at t^. It also interfaces with

a shorter "Slinky," with greater resolution at t^. Assume

also that each "Slinky" can manage its shape. However,

no "Slinky" can act independently of the others. Specifically, the "Slinky " on interval [t^, t^) must interact with

the "Slinky" operating beyond t^ in order to define the

boundary conditions at t^. To be more precise, the Task

Accepting function of the [t^, t^)-subsystem must interact

with the Tasking Assigning functions for the Assignors of

the [t^, t^)-subsystem. Similarly, the Task Assigning function for the [tj, t^)-subsystem must interact with the Task

Accepting functions of its Acceptors. Finally, the shape

of the "Slinky" between t^ and t^ is controlled by the Performance Improvement function as the [t^, t^)-subsystem

responds to forecasted external inputs that will likely act upon it during the interval [t^, t^). Given the recursive system-of-systems nature for

the system, each Acceptor for the [t^, t^)-subsystem interacts with the [tj, t^)-subsystem in a manner similar to the

way that the [t^, t^)-subsystem interacts with its Assignors.

Similarly, each Acceptor for the [t^, t^)-subsystem will similarly interface with its own Acceptor's Task Accepting functions. Note also that none of the indicated subsystems can

touch tjj because only a Process that has no Acceptors can

reach t^. (We will discuss this assertion later.)

Now, let us try to visualize the dynamic behavior

of the proposed system's response. Remember, t^^ (the current time) must continue to advance in real time. We may

assume that the entire srate trajectory is dynamic, and neither the mterface times (t| , t„ . . . ) nor the boundary conditions are fixed. Instead, the shared boundary conditions

between two adjacent "Slinkies" are constantly being re- negotiated in real time. As the boundary conditions are modified and the forecasts for the external effects upon a given subsystem are updated, the intelligent controller re- sponds by modifying the projected desired shape of the

"Slinky" between the appropriate interface times.

We have stated that only Processes can affect the

system m real time. Several important conclusions fol- low:

• No interface time at the junction of two subsystems

'

responses can ever occur. These interface times constantly change with time and must always be greater

than the current time t^,.

• Only Processes react to real inputs from the external

environment. The other subsystems plan their response

based upon forecasted inputs from the environment

and their current negotiated boundary conditions.

• The planned trajectories of the non-processing subsystems are never realized. These planned trajectories only conjecture how the system will likely respond

for planning purposes.

• The purpose ofthe intelligent controllers for the nonprocessing subsystems is simply to establish goalsfor

another subsystem. The recursive system-of-system

nature of these systems implies that these goals will

become more detailed as their interfacing times approach tj,. Figure 4 does not adequately depict the interaction between a given subsystem and its Assignor(s) and

Acceptors. In Figure 5, we provide a more detailed illustration of the proposed interaction among the subsystems

as they interact with each other. It also illustrates the evolution of time and the limitations that a given system has

in managing the response of the system.

Time advances from left to right in Figure 5. The

large sphere represents the state space for the aggregate

subsystem that projects into the most distant future. Within

that subsystem's state space, there are two smaller spheres.

The right-most of these spheres represents the goal space

that the system seeks to reach at t^. In this case, we as- sume that the final goal is established by the system's en- vironment because the Assignors for this subsystem have

not been included within the system model. Note that this

is an arbitrary choice based upon the modeler's desires

and is determined to a certain extent by how far the modeler wants to forecast the system's future response.

The left-most sphere within the largest sphere represents the forecasted state at t^ from which the aggregate

subsystem initiates its planning. Thus, the aggregate subsystem represented by the largest sphere will plan on the

interval [t^, t^). The values for both t^ and t^ are dynamic

and must increase with real-time, and t^ is always greater

than iy (The reader will note that we have not included t^ within the subsystem's planning horizon because the goal

state at t^ is specified by an agent outside of the modeled

system).

The role of the intelligent controller for the [t^, t^)- subsystem is to determine the ideal trajectory from the

anticipated state at t^ to the desired goal state at t^. During

that interval, the subsystem must also respond to other

external inputs. Because the planning interval is beyond

the current time, these external inputs must be forecasted.

Hence, the planned response on the [t^, t^) interval is a

projected response only. It will not (or cannot) be implemented as planned.

230
The [t3-t^)-subsystem cannot manage the response

of the system before t^ because it cannot address the detail

required to describe the system's response prior to t^. Rather, this detail will be addressed by two other subsystems as indicated by the second largest spheres in Fig- ure 5. The fact that the spheres are smaller has no relation

to the dimensions of each subsystem's state space. Rather,

the diameters of the spheres correspond to the relative

length of the planning interval that each subsystem addresses.

The [tj, t^) subsystem estimates its initial state at

ty Thus state is achieved by the subordinate's response on

the [t^, t^) time interval. In order to manage the subordinate subsystem's response, the [t^, t^)-subsystem must define goal states for the two subsystems at t,"* and t^^^\ respectively. However, the state variables considered by the

subordinate subsystems are different than those considered by the [t^, t^)- subsystem. Hence, a transformation

between the state spaces must occur. This transformation

is implemented by the Task Assignor for the [i^, t^)-subsystem as it interacts with the Task Acceptors within [t,,!^)- subsystem's Acceptors. This transformation involves two

types ofinteractions. With respect to the [t^ t^)-subsystem,

the first interaction determines a mutually acceptable set of feasible goals for each Acceptor. The second interaction monitors each Acceptor's progress in achieving its

assigned goals. Here, the Task Assigning function for [t^, t^)-subsystem must transform each Acceptor's projected

goal achievement into the corresponding state representation within the [t^, t^)-subsystem's state space. Moreover,

the individual Acceptor's response must be integrated to form a single composite estimate for the [t,, t^)-subsystem's

initial state at t^. The goals established for the Acceptors will cover

the time interval up to t^*'' and t^'^', respectively. In order

to insure planning across the entire time interval up to t^^, it

is essential that t^ be less than or equal to either tj^'^ or t^^^\

Thus, the planning interval for a given Acceptor usually

overlaps the planning interval of its Assignor(s). In addition, the state space for the individual Acceptors can also

overlap each other. For example, it might be possible for

both Acceptors to execute a given task. It is also possible

that the state spaces are not entirely congruent. One Acceptor might be able to execute tasks that the other Acceptor cannot.

On the other hand, the state trajectories through

the subsystem's state spaces must not intersect. Two distinct subsystems may not perform identical tasks upon the

same entity at the same time. Two or more subsystems

could possibly collaborate, but one subsystem would still assume primary control of the entity and subsystem actions upon the entity must differ from the others in some

' 'tf

I4

Figure 5. A more detailed representation of the multi-resolutional state evolution.

231
manner at a given time. A fundamental law of physics

prevents two objects from occupying the same region of

space and time simultaneously.

Given its desired final goal state at t^*", the [t,, t3*'')-subsystem plans its response through its state space.

The [t,, tj"')-subsystem interacts with its Acceptors in or- der to estimate its initial planning state at t^. This interaction also establishes the goals for each of the [t,, t^'")-

subsystem's Acceptors at t,'", t^'-' and t,'^*. Having established each of their individual goals, the [t,, t^''')- subsystem's Acceptors can determine their individual initial planning states. The same process is repeated for each

state trajectory emanating from t^ until a process, (which

has no Acceptors), is encountered. This terminating process can be managed at t^. Hence, the recursive planning

process generates a collection of state trajectories, each

beginning at t^ and terminating at t^. Figure 5 depicts a situation where hierarchical

planning occurs. Each subsystem has at most one Assignor,

and the collection of state trajectories illustrated in Figure

5 forms a tree. Observe that state trajectories continue to divide as the diagram progresses from the most distant time

t^ toward present time. Moreover, each subsystem has a

single state trajectory to manage. In Figure 5, we did not

include every possible subsystem in order to simplify the

figure. (Observe that some of the state trajectories do not

begin at t^.) If all potential subsystems for a hierarchical

system were included, then every path in the state-trajectory tree would start at t^ and terminate at a common root

at t,. Recently, hierarchical systems have fallen from

favor. Certainly, hierarchies, like all organizational structures, do have their limitations. However, most limitations occur when the Assignors dictate their goal assignments and the Acceptors cannot reject an assigned goal.

Recent management and distributed planning approaches

seek to empower the subordinate subsystems with greater

planning and control responsibilities. Contrary to popular

belief, such empowerment does not negate hierarchical

structures.Unfortunately, there are situations where hierarchies are inappropriate. For example, the government typically seeks to prevent individual corporations from collaborating in order to create a monopolistic environment.

One benefit of the proposed approach is that we

can now characterize the limitations that arise when one must employ a structure other than a hierarchy. In particular, we can now test many of the claims that the advocates of other architectures have cited.

5. Conclusions

The efficacy of current planning and control technologies

requires a monolithic statement of the system's planning

and control problems. If such monolithic statements cannot be made, then available planning and control technologies are probably irrelevant. The above discussion demonstrates that it will be impossible to provide such monolithic specifications for most complex systems.

One might question whether it is possible to provide a monolithic statement for any system's planning and

control problems. Remember that one's definition of the

system's boundary is arbitrary. In most cases, the defined

system is still dependent upon other environmental subsystems that are being managed by other entities. A subsystem seldom has complete control over its planning and

control responsibilities. Witout such control, it is impossible to demonstrate optimality of a planned /executed system response with respect to any performance criteria.

Given the current state of affairs, performance evaluations

for a given subsystem level or the composite system should

be avoided. The primary goal must be to develop improved

technologies for managing complex systems.

6. References

[1] L. S. Lasdon, Optimization Theory for Large systems, London, Macmillian Company, 1970.

[2] D. A. Wismer, Optimation Methods for Large-Scale

Systems with Applications, New York, MCGraw-Hill

Book Company, 1971.

[3] R Kokotovic, H. Khalil and J. O'reilly, Singular Perturbation Methods in Control: Analysis and Design,

New York, Academic Press, 1 986.

232
Hypothesis Testing for Complex Agents

Joanna Bryson, Will Lowef and Lynn Andrea Stein

MIT AI Lab

Cambridge, MA 02139

joanna@ai.mit.edu, las@ai.mit.edu

fTufts University Center for Cognitive Studies

Medford, MA 02155

wlowe02@tufts.edu

Abstract

As agents approach animal-like complexity, evaluating them becomes as difficult as evaluating animals.

This paper describes the application of techniques for

characterizing animal behavior to the evaluation of complex agents. We describe the conditions that lead to the

behavioral variability that requires experimental methods. We then review the state of the art in psychological experimental design and analysis, and show its application to complex agents. We also discuss a specific methodological concern of agent research: how the

robots versus simulations debate interacts with statistical evaluation. Finally, we make a specific proposal for

facilitating the use of scientific method. We propose the

creation of a web site that functions as a repository for

platforms suitable for statistical testing, for results deter- mined on those platforms, and for the agents that have

generated those results.

Keywords: agent performance, complex systems, behavioral indeterminacy, replicability, experimental design, subjective metrics, benchmarks, simulations, reliability.

1. Introduction

Humanoid intelligence is a complex skill, with many interacting components and concerns. Unless they are in an exceptional, highly constrained situation, intelligent agents can never be certain they are expressing the best possible behavior for the current circumstance. This is because the problem of choosing

an ordering of actions is combinatorially explosive [9]. Consequently, for scientists or engineers evaluating the behavior of an agent, it is generally impossible to ascertain whether a behavior is optimal for that agent. Albus [2] defines intelligence

as "the ability of a system to act appropriately in an uncertain

environment, where appropriate action is that which increases

the probability of success." Systems of such complexity are

rarely amenable to proof-theoretic techniques [26]. In general.

the only means to judge an increase in probability is to run statistical tests over an appropriately sized sample of the agent's

behavior.

Computational systems, in contrast, are traditionally eval- uated based on their final results and/or on their resource utilization [29]. The historical definition of computational process

(c.f. Babbage, Turing, von Neumann) is modeled on mathematical calculation, and its validity is measured in terms of its ultimate product. If the output is correct — if the correct value

is calculated — then the computation is deemed correct as well. More recent descriptions [e.g. 1 1] have added an assessment of

the time, space, processor, and other resource utilization, so that a computation is only deemed correct if it calculates the appropriate value within some resource constraints.

This characterization of computation is less applicable

when it comes to particular operating systems and other real- time computational systems. These systems have no final result, no end point summarizing their work. Instead, they must be

evaluated in terms of ongoing behavior. Guarantees, where they

exist, take the form of performance constraints and temporal invariants. Although formal analysis of correctness plays a role even in these systems, performance testing, including benchmarking, is an essential part of the evaluation criteria for this kind of computational system.

Computational agent design owes much to computer sci- ence. But the computationalist's tendency to evaluate in terms

of ultimate product is as inappropriate for computational agents

as it is for operating systems. Instead, metrics must be devised

in terms of ongoing behavior, performance rather than finitary

result. But what is the analog to benchmarking when the tasks

are under-specified, ill-defined, and subject to interpretation and

observer judgment?

In this paper, we will examine issues of running such evaluations for complex agents. By complex agents we mean au- tonomous agents such as robots or VR characters capable of

emulating humanoid or at least vertebrate intelligence. We will

discuss hypothesis testing, including the statistical controversies that have lead to the recent revisions in the standard experi233
mental analysis endorsed by the American Psychological Association. We will also discuss recent advances in methodologies

for establishing quantitative metrics for matters of human judgment, such as whether one sentence is more or less grammatical than another, or an anecdote is more or less appropriate. We

propose a means to facilitate hypothesis testing between groups:

a simulation server running a number of benchmark tests.

2. Motivation: Sources of Uncertainty

Although there is certainly a role for using formal methods in comparing agent architectures [e.g. 8, 6], what we as agent designers are ultimately interested in is comparing the resulting

behavior of our agents. Given the numerous complex sources of indeterminacy in this behavior, such comparison requires the

application of the same kind of experimental methodology that has been developed by psychology to address similar problems.

In this section we review some of the sources of this indeterminacy; in the next we will review analytic approaches for addressing them.

The first source of indeterminacy is described above: The

combinatorial complexity of most decision problems makes absolute optimality an impractical target. Thus even if there is a

single unique optimal sequence of actions, in most situations we cannot expect an agent to find it. Consequently, we will expect a range of agents to have a range of suboptimal behaviors,

and must find a way of comparing these. The next source of indeterminacy is the environment. Many

agents must attempt to maintain or achieve multiple, possibly even contradictory goals. These goals are often themselves uncertain. For example, the difficulty of eating is dependent on the

supply of food, which may in turn be dependent on situations

unknowable to the agent, whether these be weather patterns,

the presence or absence of other competing agents, or in human

societies, local holidays disrupting normal shopping. Thus in evaluating the general efficacy of an agent's behavior, we would

need a large number of samples across a range of environmental

circumstances.

Another possible source of indeterminacy is the development of agents. As engineers, we are not really interested in evaluating a single agent, but rather in improving the state-ofthe-art in agent design. In this case, we are really interested in what approaches are most likely to produce successful agents.

This involves uncertainty across development efforts, complicated by individual differences between developers. Many results contending the superiority or optimality of a particular the- ory of intelligence may simply reflect effective design by the

practitioners of that theory [e.g. 7].

Finally, the emphasis of this workshop is on natural, humanlike behavior. Humans are highly social animals, and social acceptability is an important criteria for intelligent agents. However, sociability is not a binary attribute: it varies in degrees.

Further, a single form of behavior may be considered more or

less social by the criteria of various societies. Evaluations of

systems by such criteria requires measurement over a population of judges.

3. Current Approaches to Hypothesis Testing

The previous section presented a number of challenges to the

evaluation of complex, humanoid agent building techniques. In

this section we review methodologies used by psychology —

the evaluation of human agents — that are available to address

these challenges.

Although it is obvious that comparing two systems requires

testing, the less obvious issues are how many tests need to be

run and what statistical analysis needs to be used in order to an- swer these questions. In this section we describe three increasingly common problems in Artificial Intelligence and discuss a

set of experimental techniques from the behavioral sciences that can be used to address them.

The first problem is variability in results: We need to know

whether performance differences that arise over test replications can be ascribed to varying levels of a system's ability or to vari- ation in lighting conditions, choice of training data, starting position, or some other or some other external (and therefore uninteresting) source. Psychology uses statistical techniques such as the Analysis of Variance (ANOVA) to address these issues. The

second problem is of disentangling complex and unexpected in- teractions between subparts of a complex system. This can also

be addressed using ANOVA coupled with factorial experimental design. The third problem is that of rigorously and meaningfully evaluating inherently subjective data. Since many psychology experiments investigate inherently subjective matters,

the field has developed a set of techniques that will be of use

to artificial agent designers as well. The next three sections describe these solutions in more detail.

3. 1 Variability in Results

The problem of comparing performance variability due to dif- ferences in ability and variability due to extraneous factors is ubiquitous in psychology. It is dealt with by procedures known

collectively as Analysis of Variance or ANOVA.

3.1.1 Standard ANOVA

In a typical experimental design for comparing performance, K

systems are tested N times each. If the variation in performance

between the K systems outweighs the variability among each

system's N runs, then the system performances are said to be

significantly different. We then examine the systems pairwise

to get information about ordering. The ANOVA allows us to infer that e.g. although there are differences overall between the

K=4 systems (i.e. some are better than others), the performance

difference between 3 and 4 is reliable, whereas the difference

between 1 and 2 is not reliable because it is outweighed by the

amount of extraneous variation across the N tests. In this case, although I may perform on average better than 2, this does not

234
imply that it is actually better on the task. If the experiment were repeated then 2 would have reasonable chance of performing on average the same as 1 , or even better. The notion of reasonable chance used above is the essence

of the concept of significant difference. System 3 is on average

better than 4 in this experiment and the ANOVA tells us that per- formances are significantly different at the .05 level (expressed

as p<.05). This means that in an infinite series of experimental replications, if 3 is in fact exactly as good as 4, i.e. there

is no genuine performance difference, then the probability of

getting a performance difference as large or larger than the one

observed in this experiment is 0.05. The smaller this probability becomes, the more reliable the difference is. In contrast, the

fact that the average performances of 1 and 2 are not significantly different means their ordering in this experiment is not

reliable because there is a more than 0.05 probability that the

ordering would not be preserved in a replication.

Notice that hypothesis testing using ANOVA does not guarantee an ordering, it presents probabilities that each part of

the ordering is reliable. This is a fundamental difference between experimental evidence and proof. Scientific method in- creases the probability that hypotheses are correct but it does

not demonstrate them with complete certainty.

The binary output of hypothesis tests (significant difference versus no significant difference) and its probability is an unnecessarily large loss of information. The American Psychological

Association have consequently recently moved to emphasize

confidence intervals over simple hypothesis testing. A confidence interval is a range, centered on the observed difference,

that in the hypothetical replications will contain the true perfor- mance value some large percentage, say 95%, of the time. In the example above, each system has a 95% confidence interval, or error bar, centered on its average performance with width

determined by the amount of variability between runs. When

two intervals overlap, there is a significant probability that a

replication will not preserve the current ordering among the av- erages and we can conclude that the corresponding performance

difference is unreliable. This method gives the same result as simple hypothesis testing above — the performances are not

significantly different — but is much more informative: confidence intervals give an idea about how much variability there is

in the data itself and yield a useful graphical representation of

analytical results.

3. 1.2 Alternative Approaches to Analysis

Stating confidence intervals is more informative than simple

significance judgments. However, it also relies on an hypothetical infinity of replications of an experiment. This aspect

of classical statistical inference is a result of assuming that the

true difference in performance is fixed and the observed data

is a random quantity. Alternatively, in Bayesian analysis the

difference is considered uncertain and is modeled as a random

variable whereas the results are fixed because they have already

been observed [5]. The result is a probability distribution over

values of the true difference. To summarize the distribution an

interval containing 95% of the probability mass can be quoted.

This takes the same form as a confidence interval, except that

its interpretation is much simpler: Given the observed results,

the probability that the true difference is in the interval is 0.95, so if the interval contains 0, there is a high probability that there

is no real performance difference between systems.

The Bayesian approach makes no use of hypothetical ex- perimental replications and is more naturally extended to deal

with complicated experimental designs. On the other hand, it does require an initial estimate (or prior distribution) for the

probabilities of various values of the performance difference

before seeing test data. There is much controversy about which

of these approaches is more appropriate. In the context of AI

however, we need not take a stand on this issue. The two ap- proaches answer different questions, and for our purposes the

questions answered by classical statistics are of considerable

interest. Unlike many of the natural sciences, the performance

of AI systems over multiple replications is not only accessible,

but of particular interest. To the extent we are engineers, AI

researchers must be interested in reliability and replicability of

results.

3.2 Testing for Interacting Components

Many unpleasant software surprises arise from unexpected in- teractions between components. Unfortunately, in a complex

system it is typically infeasible to discover the nature of inter- actions analytically in advance. Consequently /actona/ experimental design is an important empirical tool. As an example, assume that we can make two changes A

and B to a system. We could compare the performance of the

system with A to the same system without it, using the ANOVA

methods above, and then do the same for B. But when building a complex system it is essential to also know how A and B

affect performance together. Separate testing will never reveal,

for example, that adding A generates a performance improvement only when B is present and not otherwise. This is referred

to as an interaction between A and B, and can be dealt with by

testing all combinations of system additions, leading to a factorial experiment. Factorial experiments are analyzed using simple extensions to ANOVA that test for significant interactions

as well as simple performance differences. Factorial ANOVA

methods are described in any introductory statistics textbook

[e.g 23].

In the discussion above we have implicitly assumed that dif- ferences in performance can be modeled as continuous quantities, such as distance traveled, length of conversation or number

of correct answers. When the final performance measure is discrete, e.g. success or failure, then logistic regression [I, ch.4]

is a useful way to examine the effects of additions or manipulations on the system's success rate. Information about the effects of arbitrary numbers of additions, both individually and in in235
teraction, is available using tiiis method, just as in the factorial ANOVA. Logistic regression also gives a quantitative estimate

of how much the probability of success changes with various

additions to the system, which gives an idea of the importance

of each change.

3.3 Quantifying Inherently Subjective Data

Often performance evaluation involves judgments or ratings

from human subjects. Clearly it is not enough that one subject judges an AI conversation to be lifelike because we do not know how typical that subject is, and how robust their opinion

is. It would be better to choose a larger sample of raters, and to check that their judgments are reliable. When ratings are dis- crete (good, bad) or ordinal (terrible, bad, ok, good, excellent)

then Kappa [22] is a measure of between-rater agreement that

varies from 1 (perfect agreement) to -1 (chance levels of agree- ment). For judgments of continuous quantities the intraclass

correlation coefficient [13] performs the same task.

However, such discrete classifications are often clumsy. Because a rating system is itself subjective, the extra variance

added by difference in interpretation of a category can lose correlations between subjects that actually agree on the relative validity or likeability of two systems. Further, we would really

prefer in many circumstances to have a continuous range of dif- ference values. Such results can be provided by magnitude esti- mation, a technique from psychophysics. For example. Bard et

al. [4] have recently introduced the use of magnitude estimation

to allow subjects to judge the acceptability of sentences which

have varying degrees of syntactic propriety. In a magnitude esti- mation task, each subject is asked to assign an arbitrary number

as a value for the first example they see. For each subsequent

example, the subject need only say how much more or less ac- ceptable it is, with reference to the previous value, e.g. twice as acceptable, half as acceptable and so on. This allows subjects to pick a scale they feel comfortable with manipulating, yet gives

the experimenter a generally useful metric. For example, in Bard et al/s work, a subject might give the first sentence an 8, the next a 4, the following a 32 — the experimenter records Is,

.5s and 4s respectively. This method has been shown to reduce

the number of judgments necessary to get very reliable and ac- curate estimates of acceptability, relative to other methods.

Bard et al. manipulate the sentences themselves, but it is clear that magnitude estimation can equally well be used to get

fine-grained judgments about how natural the output of a natural language processing (NLP) system is, and the degree to which this is improved by adding new components. Nor is the

method limited to linguistic judgments, for it should be equally

effective for evaluating ease of use for teaching software, the

psychological realism of virtual agents or the comprehensibility of output for theorem proving machinery.

4. Environments for Hypothesis Testing: Robots

and Simulations

As the previous sections indicate, one of the main attributes of statistically valid comparisons is a large number of experimental trials. Further, these experimental conditions should be

easily replicable and extendible by other laboratories. In Section 5. we propose that a good way to facilitate such research

is to create a web location dedicated to providing source code

and statistics for comparative evaluations over a number of dif- ferent benchmark tasks. This has approach has proven useful in neural network research, and should also be useful for complex

agents. However, it flies in the face of one of the best-known

hypotheses of complex agent research: that good experimental

method requires the use of robots. Consequently, we will first provide an updated examination of this claim.

4. 1 Arguments Against Simulation

Simulation is an attractive research environment because it is easy to maintain valid controls, and to execute large numbers

of replications across a number of machines. However, there

have been a number of important criticisms leveled against this approach.

A Simulations never replicate the full complexity of the real world. In choosing how to build a simulation, the researcher

first determines the 'real' nature of the problem to be solved.

Of course, the precise nature of a problem largely deter- mines its solution. Consequently, simulations are not valid

for truly complex agents, because they do not test the complete range of problems a natural or embodied agent would

face.

B If a simulation truly were to be as complicated as the real world, then building it would cost more time and effort than can be managed. It is cheaper and more efficient to build

a robot, and allow it to interact with the real world. This

argument assumes one of basic hypotheses of the behaviorbased approach to AI [3], that intelligence is by its nature simple and its apparent complexity only reflects the complexity of the world it reacts to. Consequently, spending

resources constructing the more complicated side of the sys- tem is both irrational and unlikely to be successful.

C When researchers build their own simulations, they may deceive either themselves or others as to the validity or complexity of the agents that operate in it. Since both the problem and the solution are under control of the researcher, it is

difficult to be certain that neither unconscious nor deliberate

bias has entered into the experiments. In contrast, a robot is considered to be clear demonstrations of autonomous artifact; its achievements cannot be doubted, because it inhabits

the same problem space we do.

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4.2 Are Robots Better than Simulations?

These arguments have led to the wide-spread adoption of the

autonomous robot as a research platform, despite the known

problems with the platform [16]. These problems reduce essentially to the fact that robots are extremely costly. Although their popularity has funded enough research and mass production to reduce the initial cost of purchase or construction, they are still

relatively expensive in terms of researcher or technician time

for programming, maintenance, and experimental procedures.

This has not prevented some researchers from conducting rig- orous experimental work on robot platforms [see e.g. 10, 25].

However, the difficulty of such procedures adds urgency to the

question of the validity of experiments in simulation.

This difficulty has been reduced somewhat by the advent

of smaller, more robust, and cheaper mass-produced robot platforms. However, these platforms still fall prey to a second problem: mobile robots do not necessarily address the criticisms

leveled above against simulations better than simulations do.

There are two reasons for this: the need for simplicity and reliability in robots, and the growing sophistication of simulations.

The constraints of finance, technological expertise and researchers' time combine to make it extremely unlikely that a robot will operate either with perception anything near as rich

as that of a real animal, nor with actuation having anything

like the flexibility or precision of even the simplest animals.

Meanwhile, the problem of designing simulations with predictive value for robot performance has been recognized and addressed as a research issue [e.g. 18]. All major research robot

manufacturers now distribute simulators with their hardware. In the case of Khepera, the robot most used by researchers running

experiments requiring large numbers of trials, the pressure to provide an acceptable simulator seems to have not only resulted

in an improved simulator, but also a simplified robot, thus making results on the two platforms nearly identical. Clearly this

similarity of results either validates the use of the Khepera simulator, or invalidates the use of the robot.

When a simulator is produced independent of any particular

theory of AI as a general test platform, it defeats much of the

objection raised in charges A and C above, that a simulator is bi- ased towards a particular problem, or providing a particular set of results. In fact, complaint Cis particularly invalid as a reason

to prefer robotics. Experimental results provided on simulations can be replicated precisely in other laboratories. Consequently,

they are generally more easily tested and confirmed than those

collected on robots. To the extent that a simulation is created for

and possibly by a community — as a single effort resulting in a platform for unlimited numbers of experiments by laboratories

world-wide, that simulation also has some hope of overcoming

argument B.

This gross increase in the complexity of simulations has particularly true of two platforms. First, the simulator developed

for the simulation league in the RoboCup soccer competition

has proven enormously successful. Although competition also

takes place on robots, to date the simulator league provides far more "realistic" soccer games in terms of allowing the demonstration of teamwork between the players and flexible offensive

and defensive strategies [21, 19]. This success has encouraged

the RoboCup organization to tackle an even more complex simulator designed to replicate catastrophic disasters in urban settings [20]. This simulator is intended to be sufficiently realistic as to eventually allow for swapping in real-time sensory data

from disaster situations, in order to allow disaster relief to monitor and coordinate both human and robotic rescue efforts. The second platform is also independently motivated to pro- vide the full complexity of the real world. This is the commercial arena of virtual reality (VR), which provides a simulated environment with very practical and demanding constraints which cannot easily be overlooked. Users of virtual

reality bring expectations from ordinary life to the system, and

any agent in the system is harshly criticized when it fails to provide adequately realistic behavior. Thorisson [30] demonstrates that users evaluate a humanoid avatar with which they

have held a conversation as much more intelligent if it provides

back-channel feedback, such as eyebrow flashes and hand gestures, than when it simply generates and interprets language.

Similarly Sengers [27] reviews evidence that users cannot be- come engaged by VR creatures operating with overly reactive

architectures, because the agents do not spend sufficient time

telegraphing their intentions or deliberations. Such constraints

have often been overlooked in robotics.

In contrast, robots which must be supported in a single lab with limited technical resources are likely to deal with far simpler tasks. Robots may face far fewer conflicting goals, lower

time-related conflicts or expectations, and even fewer options

for actuation. Although robots still tend to have more natural

perceptual problems than simulated or VR agents, even these

are now increasingly being addressed with reliable but unnatural sensors such as laser range finders.

4.3 Roles for Robots and Simulations

Robots are still a highly desirable research platform. They pro- vide complete systems, requiring the integration of many forms

of intelligence. Many of the problems they need to solve are closely related to animal's problems, such as perception and

navigation. In virtual reality, perfect perception is normally

provided, but motion often has added complication over that

in the real world. Depending on the quality of the individual

virtual reality platform, an agent may have to deliberately not pass through other objects or to intentionally behave as if it were

affected by gravity or air resistance. Even in the constantly improving RoboCup soccer simulator, there are outstanding difficulties in simulating important parts of the game, such as the

goalkeeper's ability to kick over opposing team members (currently compensated for by allowing the keeper to "warp" to any

point in the goal box instantaneously when already holding the

ball.)

237
Robots being embodied in the real world are still probably

the best way to enforce certain forms of honesty on a researcher.

A mistake cannot be recovered from if it damages the robot, an

action once executed cannot be revoked. Though this is also

true of some simulations [e.g. 31], particularly in the case of

younger students, these constraints are better brought home on

a robot, as it becomes more apparent why one can't 'cheat.'

Finally, building intelligent robots is a valid end in itself. Commercial intelligent robots are beginning to prove very useful in care-taking and entertainment, and may soon prove useful in ar- eas such as construction and agriculture. In the meantime robots

are highly useful in the laboratory for stirring interest and en- thusiasm in students, the press and funding agencies. However,

given the arguments above, we conclude that the use of robots

as experimental platforms is neither necessary nor sufficient in providing evidence about complex agent intelligence. Robots,

like simulations, must be used in combination with rigorous ex- perimental technique, and even so can only provide evidence, not conclusive proof, of agent hypotheses.

In summary, neither robots nor simulation can provide a

single, ultimate research platform. But then, neither can any

other single research platform or strategy [15]. While not denying that intelligence is often highly situated and specialized

[14, 17], to make a general claim about agent methodology re- quires a wide diversity of tasks. Preference in platforms should

be given to those on which multiple competing hypotheses can

be tested and evaluated, whether by qualitative judgments such

as the preference of a large number of users, or by discrete quantifiable goals to be met, such as a genetic fitness function, or the score of a soccer game.

5. Coordinating Hypotliesis Testing

Whether there can be general solutions to problems of intel- ligence is an empirical matter that has already been tested in some domains. For neural networks and other machine learn- ing methods, the UCI Machine Learning Repository holds a large collection of benchmark learning tasks. Besting these

benchmarks is not a necessary requirement for the publication

of a new algorithm, but showing a respectable performance on them improves the reception of new contributions. Essentially,

benchmarks are one indication for both researchers and reviewers of when an innovation is likely to be of interest.

Further, Neal and colleagues at the University of Toronto

have constructed DELVE [24], a unified software framework

for benchmarking machine learning methods. DELVE contains

a large number of benchmark data sets, details of various machine learning techniques, currently mostly neural networks and

Gaussian Processes, and statistical summaries of their perfor- mance on each task. One of the most important requirements is

that each method is described in enough detail that it could be

implemented by another researcher and would obtain a similar

performance on the tasks. This ensures that the mundane but

essential decisions that are an essential part of many learning

algorithms (e.g. setting weight decay parameters, choosing k in k-nearest-neighbor rules) are not lost. We propose a complex agent comparison server or web site,

to be at least partially modeled on DELVE. This site should al- low for the rating of both agent approaches and comparison en- vironments, thus encouraging and facilitating research in both

fields. It could also be annotated for educational purposes, in- dicating challenges and environments well suited to school, undergraduate, and graduate course projects. Such a site might

provide multiple indices, such as:

• Environments, ranked by number and/or diversity of participants.

• Agent architectures (e.g. Soar, Behavior-Based AI). This

should also allow for the petition for new categories.

• Contestants and/or contesting labs or research groups . This

allows researchers interested in a particular approach to see any related work. Ranked by the number and/or diversity of

environments.

Here are some examples of already existent platforms which

might be included on the server:

• RoboCup [21, 19].

• Khepera robot competitions. Both of these two suggestions

provide simulations as well as organized robotic competitions. They test learning and perception as well as planning

or action selection.

• Tile World and Truck World, designed as complex planning

domains. [15]

• Tyrrell's Simulated Environment [31] designed to test action-selection and goal management.

• Chess.

• An analog Turing Test, using magnitude estimation to compare dialog systems.

In addition, there are at least two software environments designed specifically to allow testing and comparison of a number

of different architectures, though they contain no specific experimental situations as currently developed. These environments

are Cogent [12] and the Sim_agent Toolkit [28].

6. Conclusion

To summarize, we believe that as agents approach the goal of

being psychologically realistic and relevant, their evaluation

will require the techniques that have been developed in the psy- chological sciences. This evaluation is critical in providing a

gradient as we search for the right sorts of techniques to build

complex agents. The techniques of hypothesis testing have been

refined to describe truly complex agents. However, these are

scientific techniques, not proofs. They do not give us certain

238
answers, only more information. We believe many of the criticisms of benchmark testing made in the past failed to properly acknowledge this feature of experimentation. We should

trust increased probability, rather than proof-theoretic guarantees. The more people perform tests across competing hypotheses, the more likely we will be to achieve our research goals,

whether they are engineering complex, social agents, or understanding the nature of intelligence.

Acknowledgments

The authors would like to acknowledge early discussions with

Brendan McGonigle and Ulrich Nehmzow on this topic.

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for Cognitive Science.

240
PART II

RESEARCH PAPERS

4. MOBILITY AND BENCHMARKING

4.1 Features of Intelligence Required by Unmanned Ground Vehicles

J. S. Albus, NIST, USA

4.2 A Standard Test Course for Urban Search and Rescue Robots

A. Jacoff, E. Messina, J. Evans, NIST, USA

4.3 Assessment of the NIST Standard Test Bed for Urban Search and Rescue Competitions

R. Murphy, J. Casper, M. Micire, J. Hyams, University of South Florida, USA

4.4 Performance Metrics in the AAAI Mobile Robot Competition

A. C. Schultz, Naval Research Laboratory, USA

4.5 Performance Evaluation of Robotic Systems; A Proposal for a Benchmark Problem

S. K. Agrawal, A. M. Ferreira, S. Pledgie, University of Delaware, USA

4.6 A Multi-Sensor Cooperative Approach for the Mobile Robot Localization Problem

A. Clerentin, L. DeLahoche, E. Brassart, CREA, France

4.7 Measuring Mobile Robots Performance: Approaches and Pitfalls G. S. Sukhatme, University of Southern CA, USA

4.8 Search Graph Formation for Minimizing the Complexity of Planning A. Lacaze, ECE, University of MD, USA, S. Balakirsky,, NIST, USA

4.9 Metrics for Embedded Collaborative Intelligent Systems

M. E. Cleary, M. Abramson, M. B. Adams, S. Kolitz. Draper Lab, MA, USA

4. 1 0 Evolution of Mobile Agents

T. K. Shih, Tamkang University, Taiwan, R. O. C.

241

Features of Intelligence

Required by

Unmanned Ground Vehicles

James S. Albus

National Institute of Standards and Technology

Gaithersburg, MD 20899

ABSTRACT

A definition of intelligence is given in terms of performance

that can be quantitatively measured. Behaviors required of

unmanned ground vehicles are described and computational

requirements for intelligent control at seven hierarchical levels in a military scout platoon are outlined. Metrics and measurements are suggested for evaluating the performance of unmanned ground

vehicles. Calibrated data and test facilities are suggested to

facilitate the development of intelligent systems.

KEYWORDSJ intelligence, intelligent systems, unmanned

ground vehicles, scout platoon, autonomous vehicles, metrics, measures

1. DEFINITIONS

The definition of intelligence is a controversial subject.

Hardly any two persons define intelligence the same. Some

even question whether intelligence can be defined at all. Yet, if we are to perform serious research on intelligent

systems, we must not only be able to define intelligence, we

must be able to quantitatively measure it. Thus, for the

purpose of discussion of the issues addressed in this paper,

we will define intelligence as follows [1]:

Df: intelligence

the ability to act appropriately in an uncertain

environment

Df appropriate action

that which maximizes the probability ofsuccess Df success

the achievement or maintenance ofbehavioral goals

Df behavioral goal

a desired state of the environment that a behavior is designed to achieve or maintain

This definition of intelligence addresses both biological

and machine embodiments. It admits a broad spectrum of

behaviors, from the simple to the complex. We deliberately

do not define intelligence in binary terms (i.e., this machine

is intelligent and this one is not, or this species is intelligent

and this one is not) and we do not limit our definition of

intelligence to behavior that is beyond our understanding.

Our definition includes the entire spectrum of intellectual

capabilities from that of a Paramecium to that of an Einstein,

from that of a thermostat to that of the most sophisticated

computer system. We include the ability of a robot to spotweld an automobile body, the ability of a bee to navigate in a

field of wild flowers, a squirrel to jump from limb to limb, a duck to land in a high wind, and a swallow to catch insects

in flight above a field of wild flowers. We include the ability

of blue jays to battle in the bushes for a nesting site, a pride

of lions to conduct a coordinated attack on a wildebeest, and

a flock of geese to migrate south for the winter. We include

a human's ability to bake a cake, play the violin, read a book, write a poem, fight a war, or invent a computer.

Our definition of intelligence recognizes degrees, or

levels, of intelligence. These are determined by the

following parameters: 1) the computational power and

memory capacity of the system's brain (or computer), 2) the

sophistication of the processes the system employs for sensory processing, world modeling, behavior generation,

value judgment, and communication, and 3) the quality and

quantity of information and values the system has stored in

its memory. The measure of intelligence is success in solving problems, anticipating the future, and acting so as to maximize the likelihood of achieving goals. Success can be

measured by various criteria of performance (including life or death, pain or pleasure, reliability in goal achievement,

cost in time and resources, and others.) Different levels of

intelligence produce different probabilities of success.

Our definition of intelligence also has many

dimensions. For example, the ability to understand what is visually perceived is qualitatively different from the ability to comprehend what is spoken. The ability to reason about

mathematics and logic lies along a different dimension from

the ability to compose music and verse. The ability to choose

wisely involves both the ability to predict the future and the

ability to accurately assess the cost or benefit of predicted

future states. Along each of these dimensions, there exists a continuum. Thus, the space of intelligent systems is a

243
i

multidimensioiial continuum wherein non-intelligent

systems occup^ a point at the origin.

At a minimum, intelligence requires the ability to sense

the environment, to make decisions, and to control action.

Higher levels of intelligence may include the ability to recognize objects and events, to represent knowledge in a world model, and to reason about and plan for the future. In advanced forms, intelligence provides the capacity to predict

the future, to perceive and understand what is going on in the world, to choose wisely, and to act successfully under a

large variety of circumstances so as to survive, prosper, and

replicate in a complex, competitive, and often hostile

environment.

From the viewpoint of control theory, intelligence

might be defined as a knowledgeable "helmsman of

behavior." Intelligence is a phenomenon which emerges as a

result of the integration of knowledge and feedback into a sensory-interactive, goal-directed control system that can make plans and generate effective purposeful action to achieve goals.

From the viewpoint of psychology or biology,

intelligence might be defined as a behavioral strategy that gives each individual a means for maximizing the likelihood

of success in achieving its goals in an uncertain and often

hostile environment. Intelligence results from the integration

of perception, reason, emotion, and behavior in a sensing,

perceiving, knowing, feeling, caring, planning, and acting

system that can formulate and achieve goals.

2. REQUIREMENTS FOR UNMANNED

GROUND VEHICLES

The features of intelligence required by an Unmanned

Ground Vehicle (UGV) depends on many factors, such as: What does the UGV have to do?

Does it simply wander through a lab looking for soft drink cans?

Does it have to operate outside? Travel long distances?

Perform difficult tasks?

How complex and uncertain is the environment?

Where is it expected to operate? On well marked

roads? On unmarked roads? Gravel or dirt roads? Roads

grown up with weeds and brush? Off roads? In tall grass and weeds? In woods? Does it have to cross streams? Are

there bridges or fords available? What kind of maps are available? How accurate are they? How recent? How dynamic and hostile is the environment?

Are there moving obstacles? What are the lighting

conditions? Are obstacles located above or below ground

level? Are there other agents competing for the goal? Are

there enemy agents with deadly weapons?

What are costs, risks, and benefits?

What are the stakes? Life or death? Win or lose?

What are goals?

Attack? Defend? Escape? Detect and track enemy

targets? Remain undetected?

What are tasks?

Pick up an object? Use a tool? Dig a ditch? Cross a stream? Establish an observation post? Discover an enemy

vehicle? Analyze enemy behavior? Identify a face in a crowd?

What sensors are available?

CCD cameras? FLIRs? LADARs? Radars? Sonars?

Inertial? GPS? Beacons? Reflectors? Tactile? Force?

Encoders?

What actuators are to be controlled?

Manipulators? Grippers? Power train? Legs or Wheels? Steering? Brakes? Switches?

How much is known apriori?

Maps? Lists of objects and their attributes? State of

objects? Behavior of objects? Rules?

What skills and abilities are required?

Locomotion? Manipulation? Perception?

Communication? Reasoning? Speech understanding?

Written text understanding? In what languages?

The above questions are so open ended that it is futile to try to address all these issues simultaneously. To focus our efforts, we select an example of a problem that is

difficult enough to be challenging, well defined enough to quantitatively measure performance, easy enough that it probably can be achieved using available technology, and

useful enough that it is worth spending time and resources to solve it. The problem that we have selected it that of an unmanned ground vehicle for military scout operations.

3. A SCOUT PLATOON EXAMPLE

To illustrate the types of issues that will be addressed, an example is given below of a seven level hierarchy for a scout platoon attached to a battalion. The specific numbers

and functions described in this example are illustrative only.

They are meant only to illustrate how the generic structure and function of an intelligent system might be instantiated in

the 4D/RCS architecture [2] designed for the Army's Demo

III experimental unmanned ground vehicle program. [3]

244
Exact numbers for the actual system are still under

development.

Level 7 — Battalion

An armored battalion is a unit that consists of a group

of Ml or Bradley companies and a scout platoon. A

computational node at level 7 of the 4D/RCS architecture

corresponds to a battalion headquarters unit, consisting of a

battalion commander, several company commanders, a scout platoon leader, and support staff (In principle, any or all of

these could be humans or intelligent agent software

processes. In practice, they are all humans.)

The battalion headquarters unit plans activities and

allocates resources for the armored companies and the scout platoon attached to the battalion. Incoming orders to the

battalion are decomposed by the battalion commander into

assignments for the companies and the scout platoon.

Resources and assets are allocated to each subordinate unit,

and a schedule is generated for each unit to maneuver and

carry out assigned operations. Together, these assignments,

allocations, and schedules comprise a plan. The plan may be

devised by the battalion commander alone, or in consultation

with his subordinate unit leaders. The battalion level planning process may consider the exposure of each unit's

movements to enemy observation, and the traversability of

roads and cross-country routes. The battalion commander

typically defines the rules of engagement for the units under

his command and works with his unit leaders to develop a schedule that meets the objectives of the mission orders

given to the battalion. In the 4-D/RCS battalion node, plans

are computed for a period of about 24 hours(h) and

recomputed at least once every 2 h, or more often if necessary. Desired positions for each of the subordinate

units at about 2 h intervals are computed.

The 4D/RCS architecture provides a surrogate battalion

node in each individual vehicle to perform the functions of

the battalion headquarters unit when the vehicle is not in direct communication with its chain of command. The

surrogate node plans activities for the vehicle on a battalion

level time scale and estimates what platoon and section level operations should be executed to follow that plan. The

surrogate battalion node considers the exposure of scout platoon operations to enemy observations, and the

traversability of roads and cross-country routes.

In the surrogate battalion node in each vehicle, the 4- D/RCS world model maintains a knowledge database

containing a copy of the battalion level knowledge database

that is relevant to that vehicle. It contains names and

attributes of friendly and enemy forces and of the force

levels required to engage them. Maps have a range of 1000

km (i.e. more than the distance that a vehicle is likely to travel in a 24 h day at a Demo III speed of 36 km per hour

(10 m/s)) with a resolution of about 400 m. Maps describe

the terrain and location of friendly and enemy forces (to the

extent that they are known), and roads, bridges, towns, and

obstacles such as mountains, rivers, and woods. Battalion

level maps may be updated from intelligence reports.

4-D/RCS sensory processing in the surrogate battalion

node integrates information about the movement of forces,

the level of supplies, and the operational status of all the

units in the battalion, plus intelligence about enemy units in the area of concern to the company. This information is used to update maps and lists in the knowledge database so

as to keep it accurate and current.

The surrogate battalion node also contains value

judgment functions (e.g., calculating the risk of casualties)

that enable the battalion commander to evaluate the cost and

benefit of various tactical options. To the extent that the

knowledge, skills, and abilities in the surrogate battalion

node is identical with that in the real battalion node, the

surrogate battalion node will make the same decisions as the

real battalion headquarters node.

An operator interface allows human operators (either

on-site or remotely) to visualize information such as the

deployment and movement of forces, the availability of

ammunition, and the overall situation within the scope of

attention of the battalion commander. The operator can

intervene to change priorities, alter tactics, or redirect the

allocation of resources.

Output from the battalion level through the company

commanders and scout platoon leader comprise input

commands to the company/platoon level. Armor company

commanders and the scout platoon leader are expected to issue commands to their respective units, monitor how well

their units are following the battalion plan, and make

adjustments as necessary to keep on plan. New output

commands may be issued at any time, and typically consist

of tasks expected to require about 2 h to complete.

Level 6—Platoon

A scout platoon is a unit that typically consists of ten HMMWVs or Bradley vehicles organized into one or more

sections. For the Demo 111 project, a scout platoon will

consist of six manned HMMWVs and four UGVs. A 4- D/RCS node at the Platoon level corresponds to a scout platoon headquarters unit. It consists of a platoon

commander plus his/her section leaders. (Any of these could

be humans or intelligent agent software processes, in any

combination.) The platoon commander and section leaders

plan activities and allocate resources for the sections in the

platoon. Platoon orders are decomposed into job

assignments for each section. Resources are allocated, and a schedule of activities is generated for each section.

Movements are planned relative to major terrain features and

other sections within the platoon. Inter-section- formations

are selected on the basis of tactical goals, stealth requirements, and other priorities. At the platoon level,

plans are computed for a period of about 2 h into the future,

245
and replanning is done about every 10 min, or more often if necessary. Section waypoints about 10 min apart are computed.

The surrogate platoon node in each vehicle performs

the functions of the platoon headquarters unit when the

vehicle is not in direct communication with the chain of

command. It plans activities for the vehicle on a platoon

level time scale and estimates what vehicle level maneuvers

should be executed in order to follow that plan. Movements

are planned relative to major terrain features and other

vehicles within the platoon.

At the platoon level, the 4-D/RCS world model

symbolic database contains names and attributes of targets,

and the weapons and ammunition necessary to attack them.

Maps with a range of about 100 km (i.e. more than the

distance a platoon is likely to travel in 2 h) and resolution of

about 40 m describe the location of objectives, and routing

between them. Sensory processing integrates intelligence

about the location and status of friendly and enemy forces. Value judgment evaluates tactical options for achieving

section objectives. An operator interface allows human

operators to visualize the status of operations and the

movement of vehicles within the section formation.

Operators can intervene to change priorities and reorder the

plan of operations. Section leaders are expected to sequence

commands to their respective sections, monitor how well

their sections are following the platoon plan, and make

adjustments as necessary to keep on plan. The output from

the platoon level through the section leaders are commands

issued to sections to perform maneuvers and engage enemy

units in particular sectors of the battlefield. Output

commands may be issued at any time, but typically are planned to change only about once every 5 min.

Level 5—Section

A scout section is a unit that consists of a group of

individual scout vehicles such as HMMWVs and UGVs. A

4-D/RCS node at the section level corresponds to a section

leader and vehicle commanders (humans or intelligent

software agents). The section leader assigns duties to the

vehicles in his section and coordinates the vehicle

commanders in scheduling cooperative activities of the

vehicles within a section. Orders are decomposed into

assignments for each vehicle, and ^ schedule is developed

for each vehicle to maneuver in formation within assigned

corridors taking advantage of local terrain features and

avoiding obstacles. Plans are developed to conduct

coordinated maneuvers and to perform reconnaissance,

surveillance, or target acquisition functions. At the section

level, plans are computed for about 10 min into the future,

and replanning is done about every 1 min. Or more often if necessary. Vehicle waypoints about 1 min apart are computed.

The surrogate section node in each UGV performs the

functions of the section command unit when the UGV is not

in direct communication with the section commander. The

surrogate node plans activities for the UGV on a section

level time scale and estimates what vehicle level maneuvers

should be executed in order to follow that plan.

At the section level, the 4-D/RCS world model

symbolic database contains names, coordinates, and other

attributes of other vehicles within the section, other sections,

and potential enemy targets. Maps with a range of about 10 km and a resolution of about 30 m are typical. Maps at the

section level describe the location of vehicles, targets,

landmarks, and local terrain features such as buildings,

roads, woods, fields, streams, fences, ponds, etc. Sensory

processing determines the position of landmarks and terrain features, and tracks the motion of groups of vehicles and

targets. Value judgment evaluates plans and computes cost,

risk, and payoff of various alternatives. An operator

interface allows human operators to visualize the status of

the battlefield within the scope of the section, or to intervene

to change priorities and reorder the sequence of operations

or selection of targets. Vehicle commanders issue commands

to their respective vehicles, monitor how well plans are being followed, and make adjustments as necessary to keep on plan. Output commands to individual vehicles to engage

targets or maneuver relative to landmarks or other vehicles

may be issued at any time, but on average are planned for tasks that last about 1 min.

Level 4—Individual vehicle

The vehicle is a unit that consists of a group of

subsystems, such as locomotion, attention, communication,

and mission package. A manned scout vehicle may have a driver, vehicle commander, and a lookout. Thus, a 4-D/RCS

node at the vehicle level corresponds to a vehicle

commander plus subsystem planners and executors. The

vehicle commander assigns jobs to subsystems and

schedules the activities of all the subsystems within the

vehicle. A schedule of waypoints is developed by the

locomotion subsystem to avoid obstacles, maintain position

relative to nearby vehicles, and achieve desired vehicle

heading and speed along the desired path on roads or cross- country. A schedule of tracking activities is generated for the attention subsystem to track obstacles, other vehicles,

and targets. A schedule of activities is generated for the

mission package and the communication subsystems.

Waypoints and task activities about 5 s apart out to a planning horizon of 1 min are replanned every 5 s, or more

often if necessary.

At the vehicle level, the world model symbolic

database contains names (identifiers) and attributes of

objects — for example, the size, shape, and surface

characteristics of roads, ground cover, or objects such as

rocks, trees, bushes, mud, and water. Maps are generated

from on-board sensors with a range of about 500 m and

246
resolution of 4 meters. These maps are registered and

overlaid with 40 meter resolution data from Section level maps. Maps represent object positions (relative to the

vehicle) and dimensions of road surfaces, buildings, trees,

craters, and ditches. Sensory processing measures object

dimensions and distances, and computes relative motion.

Value judgment evaluates trajectory planning and sensor

dwell time sequences. An operator interface allows a human

operator to visualize the status of operations of the vehicle,

and to intervene to change priorities or steer the vehicle

through difficult situations. Subsystem controller executors

sequence commands to subsystems, monitor how well plans

are being followed and modify parameters as necessary to keep on plan. Output commands to subsystems may be

issued at any time, but typically are planned to change only

about once every 5 s.

Level 3—Subsystem level

Each subsystem node is a unit consisting of a controller

for a group of related Primitive level systems such as Primitive mobility. Gaze control. Communication, and

Mission package sub-subsystems. A 4-D/RCS node at the

Subsystem Level assigns jobs to each of its Primitive subsubsystems and coordinates the activities among them. A

schedule of Primitive mobility waypoints and Primitive

mobility actions is developed to avoid obstacles. A schedule

of pointing commands is generated for aiming cameras and

sensors. A schedule of messages is generated for

communications, and a schedule of actions is developed for

operating the mission package sub-subsystems. The

Primitive mobility way points are about 500 ms apart out to a planning horizon of about 5 s in the future. A new plan is generated about every 500 ms.

At the Subsystem level, the world model symbolic

database contains names and attributes of environmental

features such as road edges, holes, obstacles, ditches, and

targets. Vehicle centered maps with a range of 50 meters

and resolution of 40 cm are generated using data from range

sensors. These maps represent the shape and location of

terrain features and obstacle boundaries. The Demo III LADAR and stereo cameras measure position and range (out

to about 50 m) of surfaces in the environment. Sensory

processing computes surface properties such as dimensions,

area, orientation, texture, and motion. Value judgment

supports planning of steering and aiming computations, and

evaluates sensor data quality. An operator interface allows a human operator to visualize the state of the vehicle, or to intervene to change mode or interrupt the sequence of

operations. Subsystem executors compute at a 5 Hz clock

rate. They sequence commands to primitive systems,

monitor how well plans are being followed, and modify

parameters as necessary to keep on plan. Output commands

to Primitive sub-subsystems may be issued at any 200 ms

interval, but typically are planned to change on average

about once every 500 ms.

Level 2— Primitive level

Each node at the primitive level is a unit consisting of a group of controllers that plan and execute velocities and

accelerations to optimize dynamic performance of

components such as steering, braking, acceleration, gear

shift, camera pointing, and weapon loading and pointing,

taking into consideration dynamical interaction between

mass, stiffness, force, and time. Communication messages

are encoded into words and strings of symbols. Velocity and

acceleration set points are planned every 50 ms out to a planning horizon of 500 ms.

The world model symbolic database contains names

and attributes of state variables and features such as target

trajectories and edges of objects. Maps are generated from camera data. Five meter maps have a resolution of about 4 cm. Driving plans can be represented by predicted tire tracks on the map, and visual attention plans by predicted

fixation points in the visual field.

Sensory processing computes linear image features

such as occluding edges, boundaries, and vertices and

detects strings of events. Value judgment cost functions

support dynamic trajectory optimization. An operator

interface allows a human operator to visualize the state of

each controller, and to intervene to change mode or override

velocities. Primitive level executors keep track of how well

plans are being followed, and modify parameters as necessary to keep within tolerance. Primitive executors compute at a 20 Hz clock rate. Output commands are issued

to the Servo level to adjust set points for vehicle steering,

velocity, and acceleration or for pointing sensors or weapons

platforms. Output commands are issued every 50 ms.

Level 1 —Servo level

Each node at the servo level is a unit consisting of a group of controllers that plan and execute actuator motions

and forces, and generate discrete outputs. Communication

message bit streams are produced. The servo level transforms commands from component to actuator coordinates and computes motion or torque commands for each actuator. Desired forces, velocities, and discrete

outputs are planned for 20 ms intervals out to a planning

horizon of 50 ms.

The world model symbolic database contains values of

state variables such as actuator positions, velocities, and

forces, pressure sensor readings, position of switches, and

gear shift settings. Sensory processing detects events, and

scales and filters data from individual sensors that measure

position, velocity, force, torque, and pressure. Sensory

processing also computes pixel attributes in images such as

spatial and temporal gradients, stereo disparity, range, color,

and image flow. An operator interface allows a human

operator to visualize the state of the machine, or to intervene

to change mode, set switches, or jog individual actuators. Executors servo actuators and motors to follow planned

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trajectories. Position, velocity, or force servoing may be

implemented, and in various combinations. Servo executors compute at a 200 Hz clock rate. Motion output commands

to power amplifiers specify desired actuator torque or power

every 5 ms. Discrete output commands produce switch

closures and activate relays and solenoids.

The above example illustrates how the 4-D/RCS

multilevel hierarchical architecture assigns different

responsibilities and duties to various levels of the hierarchy

with different range and resolution in time and space at each

level. At each level, sensory data is processed, entities are recognized, world model representations are maintained, and

tasks are decomposed into parallel and sequential subtasks,

to be performed by cooperating sets of agents. At each

level, feedback from sensors reactively closes a control loop

allowing each agent to respond and react to unexpected

events.

At each level, there is a characteristic range and

resolution in space and time, a characteristic bandwidth and

response time, and a characteristic planning horizon and

level of detail in plans. The 4-D/RCS architecture thus

organizes the planning of behavior, the control of action, and

the focusing of computational resources such that functional

processes at each level have a limited amount of

responsibility and a manageable level of complexity.

4. DEMO III CONTROL HIERARCHY

Figure 1 is a high-level block diagram of the first five

levels in the 4-D/RCS architecture for Demo 111. On the

right, Behavior Generation modules decompose high level mission commands into low level actions. The text beside

the Planner and Executor at each level indicates the planning

horizon, replanning rate, and reaction latency, and the rate at which new commands are typically generated at each level. Each planner has a world model simulator that is appropriate

for the problems encountered at its level.

In the center of Figure 1, each map as a range and

resolution that is appropriate for path planning at its level. At each level, there are symbolic data structures and

segmented images with labeled regions that describe entities,

events, and situations that are relevant to decisions that must

be made at that level. On the left is a sensory processing

hierarchy that extracts information from the sensory data

stream that is needed to keep the world model knowledge

database current and accurate.

At the bottom of Figure 1 are actuators that act on the

world and sensors that measure phenomena in the world.

The Demo III vehicles will have a variety of sensors

including a laser range imager (LADAR), stereo CCD

(charge coupled device) cameras, stereo forward looking

infra red (FLIR) devices, a color CCD, a vegetation

penetrating radar, GPS (Global Positioning System), an

inertial navigation package, actuator feedback sensors, and a

variety of internal sensors for measuring parameters such as engine temperature, speed, vibration, oil pressure, and fuel

level. The vehicle also will carry a Reconnaissance,

Surveillance, and Target Acquisition (RSTA) mission

package that will include long-range cameras and FLIRs, a

laser range finder, and an acoustic package.

In Figure 1, the bottom (Servo) level has no map

representation. The Servo level deals with actuator dynamics and reacts to sensory feedback from actuator

sensors. The Primitive level map has range of 5 m with

resolution of 4 cm. This enables the vehicle to make small

path corrections to avoid bumps and ruts during the 500 ms

planning horizon of the Primitive level. The Primitive level also uses accelerometer data to control vehicle dynamics and

prevent roll-over during high speed driving.

The Subsystem level map has range of 50 m with

resolution of 40 cm. This map is used to plan about 5 s into

the future to find a path that avoids obstacles and provides a smooth and efficient ride. The Vehicle level map has a range of 500 m with resolution of 4 m. This map is used to plan paths about 1 min into the future taking into account

terrain features such as roads, bushes, gullies, or tree lines. The Section level map has a range of 5000 m with resolution

of about 40 m. This map is used to plan about 10 m into the

future to accomplish tactical behaviors. Higher level maps

(not shown in Figure 1) are used to plan section and platoon

missions lasting about 2 and 24 h respectively. These are derived from military maps and intelligence provided by the

digital battlefield database.

4D/RCS planners are designed to generate new plans

well before current plans become obsolete. Thus, action can always take place in the context of a recent plan, and

feedback through the executors can close reactive control

loops using recently selected control parameters. To meet

the demands of Demo 111, the 4D/RCS architecture specifies

that replanning should occur within about one-tenth of the

planning horizon at each level (e.g., replanning at the

Vehicle level will occur about every 5 s.)

Executors can react to sensory feedback even faster

(e.g., reaction at the Vehicle level will occur within 500 ms).

If the Executor senses an error between its output

CommandGoal and the predicted state (status from the

subordinate BG Planner) at the GoalTime, it may react by

modifying the commanded action so as to cope with that

error. This closes a feedback loop through the Executor at

that level within the specified reaction latency.

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SENSORY PROCESSING

WORLD MODELING

VALUE JUDGMENT BEHAVIOR GENERATION

SYMBOLIC STRUCTURES IMAGES

Entities. Events Labeled Regions

Attributes Attributes States Relationships

groups

SP5 classification confirm grouping

filter compute attributes grouping

attention

objects

SP4 classification confirm grouping

filter compute attributes grouping

attention

surfaces

SP3 classification confirm grouping

filter compute attributes grouping

attention

lists

SP2 classification confirm grouping

filter compute attributes grouping

attention

pixels

MAPS

Labeled Features

Attributes Icons

pixel attributes

5000 m range 40 m resolution

50 m range

40 cm resolution

5 m range

4 cm resolution

vetiicle stale sensor stale

status

5 s reaction latency

new command - every 100 min

PLANNER

SECTION LEVEL

- 10 min horizon

new plan - every minute

EXECUTOR 2 s reaction latency

status new command - every 1 min

WM

simulator PLANNER

VEHICLE LEVEL

- 1 min horizon

EXECUTOR

new plan ~ every 5 ss

500 ms reaction latency

status new command - every 5 ss

WM

simulator PLANNER

SUBSYSTEM LEVEL

- 5 second horizon

new plan - every 500 ms

EXECUTOR

status

200 ms reaction latency

5 Hz clock new command - every 500 ms

WM

simulator PLANNER

PRIMITIVE LEVEL

- 500 ms horizon

new plan every 50 ms

EXECUTOR

status

50 ms reaction latency 20 Hz clock new command every 50 ms

WM

simulator PLANNER

SP1 compute attributes, filter, classification

actuator slate

SERVO LEVEL

50 ms horizon

new plan every 50 ms

EXECUTOR 5 ms reaction latency 200 Hz clock

ladar signalsstereo CCD stereo FLIR

signals signals

color CCD radar navigational actuator

signals signals signals signals

SENSORS

actuator output every 5 ms

power

i ACTUATORS

WORLD

Figure 1. Five levels of the 4-D/RCS architecture. On the right are Planner and Executor modules. In the center are maps

for representing terrain features, road, bridges, vehicles, friendly/enemy positions, and the cost and risk of traversing various

regions. On the left are Sensory Processing functions, symbolic representations of entities and events, and segmented images

with labeled regions.

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The type of Executor reaction depends on the size and

nature of the detected error. If the error is small, the

Executor may simply modify its CommandedAction in a manner designed to reduce the error. For example, if the

status reported from the subordinate planner indicates that the vehicle is going to arrive at the goal point late, the

Executor might modify its CommandedAction to speed up

or delete some low priority activities. However, if the error

is out of range, the Executor may select a stored emergency

plan from an exception handler, substitute it for the current

plan, and modify its CommandedAction and CommandGoal

to its subordinate planner appropriately. For example, an event such as the discovery of an unexpected obstacle in the

AM planned path (generated by the Vehicle Planner) may

cause the AM planner to make a plan that deviates

significantly from its commanded goal. In this case, the

Vehicle level Executor may modify its CommandedAction

in a manner designed to buy time for the Vehicle level Planner to generate a new AM plan. For example, it may

command the AM level to reduce speed or stop and direct

AM driving cameras or RSTA sensors to collect information

about the obstacle while a new AM plan is being generated

by the Vehicle level planner. All of this Executor response

should take place within the 500 ms reaction latency of the

Vehicle level Executor.

Typically, evoking an emergency plan will cause the

Executor to request its Planner to immediately begin a new

replanning cycle. As shown in Figure 1 , the period required

for replanning at the Vehicle level is 5 s. The replanning

period at the AM level is 0.5 s. Thus, the emergency plan

evoked by the Vehicle level Executor can handle the

problem of what the AM level should plan to do over the next 5 s while the Vehicle level planner generates a new AM

plan out to its 1 min planning horizon.

5. GENERIC BEHAVIORS OF SCOUT

VEHICLES

Navigate from A to B

Point A may be several km from point B. What kind of

roads are available? How much traffic will be present? A

scout vehicle may be required to stay off of roads, to maneuver through hilly fields and woods, and cope with

fences, washes, and streams.

Avoid obstacles

The simplest obstacles are those that stick up from flat ground and are not obscured by foliage. The most difficult are ditches that are obscured by foliage. It is important to be

able to distinguish grass and weeds that the vehicle can drive

through from grass and weeds that conceal obstacles. In some cases, the only way to tell the difference is to drive

slowly and stop when the vehicle encounters stiff resistance,

or when the front wheels drop over the edge of a ditch, or

sink into the mud.

Compute terrain attributes and classify terrain

features

The first requirement is to map the terrain geometry

and topology. The second is compute attributes such as

color, texture, slope, size, and shape of regions of terrain. The third is to compare attributes of terrain regions with

class attributes so as to classify terrain regions as road, dirt, grass, rocks, brush, trees, and bogs.

Drive autonomously

Driving autonomously covers a wide range of

situations. Driving on an empty freeway is quite different

from driving in downtown Istanbul. Driving with traffic on

a freeway requires the ability to recognize lane markings,

detect and track other vehicles, detect and avoid obstacles in the roadway, and obey road signs.

Driving at normal human speeds on narrow roads and

cross country is more difficult. Road edges may be poorly

defined and lane markings often do not exist. There may be

bumps or ditches that will damage the vehicle if struck at high speeds.

Autonomous driving in suburban or downtown streets requires the ability to detect and predict the behavior of

pedestrians, other vehicles, to read road signs, and respond

to traffic signals, including hand signals from humans.

In driving cross country, there is no guarantee that a chosen path is even feasible. There may be hidden obstacles

such as ditches, streams, fences, hills, brush, or woods that are impassable. The vehicle must be able to back up, and try

alternate routes when the planned path is blocked.

Classify landmarks, objects, places, and situations

It is easy to get lost. GPS is not always available.

Critical path waypoints may not appear on a map, or may be

incorrectly represented. The unexpected appearance of an enemy may require immediate action. The ability to recognize a likely spot for an enemy sniper in time to take

evasive action may be critical to survival.

Recognize and track other vehicles, avoid collisions

On-coming traffic on narrow roads is a major problem.

One must drive very close to oncoming vehicles to stay on

the road. One must estimate whether the oncoming vehicle

is in its own lane on its own side of the road, and whether

there is room on the road for two vehicles to safely pass. To

do that one must detect the road edges at a great distance and measure the relative position of the on-coming vehicle

between the road edges. There is very little margin for error

in space or time.

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Predict behavior of pedestrians and ottier vehicles

in traffic

Driving in traffic requires the self vehicle to not only

detect, but to predict where pedestrians and other vehicles

will be in the future. For example, on a two lane road, on- coming traffic may consist of one vehicle passing another.

The self vehicle must predict whether the on-coming vehicle

in the self vehicle lane will return to its own side before a head-on collision occurs. On a one lane road, it may be

necessary for the self vehicle to pull over and let an on- coming vehicle pass, or wait for the on-coming vehicle to

pull over so that the self vehicle can pass. On a narrow

mountain road, it may be necessary to back up to a place

where it is wide enough for two vehicles to pass each other.

Learn from experience and from human instructors

Adjust behavior to situation and priorities. Use reward

and punishment from human instructors to learn skills and

behaviors. Use experience from multiple simulated

scenarios to learn from experience.

6. METRICS AND MEASURES

A metric is a unit of measure. Examples include the

meter, the second, the kilogram, the volt. Plank's constant,

and Avogadro's number.

Measurements are made by comparing something

against the unit of measure. A measurement can be made of

the length of the coastline of the British Isles, the height of

the Eiffel Tower, the mass of the Queen Mary, the length of

a day, or the charge on an electron. There are many

parameters related to measurement including accuracy,

precision, resolution, observability, and uncertainty.

What is it about intelligent systems that can be

measured? If an intelligent system is defined as a system

with the ability to act appropriately in an uncertain

environment, then we can measure the appropriateness of its behavior. And, if appropriate behavior is defined as that which increases the likelihood of achieving a goal, then the

ability of a system to achieve goals in an uncertain

environment is a measure of intelligence.

At least three things are required to measure the ability

of a system to achieve goals. First, we need to define the

goals and set criteria for achieving them. Second, we need

to provide an environment in which to make the

measurements. Third, we need to define a procedure for

scoring performance that takes into account the difficulty of

the goals, and the complexity and uncertainty of the

environment

What kinds of measurements can be used to measure

performance? One possibility is to develop one or more

benchmark tests, and measure speed, accuracy, efficiency,

level of difficulty, and cost. These measurements can then

be weighted for importance and summed to provide an

overall score.

Another approach is to devise competitions wherein

different intelligent systems can compete against each other

for a score. Competitions can involve direct physical

interactions such as in football or tennis, measurements of

time as in skiing or bobsleding, or competitions that consider

both style and difficulty as in ice skating, diving, and

gymnastics. Again, performance measurements can be

weighted for importance and summed to provide a score.

What kind of metric can be used to measure the

performance of an intelligent systems? One possible metric

is the performance of a human being. Another possible

metric is the performance of a standard baseline system. In either case, the performance of the intelligent system under

test can be compared with the performance of a human being

(or baseline system) under similar conditions. The

difference in performance, the level of difficulty of the test, and the weighting for importance of the test all combine to give a score.

Measures of performance can be devised for subsystem

performance, individual system performance, or group or team performance. For example, for subsystems, benchmark

tests can be devised to measure the performance of sensory

processing algorithms, world model predictors, or behavior

generation planners. One might measure the difference

between predictions and observations, or the difference

between plans and actions. Benchmark tests can also be

devised to measure the accuracy of knowledge about the

world. For example, one can measure the difference

between perceived terrain geometry derived from sensors and ground truth from calibrated test courses. One can measure the latency between requesting and receiving

information about the world. Individual system performance

can be measured and scored against standard tasks that are typically required of human scout vehicles. Similarly, team

performance can be measured and scored in war games

wherein opposing forces are tested in battle fighting

scenarios.

What is needed?

Calibrated test facilities are needed to test the

performance of sensors and systems in the field under

realistic conditions. High fidelity simulation facilities are needed to generate repeatable test data for software

debugging and testing. Data from calibrated sensors, mixed

with a known noise, and accompanied by ground truth are needed to test sensory processing and world modeling

algorithms. World model data with values assigned to

entities and events is needed to test behavior generation

planning and control algorithms.

Large scale test and training facilities are needed to test performance of systems in large scale operations and to

251
develop tactics and training for integration of autonomous

systems with manned forces. A wide variety of benchmark

tests and competitions are needed to test intelligent system

performance under a wide variety of environmental

conditions. A rigorous regimen of testing, debugging, and

reliability engineering will be needed before intelligent

systems become robust enough to operate reliably under a wide variety of operational conditions.

6. REFERENCES

[1] Albus, J.S., "Outline for a Theory of Intelligence,"

IEEE Transactions on Systems, Man and Cybernetics, Vol.

21, No. 3, pgs. 473-509, May/June, 1991

[2] Albus, J. 4D/RCS: A Reference Model Architecturefor

Demo III. Version 1.0, NISTIR 5994. Gaithersburg, MD,

1998

[3] Shoemaker, C, Bomstein, J., Myers, S., andBrendle, B. "Demo 111: Department of Defense testbed for unmanned

ground mobility, " SPIE Conference on Unmanned Ground

Vehicle Technology. SPIE Vol. 3693, Orlando, FA, April,

1999

252
A Standard Test Course for Urban Search and Rescue Robots

Adam Jacoff, Elena Messina, John Evans

Intelligent Systems Division

National Institute of Standards and Technology

Gaithersburg, MD 20899-8230

ABSTRACT

One approach to measuring the performance of intelligent systems

is to develop standardized or reproducible tests. These tests may

be in a simulated environment or in a physical test course. The

National Institute of Standards and Technology is developing a test course for evaluating the performance of mobile autonomous

robots operating in an urban search and rescue mission. The test course is designed to simulate a collapsed building structure at various levels of fidelity. The course will be used in robotic competitions, such as the American Association for Artifical Intelligence (AAAI) Mobile Robot Competition and the RoboCup

Rescue. Designed to be highly reconfigurable and to accommodate

a variety of sensing and navigation capabilities, this course may

serve as a prototype for further development of performance

testing environments. The design of the test course brings to light several challenges in evaluating performance of intelligent

systems, such as the distinction between "mind" and "body" and

the accommodation of high-level interactions between the robot and humans. We discuss the design criteria for the test course and

the evaluation methods that are being planned.

KEYWORDS: performance metrics, autonomous robots, mobile

robots, urban search and rescue

1. Introduction

The Intelligent Systems Division of the National

Institute of Standards and Technology is researching how to measure the performance of

intelligent systems. One approach being

investigated is the use of test courses for

evaluating autonomous mobile robots operating in

an urban search and rescue scenario. Urban

search and rescue is an excellent candidate for

deploying robots, since it is an extremely

hazardous task. Urban Search and Rescue

(USAR) refers to rescue activities in collapsed

building or man-made structures after a catastrophic event, such as an earthquake or a bombing. Japan has an initiative, based on the

RoboCup robots, that focuses on multi-agent

approaches to the simulation and management of

major urban disasters [1]. The real-world utility

and manifold complexities inherent in this domain

make it attractive as a "challenge" problem for the

mobile autonomous robots conmiunity. For a

description of the issues pertaining to intelligent

robots for search and rescue, see [2].

Figures 1 and 2 illustrate the type of

envirormient that a rescuer has to confront with a collapsed building. There is totally unstructured

rubble, which may be unstable and contain many

hazards. Victims' locations and conditions must

be established quickly. Every passing minute

reduces the chances of saving a victim.

This type of environment stresses the

mobility, sensing, and planning capabilities of

autonomous systems. The robots must be able to

crawl over rubble, through very narrow openings,

climb stairs or ramps, and be aware of the

possibility of collapses of building sections. The

sensors are confronted with a dense, variable, and

very rich set of inputs. The robot has to ascertain

how best to navigate through the area, avoiding

hazards, such as unstable piles of rubble or holes,

yet maximizing the coverage. The robot also has

to be able to detect victims and ideally, determine

their condition and location. The robot has to

make careful decisions, planning its path and

strategy, and taking into account the time

constraints.

A near-term measure of success for robots in a search and rescue mission would be to scout a

structure, map its significant openings, obstacles,

and hazards, and locate victims. The robots

would communicate with victims, leaving them

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with an emergency kit that contains a radio, water,

and other supphes, and transmit a map, including

victim locations and conditions, to human

supervisors. Humans would then plan the best

means of rescuing the victims, given the

augmented situational awareness.

Search and rescue missions are not amenable

to teleoperation due to the fact that most of the

radio frequencies are reserved by emergency

management agencies. Obstructions and

occlusions also diminish the effectiveness of radio

transmissions. Tethers are not typically practical

in the cluttered environment in which these robots

must operate.

2. Urban Search AND Rescue AS A ROBOTIC

CHALLENGE

A search and rescue mission is extremely

challenging and dangerous for human experts.

This is a highly unstructured and dynamic

environment, where the mission is time critical.

Very little a priori information about the

environment or building may exist. If any exists,

it will almost certainly be obsolete, due to the

collapse.

Urban Search and Rescie is therefore

attractive as a mission framework in which to

measure intelligence of autonomous robots. The

high degree of variability and unpredictability

demand high adaptation and sophisticated

decision-making skills from the robots. Robots

will need to quickly and continually assess the

situation, both in terms of their own mobility and

of the likelihood of locating more victims. USAR

missions are amenable to cooperation, which can

be considered another higher-level manifestation

of intelligence. We propose that any robot or team of robots that is able to successflilly and

efficiently carry out USAR missions would be

considered intelligent by most standards.

In the following sections, we will briefly

discuss how USAR missions tax specific

components of an intelligent system.

Figure 1: Partially Collapsed Building from

Turkey Earthquake

Figure 2: Totally Collapsed Building from

Turkey Earthquake

2.1 Mobility

As can be seen from Figures 1 and 2, the mobility

requirements for search and rescue robots are challenging. They must be able to crawl over

piles of rubble, up and down stairs and steep

ramps, through extremely small openings, and

take advantage of pipes, tubes, and other

unconventional routes. The surfaces that they

must traverse may be composed of a variety of

materials, including carpeting, concrete blocks,

wood, and other construction material. The

surfaces may also be highly unstable. The robot

may destabilize the area if it is too heavy or if it bumps some of the rubble. There may be gaps.

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holes, sharp drop-offs, and discontinuities in the

surfaces that the robot traverses.

2.2 Sensing

In order to be able to explore an USAR site and

successfully navigate in this environment, the

robot's sensing and perception must be highly

sophisticated. Lighting will be variable and may

be altogether missing. Surface geometry and

materials may absorb emitted signals, such as

acoustic, or they may reflect them. For truly

robust perception, the robots should emulate

human levels of vision.

The presence of victims may be manifested

through a variety of signals. The stimuli that the

robots have to be prepared to process include

• Acoustic - victims may be calling out,

moaning softly, knocking on walls, or otherwise generating sounds. There will be

other noises in the environment due to shifting

materials or coming from other USAR

entities.

• Thermal - a body will emit a thermal

signature. There may be other sources of heat,

such as radiators or hot water.

• Visual - a multiplicity of visual recognition

capabilities, based on geometric, color,

textural, and motion characteristics, will be

exercised. Recognizing human

characteristics, such as limbs, color of skin,

clothing is important. Motion of humans,

such as waving, must be detected. Confusing

visual cues may come irom wallpaper,

upholstery or curtain material, strewn

clothing, and moving objects, such as curtains

blown by a breeze.

2.3 Knowledge Representation

In order to support the sophisticated planning and

decision-making that is required, the robot must

be able to leverage a rich knowledge base. This

entails both a priori expertise or knowledge, such

as how to characterize the traversability of a

particular area, as well as gained information,

such as a map that is built up as it explores. It

must develop rich three-dimensional spatial maps

that contain areas it or other robots have and

haven't yet seen, victim and hazard locations, and

potential quick exit routes. The maps from

several robots may need to be shared and merged.

A variety of types of knowledge will be

required in order to successfiilly accomplish

search and rescue tasks. Higher-level

knowledge, which may be symbolic, includes

representations of what a "victim" is. This is a multi-facetted definition, which includes the many

manifestations that imply a victim's presence.

2.4 Planning

An individual robot must be able to plan how to

best cover the areas it has been assigned. The

time-critical nature of its work must be taken into

account in its planning. It may need to trade off

between delving deeper into a structure to find

more victims and finding a shortcut back to its human supervisors to report on the victims it has

already found.

2.5 Autonomy

As mentioned above, it is not currently practical

to assume that the robots will be in constant

communication with human supervisors.

Therefore, the robots must be able to operate

autonomously, making and updating their plans

independently. In some circumstances, there

may be limited-bandwidth communications

available. In this case, the robots may be able to

operate under a mixed-initiative mode, where they

have high-level interactions with humans. The

communications should be akin to those that a human search and rescue worker may have with

his or her supervisor. It definitely would not be of

a teleoperative nature.

2.6 Collaboration

Search and rescue missions seem ideally suited

for deploying multiple robots in order to

maximize coverage. An initial strategy for

splitting up the area amongst the robots may be

devised. Once they start executing this plan, they

will revise and adapt their trajectories based on

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the conditions that they encounter. Information

sharing between the robots can improve their

efficiency. For example, if a robot detects that a

particular passageway that others may need to use

is blocked, it would communicate that to its peers.

The robots should therefore collaborate and

cooperate as they jointly perform the mission.

They may be centrally or decentrally controlled.

The robots themselves may all have the same

capability, or they may be heterogeneous,

meaning that they have different characteristics.

Heterogeneous robot teams may apply the

marsupial approach, where a larger robot

transports smaller ones to their work areas and

performs a supervisory ftinction.

3. Measuring the Performance of USAR

Robots

We have described briefly the requirements for

autonomous urban search and rescue robots. We

will now discuss approaches to testing their

capabilities in achieving a USAR mission.

The approach being taken by the upcoming

USAR robot competitions that will use the NIST

test course is based on a point system. The goal

of the robots is to maximize the number of

victims and hazards located, while minimizing the

amount of time to do so and the disruption of the

test course.

Specifically, the AAAI Mobile Robot competition

[1] will use Olympic-style scoring. Each judge

will have a certain number of points that can be

awarded based on their measuring certain

quantitative and qualitative metrics. Robots

receive points for

• Number of victims located

• Number of hazards detected

• Mapping of victim and hazard locations

• Staying within time limits • Dropping off a package to victims

representing first aid, a radio, or food and

water

• Quality of communications with humans

• Tolerance of communications dropout

They lose points for

• Causing damage to the environment, victims, or themselves (e.g., destabilizing a structure)

• Failing to exit within time limits

In certain sections of the test course, robots

are allowed to have high-level communications

with humans. These communications must be

made visible to the judges. Metrics for evaluating

the quality of the communications include

"commands" per minute and/or bandwidth used.

Fewer commands per minute and less bandwidth

per minute receive better scores. Tolerance of

communications disruption is an important

capability and will be given greater difficulty

weighting. A team may request that the judges

simulate communication disruptions at any point

in order that the robots demonstrate how to recover. Examples of recovery would be to move

to a location where there is better chance of

communication, making decisions autonomously

instead of consulting humans, or utilizing

companion robots to relay the information to the

humans.

For teams consisting of multiple robots, the

advantage of cooperating or interacting robot

must be demonstrated. This can be either in

performing the task better, or performing the task

more economically. Multi-robot teams should

have a time speedup that is greater than linear, or may be able to perform the tasks with less overall

power consumption or cost. The scoring will

factor in the number of robots, types of robots,

types or mixture of sensors, etc., in determining

the performance of a team.

The RoboCup Rescue competition, sponsored by

Robot World Cup Initiative, takes an evaluation

benchmarking approach. Initially, there are 3

benchmark tasks. The current tasks are victim

search, victim rescue, and a combination of victim

search and rescue. Additional ones will be added

as the competition and participants evolve. The

RoboCup Rescue includes a simulation

infrastructure in which teams can compete, as well as the use of the NIST test course.

256
Their evaluation metrics are still under

development. Examples of criteria that have been

published on their web site [4] include:

• Recovery rate, expressed as percentage of

victims identified versus number under the

debris.

• Accuracy rate, computed as the number of

correctly identified victims divided by the

total number of identified victims.

• Operational loading, which is the number of

operations that a human has to perform in

order to enable to robots to perform their

tasks.

• If rescuing victims, the total time it takes to rescue all victims.

• Total damage caused to victims in attempting

to rescue them.

4. The TEST COURSE DESIGN

The test course which NIST designed for the

AAAI Mobile Robot Competition was designed

with three distinct areas of increasing

verisimilitude and difficulty. Overall, the course is

meant to represent several of the sensing,

navigation, and mapping challenges that exist in a

real USAR situation. As discussed above, these

are challenges that correlate well with general

characteristics desirable in mobile, autonomous

robots that may operate in other types of missions.

In the design of the course, tradeoffs were made

between realism and reproducible and controlled

conditions. In order to be able to evaluate the

performance of robots in specific skill areas,

certain portions of the course may look unrealistic or too simplified. This idealization is necessary

in order to abstract the essential elements being

exercised, such as a the ability to deal with a

particular sensing challenge.

Given the controlled conditions that the test course provides, it is possible to have multiple

robots or teams face the identical course and have

their performances compared. This should yield

valuable information about what approaches to

robotic sensing, planning, and world modeling

work best under certain circumstances.

The course is highly modular, allowing for

reconfiguration before and during a competition.

Judges may swap wall panels that are highly

reflective for some that are fabric-covered, for

example, or victims may be relocated. This

reconfigurability can serve to avoid having robot

teams "game" the course, i.e., program their

robots to have capabilities tailored to the course

they've seen previously. The reconfigurability can serve to provide more realism as well. A

route that the robot used previously may become

blocked, forcing the robot to have to find an

alternative way.

The three areas of the course are described

below. Note that the use of color in the names of

the section is for labeling purposes only and does

not mean that the courses are primarily colored in

their namesake color. A representative

schematic of the test course is shown in Figure 3,

at the end of this paper.

4.1 Yellow Course

Given the fact that participating teams, at least

initially, will primarily be from universities that

may not have access to new agile robotic

platforms, one design requirement was to have an

area within the course where the mobility

challenges are minimal. We call this area the

"Yellow course." The floor of the yellow course

is flat and of uniform material. Passageways are wide enough to permit large robots, up to about 1 meter diameter, to pass easily.

Yet the Yellow course allows teams with

sophisticated perception and planning to exercise

their robots' capabilities. Some sensing

challenges are as difficult in this section as in the

others. There will be highly reflective and highly

absorbent material on walls. Certain wall panels

will be clear Plexiglas, whereas others will be

covered in brightly patterned wallpaper. Some

areas may be dimly lit or accessible only from one

257
direction. Victims will be represented in all modalities (i.e., acoustically, visually, through

motion, thermally, etc.) and may be hidden from

view under furnishings or in closed areas.

4.2 Orange Course

The Orange course is of intermediate difficulty.

A second story is introduced, and there are routes

that only smaller robots may pass through. The

robots may have to climb stairs or a ramp in order

to reach victims. Flooring materials of various

kinds, such as carpeting, tile, and rubber, are introduced. Hazards, such as holes in the floor,

exist. In order to be effective, the robot will have

to plan in a three-dimensional space. Larger

robots will be able to navigate through some

portions of this course, but hot all.

4.3 Red Course

The Red course poses the most realistic

representation of a collapsed structure. We do

not anticipate that any of the contestants will be

able to successfiilly complete the red course in the

first or perhaps even second years. However, this

section provides a performance goal for the teams

to strive for. In the Red section, piles of rubble

abound, lighting is minimal or non-existent, and

passageways are very narrow. The course is highly three-dimensional, from a mapping

perspective. Not only are there two floors, but

the rubble piles that the robot has to traverse may

need to be mapped as well. Passageways under

the rubble or through pipes may have to be used

by the robots to reach certain areas or to get closer

to victims. There are some portions of this course that can be traversed by the larger class of

robots, but they would not be able to reach most

of the victims. Larger robots would be best suited

in marsupial configurations in this area.

5. Conclusion

An Urban Search and Rescue application for

autonomous mobile robots poses several

challenges that can be met only by highly

intelligent systems. The variability, risk, and

urgency inherent in USAR missions makes this a good framework in which to begin measuring

performance in controlled and reproducible

situations. We believe that the test course we are developing can serve to elucidate performance

measures for overall systems, as well as for

components of intelligent systems.

6. References

[1] http://www.aic.nrl.navy.mil/~schultz/aaai2000

/menu-bar-vert . html

[2] Casper, J., and Murphy, R.R., "Issues in

Intelligent Robots for Search and Rescue,"

SPIE Ground Vehicle Technology II, Orlando,

FL, April 2000.

[3] Kitano, H., et al., 'RoboCup Rescue: Search

and Rescue in Large-Scale Disasters as a Domain for Autonomous Agents Research,"

Proceedings of the IEEE Conference on Man,

Systems, and Cybernetics, 1999.

[4] http : //www. robocup. org/games/3 6 . html

Acknowledgements

The development of the USAR test course was

carried out with the support from the Defense

Advanced Research Agency (DARPA)

MobileAutonomous Robot Software Program and

the Naval Research Laboratory.

258
Figure 3: Overall USAR Test Course Layout

Overall dimensions are approximately 1 7 by 20 meters

1 Meter Robot drawn to show scale

Represents a victim "signature", such as a thermal emission, clothing, or other manifestation

259
Assessment of the NIST Standard Test Bed for

Urban Search and Rescue

Robin Murphy, Jenn Casper, Mark Micire, Jeff Hyams

Computer Science and Engineering

University of South Florida

Tampa, FL 33620

{murphy, jcasper, mmicire, hyams}@csee.usf.edu

ABSTRACT

The USF team in the 2000 AAAI Mobile Robot Competition had the most extensive experience with the NIST

Standard Test Bedfor USAR. Based on those experiences,

the team reports on the utility of the test bed, and makes

over 20 specific recommendations on both scoring competitions and onfuture improvements to the test bed.

1 INTRODUCTION

A team of three operators and two robots from the University of South Florida (USF) tested the NIST standard test bed for virban search and rescue (USAR) as part of the 2000

AAAI Mobile Robot Competition USAR event. The test bed consisted of three sections, each providing a different

level of difificuhy in order to accommodate most competitors (see Fig. 1). The easiest section. Yellow, contained

mainly hallways, blinds, and openings to search through.

The course could be traversed by a Nomad type robot. The

intermediate Orange Section provided more challenge with

the addition of a second level that was reachable by stairs or ramp. Other challenges included those found in the yellow as well as some added doors. The Red Section was

intended to be the most difficuU. It contained piles of rubble and dropped floorboards that represented a pancake-like

structure. The Orange and Red sections clearly required

hardware that was capable of traveling such spaces.

In addition to USF, three other teams entered the AAAI

competition's USAR event: Kansas State, Swarthmore College, and University of Arkansas. The Kansas State team

dropped out due to hardware failures on site. The Swarth- more and Arkansas teams fielded Nomad scout types of

robots that operated only in the Yellow Section. The per- formance of each team is unclear as the judges did not record how many victims were found and how many victims were missed. At the time of publication of this paper,

Figure 1 : Overview of the NIST USAR arena.

the awards for the event were under protest. Swarthmore

had a single robot which attempted to enter a room, per- form a panoramic visual scan for possible victims, mark

the location on a map, and then enter another room and so

on. At the conclusion of their allotted time, the robot was

retrieved and the contents of the map was made available

to the judges. They entered one room successfully and it

is believed they identified up to two surface victims. The

Arkansas team used two Nomad scout type robots; however, each robot was physically placed in a room, and the

team was allowed to repeatedly move and reset the robots as needed. The Arkansas team found at least one victim, and

communicated this by repeatedly ramming the marmequin.

The USF team used two outdoor robots: 1) a RWl

ATRV with sonar, video, and a miniature uncooled FLIR

and 2) a customized RWl Urban with a black and white camera, color camera, and sonars. This was intended to be

a marsupial pair, but the transport mechanism for the team was still under construction at the time of the competition.

The USF team used a mixed-initiative or adjustable auton- omy approach: each platform was teleoperated for purposes

260
of navigation but ran a series of software vision agents for

autonomous victim detection: motion, skin color, distinctive color, and thermal region. The user interface displayed

the extraction results from each applicable agent and highlighted in color whenever the agent found a candidate. A

fifth software agent ran on the ATRV which fused the out- put of the four agents, compensating for the physical separation between the video and FLIR cameras. It beeped the

operator when it had sufficient confidence in the presence

of a victim, but the beeping had to be turned off due to a high number of false positives generated by the audience.

The ATRV found an average of 3.75 victims per each of the

four runs recorded, while the Urban found an average of

4.67 victims. A fifth run was not recorded and no data is

available.

Figure 2: The USE USAR robot team, Fontana (ATRV) and

Klink (Urban) (named after two women Star Trek writers).

In addition to participating in the competition (both a preliminary and a final round), the USF team hosted three

complete exhibition runs as part of the AAAl Robot Exhibition Program and did numerous other partial exhibitions

for the news media at the request of AAAl. The other teams

did not exhibit. As such, the USF team had the most experi- ence with the most difficult sections of the test bed and can

claim to represent user expertise.

This paper discusses the NIST test bed from the USF

experience, and makes recommendations on scoring, improving the test bed, and staging a more USAR-relevant

event at RoboCup Rescue in 200 1

2 ASSESSMENT OF THE THREE SECTIONS

The NIST test bed is an excellent step between a research

laboratory and the rigors of the field. For example, USF has

a USAR test bed (Fig. 3), but it is somewhere between the

Yellow and Orange sections in difficuhy and cannot provide the large scale of the NIST test bed. One advantage

is that the test bed sections can be made harder as needed.

An important contribution that should not be overlooked is

that the test bed appeared to motivate researchers we talked

to: it was neither too hard nor too trivial. This is quite an

accomplishment in itself.

Figure 3: The USF USAR testbed, a mock-up of a destroyed bedroom.

2.1 Yellow

The USF team did not compete or exhibit in the Yellow

Section, entering only for about 1 hour of practicing col- laborative teleoperation. Our assessment was that the sec- tion was far too much of an office navigation domain- the

over-turned chair in one of the rooms was the only real surprise. Only one room had a door and neither Swarthmore

nor Arkansas reached it. The arena was at about the level of complexity seen in the Office Navigation Event thread of

the AAAl Robot Competition in the mid- 1 990 's.

2.2 Orange

The Orange Section consisted of a maze plus a second story

connected by a ramp and stairs. Unlike the Yellow Section,

the doorways into the Orange and Red Sections had cross- members crowning the doorway at about 4 feet high. This

added some feel ofconfined space. The USF robots entered

a very confined maze of corridors to find a surface victim.

The Urban served as point man, exploring first, then guiding

the ATRV if it found something requiring confirmation or iR sensing. The maze had hanging Venetian blinds in the

passage way, and the Urban almost got the cord tangled in her flipper.

The Orange Section also had two forms of entry in the

main search area after the robots had navigated the maze. One entry was through the X made by cross-bracing the

261
second story. The Urban could navigate under the cross- bracing, but the ATRV could not. The second form of entry was through a door on hinges. The Urban pushed the door

open for the ATRV to enter the main search space (Fig. 4). The Urban attempted to climb the stairs, but the first step was too high for the design. (A Matilda style robot also

attempted to climb the stairs but could not either.) It went

to the ramp and climbed to the second story.

Figure 4: The Urban holds the door for the ATRV in the

Orange Section.

The USF robot was able to avoid negative obstacles (a

stairwell and uncovered HVAC ductmg in the floor of the

second story) to find victims on the second story (Fig. 5). The modified Urban actually flipped its upper camera onto

the HVAC hole and peered inside the duct. This shows the

utility of having multiple sensors and in different locations.

The Orange Section is also to be commended for pro- viding some attributes of 3D or volumetric search. For ex- ample, an arm was dangling down from the second story

and should have been visible fi^om the first floor. Note that the dangling arm posed a classic challenge between navigation and mission. The mission motivates the robot or res- cuer to attempt to get closer and triage the victim, while the navigational layout prevents the rescuer from approaching

without significantly altering course, and even backtracking

to find a path.

2.3 Red

The Red Section at first appeared harder (Fig. 6), however,

in practice it was easier for the ATRV than the Orange Section due to more open space. The floor was made up of

tarps and rocks on plywood. The ATRV and Urbans were

built for such terrain. The Red Section contained two layers

of pancaking, with significant rubble, chicken wire, pallets,

and pipes creating navigation hazards for the Urban. Only

about 30% ofthe area was not accessible to the larger ATRV

due to the large open space.

Figure 5: Close up of victim lying on the second floor of

the Orange Section.

One nice attribute of the Red Section is that it lends

itself to booby-traps. The pancake layers were easily modified between runs to create a secondary collapse when the

Urban climbed to the top. Using current technology, the Ur- ban operator and/or software agents could not see any signs

that the structure was unstable.

Figure 6: Overview of the Red Section.

3 RECOMMENDATIONS ON SCORING

The AAAl Competition did not use any metric scores for

their USAR event, relying entirely on a pamel of fourjudges, none ofwhom had any USAR experience. The AAAl Competition had published metrics prior to the competition that were to be used in scoring, [5] but did not use those metrics on site and the scoring was subjective. The published

metrics appeared to be a good first start (with our reser- vations given below) and no reason was given why AAAl

abandoned them.

1 . Use quantitative scoring, at least as a basis for the com262
petition. The scores might be modified by a quahtative

assessment of the Al involved, but there should be a

significant numerical aspect to the scoring.

2. Distribute victims in same proportions as FEMA

statistics given in FEMA publication USFA/NFARS1-SM1993 and award points accordingly. Detecting a surface victim and an entombed victim require

much different sensing and intelligence.

Surface 50%

Lightly trapped 30%

Trapped in void spaces 15%

Entombed 5%

3. Have a mechanism for unambiguously confirming that the victims identified were identified. It was not clear

to the audience when a victim had been correctly detected or when the robot had reported a false positive.

Perhaps an electronic scoreboard showing the number

of false positives and false negatives (missed victims)

could be displayed and updated during the competition. (Swarthmore used beeping and USF flashed the

headlights. The judges appeared to accept that if there was a victim in the general direction of the robot's sensors at the time of the announced detection that a victim had been found, in the case of U SF, only one judge

took time during the competition look at the technical rescue display workstation, which provided both the sensor data and the fiised display, to confirm what the

robot was seeing.)

4. Points for the detection of a victim should also depend on the time at which the technical rescue crew is in- formed of the discovery and the accuracy of the lo- cation, either in terms of absolute location or a navigable path for workers to reach the victim. Robots

which overcome inevitable communications problems

by creating a relay of "comms-bots" or returning to lo- cations where broadcasting worked are to be rewarded.

(The Swarthmore robot beeped when it thought it found a victim, but in terms of truly communicating

that information to rescue workers, it stored the location of all suspected victims until the competition was

ended. In practice, if the robot had been damaged, the

data would have been lost. Also, the map was not compared quantitatively to the ground truth.)

5. Contact with the victims should be prohibited unless

the robot is carrying a biometric device that requires contact with the victim. In that case, the robot should

penalized or eliminated from competition if contact is too hard or otherwise uncontrolled. (The Arkansas

robots repeatedly struck the surface victim it had detected.)

6. Fewer points should be awarded for finding a disconnected body part (and identifying it as such) than for

finding a survivor.

7. Require the robots to exit fi:om the same entry void

that they used for entry. This is a strict requirement for

human rescue workers in the US, intended to facilitate accounting for all resources. (The AAAl Competition

permitted exiting firom anywhere on the grounds that the robot may need to find a clear spot to communicate

its results.)

8. Have all competitors start in the same place in the

warm zone, £md do not permit them to be carried by

human operators inside the hot zone. The exception

is if the robot has to be carried and inserted in an above grade void from the outside. (Swarthmore and

Arkansas manually placed their robots in the yellow

section, with Arkansas actually placing their robots

within specific rooms in the yellow section.)

9. Do not permit human operators to enter the hot zone and reset or move robots during the competition.

(Arkansas team members repeatedly entered the hot zone to reboot errant robots and to physically move

robots to new rooms to explore.)

10. Have multiple runs, perhaps a best of three rounds approach used by AUVSl. (NIST "booby-trapped" the

Red Section after the AAAl Preliminary Round, making it extremely easy to create a secondary collapse.

This was done to illustrate the dangers and difficulties of USAR. However, if the AAAl rules had been

followed, this would have resulted in a significant deduction of points from the USF team, and quite a

different score between runs. The difficulty of the courses should be fixed for the competition events, and

changed perhaps only for any exhibitions.)

It should be clear from the above recommendations

that a quantitative scoring system which truly provides a

"level playing field" is going to be hard to construct. Unlike

RoboCup, where the domain is a game with accepted rules and scoring mechanisms, USAR is more open. In order

to facilitate the relevance of the competition to the USAR

community, we recommend that scoring mechanisms be derived in conjunction with USAR professionals outside of

the robotics community and with roboticists who are trained

in USAR. We propose that a rules committee for RoboCup

Rescue physical agent be established and include at least one representative from NIST, NIUSR, and one member of

the research community who had worked and published in

USAR.

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4 RECOMMENDATIONS FOR IMPROVING THE NIST TESTBED

The NIST testbed was intended to be an intermediate step

between a research laboratory and a real collapsed building. The three sections appeared to be partitioned based on

navigability, rather than as representative cases of severity

of building collapses or perceptual challenges. For example, the basic motivation for the Yellow versus the Orange

and Red Sections appeared to be to engage researchers with

traditional indoor robot platforms (e.g., Nomads, RWl B series, Pioneers, and so on). An alternative strategy might be

to consider each section more realistically, where the Yellow Section would be a structurally unsound, but largely

navigable, apartment building, the Orange Section might be

an office building in mixed mode collapse such as many of

the buildings in the 1995 Hanshin-Awaji earthquake, and

the Red Section might be a pancake collapse such as seen

in the front of the Murrow building at the Oklahoma City

bombing. This approach would permit traditional indoor

robot platforms to navigate, but require advances in detection of unfriendly terrain such as throw rugs or carpet,

doors, etc.

4.1 For All Sections

In addition to the suggestions made above, we offer some

possible improvements to the test bed:

1. Create void spaces in each section more typical of

USAR (Fig. 7). In particular, there were no lean-to and

V void spaces in any of the 3 sections. The red section

did have some light pancaking. Victims in even the

Yellow Section should be placed behind furniture and

occluded by fallen furniture or even sheet-rock or portions of the ceiling.

Figure 7: Infrared images of a lightly trapped, void trapped,

and entombed victim.

2. Put tarps and high powered lights ("beams of sunlight") over portions of all courses to create significant

changes in lighting conditions, most especially darkness. As it stands now, the testbed is a poor test of the

utility of infra-red.

3. Entries were all doors at grade. Many voids are actually above grade, irregular, and have been knocked

in the wall, even in buildings that have not collapsed.

Each section should have one or more above grade entry voids from the "outside". This will support the test- ing of concepts for automating the reconnaissance and

deciding how to deploy resources, as per the rescue and recovery of lightly trapped victims, use of reconnaissance results to locate lightly trapped victims, and

searching void spaces after hazard removal phases of a

structural collapse rescue. [4]

4. Each section should contain more human effects. For

example, the Yellow and Orange Sections should have

throw rugs on the floors, fallen debris such as magazines, books, bills, toys, etc. Otherwise, the Yellow Section is actually easier than the Office Navigation thread in the AAAI competitions during the mid1990's.

5. Each section should contain real doors with door

knobs or at least the commercial code handles for dis- abled access. The doors in the Yellow and Orange section were both easily opened panels. (USF was able

to easily identify the swinging door in the Orange Section and use the Urban to open the door for the ATRV

to pass through. None of the other teams got to the room with the door in the Yellow Section). All rooms

in any section should have doors and some of those

doors should be off their hinges or locked. This will

test the advances in object recognition, reasoning, and

manipulation.

6. If possible, victims should produce a more realistic heat profile than a heating pad. This is needed for detection and to test advances in assessment of the context of the victim (how much they are covered, etc.).

4.2 For the Orange and Red Sections

1. Cover everything with dust to simulate the cinder

block and sheet-rock dust that commonly covers ev- erything in a building collapse. Victims who are alive

often move enough to inadvertently shake off some

of this dust, making color detection a very important

component of victim detection. (USF used a "distinctive color detector" as one of their four vision agents.

The distinctive color agent looked for regions of color

that were different than the average value. This appeared to work during the competition for the Red Section, which was less colorful (no wallpaper, etc.), but

there wasn't enough data to draw any statistical conclusions.)

2. Make the surfaces uneven. All the surfaces were level

264
in their major axis; even the ramp in the Orange Section was flat, not canted to one side.

3. Use real cinder blocks. The USF Urban was able to move the faux cinder blocks on the ramp in the Orange

Section rather than navigate around (Fig. 8).

Figure 8: The Urban has pushed the cinder block around

rather than traversed over it.

4. Make a "box maze" for entry to introduce more confined space. Rescue workers who are certified for confined space rescue use a series ofplywood boxes which

can be connected together to form long, dark, confined mazes. The mazes are easily reconfigured. A similar

box maze could be constructed firom the lightweight

paneling material.

5. The terrain of both sections was still fairly easy compared to the field, and dry. Perhaps as robot platforms

evolve, the courses should contain water.

5 OTHER SUGGESTIONS

The testbed is primarily intended to be a standard course for

experimentation. The AAAl Competition did not especially

further experimentation, as that the competition judges collected no metric data. However, the AAA! Competition per- formed a valuable service by illustrating the potential conflict between science and exhibitions. The public viewing

interfered with testing and validating aspects of AI in two

different ways. Public viewing may also lead to a tendency

towards "cuteness" at the expense of showing direct rele- vance to the USAR community.

5.1 Viewing versus Validation

The conflict between spectator viewing and validation is best seen by the following example. One of the USF vi- sion agents identified large regions of heat using a FLIR,

then fused that data with regions of motion, skin color, and

distinctive color extracted by software agents operating on

video data. If there was a sufficiently strong correlation, the

operator interface began beeping to draw the operator's at- tention to the possibility of a survivor. (The RWl supplied

user interface for the Urban requires almost full attention

just to navigate, detection assistance is a practical necessity.)

Unfortunately, the test bed has Plexiglas panels to facilitate judge and spectator viewing. AAAl permitted spectators to ring the sections during the competition. Between

the low height of walls and the Plexiglas, these spectators were visible and produced color, motion, and IR signatures even when the USF robots were facing interior walls due to views of exterior walls in other sections. As a result, USF

had to turn off automatic victim notification through audio

and rely strictly on color highlighting in the display windows.

A long-term solution is to insert cameras into the

testbed to record, map, and time robot activity as well as broadcast the event to a remote audience. The competition

chair stated that the audience should be allowed viewing

access on the grounds that rescue workers would be visible

in a real site. We note that at a "real site", access to the

hot zone is strictly controlled and very few, certified technical rescue workers are permitted in the hot and warm zones. The rest must wait in the cold zone at least 250 feet from the

hot zone. [4] Also, at a real site, walls would have blocked

views of people versus the half height panels.

Second, in order to record and broadcast the event, photographers and cameramen were permitted in the ring during the exhibitions and competition. During the exhibition, a cameraman repeatedly refused to move out of the robots'

way. When the robot continued on, it almost collided with

the video recorder

Therefore, we recommend:

1 . At least the Red Section should be fitted with walls and

ceilings to block the view ofnon-testbed elements and

the audience.

2. The test bed sections should be fitted with cameras and no one should be permitted in the test bed during timed

events. If a robot dies (such as the USF Urban due to a faulty power supply or the Arkansas robots due to software failures), the robot should remain there until the session is complete.

5.2 Relevance to the USAR Community

In our opinion, the AAAl Competition missed several opportunities to show a clear relevance of the NIST test bed

and robots to the USAR community. As discussed earlier,

265
USAR professionals should be involved in setting the rules

as well providing realistic scenarios, in general, any further

competition venues, such as RoboCup Rescue, should actively discourage anything that might be construed as trivializing the domain. For example, Swarthmore costumed

their robot as a Florence Nightingale style nurse, which res- cue workers were likely to find offensive. Likewise, a handwritten "save me" sign was placed next to a surface victim.

The test bed may also miss relevance to the USAR field

if it focuses only on benchmarking fully autonomous sys- tems rather than on more practicable mixed-initiative (adjustable autonomy) systems. The Urban type of robot in a hardened form capable of operating in collapsed structures must be controlled ofF-board: they do not have suffi- cient on-board disk space to store vision and control routines. Therefore, the test bed should measure communications bandwidth, rate, and content in order to categorize the extent of a system's dependency on communications. Also,

the test bed should include localized communications dis- rupters to simiilate the effect of building rubble on communications systems.

6 CONCLUSIONS

Based on our five complete runs in the NIST test bed at AAAl and numerous informal publicity demonstrations, the

USF team has had the most time running robots in the test bed. We conclude that the NIST test bed is an excellent

halfway point between the laboratory and the real world.

The test bed can be evolved to increasingly difficult situations. The initial design appears to have focused on pro- viding navigational challenges, and it is hoped that future

versions will add perceptual challenges.

Our recommendations fall into four categories. First,

scoring or validation will be a critical aspect ofthe test bed.

The AAAl competition did not implement a quantitative

scoring system and thus provides no feedback on what are reasonable metrics. We recommend many metrics, but our

guiding suggestion is to get knowledgeable representatives

from the USAR community involved in setting up scenarios

and metrics, in particular, we note that the victims should

be distributed in accordance to FEMA statistics for surface,

lightly trapped, void trapped, and entombed victims, and

then points awarded accordingly. One major issue that

Eirose from the USF team trying to reconstruct its rate of

victim detection was that there needs to be an unambiguous

method for signaling when a victim has been detected.

Another aspect of scoring is to complement the proposed

AAAl "black box" (external performance) metrics with a

rigorous "white box"(software design and implementation)

evaluation. Second, the test bed should be made more

representative of collapsed buildings. We believe this can

be done without sacrificing the motivation for the different

sections. For example, all sections need to have void

spaces representative of the three types discussed in the

FEMA literature (lean-to, V, and pancake). The Yellow

Section can still have a level, smooth ground plane but the

perceptual challenges can be more realistic. Third, the test bed should resolve the inherent conflict between spectator

viewing and validation. We believe this can be done by

inserting cameras into the test sections as well as adding

tarps and walls. Finally, we strongly urge the mobile

robotics community to concentrate on making the NIST

test bed and any competition venue which uses the test bed

to be relevant to the USAR community. The community

should resist the tendency to "be cute" and instead use the

test bed as a means of rating mixed-initiative or adjustable

autonomy systems that can be transferred to the field in the near future as well as the utility of fully autonomous

systems.

ACKNOWLEDGMENTS

Portions of the work reported in this paper were conducted

as part of work under the DARPA BAA 9835 program and

through a donation firom SAIC. The authors would like to thank AAAl, the USF Department of Computer Science

and Engineering, and the USF Engineering College for

travel support, and Mark Powell and Brian Minten for their

help in programming and maintaining the robots.

References

[1] Rescue Systems J. National Fire Academy, 1993.

[2] Technical Rescue Program Development Manual.

United States Fire Administration, 1996.

[3] Standard on Operations and Training for Technical

Rescue Incidents. National Fire Protection Association, 1999.

[4] Casper, J ., Micire, M., Murphy, R.R. "Issues in Intelli- gent Robots for Search and Rescue," in SPIE Ground

Vehicle Technology 11, 2000.

[5] http://www.cs.swarthmore.edu/ meeden/aaaiOO/ contest.html#usar

266
PERFORMANCE METRICS IN THE

AAAI MOBILE ROBOT COMPETITIONS

Alan C. Schultz

Naval Research Laboratory

Code 5515

Washington DC 20375

schuhz@aic.nrl.navv.mil

Abstract

In this paper, performance metrics used in the

AAAI Mobile Robot Competition and

E.xhibition over the nine years of the contest

are compared. Performance metrics have

tended from more explicit quantitative measures to more qualitative measures. The

author believes that this trend is the result of

more complex tasks where more aspects need

to be measured. The paper will end by

claiming that competitions that are to measure intelligence in robots should include

tasks that require adaptation and learning,

which the author believe are the hallmarks of

intelligence.

Keywords: mobile robot contests,

competitions, metrics, muhi-agent

robotics, learning and adaptation,

autonomous robots.

1. Introduction

Although there are several annual mobile

robot competitions, the American

Association for Artificial Intelligence's

(AAAI) Mobile Robot Competition and

Exhibition has distinguished itself by

attempting to reward those contestants that

show the greatest amount of "intelligence"

in solving a given task [1-6].

Since this event is organized as a competition, metrics are required for

measuring performance in a task that also

try to measure the degree of intelligence

the robot has exhibited.

Because the contest is organized under the

sponsorship of the AAAI, a goal of the

competition is to foster research and

education in artificial intelligence. As

such, tasks selected for the competition

were picked because they required some

level of "intelligent" behavior or knowledge representation.

2. Early Years: Quantitative

Metrics

In the first two years of the competition,

less explicit quantitative metrics were

used. However, many teams complained

that the rules were not explicit enough

leading to ambiguities in scoring and in

problems interpreting the rules. Starting in

the third and subsequent years of the

competitions, more explicit and published

quantitative measures of performance in

the task have been used. It was assumed

that completion of the task itself was

indicative of intelligence. Points would be

awarded to various activities (subtasks)

and for abilities and competencies

achieved by the robot. The final score would be a summation on the individual

points. In some events, points could be

removed for exhibiting some undesired

behavior. Depending on the task, time

267
would be factored into the score so that

achieving the goal faster would generate

more points.

The critical point is that every task and

competence had points that were on a comparable absolute scale. A robot

missing some skill could still win the

competition. The point system would be

published before the event so that teams

knew exactly what score they could obtain

(given that their robot performed as designed). This also allowed teams to

make design decisions about what to

implement on their robot.

One problem with the explicit quantitative

scoring is trying to properly assign the

proper score to the various competencies.

As observed by Reid Simmons in the third

competition [6], the virtual manipulation

penalty [for not using real manipulation]

"was much too small, providing a big

disincentive for actually trying to grasp

objects."

Another problem with using an explicit

metric has been "gaming," where teams

tailor their approaches to maximizing the

metric. In some cases these high scoring

entries violated the spirit of the particular

competition. It was possible to exploit the

metric in ways that gave less "intelligenf

robots advantages in scoring.

Here is an illustrative example. Consider

a "smart" robot that successfully exhibits

all of the competencies; that is, it performs

all of the aspects of the task itself,

autonomously. The only problem is that

this robot is slow, because of all of the

processing. Now consider a not-quite-assmart robot. Much less competent than

the smart robot, it explicitly skips parts of

the contest, gets help from the human, and

consequently gets less competency points.

But its so much faster that the overall total

number of points is higher. In essence,

speed wins even though part of why it was

faster was because it skipped the slower,

harder parts of the task.

To prevent these problems, it is necessary

to design point systems where

competencies define strict boundaries

where lower level competencies cannot

outscore higher-level competencies. But in

complex tasks, this can become difficult to

achieve.

3. Recent Years: Qualitative

Metrics

In recent years, as the scope of the tasks in

the contests have become more complex,

we have found explicit quantitative

metrics more difficult to implement, while

at the same time having a desire to reduce

gaming.

There are two reasons why the added

complexity in the tasks have lead to

difficulty. First, the tasks generally have

multiple, sometimes conflicting aspects,

and second, some of the required

competencies are difficult to measure

quantitatively themselves.

Human-robot communications is one

competency that has proved difficult to

judge in some domains. As observed in

the second competition, "...because robots

must often interact with humans, we tried

to emphasize communications between

man and machine. With a few exceptions,

this aspect of the competition is still disappointing, and it is difficult to design

tasks that reward appropriate

communication."

Starting in the seventh annual competition, an hors d'oeuvres serving contest required

268
the robots to serve conference attendees at a reception. Human-robot interaction was

an important judged competency of the

robots behavior, as was how much of the

reception area was covered, and whether

the robots could perceive when they

needed to refill their trays. Obviously a

single explicit quantitative metric is

difficult.

Interesting, the first year of this contest,

they awarded two separate awards based

on different metrics. In addition to

judging technical performance (which

included the "intelligence," they also had a popular vote where conference attendees

voted for their favorite entry. Its noteworthy that the robot that won first

place in technical achievement did not win

the popular vote.

We have tried various approaches that in

general use more qualitative measures

externally, while in some cases retaining

internal quantitative metrics. In general,

this means publishing more qualitative

metrics, and hiding any explicit

quantitative measures from the teams.

This is more of an "Olympic Figure

Skating" style of scoring: a series of

internal metrics are used in several

categories that try to capture certain

qualitative competencies. Judges, who are instructed in the qualitative aspects of

these competencies, then assign a score from one to ten in each aspect, based,

where possible, on an internal quantitative

score. The external scores are then

averaged, and each team is assigned a

score fi"om one to ten. By eliminating the

external, published metrics, gaming could

be avoided.

However, this style of scoring is generally

difficult to implement. It also requires that

the judges are careflilly instructed, and that

the qualitative aspects of the competencies

are very well described such that teams are not mislead as to the way to achieve good

scores, and to reduce the ambiguities like

those that were present in the first two

years of the competition.

This Olympic style of scoring is not

appropriate for all competitions. For

example, in the RoboCup competitions,

where simulated and real robot teams

compete in soccer competitions, there is a

clear and natural quantitative score - the

number of goals each team makes against

the other.

Scoring Multiple Robots

One ongoing debate is how to measure the

performance of multi-agent teams. The

question is whether multi-robot entries

need to exhibit better than linear

improvements in performance over single

robot teams.

Those who believe in super-linear

improvement believe that the additional

robots should introduce improvements that

cannot be obtained by simply adding more

robots to perform in parallel. Others

believe that the proper metric involves

looking at the total cost of implementing

the team. Here the belief is that having

multiple, inexpensive robots is equal to

single expensive robots. There are several

excellent articles in this proceedings on

metrics for multi-agent systems.

Learning and Adaptation in

Future Contests

One competency that distinguishes

intelligence is the ability to learn and adapt

to unanticipated events and conditions.

269
I would like to see competition events that

require learning and adaptation in order to

be most successful in the task. The

learning and adaptation would not need to

be directly scored, per se, but the tasks

should be designed so that success is

easier with those capabilities.

Although earlier competitions have stated

this as a desired feature, learning usually

just required building maps and learning

locations of items in the environment, and

adaptation was usually involved changing

the robot's internal representations of the

environment.

In particular, events where features of the

environment change which require

different sensing modalities or changes in

strategies would allow for real indication

of a robots "intelligence." Allowing judges

to introduce failures in robots capabilities

would be an ultimate test of the robots

capability to adapt!

Acknowledgements

I want to thank Illah Nourbakhsh for his

comments on previous AAAI contests. I also wish to thank all of the people who

have contributed to past contests, either as organizers, sponsors, or as contestants.

References

[1] Arkin, R.C., 1998. "The 1997 AAAI

Mobile Robot Competition and

Exhibition:' AI Magazine, 19(3), 13-17.

[2] Dean, T. and R. P. Bonasso, 1993.

"1992 AAAI Robot Exhibition and

Competition," AI Magazine, 14(1), 49-

57.

[3] Hinkle, D., Kortenkamp, D., and

Miller, D., 1996. "The 1995 Robot

Competition and Exhibition," AI

Magazine, 1 7( 1 ), 3 1 -45

.

[4] Konolige, K., 1994. "Designing the

1993 Robot Competition," AI Magazine,

15(1), 57-62.

[5] Kortenkamp, D., Nourbakhsh, I., and

Hinkle, D., 1997. "The 1996 AAAI

Mobile Robot Competition and

Exhibition," AI Magazine, 18(1), 25-

31,1997.

[6] Simmons, R., 1995. "The 1994

AAAI Robot Competition and

Exhibition,'' AI Magazine, 16(2), 19-30.

270
Performance Evaluation of Robotic Systems:

A Proposal for a Benchmark Problem

Sunil K. Agrawal*, Armando M. Ferreira**, Stephen Pledgie**

Mechanical Engineering Department - University of Delaware

Newark DE 19716 USA - agrawal@me.udel.edu

Abstract

For highly agile autonomous systems, the dynamics plays a central role in the development of planners and feedback controllers to achieve a certain desired task. Trajectory

plans that do not satisfy the system dynamics and constraints have a small likelihood

for implementation without placing undue demands on the controllers. Coordinated control of such systems in groups becomes even more challenging because of the potential of

dynamic interaction between members of the group, distributed nature of sensing, computation, and control. Among other desirable criteria, such as low energy consumption and

constraint satisfaction, a measure of performance for robotic systems is compliance with

its own dynamics and those of the other co-players in the group.

In this paper, we propose a benchmark problem for controller performance evaluation

of a group of mobile robots. This benchmark experiment is inspired by a platoon of

autonomous vehicles with the goal to change its formation over time. The objective is

to obtain these formation changes while minimizing certain meaningful cost criteria. We

assume that the physical models that describe the system are subject to errors. The sensor

is not perfect and the structure of the controller has been selected by a user. For such a

system, we can obtain the theoretically optimum trajectory with a measure of the cost.

This cost can then be compared to the actual cost during hardware implementation on an

experiment set up.

*AssociateProfessor,**GraduateStudents

111
We propose the following hardware set up with four vehicles in our Mechanical Systems

Laboratory at University of Delaware. We plan to make this physical facility available to

other members of the research community to test the effectiveness of their algorthims and

controller implementations. Within such a facility, the different parameters of the model

and controller can be altered to evaluate the performance sensitivities as a result of these

change in parameters.

Our implementation on this experiment setup will be based on a two degree-of-freedom

controller approach: (i) development of a reference trajectory for the system consistent

with dynamics and constraints; (ii) an exponentially stable controller implemented around

the reference trajectory. The reference trajectory development will be based on results

from nonlinear systems theory and feedback linearization to efficiently solve the problem

in a higher-order space, with a large fraction of computations done off-line ([1], [2], [3]).

Such a study will bring out the issues of performance degradation during an experimental

task and will provide a rich test-bed for comparing the effectiveness of different paradigms

of control.

References

[1] Veeraklaew, T. and Agrawal, S. K., "A New Computation Framework for Optimization of Higher-Order

Dynamic Systems", to appear in AIAA Journal of Guidance, Control, and Dynamics, 2000.

[2] Veeraklaew, T. and Agrawal, S. K., "Designing Robots for Optimal Performance During Repetitive Motion",

IEEE Transactions on Robotics and Automation, Vol. 14, No. 5, Oct 1998, 771-777.

[3] Ferreira, A. M. and Agrawal, S. K., Pledgie, S., "Planning for Autonomous Vehicle Platoons Using Differential

Flatness" , to appear in Proceedings of 5th International Symposium on Advanced Vehicle Control and Control,

2000.

272
A MULTI-SENSOR COOPERATIVE APPROACH FOR THE

MOBILE ROBOT LOCALIZATION PROBLEM

Amaud CLERENTIN, Laurent DELAHOCHE, Eric BRASSART

CREA

Centre de Robotique, d'Electrotechnique et d'Automatique

lUT, departement Informatique, Avenue des Facultes, 80000 Amiens - France

Amaud.Clerentin@iut.u-picardie.fr , Laurent.Delahoche@u-picardie.fr

Abstract

In this paper, we present a multi-sensor cooperation

paradigm between an omnidirectional vision system and a low cost panoramic range finder system using to localize a mobile robot in its environment. These two sensors, which have been used independently until now, provide some complementary data. This association enables us to build a robust sensorial model which integrates an

important number of significant primitives. We can thus

realize an absolute localization of the mobile robot in particular configurations, like symmetric environments,

where it is not possible to determine the position with the use of only one of the two sensors. In a first part, we

present our global perception system. In a second part, we

describe our sensorial model building approach and our segment classification method which takes into account

the belief notion concerning a sensor. Finally we present an absolute localization method which uses three

matching criteria fused thanks to the combination rules of

the Dempster-Shafer theory. The basic probability

assignment got for each primitive matching enables to estimate the reliability of the localization. We test our

global absolute localization system on several robot's

elementary moves in an indoor and symmetric

environment.

1 Introduction

Autonomous mobile robots cannot rely solely on deadreckoning to determine their configuration because deadreckoning errors are cumulative. That's why they must use

exteroceptive sensors that get information from the

environment in order to estimate the robot's location more

accurately. This leads to a classical localization method

based on the fusion of dead reckoning data and

exteroceptive data. The fusion method generally used is based on the extended kalman filter (EKF). The

perception systems used both with the dead reckoning can be of different natures: a goniometer [3], the SYCLOP

system [4], a laser range scanner [2]. Another approach consists in using only exteroceptive

data: the robot's configuration is calculated in the

environment reference without using previous

information. To answer to this problem, two strategies are generally used. The first consists in marking the robot's

evolution world with artificial beacons [5]. The second

one consists in using the intrinsic features of the

environment (doors, edges, comers. ..)[4] [1]. Artificial beacons can be detected fast and reliably and

provide accurate positioning information with minimal

processing. This kind of system is generally employed for

industrial applications [10]. Unfortunately, these methods

lack flexibility and modularity because it is necessary to

fit out the robot's evolution environment.

The other solution consists in referencing on environment characteristic elements and offers a great

modularity because the robot can localize itself directly in accordance with the landmarks. This kind of localization

is founded on a matching stage between a sensorial model

and a theoretical map of the environment. The perception

systems used in that case are often the vision systems and

the range finding ones. Perez in [6] determines with a panoramic laser range finder the absolute position of its robot by using the line segments as sensorial primitives.

Similarly Yagi uses an omnidirectional vision system to develop navigation and environment map building

methods [1]. We can notice that the robustness of these

methods is mainly linked to the matching stage. The more

precise and rich information the sensorial model will give,

the more robust the matching stage will be. That is why we have worked on a localization approach

based on the cooperation of two omnidirectional

perception systems: the vision system SYCLOP and a low

cost range finder system. The association of these two

kinds of complementary information permits to generate a sensorial model with a high descriptive level. Then, the

matching stage provides an unique solution and we obtain

a robust absolute determination of the robot's

configuration.

The first part of this paper presents the global

omnidirecfional perception system. The second part deals

with the sensorial model building method based on the

management of two types of information. We describe

also our classification method of the obtained segments on two classes according to their reliability. Our absolute

localization method, based on a Dempster-Shafer

multicriteria fusion approach, will be presented in the last

part. In the conclusion we will analyze the experimental

results reached with our mobile robot SARAH.

273
2 The global omnidirectional perception

SYSTEM

To localize our mobile robot, we use an original

perception system making cooperate two omnidirectional

sensors: an omnidirectional vision system (SYCLOP) [4]

and a low cost and fast panoramic range finder system

(Figure 1). These two sensors have been developed and

used independently within our laboratory [4] [9]. The

rotation axis of the laser is in line with the center of the

conic reflector. This geometric constraint is taken into

account at the time of a previous phase of calibration.

CCD

cam era

HF

transm ission

CCD

cam era

Figure I : The globalperception system

The range finder system is an active vision sensor. This

method consists in projecting on the scene a visible light

with known pattern geometry (a laser spot in our case). A

camera images the illuminated scene with a given

parallax. The desired 3D-information can be deduced

from the position of the imaged laser point and the lateral distance between the projector and the camera (Figure 2).

Figure 2: The geometric configuration ofan active triangulation system.

The laser beam intersects the landmark Ml in the point PI figure 2). This point is projected on the retinal plane

through the focal point F to a point ul. A landmark M2,

located at an other distance, generates a point u2. The

distance of the landmark or the object Mi can be

determined from the position of the point ui. This perception system allows to obtain an

omnidirectional range finding sensorial model. We

manage in the sensorial model reference the cartesian

distance between the laser spot and the sensor. The kind

of primitives is the same that a classical range finder laser. The interest of this system is on the one hand its low cost and on the other hand its rapidity.

The prototype we built is constructed from a laser diode

and a CCD camera. An infrared filter is used to extract only the light of the laser. The effective measurable

distance region is designated as 0.8m-5m: this distance is thought to be a sufficient distance for a mobile robot to detect obstacles and maneuver around them.

The experimental study of this sensor is presented in [9]. The SYCLOP system, similar to the COPIS one [1], is composed of a conic mirror and a CCD camera. It allows

to detect all the vertical landmarks of the environment

thanks to a two dimensional projection. (Figure 3). The

vertical landmarks are characterized by a radial straight

line corresponding to a high contrast variation. These

radial straight lines are extracted with a treatment based on the Sobel gradient. We can note that we work in fairly

constraint environments, which not generate an excessive

number of detected landmarks.

radial stra ght

lines

Camera

plane

Figure 3: Principle ofthe omnidirectional sensor SYCLOP

This two omnidirectional sensors association permits to manage some complementary and redundant information

within the same sensorial model. With the SYCLOP

system we exploit, after the segmentation phase [1], the

radial straight lines which characterize angles of every

vertical object as, for example, doors, corners, edges,

radiators. With the vision system, the information of depth

cannot be gotten on an unique acquisition. For example, it

is not possible to differentiate with this only sensor use

the notion of opening (corridor, opening of door....) and

the notion of vertical object (closed door, radiator,...)

(Figure 4). For a higher description level, it is therefore interesting

to use a sensor providing some complementary

information. Then we have associated to SYCLOP an inexpensive range finding sensor capable to be fast. Following a segmentation stage [9], this sensor permits us

to exploit sensorial primitives that are segments figure

4). These segments characterize straight partitions of the

environment. In this case we have the notion of depth, but

it is impossible to differentiate two vertical objects placed

in the same alignment: for example two closed doors

placed on the same wall (Figure 4). It misses the notion of

angle that will be provided by the SYCLOP system.

274
environment

doors

final segments

range finder^^j^^^^/ange finding

primitives (segments)

sengoralaegm ents oblaiied w ih Hie ccnpeiatbn ofIhe two om niiuect±inalsBnaois

lEdondantdatB

Sll

Figure 4: Principle ofthe omnidirectional sensorial cooperation.

Finally this cooperative approach permits to construct a sensorial model whose descriptive level is high. This

descriptive level is superior to the one obtained with each

sensor individually. Moreover with an appropriated

management of the redundant data (separation between

two segments for the range finder and radial straight line for the SYCLOP system) we can compensate a sensorial

information absence on one of this two sensors (Figure 4).

3 Sensorial Model construction

The sensorial model of the evolution world is based on

the taking into account of two types of data (Figure 4): the

vertical landmarks angles and the segments characterizing

walls. Segments are managed with two points whose

coordinates are expressed in the robot's reference. The

managed primitive in the final sensorial model will be

segments. These segments will be determined with two

types of approaches :

An approach based on the data complementarity: this treatment consists in cutting up segments gotten with

the range finder in subsegments (Figure 5). The

carving is realized with the radial straight lines of the

vision system.

An approach based on the data redundancy: the

redundant aspect is characterized by the detection of a

vertical landmark with the two sensors (Figure 5). In certain cases a vertical landmark is detected by the

range finder with the end points of segments. We will

be able to confirm the existence of a segment

extremity if a radial straight line corresponds to it. In case of radial straight line absence we will keep the

segmentation obtained with the range finding

sensorial model.

end point segment

(complQmdntary data)

missing tiala (case 3)

range finding

segment

conic

reflector

robot reference

redundant

dsita {case 1

)

Figure 5: The Different cases ofthe cooperation algorithm.

We have integrated these different cases of cooperation

in the sensorial model building algorithm shown on

Figure 6. The first step consists in extracting line segments from

the set of points given by the sensor. We use the recursive

Duda-Hart segmentation algorithm [7] [9]. To decrease

the noise sensitivity of this algorithm we have added a pre-processing stage on the set of points in order to eliminate the aberrant points. Besides, in order to fit as

better as possible the set of points, we apply a least square

algorithm on the obtained segments.

RANGE FINDER MODEL SYCLOP IMAGE

Duda-Har segmentation

1

Hough segmentat:io i

PI range f ind( Syclop data fusion

P2 : redundant dat detection and mergir

P3 : non s igni f i cai it segments eliminatio

T SENSORIAL MODEL

Figure 6: Principle ofthe global sensorial model building algorithm

From the SYCLOP image, we treat the radial lines with

a segmentation algorithm based on a simplified Hough

transform. We fixed the threshold detection of a radial

line (number of pixels composing a radial line ) to an

important value in order to keep the significant radial

primitives.

The fusion step, described on Figure 4 and Figure 5, is based on the taking into account of three cases : The treatment of redundant data (case 1 of Figure 5). In this case we take as hypothesis to use the radial

line systematically to determine the end point of a segment. The angle of a vertical landmark is determined more precisely with the vision system that with the range finder.

The treatment of complementary data (case 2 of

Figure 5). This treatment consists in cutting up a range finding segment into several final subsegments.

This stage is based on the segment intersection

determination.

275
The treatment of missing data (case 3 of Figure 5). The notion of missing data is here characterized by a

vertical landmark which is not detected with the

vision sensor. In this case the range finding

breakpoint is considered directly.

During this stage, we classify the segments and

subsegments we get in two classes of reliability: a class

"SURE" and a class "UNCERTAIN". In this purpose, we

take into account five criterion for each segment.

The first criteria is the mean distance between the range

finding points contained by the segment and this segment.

If this mean distance is high, it means that the points are not very well aligned, so this segment is not very sure. The second criteria is the number of points supported by

the segment. This criteria is only discriminative when the

segment contains very few points. In this case, it is not

sure. The third criteria is the segment density of points. As

shown in [9], a major drawback of this kind of

triangulation depth sensor is a decreasing resolution with

increasing distance. So, this criteria, which is linked to the mean distance between the sensor and the set of point, is a good indicator of the segment reliability (more distant the

set of points is, less the precision is). Considering the measure extent of the sensor (0.8m from 5m), the minimal

and maximal density are as shown on Figure 7.

derslty

wall

angular

lesoluton

rotint (ranga firdar)

Figure 7: quantification ofthe density criteria

The fourth criteria analyzes if the segment has been

detected by one or by the two sensors. The different cases

are: The two extremities of a segment are detected only by

the laser range finder (segment SI in the case 1 of

Figure 8). This segment has a weight of 1. One extremity of a segment is detected by the laser range finder and the other extremity is detected only

by the conic mirror (segment SI in the case 2 of

Figure 8). This segment has a weight of 2 because, as we say before, we think that the radial straight lines are more precise and reliable. One extremity of a segment is detected by the two

sensors and the other extremity is detected only by

the laser, or the two extremities are detected only by

the conic mirror (segment SI in the case 3 of Figure

8). This segment has a weight of 3. One extremity of a segment is detected by the two

sensors and the other extremity is detected only by

SYCLOP (segment SI in the case 4 of Figure 8). This

segment has a weight of 4. The two extremities of a segment are detected by the

two sensors (segment SI on the case 5 of Figure 8). This segment has a weight of 5.

Range find np

case 1^ Robot

refsnErce

linHM

case 3

Radial

line

case 2

Range Fnding

case 4

Range frdiig segm ents case 5

Figure 8: powdered segment, the four cases.

The last criteria concerns a gray level curves extracted

from the SYCLOP image. We take into consideration five concentric gray level circles whose average is made. We

obtain thus one gray level curve from 0 to 360 degrees.

We apply on the portions of curve which represent a segment a least square algorithm. We obtain a straight line and we compute the mean difference of the gray level value from this line. If this mean difference is high, this means that the gray level sector is not constant. This case occurs generally when a landmark has not been detected

by SYCLOP, so this segment is not sure.

Figure 9 : the gray level curve ofthe experimental result shownfig. 14

276
The fusion of these five criteria is made thanks to the

combination rules of the Dempster-Shafer theory [8][1 1]. We use this theory because it is an interesting formalism

which enables to represent ignorance. Our frame of

discernment is composed of two elements: "SURE" and

"UNCERTAIN". The basic probability assignments m/,

mi, iriji, m4 and Wj- for this five criteria are shown in Figure

10. We can see that, for certain values, the criterion are

not discriminative and Dempster-Shafer enables to

represent this ignorance (for example, if the density is equal to 0.12 points/cm, this value does not permit to take

a decision SURE or UNCERTAIN for this criteria).

ml: mean distance of the range findings points from the segment

—• a . P.A. of SURE « - B.P.A. Of (SURE, UNCERTAIN) •-A— B . P . A . of UNCERTAj

m2: number of points

B.P.A. of {"SU "UNCERTAIN"

}

B. P -A- of "UNCERTAIN"

m3; density

0,4-

0,3-

0, r

OS

-B.P.A, o£ SURE

- a- -B.P.A. o£ (SURE, UNCERTAIN} —A- —B.P.A. of UNCERTAHn

m5: variation of grey levels from the least square straight

line

-B.P.A. of SURE

B.P.A. of

{SURE, uncertain; -B.P.A. of UNCERTAl

(gray level)

Figure 10: the B.P.As of the four classification criteria ({SURE. UNCERTAIN} =G)

For the fourth criteria, the B.P.A. are: weight l:mt(SURE)=mi(0)=O.6, m,(UNCERTAIN)=0.4

weight 2:m4(SURE)=0, mi(0)=l, mt(UNCERTAIN)=0

weight 3:mi(SURE)=O.3,mt(0)=O.7,mt(UNCERTAIN)=O

weight 4:mi(SURE)=O.6,mj(0)=O.4,m4(UNCERTAlN)=O

weight 5:m;(SURE)=l, mi(0)=m4(UNCERTAlN)=O

We can then perform the combination calculation thanks

to the Dempster-Shafer rules [8][11]. If the conflict

coefficient k between the elements of the frame of

discernment is superior to 0.7, it means that our criteria

disagree. In this case, we decided that our segment is uncertain. IfA:<0.7, we compute the combination of belief

functions for each element of the frame of discernment

and we choose the class which has the maximal B.P.A.

The last stage (P3 on Figure 6) consists in eliminating

the non significant segments in the final cooperative

sensorial model. A non significant segment is characterized by a number of range finding points equal to 0 and a length (Cartesian distance) inferior to a predetermined threshold.

This stage permits to decrease the combinatory aspect of

the matching stage and to increase the robustness.

This building algorithm enables to get a sensorial model

where the number of exploitable primitives is more

important than the number of primitives got by each

sensor when it works individually. Besides, we obtain a

certainty information of a segment by considering five

criteria. This information will be used in the matching

phase.

4 Absolute localization method

The robot configuration is determined by matching the

sensorial model, got by multisensor cooperation, with a

theoretical map of the environment. The primitives used

for this matching phase are segments. Therefore, all environment's elements like doors, walls, windows,

radiators... are indexed as segments in the theoretical

map.

For each segment, we have considered three

correspondence tests, which are similar to these used by

Crowley [7]: the angular difference between the two segments,

the difference in length between the two segments,

the distance between the centers of the two segments.

•srd

Map Seg.

Sen S&g'

^^"•^'^a- ' Sen. Seg

Figure II: The three matching criteria. The fusion of these three treatments is made thanks to the

combination rules of the Dempster-Shafer theory [8]. Our

frame of discernment is composed of two elements: YES

and NO corresponding to those assertions : "Yes, we can match the two segments" and "No, we can not match the

two segments". For each criterion, we have determined

the Basic Probability Assignments (B.P.A.) mj, rr^ , shown Figure 1 2.

277
m1: difference in angle between the two segments

.p. A. o£ YES

.P. A. Of {YES P. A. Ot NO

m2: difference in length between the two segments

— B.P.A. Of YES -iS'-B.P.A. of {yes

-Tfe B.P.A. of NO

(mm)

(0}

m3: distance between the center of the two segments

- P A. of YES

. . Q. . . B . P A. of {YES,

A-— B .P A. of NO

100 200 (mm)

Figure 12: basic probability assignments oj matching criteria (,'YES.NO> = Q

We can then perform the combination calculation thanks

to the Dempster-Shafer rules [8]. Since we have three

criteria, we first fuse the two first criteria. The conflict coefficient between these two first criteria

is:

k,: = mi(YES).m2(N0) + mi(NO).m.(YESJ ( 1

)

If ki2<\, the conflict is not complete and the combination of belief functions mi2 for these two elements

of the frame of discernment is given by:

m,{YES)m,{YES)+m,{YES)m,{e)+m,{e]m,{YES) m {YES) = -

m,,{NO)-- m,(NO}m,(NO)+ m,(NO)m._(e)+ m,{e\m,(NO)

Aw,(0)w,(0) (2)

1-A-, Then we fuse the last criterion. We compute the conflict

coefficient k (3) between this criterion and the two criteria we have fused above:

k = m,2(YES).m3(NO) + mi2(NO).m2(YES) (3)

If k>OJ, we think that the conflict is too high. So we

decide to take a prudent decision: we don't match the two

segments. IfA;<0.7, we compute the combination of belief

functions for each focal element:

m

Wi

;

{YES}m^{YES )+ m,,{YES)mj{e)+ m,,{e).m, (YES)

\-k

jj^Q-. ^

nijNO)j,i, (NO) +m,,{NO}m,{e) (6 {NO)

^ ' \-k

^ ' \-k \-k

(4)

The segments are matched if B.P.A. for the YES m(YES)

is superior to the B.P.A. for the NO m(NO).

The first stage of this localization algorithm consists in determining a list of sensorial segments Ls which have a strong probability of existence. This segments are the

"SURE" segments obtained during the fusion stage.

We consider that the length of these segments has been

determined with a good accuracy. So, our starting

correspondence test is the length of a segment.

In the second stage, we consider a segment Ls^ from the

list Ls and we search the theoretical map segments which

length is similar to the Lsi^ segment length. Each found

theoretical segment is superposed on the sensorial

segment L^t and we apply the third step in order to test the

correspondence of the other sensorial segments.

The third step consists in applying the three criteria describe above on all the segments on the list Ls except

the segment Ls^. A segment is matched if the B.P.A. for the YES is superior to the B.P.A. for the NO. To choice

the optimal matching solution we calculate a V criteria. For each matched segment pair, we increment this V

coefficient which characterizes the robustness of the

global matching. V is managed with the following

algorithm:

Given

:

B the B.P.A. for the YES of the matched

segment pair

W a weight linked to the segment ' s class

(SURE segment: w=3, UNCERTAIN segment:

w=l) . FOR each global matching

v=o FOR each segment matched

V = V + (B*W)

END

END

So we can see that V is an interesting and discriminative

indicator of the global matching relevance since K takes

into account the class of each matched segment ("SURE",

"UNCERTAIN") and the quality of each matched pair

(through the B.P.A. for the YES).

These three steps are then repeated for all the Ls list segments. The final solution is the one which permits the

maximal V.

5 Experimental results

To test the robustness of our localization algorithm, we have performed it on several sensorial acquisitions

made in an indoor environment figure 13). The two

omnidirectional acquisitions are made when the robot is stopped. The omnidirectional acquisitions and the

localization algorithm are computed in a Pentium PC

located on our mobile robot. A Matrox Meteor I! video

card is used to acquire the omnidirectionnal image and the

laser acquisition. Our experimental perception system is

278
shown on Figure 13.

Figure 13: Our global omnidirectional perception system and the experimental indoor environment.

In order to show the interest of our cooperative

approach, we have tested our localization method on symmetric environments (the first picture on Figure 13).

The use of one sensor individually instead of the two

sensors emphasizes the robustness problem: a strong

failure rate has been observed for the matching phase

when we use only one sensor [9]. The first environment is a long corridor (length: 50

meters). Figure 14 shows a sensorial model got with our

cooperative approach. The robot is located in the middle

of the corridor (Figure 13). We can see on Figure 14 the

final decomposition on an set of segments which represent

doors and parts of wall. We show on this figure the radial

straight lines obtained with the omnidirectional conic

mirror. We must note that, for this environment, the depth

sensor would not have been able to localize the robot: two

parallel identical segments would have been detected. The

SYCLOP system used alone would have posed the

problem of environment symmetry. We can also remark

that uncertain segments are the segments which are far from the robot (not well aligned) or which correspond to the pillars of the corridor (not detected during the DudaHart segmentation stage). The robot final position

successfully obtained shows the robustness of our method

and its accuracy. We have indeed a position error of 8cm

and an orientation error of 3 degree.

3 : 0,S2

U: ,0 . 72

' , : °-\ S : 0,S4 /V-.y

. s = 0.3

f»iijii::iiiii:iili! i i jiWi!aiitgi(i|4»^i)i:::a^^ i* i

6000-

(mm)

, theoretical map

robot's position

after the matching stage

cooperative

sensorial model

(mm)

1.2 1.4 1.6 lA

Figure 14: the cooperative sensorial model with the segments

classification and the BPA (U^UNCERTAIN. S=SURE) (firstfigure) and

thefinal position determination corresponding to the optimal matching

We show on Figure 15 results obtained in an other

symmetric environment: a laboratory square hall.

• (ml /

2

5 — 1

6

\ \ .

(m)

-3.0 -2-0 -1-0 0 1.0 2.0 3.0

robot's

position

after the matching

stage

Figure 15: cooperative sensorial model andfmal position determination

in a hall environment.

The same remarks can be done: the use of the two

sensors provides enough sensorial information to enable

the matching algorithm to converge to a coherent solution.

The third environment is the end of the corridor shown

Figure 13. This environment constitutes a favorable

experimental configuration: it is not symmetric and it has an important number of exploitable landmarks (figure 10).

We can note here on several robot's configuration

determination that our matching selection criteria is highly

discriminative: the good configuration has been computed

on all the acquisitions.

U: 1 S : 0.79 / s : 0. : O.lV:]' s : 0.

279
800C -

-200t__i , , ,

. -6000 -4000 -2000 0 2000 4000 6000

(mm)

Figure 16: cooperative sensorial model (first figure) andfinal position determination. Finally on a complete path makes in the corridor by

our robot mobile SARAH, we could note on 40

acquisitions that, on the one hand, all the absolute

configurations have been determined correctly, and, on

the other hand, the mean error was equal to 11 cm in position and 3 degree in orientation.

In spite of an important combinatory aspect, our

cooperative localization method proves to be robust and

particularly accurate.

6 Conclusion

We have presented in this study an absolute localization

approach based on the cooperation between two

omnidirectional sensors: an omnidirectionnal vision sensor and a range finding sensor. This association allows

to treat two types of complementary data. Then we obtain

a highly descriptive sensorial model which integrates an

important number of primitives and enables to increase

the robustness of the matching stage. We classify also

every sensed segment in two reliability classes according

to five criteria fused thanks to the Dempster-Shafer rules. The absolute localization paradigm based on this matching stage takes into account several criteria which

are merged with the Dempster Shafer rules. The choice of

the optimal matching is based on a highly discriminative

criteria which associates the segment reliability classes

and a B.P.A. linked to the matching stage. We have tested our cooperative absolute localization algorithm on several

particular environment like for example symmetrical

environment. On the one hand, we can note on these

experimental results that the robot's configuration

determination is realized in a unique way and on the other

hand the absolute robot's configuration is calculated with

a relatively weak systematic error.

References

[I] Y. Yagi, Y. Nishizawa, M. Yachida, "Map-based

navigation for a mobile robot with omnidirectional image

sensor COPIS", IEEE Trans, on Robotics and Automation

Vol. 1 1, pp. 634-648, October 1995.

[2] J.Gomes-Mota, M.I. Ribeiro, "A multi-layer robot

localisation solution using a laser scanner on reconstructed 3D models", Proc. on the 6* Int. Symposium on

Intelligent Robotic Systems, Scotland, 1998.

[3] P. Bonnifait, G. Garcia, "Design and Experimental

Validation of an Odometric and Goniometric Localization System for Outdoor Robot Vehicles.", IEEE Trans, on

Rob. and Aut. Vol. 14, No 4, pp. 541-548, August 1998.

[4] C. Drocourt, L. Delahoche, C. Pegard, C. Cauchois , "Localization method based on omnidirectional

stereoscopic vision and dead-reckoning", Proc. of the IEEE Int. Conf on Int. Robots and Systems, Korea,

October 1 999

[5] H.R Beom, H.S. Cho, "Mobile robot localization using a single rotating sonar and two passive cylindrical beacons",

Robotica, Vol. 13, pp. 243-252, 1995.

[6] J. A. Perez, J. A. Castellanos, J.M.M. Montiel, "Continuous

localization: vision vs. laser", IEEE Proc. of Int. Conf on

Rob. and Aut. pp 2917-2923, Detroit, May 1999.

[7] J. Crowley, "Navigation for an intelligent mobile robot",

IEEE Journal on Robotics and Automation, Vol. RA-I,

n°l, pp. 31-41, March 1985.

[8] G.A. Shafer, "A mathematical theory of evidence",

Princeton : university press, 1976.

[9] A. Clerentin, C. Pegard, C. Drocourt "Environment

Exploration Using an Active Vision Sensor", Proc. of the IEEE Int. Conf on Intelligent Robots and Systems

(IROS'99), Korea ,October 1999.

[10] J. Hollingum, "Caterpillar make the earth move : automatically'^. Industrial Robot, Vol. 18, N° 2, pp. 15-18, 1991.

[II] A. Dempster, "Upper and lower probabilities induced by a multivalued mapping". Annals of mathematical statistics 38:325-339, 1967.

280
Measuring Mobile Robot Performance: Approaches and Pitfalls

Gaurav S. Sukhatme

gaurav\@usc.edu

Robotics Research Laboratories

Department of Computer Science

Institute for Robotics and Intelligent Systems

University of Southern California

Los Angeles, CA 90089-0781

Abstract

We consider the problem of measuring the performance of an intelligent mobile robot system. We believe that systems are intelligent because their capabilities are more than the sum of their parts. Therefore

any piecemeal efforts to measure the performance of an

intelligent system are bound to fail. Further, metrics

of utility are more useful to designers than something

as abstract as intelligence. We describe a task-based,

multiple-criteria technique that combines two benchmarks to result in a metric for navigation. A case study of two robots is presented, which were evaluated

and compared using the metric.

1 Introduction

We consider the problem of measuring the performance of an intelUgent mobile robot system. We believe that systems are intelligent because their capabilities are more than the sum of their parts. Therefore any

piecemeal efforts to measure the performance of an intelligent system are bound to fail. Only measuring performance along a single skill axis is also clearly limiting

since intelligence does not boil down to a single skill or capability but rather arises due to a complex interplay

between a multitude of capabilities. We strongly advocate the measurement of task-oriented quantities which

establish the utility of a system. To this end, measuring performance along several axes is clearly important

but brings with it several challenges:

• What should the axes be ?

• How do we ensure the axes span the space we want

to benchmark ?

• Does an "orthogonal" set of axes exist ?

• How should the performance measures along these axes be combined ?

In this paper we describe a task-based, multiplecriteria technique that combines two benchmarks to

result in a metric for navigation. A case study of two

robots is presented, which were evaluated and compared using the metric.

2 Previous Work

Due to space limitations we limit ourselves to a brief

survey of evaluation techniques for mobile robots. The

so-called static evaluation techniques are specifically

designed for measuring stability when the robot is sta- tionary and when it is moving in a statically stable

fashion. The primary method of choice is an energy

based stability measure as an evaluation function. In work by Nagy et al. [7] two modes of walker stability are characterized namely stance stability and walker

stability. Both use the amount of energy needed to destabilize the walking robot as a measure of the stability of the robot. The stance stability is identical to the energy stability margin defined by Messuri et al. in

[5] as the minimum work that must be done on a robot

walker to tip it over an edge of a support boundary.

Early work on robot stability was due to McGhee et

al. [4] who defined the support polygon as the convex

hull of the projections of all contacting points on a horizontal plane. In [3] the authors define a conservative

support polygon with the motivation that the walking

robot should retain its stability in the event of a single

leg failure. Of the above energy based measures of stability the work of Nagy et al. is the most general since

it includes compliance of the mechanism and depends on the terrain that is underfoot.

In [1] the authors discuss several evaluation criteria for comparing three configurations for the design

281
of a walking robot. Some of the evaluation criteria were foothold selection area, stride length, static stability and energy stability. The important tradeoff was

stride vs. stability, based upon which the circulating

configuration for Ambler was chosen.

Dynamic evaluation techniques are so named be- cause they focus on properties related to motion.

Wilcox [10] introduced a metric called the MCC (Mobility Characteristic Curve) to measure the ability of a robot to surmount obstacles. The obstacle was a

cylinder of (theoretically) infinite length and diameter d which was buried to a depth (f/3 in an inclined

plane of slope s composed of loose sand. The MCC

was defined as the plot with s on the horizontal axis

and the diameter of the largest cylinder that the robot

could surmount (in dimensionless units based upon its length) on the vertical axis. The proposed figure of

merit was the area between the co-ordinate axes and

the MCC. The two main achievements of this method

were its independence of scale and easy reproducibility.

Its chief drawback was that it used a simple obstacle

geometry and did not evaluate the entire system in a mission oriented way.

Lietzau [2] proposed a set of benchmarks to assess the performance of a Mars microrover. These benchmarks were divided into five categories namely, mobility, navigation and control, science, autonomy and

environmental. A set of weights was assigned to these

categories based upon their importance by the system

designers and mission specialists. The weighted sum

of the individual benchmarks was then proposed as a

figure-of-merit. Lietzau 's work is a thorough description of the individual subsystem tests that are a neces- sary part of evaluation but does not focus on the system

level evaluation that we emphasize here. Though it was

never formally characterized as such, Lietzau 's evaluation technique is an example of a Linear Programming

approach to solve the problem of evaluation.

3 Case Study

The evaluation methodology that we propose here

is for a particular robot mission - exploration of an unknown planetary surface. The area to be explored

is assumed to contain rocks whose positions are not

known a prion to the robot since it is presumed to be in unfamiliar surroundings. The robot mission is to perform scientific experimentation on rocks that are

"interesting" . We propose two evaluation functions in this study based on robot displacement as a function

of mission time and energy consumption.

time (t)

Figure 1: A Schematic of P(r > rg) vs. Time

3.1 The Cost Functions r and t]

The basic intuition behind the two cost functions

proposed is to develop a nondimensional measure of

the robot's ability to cover distance. The idea is to measure how "good" a particular robot design is by

measuring how far the robot travels from the start lo- cation as a function of the time elapsed and the en- ergy consumed by it. At first sight it may seem like the consumption of these two resources is extremely

well correlated. This is indeed the case for straightline travel on level ground with no obstacles. However,

in the presence of obstacles it is not so - especially since

the energy consumption of the system changes dramatically depending on whether it is at a standstill or in

motion.

We define a trial as an autonomous traverse of the

terrain by the robot in a particular instantiation of obstacle placement from start to goal. Using multiple trials we estimate the probability that the displacement

r > ro for different values of the time t. A schematic

of this probability as a function of time is shown in Figure 1. The main intuition is that the quicker this curve rises (close) to 1, the better the time utilization

of the robot. Further, good time utilization also dic- tates that this curve be monotonic increasing. For the

purposes of evaluation one is interested in the robot

covering some displacement rg within some time to. In other words we expect some minimum performance for a limited resource (time).

The above requirement means that Robot A should

be assigned a higher score than Robot B in Figure 2. This can be achieved by defining the area under the curve from Z = Oto/ = foasa metric. In order to compare robots of different size we measure displacement (r = kl) in terms of the number k of robot lengths

/. We also measure time in nondimensional terms by

282
P(r> r^)

time (t)

m

^^^^^ 1

w

Figure 2: A Schematic Comparison of P(r > ro) vs. Time for Robots A and B

Figure 3: MENO and Marscar in a Simulated Martian

Environment

multiplying it with v/l where v is the robot velocity.

Let nki{t) denote the probability of reaching a displacement kl as a function of time.

Definition 1 The time figure of merit is defined as

T= 7Vki{t)dt (1)

Jo

In a similar manner, we plot the probability of reaching a displacement kl as a, function of the energy e consumed. Energy is converted to a nondimensional

quantity by dividing it by mgl where m is the mass of

the robot and g is the acceleration due to gravity.

Let 7r/c/(e) denote the probability of reaching a displacement kl as a function of energy.

Definition 2 The energy figure of merit is defined as

r]= 7rfe,(e)c/e (2)

Note than both figures of merit are non-dimensional.

3.2 The Robots: MENO and Marscar

MENO is a 12 DOE statically stable quadruped

designed and constructed for this study in the USC

Robotics laboratory. Each leg is a rotary-rotaryprismatic (RRP) design. The body of the robot and

the first two links of each leg are in the horizontal plane

and the prismatic joints (the most distal joint of each

limb) are in the vertical plane. This orthogonal design was inspired by the design of Ambler [1]. The wheeled robot Marscar is 4 wheeled rover with

Ackerman steering.^

^Ackertnan steering maintains a particular relationship be- tween the steer angles of the inner and outer wheels in order

that the entire robot turn about a single point.

avold_ob8tacle8_iaove (

}

X"

data flow

control flow

aiobal

Blacldboard

coispaas (

sonar<

IRO

encoders (

Figure 4: The Control Architecture for the Wheeled

Robot

There are two main behaviors that drive both

robots. They are avoid_obstacles jtiove( ) and

reorient _tO-5oal( ) . A schematic of the control ar- chitecture is shown in Figure 4.

Onboard computing is all done on a custom board

built around a Motorola 68332 microcontroller. A

tether is used to supply offboard power for extended

testing and for gathering telemetry. The testing is all done in a 3.5 m x3.5 m sandbox. A single camera sus- pended 3 m above the center of the sandbox is used for

tracking the robot's position. We do not use the over- head camera as a source of information for navigation;

navigation is done by dead reckoning using information

measured by onboard sensors only. The sand surface is nominally flat but not precisely so.

283
Loop luitll at goal:

If obstacle In front Compute 'good' detour direction

Detour

Else

If goal within angular range limits Move forward

Else Reorient towards goal Endif

Endif

EndLoop

Figure 5: The Navigation Algorithm

3.3 The Navigation Algorithm

Both robots above use the same behavior-based navigation algorithm. There are two- basic behaviors; 1. Reorient towards goal and 2. Avoid obstacles. The

basic idea is for the robot to keep track of its current

position using knowledge of its kinematics and proprioceptive sensors (such as wheel encoders on Marscar and

joint angle measurements on MENO). The estimator

running on board the robot performs a simple deadreckoning calculation to estimate position and orientation at every move. The 'avoid obstacles' behavior

is also fairly simple - if an obstacle is seen the robot

will attempt to detour around it (while keeping track

of its position as mentioned above). If no obstacle is blocking the robot, it will attempt to move towards

the goal, re-orienting itself if necessary. The navigation algorithm is reactive. A schematic outline of the

algorithm is given in Figure 5. An interesting part of the detour behavior is the use

of global information. When an obstacle is detected the

reactive strategy is to backup and turn. The direction

of the turn is dependent on the current location of the

robot and the commanded goal location in global coordinates. The turn direction that reduces the difference

between the robot angle and the desired goal angle 6g

is chosen and executed. A purely local strategy would

pick one direction at random but the reactive obstacle avoidance behavior is modified to use some global

information viz. the goal position.

We also adapt the angular range during a traverse. The basic observation is that small angular errors when

the robot is far away from the goal lead to large position errors later. To avoid this we keep the angular

range limits (within which no reorientation is neces-

^The legged robot also has balancing and gaiting behaviors

at a lower level. They are discussed elsewhere [8]

sary) small when the robot is far away from the goal.

These limits are progressively increased as the robot

nears the goal.

The experiments were performed in a simulated

Mars terrain comprised of a crushed red brick sand

mixture. The mixture was spread evenly in a 3.5 m by

3.5 m sandbox to a depth of 0.25 m. The sandbox was

populated with rocks of varying size (between 0.04 m

and 0.2 m in diameter) to simulate Martian rock dis- tributions. The density of the rocks was equal to the

Mars nominal density from the Moore distribution [6]. Since the evaluation functions use probability esti- mates from numerous mission trials, the experimental

protocol consists of many robot traverses from start to goal locations in different instantiations of Mars nominal terrain. There are three main loops. During a particular instantiation a number of trials are performed

with different start and goal locations. During the course of each of these trials (as the robot is navigating

from start to goal) the offboard computer is monitoring time. When a certain time interval St is reached

the overhead vision system images the robot and the

image is stored with a timestamp. When the current

trial is over the sequence of images taken is postprocessed to extract the {x,y) location of the robot as a

function of elapsed time. This information is stored

in a file and the next trial begins. The procedure is terminated when all the exemplar start/goal locations

have been used in every exemplar terrain. The protocol for energy is exactly the same as the time trials but

instead of monitoring the time elapsed, the power draw

is monitored. Using this a running total of the energy

consumed is maintained. When the energy consumption reaches a threshold Se the robot is imaged.

Once the data recording the position and orientation

of the robot is obtained using the protocol described

above, it is processed to create plots of the required

probability estimates that yield the previously defined

figures of merit that we are interested in. The data

processing steps for the time trials are as follows:

• Fix a given time resource value (to)

• Fix a required minimum displacement (ro)

• Build a plot of 7r(?' > ro) vs. t

1. for each of the n data sets, V/ < to compute

r = y/{x - Xs)~ + (y- VsY'

2. a = number of r values greater or equal to 7*o

3. use a/n as the required probability estimate

• Compute nondimensionalized r = f^" Trr^^{t)dt

284
• Repeat above steps for different values of 7'o and

to

The data processing steps for the energy trials are

similar. In both outlines above {xs,ys) is the robot

start location and 7r(r > ry) denotes the probability that the displacement r from the start location is greater than j-q4 Data Analysis

The experiments were performed in simulation and

with the physical robots. The datasets discussed here

thus contain results from both. We will however restrict ourselves to a discussion of the datasets from the

physical robots since space constraints do not allow a complete discussion here. The interested reader is re- ferred to [9] for a complete account.

4.1 Mobility Trials

Tradeoff Space

and Clustering in

The first step in calculating the figures of merit is to calculate the probability of reaching k robot lengths

as functions of time and energy. Since we have multiple trials we estimate this probability as the fraction

of trials in which the displacement was greater than kl as functions of time and energy consumption. In Fig- ure 6 the probability of Marscar reaching the threshold

displacement kl is shown for various values of k. The

quantity / is intended to be a measure (with dimensions of length) of the robot size. We use the cube

root of the volume of the smallest rectangular box in

which the robot can be packed. For Marscar / = 0.35 m. All the trials were done in Mars nominal distri- butions. One can see a reasonable agreement between

the simulated dataset and the dataset collected from

the physical robot. The simulated dataset consisted of

200 trials and the physical dataset consisted of 40 trials. The probability estimates of the simulated dataset

are smoother compared to the physical dataset due to the larger sample size. The general behavior of the

family of curves shown in Figure 6 is a monotonic rise to saturation. The interpretation of these curves is the likelihood of success (at navigating through the

obstacle field) as a function of the available resource

(time). A higher k value corresponds to a longer tra- verse and thus involves greater ability in penetrating

obstacle fields. As k is increased for the same robot

the probability of achieving the same degree of success

decreases.

In Figure 7 a family of curves is shown which plot

the probability of Marscar achieving a threshold displacement kl as a function of energy consumed. As in

o

o robot + simulation

200 400 600 BOO 1000 1200

time (s)

Figure 6: Marscar - Probability of reaching threshold

displacements vs. time in Mars nominal terrain

k = 2 $ $ * * * 4 S «

'

mbol

simulation

Figure 7: Marscar - Probability of reaching threshold

displacements vs. energy in Mars nominal terrain

the case of the plots in the previous figure, the probability of greater success shows an asymptotic rise to saturation. Figure 7 shows the probability estimates

for the simulated as well as physical datasets. As one

can see there is a good match between the two. As

in the previous case larger k values imply longer missions and thus are harder to achieve for the same value

of the energy resource. Performance degrades as k is increased. As in the time trials with Marscar, the physical datasets in Figure 7 are the result of 40 trials and

the simulated datasets are the result of 200 trials.

In order to compute the figures of merit for MENO

in Mars nominal terrain we follow the same data analysis procedure as before. The curves showing the plots

of the probabilities of achieving the threshold displacement kl as & function of time elapsed are shown in

Figure 8. As in the previous cases increasing values of

k signify longer missions. For MENO / = 0.47 m. The

285
5 s s * * *

o rebol + simulaiion Table 1: The Figures of Merit for MENO and Marscar

for Different Traverse Lengths

9 • *

*

8"

^0.4 -

-J-8-

1000 2000 3000 4000 5000 6000

tim« (s)

k Marscar MENO

T T) T 7] 2 14.7 11504 2.7 6911

4 12.1 11429 2.1 5957

6 9.4 9718 1.1 3609

8 7.3 9635 0.8 2745

Figure 8: MENO - Probability of reaching threshold

displacements vs. time in Mars nominal terrain

' k= 1 robot simulaiion

0 100 200 300 400 500 600 700

energy (kJ)

Figure 9: MENO - Probability of reaching threshold

displacements vs. energy in Mars nominal terrain

o k = z + k = 4

K = 6

X k = s

Wheeled

Legged

time figure of mefil 20 25

physical datasets shown in Figure 8 were computed us- ing 40 trials in Mars nominal terrain and the simulated

datasets were generated using 200 trials in simulation.

The last datasets of interest in the current series are the behavior of MENO as a function of the energy

consumed in Mars nominal terrain. The relevant plots are shown in Figure 9.

In the notation of Chapter 4 we now have plots of

iTki{t) and TTki{e); the probabilities of the achieving cer- tain threshold displacements as functions of time and

energy. Using = 40 min and eo = 200 kJ as repre- sentative numbers for the mission under study we cal- culate the two figures of merit using Equations 1 and

2 for different values of k. These values are shown in

Table 1. Figure 10 shows the r and rj values for the two robots

in the tradeoff space. The lower left hand side of the

plot (signifying lower evaluation scores) is the space oc- cupied by the legged robot. The wheeled system has

better scores on both time and energy axes. The evalFigure 10: A Comparison of MENO and Marscar in

Mars Nominal terrain for Different values of k

uation functions are evaluated for 4 different values of

k. Irrespective of the k value the wheeled robot out- performs the legged robot. The functions r] and r thus

partition the design space.

To illustrate the cause of the difference in the evaluation scores it is useful to re-examine Figures 6 and

7 on the same scale. This is done in Figure 11 where we show the probability estimates for both MENO and

Marscar with /; = 5 as a function of the time elapsed.

Seen on the same axis it is obvious that the wheeled

system does better with the 'area under the curve' metric since it is a lot faster than the legged system in this

terrain (the Mars nominal rock distribution).

If Figures 8 and 9 are plotted on the same axis a

similar conclusion can be drawn regarding the energy

286
iI

jBO.4

500 1000 1 500 2000 2500 3000 3500 4000 4500 5000

tlrrw (si

Figure 11: A Comparison of MENO and Marscar in

Mars Nominal terrain for A; = 5 as a Function of Time

Elapsed

lin>e Tigure of merit

0 20 43 60 80 100 120 140 160 180

energy (kJ)

Figure 12: A Comparison of MENO and Marscar in

Mars Nominal terrain for k = 5 as a. Function of Energy

Consumed

scores. This is shown (again for = 5) in Figure 12. The wheeled robot needs far less energy to cover the

same distance compared to the energy consumption of

the legged robot over a similar distance for this particular rock distribution.

4.2 Sensitivity Studies - Environment

One of the objectives of this study was to measure

the effects of changes in environmental parameters on

the mobility metrics. The environment model used in

this study is the distribution of rocks called the Moore

distribution. In the vicinity of a previous mission to Mars (the Viking II mission) the density of rocks is much higher than the Mars nominal distribution used

thus far. The effect of terrain clutter is very clearly seen

in the two metrics. In the case of both robots, increased

clutter leads to performance degradation. However it is

Figure 13: MENO and Marscar mobility in Viking II (cluttered) terrain for different values of k

interesting to note that the wheeled system is affected

far more than the legged system. This is largely due to the fact that the increased clutter leads to significantly

longer paths for the wheeled system whereas the legged

system is able to go over many more obstacles and even though it is slower its performance is comparable to the

legged robot. This is shown in Figure 13. As one can see in Figure 13 the Marscar cluster

moves dramatically to the left and down when the

terrain was changed from Mars nominal to Viking II. MENO performance also suffered as seen in Figure 13

but not as dramatically. For this environment, its en- ergy figure of merit is better than Marscar.

4.3 Scalarization of the Metrics

The metrics r and j] can be combined into a single scalar metric using a weighted linear combination.

From the data presented in this Chapter we see that

the wheeled robot outperforms the legged vehicle along

both dimensions in Mars nominal terrain. The scalarization chosen should preserve this ordering. A stan- dard technique is to use a weighting function which is either linear or quadratic and maximize the combination of the two metrics. However the problem of how

to choose the weights still remains. Instead of an ad

hoc solution we use domain knowledge to postulate a

feasible scalarization technique.

287
On one axis (r) we are measuring the robot's ability to use time effectively and on the other (?;) we measure

effective energy utilization. The fundamental unit of

conversion between them is the maximum power delivered by the onboard power source. If the power source

is capable of delivering a W then we weight energy and

time in the ratio 1 : a. We computed the scalarized scores for k = 6 for the

different cases reported in this Chapter using ai = 30,

a-> = 40 and a-^ = 50. Using this scalarization technique it is clearer that in sparse obstacle distributions

the legged system should be the preferred design while

in dense obstacle distributions (such as the Viking II

site) the nominal configuration of the legged robot

MENO is the better design using these metrics and

this particular linear scalarization.

5 Discussion

Values of the two metrics, r and t] for Marscar are

significantly superior to the MENO values. The effect of obstacle clutter, though, is more pronounced on the

wheeled robot.

There are three interesting aspects of the data pre- sented here which form the basis for substantial fu- ture research. The first deals with the following design

question: "In what parts of the design space are good

designs found ?" . At first glance it may seem like the answer is obvious - by definition it would seem like the

designs leading to the highest values of the evaluation

functions are the good parts of the design space. However, a closer look suggests that the real 'sweet spots'

in the design space are those where the design is in- sensitive to changes in the environment. For example,

MENO in its nominal configuration is insensitive to changes in rock density. If there is large variability in

the expected terrain density it may be a better decision

to pick a design like MENO even though it has low evaluation scores compared to other designs. We are thus

led to believe that future scalarization efforts should

include weighted contributions from select components

of the evaluation gradient in addition to the values of

the evaluation functions themselves.

The second interesting point also concerns the evaluation gradient. Locations in the design space where

the evaluation gradient becomes very large also provide interesting insight into design methodology. We

suggest that these locations in the design space signal a 'breakdown' in the current kinematic design and

a discrete jump to a new structure is indicated (with

higher articulation perhaps or with a larger number of

wheels)

.

A third application of the metrics proposed here is to global optimization. While the technique for extrapolating performance shown here is local, it is possible

to extend it by instantiating a chain of local models

and following the evaluation gradient to an optimal set of parameter values.

6 Acknowledgments

The author gratefully acknowledges help from George Bekey

in the development of these ideas. The author is partially

supported by two DARPA programs; the Tactical Mobile

Robotics (TMR) program under contracts F04701-97-C-0021

and DAAE07-98-C-L028, and the Mobile Autonomous Robot

Software (MARS) program under grant DABT63-99- 1-0015. The

case studies described here were performed under the auspices of NASA grant #9.59816 from Caltech/JPL.

References

[l] J. Bares and W. Whittaker. Configuration of autonomous

walkers for extreme terrain. International Journal of Robotics Research, 12(6):535-559, 1993.

[2] K. R. Lietzau. Mars micro rover performance measurement

and testing. Master's thesis, Massachusetts Institute of Technology, Department of Aeronautics and Astronautics,

December 1993.

[3] S. Mahalingam and W. L. Whittaker. Terrain adaptive gaits

for walkers with completely overlapping leg workspaces. In Proc. Robots 13, May 1989.

[4] R. B. McGhee and A. A. Frank. On the stability properties of quadruped creeping gaits. Mathematical Biosciences,

3(3/4):331-351, 1968.

[5] D. A. Messuri and C. A. Klein. Automatic body regulation

for maintaining stability of a legged vehicle during rough

terrain locomotion. IEEE Journal of Robotics and Automation, RA-1(3):132-141, 1985.

[6] H. J. Moore and B. M. Jakosky. Viking landing sites, remote-sensing observations and physical properties of martian surface materials. Icarus, 81:164-184, 1989.

[7] P. Nagy, S. Desa, and W. L. Whittaker. Energy-based stability measures for reliable locomotion of statically stable walkers: Theory and application. The International Journal of Robotics Research, 13(3):272-287, June 1994.

[8] G. S. Sukhatme. The design and control of a prototype

quadruped microrover. Autonomous Robots, 4(2):211-220,

April 1997.

[9] G. S. Sukhatme. On the Evaluation of Autonomous Mobile

Robots. PhD thesis, University of Southern California, May

1997.

[10] B. Wilcox. Mobility characteristic curve. Jet Propulsion

Labs, lOM 3472-91-019, 1991.

288
Search Graph Formation for Minimizing

the Complexity of Planning

Alberto Lacaze

Computational Intelligence Laboratory, ECE

University of Maryland, College Park

Abstract

A large number of path planning problems are solved

by the use of graph based search algorithms. There

are a variety of techniques available to optimize the

search within these graphs as well as thorough studies

of the complexity involved in searching thrvugh them.

However, little effort has been dedicated to constructing the graphs so that the results of searching will be optimized.

The commonly used approach for the evaluation of

complexity assumes that the complexity of a path plan- ner can be evaluated by the number of nodes in the

graph. However, in many path planning problems (especially in complex, dynamic environments) the evaluation of the cost of traversing edges is the major culprit

of computational complexity. In this paper we will as- sume that the complexity associated with the computation of cost of traversing an edge is significantly larger

than the overhead of searching through the graph. This

assumption creates non-trivial complexity results that

allows to optimize the creation of the graph based on

the computational power available.

We will present a numerical evaluation of several

graph creation algorithms including the commonly used

four and eight connected grid. Different scenarios for

which ground truth is available are explored. Comparison among the graph creation algorithms reveals se- rious downfalls that are common practice throughout

the literature.

1 Introduction

Planning can be defined as the process of finding

the steps necessary to bring a system from an initial (current) state to a final (desired) state. Most planning techniques represent the planning problem in a graph G{V,E). Where V is a set of vertices, and E

is a binary relation on V [6, 7, 9]. The elements of

Stephen Balakirsky

Intelligent Systems Division

National Institute of Standards and Technology

the set V are called vertices and represent states. The

elements of the set E are called edges and represent

the ability of the system to move from one state to another. In planning graphs, the edges are ordered or unordered pairs of vertices, {vi,Vj) where Vi G V and

Vj E V. A walk is an alternating sequence of vertices

and edges, a trail is a walk with distinct edges, and a path is a trail with distinct vertices.

When solving a planning problem, we must find a path or plan from a starting vertex Vg to an ending vertex Vg while minimizing a cost function C =

Y^l Wij where wij is the cost of traversing the edge

{vi,Vj). Some planning problems can be solved by al- gorithms with polynomial complexity. Unfortunately,

these tractable set of problems covers only a few of the

relevant problems encountered in path planning. Most

problems, however, can only be solved by polynomial

algorithms on non deterministic machines, ie NP. For

a thorough study on the problem of tractability and

its taxonomy see [8]. One very useful tool when fighting the computational complexity of planning is the creation of hierarchies of planners. The Real-time Control System

(RCS) reference model architecture is one such architecture and it has been successfully applied to multiple diverse systems [1, 3]. The target systems for RCS

are in general, complex control problems. Although it has been shown [2, 10] that the complexity of a control

problem is reduced by the use of a hierarchical control

system, the reduction of error as a function of complexity at one level of the hierarchy has been mostly

overlooked.

The complexity of search algorithms inside a graph

has been thoroughly studied [11, 13, 14]. However,

with few exceptions [4, 12], little attention has been

paid on how the graph should be built with some ex- ceptions [4, 12]. In most cases, it is recommended that

the graph for search on "empty space" should be built

using grids, Voronoi diagrams, or visibility graphs. It

289
Figure 1: Average error for a 4 connected grid.

is not clear from the literature which of these methods

should be used and when. Moreover, in most cases the

complexity of algorithms is calculated solely based on

the number of vertices in the graph. In most path

planning problems, the computational complexity of

calculating the cost of the edges is orders of magnitude

higher than the actual time spent searching through

the graph once these values have been calculated.

2 Numerical Exploration of Graph Creation

In order to compare the different graph formation

algorithms, we started by defining a simple test sce- nario. The analytical closed form evaluation of the

complexity of finding the optimum path taking under

consideration the placement of the vertices in the so- lution space becomes easily intractable. Therefore, we

decided to study the problem numerically. In the ex- periments presented in this paper, simple Euclidean

distances were used to calculate the cost of travers- ing the edges. The advantage of using this measure

is that we have ground truth. We assumed that the

Euclidean distance is calculated with an accuracy of

five significant figures.

2.1 Grid Based Graphs

By far, the most commonly used graph for search

in planning algorithms is the four-connected square

grid. In this kind of graph, the vertices are placed at regular intervals and it is assumed that each vertex

is connected to four (or eight) of its closest neighbors.

edges

Figure 2: Average error for a 8 connected grid.

Figure 3: Average distance to the mean.

290
We built a two dimensional four-connected square grid

with a random number of vertices. We repeated this

experiment several times. Figure 1 shows log{error)

where error is defined as

ve error = abs{ds.e - + ds,v, +4,i.J) (1)

vs

s is a randomly selected starting point, e is a randomly

selected ending point, Ve is the closest vertex in the

graph to e, Vg is the closest vertex in the graph to s, d{i,j) is the Euclidean distance between two points.

Please note that this cost function may underestimate

the real error of traversing the planned graph as it is assuming that ds^v, and d^^v^ ^-re Euclidean. This is a

best case scenario.

The summation in the equation represents the added

cost of the optimal path through the graph. The av- erage error (marked with a black star in the Figure)

is kept constant as the number of edges is changed.

The different values at a particular number of edges

correspond to the different number of times that the

experiment was performed using different e and s. Figure 2 shows the error function shown in 1 applied to a eight-connected grid. As expected, the er- ror function settles at a lower error. By comparing

the 4-connected grid to the 8-connected grid we can

appreciate that the average error decreases with the

higher connectivity, however in both cases, the error quickly settles to a constant value.

Please note that in both cases, increasing the number of edges, and therefore increasing the computational complexity gives us very modest improvements

of the final cost. Another problem found experimentally with the 4 and 8 connected grids using this cost function is that there are many paths that have ex- actly the optimal cost. This has the effect that the

optimal path that the algorithm will choose, may wander off the "expected" straight path line from e to s. In other words, many paths within the parallelogram

defined by Vs and Ve have exactly the same "optimal"

cost. Another effect that results from square grids is that the error varies significantly depending on the di- rection of travel. A numerical evaluation of this deviation can be appreciated by examining Figure 3. The

large average distance to the mean is due to the fact

that some s and e happened to be horizontal or vertical, therefore giving small error, while some created a

very costly stair-step paths through the graph.

2.2 Shaking the Grid

Some of the pitfalls of the grid based graphs can be

avoided by:

1. Shaking the vertices within the grid. In other

words, building a square grid, adding a random

displacement to the vertices, and finally connecting all the vertices that are within a neighborhood. The size of the neighborhood dictates the

vertices to edges ratio. This has two effects:

(a) Break the ties among optimal paths so that

only one path is found to be optimal. This

is very helpful in re-planning systems as it forces to commit instead of randomly flipping among the set of "optimal" paths.

(b) Create a more uniformly distributed set of

vertices where all " directionalities" are represented.

2. Create higher connectivity rates (higher than in

the 8-connected grid).

Figure 4 through Figure 7 shows the results of a

set of experiments run using the above principles. To

compute these figures, the vertices of the grid are placed first in a grid pattern where each point is / apart from its closest neighbor. Next, a random vec- tor is added to each vertex of maxinmm amplitude 3/. All vertices within a distance threshold are then connected. By varying the connection threshold, different

ratios between the number of nodes and the number

of edges are achieved. We can see from Figure 4 that

the error decreases as the number of edges increases,

approaching the lOe-5 mark set by the 5 significant

figures used to calculate the Euclidean distances. Fig- ure 5 shows a top view of the same numerically found

error. We can see that even a simple Euclidean cost function creates ripple effects in the final cost.

If we take the assumption that the computational

complexity is directly proportional to the number of

edges (as it is in most cases), we can see in Figure

8 the error function as a function of the number of

nodes. The almost counter-intuitive results can be

explained from the fact that by increasing tht .ber

of vertices the average cost of an edge cecreases. la

Figure 9 we assumed that we could only calculate the

cost of 40000 edges. By visual inspectioi; of Figure

9 we can determine that the least error is given by

about 2000 vertices, and therefore creating a graph

where each vertex has 20 connected neighbors.

3 Vehicle Planner E mple

In order to validate the above rules of thumb, several experiments were conducted using the Demo III

291
'

7

0

edges

nodes

Figure 8: Error for different complexities and varying

number of vertices

292
He Set

600 1000 1500 2000 2500 3000 3500 4000 4500 5000 Number of nodes

Figure 9: Error for fixed complexity and varying number of vertices

fie Se»

Map Size |l km ^

P" North Orient F AM Obstacles

F" Veh. Obstacles

CoordSync N¥L Reset

N

Path sent

Figure 10: Planning result for complex cost map with

four-connected graph.

Map Size

P" North Orient

P" AM Obstacles

P" Veh. Obstacles

N

w E

Path sent

Figure 11: Planning result for complex cost map with

many-connected graph.

Vehicle Level Planner [5]. In these experiments, a

four-connected graph and a shaken graph of the form

of section 2.2 were run using a complex world model

and cost function. The four-connected graph had a

grid size of 8 meters with 61012 connections and the

shaken graph had a grid size of 11 meters with 45086

connections (26% fewer connections) and was shaken

±5.5 meters. The world model contained a priori in- formation on the NIST grounds at 4 meter resolution

including the locations of wooded areas, buildings,

roads, and fences. It should be noted that the world

model resolution is twice that of the four-connected

graph and almost three times that of the highly-connected

graph.

In the Demo III Vehicle Level Planner, the planning

module passes path segment endpoints (the vertices of

the planning graph) to the world model for evaluation.

The world model simulates driving a straight hne path

(the edges of the planning graph) between these end

points and returns the cost of traversal to the planner. The planner then conducts an optimal search

algorithm to find the cheapest path (in reference to the cost function used by the world model). The cost function used by the world model favored paths that

avoided roads and buildings, and drove next to, but

not in wooded areas combined with the time of traversal of the route (assumed uniform vehicle velocity over the route segment).

The straight line segments used by the world model

may cause plan failures when the resolution of the

planning graph is less then that of the world model.

293
Tliis occurs when a very narrow low-cost corridor is surrounded by a very high cost area. It may occur

that there are no straight Vine segments at the graph

resolution that traverse this low-cost corridor. This

phenomenon can be avoided in the highly-connected

graph by adding additional vertices in these high payoff areas. This approach was not taken in the experiments described below.

Using this planning system, we found that the highlyconnected graph performed as much as 27% better

then the four-connected graph, even though it used

26% fewer connections. Sample output paths may be

seen in Figure 10 for the four-connected graph and

Figure 11 for the highly-connected graph. A snap-shot

of the world model may be seen as the background of

these images. As one would expect, the benefit of us- ing the highly-connected graph is directly tied to the

shape of the optimal path. For straight paths, the two

graphs performed on par with each other. For paths

which required many turns, the highly-connected graph

significantly outperformed the four-connected graph.

4 Conclusion

• "Optimal" paths found using the four-connected

grid based graph are in general, directionally bi- ased, favoring the traversal of the space in cer- tain directions and not in others. They also

create symmetries that result in noncommittal

paths. Shaken grids and high connectivity between vertices was shown numerically to improve

these pitfalls.

• The number of edges in the graph and their cost evaluation are in most cases, the major culprit for computational complexity. Therefore, it

is recommended that the graph design process

starts by determining the number of edges that can be evaluated, and then selecting the immber

of vertices that give the least error.

• Numerical evaluation of the error are in most

cases the only way to select parameters for the

formation of search graphs in complex environments. Most analytical evaluations of the complexity in the literature make the assumption

that the burden of computational complexity is

in the "opening" of the vertices in the search

graph, and are not readily applicable to planning problems.

References

[1] J. Albus. Outline for a theory of intelligence.

IEEE Transactions on Systems, Man, and Cybernetics, 21:473-509, 1991.

[2] J. Albus, A. Meystel, and A. Lacaze. Multiresolutional planning with minimum complexity. Intelligent System and Semiotics, 97.

[3] J.S. Albus. Brain, Behavior, and Robotics.

McGraw-Hill, 1981.

[4] R.S. Alexander and N.C. Rowe. Path planning by

optimal-path- map construction for homogenouscost two-dimensional regions. In IEEE International Conference on Robotics and Automation -

1990, 1990.

[5] Stephen Balakirsky and Alberto Lacaze. World

modeling and behavior generation for au- tonomous ground vehicles. In Proceedings IEEE

International Conference on Robotics and Automation, 2000.

[6] J. Bondy and U. Murty. Graph Theory with Applications. North Holland, 1976.

[7] N. Deo. Graph Theory with Applications to Engineering and Computer Science. Prentice Hall,

1974.

[8] M. Garey and D. Johnson. Computers and Intractability. Freeman, 1979.

[9] F. Harary. Graph Theory. Addison Wesley, 1972.

[10] Y. Maximov and A. Meystel. Optimum design

of multiresolutional hierarchical control systems.

In Proceedings of IEEE Int'l Symposium on Intelligent Control, pages 514-520, Glasgow, U.K.,

1992.

[11] C.H. Papadimitriou. Computational Complexity.

Addison Wesley, 1994.

[12] F.P. Preparata and M.I. Shamos. Computational

Geometry, An Introduction. Springer Verlag,

1988.

[13] J.H. Reif. Complexity of the generalized moving

problem. In et al Schwartz, editor, Planning Geometry and Complexity of Robot Motion. Ablex

Publishing Corporation, 1987.

[14] J.T. Schwartz, M. Sharir, and J. Hopcroft, editors. Complexity of the Generalized Moving Problem. Ablex Publishing Corporation, 1987.

294
Metrics for Embedded Collaborative Intelligent Systems

Michael E. Cleary, Mark Abramson, Milton B. Adams, Stephan Kolitz

Draper Laboratory

555 Technology Square, MS 3F, Cambridge, MA 02139

{mcleary, mabramson, adamsm, kolitz }@draper.com

ABSTRACT

The intelligence of a network of agents is reflected in the

complexity of missions that can be accomplished, the

degree ofcoordination/cooperation among the agents, and

the level of uncertainty the system can tolerate and still accomplish its missions. The networked system must be

able to evaluate a situation, devise an appropriate

response, and act accordingly. Metrics must be devised

to capture the complexity and surprises of the real world,

and to capture the system's need to reason about its situation so as to uncover unanticipated problems and

opportunities. Inputs for developing autonomous

capability specifications (and thus metrics of interest)

include (1) descriptions of expected missions, (2) the

space ofmission parameters, and (3) the cost/benefitratio

for operational concepts. These inputs come from both

current and anticipated missions. Several of our recent

projects have sought to quantify operational metrics for

autonomous ground, air and undersea vehicles. This

paper presents our approach to high-level design of

autonomous vehicles that produces the three inputs for

metric development. The approach and parameter spaces

are illustrated with examples derived from several vehicle

projects.

Keywords: metrics, intelligence quotient, intelligent

systems, autonomous systems, collaborative systems,

situation awareness, planning under uncertainty, orders of

intelligence.

1 Introduction

The intelligence of a network of agents is a complex

characteristic that can be quantified and measured in a wide variety of ways. Our work on the design of

intelligent autonomous vehicles and programs to develop

such vehicles has made clear that the type of metric we

develop will be chosen to meet a particular objective. For

instance, commercial sponsors will likely optimize some

functionality, while researchers may try to optimize some

measure of "pure" intelligence. After reviewing a number of systems in ground, air and undersea domains,

it becomes clear that the .major characteristics of

intelligence for any complex set of vehicles are the broad

areas of multi-vehicle collaboration, understanding the

world they operate in (situation awareness) and

responding appropriately (planning under uncertainty).

Syst«m and Operational R«quir«in«nts

I

Develop Mission Descrip

1 Develop Mission Parameter:

"

• : 2 Analyze Parameter Space:

2 Current and Future Ops

J Estimate Benefit of Futi*re

I Concepts

LtteratufO Exp»ri*nc9

Sii» Vi»H» to R«««»roh«f$y

Identify Required Technol(|g(i Future Ops

(Intelligent Autonomy Roddln.

Estimate Cost to Develjop

Technologies

Op s

es for

lap)

Trade Study:

Cost/Benefit for Future Ca^bilitieS;

J Metric development acti Development Roadmap

(Goals and Objectives

Figure 1 -- Development Roadmap

To guide the design of intelligent vehicles for particular

domains, we have used the process illustrated in Figure 1

There are two major efforts shown - the left column

focuses on the missions the vehicle is intended to accomplish, while the right column focuses on the

technologies required to accomplish those missions. The

two columns could be loosely labeled requirements pull

and technology push, respectively. The areas we have

considered for metric analysis to date are those shown

surrounded with dotted lines. Once thorough descriptions

of the vehicles' missions are developed, those are reviewed to extract parameters that affect performance.

The mission descriptions are then extended to probe the

space of the identified parameters. This process is

illustrated in detail in Section 2. A more humanly intuitive representation of the parameter

space was sought, since the bare listing of parameters can

be daunting (Section 3). This introduces significant

subjectivity, but allows aspects of intelligence to be

clustered that seem to lead to strong collaborative

systems.

Section 4 discusses an attempt to quantize intelligence

into "orders" of intelligence. It begins with the point that

Copyright 2000 The Charles Stark Draper Laboratory, Inc.

295
"intelligence" is still a relatively undefined area, needing

substantial work in the component technologies and in the

development of appropriate metrics. Despite that reservation, candidate levels of intelligence capability are described that might serve as an IQ for autonomous

systems.

Costs are another aspect of intelligence that require

attention and metrics (Section 5). For instance, a sponsor

may seek to develop a comprehensive technology

roadmap that will determine what technologies need

investment to meet a particular set of system and

operational requirements.

The paper concludes with a brief discussion of some

future directions for our work (section 6) and a summary

(section 7).

2 Construction of Parameter Space

The simplest way to evaluate a system's success or

failure at its task is often binary - did it accomplish some

goal? For instance, in RoboCup Soccer [3] as in human

games, a single score is the final arbiter of success. However, the single score does not capture the

complexity of the domain or of the team's approach to various elements of the problem. Thus additional "scores" are developed that rate game players on the

skills that contribute to the final game score. Such more

detailed scores can be combined into a single weighted

score, using multi-objective optimization techniques

[1,2]. However, that requires significant work to determine appropriate weightings and combination

techniques.

The first step toward such a development is to flesh out

the parameter space of the task. A large number of

factors can be considered in a thorough analysis of a collaborative group of vehicles. We use the three

characteristic areas named above (collaboration, situation awareness, and planning under uncertainty). The

following incomplete lists indicate some ofthe important

elements for robots facing dangerous situations (military or other). Each metric on the list requires a range of

acceptable values and a weighting factor for combining

them with other components. The factors can then be

processed to produce a combined metric if such a score is desired.

• Multi-vehicle collaboration factors

• number of interacting agents

• degree of coordination/cooperation among the

agents

• degree of improvement in situation awareness due to multiple vehicles

• success of dynamic replanning to maintain

configuration for communication

• Situation awareness

• amount of complexity and surprise of real world

captured

• number of elements

• level of interactions between elements

• dynamism

• model complexity for target identification

• observability

• environmental challenge + clear air/daylight - to - storms at night + desert (all is visible) - to - mountainous

(hard to see details) + textured (landmarks differ) - to - desert/no

texture

• threat types + from known type/location - to - suspected -

to - unknown till aggression + from id is straightforward (e.g., surface-toair-missile (SAM) radar) - to -

difficult/uncertain (visual or synthetic

aperture radar (SAR), near friendlies,

signature similar to neutral or friendly

• neutrals

-I- known type/location - to - threats

masquerading as neutrals

• friendlies

-1- known type/location - to - identify-friendfoe (IFF) transponders off/broken or known

but near threats

• navigation

-I- sensors functioning and low uncertainty - to - sensors dropping out/damaged or high

uncertainty

• vehicle state (including equipage)

-I- sophistication of health monitoring and

reconfiguration

• time to sense and assimilate (separate from time

to plan) + enough time - to - insufficient time due to tempo or number of targets (so need to

prioritize sensing and assimilation)

• can successfully identify a target

• can detect environmental changes of the

following types: + threats

-I- terrain + collision

-1- targets of opportunity

• Decision making and executing under uncertainty

• extent that system reasons about its .situation

-I- uncovers unanticipated problems + uncovers opportunities

• level of uncertainty the system can tolerate

• performs under available time to plan

296
dynamic time constant that system can reason

within

stochasticity - number of contingencies handled

by system

number of decisions (i.e., size of planning

problem)

quality of plan generation / selection algorithms

quality of planning approach (algorithms and

representations)

complexity of mission / problem

complexity of controllable system

number of plan elements in flux simultaneously

number of levels in planning problem

• ability to perform dynamic replanning due to; + change in mission objectives + environmental change detected

Such a list of parameters is daunting, and only becomes more difficult to grasp and synthesize as the level of

detail grows. A more intuitive representation was sought

to support analysis of the trade-offs involved in system

design and funding. The result is discussed in the

following section.

integrated multi-sensor

fusion

single-sensor model-matching

low-level sensor proc'g

e.g. visual servoing (template tracking)

none,

teleoperateonly

or sensor as conduit

Systems

1. eyes on wall with teleoperated camera direction

2. Micro Air Vehicle or helicopter with visual mapping

3. bat

4 vision augmented navigalorAnapper without own mobility (e.g., spy briefcase) RC helicopter beyond line of sight (pilot has only the view from

on-board cam)

6. smart intrusion sensor alarm (SISA)

7. a general intelligence in a human invalid

8. mosaicking visual mapper (creating 3d mosaicked map) and visual servoing to navigate with respeci to map

9. DARPA'sautonomous submarine project (Autonomous Mapping

and Minehunting Technologies)

1 0. UGV with flow-based OD/OA + feature-assisted retron^verse, and run and hide

Task Planning

Vi^'!^ OA

v* none, teleoperateonly

integrate multiple actions

get to waypoint, do one feature-based command

J^

E -5

Figure 2 -- Three-Dimensional Intelligence Space

3 Graphical Parameter Space

A three-dimensional graphical approach was used to

illustrate where various systems and system designs fell

in the overall parameter space (Figure 2). This shows a

particular three axes in the parameter space, recognizing

that the whole estimation and metrics space is highly

multi-dimensional. Several such charts were prepared,

but no canonical axes were identified that best serve all analysis purposes for all autonomous systems. The figure

shows axes of situation awareness, mobility, and task

planning as creating a 3D intelligence space. A variety of

autonomous and non-autonomous systems are included in

the figure to highlight key parts of the resulting space.

297
The representation's key weakness is inherent in the

choice of any set of 3 dimensions- key information from

a fuller, higher-dimensional space is lost. Also

problematic is the apparent linearity between ticks along

any axis - what conclusions can be drawn by systems

shown N ticks apart? Still, there is the strong sense that

this captures something fundamental and accurate about

the intelligence present in a variety of compared systems.

The primary difficulty with this approach, however,

remains the subjective judgement that only a small

number of axes is enough to grasp the entire intelligence space.

4 An Intelligence Quotient?

We have been asked at various junctures to provide

metrics for autonomous systems development, in a similar vein to those provided by (for instance) engineers

working in other disciplines who do not hesitate to propose metrics. That has been a difficult request to answer, until the various exercises reported above led us

to a key conclusion: Mature technologies can support more precise performance targets than immature

technologies. For instance, a group researching

automatic target recognition (ATR) can aim to decrease

the false alarm rate by 5%. However, what similar metric

applies to the broader aim of "increase intelligent

autonomy"?

This section discusses a reservation about characterizing

intelligence, then proposes levels of capability that are our best-yet "intelligence quotient" for autonomous

systems.

4.1 A Philosophical Reservation

Answering the above question may depend on how the

question is phrased, but consider this goal: enable

autonomous dynamic mission replanning, based on

discovered targets and conditions expected in the target

area, while out of communication with the human

operator. Several questions spring to mind. What

technologies apply? What are their margins for

improvement? Do we even know what is necessary to achieve the goal? One approach is to consider finer- grained technologies rather than the broad term of

"autonomy". For instance, the following appear more

susceptible to metrification.

• Decrease route planning time-to-plan by 20% given

contingencies of type A.

• Increase ATR reliability for particular

target/environment pairs by 1 0%.

• Increase situation recognition capability by

increasing contingency representation flexibility by

10 times.

We conclude that "intelligent autonomy" is an immature

"technology" that is actually a composition of underlying

technologies, all of varying maturities. A small set of

examples of component technologies with clear

deficiencies (compared to human-level capabilities)

follows.

• Sensor data interpretation

• Situation awareness and assessment

• Communication

• Efficient - perhaps better named "data

communication" (bandwidth, rates, etc) • Effective - perhaps better named "knowledge

communication" (content, concepts,

transparency of thought processes)

• Knowledge representation - know, represent and

share:

• Wliat data toward what goals in what

timeframes?

• Why does datum A or set of data B matter?

• Timeliness of concern + Damage is expected to occur by time T

(e.g., hostile strike group detected headed

for barrier) + Unless used by time T, data C not useful

(e.g., a moving surface-to-air-missile

launcher is detected 1 mile from bunker

moving 10 mph - must use information

within 6 minutes)

• Relatedness of data

• Collaboration

-I- Understand others' goals + Infer intent from observed behavior

Thus finding ways to divide intelligence and autonomy

into appropriate sub-technologies that can be weighed

and combined properly is a critical problem facing this

effort. Lacking such a reliable analysis tool, we next consider one way to approach its formulation.

4.2 Orders ofIntelligence

Given the above reservation, let us proceed to characterize intelligence by asking: how hard is a planning and execution problem? Time to plan (TTP)

depends on the size of the planning problem, but Moore's

Law will reduce TTP significantly by increasing the

feasible size of planning problems. However, TTP also

depends on (a) the planning approach (algorithms and

representations) and (b) the problem complexity. Size of

the problem is the easiest to provide metrics for. The

other two factors are used to modulate the metrics. If a planning agent is only concerned with a certain time

horizon (e.g., 10 milliseconds, I hour, 1 day), the level of

detail it considers is similarly bounded. Thus planning

298
problems can be of similar sizes whether at the level of a

single vehicle or a fleet of vehicles.

There are numerous planning approaches. For wellcharacterized and well-formulated domains, search in a pre-defined state space is satisfactory. For other

problems, current pure research effots are unable to provide a well-defined solution. More pragmatically,

planning and execution systems can use a variety of

hybrid approaches, the integration of which pose at least engineering issues.

Problem complexity addresses characteristics beyond the

simple size of the problem. The characteristics that make

planning, estimation and control difficult include the

following elements. Since planning needs to be

concerned with what can be expected to occur, it must be

concerned with expected results from estimation and

control, that are affected by the following elements.

• observability - the degree of hidden state (in

controlled system or in situation being monitored)

• complexity of the controllable system. E.g., number

and type of actuators, static and dynamic stability of

the vehicle.

• situation awareness complexity. E.g.: • number of elements

• interactions between elements

• dynamism (e.g., likelihood to loose lock in tracking subsystem)

• model complexity for target identification (e.g.,

2D image templates, 3D shape, functional

analysis based on shape, behavioral)

• degree to which situation awareness (SA) fulfills expectations

• number of interacting agents. Especially if multiple

agents are simultaneously planning

• number of plan elements in flux simultaneously.

E.g., (a) is plan in place before SA is received, or (b)

is SA being integrated while plan using it is being

created? Regarding example (a) consider the plan

"go to area X and find tanks" (where "tanks" will be

bound to those found by SA), whereas for (b)

consider what the system needs to do when it finds

itself unexpectedly under attack from unknown

quarters.

• number of levels in planning problem due to (i) number of elements, (ii) number of time horizons,

etc. One approach to creating metrics for these problems is to

classify problems from the domain into nominal orders of

difficulty, then set targets for various demonstrations

which move along the spectrum of difficulty. For

instance, reasonable goals might be created by aiming to solve a problem in 1 second in each demo year, where the

size and complexity of the problem increases over time.

Based on the nominal characterization below of levels of

difficulty, the solvable problem size could increase from

1 0'' in demo 1 (say year 2), to 1 0' in demo 2 (year 4), and

1 0" in demo 3 (year 6). This folds together the expected

advances in processor speed and capacity embodied in Moore's Law with improvements in planning approaches

resulting from pure and applied research progress. Table

1 captures this approach and leaves space for additional

metrics at various levels of maturity.

10" 10' 10- 10'

Demo 1 1 second

(TTPO)

Demo 2 1 second

(TTPl)

Demo 3 1 second

(TTP2)

Beyond 1second

(TTP3)

Table 1 ~ Problem Size, and Plan for Increasing

Demonstrable Complexity

4.3 Nominal candidate orders ofintelligence

The following lists indicate relative order of magnitude

capabilities that could be grouped together to assess the

maturity of a system's intelligence. These are illustrative, not final. Order 0 activities may exist in preliminary

commercial research forms or may need applied research

and engineering to be tlelded. Higher order activities are believed to be beyond the current state of the art.

Order 0 activities:

• Single vehicle plans including (a) multi-waypoint

path planning and execution cognizant of known

threats, (b) obstacle avoidance given some warning,

(c) deck landing in relatively benign environment

• Multiple vehicle plans, for non-interacting vehicles

• Plan to search area of regard ( AOR) for target, where

AOR is essentially flat and open, and target can be

found by template matching.

• Re-plan communication relay service due to disruption of channel, using prior known assets. • Re-plan for changed objective, where

accomplishment of the objective is in the future from

the current time-horizon.

• Re-plan task particulars due to change in SA. E.g.,

arrive in kill box and discover that the targets to be

hit are tanks instead of a column of trucks.

• identify targets of opportunity based on their

appearance

Order 1 activities:

• single vehicle obstacle avoidance given less warning

and/or more constraints on response (e.g., in

299
confined airspace due to terrain or other vehicles, near vehicle limits for responsiveness)

• single vehicle deck landing in moderate sea state and/or moderate visibility • Plan to act as autonomous communication relay

between moving communication partners, where the

partners are moving in ways that are expected to disrupt communication within foreseeable future. Thus plan must include a plan to identify and involve

additional communication relays. Alternative

contingencies would include planning for disruptions

that might occur due to weather, jamming, or other

hostile activity. • Re -plan for changed objective, where

accomplishment is within current time-horizon,

requiring current SA to be integrated while planning

is underway using the being-acquired SA.

• identify targets of opportunity based on their

appearance where (e.g.) detection depends on sensor

angle, so vehicle must do more extensive search to cover the space of AOR-cross-sensor-attitude. E.g.,

tanks at edge of forest need to be sensed from the

open side. Vehicle should understand the constraints

(not just fly more lanes of a survey pattern).

• multi-vehicle plans for interacting vehicles

• strike group flight plan through waypoints and

around known threats • re-plan task goals due to change in SA. E.g., while on wild weasel mission switch to coordinated multivehicle SAM attack.

Order 2 activities:

• single vehicle deck landing in high sea state and/or

low visibility and/or high and gusty winds

• coordinated obstacle avoidance for a strike group

flying very close together

Order 3 activities:

• identify targets of opportunity based on their

behavior (from prior planning/SA need model of

behavior and identification of behavior based on model)

These characterizations build on those detailed in Section

2 - as vehicles increase their ranking in the Orders of

Intelligence, they exhibit more capability in the parameter

spaces. For example, consider the multi-vehicle

collaboration factor of degree of coordination / cooperation among the agents. The Order 0 system

includes multiple vehicle plans for non interacting

vehicles. This could include an system that distributes the

team goals among the individual agents for separate

completion. The Order 1 system includes a higher level capability in this area of multi-vehicle plans for

interacting vehicles. Here agents can communicate to one

another when they fail or if they are able to take on an

increased set of tasks. The Order 2 system increases the

requirements on coordination and cooperation to coordinated obstacle avoidancefor a strike groupflying

very close together. The system will be required to share

situation awareness information and plan coordinated

responses at very short time constants.

5 Metrics For Costs

To create a plan for funding toward a goal, an assessment

must be made of the state of the technology against the

require capabilities. Figure 3 shows such an assessment.

It was constructed by asking technology experts to determine the state of maturity of their technologies for

solving various parts of a vehicle's parameter space. The

colors indicate technological maturity levels:

red pure research needed (6. 1

)

yellow applied research needed (6.2)

green ready for engineering (6.3)

blank not applicable

Although this is not a measure of intelligence per se, it supports analyses leading to the construction of

intelligence vehicles and groups of vehicles.

1 ! j

! : •jfrfMN^F^! ! z : ! f . _ ,

. A?i»«„

; iter ! !Sjr

BHBHH' SSI Ml . MSSMt : .-

'

' BHSP*'*''' mmmm^^^^ ^^^^d

Figure 3 -- Technology Roadmap (partial).

Columns are technologies considered appropriate for addressing the domain, while rows are elements of the vehicle's

parameter space.

300
Figure 4 — Cost/Benefit Analysis,

(left) Where in the parameter space future intelligently autonomous operations exist,

(right) Part of a cost-benefit analysis to select among various operations based on cost.

The notional charts in Figure 4 illustrate how required

capabilities can be mapped against mission descriptions

of current and future operations, to help determine which

are more valued, and to help determine which are expected to be more expensive. Formal methods for such

cost projections would be very helpful.

6 Future Directions

Substantial work has been done in applying valuations to multi-attribute (multi-criteria) problems. Besides a number of good textbooks (e.g., [1 ,2]), various techniques

have been formalized to assist in this process. We intend

to extend the work reported here by investigating and

applying formal tools to the domain characteristics

discussed above.

7 Summary

The intelligence of an autonomous vehicle is a complex

multi-dimensional characteristic evaluated in a wide

variety of dynamic situations, for which no obvious

algorithmic measures exist. Several attempts to analyze

system complexity and intelligence have been presented

in this paper that are drawn from work done for recent and current projects working toward intelligent

autonomous vehicles. These analyses have sought to uncover the collaboration, planning and situational awareness challenges facing an autonomous vehicle in

difficult conditions, to assist engineers and sponsors in focusing project efforts. Although the analyses reported

here have been useful first steps toward the significantly

complex vehicles imagined, more work is clearly required

before intelligence and intelligent systems can be

automatically analyzed and measured.

References

1. Clemen, Robert T., Making Hard Decisions: An

introduction to decision analysis, PWS-Kent Publishing

Co., Boston, 1991

2. French, Simon, Decision Theory: An introduction to the Mathematics of Rationality, Ellis Harwood, Ltd.

Chichester, West Sussez, England, 1986

3. RoboCup-97: Robot Soccer World Cup I, SpringerVerlag, 1998.

301
Evolution of Mobile Agents

Timothy K. Shih

Multimedia Information NEtwork (MINE) Lab

Department of Computer Science and Information Engineering

Tamkang University

Tamsui, Taipei Hsien. Taiwan 251. R.O.C.

email: TSHIH@CS.TKU.EDU.TW

Abstract

Mobile agents are powerful. A mobile agent can travel on the Internet, perform tasks, and report to its owner

the cLchievement. Mohik agent techniques are used in E- commerce, distrihuted applications, distance learning, and

others. However, it is hard to find a strategic method, which

tells how mobile agents should behave on the Internet. In

this paper, we propose such a mechanism. Based one the

concepts of Food Web, one of the laws that we may learn from the natural besides neural networks and genetic al- gorithms, we propose a theoretical computation model for mobile agent evolution on the Internet. We define an agent

niche overlap graph and agent evolution states. We also propose a set of algorithms, which is used in our multimedia search programs, to simulate agent evolution. Agents

are cloned to live on a remote host station based on three

different strategies: the brute force strategy, the semi-brute

force strategy, and the selective strategy. Evaluations of dif- ferent strategies are discussed. Guidelines of writing mobile

agent programs are proposed. The technique can be used in distributed information retrieval which allows the computation load to be added to servers, but significantly reduces

the traffic of network communication.

1 Introduction

Mobile agents are software programs that can travel over the Internet. Mobile search agents find the information specified by its original query user on a specific station, and send back search results to the user. Only queries and results are transmitted over the In- ternet. Thus, unnecessary transmission is avoided. In other words, mobile a.gpnt computing distributes computation loads among networked stations and reduces

network trafhc.

The environment where mobile agents live is the In- ternet. Agents are distributed automatically or semiautomatically via some communication paths. Therefore, agents meet each other on the Internet. Agents

have the same goal can share information and co- operate. However, if the system resource (e.g., net- work bandwidth or disk storage of a station) is insuffi- cient, agents compete with each other. These phenomena are similar to those in the ecosystem of the real world. A creature is born with a goal to live and re- produce. To defense their natural enemies, creatures of the same species cooperate. However, in a perturbation in ecosystems, creatures compete with or even

kill each other. The natural world has built a law of

balance. Food web (or food chain) embeds the law of creature evolution. With the growing popularity of In- ternet where mobile agents live, it is our goal to learn

from the natural to propose an agent evolution computing model over the Internet. The model, even it is applied only in the mobile agent evolution discussed in this paper, can be generalized to solve other computer

science problems. For instance, the search problems in distributed Artificial Intelligence, network traffic control, or any computation that involves a large amount

of concurrent/distributed computation. In general, an

application of our Food Web evolution model should

have the following properties:

• The application must contain a niimher of concurrent

events.

• Events can be simulated by some processes, which can be partitioned into a number of groups according to the properties of events.

t There must exists some consumer-producer relation- ships among groups so that dependencies can be deter- mined.

• The uuuiber of processes musl. be large enough.

For instance, with the growing popularity of Internet, Web-based documentation are retrieved via some

search engine. Search processes can be conducted as

several concurrent events distributed among Internet

stations. These search events of the same kind (e.g.,

pursuing the same document) can be formed in a group.

Within these agent groups, search agents ca.n provide

information to each other. Considering the amount

of Web sites in the future, the quantity of concurrent

search events is reasonably large.

302
We have surveyed articles in the area of mobile

agents, personal agents, and intelligent agents. The

related works are discussed in section 2. Some terminologies and definitions are given in section 3, where we

also introduce the detail concepts of agent communication network. In our model, an agent evolves based on

state transitions, which are also discussed. A graph theoretical model describes agent dependencies and competitions is also given. Agent evolution computing al- gorithms are addressed in section 4. And finally, we

discuss our conclusions in section 5.

2 Related Works

The concept of mobile agent is discussed in several articles [3, 4]. Agent Tel, a mobile-agent system providing navigation and communication services, security

iiiechanisins, and debugging and tracking tools, is proposed in [1]. The system allows agent programs move

transparently between computers. A software technology called Telescript. with safety and security fea- tures, is discussed in [7]. The mobile agent architecture. MAGNA, and its platform are presented in [3]. Another agent infrastructure is implemented to support

mobile agents [4], A mobile agent technique to achieve

load balancing in telecommunications networks is proposed in [6]. The mobile agent programs discussed can

travel among network nodes to suggest routes for better communications. Mobile service agent techniques

and the corresponding architectural principles as well as requirements of a distributed agent environment are

discussed in [2].

3 Definitions

Agents communicate with each other since they can

help each other. For instance, agents share the same

search query should be able to pass query results to each

other so that redundant computation can be avoided.

An Agent Communication Network (ACN) serves this

purpose. Each node in an ACN represents an agent on

a computer network node, and each link represents a

logical computer network connection (or an agent communication link). Since agents of the same goal want

to pass results to each other, agent communication rela- tions can be described in a complete graph. Therefore, an ACN of agents hold different goals is a graph of complete graphs. Since agents can have multiple goals (e.g.,

searching based on multiple criteria), an agent may belong to different complete graphs.

We define some terminologies used in this paper. A

host station (or station) is a networked workstation on

which agents live. A query station is a station where

a user releases a query for achieving a set of goals. A

station can hold multiple agents. Similarly, an agent can pursue multiple goals. An agent society (or society) is a set of agents fully connected by a complete

graph, with a common goal associated with each agent

in the society. A goal belongs to different agents may

have difi"erent priorities. An agent society with a com- mon goal of the same priority is called a species. Since an agent may have multiple goals, it is possible that

two or more societies (or species) have intersections. A

communication cut set is a set of agents belong to two

distinct agent societies, which share common agents.

The removing of all elements of a communication cut

set results in the separation of the two distinct societies. An agent in a communication cut set is called an articulation agent. Since agent societies (or species) are represented by complete graphs and these graphs

have communication cut sets as intersections, articulation agents can be used to suggest a. shortest network

path between a query station and the station where an

agent finds its goal. Another point is that an articulation agent can hold a repository, which contains the

network communication statuses of links of an agent

society. Therefore, network resource can be evaluated

when an agent checks its surviving environment to decide its evolution policy.

An agent evolves. It can react to an enviroiiiiienl.

respond to another agent, and communicate with other

agents. The evolution process of an agent involves some

internal states. An agent is in one of the following states

after it is born and before it is killed or dies of natural:

• Searching: the agent is searching for a goal

• Suspending: the agent is waiting for enough resource

in its environment in order to search for its goal

• Dangling: the agent loses its goal of surviving, it is waiting for a new goal

• Mutating: the agent is changed to a new species with

a new goal and a possible new host station

An agent is born to a searching state to search for

its goal (i.e.. information of some kind). All creatures must have goals (e.g.. search for food). However, if its surviving environment (i.e., a host station) contains no

enough resource, the agent may transfer to a suspending state (i.e., hibernation of a creature). The searching process will be resumed when the environment has

better resources. But. if the environment is lack of re- sources badly (i.e., natural disasters occiu). the agent

might be killed. When an agent finds its goal, the agent

will pass the search results to other agents of the same

kind (or same society). Other agents will abort their

search (since the goal is achieved) and transfer to a

dangling state. An agent in a dangling state can not

survive for a long time. It will die after some days

(i.e.. a duration of time). Or, it will be re assigned to a new goal with a possible new host station, which is a

303
new destination where the agent should travel. In this case; the agent is in a mutating state and is reborn to search for the new goal. Agent evolution states keep the

status of an agent. Tn order to maintain the activity of

agents, in a distributed computing environment, we use message passing as a mechanism to control agent state

transitions.

Agents can suspend/resume or even kill each other.

We need a general policy to decide which agent is killed. By our definition, a species is a set of agents of the same

goal with a same priority. It is the priority of a goal we

base on to discriminate two or more species.

We need to construct a direct graph which represents

the dependency between species. Wc call this digraph an species food web (or food web). Each node in the

graph represents a species. All species of a connected

food web (i.e., a graph component of the food web) are of the same goal with possibly different priorities. We

assume that, different users at different host stations

may issue the same query with different priority. Each

directed edge in the food web has an origin represents a

species of a higher goal priority and has a terminus with a lower priority. Since an agent (and thus a species) can have multiple goals which could he similar to other

agents, each goal of an articulation agent should have an cissociated food web. Therefore, the food web is used

as a competition base of agents of the same goal in the

same station.

Each food web describes goal priority dependencies

of species. Form a food web, we can further derive an niche overlap graph. In an ecosystem, two or more

species have an ecological niche overlap (or niche over- lap) if and only if they are competing for the same re- source. A niche overlap graph can be used to repre- sent the competition among species. The niche overlap

graph is used in our algorithm to decide agent evolution

policy and to estimate the effect when certain factors are changed in an agent communication network. Based on the niche overlap graph, the algorithm is able to suggest strategies to re-arrange policies so that agents can

achieve their highest performance efficiency. This con- cept is similar to the natural process that recover from

perturbations in ecosystems.

4 Agent Evolution Computing

The algorithms proposed in this section use the agent

evolution states and the niche overlap graphs discussed

for agent evolution computing. An agent wants to

search for its goal . At the same time, since the searching

process is distributed, an agent wants to find a destination station to clone itself. Searching and cloning are

essentially exist as a co-routing relation. A co-routine can be a pair of processes. While one process serves as

a producer, another serves as a consumer. When the consumer uses out of the resource, the consumer is sus- pended. After that, the producer is activated and pro- duces the resource until it reaches an upper limit. The

producer is suspended and the consumer is resumed.

In the computation model, the searching process can

be a consumer, which need new destinations to proceed

search. On the other hand, the cloning process is a producer who provides new URLs.

Agent evolution on the agent communication net- work is an asynchronous computation. Agents live on

different (or the same) stations communicate and work

with each other via agent messages. The searching and

the cloning processes of an agent may run as a co- routine on a station. However, different agents are run on the same or separated stations concurrently. We use a formal specification approach to describe the logic of our evolution computation. Formal specifications use

first order logic, which is precise. In this paper, we use the Z specification language to describe the model and

algorithms.

Each algorithm or global variable in our discussion

has two parts. The expressions above a horizontal line are the signatures of predicates, functions, or the data

types of variables. Predicates and functions are con- structed using quantifiers, logic operators, and other

predicates (or functions). The signature of a predicate

also indicate the type of its formal parameters. For

instance. Agent x Goal x Ilost^Siaiion are the types

of formal parameters of predicate AgentSearch . The

body, as the second part of the predicate, is specified

below the horizontal line. We use some global variables through the formal

specification. The variable goal_ar.hier,e.d is set to TRUE when the search goal is achieved, FALSE otherwise. We also use two watermark variables, q and (i. where Q is the basic system resource requirement and

13 is the minimal requirement. Note that, q must be

greater than /3 so that different levels of treatment are used when the resource is not sufficient.

Global Variables and Constants

goal—achieved : Goal^Achieved

a: BEA 1,

l3 : REA L a> fi

Algorithm Agent-Search is the starting point of agent

evolution simulation. If system resource meets a basic

requirement (i.e.. q), the algorithm activates an agent

in the searching slate wilhin a local station. If the

search process finds its goal (e.g., the requested in- formation is found), the goal is achieved. Goal abortion of all agents in a society results in a dangling

state of all agents in the same society (including the

agent who finds the goal). At the same time, the

304
search result is sent back to the original query sta- tion via Query—Return— URL. Suppose that the goal can not be achieved in an individual station, the agent

is cloned in another station (agent propagation). The

Agent-Clone algorithm is then used. On the other

hand, the agent may be suspended or even killed if the

system resource is below the basic requirement (i.e., Resource-Available{A. G,X) < a). In this case, algorithms Agent-Suspend is used if the resource available

is still feasible for a future resuming of the agent. Otherwise, if the resource is below the minimal requirement,

algorithm Ageni._Kill is used

Ageut, Searching Algorithm

Agent—Search : Agent x Goal x Host—Station

V/4 : Agent, G : Goal. X : Ho-itStnttnn • AgentSearch[A, G, X] <^ Resource—Available( A, G , X ) > a => [G e LocalSearch{A,X] =^ Abort.-All(A t Agent-Society] A

send-restilt(X . UftJ,, G. Query-Refiirn-URL) A

goal-achieved = TRUE

V G ^ Local-Searcti(A.X) => Agent—Clone[A, G,

A I Agent-Society)]

V Resource-Available( A, G, X] > l3 ^

AgentSuspend( A, G, X)

V Resource-Availahle(A, G, X] < /? ^

Agent-Kill(A, G,X)

Agent cloning is achieved by the Agent-Clont algorithm. When the cloning process wants to find new

stations to broadcast an agent, two implementations can be considered. The first is to collect all URLs

of stations found by one search engine. But, considering the network resource available, the implementation may check for the common URT,s found by two

or more search engines. New IJRT.s are collected by

the Search-ForStations algorithm, which is invoked in

the agent cloning algorithm. Agent propagation strat- egy decides the computation efficiency of our model. In

this research, we propose three strategies:

• the brute force agent distribution

• the scmi-brutc force agent distribution, and

• the selective agent distribution.

The first strategy simply clone an agent on a remote

station, if the potential station contains information

that helps the agent to achieve its goal. The semi-brute

force strategy, however, finds another agent on a potential station, and assigns the goal to that agent. The se- lective approach not only try to find a useful agent, but

also check for the goals of that agent. Cloning strategies affect the size of agent societies thus the efhciency

of computation.

Agent Cloning Algorithm: the Brute Force

Strategy

Agent—Clone : Agent x Gocd x Agent-Society

V A : Agent, G : Goal, S : Ac/entSociety • Agent.-Clone{A,G,S) <^

[V X : Host-Station • X G Search-For-Stations(G] ^

(3A': Agent • .4' = copy(A} A

X. Agent-Set = X.Agent^etU { -4' } A

S = SU{A' } A

AgentSearch(A', G.X))]

V [Search-For-Stat>.ons{G) = 0 => goal—achieved = FAt^SE]

The brute force agent distribution strategy makes a copy of agent A. using the copy function, in all stations

returned by the Search-ForStaiions algorithm. Agent

set in each slalioii is updated and the sociely S where

agent A belongs is changed. Agent A' . a clone of agent

A is transmitted to station A' for execution.

Agent Cloning Algorithm: the Semi- brute

Force Strategy

Agent—Clone : Agent x Goal x Agent—Society

V ,4 : Agent, G : Goal.S : Agent-Society • Agent-Clone{A. G, S) O

[V A : Host-Station • X € Search—For—Stations{G] =i-

[3 A' : Agent • A' e X .AgentSet ^

[A'. Goal-Set = A'.Goal-SetU

{G}A

S = SU { A' }

/\ Agenf-.Search[A', G. X)]]]

V [Search-For-Stations( G') = 0 ^

goal—achieved = FALSE]

The semi-brute force agent distribution approach is similar to the brute force approach, except that it does

not make a copy of the agent but give the goal to an

agent on its destination station. The agent which ac- cepts this new goal (i.e.. A') is activated for the new

goal in its belonging station.

Agent Cloning Algorithm: the Selective

Strategy

305
Agent—Clone : Agent x Goal x AgcntSocicty

V A : Agent, G : Goal.S : AgentSociety • Ag€nt-Clone{A, G, S) <:>

[V X : Host^Station • X £ S€arch-^For^Stations(G) =>

[3 A' : Agent • A' G X.Agent^Set =^ [G e A'. Goal-Set => S = S U A' l AgtrttSoeiety

V G^ A'.GoaUSet => {A'.GonUSet^

A'.Goal^et U { G' }

A S = SU{ A' })]

A Agcnt_Search{A' . G, X )] V [X. Agent-Set = 0 [3A": Agent • A" = copy(A) A

X. Agent-Set = { A" } A

S = S U { A" } A

Agent-Search(A"., G. X)]]]

V [Search-ForStations{G) = 0 => goal—achieved = FALSE]

The last approach is more complicate. The selective

appi oach of cloning algorithm must check whether there

is another agent in the destination station (i.e., A'). Tf so, the algorithm checks whether the agent (i.e., A') at that station shares the same goal with the agent to be

cloned. If two agents share the same goal, there is no

need of cloning another copy of agent. Basically, the

goal can be computed by the agent at the destination

station. In this case, the union of the two societies is necessary (i.e., S = S U A' ] AgentSociety). On the

other hand, if the two agents do not have a common

goal, to save computation resource, we may ask the

agent at the destination station to help searching for an additional goal. This case makes a re-organization

of the society where the source agent belongs. The

result also ensure that the number of agents on the

ACN is kept in a minimum. Whether the two a.gents

share the same goal, the Agent—Search algorithm is used

to search for the goal again, in this case. Agent A'

is physically transmitted to station A' for execution.

When there is no agent running on the destination sta- tion, we need to increase the number of agents on the

ACN by duplicating an agent on the destination sta- tion (i.e., the invocation of A" = cnj)y(A)). The society is reorganized. And the Agent-Search algorithm is called again. In the acse that no new station is found

by the Search- ForSt attons algorithm, the goal is not

achieved.

The agent search and agent clone algorithms use some auxiliary algorithms, which arc discussed as fol- lows. The justification of system resource available depends on agent policy, defined in A. Policy. Agent

policy is a set of factors indicated by name tags (e.g.,

NET WORK-BOUND). The estimation of resources is represented as a real number, which is computed based on X .Resource of station X. Note that, in the algorithm, w\ and Vj2 are weights of factors {w\ w'2 =

1.0). Wc only describes some cases of using agent policies. Other cases are possible but omitted. Moreover, we consider the priority of goal G. If the priority is lower than some watermark (i.e.. G. Priority < 0), we

let rl be a constant less than 1.0. Therefore, resources

are reserved for other agents. On the other hand, if the priority is high, we consider the value returned by

Rcsourcc-Avatlable should be high. Thus the potential

agent can proceed its computation immediately. The

values of ^ and u depend on agent applications.

Auxiliary Algorithms

Resource—A vailable : Agent x Goal x Host-Station —

REAf

V .4 : Agent, G : Goal.X : Host-Station, R : HEAL*

3 wl; w2, rl. r2 : REAL*

Rc3ourcc-Availablc(A. G . X) = R f^ [NETWORK-BOUND e A. Pokeys

R = X .Resource. Network.

V CPU-ROUND e A.Pohcy=>

R = X. Resource. CPU

V MEMORY-BOUND £ A. Policy

R = X .Resource Memory

V CPU-BOUND e A. Policy A

MEMORY-BOUND e A. Policy^

R = X. Resource. CPU + u' l-f X .Resource. Memory * w)'2 A

M'l -1- iu2 = 1.0 V ...] A36,uj : Priority • [G. Priority <6 ^

(R = R* r] A rl < 1.0) V G. Priority > w => (R = R* r2 A r2 > 1,0)]

The above algorithms describe how an agent evolves

from a state to another. How agents aifect each other

depends on the system resource available. However,

in an ACN, it is possible that agents suspend or even

kill each other, as we described in previous sections.

The niche overlap graphs of each goal play an important role. We use the AgentSusprnd and Agent^Kill

algorithms to take the niche overlap graphs of a goal

(i.e.. mche-Compete{G)) into consideration. In the

AgeniSuspend algorithm, if there exists a goal that has

a lower priority comparing to the goal of the searching

agent, a suspend message is seiil. to I he goal l.o delay its search {\ .e., \-\eL suspend{G' \ Agent)). The searching

agent may be resumed after that since system resources may be released from those goal suspension. In the

Agent-Kill algorithm, however, a kill message is sent instead (i.e., via terminal c{G' ] Agent)). The system

resource is checked against the minimum requirement

p. If resuming is feasible, the AgentSearch algorithm

in invoked. Otherwise, the system should terminate the

searching agent.

306
Search Jor—Stations : Goal —> P Host-Station

V G : Goal, X^Set : P Host-Station • Search-For-Stations { G) = X^Set

X-Set = { X : Host-Station \ candidate—station(G , X ) }

Agent—Suspend : Agent x Goal x Host—Station

V A : Agent, G : Goal. X : Host-Station • Agent—Suspend{A, G, X ) O

3 GS : Goal-,Set • GS — niche—compete(G)

A (V G' : Goal • G' £ GS A

G' .Priority < G .Priority ^

susptud{G' 1 Aytui))

A (Resourc€-Availabl€(A ,G , X) > /? AgentSenrch(A,G, X)

V He30urce-Available[A, G, X] < => suspend(A))

Agent—Kill : Agent x Goal x Host—Station

\l A : Agent, G : Goal.X : Host-Station • Agent-I<ill{A, G, X) <^ 3 GS : Goal-Set • GS = niche—compet€(G)

A (V G' : Goal • G' e GS A

G' .Priority < G .Priority

t.erminat.e{G' \ Agent))

A (Resource-Available(A, G . X) > j3 =? Agent-Search{A, G, X)

V Rc3ourcc-Availablc[A, G, X) < /? terminate {A])

The other auxiliary algorithms are relatively less complicated. Function Local-Search takes as input an agent

and a station. It returns a set of goals found hy the

agent in that station. A match predicate is used. This

match predicate is apphcation dependent. It could be a search program which locates a key word in a Web

page, or a request of information from a user (e.g., a

survey questionnaire). The Aborl-AU predicate lakes

as input an agent society and terminates all agents

within that society. The Search-ForStations function

takes as input a goal and returns a set of host sta- tions. The stations should be selected depending on the

candidaiestaiton function, which estimates the possibility of goal achievement in a station. This function can be implemented as a Web search engine which looks

for candidate URLs. We have omir.ted some detailed

definitions of the above auxiliary algorithms, as well as some primitive functions which are self-explanatory.

Local—Search : Agent x Host—Station —^ Goal—Set

V/l : Agent, X : Host-Station, GS : Goal-Set • r^cal-Search{A,X) = GS

GS = { G : Goal \ G € A.GoalSet A

match( G. Query,

X. Resource. Information) }

Ahort—All : Agent—Society

V5 : Agent—Society • Abort-All{S] -S^^ V A : Agent • A G S =^ terminate{ A)

5 Conclusions

Mobile agent based software engineering is interesting. However, in the literature, we did not find any

other similar theoretical approach to model what mobile agents should act on the Internet, especially how

mobile agents can cooperate and compete. A theoretical computation model for agent evolution was pro- posed in this paper. Algorithms for the realization of our model were also given.

References

[1] David Kotz, Robert Gray. Saurab Nog. Danicla Rus,

Sumit Chawla, and George Cybenko, "Agent Tel: targel. iiig l.lie needs of mobile coiiipulers." TEEF Tiiteriiet Gomputing, Vol. 1. No. 4, .Tuly 1997. pp. .58 - 67.

[2] S. Krausc and T. Magcdanz. ''Mobile service agents en- abling intelligence on demand in telecommunications^"

ill Proceedings of the 199fi IEEE Global Telecom xiiuui- cations Gonference, London, UK, 1 996. pp 78 - 84.

[3] Sven Krause, Flavio Morais de Assis Silva, and Thomas

Magedanz, "MAGNA - a DPE-based platform for mobile agents in electronic service markets," in Proceedings of the 1997 3rd International Symposium on Autonomous Decentralized Systems (ISADS'97), Berlin,

Germany, 1997, pp. 93 102.

[4] Anselm binguau and Oswald Drobnik, '"Making mobile agents communicate: a flexible approach." in Pro- ceedings of the 1996 1st Anniia.l Gonference on Emerging Technologies and Applications in Gommunications,

Portland, OR, USA. 1996, pp. 180 183.

[.5] Michael Pazzani and Daniel Rillsus, "Learning and Revising User Profiles: The Identification of Interesting

Web Sites", Machine Learning, Vol. 27, 1997, pp. 313

331.

[6] Ruud Schoonderwoerd. Owen Holland, and lanet

Rriiten, "Ant-like agents for load balancing in telecommunications networks," in Proceedings of the 1997 1st International Gonference on .'^utonomoiis Agents, Marina del Rey. California, U.S.A., 1997. pp. 209 216.

[7] .loseph Tardo a.nd Lnis Valente, "Mobile a.gent security

and telescript," in Proceedings of the 1996 41st IEEE

Gomputer Society International Conference (COMPGON'96), Santa Clara. CA, USA, 1996, pp. 58 63.

307

PART II

RESEARCH PAPERS

5. MODELING THE BIOLOGICAL PROTOTYPES

5.1 Some Constraints on Intelligent Systems: Autonomous Computation in a Changing World

S. Grossberg, Boston University, USA

5.2 The Neurodynamics of Intentionaiity in Animal Brains May Provide a Basis for Constructing Devices that are Capable of Intelligent Behavior

W.J. Freeman, University of California at Berkeley, USA

5.3 Measuring Intelligence: a Neuromorphic Perspective

M.A. Jabri, University of Sydney, Australia

5.4 Grading Intelligence in Machines: Lessons from Animal Intelligence

S. Kak, Louisiana State University, USA

5.5 Biometric Techniques: The Fundamentals of Evaluation T.A. Chmielewski, P.R. Kalata, Drexel University, USA

5.6 Measuring the Quality of Visual Learning

G. Bianco, University of Vemona, Italy

5.7 Automatic Mental Development and Performance Metrics for Intelligent Systems

J. Weng, Michigan State University, USA

309

SOME CONSTRAINTS ON INTELLIGENT SYSTEMS

Autonomous Computation in a Changing World

Stephen Grossberg

Department of Cognitive and Neural Systems, Boston University, 677 Beacon Street, Boston, MA

02215

Steve@bu.edu, http : //www.ens.bu. edu/Profiles/Grossberg

Among the many possible topics concerning how

autonomous intelligent systems should be designed, 1

will focus on one that is close to work with which I am

familiar. A core problem on which much more work

needs to be done is how to design systems that can autonomously learn, recognize, and perform complex

tasks in a rapidly changing environment. Such self- organizing systems should also be able to interact

effectively with humans and other self-organizing

systems in order to achieve goals cooperatively.

In order to make the interface between human and

system as seamless as possible, biological designs,

notably designed inspired by and even emulating

brain architectures, will be helpful. The list of possible

applications is incredibly long, ranging from

autonomous search and data base management tools on the world wide web, medical data base prediction

to help doctors and other health professionals,

classifiers of complex imagery of multiple types, new

approaches to speech perception in noisy multispeaker environments, and controllers of autonomous

mobile robots, to models of normal brain and

behavior, and predictions about how different brain

lesions can generate the behavioral symptoms of

mental disorders.

Available results have already suggested that the

brain designs for sensory and cognitive processes

differ from, and are even computationally

complementary to, the designs for spatial navigation

and action. This complementarity can be noticed by

observing that cognitive knowledge needs to accumulate in a stable way over a period of years, with new knowledge not accidentally erasing

previously learned, but still useful, knowledge. This is the familiar problem of "catastrophic forgetting". In contrast, the parameters that control action need to be

continually updated in order to adapt to changes,

including damage, to motor effectors. Here,

catastrophic forgetting is a useful property. Thus,

these systems will need to incorporate new ideas

about parallel processing between information

subsystems that compute complementary properties.

The design of increasingly autonomous intelligent

agents will also require an end-to-end approach, in which all the aspects of perception, cognition,

emotion, and action are realized in a single system.

Feedback cycles of information processing need to be

designed from perception through action and then

back to perception again, mediated by feedback

through the environment. Such cycles of information

processing can evaluate the effects of system

performance on the environment, and modify the

system where needed to achieve better environmental

control. It has also become clear that, in addition to these externally mediated cycles of information

processing with the environment, internally mediated

feedback is needed to achieve autonomous system

properties. Such internal feedback realizes properties

of intentionality and attention that are characteristic

of biological intelligence. The design of self- organizing feedback systems will require a deeper

analysis of nonlinear systems, since various types of

nonlinearity are needed to achieve key system

properties that depend on feedback, such as the

stability of fast learning in a changing environment.

One example of such an autonomous system is the

primate cerebral cortex. All sensory and cognitive

neocortex is organized into laminar circuits, wherein

bottom-up, top-down, and horizontal connections are synthesized into a unified design. Recent modeling

(Grossberg, 1999; Grossberg, MingoUa, and Ross,

1997; Grossberg and Raizada, 2000; Grossberg and

Williamson, 2000) has clarified how these laminar

circuits are designed (Figure 1) to simultaneously

achieve at least three properties: (1) stable development and learning of circuit connections and

adaptive weights in response to a changing world,

thereby providing a solution of the stability311
plasticity dilemma ; (2) a seamless fusion of bottomup data-driven processing and top-down intentional

processing whereby high-level constraints can

selectively focus attention upon important

information; and (3) the coherent grouping or binding

of spatially distributed information into

representations of objects and events, while

suppressing noise and weaker groupings, without a

loss of analog sensitivity to input values, the so- called property of analog coherence.

The design of more subtle decision making processes

in an autonomous agent will require more

sophisticated cognitive-emotional interactions,

whereby the information acquired through cognitive

processing is evaluated and selected in terms of

Illustrative References

Brown, J., Bullock, D. and Grossberg, S. (1999). How

the basal ganglia use parallel excitatory and

inhibitory learning pathways to selectively

respond to unexpected rewarding cues. Journal ofNeuroscience, 19, 10502-1051 1.

Fiala, J., Grossberg, S. and Bullock, D. (1996).

Metabotropic glutamate receptor activation in cerebellar Purkinje cells as substrate for

adaptive timing of the classically conditioned

eye-blink response. Journal of Neuroscience,

16,1,160-1,11A.

Grossberg, S. (1999). How does the cerebral cortex work? Learning, attention, and grouping within

the laminar circuits of visual cortex. Spatial

Vision, n, 163-187.

Grossberg, S. (1999). The link between brain learning,

attention, and consciousness. Consciousness

and Cognition, 8, 1-44.

Grossberg, S. (2000). The complementary brain: Unifying brain dynamics and modularity.

Trends in Cognitive Sciences, 4, 233-246.

Grossberg, S (2000). The imbalanced brain: From

normal behavior to schizophrenia. Biological

Psychiatry, in press.

Grossberg, S., Boardman, I. and Cohen, M.A. (1997).

Neural dynamics of variable-rate speech

categorization. Journal of Experimental

Psychology: Human Perception and

Performance, 23, 481-503.

internal system values and goals. Such interactions

help to direct attention selectively to those subsets of

information that predict future success in achieving

system goals. Designs for such systems need to be

able to use unsupervised learning when no evaluative

feedback is available, but to be able to switch to supervised learning whenever such feedback is available. In a self-organizing autonomous learning

system, both unsupervised and supervised learning

need to be able to operate without a change of system

design. Taken together, these constraints point to the

development of new types of self-organizing parallel

processing systems wherein nonlinear feedback

within the system and between system and world help

the system to rapidly adapt to a changing world, and

thereby to better represent, predict and control it.

Grossberg, S. and Memll, J.W.L. (1996). The

hippocampus and cerebellum in adaptively

timed learning, recognition, and movement.

Journal of Cognitive Neuroscience, 8, 257-277.

Grossberg, S., Mingolla, E. and Ross, W. (1997).

Visual brain and visual perception: how does

the cortex do perceptual grouping? Trends in Neurosciences, 20, 106-1 1 1

.

Grossberg, S. and Myers, C.W. (2000). The resonant dynamics of speech peiception: Interword

integration and duration-dependent backward

effects, Psychological Review, in press.

Grossberg, S. and Raizada, R.D.S. (2000). Contrastsensitive perceptual grouping and objectbased attention in the laminar circuits of

primary visual cortex. Vision Research, 40,

1413-1432.

Grossberg, S. and Williamson, J.R. (2000). A neural

model of how horizontal and interlaiminar

connections of visual cortex develop into adult

circuits that carry out perceptual grouping and

learning. Cerebral Cortex, in press.

312
Figure Caption

Figure 1. Some model cell interactions between the

lateral geniculate nucleus (LGN) and cortical areas VI

and V2 for perceptual grouping and attention:

Excitatory connections are shown with open symbols.

Inhibitory intemeurons are shown filled-in black, (a): The LGN provides bottom-up activation to layer 4 via

two routes. Firstly, it makes a strong connection

directly into layer 4. Secondly, LGN axons send

collaterals into layer 6, and thereby also activate layer

4 via the 6 -+ 4 on-center off-surround path. Thus, the

combined effect of the bottom-up LGN pathways is to stimulate layer 4 via an on-center off-surround, which

provides divisive contrast normalization of layer 4 cell responses, (b): Folded feedback carries attentional

signals from higher cortex into layer 4 of VI, via the

modulatory 6^4 path. Corticocortical feedback axons tend preferentially to originate in layer 6 of the

higher area and to terminate in the lower cortex's layer

1, where they can excite the apical dendrites of layer 5 pyramidal cells whose axons send collaterals into

layer 6. Several other routes through which feedback

can pass into VI layer 6 exist. Having arrived in layer

6, the feedback is then "folded" back up into the

feedforward stream by passing through the 6 ^ 4 oncenter off-surround path, (c): Connecting the 6 4

on-center off-surround to the layer 2/3 grouping

circuit: like-oriented layer 4 simple cells with opposite

contrast polarities compete (not shown) before

generating half-wave rectified outputs that converge

onto layer 2/3 complex cells in the column above

them. Like attentional signals from higher cortex,

groupings which form within layer 2/3 also send

activation into the folded feedback path, to enhance

their own positions in layer 4 beneath them via the 6

-> 4 on-center, and to suppress input to other

groupings via the 6 -> 4 off-surround. There exist

direct layer 2/3 6 connections in macaque VI, as well as indirect routes via layer 5. (d): Top-down

corticogeniculate feedback from VI layer 6 to LGN

also has an on-center off-surround anatomy, similar to the 6-^4 path. The on-center feedback selectively

enhances LGN cells that are consistent with the

activation that they cause, and the off-surround

contributes to length-sensitive (endstopped)

responses that facilitate grouping perpendicular to

line ends, (e): The entire V1/V2 circuit: V2 repeats the laminar pattern of VI circuitry, but at a larger spatial

scale. In particular, the horizontal layer 2/3

connections have a longer range in V2, allowing

above-threshold perceptual groupings between more

widely spaced inducing stimuli to form. VI layer 2/3

projects up to V2 layers 6 and 4, just as LGN projects

to layers 6 an 4 of VI. Higher cortical areas send

feedback into V2 which ultimately reaches layer 6, just

as V2 feedback acts on layer 6 of VI . Feedback paths

from higher cortical areas straight into VI (not shown)

can complement and enhance feedback from V2 into VI.

313
The Neurodynamics of Intentionality in Animal Brains May Provide a

Basis for Constructing Devices that are Capable of Intelligent Behavior

Walter J Freeman

Department of Molecular and Cell Biology

University of California, Berkeley CA 94720-3200 USA

ABSTRACT

Intelligent behavior is characterized by flexible and creative pursuit of

endogenously defined goals. It has emerged in humans through the stages of evolution that are manifested in the brains and behaviors of

other animals. Intentionality is a key concept by which to link brain dynamics to goal-directed behavior. The archetypal form of

intentional behavior is an act of observation through time and space, by which information is sought for the guidance of future action. Sequences of such acts constitute the key desired property of freeroving, semi-autonomous devices capable of exploring remote environments that are inhospitable for humans. Intentionality

consists of the neurodynamics by which images are created of future

states as goals, of command sequences by which to act in pursuit of

goals, of predicted changes in sensory input resulting from intended

actions (reafference) by which to evaluate performance, and

modification of the device by itself for leaming from the consequences of its intended actions. These principles are well known among psychologists and philosophers. What is new is the development of nonlinear mesoscopic brain dynamics, by which

using chaos theory to understand and simulate the construction of

meaningful patterns of neural activity that implement the perceptual process of observation. The prototypic hardware realization of

intelligent behavior is already apparent in certain classes of robots. The chaotic neurodynamics of sensory cortices in pattern recognition

is ready for hardware embodiments, which are needed to provide the

eyes, noses and ears of devices for survival and autonomous

operation in complex and unpredictable environments.

Key Wordsi Chaos theory, Intentionality, Mesoscopic Brain

dynamics. Perception, Reafference

1.0 Neurodynamics of intentionality in the

behavioral act of observation

1. 1 The properties ofintentionality

The first step in pursuit of an understanding of intentionality

is to ask, what happens in brains during an act of observation?

This is not a passive receipt of information from the worid. It is a purposive action by which an observer directs the sense organs toward a selected aspect of the wodd and interprets the resulting

barrage of sensory stimuli. The concept of intentionality has been

used to describe this process in different contexts, since its first use by Aquinas in 1272 [1]. The three salient characteristics of

intentionality as it was developed by him are (a) intent or

directedness toward some future state or goal, (b) wholeness, and

(c) unity [12]. These three aspects correspond to current use of

the term in psychology [with the meaning of purpose], in medicine

[with the meaning of mode of healing and integration of the body],

and in analytic philosophy [with the meaning of the way in which

beliefs and thoughts are connected with ("about") objects and

events in the worid, also known as the symbol-grounding problem].

Intent comprises the endogenous initiation, construction, and

direction of behavior into the world. It emerges from brains. Humans, animals and autonomous robots select their own goals,

plan their own tactics, and choose when to begin, modify, and stop sequences of action. Humans at least are subjectively aware of

themselves acting, but consciousness is not a necessary property

of intention. Unity appears in the combining of input from all sensory modalities into Gestalts, in the coordination of all parts of

the body, both musculoskeletal and autonomic, into adaptive,

flexible, yet focused movements. Subjectively, unity appears in the awareness of self and emotion, but again this is not intrinsic to

intention. Wholeness is revealed by the orderly changes in the self and its behavior that constitute the development, maturation and

adaptation of the self, within the constraints of its genes or design

principles, and its material, social and industrial environments.

Subjectively, wholeness is revealed in the remembrance of self through a lifetime of change, although the influences of accumulated

and integrated experience on current behavior are not dependent on

recollection and recognition. In brief simulation of intentionality

should be directed toward replicating the mechanisms by which

goal states are constructed, approached and evaluated, and not toward emulating processes of consciousness, awareness, emotion,

etc. in machines.

1.2 The limbic system is the chieforgan ofintentional

behavior

Brain scientists have known for over a century that the necessary and sufficient part of the vertebrate brain to sustain minimal intentional behavior is the ventral forebrain, including those components that comprise the external shell of the phylogenetically oldest part of the forebrain, the paleocortex, and

the deeper lying nuclei with which the cortex is connected. These

components suffice to support remarkably adept patterns of

intentional behavior, in dogs after all the newer parts of the

forebrain have been surgically removed [17], and in rats with neocortex chemically inactivated by spreading depression [3]. Intentional behavior is severely altered or absent after major

damage to the medial temporal lobe of the basal forebrain, as manifested most widely in Alzheimer's disease.

314
Phylogenetic evidence comes from observing intentional behavior in salamanders, which have the simplest of the existing

vertebrate forebrains [21, 28]. The three main parts are sensory

(which, as in small mammals, is predominantly olfactory), motor,

and associational (Figure 1). These parts can be judged to comprise

the limbic system in all vertebrates, but in the salamander they

have virtually none of the "add-ons" found in brains of higher

vertebrates, hence the simplicity. The associational part contains

the primordial hippocampus with its interconnected septum and

amygdaloid nuclei, striatal nuclei, which are identified in higher

vertebrates as the locus of the functions of spatial orientation (the

"cognitive map") and temporal integration in learning (the

organization of long and short term memory). These processes are

essential, inasmuch as intentional action takes place into the world,

and even the simplest action, such as searching for food or evading

predators, requires an animal to know where it is with respect to

its world, where its prey or refuge is, and what its spatial and

temporal progress is during sequences of attack or escape. The

feedback loops that support the flow of neural activity in the neurodynamics of intentionality are schematized in Figure 2.

14, 22, 23]. The construction is not by recall of stored patterns

but by pattern formation in distributed nonlinear systems with

connections that have been modified cumulatively through learning.

The manner in which this take place involves hierarchical ordering

of neural activity between microscopic, mesoscopic and

macroscopic levels having differing time and space scales. Cortical neurons are selectively activated by sensory receptors and made to generate microscopic activity in the form of trains of action

potentials (pulses) on their axons.. These and neighboring neurons by their synaptic interactions form a population forms that "binds"

their activity into mesoscopic patterns 14, 18, 19, 29, 30]. These

mesoscopic brain activity patterns are revealed by electrical fields of potential (EEGs) generated by interactive masses of neurons are induced by the arrival of stimuli, which trigger sequences of 1st order state transitions. These sequential states in turn converge

into integrated macroscopic patterns that occupy the entirety of

each cerebral hemisphere and give rise to the global patterns of

brain activity, that may be related to the patterns of metabolic

activity that are revealed by non-invasive brain imaging (fMRI,

PET, SPECT, etc.).

environment

sensory

nnotor

body

image

cognitive

mop

Figure 1. This schematic illustrates the sensory, motor, and

associational components of the right hemisphere (seen from

above) of the simplest extant vertebrate brain in the salamander.

The bidirectional connections between these 3 major subdivisions

of the forebrain provide for the macroscopic interactions that support the neurodynamics of the process of intentionality: goal

formation, action, perception, and learning from the sensory consequences of the action taken into the environment. These

components are form the prototype of the limbic system, which is found in all vertebrate brains, typically buried within exuberant

growth of other "add-on" structures that operate in concert with

the limbic system.

1.3 Neurodynamic manifestations ofintentionality in brain

activity ofthe primary sensory cortices: the EEG

(electroencephalogram , 'localfield potential'

)

The crucial question for neuroscientists is, how are the patterns of neural activity that sustain intentional behavior

constructed in brains prior to perception? An answer is provided

by studies of electrical activity of the primary sensory cortices of

animals that trained to respond to conditioned stimuli [2, 8, 10-12,

motor loop

proprioceptive loop

SOCY

reafference

Ian0 ALL SENSORY

SYSTEMS i

CORTEX r

i lime 1 loop

HM'PCCAriPuS

Figure 1 This diagram of brain state space maps the multiple

feedback loops that support the intentional arc. Flow of neural

activity inside the brain is in two directions. Forward flow from

the sensory systems to the entorhinal cortex and on to the motor

systems is by spatial AM patterns of action potentials at the microscopic level, by which transmitting cortices drive the neurons

in their targets. Feedback flow from the motor systems to the entorhinal cortex by control loops, and from the entorhinal cortex

to the sensory systems inside the brain, is by spatial AM patterns

of action potentials at the mesoscopic level. This feedback

constrains and modulates the microscopic activity in the forwardly

transmitting populations. The mesoscopic feedback messages are order parameters that bias the attractor landscapes of the sensory

cortices in preafference. Forward flow supports motor output and

provides the content of percepts. Feedback flow supports

integrative processes in learning that lead to the wholeness of

intentionality. They enable the formation of a macroscopic AM

pattern that reflects the integration of the activity of an entire hemisphere.

315
Owing to the nonlinear state transitions by which they form,

these mesoscopic brain states are not representations of stimuli, nor are they simple effects caused by stimuli. Each learned stimulus serves to elicit the construction of a pattern that is shaped

by the synaptic modifications between cortical neurons from prior

learning, which vastly outnumber the synapses formed by

incoming sensory axons, and also by the brain stem nuclei that bathe the forebrain in neuromodulatory chemicals. Each cortical

activity pattern is a dynamic operator that creates and carries the meanings of stimuli for the recipient animal, it reflects the

individual history, present context, and expectancy, corresponding

to the unity and the wholeness of the intentionality. The patterns

created in each cortex are unique to each animal. All sensory

cortices transmit their signals into the limbic system, where they

are integrated with each other over time, and the resultant

integrated meaning is transmitted back to the cortices in the processes of selective attending, expectancy, and the prediction of

future inputs, which together comprise the neural process of

"reafference".

The same kinds of EEG activity as those found in the sensory and motor cortices are found in various parts of the limbic system.

This discovery indicates that the limbic system also has the capacity to create its own spatiotemporal patterns of neural

activity. They are related to past experience and convergent

multisensory input, but they are self-organized. The limbic system

provides a neural matrix of interconnections, that serves to generate

continually the neural activity that forms goals and directs behavior

toward them. EEG evidence shows that the process occurs in discontinuous steps, like frames in a motion picture. Each step follows a dynamic state transition, in which a complex assembly of neuron populations jumps suddenly from one spatiotemporal

pattem to the next, as the behavior evolves. Being intrinsically

unstable, the limbic system continually transits across states that emerge, spread into other parts of the brain, and then dissolve to give rise to new ones, a process that Japanese mathematicians have

described as "chaotic itinerancy" between "attractor ruins" [34].

Its output controls the brain stem nuclei that serve to regulate its own excitability levels, implying that it regulates its own

neurohumoral context, enabling it to respond with equal facility to changes that call for arousal and adaptation or rest and recreation,

both in the body and the environment. It may be said that the neurodynamics of the limbic system, assisted by other parts of the

forebrain such as the frontal lobes, initiates the novel and creative behavior seen in search by trial and error.

The limbic activity patterns of directed arousal and search are

sent into the motor systems of the brain stem and spinal cord. Simultaneously, patterns are transmitted to the primary sensory

cortices, preparing them for the consequences of motor actions. This process has been called "reafference" [12, 35], "corollary

discharge" [32], "focused arousal" [29] and "preafference" [22, 23].

It sensitizes sensory systems to anticipated stimuli prior to their expected times of arrival Sensory cortical constructs consist of

brief staccato messages to the limbic system, which convey what is

sought and the result of the search. After multisensory convergence, the spatiotemporal activity pattern in the limbic

system is up-dated through temporal integration in the hippocampus. Between sensory messages there are return updates from the limbic system to the sensory cortices, whereby each

cortex receives input that has been integrated with the output of

the others, reflecting the unity of intentionality. Everything that a human or an animal knows comes from this iterative circular process of action, reafference, perception, and up-date. It is done

by successive frames that involve repeated state transitions and

self-organized constructs in the sensory and limbic cortices. This

neurodynamic system is defined here as the "limbic self in the brain of an individual, where intentional behavior is created, with

help from other parts of the forebrain.

An act of observation comprises Aquinas' intentional action of

"stretching forth" and learning from the consequences. It embodies

the existential "action-perception cycle" of Merleau-Ponty [26]. It corresponds to Piaget's [27]cycle of "action, assimilation, and

adaptation" in the sensorimotor stage of childhood development.

His postulated sequences of equilibrium, disequilibrium, and reequilibration conform to state transitions in brain dynamics, which

initiate and sustain action, construct dynamic patterns in the sensory cortices, and up-date the limbic patterns by modifying

synapses in the learning that follows the sensory consequences of

intended actions. For Piaget, cause and effect are chains of events

that have the appearance of linkage corresponding to the unfolding

experience of that exploration, by which a child is trying to make

sense of its world by manipulating objects in it. The origin of

causal inference is buried deeply in the pre-linguistic exploratory

experience of each of us. It is not easily accessed by cognitive

analysis or introspection.

We are all aware of our acts of observation. It is partly by

expectation of what we are looking for through reafference, partly

by perceiving the changes that our actions make in the dispositions

of our bodies through proprioception, and partly by our selection of stimuli from the environment through exteroception. We

perceive our intentional acts as the "causes" of changes in our

perceptions, and the subsequent changes in our bodies as "effects"

[12]. If this hypothesis of limbic dynamics is correct, then everything that we know we have learned through the action- perception cycle, including the iterative state changes by which it is produced in brains of animals and humans. It is this cycle, in prototypic form without need for appeal to consciousness, that must be simulated in our attempts to devise intelligent machines.

2.0 Characteristics of brain states as they are revealed by EEGs

The "state" of the brain is a description of what it is doing in some specified time period. A state transition occurs when the

brain changes and does something else. For example, locomotion is

a state, within which walking is a rhythmic pattem of activity that

involves large parts of the brain, spinal cord, muscles and bones. The entire neuromuscular system changes almost instantly with the

transition to a pattem of jogging or running. Similarly, a sleeping

316
state can be taken as a whole, or divided into a sequence of slow wave and REM stages. Transit to a waking state can occur in a

fraction of a second, whereby the entire brain and body shift gears,

so to speak. The state of a neuron can be described as active and

firing or as silent, with sudden changes in the firing manifesting

state transitions. Populations of neurons also have a range of

states, such as slow wave, fast activity, seizure, or silence. The

mathematics of nonlinear dynamics is designed to study these

states and the transitions by which they are accessed and

abandoned.

2. 1 The problem ofstability ofcortical states

The most critical question to ask about a state is its degree of

stability or resistance to change. Evaluation is done by perturbing an object or a system [8]. For example, an object like an egg on a

flat surface is unstable, but a coffee mug is stable. A person

standing on a moving bus and holding on to a railing is stable, but someone walking in the aisle is not. If a person regains his chosen

posture after each perturbation, no matter in which direction the displacement occurred, that state is regarded as stable, and it is said

to be governed by an attractor. This is a metaphor to say that the system goes ("is attracted") to the state through an interim state of

transience. The range of displacement from which recovery can occur defines the basin of attraction, in analogy to a ball rolling to

the bottom of a bowl. If the perturbation is so strong that it causes concussion or a bioken leg, and the person cannot stand up again, then the system has been placed outside the basin of attraction, and

a new state supervenes with its own attractor and basin.

Stability is always relative to the time duration of observation

and the criteria for what is chosen to be observed. In the perspective of a lifetime, brains appear to be highly stable, in their numbers of neurons, their architectures and major patterns of

connection, and in the patterns of behavior they produce, including

the character and identity of the individual that can be recognized

and followed for many years. Brains undergo repeated transitions from waking to sleeping and back again, coming up refreshed with a good night or irritable with insomnia, but still, giving the same

persons as the night before. Personal identity is usually quite

stable. But in the perspective of the short term, brains are highly

unstable. Thoughts go fleeting through awareness, and the face and

body twitch with the passing of emotions. Glimpses of their

internal states of neural activity reveal patterns that are more like hurricanes than the orderly march of symbols in a computer. Brain

states and the states of populations of neurons that interact to give

brain function, are highly irregular in spatial form and time course. They emerge, persist for a small fraction of a second, then disappear and are replaced by other states. It is the flexibility and

creativeness of this process that makes it so successful in animals

for their adaptation to rapidly changing and unpredictable

environments, and that makes it the desired platform on which to base the design of intelligent machines.

2.2 Three types ofstable cortical states

In using dynamics we approach the problem by defining three kinds of stable state, each with its type of attractor. The simplest

is the point attractor. The system is at rest unless perturbed, and

it returns to rest when allowed to do so. As it relaxes to rest, it has

the history of what happened, but that history is lost after convergence to rest. Examples of point attractors are silent neurons or neural populations that have been isolated from the

brain, and also the brain that is depressed into inactivity by injury or a strong anesthetic, to the point where the EEG has gone flat (Figure 3, bottom trace). A special case of a point attractor is

noise. This state is observed in populations of neurons in the brain

of a subject at rest, with no evidence of overt behavior. The

neurons fire continually but not in concert with each other. Their

pulses occur in long trains at irregular times. Knowledge about the

prior pulse trains from each neuron and those of its neighbors up to the present fails to support the prediction of when the next pulse

will occur. The state of noise has continual activity with no

history of how it started, and it gives only the expectation that its amplitude and other statistical properties will persist unchanged.

A system that gives periodic behavior is said to have a limit cycle attractor. The classic example is the clock. When it is viewed in terms of its ceaseless motion, it is regarded as unstable

until it winds down, runs out of power, and goes to a point

attractor. If it resumes its regular beat after it is re-set or otherwise

perturbed, it is stable as long as its power lasts. Its history is

limited to one cycle, after which there is no retention of its

transient approach in its basin to its attractor. Neurons in populations rarely fire periodically, and when they appear to do

so, close inspection shows that the activities are in fact irregular

and unpredictable in detail, and when periodic activity does occur,

it is either intentional, as in rhythmic drumming, clapping and

dancing, or it is pathological, as in the periodic oscillations of the eyes in nystagmus, or of the limbs during Parkinsonian tremor, or of the cortex during the hypersynchrony of partial complex

seizures that are revealed by near-periodic spike trains (Figure 3, top trace).

1 1 MV

Moti vof lon

nholatjon

RESP

Waking Rest 50>iV

De«p Anesthesio 50>jV

0.5 sec

317
Figure 3. Four levels of function of the olfactory system are revealed by EEC recording. The lowest is the non-interactive

'open loop' state imposed by deep anesthesia, which suppresses

brain activity. The ne.xt is the resting steady state with broad

spectrum \/f^ aperiodic waves. The aroused level in which

behavior is generated is shown by the repeated state transitions,

by which bursts are formed that reveal spatial patterns of .A.M (amplitude modulation) relating to odorant recognition with

inhalation. The upper trace shows the pattern of high-amplitude

spikes when an epileptic seizure has been triggered by powerful

electrical stimulation. This state is likewise chaotic, but with a reduced correlation dimension. This state also occurs during recovery from deep anesthesia on the way to the resting state [9,

31].

The third type of attractor gives aperiodic oscillation of the kind that is observed in recordings of EEGs. There is no one or

small number of frequencies at which the system oscillates. The

system beha\ ior is therefore unpredictable, because performance

can only be projected far into the future for periodic behavior.

This type is now w idely know n as "chaotic". The existence of this type of oscillation was known to Poincare a century ago, but systematic study was possible only recently after the full development of digital computers. The best know n systems with

chaotic attractors have a small number of components and a few

degrees of freedom, as for example, the double-hinged pendulum,

the dripping faucet, and the Lorenz, Chua. and Rossler attractors

[13]. These simple models are stationary, autonomous, and noisefree, forming the class of "deterministic chaos". Large and complex

real-world systems, which include neurons and neural populations

are noisy, infinite-dimensional, nonstationary, non-autonomous,

yet capable of chaotic behavior which has been called "stochastic chaos" [14]. The source is postulated to be the synaptic

interaction of millions of neurons, which create fields of

microscopic noise in cortex, but which are constrained by their own

interactions to generate mesoscopic order parameters that regulate

the spatiotemporal patterns of cortical activity revealed by the EEC. These spatiotemporal patterns are revealed by spatial

patterns of amplitude modulation (".\M patterns") of a spatially coherent aperiodic carrier wave in the gamma range of the EEC

They appear in time series as bursts of oscillation (Figure 3), and

their spatial pattemnig indicates the existence of an attractor landscape, which is actualized in the olfactory system with each

inhalation (Figures 4 and 5 durinsj intentional behavior.

SEIZURE

/ MOTIVATION

Inhalation

Exhalation

WAKING REST

SLEEP

— DEEP ANESTHESIA

Figure 4. A bifurcation diagram of the olfactory system state space

is constructed from the EEGs in Figure 3.

The discovery that brain dynamics operates in chaotic domains has profound implications for the study of higher brain function [31]. A chaotic system has the capacity to create novel and unexpected patterns of activity. It can jump instantly from

one mode of behavior to another, which manifests the facts that it has a collection of attractors, each with its basin, and that it can move from one to another in an itinerant trajectory [34]. It retains

in its pathway across its basins its history, which fades into its

past, just as its predictability into tts future decreases. Transitions

between chaotic states constitute the dynamics that we need to understand how brains perform such remarkable feats as

abstraction of the essentials of figures from complex, unknown and

unpredictable backgrounds, generalization over examples of

recurring objects never twice appearing the same, reliable assignment to classes that lead to appropriate actions, and constant

up-dating by learning.

318
Seizure

Inhalation

Exhalation

Motivation

^S}^^^^ Waking Rest

Deep Anesthesia

Figure 5. This perspective drawing of a projection from an

infinite dimensional brain state space into 3-space offers a view of

how an attractor landscape of learned basins of attraction is created with each inhalation. The selection is made by the input odorant.

If the stimulus is novel or unknown, the system goes into the chaotic well, which provides the aperiodic umpattemed activity

that drives Hebbian learning for new basin formation.

2.3 The hi order cortical stale transition is an elemental step in

intention

Systems such as neurons and brains that have multiple chaotic

attractors also have point and limit attractors, each with its basin of

attraction, which serves to provide the generalization gradient

required for perception of recurring stimuli that are never twice the same. If the basin is that of a point or a limit cycle attractoi, the system can proceed predictably to an identical end state. If the basin leads to a chaotic attractor, the system goes into ceaseless

fluctuation, as long as its energy lasts. If the starting point is

identical on repeated trials, which can only be assured by

simulation of the dynamics on a digital computer, the same

aperiodic behavior appears. If the starting point is changed by an

arbitrarily small amount, although the system is still in the same

basin, the trajectory is not identical. A deterministic chaotic system that is in the basin of one of its chaotic attractors is legendary for its sensitivity to the initial conditions. If the

difference in starting conditions is too small to be originally

detected, it can be inferred from the unfolding behavior of the system, as the difference in trajectories becomes apparent. This

observation shows that a chaotic system has the capacity to create

novel patterns constituting endogenous increases in information in

the course of continually constructing its own trajectory into the

future.Our EEG evidence indicates that every primary sensory

cortex maintains multiple basins corresponding to previously

learned classes of stimuli, as well as to the unstimulated state, which together form an attractor landscape. They all show evidence

that the vehicle they use for transmission of their output is an

aperiodic carrier wave that is amplitude-modulated in the two

spatial dimensions of cortical coding, and that is gated by extracortical forcing functions in the theta range (2-7 Hz). We note that we predicted a common code for all sensory systems, on the basis

that the signals from all sensory cortices must be combined in the limbic system to form gestalts. We postulate that preafferent input

from the limbic system can serve to bias the landscapes in such a way as to facilitate the capture of the multiple sensory systems by

basins of the attractors corresponding to the goal of the intended

observation, perhaps in the manner of the variable tiling in a Voronoi diagram. This chaotic prestimulus state of expectancy

establishes the sensitivities of the cortices, so that the very small number of sensory action potentials evoked by the expected

stimuli can simultaneously carry the cortical trajectories into the basins of the appropriate attractors as they are created by the forcing function, in the case of olfaction by inhalation (Figure 5), irrespective of which equivalent receptors actually receive the expected stimuli in the different sensory modalities. In the absence

of the stimulus, the cortices continue to transmit their outputs to

the limbic system, confirming the continuing absence. The stimuli

are also selected by the limbic system through orientation of the sensory receptors in space by sniffing, looking, and listening. We

believe that the basins of attraction in each of the sensory cortices

are shaped by limbic input to sensitize them for receiving and

processing the desired class of stimuli in every modality, whatever

may be the goal at the moment of choice.

3.0 Problems in use of chaotic dynamics in the

development of advanced machine

intelligence

Chaotic dynamics has proved to be extremely difficult to harness in the service of intelligent machines. Most studies that purport to control chaos either find ways to suppress it and

replace it with periodic or quasiperiodic fluctuations, or to lock

two or more oscillators into synchrony sharing a common aperiodic wave form, often as an optimal means for encryptation and secure

transmission. Our aim is to employ chaotic dynamics as the means

for creating novel and endogenous space-time patterns, which must

be the means to achieve any significant degree of autonomy in devices that must operate far from human guidance, where in order

to function they must make up their courses of action as they go

along. We know of no other way to approach a solution to the problem of how to introduce creative processes into machines,

other than to simulate the dynamics we have found in animal

brains. To be sure, there are major unsolved problems in this approach, chief among them that we know too little about the dynamics of the limbic system. Hence we find it necessary to

319
restrict the development of hardware models to the stage of brain- world interaction that we know best, which is the field of

perception. In brief, what are the problems in giving eyes, ears and

a nose to a robot, so that it might learn about its environment in something like the way that even the simpler animals do - by

creating hypotheses and testing them through their own actions?

3. 1 Noise stabilization of chaotic dynamics, opening the way to

analog-digital hybrid embodiments

The operations in the olfactory system by which the state

transitions and pattern constructions for pattern classification are simulated in software and hardware embodiments have been

described in a series of publications [9-12, 14]. Our simulations are done with a set of approximately 920 interconnected first-order nonlinear ordinary differential equations, forming what we have

named the Kill model [8]. The basic element, the KO set, is a 2-

stage linear integrator simulated in hardware [6, 7] by 2 operational

amplifiers, whose output is passed through an asymmetric sigmoid

function modeled by 2 diodes back-to-back. Connections between

64 elements are time multiplexed (Figure 6) through a MUX, an

amplifier with voltage-controlled gain, and a DMUX [10]. Switching is controlled by a digital computer at a clock rate suitable

for the pass band of the carrier wave. For each connected pair the gain is stored in memory, so that the connection strengths are

easily modified during learning. With this device the connectivity

grows by 2-N instead of by N^'. In digital embodiment the equations have been solved by numerical integration on Unix,

Macintosh, and PC platforms, and by vector programming on the Crav M/X.

frequencies are incommensurate, and the feedback delays between

the 3 layers are distributed to act as low-pass filters, the solutions

of the equations give the aperiodic waveforms and broad 1/f^ spectra (Figure 7) of EEGs trom the 3 layers. The asymmetric

sigmoid endows the system with the property of nonlinear state

transitions on step inputs, owing to the amplitude-dependent gain

of the K.0 elements.

In the course of digital simulation it has become apparent that a minimum of 64 elements will suffice for 2-D pattern classification under Hebbian and non-Hebbian reinforcement learning [16, 24, 25,

37, 38]. The large number of equations leads to attractor crowding

[15], in which the basins of attraction shrink close to the size of the

digitizing step in using rational numbers for computation, so that sooner or later the system jumps out of its designated chaotic basin

into a neighboring basin that is most likely to be that of a point or

limit cycle attractor, which kills the system. This problem has been

solved by use of additive noise on the order of 1 5% of the amplitude of the aperiodic state variables [4, 5, 13, 15], giving

robust attractor landscapes for learning and pattern classification

[24, 25]. The lesson learned is that deterministic chaos, in which

the system is low-dimensional, stationary, strictly autonomous,

and noise-free, is inappropriate for modeling biological and machine

intelligence. Brains operate with what we call 'stochastic chaos'

[13], which is high-dimensional, nonstationary with regularly

repeated state transitions, engaged with its surround, and deeply

embedded in noise created by Kle sets and manifested in high

densities of action potentials. The noise in digital models is simulated with random number generators, either rectified to simulate Kle sets or off-set with d.c. bias to simulate the noise in

Kllei sets.

KJLj ,i = 1, 8 subsets

CONTROL I "'I OUTPUT

Figure 6. Schematic for connecting KII sets by multiplexing.

Interaction of KO sets of like kind (excitatory or inhibitory)

giving point attractors is modeled by KI sets; interaction of Kle

and Kli sets giving limit cycle attractors is modeled by KII sets. Three serial KII sets in layers that correspond to the olfactory

bulb, prepyriform cortex, and an intervening control nucleus called

the AON is modeled by the Kill set; if the 3 characteristic

0.5 1.0 1.5 2.0 10E(F)

Wave Amplitude

Figure 7. The power spectrum and amplitude histogram for a simulated EEC trace from the Kill model, with a section of

the asymmetric nonlinear gain curve, showing the nature of the

nonlinearity that provides for destabilization by the input.

The interactive gain increases with excitatory input.

320
The finding in digital embodiments that noise is not only

unavoidable but is necessary for stable high-dimensional chaotic dynamics opens the way to analog embodiments [7], in which

noisy components resemble the characteristics of local pools in nerve cell assemblies, but which offer much higher rate of temporal

and spatial integration, the use of continuous variables in place of

rational numbers, and the feasibility of implementing the dynamics

on chips suitable for incorporation into mobile devices.

3.2 Embedding devices for perception into autonomous cognitive

machines

The Kll sets have multiple robust limit cycle attractors, which

become embedded as chaotic attractors when coupled in serial

layers with distributed delayed feedback. The Kill model is offered

as the prototype for constructing devices in hardware and software

to implement the elementary steps of perception, thus providing

robots with the sensory ports that they need to guide them through

their environments. These steps are the interpretive operations necessary to normalize, compress, abstract and generalize over

successive inputs preparatory to classification [5, 16, 25, 33, 36].

These cognitive operations are done by the nonlinear operations in

the input stage and by the basins of attraction in the landscape

formed by learning in each of the sensory systems. They are required in each of the ports providing information to the mobile

device about its visual, auditory, tactile, and chemical

environments. Our tests of the Kill model have shown that it can

learn a new class in half a dozen trials instead of the thousands of

trials required by MLPs, and that new learning occurs without

degradation of previous attractors, although, as in the case of the olfactory system, the attractors are modified through attractor crowding. The superior level of 'intelligence' is demonstrated by

the capacity of the Kill model to separate items in 64-space that belong to identifiable classes but are not linearly separable. The

classes are, in fact, constructed by the model and are not imposed

from outside, constituting an aspect of autonomy. In other words,

the system creates its own features from its own experience of the constancy of relations between channels in the 8x8 64-channel

input array. Formation of a world-view by which the device can guide its explorations for the means to reach its goals depends on the

integration of the outputs of the several sensory systems, in order

to form a multisensory percept known as a gestalt. This

integration is easily done when all of the ports have their outputs

in the same form: a vector consisting of a 2-D spatial pattern of

amplitude modulation of a 1-D aperiodic wave form in the gamma

range (nominally 30-60 Hz), which is segmented in time at a frame

rate of nominally 2-7 Hz and frame durations on the order of 0.1

sec. Precise clocking and synchronization are not prerequisite.

The sequential frames deriving from sampling the environment

must then be integrated over time and oriented in space. An

example of how these higher operations might be done was

provided by W. Gray Walter [36] with his electronic tortoises,

which had the capacity for autonomous goal-directed search involving the adjudication of conflicting needs in an uncertain environment.

The performances of these devices set a challenging level of

'intelligence' to which to aspire, and they also serve to highlight some of the difficulties in using the descriptive term "autonomous". As with animals the devices were untethered, and they learned to avoid obstacles without need for instruction or intervention, if within their limited capacities for locomotion. However, they were programmed to satisfy their own needs without regard for or comprehension of anything else's, perhaps in analogy to house

pets, whose sole purpose, however inadvertent, is to provide

enjoyment to their owners, and seldom to do useful work or bend

their talents to the benefits of the owners, or, in the case of the machines, the designers and builders.

It is already apparent that fully autonomous vehicles are not

in the best interest of researchers and the general public, except as demonstrations of what might emerge as major problems from this

line of study. It is also clear that such devices can and will be built, and that the proper path of future management will not be by

techniques of training and aversive conditioning, but by education,

with inculcation of desired values determined by the manufacturers

that will govern the choices that must by definition be made by the newly autonomous mechanical devices.

Acknowledgments

This work was supported by grants from the National Institute of

Mental Health MH06686, the Office of Naval Research N00014-

90-J-4054, and from ARO MURl DAAH04-96-1-0341.

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Peripheral nois* Peripheral noise Peripheial noise

: CotmecCion to

all lateral node except itself

: Connection to all lateral nodes

I Connection from

all lateral nodes

322
Measuring intelligence: a neuromorphic perspective

Marwan A. Jabri

Electrical and Computer Engineering

Oregon Graduate Institute, 20000 NW Walker Rd,

Beaverton, OR 97006 USA

and

School of Electrical and Information Engineering

The University of Sydney, NSW 2006 Australia

marwan@ece.ogi.edu

ABSTRACT

Neuromorphic engineering is about the development of

biologically inspired roving machines that can exhibit

intelligent behaviour, learn on-line and in real-time. The

question of how to assess and measure the intelligence of

such machines is essential if progress in neuromorphic

engineering is to be assessed. However, it is awkward to talk about measuring intelligence without a clear understanding

of the capabilities that researchers aim or dream to equip

neuromorphic systems with. In this communication we

promote the position that metrics for measuring of the intelligence of neuromorphic systems should be task-based,

should factor in the computational resources, the on-line learning efficiency, the capability to learn from intermittent reward that can vary in frequency and importance to the task

at hand, the capability to anticipate events and to modify

decision making processes based on anticipated events, the capability to balance exploration and exploitation as to discover new methods or to fine-tune existing methods, and

the ability to optimize the utilization of its resources using

ground rules that maximizes it success. To factor in all these aspects requires a fundamental assessment of what such

machines achieve as goals and at what cost. We propose that a simple achievement rule, energy and resource oriented

metric be used.

Keywords: neuromorphic engineering, on-line

learning, reward-based learning, anticipation, exploration

and exploitation, regularity! and modularity.

1 Introduction

Neuromorphic engineering was a term coined

by Carver Mead and described the process of

building systems based on biological models

and embedding them in roving machines. In

the last 20 years, neuromorphic engineering

addressed the development of various

biological like processing system such as

retinas, cochleas (Schaik 2000), legged

robots and creatures (Tilden 1994) (Lewis,

Etienne-Cummings et al. 2000), sensorimotor

control (Horiuchi and Koch 1999) (EtienneCummings, Spiegel et al. 2000) and

integration systems (Jabri, Coenen et al. 1997).

Although analog microelectronics was

initially promoted (and continue to some

extent) as the ideal substrate for

neuromorphic information processing

systems (Mead 1989), current works tend to use many implementation technologies,

hardware and software.

The aim of many neuromorphic engineering

groups is to develop active perception

systems, systems that interact with the

environment in a closed loop fashion'

Neuromorphic engineering is a synergy

between neuroscience and engineering. The

common neuromorphic methodology is to

identify a task or a function, to explore and

identify brain areas from neuroscientific

knowledge (anatomy, physiology,

The Telluride Neuromorphic Engineering workshop

is a yearly meeting where research groups meet and

collaborate. See http://zig.ini.unizh.ch/teiluride2000.

323
psychophysics, ...), to develop

computational models that encapsulate the

information processing at some level of

abstraction, and to develop implementations

of the models. The determination of an

acceptable level of abstraction of the

biological systems during the computational

model development is a challenging task, and

is typically done as to preserve some essence of the biological information or mechanical

processes.

In assessing the intelligence of engineering

machines, and because of its close

relationship to the neuroscientific

community, neuromorphic engineering has

traditionally relied on several levels of

metrics. Not all metrics are necessarily

directly related to the behaviour of the

machines and the classification of the

intelligence of such behaviour. The common

levels are:

• Device/circuits

• Representation

• Organization

• Behaviour with and without artificial

lesions

Learning & behaviour adaptation

In these assessments, tasks have commonly

been related to the biological systems being

modeled: specific brain areas, the central nervous system, and the mechanical

apparatus. We elaborate on these tasks in the

next section.

2 Neuromorphic Tasks - Present and

Future

2. 1 Peripherals systems

Biologically based or inspired peripheral

systems are probably the most researched

neuromorphic systems. The development of

silicon-based implementations of retinas and

cochleas has been pioneered in Carver

Mead's laboratory in the eighties. Artificial

olfactory and somatosensory systems have

also been researched and developed.

It is clear why most early neuromorphic

research focused on peripheral systems: They

are the sensors and they drive the motor

responses of biological systems, and they are

the most understood, in particular in the case of primates.

The research and development of

neuromorphic peripheral systems has also

contributed to better understanding of the

biological devices and they incorporation in

systems.

2.2 Sensorimotor Systems

Over the last decade sensorimotor systems

have been developed. Sensorimotor system

are broad in their definition but are supposed

to implement forms of sensory (visual,

auditory, infrared, sonar) to motor mapping,

where the motor are actions that aim at performing forms of active perception,

navigation and tracking, or orienting to

stimuli in the environment.

Experimental sensorimotor systems have

incorporated abilities by incorporating

simplified models of the superior colliculus,

goal reaching and simple navigation abilities

by incorporating computational models of the

basal ganglia and ventral tegmental area, predictive control abilities by incorporating

324
computation models of the cerebellum, and

spatial representation learning by

incorporating computational models of the

hippocampal formation.

Sensorimotor systems have so far included

sophisticated adaptive learning abilities

implemented in software. The measuring of

the intelligence of such systems has largely

been a matter of retrieving behavioral

properties that resemble those of animal,

when the systems are implementing

sensorimotor tasks, or by observing the

behaviour of the system when software

lesions are performed. In that case the deficit

of the systems are typically compared to

those of animal that have had specific brain

areas severed or temporarily disabled.

Analog Very Large Scale Integration

(aVLSI) systems with adaptive abilities have

also been implemented and typically mimic

to some extent their biological counter parts.

The assessment of the intelligence of such

system has largely been a matter of

comparing the signal processing or collective

computation of the devices to the biological

counterparts. These could also be seen as task-oriented comparison. An example is an

implementation of the retina with adaptive

intensity saturation control. Another example

is a silicon cochlea that implements adaptive

gain control.

2.3 Cognitive Systems

If one defines cognitive systems as being

capable of performing higher order

processing by utilizing first order information

and generating higher order knowledge,

some sensorimotor systems would qualify of

being "cognitive".

An example is a sensorimotor system similar

to that of Fig 1 which implements abstract

computational of the cerebellum, basal

ganglia and ventral tegmental area.

In this system the cerebellum perfonns

sensory prediction and coordinate

transformation from world coordinate to

robot centered coordinate. The predicted

sensory signal (visual target position) is then

used by the basal ganglia to associate a motor

command with the visual target as to keep it as much as possible in front of it. If the basal

ganglia are lesioned, the robot looses its motor ability. If the cerebellum is lesioned,

the tracking lags the object.

In survival terms, and such a sensorimotor

system is controlling the hunting abilities of

an animal, a lesion of the cerebellum would

most likely lead to the animal death, although

it can track its prey, though not predictively

to the point that it can catch it (assuming a mobile prey), or it cannot escape a predator

by anticipating potential contact points.

Interestingly, the hypothetical animal would

be able to anticipate the position and perform

all desired coordinate transformation,

however a lesion of the basal ganglia will

also lead to its death.

2.4 Future ofNeuromorphic Systems

With the rapid development in neuroscience

research brought by phenomenal growth in

computation, sensing, signal processing and

imaging technologies, neuromorphic

engineering will increasingly focus on the

implementation of complex motor, sensory

and cognitive processing. The development

of computational models of sub-cortical and

cortical will permit the development of

sophisticated real-time systems, that will go

beyond present sensorimotor loops and will

integrate aspects such as planning, object

recognition, motion and auditory analysis,

and perception. This will put additional

pressure in comparing the performance of

such systems, and hence on the issue of

measurement metrics.

325
3 Computational Resources

Computational resources in neuromorphic

systems, in particular aVLSI systems tend to

be a central criterion of design. One

attraction of aVLSI neuromorphic systems is the low power requirements (Mead 1989;

Jabri, Coggins et al. 1996). However, beside

the elegance of the implementation, and

specific application requirements, it is becoming more difficult to promote analog

as a preferred design methodology, except in

some fairly narrow areas such as world

interfaces. This is not to say that analog

asynchronous parallel computation does not

provide any conceptual computational

advantages. Only that the inspirations for

such advantages have not been met with clear

theoretical support over digital computation

as yet.

Computational resources have also been

considered from the point of view of

compactness, efficiency of representing basic

computational elements such as sensors and

signal processors. Here applications that have

specific requirements such as ultra low

power and high fauU tolerance capability

could benefit more from analog than digital

representation. This is particularly the case if

sparse representation is being used. In a sparse representation of neural networks, neurons within a hierarchy of computation do

not fire concurrently. The receptive fields of

the neurons are highly tuned/selective and

are independent of each other. This translates

into data-driven architecture with attractive

low power consumption properties.

Given the infancy of neuromorphic systems,

autonomous behaviour has not been

developed beyond adaptive sensing and

signal processing tasks.

4 On-line Learning

Continuous on-line learning with bound

resources represents a challenge because of

the following problems:

1- Frequency of the associations to be

learnt is not sufficiently high to be

captured in a distributed

representation. Note the tuning pf

learning parameters do not

necessarily solve this problem as for

example, the use of large learning rate can lead to prior information to be

forgotten (catastrophic learning

effects) if no processes are implemented to move and consolidate

information from soft-term memory

to long-term memory store.

2- In cases where statistical properties of

the sensed signals are to be

discovered on-line, sample size

effects, and non-stationarity of the

signals are very problematic. For

example if independent component

analysis techniques are being used to discover feature detectors (Bell and

Sejnowski 1995), such discovery

using information maximization

techniques and mutual independence

criteria of the features would be more

difficult to achieve if performed online.

3- Rapid and flexible learning schedules

is necessary in situations where

autonomous systems requires to learn

at various rates and in real-time. This

imposes constraints on propagation of

information in the system and on its time constants. For example systems

doing sequence learning require

significant memory resources in the

form of analog or digital delay lines

and the performance of credit

326
assignment through time over the

present and historical information.

The learning issues above represent

significant challenges to the incorporation of

online and continuous learning. The

proposition of metrics for these sort of

capabilities is premature, given we do not

really know the how, when and where of

such capabilities.

5 Anticipation

An important element in neuromorphic

systems research is the development of the

concept of anticipation within the context of

autonomy. The system described earlier in

Section 2.3 is an example demonstrating an

anticipation property. A roving robot that can

anticipate undesirable events would

maximize its mission success. Anticipation or prediction of sensory or motor control

(predictive control) have been attributed as a

role to the cerebellum (Coenen and

Sejnowski 1996; Coenen 1998), in addition

to the traditional attributed role of motor

learning (Marr 1969; Albus 1971).

Present computational models of the

cerebellum have addressed individual

sensory (or a few) and motor prediction

capability. Computational models that

demonstrate abilities to adaptively and

continuously deal with a large number of

sensory modality and motor learning skills are still to be developed. Such skills will be

essential to autonomous machines that are expected to perform tasks such as navigation

in complex terrains or to perform object

manipulation. Anticipation is also important

for planning because it affects the

performance of the machine and its interaction with the environment and its objects.

Measuring anticipation can be very

subjective. However factoring anticipation in

the overall goal of a machine will provide

easier means for assessment.

6 Curiosity, Exploration and

Exploitation

Autonomous machines should possess

elements of "curiosity". For instance, it is known that reinforcement based learning

algorithms depend on forms of exploration

(Sutton and Barto 1981). However,

exploration has so far been implemented in

terms of probabilistic random actions aimed

at exploring the state-space with hope of

discovering policies that can be effective in

achieving specific goals. The issues of either

exploring more effectively or in a directed

way, or to explore better policies and

solutions are not well understood.

Another important aspect of autonomous

systems is that of exploitation of infrequent,

but yet important information encountered

during machine experiences. The interactions

between exploration and exploitation are fundamental in that regard. Reinforcement

based learning algorithms have assumed that

rewards are specified as end-achievements to

the learning machine. The ability to discover

and capture sensorimotor associations to yet

unspecified goals (and reward) is essential to

the rapid learning and the effective

exploitation of sensorimotor experiences. To

achieve this, the learning machines must be

able to recognize unspecified or unscheduled

rewards by forms of assessment of its sensory state and its sensory-reward memory.

7 Robustness and fault tolerance

Autonomous systems have to be robust and

fault tolerant. We discussed in Section 3

sparse representation and their low power

327
property as well as their potential role in

more effective learning by decorrelating

features. It is not clear however, without

clear redundancy in the underlying resources

(e.g. synapses and neurons), that sparse

representation alone lead to more fault

tolerance. It is also conceivable that other

additional encoding representations, such as population-based be a source of fault

tolerance (see for instance motor population

coding (Georgopoulos 1995)).

Fault tolerance has been attributed to

traditional neural network representation

because of the distributed representation that

develop during learning or that have been

hand-crafted. The relationship between pure

distributed representation and neural

correlate is not trivial, nor automatic.

Biological systems have various level of

fault-tolerance, some of which is not

graceful. Although biological systems

survive significant faults, behaviour is commonly degraded or lost. For example in

humans or monkeys, the level of behaviour

change depends greatly on brain areas that are damaged.

Then, what role does fault tolerance plays in

measuring intelligence? From an application

point of view, fault tolerance is an important

property of designs and system operation.

Furthermore, with continuous shrinkage in

transistor sizes, the importance of faulttolerance in highly complex processing

system will become increasingly important.

Another more important aspect of fault

tolerance requirement is in autonomous

system. Here clearly fault tolerance becomes

a critical element of endurance and graceful

degradation. But is this an important element

of intelligence? Although present machine

intelligence paradigms only addresses fault

tolerance from "an emergent property"

perspective, it is possible that fault-tolerance

was used a ground-rule for evolutionary

development of biological systems, and may

lead to yet unknown computational

architectures.

Hence, for the short-term, the issue of

metrics for fault-tolerance appear to be

relevant for autonomous systems in the

context of performing tasks in harsh

environments and where mechanical and

information resource tolerance are important.

The tolerance can be graded according to the

task and the ability of the machine to

complete it in the presence of faults.

8 Consciousness and control

The debate over the neural correlate of

consciousness is obviously of most interest to

neuromorphic engineering. Our present poor

understanding of the underlying neural

circuits does not imply that it is not a

necessity for autonomous machines. The

complex interactions between awareness,

planning and survival dictates equipping

machines with some level of the "self. The

level may be primitive at first. Practical

awareness can address sensory representation

of the environment and its representations in

terms of goals and necessities to survival

(e.g. battery charging). The competition of

sensory on motor behaviour will need to

address priorities and dynamic reward. The

representations that emerge from this

computation will represent primitive forms of

awareness that machines will be capable of

processing, but not necessarily of realizing.

Realization may emerge as a balanced

competition between motor plans, behaviour

and reward obtained from behaviour. Hence

the development of task-oriented

neuromorphic systems will allow the

exploration of computational structures and

information processing paradigms that can

embed such a competition.

328
In the context of intelligence metrics, the

question of consciousness can be stated as

that of resource management. The

development of a metric framework will

have to account for a broad spectrum of

sensory, motor and reward situations that

could be too complex to represent. One can

envision a metric that measures final

outcomes based on the essence of task

completion measured in terms of energy and

survival. That is to be, and to be there in the

right time.

9 Complexity, Hierarchy, Regularity

and Modularity

The development of design methodologies

for highly complex integrated circuits

containing tens of million of transistors have

taught engineers a number of golden rules in

the management of complexity: Hierarchy,

regularity and modularity. These humanmade engineering rules are similar to the

rules that underlie biological systems

structures. Representation and learning

efficiency (in particular online continuous

learning) are the most concerned and affected

by the hierarchy, regularity and modularity

(HRM) of the underlying structures. The

issue of whether HRM issues are relevant to

intelligence metrics is similar to those

discussed earlier in the context of fault

tolerance and representation (e.g.

sparseness). HRM of computational

structures may affect the optimality of an

autonomous system, but may not be critical

to its successful operation. Again, the

importance of HRM as a ground-rule for

autonomy may go beyond optimality and

may be critical to the scalability of the

architecture and representations. Scalability

is relative to the initial conditions and desired

bounds. From a practical point of view, it is evident that a HRM-based design will be

superior to a design that is flat and that lacks

modularity and regularity.

10 Summary and Conclusions

The issues discussed in this position paper

converge to the conclusion that in the context

of autonomous systems, intelligence metrics

should be task oriented and should embed

factors such as completion, resources and

energy. Completion is easy to assess, with

the distinction that it is for practical systems

and not for simulations. Resources and

energy could be cast to specific

implementation, whether software or hardware. Resources will cover aspects of

resources used to perform a task, and those

available to capture the skills to perform the

task. The energy measure will represent the

total energy required to perform a task and

can easily be measure for software and

hardware implementations.

9 Visual inputs froir 0

Coordinate

ftansfbnration

(ParietEri cortex)

5 Spatial

locatic,

2 Velociti

Orimtation

preprocossing

(Hippocanr^xis)

Cerebelhm

Model

11 orientations

2 points of

reference

(landmarks)

Predicted sensory inputs of robot in the environment

BasaiGati^ia ^cSonNetwwk)

VTA

(Reward

Preciction

winning motor speed

Winning motor conmand

Predicted future

reward

Reward

Figure 1. Sensorimotor system implementing anticipation and reinforcement learning

allowing a Khepera robot to track (by rotating in place) a moving target.

329
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330
Grading Intelligence in Machines: Lessons from Animal Intelligence

Subhash Kak

Department of Electrical & Computer Engineering

Louisiana State University

Baton Rouge, LA 70803-5901; kakOee . Isu. edu

July 20, 2000

In this note I argue that to find a Vector of Intel- ligence (VI) for a performance metric for machines,

it is helpful to look at animal intelligence, which is clearly defined as a spectrum.

All animals are not equally intelligent at all tasks;

here intelligence refes to performance of various tasks,

and this performance may depend crucially on the animal's normal behavior. It may be argued that all an- imals are sufficiently intelligent because they survive

in their ecological environment. Nevertheless, even in cognitive tasks of the kind normally associated with

human intelligence animals may perform adequately.

Thus rats might find their way through a maze, or dolphins may be given logical problems to solve, or the problems might involve some kind of generalization. These performances could, in principle, be used

to define a gradation.

If we take the question of AI programs, it may be

argued that the objectives of each define a specific

problem solving ability, and in this sense AI programs

constitute elements in a spectrum. But we think that

it would be useful if the question of gradation of intel- ligence were to be addressed in a systematic fashion.

The question is best examined in an ecological context; a similar case for an ecological study of machine

vision has been made by Gibson.

The issues that we leave out are those related to defining consciousness and quantum approaches to brain processes and intelligence. Although I have

personally worked on these issues, I believe they lie outside the scope of the NIST Conference on Performance Metrics for Intelligent Systems.

On Animal Intelligence

According to Descartes, animal behavior is a series

of unthinking mechanical responses. Such behavior

is an automatic response to stimuli that originate in

the animal's internal or external environments. In

this view, complex behavior can always be reduced

to a configuration of reflexes where thought plays no

role. According to Descartes only humans are capable

of thought since only they have the capacity to learn

language.

Recent investigations of nonhuman animal intelli- gence not only contradict Cartesian ideas, but also

present fascinating riddles. It had long been thought

that the cognitive capacities of the humans were to be

credited in part to the mediating role of the inner lin- guistic discourse. Terrace Te85 claims that animals

do think but cannot master language, so the question arises as to how thinking can be done without

language:

Recent attempts to teacli apes rudimentary

grammatical skills have produced negative

results. The basic obstacle appears to be

at the level of the individual symbol which,

for apes, functions only as a demand. Evidence is lacking that apes can use symbols as names, that is, as a means of simply transmitting information. Even though

non-human animals lack linguistic competence, much evidence has recently accumulated that a variety of animals can represent particular features of their environment. What then is the non-verbal nature

of animal representations?... [For example]

learning to produce a particular sequence

of four elements (colours), pigeons also ac- quire knowledge about a relation between

non-adjacent elements and about the ordinal position of a particular element. ([6], page 113)

Clearly the performance of animals points to representation of whole patterns that involves discrimination at a variety of levels. But if conceptualization

is seen as a result of evolution, it is not necessary

that this would have developed in exactly the same

331
manner for all species. Other animals learn concepts

nonverbally, so it is hard for humans, as verbal animals, to determine their concepts. It is for this reason

that the pigeon has become a favourite with intelli- gence tests; like humans, it has a highly developed

visual system, and we are therefore likely to employ

similar cognitive categories. It is to be noted that

pigeons and other animals are made to respond in extremely unnatural conditions in Skinner boxes of

various kinds. The abilities elicited in research must

be taken to be merely suggestive of the intelligence

of the animal, and not the limits of it. In an ingenious series of experiments Herrnstein

and Loveland He64 were able to elicit responses about

concept learning from pigeons. In another experiment Herrnstein He85 presented 80 photographic

slides of natural scenes to pigeons who were accus- tomed to pecking at a switch for brief access to feed. The scenes were comparable but half contained trees and the rest did not. The tree photographs had full views of single and multiple trees as well as obscure

and distant views of a variety of types. The slides were shown in no particular order and the pigeons were rewarded with food if they pecked at the switch

in response to a tree slide; otherwise nothing was

done. Even before all the slides had been shown the

pigeons were able to discriminate between the tree and the non-tree slides. To confirm that this ability,

impossible for any machine to match, was not some- how learnt through the long process of evolution and

hardwired into the brain of the pigeons, another ex- periment was designed to check the discriminating

ability of pigeons with respect to fish and non-fish scenes and once again the birds had no problem doing so. Over the years it has been shown that pigeons can also distinguish: (1) oak leaves from leaves

of other trees, (ii) scenes with or without bodies of

water, (iii) pictures showing a particular person from

others with no people or different individuals.

Herrnstein HeSo summarizes the evidence thus:

Pigeons and other animals can categorize

photographs or drawings as complex as those encountered in ordinary human ex- perience. The fundamental riddle posed by

natural categorization is how organisms devoid of language, and presumably also of

the associated higher cognitive capacities, can rapidly extract abstract invariances for some (but not all) stimulus classes containing instances so variable that we cannot

physically describe either the class rule or the instances, let alone account for the underlying capacity.

Amongst other examples of animal intelligence are mynah birds who can recognize trees or people in pictures, and signal their identification by vocal utterances—words—instead of pecking at buttons

Tu82, and a parrot who can answer, vocally, questions about shapes and colors of objects, even those

not seen before Pe83.

Another recent summary of this research is that of

Wasserman Wa95:

[Experiments] support the conclusion that

conceptualization is not unique to human

beings. Neither having a human brain nor being able to use language is therefore a

precondition for cognition... Complete un- derstanding of neural activity and function

must encompass the marvelous abilities of

brains other than our own. If it is the busi- ness of brains to think and to learn, it should

be the business of behavioral neuroscience

to provide a full account of that thinking

and learning in all animals—human and

nonhuman alike.

Gradation of Intelligence

An extremely important insight from experiments of

animal intelligence is that one can attempt to define

different gradations of cognitive function. It is obvious that animals are not as intelligent as humans;

likewise, certain animals appear to be more intelli- gent than others. For example, pigeons did poorly

at picking a pattern against two other identical ones,

as in picking an A against two B's. This is a very

simple task for humans. Herrnstein He85 describes

how they seemed to do badly at certain tasks:

• Pigeons did not do well at the categorization

of certain man-made and three-dimensional objects.

• Pigeons seem to require more information than

humans for constructing a three-dimensional image from a plane representation.

• Pigeons seem to have difficulty in dealing with

problems involving classes of classes. Thus they

do not do very well with the isolation of a rela- tionship among variables, as against a representation of a set of exemplars.

In a later experiment Herrnstein et al. He89

trained pigeons to follow an abstract relational rule

by pecking at patterns in which one object was inside,

332
rather than outside of a closed linear figure. Wasserman Wa93,Wa95 devised an experiment to show that

pigeons could be induced to amalgamate two basic categories into one broader category not defined

by any obvious perceptual features. The birds were

trained to sort slides into two arbitrary categories,

such as category of cars and people and the category

of chairs and flowers. In the second part of this ex- periment, the pigeons were trained to reassign one

of the stimulus classes in each category to a new re- sponse key. Next, they were tested to see whether

they would generalize the reassignment to the stimulus class withheld during reassignment training. It was found that the average score was 87 percent in the case of stimuli that had been reassigned and 72

percent in the case of stimuli that had not been re- assigned. This performance, exceeding the level of

chance, indicated that perceptually disparate stimuli

had amalgamated into a new category. A similar ex- periment was performed on preschool children. The

children's score was 99 percent for stimuli that had

been reassigned and 80 percent for stimuli that had

not been reassigned. In other words, the children's

performance was roughly comparable to that of pigeons. Clearly, the performance of adult humans at

this task will be superior to that of children or pigeons.

Another interesting experiment related to the abstract concept of sameness. Pigeons were trained to distinguish between arrays composed of a single, re- peating icon and arrays composed of 16 different icons

chosen out of a library of 32 icons Wa95. During

training each bird encountered only 16 of the 32 icons;

during testing it was presented with arrays made up

of the remaining 16 icons. The average score for training stimuli was 83 percent and the average score for testing stimuli was 71 percent. These figures show

that an abstract concept not related to the actual

associations learnt during training had been internalized by the pigeon. And the performance of the pigeons was clearly much worse than what one would

expect from humans.

Animal intelligence experiments suggest that one

can speak of different styles of solving AI problems.

Are the cognitive capabilities of pigeons limited be- cause their style has fundamental limitations? Can

the relatively low scores on the sameness test for pigeons be explained on the basis of wide variability in

performance for individual pigeons and the unnatural

conditions in which the experiments are performed?

Is the cognitive style of all animals similar and the

differences in their cognitive capabilities arise from

the differences in the sizes of their mental hardware?

And since current machines do not, and cannot, use

inner representations, is it right to conclude that their

performance can never match that of animals?

Another issue is whether one can define a hierarchy of computational tasks that would lead to varying

levels of intelligence. These tasks could be the goals

defined in a sequence, or perhaps a lattice, that could

be set for AI research. If the simplest of these tasks

proved intractable for the most powerful of computers

then the verdict would be clear that computers are designed based on principles that are deficient compared to the style at the basis of animal intelligence.

Recursive Nature of Animal Behavior

A useful perspective on animal behavior is its re- cursive nature, or part-whole hierarchy. Considering this from the bottom up, animal societies have

been viewed as "superorganisms" . For example, the

ants in an ant colony may be compared to cells, their castes to tissues and organs, the queen and her drones

to the generative system, and the exchange of liquid

food amongst the colony members to the circulation

of blood and lymph. Furthermore, corresponding to morphogenesis in organisms the ant colony has so- ciogenesis, which consists of the processes by which

the individuals undergo changes in caste and behavior. Such recursion has been viewed all the way up

to the earth itself seen as a living entity. Parenthetically, it may be asked whether the earth itself, as a

living but unconscious organism, may not be viewed

like the unconscious brain. Paralleling this recursion

is the individual who can be viewed as a collection of

several "agents" where these agents have sub-agents

which are the sensory mechanisms and so on.

Logical tasks are easy for machines whereas AI

tasks are hard. It might well be that something fundamental will be gained in building machines that

have recursively defined behavior in the manner of

life. But how such machines could be designed is not

at all clear. A hierarchy of intelligence levels can be useful also

in the classification of animal behavior. There does

not appear to be any reason that experiments to check for intelligent behavior at diflTerent levels could

not be devised. Furthermore, experiments could be

conducted to determine the difference in ability for individual animals. That such experiments have not

been described until now is merely a reflection of the

peculiar history of the field.

333
Concluding Remarks

Study of animal intelligence provides us with new perspectives that are useful in representing the performance of machines. For example, the fact that pigeons learn the concept of sameness shows that this

could not be a result of associative response to cer- tain learnt patterns. If evolution has led to the development of specialized cognitive circuits in the brain

to perform such processing, then one might wish to endow AI machines with similar circuits. Other questions arise: Is there a set of abstract processors that

would explain animal performance? If such a set can be defined, is it unique, or do different animal species

represent collections of different kinds of abstract processing that makes each animal come to achieve a unique set of conceptualizations?

Animal behavior ought to be used as a model to define a hierarchy of intelligence tasks. This hierarchy is likely to be multidimensional. Various kinds of

intelligence tasks could define benchmark problems

that would represent the various gradations of intel- ligence.

Should VI reflect the degree of recursion in the or- ganization of the intelligence in the machine? Given

that the neural organization of the brain consists of

"networks of networks", it appears that this be so. On similar grounds, one may assert that the performance of the machine should span several scales. The

relative scale invariance of the performance will be a measure of the "quality" of the intelligence.

[6] Terrace, H.S. (1985). "Animal cognition: thinking without language." Phil. Trans. R. Soc. Lond. B 308, 113-128.

[7] Wasserman, E.A. (1993). "Comparative cognition: Beginning the second century of the study

of animal intelligence." Psychological Bulletin,

113, 211-228.

[8] Wasserman, E.A. (1995). "The conceptual abilities of pigeons." American Scientist, 83, 246-255.

References

[1] Gibson, J.J. (1979). The Ecological Approach to Visual Perception. Houghton Mifflin, Boston.

[2] Herrnstein, R.J., Loveland, D.H. (1964). "Complex visual concept in the pigeon." Science 146,

549-551.

[3] Herrnstein, R.J. (1985). "Riddles of natural cat- egorization." Phil. Trans. R. Soc. Lond. B 308,

129-144.

[4] Herrnstein, R.J., W. Vaughan, Jr., D.B. Mumford, and S.M. Kosslyn. (1989). "Teaching pigeons an abstract relational rule: insideness."

Perception and Psychophysics 46, 56-64.

[5] Kak, S. (1996) "Can we define levels of artificial intelligence?" Journal of Intelligent Systems, vol. 6, 133-144.

334
Biometric Techniques: The Fundamentals of Evaluation

Thomas A. Chmielewski and Paul R. Kalata

Drexel University

Philadelphia, PA 19104

tchmiele@ cbis.ece.drexel.edu, kalata@cbis.ece.drexel.edu

ABSTRACT

While the term biometrics may connote high technology, it simply

stands for the concept of recognizing a human being. Today, we use technology to automate the measurements of physical or behavioral

characteristic of an individual, so that these measurements may be

compared against previously stored data to authenticate an

individual's claimed identify. Current biometric systems use diverse

measurements, technology and algorithms making it difficult to compare their performance on an equal basis. In general, a candidate

biometric system needs to be accessed against the performance

requirements of an application. The biometric community uses

statistical measures to define the performance of systems. The

objective of this paper is to provide a brief tutorial on biometrics and

the current measures used to define performance in order to provide

information to new biometric users and to stimulate the research

community in this rich problem.

Keywords: acceptance threshold, biometrics, confidence

intervals, false accept rate, false reject rate, FAA, FAR, d', performance, pdf, receiver operating curves, ROC

1. Biometrics: A Brief Tutorial

Humans identify one another by the way faces look and can sometimes identify individuals at a distance based on their stature and gait. Over the years, inventive humans extended

their innate identification capability by applying engineering

techniques to allow identification of individuals without

having the need for someone who explicitly knew the

individual. Specifically, the identification problem was solved

by relating a physical entity or "secret" information to a person by using:

• something you "have" such as a card or key,

• something you "know" such as a password or personal identification number (PIN).

Either alone or in combination, possession and knowledge

can enable the use of technology to identify a person. ATMs

for example require the use of the ATM card (have) plus a Pn^ (know) to gain access. Since using possession and

knowledge for identification purposes cannot distinguish

between the correct person and a potential impostor who

acquired the possession/knowledge, there is clearly a need for a higher level of positive personal identification.

It is possible to eliminate the aspect of possession and/or

knowledge and rely rather on something that the person "is",

specifically a physiological or behavioral characteristic that can be easily detected, that is time invariant, and that is significantly different across the population of people who

will be identified by it. The term biometric is used to describe

these characteristics which allow identification of an

individual. The key advantage of using biometric data to identify a person is that the biometric cannot be stolen,

misplaced or forgotten because it is something that the person

"is", as contrasted to "possession" and/or "knowledge".

Biometric Systems use technology to automate the

measurements of physical or behavioral characteristic of an

individual, so that these samples may be compared against

previously stored data to determine if significant similarities

exist in order to confirm the samples sufficiently match the

stored data hence confirming or denying the individual's

identity. In essence, biometric identification is a pattern

recognition problem. In order to allow good decisions to be

made, we would like maximum variations across individuals,

but minimum variation for any given person across time or environmental conditions.

There are two types of problems that a biometric system

must handle namely:

• Verification Problem (authentication): confirming or denying a person's claimed identity (Am I who I claim I am ?); this is a one to one matching process.

• Recognition Problem (identification): establishing a person's identity from a set of stored identities; this is a one to many matching process.

Biometrics currently in commercial use for either

identification or recognition include: fingerprints, hand

geometry, handwritten signatures, voiceprints, face, iris,

retinal patterns and thermograms [1]. Certain, physical

characteristics such as fingerprints and iris texture, are considered to be "invariant". Behavioral characteristics such

as voice and signature, are considered to be "somewhat

variable" since they are influenced by physical and emotional

conditions and evolve over time.

The retina, the iris and fingerprints are considered truly

unique and provide the greatest precision for biometrics [9].

However, other biometrics should not be dismissed, since each

biometric provides unique advantages which can be exploited

by selecting the correct biometric for the correct application.

For instance, INSPASS (Immigration and Naturalization

Service Passenger Accelerated Service System) uses a hand

geometry system to quickly verify the identity of arriving

passengers in speeding up international arrivals at certain

North American international airports. People enrolled in

INSPASS are given a magstripe card encoded with

appropriate data for their hand geometry. Upon arrival,

INSPASS travelers swipe their card (have), place their hand in a reader (are), and then proceed to the customs gate.

Coupling the "have" and "are" makes hand geometry a good

335
solution for this application (even though this biometric is not

as unique as others) since it is cost effective, readily accepted

by most users and exhibits low failure rates in acquiring data.

2. System Functionality

Before a biometric system can operate, a quality sample(s) of

the biometric signal, such as a fingerprint, the image of an iris, or speech from users of the system must be obtained. This is called the enrollment process. Enrollment usually involves an

operator who coaches the users to provide the best biometric

input. These inputs are processed and stored as templates or

feature vectors that contain the pertinent information used for

later biometric data comparison. Additionally, the person's

identity in the form of an "ID number" or some data structure

is associated with the template. Enrollment should be done

under the best of conditions since the quality of data stored

during enrollment effects the performance of the system.

Figure 1 illustrates a verification system block diagram.

Sensor,

signal

processing,

feature extraction

Stored

Feature Vectors

Feature Vector

Accept or Reject

Score

Figure 1. Biometric Verification System Block Diagram

Here it is assumed that a number of individuals have been

enrolled in a data base and have been given an " ID number"

(such as a bank account). When a person uses the system,

their ID number (have) is used to reference a stored feature

vector or template in a database. A sensor obtains a biometric

sample from the person and then extracts the relevant features

from the biometric data into a feature vector. A comparison of

the stored feature vector and the computed feature vector is made {one-to-one process) generating a matching score. The

score is then passed to a decision process. The results of the

decision process are acceptance or rejection of the premise

that the person at the device is the same as the one who

originally generated the feature vector during the enrollment

process as referenced by the ID number. Verification

acceptance or rejection is based on comparing the matching

score to a decision threshold defined by a priori statistic of

system performance and the application.

The recognition process is significantly different. No ID

number is input and the system must compare the feature

vector of the person at the device against all stored feature vectors {a one-to-many process). If a match is found, then an ID number (and/or other data structure) is retrieved and the

person is identified and coupled to this data allowing them

access to a building, bank account, etc.

Consider the possible resulting outcomes for a system if a person walks up and attempts to be verified (or identified).

There are two possible descriptions of the user: he/she is the correct person (and should be given access) or he/she is an

impostor (trying to gain access). Ignoring the case where the

biometric system chooses to make no decision, there are two

possible outcomes (pass or fail), generating four possible

conditions. Table 1 shows these outcomes and conditions:

either the correct result occurs, or the system falsely rejects

the authentic or falsely accepts the impostor. In the case that

the system chooses to make no decision, the user may be

given another chance. The performance of a biometric or biometric system is measured by the frequency of false accepts and false rejects.

Table 1. Results of Verification or Identification

for a Biometric System User

User Pass Fail

Authentic correct acceptallow access

false reject -

refuse access Impostor false acceptallow access

Correct rejectrefuse access

3. Performance

The quantitative measure of the performance of a biometric

verification system is defined by the frequency of false accepts and false rejects [1,12]. These probabilities define

how correctly the biometric returns a matching score when a correct individual (authentic) or an incorrect individual

(impostor) is presented to the system. While other

performance metrics such as the speed of operation, number of

templates capable of being stored, and cost are important they

are not the focus of this paper.

While we would like to have ideal performance for every

biometric (i.e. no False Accepts and no False Rejects), this is unachievable when we consider real world factors such as noise, environmental conditions or the actual discriminating

capability of the biometric itself. While not perfect, biometrics

are used in many successful applications. Generally, the

application dictates the required performance of a biometric.

Banks may be willing to accept a certain level of False

Accepts but no False Rejects at an ATM in order to keep their

customers happy. Alternatively, access to a highly secure

facility may not allow any False Accepts but allow False

Rejects, since real authentics would be willing to try the

multiple times necessary to gain access. A Cost Functional

(probability of the decision times a "cost") may also be used to best determine the False Accept versus False Reject trade-off.

3.1 Population Issues: Failure-to-Enroll (FTE)

Even though we would like a biometric to be universally

applicable across all users in a given population, there may be some people who cannot use the system due to abnormalities,

diseases, injuries, accidents, or degradation of the biometric

336
signal due to their occupation. For instance, masonry workers

may wear down their fingerprints so as to make this biometric

unreliable. The subset of a population who cannot use a

particular biometric are called outliers.

Outliers will generally not be able to enroll in a system

due to the absence of the biometric or a signal that cannot be

converted into a biometric template due to such factors as signal strength, missing characteristics or characteristics

present in their biometric not considered by the system. The

performance of outliers are categorized by a failure-to-enroll

(FTE) rate. Numbers to boimd the FTE for a biometric can be

estimated from the frequency of abnormalities, permanent

injuries, or permanent diseases in a given population (or for

the world by geographic location) that prevent use of the

characteristic. When testing any population, FTE should be

acctmiulated and analyzed.

3.2 System Issues: Failure-to-Acquire (FTA)

Failure-to-Acquire (FTA) is defined as the failure of a biometric system to capture information prior to the extraction

of biometric data for the feature vector. This failure may

occur during the enrollment process, the verification process

or the identification process. It is dependent on the ancillary

processes, human factors and external disturbances that may

affect the sensor used to acquire the biometric sample.

Factors that cause FTAs include: user distraction, or acute

injuries or diseases that prevent acquisition of the biometric

signal. During testing, FTAs should be accumulated and

included in the false reject rate computation (for persons

previously enrolled) especially if the entire system

performance is being considered rather than just the raw

biometries' performance.

3.3 False Accept Rates and False Reject Rates

(FAR, FRR)

Biometric Systems suppliers typically use FAR (False Accept

Rate) together with FRR (False Reject Rate) to describe the

capabilities of their system (Table 1). FRR is the error rate at which a true authentic (i.e., an individual claiming to be who

they actually are) is rejected by the system. FAR is the error

rate at which a false authentic (i.e., an individual claiming to be who they are not, i.e., an impostor) is falsely allowed to use

the system. FRR and FAR are interrelated by statistics and are dependent on the acceptance or decision threshold of the

biometric system under consideration. Being more liberal in

the acceptance criterion (a lower threshold), will generally

allow more people (both authentics and impostors) into the

system. Conversely, being stricter in the acceptance criterion

will reject more people (both authentics and impostors). The

setting of the threshold is dictated by the requirements of the

application.

The relationship between FAR and FRR is easily

understood by plotting a distribution of the matching scores of

authentics and impostors on the same graph. Sometimes

referred to as Authentics-Impostor Distribution Curve or a

Performance Histogram, this graph shows the distribution of

the population versus a given score of the biometric. The

match (or mismatch) score (how well the template matched

the biometric sample) is plotted on the horizontal axis and the

population frequency on the vertical axis. For statistical analysis, the curves can be normalized so that the area under

each curve is one. When normalized in this way the curves become probability distribution functions and may be used to compute probabilities or error rates. Figure 2 shows a typical

set of curves. As can be seen the authentics curve (left) and

impostor curve (right) overlap. Note that these curves could

be interchanged based on the meaning of the horizontal axis.

For Figure 2, the better the match between a user and the

stored template, the lower the value, with zero (a perfect

match) at the far left. The setting of the decision threshold

(shown by the vertical bar) defines both the false accept rate (FAR) and false reject rate (FRR). FAR is the area under the

impostor curve to the left of the decision threshold. FRR is the area under the authentics curve to the right of the decision

threshold. From Figure 2, it is clear that FAR and FRR are

interrelated, one cannot specify each value independently!

1

o

a.

/ Authentics \ / Impostor \ / Distribution \ / Distribution \

y FAi /

Acceptance Threihold

Matching Scot?

Figure 2. Hypothetical Authentic and Impostor Curves

Impostor and Authentic histograms are different for each

biometric. In fact, the actual curves for a given type of

biometric may be platform, sensor or algorithm dependent.

Obtaining histograms which have sufficient data to perform a

reliable curve fit for analysis of their tails requires large

amounts of data. The impostor and authentics histograms are usually generated off line by using multiple samples of a closed set of individuals. Hence, for a set of N individuals

there will be some number of samples mN for each individual.

By verifying each sample against each other for a given

individual an individuals authentics histogram can be

generated. The authentics curve for the tested population is obtained by combining all the individual authentics data.

Verifying each sample for a given individual against all other

individual's samples produces the impostor histogram. Hence,

the authentic histogram is a combination ofN people having

mN samples each while the impostor curve is the combination

of the verification of each sample for a given person against

337
all other persons' samples.

Performance curves are not necessarily Gaussian.

Generally, there is a binning process associated with these

performance curves. Binned histograms may show no

overlap, incorrectly indicating separation between authentics

and impostors. Fitting theoretical curves to the measured data

allows computation of FAR/FRR for various thresholds and

produces the theoretical tails of the performance curves. Without curve fitting, the granularity of the data bins may not

provide sufficient resolution for computation of the FAR/FRR

as the decision threshold changes or at extreme points. Fit

data may provide more conservative (or more liberal) values

than are actually descriptive of the system. It is important to exercise caution with curve fitting since the results may not

accurately model the system (especially in the tails) leading to inaccurate probability computations.

Further modifying the shape of performance curves are "goats" and "sheep". Goats are users who consistently return

large distance measures when new samples are compared to their enrolled templates. Sheep, generally a larger part of the

population, return small distance measures compared to their

enrolled data. The sheep (small variance) and goat (large

variance) performance can cause the histogram to exhibit

bimodal authentics plots with the goats associated with a smaller secondary mode [1].

3.4 An Empirical Bound on FAR

Consider a biometric that may not be capable of clearly

distinguishing characteristics of identical twins (possibly a

face biometric). One can put the following bound on the FAR

due to identical twins [1]. Statistically, 1 in 80 births are twins

and about 1/3 of twins are identical (monozygotic). If we

consider 240 births, there are 243 individual and one pair of

them are identical twins. The chance of selecting a person at random who has an identical twin is roughly 2/243 = 0.82%.

With the assumption that a particular biometric cannot

distinguish between identical twins, one can define the

minimum False Accept rate at 0.82% due just to the birth of

identical twins. Besides user cooperation or technical factors,

fraternal twins and parent/offspring may also share the same

biometrics value (consider that many fraternal twins still look

alike). Putting a number on this contribution to FAR is significantly more difficult.

4.0 The Underlying Probability for Decisions

In biometric identification, a decision to the authentic or impostor must be made based on noisy measurements. To

this extent, one must understand the probabilistic nature of the

measurement, how it is processed to make a decision and the

performance of the decision itself. Consider a noisy biometric random variable, x, characterized by its probability density function (pdf), fx(x)

with properties [6,7]:

fx(x)>0, jfx(x)dx=l.

Common pdf shapes include: Gaussian, Uniform, Exponential,

Binomial and Poisson. Two important parameters which

describe the pdf are the mean and standard deviation (the

square root of the variance):

mean: x = | x fx(x) dx

variance: a| =

j (x-x )^ fx(x) dx.

The mean, x , is "where" the pdf mass is concentrated and the

standard deviation, Ox, is the "spread" of the mass about the

mean as illustrated by the triangular pdf in Figure 3.

X - a X X +a

Figure 3. Probability density function

4.1 Biometric Measurement

Consider noisy measurements (or score) of authentic and

impostor alternatives {ma, mj} of {xa, xj} with zero mean

measurement noise {e^, ei}as illustrated by Figure 4.

authentic measurement: ma = Xa + Ca

impostor measurement: mj = xi + ej

The variability of the authentic and impostor measurements

invariably overlap each other as illustrated by Figure 4.

m

Figure 4. Authentic and impostor measurements

4.2 Biometric Decision

For our analysis, high valued measurements imply an

authentic biometric source and low valued measurements

imply an impostor source. The decision design [7] is to select a measurement threshold, mth: for measurements exceeding

the threshold, decide an authentic source (da); alternatively, if

the measurement is less than the threshold, decide an impostor

source (dj) as illustrated by Figure 5, i.e.,

da: if m > mth df: if m < mth

338
m

Figure 5. Authentic or impostor threshold decision

For the binary source/decision process there are 4 possible

outcomes as illustrated by Table 2 in that: • an impostor decision with an impostor source results

in a correct rejection (dismissal),

• an impostor decision with an authentic source results

in a false rejection (mis-detection),

• an authentic decision with an impostor source results

in a false acceptance (false alarm), and

• an authentic decision with an authentic source results

in a correct acceptance (detection).

The terms {dismissal, mis-detection, false alarm,

detection} are common in decision theory and can be

interchanged with {correct rejection, false rejection, false

acceptance, correct acceptance} respectively as in Table 2.

Table 2. Binary Source/Decision Outcome

Decision Source

authentic, Xa impostor, xi

authentic da correct accept

(detect)

false accept

(false alarm)

impostor dj false reject

(mis detect)

correct reject

(dismissal)

In virtually all decision cases, no matter where the threshold is

set, there will be correct decision and there will be incorrect

decisions. The decision design problem is to select the

threshold to maximize the (weighted) correct decisions and

minimize the (weighted) incorrect decisions.

4.3 Biometric Performance

The decision process performance is determined by the

authentic and impostor probabilities which are determined by

areas imder the pdfs depending. In particular:

Probability of a correct acceptance, P(da|xa), is the area under f(ma) for m>mth,

Probability of a false acceptance, P(da|xi), is the area under f(mi) for m > mth,

Probability of a correct rejection, P(di|xi), is the area under f(mi) for m < mth, and

Probability of a false rejection, P(dilxa), is the area under f(ma) for m < mth.

Figures 6a, 6b and 6c illustrate these decision probabilities.

m

Figure 6a. Probability of Correct Accept and False Reject

m

X, '"th

Figure 6b. Probability of Correct Rejection and False Accept

m

Xj "^th Xa

Figure 6c. Probability of False Reject and False Accept

4.4 Cross-Over-Error-Rate (CER)

The value at which the FAR equals the FRR defines the cross- over-error-rate (CER) or equal-error-rate (EER). The CER

may be correlated to the decision threshold that allows this

equality. CER provides one method of comparing biometric

performance since it is a characteristics of the set of

histograms and predefines the threshold setting.

4.5 ROC curves

Another convenient ways to compare the decision process is with "Receiver Operation Characteristic" (ROC) curves which

illustrates performance probabilities generated by varying the

threshold decision. Figure 7a illustrates the ROC-P(D)/P(FA)

performance: Probability of detection (correct acceptance)

verses the Probability of false accept. Varying the threshold

decision, we can improve in the detection probability, but we

also increase the false accept probability. The biometric

performance objective is to make this curve as convex to the

left as possible and then select which point on this curve is acceptable for biometric identification operation.

339
0

detection

probability

0

false alarm

probability

Figure 7a. ROC : P(detection) verses P(false accept)

Another ROC curve is illustrated by Figure 7b, the ROCP(FR)/P(FA) performance: Probabihty of false rejects verses

the Probability of false accept. This is the ROC curve normally used in biometric literature. Each point on the curve corresponds to a decision threshold and the corresponding

FRR and FAR may be easily seen. Varying the threshold

decision, we can decrease the false reject probability, but we

also increase the false accept probability. The biometric

performance objective is to make this curve as concave to the

left as possible and then to pick which point on this curve is acceptable for biometric identification operation. As

illustrated in Figure 7b the CER (where FAR = FRR) can be

easily found from a ROC curve.

1

0

0

false accept

probability

Figure 7b. ROC: P(false reject) verses P(false accept)

1

4.6 Biometric Confidence

The generation of the biometric pdfs as described above must

be made by acquiring test data from the biometric system.

Once this is done, the estimates of the means and variance are calculated by the sample means and sample variance:

sample mean: Ss^Zxn

sample variance:

1 St =

'X Z (xn - X s)' N-1

However, these are only estimates of the true mean and

variance. Performance as determined by previous sections assumes that we have the true mean and variance. To

compensate for only having sample means and sample

variances, we have to use "Confidence Levels" and

"Confidence Intervals" [8] in describing the biometric

performance.

Given N biometric samples, with probability "1-a"

confidence level, the sample mean will lie somewhere within a confidence interval between an upper and lower bound which

depend on the number of samples, sample mean, the sample

variance and a confidence level:

X c

- ta/2,N-l < X < X c + ta/2,N-l

where ta/2,N-l is the t-distribution point [8]

P(tN-l >ta/2,N-l)=f

Similarly, with probability "1-a" confidence level, the sample

variance will lie somewhere within a confidence interval

between an upper and lower bound which depend on the

number of samples, the sample variance and a confidence

level: (N-l)s2 ^ (N-l)s2

^a/2;N-l

^x

<

where

'^l-a/2;N-l

^a/2 N 1 chi-squared point [8]

2 ^,,2

-'^a/2;N-l^ 2

P(%-1

In biometric system design, we desire to have tight bounds to evaluate/ensure decision performances. Hence, it is obvious

from the bound on the sample means, we wish to have a large

number of samples to obtain the sample mean with a tight

bound. From the "N-l" numerator term in the sample

variance bounds, it initially appears that the bound increases

with increasing number of samples, but X(^/2-n j t^J

decreases more rapidly than N- 1 increases and the net effect is

to decrease the sample variance as N-l increases.

4.7 Decidability Index

The Decidability Index, d' is a measure of the authentic and

impostor distributions separation, given by:

340
d

X - X . a I 2 2 a + (J 7

a I

were X and Xi are the means of the authentic and impostor

2 2 histograms with variances (5 a and <5 \. Since the design

performance is to maximize the probabihty of correct decisions and minimize the incorrect decisions, there are two

biometric separation properties which can influence the

decision performance:

i) maximum separation of the authentic and impostor

means, i.e. max: | x g

- x j |, and

ii) minimize both the authentic and impostor variances,

i.e. min: {a 2^ , a 2.

}

If either/both of the above biometric separation properties

improve, Figure 8 illustrates the pdfs.

dimpostor

^authentic

\ f(ma)

Figure 8. Authentic/Impostor pdfs and threshold decision

Mathematically, d' is independent of the decision threshold

and reflects the degree to which any improvement in FAR

must be paid for by relaxing of the FRR. As can be seen from

the equation the larger d', the better the separation, hence for

the best possible discrimination we want a large value of d'. It

is obvious that the corresponding Probability of

correct/incorrect decisions will improve no matter what the

threshold setting is whenever d' is increased.

5. Testing Paradigms

As with using anything new, unfamiliarity can contribute to

errors. Biometric systems also exhibit a learning curve. Generally, as users in a test suite become more familiar with

the system (habitation) they learn techniques or tricks needed

to properly interface to the system and false rejection errors tend to decrease. Proper training can significantly reduce the

time to gain system familiarity hence minimizing the initial

false reject rate since clues can be given as opposed to being

learned by trial and error. Uncooperative test subjects or unwilling users can skew

collected data effecting the measured operative accuracy of a system under test. Thus, it is imperative to design tests that

identify or minimize potential human bias during the

collection of biometric performance data. Consider the

conditions under which data for biometric performance can be

collected. Three conditions apply:

• Ideal Data Collection,

• Controlled Real World Data Collection and

• Uncontrolled Real World Data Collection.

In the first case, data is collected and analyzed under the best

possible conditions, for instance a user's head may be placed

in a chin rest for facial or iris recognition, environmental

factors such as ambient temperature and lighting can be

controlled. For the second case, data is collected in a real

world operational application but under a well controlled

experimental situation, for instance having a set of known

cooperative subjects use the equipment. The third approach

uses data collected at an actual application by actual users, hence it is in an unsupervised, uncontrolled environment.

Each of these scenarios can be used to provide a different

perspective on the performance of the system. Ideal data

collection allows evaluation of the raw biometric

implementation along with its sensors and other components.

Controlled real world data introduces the environment as well

as an application (but with cooperative users) thereby allowing

testing of the biometric system under an actual application.

Uncontrolled real world data collection introduces the most

variability and is the most difficult. For the last case, it is envisioned that there is an automatic data collection that

would report the results of the system's performance over some operational period, there are some issues in this approach as actual data (is it really a false reject or an

impostor trying to gain access) would not be known.

5.1 Dual Thresholds

Sometimes "upper and lower bound" acceptance thresholds

are sometimes used. This approach allows the user to try a second time if the initial score is between an upper threshold

(which would passes the user) and a lower threshold (which

would fail the user). Clearly, dual thresholds affects the

reported FAR and FRR of the biometric data.

5.2 Attempts and Tries

Collecting statistics for false accepts and false rejects requires

a definition of whether the data is collected for a "single try" or is decided after "n tries". The word "try" is used to define

a single presentation of the user to the biometric system for

measurement (verification). The word "Attempt" defines a cycle of an individual using the biometric system. Most

verification devices allow more than one try per attempt and

may even internally take multiple samples of the user's biometric for each try. Hence, when collecting and reporting

data it is important to define how the counting is done.

Table 3 describes how to count accept and reject data for a "maximum three try" decision process [2]. In this case, accept and reject are defined as whether the user's biometric

score is greater or less than a predefined acceptance threshold.

If data is collected for multiple users, with multiple attempts

per user, the false-reject rate for one-try, two-try or three-try

341
statistics can be computed by using the total of rejects divided

by the total attempts, hence for the table shown (assuming

each verification results is from a user): FRR = Va for one-try

statistics, FRR = 'A for two-try statistics and FRR = Va for

three-try statistics. Other ways of reporting the performance

statistics are also possible using this approach for instance one

could report the FRR statistics for individual tries.

Table 3 Three Try Decision Counting for a Verification System

Verification Result

One-try

Statistics

Two-try

Statistics

Three-try

Statistics

Accept on 1" try Accept Accept Accept

Accept on I"** try Reject Accept Accept

Accept on 3'^ try Reject Reject Accept

No Accepts in 3 Reject Reject Reject

tries

5.3 Some Formal Test Reports

In 1991, Sandia National Laboratories released a report

entitled "A Performance Evaluation of Biometric

Identification Devices" which describes the testing of a select

set of vendors' biometric devices [2]. Specifically,

fingerprints, hand geometry, signature, retina and voice

biometrics were evaluated and error rate curves generated. A

second report titled "Laboratory Evaluation of the IriScan

Prototype Biometric Identifier" was issued in 1996 [3]. Both

of these reports are a good reference on the procedures and

issues that need to be considered for testing of biometric

devices. For instance, it is seen that as a user becomes more

familiar with a biometric, the false rejection rate decreases,

additionally, it was found that retraining and reenroUment of

problem users may not always result in higher performance.

Sandia's testing attempted to get as many transactions as possible (without too much fatigue or loss of participation

from disinterest) from a limited set of users/volunteers.

The FERET (Face-Recognition Technology) program

administered by the US Army Research Laboratory [4,5,10]

provided a large database of faces collected in a controlled

setting (consistent with mug shots or drivers license photos) so

that four face recognition algorithms could evaluated under

double-blind testing conditions. Results showed dependence on illumination and sensors as well as time between

enrollment and verifications (comparison of images taken the same day verses a year apart).

6. Conclusions/Recommendations

Today's automated biometrics technologies are changing due

to advances in computer hardware, sensors and algorithms.

There are potentially large and diversified uses for biometrics

including such applications as: securing Internet credit card

based transactions, secure computer logon, ATM access, law

enforcement related identification, building access, access to

medical records and access to secure facilities. A significant

challenge faced in implementing a biometric system is understanding the performance of the system in terms of the

tradeoff between FAR and FRR for the particular application.

This paper presented an overview of biometrics and statistical measurements currently used to describe the performance of

biometric systems. Measuring the performance of a biometric

system requires well defined testing and sufficient data to extrapolate performance to the actual user population.

Providing quantitative performance to compare different

biometrics or even the same biometric using a different

platform represents a larger task requiring numerous test subjects, a good definition of the process and a standard data

analysis approach. The issue of comparative performance

measurements represents a challenge to the research

community as well as the biometric industry.

References

1. A. Jain, R. Bolle and S. Pankanti, Biometrics Personal

Identification in Networked Society. Kluwer Academic

Publishers, Boston, July 1999.

2. J. P. Holmes, et al., "A Performance Evaluation of

Biometric Identification Devices", Sandia National

Laboratories, SAND9 1-0276, June 1991.

3. F. Bouchier, J. Ahrens, G. Wells, "Laboratory Evaluation

of the IriScan Prototype Biometirc Identifier", Sandia

National Laboratories, SAND96- 1033, April 1996.

4. P.J. Philips, "FERET (Face-Recognition Technology)

Recognition Algorithm Development and Test Results",

Army Research Laboratory, ARL-TR-995, October 1996.

5. P.J. Philips, et al., "FERET (Face-Recognition

Technology) Recognition Algorithms", Proceedings of

the ATRWG Science and Technology Conference, July

1996.

6. A. Papoulis, Probability, Random Variables and

Stochastic Processes, 3rd ed, McGraw Hill, New York,

1991.

7. H. Urkowitz, Signal Theory and Random Processes,

Artech House, 1982.

8. D. Montgomery, Introduction to Statistical Quality

Control, 3rd ed, John Wiley and Sons, New York, 1996.

9. J.D. Woodward, Believing In Biometrics, Information

Security Magazine, March 1998.

10. A. Pentland, T. Choudbury, Face Recogntion for Smart

Environments, IEEE Computer Magazine February 2000,

Vol 3, No. 2.

11. M. Negin, T. Chmielewski et al, An Iris Biometric System

for Public and Personal Use, IEEE Computer Magazine

February 2000 Vol 3, No. 2.

12. P. J. Philips, A. Martin et al.. An Introduction to Evaluating Biometric Systems, IEEE Computer Magazine

February 2000 Vol 3, No. 2.

342
Measuring the quality of visual learning

Giovanni Bianco

Computer Science Serv., University of Verona, Italy

bianco@chiostro . univr . it

ABSTRACT

Biology often offers valuable example of systems both

for learning and for controlling motion. Work in robotics

has often been inspired by these findings in diverse ways.

Nevertheless, the fundamental aspects that involve visual

landmark learning has never been approached formally. In

this paper we introduce results that explain how the visual

learning works. Furthermore, these tools provide bases to measure the quality of visual landmark learning. Basically,

the theoretical tools emerge from the navigation vector field produced by the visual navigation strategy. The learning

process influence the motion vector field whose features are addressed.

1 INTRODUCTION

Animals are proficient in navigating and diverse methods of biological navigation have been recently studied and

categorized as [20]: guidance, place recognition - triggered response, topological and metric navigations. In order to perform such tasks animals usually deal with identifiable

objects in the environment called landmarks [21]. The use of landmarks in robotics has been extensively

studied [4]. Basically, a landmark needs to possess characteristics such as the stationarity, reliability in recognition,

and uniqueness. These properties must be matched with

the nature of a landmark: landmarks can be artificial or

natural. Of course it is much easier to deal with artificial landmarks instead of dealing with natural ones, but the

latter are more appealing because their use requires no engineering of the environment. However, a general method

of dealing with natural landmarks still remains to be intro- duced. The main problem lies in the selection of the most

suitable landmarks [19]. ' Recently it has been discovered that wasps and bees

perform specific flights during the first journey to a new

place to learn color, shape and distance of landmarks. Such

flights are termed Turn Back and Look (TBL) [11]. Once

the place has been recognized using landmarks, insects can

then accomplish navigation actions accordingly.

Starting from Biological bases, the system described in this paper selects natural landmarks from the surrounding

environment adopting the TBL phase. Once landmarks

have been selected suitable navigation movements are computed. Iterating the process of computation of the navigation vector over the whole environment, a vector field is produced.

Studying the navigation vector field two main results are provided:

• the visual potential function generating the navigation

vector field represents the driving principle to perform

visual guidance. When proven to be a Lyapunov compliant function, we can state the navigation system

exhibits convergence to the goal.

• The conservativeness of the navigation vector field provides key information about the quality of landmark learning.

Details about the navigation system and the computation of the potential function can be found in [2] and [3]. This paper addresses the learning process and its organization is as follows. In Section 2 aspects both related to findings on biological learning and to biological navigation

will be introduced. In addition, this section addresses for- mer work and research in the field of landmark learning in Robotics. In Section 4 the theoretical principles specifically

involved with visual learning are detailed. Final remarks

conclude the paper.

2 BIOLOGICAL FOUNDATIONS

Over many decades, studies of the visual performance

of bees have exploited the fact that bees keep returning

to a profitable feeding site once found, even when it is an

artificial food source established by an experimenter.

2. 1

^ Landmark learning

As soon as the bee encounters a novel place, she turns by 180 degrees to inspect the place and performs the initial phase of training, termed the Turn- Back- and-Look (TBL)

phase [10]. A similar behavior was also observed in other

insects thus categorizing this phase a typical behavior of an

insect when a new visual learning phase is needed [22, 23]. In references [10, 11] and [14] the details and results on the visual parameters learned by TBL are introduced.

Basically, findings show that TBL performed on departure

serves primarily for acquiring depth information by exploiting image motion, whereas color, shape and size of landmarks are mainly acquired on arrival. Attempts to understand in detail the geometric signifi- cance of learning flights have only recently been made. Essentially, the flights are invariant in certain dynamic and

geometric structures thus allowing the insects to artificially

produce visual cues in specific areas of the eyes [24]. Perhaps, the main reason is that the precision for the homing

mostly depends upon the proximity of chosen landmarks

to the goal [6]. In fact, those flights need to be repeated

whenever some changes in the goal position occur [12].

343
2.2 Landmark guidance

Landmarks guidance in insects is retinoptically driven

and animals tend to reduce the discrepancies between the

stored view and the actual one by a matching procedure

(reviews in [7] and [21]). The survey work presented in [20] addresses biological navigating behaviors from a robotics

point of view.

Referring to landmark guidance in bees, the seminal

work is presented in [5]. The authors show how bees

learn landmarks by storing an unprocessed two dimensional

snapshot of the panorama. The model matches landmarks

in the stored snapshot with landmarks in the actual image.

If this match is performed far from the goal every matched

pair could differ both in angular size and compass bearing.

These differences drive a bee toward the right position.

3 RELATED WORK

The guidance model introduced in [5] has some shortcomings and interesting extensions have been addressed in recent works. Basically, a guidance strategy that operates

with landmarks strives to reduce the differences between

the pre-learnt landmarks at the goal position and the same

landmarks viewed from a different place. The extraction

of landmarks follow different schemas such as in [18] where

visual moments are applied on the panorama image to extract prominent features or as in [8] and [16] where unique

(small) portions of the whole image, called templates, are

extracted.

Operating with landmarks extracted from the pano- rama, navigation vectors can be computed. Unfortunately, none of the work previously reported tries to handle the

mathematical features of the navigation vector fields thus

produced.

A formal interpretation of the visual guidance behavior

is firstly presented in [1] where two fundamental principles are extracted from the strategy navigation field: the

visual potential function and the measure of conservativeness. The latter has been proved to measure the quality of landmark learning whereas the former is a funnel-shaped

function that explain why guidance strategies operate with

a gradient process to lead the robot to the goal (the global

minimum).

4 THE MOTION FIELD

According to what has been previously expressed, start- ing with local visual information, a vector needs to be computed by the agent which will be used it to perform the next movement. In our case, the computation of the navigation

vector is based on information involving the chosen landmarks. How to get navigation information from landmarks

is briefly introduced here for completeness and details can be found in [2, 3]. Basically, once landmarks have been learned, they can

provide two kind of information to perform motion:

• their actual size, compared to the size learned at the

goal site, reports how far/close the agent is to the goal

position

• their actual orientation, compared to the orientation

learned at the goal site, speaks about the actual

left/right shift of the agent.

This kind of data come from each individual landmark

and we need to fuse them in order to get the overall navigation vector. Intriguingly, the fusion procedure has strong

biological bases as detailed in [20]. To formalize aspects related to the motion field gener- ated in the environment, we call p the vector representing

the robot's Cartesian position \x y\ in a world reference W.

We also define step fc the discrete time k of robot dynamic

state. Let V^(p(/fc)) = [Vr.{p{k)) Vj,(p(fc))] be the output of the

motion strategy at a given step k, i.e. the robot movement

at step k. If the robot operates in position mode, i.e. at each step it updates its Cartesian position, then

p{k+ l}=p{k)+V{p{k)) (1)

where p{k) represents the coordinates of robot at step fc, and p{k + 1) represents the new position at step fc -|- 1. The goal position is defined as an equilibrium point p* for the system.

The computation over the whole environment of vector V defines a vector field V. Let us consider a partial set of

equivalent statements about a generic vector field V [15].

• any oriented simple closed curve c: £ V • ds= 0

• V is the gradient of some function U: V= V U

The former is related to the concept of conservativeness

of the field. The latter is concerned with the existence of a

potential function generating an unique field. From a dif- ferent point of view, conservativeness is a measure of the

quality of landmark learning, whereas the existence of a Lyapunov potential function indicates the robot's capability to reach the goal. The following Section addresses the

former aspect. Details of the other aspect can be found in

[2, 3]. The robot Nomad200 was used to accomplish the tests.

It includes the Fujitsu Tracking Card (TRV) which per- forms real-time tracking of full color templates at a NTSC

frame rate (30Hz).

5 PRINCIPLES FOR LANDMARK

LEARNING

A landmark must be reliable for accomplishing a task

as detailed in Section 2.1. Landmarks that appear to be

appropriate for human beings are not necessarily appropriate for other agents (animals, insects or artificial beings) because of the completely different sensor apparatus

and matching systems [19]. Therefore we need to state the

meaning of landmark reliability in advance for the system

in use before to solve the problem of selecting landmarks.

For our system, a template is a region of the grabbed

image identified by two parameters mx and ruy representing the sizes along X and Y axes. The size ranges from

1 to 8, i.e. from small (2^ pixels wide) to large (2* pixels wide) templates. The TRV can simultaneously track

many templates. For each template the card performs a match in a sub-area of the actual video frame adopting the

block matching method [9]. This introduces the concept of

344
0 0

Figure 1: Examples of correlation matrices. These are computed within the local sub-area of the templates

(square box in the pictures).

correlation between the template being used and the actual

video frame. The sub-area is composed of 16 x 16 positions

in the frame usually taken around the origin (ox, Oy) of the

template (its upper-left corner). The whole set of computed correlation measures forms the correlation matrix.

Examples of correlation matrices are reported in figure 1. We can take advantage of the matrices to compute a measure that states upon the reliability of the template

under study [17]. As reported in [2, 13] we calculate a figure

r, ranging from 0 to 1, which states how deep is the global

minimum of the matrix in relation to its neighborhood.

Therefore, we define reliable landmarks as templates which

are uniquely identifiable in their neighborhoods: the greater r the more uniquely identifiable the landmark in its subarea.Once that the measure for the reliability of a landmark

has been stated, the next step consists of searching the

whole panorama for landmarks. There are several degrees

of freedom in searching for the best landmarks within a

video frame [2], but some simplifications can be introduced:

only square templates are used, and the position of a landmark is searched for by maximizing the following:

(o*,o*) = arg max ri{ox,Oy) (2)

(Oj.,Oy)egrid

where ri{ox,Oy) identifies the reliability factor for a landmark / whose origin is located in {ox,Oy) representing a

generic place on the grid. The position (o^,Oy) represents

the cells with the highest r. In order to assure that differ- ent landmarks occupy different positions, previously cho- sen coordinates are not considered. In figure 2, examples of

landmarks chosen have been reported. When different sizes are considered, different sets of landmarks are extracted.

The landmarks which have been statically chosen are used for navigation tasks. This is done by testing the landmarks to verify that they represent good guides for navi345
Figure 2: Different clioices of landmarks for different landmark sizes. Landmarks are box-shaped.

346
gation tasks. TBL helps to verify landmarks by testing whether during the motion the statically chosen landmarks are robustly

identifiable. Through a series of stereotyped movements

small perturbations (local lighting conditions, changes in camera heading, different perspectives and so on) can in- fluence the reliability of the statically chosen landmarks.

These perturbations to images naturally occur in typical robot journeys thus allowing us to state that the TBL

phase represents a testing framework for landmarks. In other words, the robot tries to learn which landmarks are

suitable for use in real navigation tasks by simulating the

conditions the robot will encounter along the paths. At

the end of the TBL process only those landmarks whose

reliability r/ is above a certain threshold e are suitable to be used in navigation tasks. The reliability factor r/ for landmark I is continuously

computed during the TBL phase through the following:

Vxy-Vyx

ri TBL (3)

where TBL is the total number of steps exploited till that

time, and r'l is the reliability of landmark I calculated at time i. In the tests, at the end of the phase, TBL usually

consists of 400 steps (it takes about 13 seconds to be per- formed). The set of landmarks is tracked along the whole

TBL phase and r; is continuously monitored for each landmark (details in [13]).

5. 1 The quality of learning

There are strong connections between the learning phase

and navigation actions. The conservativeness of the motion

field bridges these two aspects.

A vector field V is said to be conservative when the

integral computed on any closed path is zero. Conversely, if the field is not conservative then diverse potential functions can be associated with the field. This translates into nonrepeatability of robot navigation trails in [13]. If the vector field is defined on a connected set in the

environments, then the null circuitation property is equivalent to [15]:

dVx{x,y) _ dVy{x,y)

dy dx (4)

We can measure how this equation differs from the theoretical null value as follows:

Figure 3: Conservativeness of a vector field computed

with a TBL threshold of 0 and landmarks sized 6

The situation obtained with a threshold of TBL set to

0.2 has been reported in figure 5. A large area of the en- vironment has a degree of conservativeness that roughly

equals 0. Similar considerations can be expressed dealing with a

different landmark size [1]. The template of the graph is the same as before. Therefore, with a good choice of threshold

the field becomes conservative regardless of the size of the

landmarks.

6 CONCLUSIONS

Landmarks learning for robots can take inspiration from

Biology but it needs to be well formalized for its efficient implementation in artificial agents. First, a definition for landmark reliability must be stated. Second, a measure

that can assess about the quality of the learning phase

needs to be introduced. In this paper, we have shown how both these aspects can be efficiently addressed. Particularly, we have shown

how the learning phase affects the navigation motion field. Further improvements to this study can be achieved by the

use of omni directional visual sensors.

dyxix,y) dVy{x,y)

dy dx

The property expressed by Equation 5 is referred to as degree of conservativeness. The degree of conservativeness

of the vector field computed with a threshold set to 0 and

landmarks sized 6 is shown in figure 3. Only small regions

of the whole area roughly satisfy the constraint.

A small change in the threshold for TBL can dramatically change the situation. In figure 4 the degree of conservativeness for each point is plotted.

A key consideration is concerned with the scale along

Z: it is about one order of magnitude less than the one

reported in figure 3. A trend toward a conservative field is thus becoming evident.

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348
Autonomous Mental Development and

Performance Metrics for Intelligent Systems

Juyang Weng

Department of Computer Science and Engineering

Michigan State University

East Lansing, MI 48824

http://www.cse.msu.edu/~weng/

Abstract

In this paper, some resent advances in neuroscience, psychology, robotics and machine

intelligence are briefly reviewed. They prompt

us to pay attention to the fundamental difference

between the way human intelligence is developed

and the traditional engineering paradigm for

developing a machine. They make us rethink the

issue of intelligence. This position paper

proposes that a fundamental criterion for a true

intelligent system is not really what it can do in a

special setting, but rather, its capability for

autonomously and incrementally developing its cognitive and behavioral capability through

online real-time interactions with its environment, directly using its sensors and

effects, a process called mental development in neuroscience and psychology. The term

' 'mental" here includes cognitive, behavioral,

sensorimotor and other mental skills that are

exhibited by animals and humans. The new

direction of autonomous mental development for

machines will create a new kind of machines,

called developmental robots. With new

perspectives from developmental robots, the

performance metrics for machine intelligence

will undergo a revolution. They will

fundamentally change the current fragmented

landscape ofthe A Ifield by shifting the emphasis

of measuring ad hoc capability of performing a

task-specific application to a systematic

measurement of mental developmental

capabilities. Such performance metrics can be

adapted from those for humans — a series of

tests well developed by a well-established field

called psychometrics.

1 Background

Human understanding of the ways our own

minds work, the power and limitation of existing

machines, as well as the relationship between

humans and machines have greatly improved

over the last 50 years. It is now clear that a developed human mind, that of a normal human

adult, is extremely complex. It is also clear that the early optimism in the 60's and the 70's about

a quick progress in artificial intelligence such as

vision, speech, and language, was not well

founded, at least not so with the traditional

approaches that have been extensively

experimented with so far. However, the past

work with the traditional approaches is by no means unimportant. In fact, they are the womb

and incubator for the birth and growth of a

drastically different approach — autonomous

mental development. This new direction is expected to become a revolution in the course of

machine intelligence'. As Thomas S. Kuhn

wrote in his book titled The Structure of

Scientific Revolution [1]: "Because it demands

large-scale paradigm destruction and major shifts

in the problems and techniques of normal

science, the emergence of new theories is generally preceded by a period of pronounced

professional insecurity. As one might expect,

that insecurity is generated by the persistent

failure of the puzzles of normal science to come

out as they should. Failure of existing rules is the prelude to a search for new ones."

The puzzle pieces from recent advances in

related fields start to reveal a picture of mental

development, which is no longer a total myth that

is beyond human comprehension, but can be

explained in terms of computation. In the

following we briefly summarize these new

thought-provoking advances.

A more detailed discussion on this issue is available

in the proceedings of Workshop on Development and

Learning, funded by NSF and DARPA, held at Michigan State University, East Lansing, MI, April 5 - 7, 2000 (http://www.cse.msu.edu/dl/). This

workshop was attended by about 30 distinguished

researchers in neuroscience, developmental

psychology, machine intelligence and robotics who

are working on related subjects in their fields. The

goal of this workshop was to discuss the state-of-theart in research on mental development and to discuss,

initiate and plan future research on this subject.

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1 . 1 Neuroscience and psychology

A traditional view is that human brain is very

much pre-determined by human genes. With

this view, the brain unfolds its pre-determined

structure during the development, which starts from the time of conception. This structure serves as a placeholder of information that is acquired from the environment. However,

recent advances in brain plasticity have begun to

reveal a very different picture of brain

development. For example, researchers at MIT

[2] have discovered that if the optical nerves from the eyes is rewired into the auditory cortex of the primate (ferret) early in life, the primate's

auditory cortex gradually takes on representation

that is observed in normal visual cortex. Further, the primates have successfully learned

to perform vision tasks using the auditory cortex.

In other words, the rewired ferrets can see in the

sound zone. This discovery seems to suggest

that the cortex is governed by self-organizing

mechanisms, which derive representation and

architecture according to the input signals, either

visual or auditory. As another example, studies

by researchers at the University of California at San Francisco [3] showed that the fmger skin areas from which a neuron in somatic cortex

receives sensory signals (called receptive field of

the neuron) can change according to sensory

experience. if multiple fingers of the adult monkeys receive consistent synchronized pulse

stimuli from a cross-finger bar for several days,

the receptive field changes drastically, from

covering only a single finger in normal cases to covering multiple fingers. This result appears to indicate that the self-organizing program of our

brain autonomously selects the source of sensory

input within a candidate area according to the

statistical properties of the actual sensory signal

that is received. These and other related studies on the brain plasticity prompt us to rethink the

traditional rigid view about the brain. It appears

that the developmental program of the brain does

not rigidly determine the brain's architecture and

representation. For example, it might determine

what statistical properties of the sensory signals

should be used and how these properties are used

to derive the representation and architecture of

the brain.

In recent years, computational modeling of

neural development has become a very active

subject of study in neural science and

psychology. For example, there have been

several computational models for the

development of response patterns in the retina,

the lateral geniculate nucleus, and simple cells in the visual cortex. A subject that is now very

actively studied is the mechanisms for developing orientational selectivity in the simple

cells of the visual cortex. Although most

computational models of developmental

mechanisms have been concentrating on early

processing (early in the order of processing steps

in the brain), such a trend will certainly extend to

later processing when global developmental

models are increasingly studied for robots. Psychology has begun to move from qualitative

descriptive models to more rigorous quantitative

models for studying cognitive and behavioral

processes. Some recent works in psychology has

started to explain the global process of mental

development using the computational element of

networks [4]. Another new trend in psychology

is to use explicit dynamics models to explain some well-known developmental facts about

infant behaviors (e.g., the work at Indiana

University [5]). These quantitative studies have

begun to produce results that are more clearly

understandable and verifiable than vague verbal

theories and arguments.

1 .2 Robotics and Machine Intelligence

Although autonomous mental development in humans is a well-known fact, the counterpart for machines did not receive serious attention until middle 90's. It has long been believed that the

approach to machine intelligence does not have

to follow what human minds do, just like modem

airplanes which do not fly like birds. Gradually,

many Al researchers started to realize that machine intelligence requires much more

cognitive and behavioral capabilities than most

had realized. Flying is a very simple problem in comparison with machine intelligence. Further,

many AI researchers have already realized that machine intelligence requires "grounding" —

concepts must be grounded on real sensory

experience about the physical world, which in turn requires the machine to have a sensor-rich

body (i.e., embodiment) that can directly sense

stimuli from the physical world and act upon

what it senses. However, grounded sensing and

action, including learning, has been extensively

studied and experimented with in robotics for

many years. Why then does the reality of

intelligent machines seem so remote? Since

1996, I argued [6] that what has been sorely

missing from machines is autonomous mental

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development, or simply called mental

development.

Autonomous mental development requires a true revolution in the way engineering has been done

(i.e., paradigm) for thousands of years. The

current manual developmental paradigm is as follows:

1. Start with a task: Given a task to be

executed by a machine, it is the human

engineer who understands the task (not the

machine).

2. Design task-specific representation: The

human engineer translates his understanding

into a representation (e.g., giving some

symbols or rules that represent particular

concepts for the tasks and the

correspondence between the symbols and

physical concepts). The representation

reflects how the human engineer

understands the task.

3. Task-specific programming: The human

engineer then writes a program (or designs a mechanism) that controls the machine to perform the task using the representation.

4. Run the program on the machine. Sensory

data may be used to modify the parameters

of the task-specific representation.

However, since the program is of special

purpose for the task, the machine does not even know what it is doing at all. All it does

is running the program.

The new paradigm, autonomous developmental

paradigm, for constructing developmental

machines or robots, is as follows:

1. Design body: According to the general

ecological condition in which the robot will work (e.g., on-land or underwater), human

designers determine the sensors, the

effectors and the computational resources

that the robot needs and the human designs a sensor-rich robot body.

2. Design developmental program: Human

designer designs the developmental program

for the robot and starts to run this program.

3. Birth: The human operator loads the

developmental program onto the computer

in the robot body.

4. Develop mind: Humans mentally "raise" the

developmental robot by interacting with it. The robot develops its mental skills through

real-time, online interactions with the

environment, including humans (e.g., let them attend special lessons). Human

operators teach robots through verbal,

gestural or written commands very much

like the way parents teach their children.

New skills and concepts are learned by the

robots daily. The software (brain) can be

downloaded from the robots of different

mental ages to be run by millions of other

computers, e.g., desktop computers.

Mental development has long been mistakenly

thought of as being simulated by traditional

machine learning techniques (e.g., neural

network techniques). In fact, all the traditional

machine learning uses the manual developmental

mode but mental development requires the

autonomous developmental mode. What is the

basic difference? With autonomous mental

development, machines will be able to learn

subjects that their programmers do not know, or have not even thought about, just like human

children who can learn subjects that their

parents do not know. The essence of autonomous

mental development by machines is the

capability of learning directly, interactively, and

incrementally from the environment using the

learner's own sensors and effectors. Therefore,

a computer that has only impoverished sensors and effectors cannot do mental development

well. A neural network that can only accept

human edited offline sensory data does not develop its mind either, even if it can learn

incrementally. A developmental robot is a robot

that runs a developmental program and is allowed to learn and practice autonomously in the real physical world.

Although the concept of developmental program

for machines is very new, a very rich set of

techniques useful for developmental programs

have already been developed in the past several

decades in the fields of pattern recognition,

robotics and machine intelligence, especially

techniques applicable to high-dimensional data. These new techniques are being used in very

innovative ways for developmental programs.

Several developmental programs have been

designed and tested on robots. Running a developmental program, the robots interact with

the environment in real time using their sensors and effectors. Internal representation,

perceptual capabilities and behavioral

capabilities are developed autonomously as a

result of interaction of the developmental

program with the environment. Humans interact

with such robots only through the robot's

sensors, as a part of the environment. Just like the nature-nurture interaction for human mental

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development, the cognitive and behavioral skills of such a robot result from extensive interaction

between what is programmed ("innate"

developmental program) and what is sensed

through real-time online experience. The mind

and intelligence emerges gradually from such

interactions.

Early examples of such developmental robots

include Darwin V at The Neurosciences Institute,

San Diego and the SAIL at Michigan State University, developed independently around the same time with very different goals. The goal of

Darwin V [7] was to provide a concrete example

for how the properties of more complex and

realistic neural circuits are determined by the

behavioral and environmental interactions of an autonomous device. Darwin V has been tested

for the development of generalization behaviors

in response to visual stimuli at different positions

and orientations (visual invariance learning). It has also been tested for the association of

aversive and appetitive stimuli with visual

stimuli (value learning). SAIL was designed as an engineering testbed for developmental

programs that are meant for scaling up for complex cognitive and behavioral capabilities

[8]. SAIL-2 developmental program has been

tested for automatic derivation of representation

and architecture through development of

association between visual stimuli of objects and

eye aiming for the objects (object evoked visual

attention), between visual stimuli of objects and arm pre-reaching for the object (vision evoked

object reaching), between voice stimuli and arm

actions (verbal command learning and execution)

and between visual stimuli and locomotion

effectors (vision-guided navigation). Other

studies for online learning directly from sensors

are in the direction towards fully autonomous

developmental systems. The work at MIT

associates video images of objects with

synchronized voices (pronounced verbal name of

the object) [9]. The work at the University of

Massachusetts at Amherst investigated the use of

coupling of robot leg joints that have been

observed in infants to reduce the search space for a desirable turning gait [10]. Although the

history of developmental robots is very short, some experiments by the above studies have

demonstrated capabilities that have never been

achieved by the traditional methods, such as in

visual recognition, verbal communication, handeye coordination, autonomous navigation, value

acquisition (learning the value of actions), and

multimodal association in real time. We are

aware that more groups in the US and other

countries have already started to investigate this new direction.

2. Some Major Characteristics of

Research! on IVIental Development

2.1 More tractable

It is known that a developed adult human brain is extremely complex, as an epigenetic product of

long-term and extensive interactions with the

complex human world. The developmental

principles for the brain in the complex human

world, however, should not be as complex as the

human world itself For example, the visual

world is very complex, but the developmental

principles that are used by the brain to derive

various filters for processing visual signals

should not be as complex as the visual world

itself Therefore, computational study of

cognitive development could be more tractable than traditional approaches to understanding

intelligence and constructing intelligence

machines.

2.2 Unified framework

Studies of cognitive development will establish a unified framework for our understanding of a wide variety of cognitive and behavioral

capabilities. Discovery of mechanisms

responsible for developing cognitive and

behavioral capabilities in humans requires more

systematic work than an account of a particular

individual capability, such as visual recognition

in a simplified setting alone or stereotyped

walking alone. Sharing of common

developmental principles by visual and auditory

sensing modalities, as revealed by recent neuroscience studies, will encourage scientists to discover further underlying developmental

principles that are shared not only by different

sensing and effector modalities, but also by

different higher brain functions.

Traditionally, vision and speech have been

considered very different, both for humans and

for machines. For the same reason, traditional

methods for different A! problems are typically

very different, resulting in what is well known

now as the fragmentation of the AI field. Potentially, Al can be applied to all possible

areas of human life and each application area

potentially can lead to a fragment of Al if it is

352
treated in an ad hoc way. The unified

framework of cognitive development will fundamentally change the current fragmented

landscape of AI in the years to come, since

different applications correspond to different

lessons that can be taught to the same

developmental robot at different mental ages. We will also see much more interactions and

collaborations among scientists and engineers in neuroscience, psychology, robotics, artificial

intelligence and other related fields, due to the

very similar research issues these fields face

under the theme of autonomous cognitive

development.

2.3 Task-nonspecific

In contrast to the task-specific nature of the

traditional engineering paradigm in Al,

developmental programs for machines will be

task-nonspecific. The power of a developmental

program is its general applicability to many

different tasks. A developmental program may

contain certain pre-processing stages that are

specific to some type of sensors or effectors,

such as camera or touch sensors. In this sense, it

is body-specific (or species-specific). However,

it is not task-specific. A developmental

program can be run to develop skills for many

different tasks, with simpler skills being learned

to prepare skills for learning more complex

skills. Recently, the scientific community has

gained a more complete understanding of human

intelligence. As Howard Gardner put it in his book Multiple Intelligences [11], human

intelligence is multiple, including linguistic,

logical-mathematical, musical, bodilykinesthetic, spatial, interpersonal, and

intrapersonal intelligences. This is a rough

classification of a very rich ensemble of inter- related cognitive and behavioral capabilities that give rise to human intelligence. The same is true for machine intelligence. Any particular

capability that we regard as intelligence in a general setting, such as the visual capability of

recognizing various persons on a busy street or

the language capability of talking about

technology, is not an isolated single thing. It requires the support of many skills developed

through extensive real-world experience via sensors and effectors.

2.4 Computational

Further, developmental mechanisms seem to be

very much quantitative in nature and thus require

clear computational models. We will see more

complete computational models for mental

development that can be simulated on computers

and robots for many different environmental

conditions and the results can be verified against

studies about humans. We will see more efforts on computational modeling of mental

development, for humans and machines, that are

clearly understandable, implementable on machines and can be subject to rigorous

verification and comparison. This will indicate

the maturation of the related fields.

2.5 Recursive and active

Development discourages any static or rigid

view of the mind. A developed human mind is a snapshot of many years of recursive and active

mental construction by the developmental

program in the human genes, utilizing the

sensory and action experience through life time.

The term recursive means that later mental

development relies on the cognitive and

behavioral capabilities that have been developed

earlier The term active means that each

individual plays an active role in the

development of his or her mind — different

actions lead to different experience. The same

is true for developmental robots. The recursive

and active nature of development discourages the

approach of collecting offline data and spoonfeeding them into a machine, which is a prevailing approach in current machine learning

studies. Sensory data cannot be pre-specified

since what sensory data is sensed depends on

online action executed in real time.

2.6 Developmental capabilities as unified

metrics for machine intelligence

The criteria for measuring machine intelligence

will fundamentally change. The metrics that can

be used to measure the power of such a new kind

of machine is primarily their autonomous

interactive learning capabilities in complex

human environments. In other words, it is the

capability of mental development instead of what

the machine can do under a pre-specified setting.

Such performance metrics can be adapted from

those used by clinical psychologists for testing

the cognitive development of human infants

(e.g., The Bayley Scales of Infant Development)

and children (e.g.. The Leither International

Performance Scale). The mental age that is used

for measuring human intelligence in these tests

353
will be adapted to a scale for measuring machine

intelligence. This is a fundamental change from

the current metrics that measure what a machine

can do under a specific setting. What a machine

can do under a specific setting is the intelligence

of the machine programmer, not the machine

itself. For example, an interactive dictionary

stores a lot of human knowledge and it can do

remarkable things for humans, but it is not

intelligent. Test criteria for machine

intelligence may also provide quantitative

feedback for improving the intelligence tests for humans.

3 Predicted Impacts

The history of science and technology has shown

that impressive technical improvement and

persistent cost reduction will follow an important

scientific revolution. The amount of technical

improvement and cost reduction can be so great

that it was difficult to foresee at the time of

revolution. Two well known examples are the

internal combustion engine technology to today's automobiles and Von Neumann machine

idea and the semiconductor technology to today's popular computers. The following

predictions may seem to be overly optimistic

today, but the history could prove them to be

true.

3.1 Human life

This revolution will greatly improve the quality

of human life. The introduction of engines

greatly relieved humans from hard manual

labors. The introduction of computers greatly

relieved humans from mechanical computation

labors, especially those that humans cannot do as

fast, such as doing calculations, controlling a complex machine or generating synthetic

graphics images in real time. The introduction

of developmental robots could relieve humans

from tedious thinking labors. Those are lowlevel thinking tasks, mainly to execute human

high-level commands. The quality of human life could be greatly improved with the arrival of the

age of developmental robots. Developmental

robots will be used as human assistants, from

factories to households. Their developed

"brains" are downloaded as software to be run on

desk-top computers to do various tasks, from

reading emails to helping children to learn, in the

past, thinking robots have been only discussed in science fiction because machine thinking has not

been sufficiently understood. Thinking seems a

collection of internal behaviors of a developmental being (animal or machine) and it must be developed through autonomous mental

development just like humans and higher

animals. Infants think using their simple

internal behaviors and adults think using their more developed internal behaviors. A robot that runs a developmental program is like a machine

that writes mental program autonomously, when

the developmental program interacts with the

sensory information from the real world. Its developed internal behaviors represent the true thinking by a machine.

Why did all these advances not occur in the past?

This is mainly because the Al field did not pay

sufficient attention to, or at least was not serious

about, autonomous mental development for machines until just a few years ago. Currently,

all the efforts for building AI systems follow the

traditional manual development paradigm, with a few recent exceptions mentioned above. With

the new paradigm, human programmers are not required to write a particular program for each of

the tasks that we want the machines to perform,

which has been proved extremely difficult if the

task requires what we consider as intelligence.

Instead, what the human programmers need to do

is to write a developmental program, which is of

general purpose. Although developmental

programs are by no means easy to design, they

are easier to understand and to improve than

many special systems designed for specific AI

tasks. The practical aspect of developmental

robots also rests in the ease of training. The user of a developmental robot does not need to write a program or manually feed data if he wants to teach the robot. He just trains the robot very

much like the way he trains a human child,

showing it how to do something while talking to

it, encouraging or discouraging what the robot

does from time to time. Thus, everybody can

train a highly improved developmental robot, a

child, an elderly, a teacher, a worker —

anybody. This is the basic reason why the

developmental robots could become popular.

Computers would not have been that popular

today if they are not as easy to use as today's

computers with very intuitive graphical user

interfaces.

3.2 Economy and jobs

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The economic impact of developmental robots

will be enormous. The country that takes the

lead in developmental robots will first create a new industry for this new kind of machine. This new industry will take advantage of the advanced

automobile industry to develop sensor-rich

humanoid robots (Honda in Japan has already

started it). It will also take advantage of the fast progress of the computer industry to build

computers and memories best suited for the

computational need of developmental robots.

The cost for large storage will drop consistently

when the market grows. For example, the cost of hard-disk storage that is of human brain size

in terms of number of bytes has already dropped

from about $5M in 1998 to around $250,000

today (June 2000). Real-time speed with large

memory is reached through coarse-to-fme

memory search schemes. There will be a new

industry for humanoid robots, fueled by the need

for building bodies for developmental robots.

Many different types of bodies, designed for

different working conditions and environments

will be made to satisfy increased application

scope of developmental robots. It is expected

that in the next 10 to 20 years, the developmental

robot industry will primarily aim at professional

applications, such as research institutions,

arnusement parks, public service areas, and the

defense industry. During this period, consumers

can benefit from the software that is developed

on professional robots. Eventually,

developmental humanoid robot may cost the same as a car plus a high-end personal computer.

The country that takes the lead in this new

endeavor will create an abundance of economical

activities and well-paid jobs related to this new

industry.

3.3 Understanding of human mind

The impact on the scientific understanding of our mind will be far reaching. This revolution will

drastically improve our understanding about one

of the most complex subjects that faces mankind

today —our own minds. For example, what are the basic mechanisms that govern the ways in which our minds develop? To what degree can

the environment change the formation of the

mind? What can the environment do to effectively and positively influence the human

mind and improve the life of mankind? The

answers to these questions require the knowledge

about the developmental root of the mind.

Without studying the computational models of

mental development, these questions cannot be

sufficiently and clearly answered.

3.4 Medicine

The knowledge created by this revolution will also improve medical care. It will provide basic

knowledge useful for treating learning

disabilities, mental disorders, and mental

problems associated with aging. For example,

what developmental mechanisms are responsible

for attention deficiency? What developmental

mechanisms are responsible for enabling an

individual to establish the value of an event, a behavior, or the social norm? What techniques

are effective for teachers to improve the

development of certain cognitive and behavioral

capabilities? Computationally, which areas of the

brain are responsible for certain mental

disorders? During aging, which mechanism of

the brain is likely to deteriorate first and what

remedies are possible?

4. Why now?

As we discussed above, the recent new

discoveries about human brain tell us loud and

clear that our human brain utilizes the

developmental principles that are shared by

different sensing and effector modalities. Since

higher brain functions appear to be even more

plastic than early sensory processing, it is expected that the higher brain functions also use developmental principles that are generally

applicable to different subject matters that humans learn. The time is right to study what

these developmental principles really are.

Technically, it is now possible to study

massively parallel, distributed brain activities and relate them to mental development. The

advances in neural imaging techniques, such as EEG, EMG and fMRI, now allow high

resolution, concurrent, and real-time

measurement of brain activities.

In the machine intelligence and robotics fields,

the fundamental difference between the way

human mind is developed and the traditional

engineering paradigm for machine development was recently identified as the fundamental reason

for the difficulties in Al. The studies about the

fundamental limitations of the current engineering paradigm have recently started.

355
Some preliminary computational models for developing the mind by machines were recently

proposed the tested. These early efforts have

achieved some results that have not been

possible using the traditional engineering

paradigm. Therefore, computational models of

mental development for machines are not beyond

human comprehension and they are within the

manageable scope for humans to model

computationally.

The performance-to-cost ratio of computers has

reached a critical level that now it is practical to simulate brain development in real time on a robot, with a storage whose size is equivalent to a considerable fraction of human brain. Further,

this can now be done at a very moderate cost. For example, the development of the most

computational challenging modality, vision, can now be simulated on real robot in real time by

software running on a PC workstation.

Technology for building robots has also been

improved significantly. In recent years, research

laboratories and related industries in US and

Japan gained remarkable experience in actually

building robots that resemble human and animal

bodies with similar articulate structures, from

human-size humanoid robots (e.g., the series of

Honda humanoid robots) to advanced consumer

toy robots (e.g., Sony AlBO dog robots). The

robotic technology is ready for building various

humanoid or animal robots as bodies for

developmental machines.

5 Research issues

in some sense, the task-nonspecific nature of

mental development makes the studies of mental

development easier than the traditional task- specific approaches. This is true for both human

subject (neuroscience and psychology) and

machine subject (Al and robotics). From the

computational view of mental development, the

research issues are around sensory signals and

effector signals with internal autonomously

generated numerical states. A developmental

program will associate signals that are from

different sensors, stored in internal status and

sent to effectors, but its programmer does not need to know what those signals actually mean!

To put it intuitively, it is easier to model how an

interactive program looks up words from its word memory than to model how the meanings

of words in The Merriam-Webster's Dictionary

relate to one another. The former is like what a

developmental program does for many tasks that a developmental being will come across and the

later is like what all the traditional programs do

for a particular task.

To understand this fact better, we take a complex

behavior as an example. Modeling attention

selection in a traditional task-specific way

requires the researcher to understand the nature of the task (e.g., driving) and then to study the

rules of attention selection based on the steps of

the task. Such rules are extremely complex (e.g.,

due to the complex road situation during driving)

and the results are ad hoc in the sense that they

are not directly applicable to other tasks or even

to the same task under different scenarios. In contrast, attention selection by a developmental

being is just a part of behaviors that are being

developed continuously and constantly. As long

as the effectors for attention selection are defined

for the body (external effector) and the brain

(internal effector), the attention selection

principles are developed autonomously by the same developmental program in a way very

similar to the behaviors for other effectors, such

as arms and legs.

Consequently, a series very interesting and yet

manageable new research problems are opened

up for study, for fields that have either human or machine as study subjects. Some of the tractable research problems that can be immediately

studied are suggested below.

1. Schemes for autonomous derivation of

representation from sensory signals (from

the environment and the body).

2. Schemes for autonomous derivation of

representation from effector signals (from

the practice experience)

3. Autonomous derivation of receptive fields,

in both the classic and nonclassic sense. That is, how later processing elements in the

brain group outputs from earlier processing

elements or sensory elements.

4. Long term memory growth, self- organization and retrieval, for highdimensional neural signal vectors.

5. Working memory formation and self- organization, for high-dimensional neural

signal vectors. The working memory may

include short term sensory memory and the

system states.

6. Developmental mechanisms for mediation

of conscious and unconscious behaviors.

That is, those for mediation among higher

356
and lower level behaviors, such as learned

behaviors, learned emotional behaviors,

innate emotional behaviors and reflexes.

7. Mechanisms for developing internal

behaviors — those that operate on internal nervous components, including attention

selection. This subject includes both

developmental mechanisms and training

strategies for humans and robots.

8. Attention-directed time warping from

continuous states. This subject deals with

time inconsistency between different

instances of experience, with the goal of

both generalization and discrimination.

9. Autonomous action imitation and self- improving. The developmental

mechanisms for a developmental being to derive an improved behavior pattern from

individual online instances of related

experience.

10. Mechanisms for communicative learning

and thinking. The developmental

mechanisms that allow later learning

directly through languages (auditory, visual,

tactile, written etc) as children do when they

attend classes. These mechanisms enable

development of thinking behavior, which is responsible for planning, decision making

and problem solving.

6 Performance metrics

The current fragmentation landscape of Al is a reflection on how different Al problems can be

measured by very different metrics, if intelligence is measured as the capability of

performing a specific task. However, what a machine can do under a specific setting

represents the intelligence of the machine

programmer, not necessarily the machine's own

intelligence. Further, a special purpose machine

that can only work for a particular problem

cannot deal with complex problems that require

true intelligence, such as vision, speech and

language capabilities.

The criteria for measuring machine intelligence

will fundamentally change. The metrics that can be used to measure the power of developmental

robots should emphasize the autonomous

interactive learning capabilities in complex

human environment. In other words, it is the

capability of mental development instead of what

the machine can do under a pre-specified setting.

This is indeed the case with well-accepted test scales used by clinical psychologists for

measuring mental and motor scales of human

children. Two such well known scales are The

Bayley Scales of Infant Development (for 1 to 42

months old) and The Leither International

Performance Scale (for 2 years to 12 years old).

These scales have a very systematic

methodology for the administration of tests and

scoring. The reliability and calibration of these

scales have been supported by a series validity

studies, including constuct validity, predictive

validity, and discriminant validity that cover

very large number of test subjects and different

age groups across very wide geographic, social,

and ethnic populations.

Here let us take a look at an example of tests in the Leither International Performance Scale for a two years old. The name of the test is Matching

Color. The test setup is a row of 5 stalls. Above each stall pasted a color card, black, red,

yellow, blue, and green, respectively. During the

test, color blocks are presented, one at a time in the order: black, red, green, blue, and yellow.

The examiner places the black block in the first

stall and tries to get the subject to put the red

block in place by placing it on the table before

him, then in the appropriate stall, then on the

table again, nodding to him to do it and at the same time pointing to the second or red stall. As

soon as the subject begins to take hold of the

test, the final trial can be attempted. In this test, the examiner tries to get the subject to imitate his procedure. The test is scored as passed if the

subject is able to place the four colors (the first one is placed by the examiner) in their respective

stalls entirely by himself during any one trial, regardless of the number of demonstrations or

the amount of help previously given by the

examiner. As we can see, the test does not

really concern about whether the child has

learned the abstract concept of color, but rather

the capability of imitating the action of the

examiner using visual color information as a cue

in coordination of his motor effectors (hand and

arm).

The mental age that is used for measuring human

intelligence in these tests can be used as a scale

for measuring machine intelligence. Currently

metrics that have been used for various Al

studies mainly measure what a machine can do

under a specific setting, instead of the capability

of mental development. Such a capability

357
requires online, interactive learning capability as the above test demonstrates. For example, an

interactive dictionary stores a lot of human

knowledge and it can do remarkable things for

humans, but it is not intelligent. If a machine

that can pass the systematic tests like the one shown above, it must have already learned many

others skills that no traditional machine has. Therefore, although autonomous mental

development is a new direction, its impact on the

future of machine intelligence and our

understanding of human intelligence will be far reaching. The performance metrics for measuring intelligent machines can be adapted

from those used by clinical psychologists for

testing the mental development of human

infants. The Bayley Scales of Infant Development and The Leither International

Performance Scale are two such examples.

References

[1] T. S. Kuhn. The Structure of Scientific

Revolution, University of Chicago Press, third

addition, page 68, 1996.

[2] L. von Melchner, S. L. Pallas and M. Sur. Visual behavior mediated by retinal projections

directed to the auditory pathway. Nature, vol. 404, April 20, pages 871-876, 2000.

[3J X. Wang, M. M. Merzenich and K. Sameshima and W. M. Jenkins. Remodeling of

hand representation in adult cortex determined

by timing of tactile stimulation. Nature, vol. 378, no. 2, pages 13-14, 1995.

[4] J.L. Elman, E. A. Bates, M. H. Johnson, A. Karmiloff-Smith, D. Parisi and K. Plunkett.

Rethinking Innateness: A connectionist

perspective on development. MIT Press,

Cambridge, MA, 1997.

[5] E. Thelen, E., G. Schoner, C. Scheier, and

L.B. Smith (In press). The dynamics of

embodiment: A field theory of infant

perseverative reaching. Behavioral and Brain

Sciences, to appear.

[6] J. Weng. The Living Machine Initiative. Department of Computer Science Technical

Report CPS 96-60, Michigan State University, East Lansing, MI, Dec. 1996. A revised version

appeared as a chapter: J. Weng. Learning in Image Analysis and Beyond: Towards

Automation of Learning, in Visual

Communication and Image Processing, C. W.

Chen and Y. Q. Zhang (eds.). Marcel Dekker,

New York, NY, 1998.

[7] N. Almassy, G. M. Edelman and O. Sprons,

Behavioral constraints in the development of

neural properties: A cortical model embedded in a real-world device. Cerebral Cortex, vol. 8, no.

4, pages 346-361, 1988.

[8] J. Weng, W. S. Hwang, Y. Zhang and C. Evans, Developmental robots: Theory, Method

and Experimental Results, in Proc. 2nd Int'l Symposium on Humanoid Robots, Tokyo, Japan,

pp. 57- 64, Oct. 8- 9, 1999.

[9] D. Roy, B. Schiele, and A. Pentland.

Learning Audio-Visual Associations using

Mutual Information. In Proc. International

Conference on Computer Vision, Workshop on

Integrating Speech and Image Understanding.

Corfu, Greece, 1999.

[10] M. Huber and R. A. Grupen. A feedback

control structure for on-line learning tasks. Robotics and Autonomous Systems, vol. 22, no.

3-4, pages 303-315, 1997.

[11] H. Gardner. Multiple intelligences: The

theory in practice. Basic Books, New York, NY,

1993.

358
PART II

RESEARCH PAPERS

6. LEARNING: TOWARD SELF-EVOLVING ARCHITECTURES

6.1 Evolution of Intelligent Systems Architectures: What Should Be Measured?

A. Meystel, Drexel University, USA

6.2 Machine IQ with Stable Cybernetic Learning With and Without a Teacher

H. Szu, Digital Media Lab, Wash DC, USA

6.3 Properties of Learning Knowledge Based Controllers

E. Grant, G. Lee, NC State University, USA

6.4 Examining the Resource Requirements of Artificial Intelligence Architectures

S. A. Wallace, J. E. Laird, K. J. Coulter, University of Michigan, USA

6.5 A Metric for Monitoring and Retaining Flight Software Performance

C. Peterson, Computer Sciences Corp., USA

6.6 Flexible Robotic Assembly

D. P. Gravel, W. S. Newman, Ford, Case Western Reserve University, USA

359

Evolution of Intelligent Systems Architectures:

What Should Be Measured?

A. Meystel

Drexel University, Philadelpliia. PA 19104

Abstract

Various degrees of intelligence evolve in the intelligent systems as a result of their development by

the virtue of external design and/or self-organization. The increase in degree of mtelligence is achieved via evolution of its architecture. Tins paper is intended to establish a conceptual and

methodological background required for design and evaluation of performance and the degree of

intelligence of intelligent systems. Tlie paradoxical ability to increase redundancy while reducing

complexity is described as a hallmark of intelligence. The naturally evolved architectures of

intelligence are constructed in such a manner that the tools of complexity reduction do not curb the

combinatorial capabilities of tlie system.

1. Intuitive Approaches to the Concept of Intelligence

An attempt is made to approach the concept of intelligence constructively and from the scratch. This

analysis is motivated by the need for using tlie results for constructed (primarily, engineering) intelligent

systems and agents. In the auUior's view, tlie Descartes' problem (of the Mind existing separately from the

Body) simply doesn't exist, because the Mind of the constructed Machine is undoubtedly produced by its physical components ("body"). Yet all phenomena of intelligence in living creatures seem to allow for their

computational modeling. Nevertheless, the author doesn't adhere to the teclmological paradigm alone. Both

the e.xamples of intelligence and its architectures will be discussed for all domains shown in Figure 1.

Figure 1. Techniques linked with and stemming from the concept of intelligence.

361
The goal to construct the architectures of inteUigence and analyze their evolution can be achieved if a comprehensi\ e definition of intelligence is introduced. It seems meaningful to derive the definition from

integrating the phenomena characteristic for inteUigence. Obviously, they can be demonstrated in relevant

systems belonging to all domains shown in Figure 1. Interestingly enough, witliin each of these domains,

there are common habits of discussing intelligence. Possibly, this is a result of the fact that all of them

depend on tlie linguistic domain. Tlie main habits of talking about intelligence can be listed as follows:

1. Functioning of intelligence is frequently characterized in the anthropomorphic terms of mental

conduct.

2. Intelligent activities are attributed to levels of generality (levels of scope)

1.1 Features of Mental Conduct

The tenns of natural language that characterize intelligence both positively and negatively, can be

used for evaluating the riclmess of die concrete domain of discussion and judging whether domains from

Figure 1 are well represented. One can make an observation that all of these properties can be

quantitati\'ely evaluated in a crisp or fuzzy manner.

Table 1. Antonyms characterizing Intelligence (From [1])

clever o dull observant unobservant

sensible silly critical uncritical

careful careless experimental unexperimental

methodical unmethodical quick-witted o slow

inventive o uninventive cunning simple

prudent o rush wise unwise

acute dense, obtuse judicious injudicious

logical o illogical scrupulous unscrupulous

witty humorless smart stupid

The tendency to using these adjectives for characterizing intelligent systems in all domains is unavoidable.

Although, tliey could be called anthropomorphic, their use seems to be justified even as applied to living creatures different from humans such as apes. cats. dogs, horses, mice. Then, we might agree with using at

least some of these terms to analyze intelligence of birds, fishes, reptiles. After getting used to see the

common patterns \\e can expand some terms related to intelligence into domain of insects, and then,

proceed toward bacteria, too.

Analysis of the intelligence related \ ocabular>' helps to discover a number of other phenomena that should

be taken in account in constructing definitions and models for intelligence. Indeed, from the fact that

Stupidity ^ ignorance we can conclude that intelligent ^ possessing knowledge.

Tlius. having knowledge, or being informed could not be considered a base for defining

intelligence. On tlie oUier hand, intelligence is frequently associated with a comparably vague concept of

the acti\'it}' of thinking. The latter contains as a part, such activity as theorizing, and one can expect

tliat theory formation should be represented in the architecture of intelligence. It is the capacit}' for

creation of a rigorous theor> that la> s the superiority of men over animals not the capacit>' to attain

knowledge.

It would be desirable to embark on constructing the definition of intelligence focusing upon most of the

factors tliat is linked w itli this complex phenomenon of mental conduct. Before introducing architectures of

intelligence a set of mental conduct epithets was analyzed including such terms as:

362
careful stupid logical unobservant ingenious

vain methodical credulous witty self-controlled

and their correlations so that they could be represented in the definitions and architectures..

1.2 Intelligence is a Property existing at all levels

In addition to muUiple properties and phenomena related to the mental conduct, intelligence invokes

talking about level of intelligence. The term intelligence is attributed to each of the interrelated levels

including

societal phenomena

group activities

individual activities

organ functioning

cell functioning

DNA functioning

scaled by the unit of intelligent agent (1)

These levels are apparently associated with the scale (resolution, granularity) of representing the external

reality by the functioning intelligence. Some of these levels emerged because humans introduced them.

Some of them evolved naturally (biologically, ecologically, or psychologically). In all cases, the

muUiresolutional organization improves the efficiency of functioning [2]. Each particular level of

resolution is scaled by the nature of tlie liierarchy (1). At the same time, for each agent within the hierarchy

(I) another multiresolutional scaling can be introduced for units of interest existing within a level and

requiring its own hierarchy of levels that makes operations with this unit more efficient. It seems that the

ability to come up with a multiplicity of levels of resolution is a property of intelligence that produces these

levels of resolution.

On tlie other hand, each of the levels mentioned above can be characterized by the ability to build and

construct rules associating objects and activities at the level, and by the ability to introduce and use

tlieories. Both rules and theories are formulated by the researcher observing and analyzing external

intelligence. However, tliey reflect the properties and laws existing within the system of objects under

consideration. Both rules and theories are applicable for tlie decision making processes that are utilized to control

• objects at a level • levels as a whole

• the overall s\'stem that is combined out of levels and contains these objects.

Let us notice that the organization of the system to be controlled is affected by the intelligence, and the

introduction of rules and theories is done by the intelligence, too. The source of the intelligent in both cases

is not determined, and the intelligence as a phenomenon is undefined.

2. Introducing Formal Approaches

2.1 General Statements

It looks like tlie Theory of Control that does not take in account tlie phenomenon of intelligence, is not fiilly equipped for solving problems for the domain of intelligent systems, e.g. in robotics. The

particular problem is in detenuining vft'TOR of intelligence of a controller and putting it in a correspondence with the vector of performance. Designers are dealing with systems that are underspecified even as far as their inputs and outputs are concerned.

Thus, die first two emerging questions are: 1. Wliat are the inputs into the system under consideration? and

2. What are the outputs of this system? The input can be introduced by the designer of the architecture and

by the values of variables provided by a specific architecture (intelligence). The output is always

understood in the temis of performance.

363
An attempt to answer the questions requires to revisiting tlie logical categories Uiat are used for analysis of

systems with intelligence (in particular, intelligent control systems). Simultaneously, we must determine

whether we will discuss tliese issues in the tenns of predicate calculus of the first order, in tlie terms of

other logical systems, or in tlie tenns of meaning extraction and interpretation of the Natural Language. The

lists of inputs and outputs contain concepts that entail a diversity of various schemes of reasoning and

logical categories. The logical type of category', to which a concept belongs is a set of ways, in which it is consistent, i.e. it is logically legitimate to operate with. To determine a logical network of concepts is to review the logic of propositions, in which tliey are utihzed including the following:

1. Witli w hat propositions of the classical control tlieory. tlie propositions related to intelligence are consistent and/or inconsistent

2. Wliat are tlie new propositions of control theor>' that follow from the propositions related to intelligence

One of tlie challenges is to determine whetlier for the alternative definitions of intelligence and the

associated processes we selected correcth' the logical categories in terms that are consistent witli the

practice of design and application in the domains shown in Figure 1 . In particular, we would be interested

whether tlie concepts of tlvinking. mental powers, smartness, Uieir components and operations they entail

have been coordinated consistently. It should be demonstrated that there is no operations with these

concepts and processes that breach logical rules. We suspect that the consistent system can be built if the

logical consistenc> will be determined not in tlie terms of predicate calculus of the first order but in the

tenns of law s of interpretation detennined for tlie Natural Language used for describing the real systems

and situations.

2.2 List of Premises that Are Characteristic for Intelligent Control

The following premises can be considered as following from Oie experiences in all domains of

Figure 1 in tlie cases of exploring intelligent systems as objects and intelligence as a phenomenon of these

objects.

2.2.1 Cultivating redundancy is a prerequisite ofintelligence

Redundanc> of systems is understood as having their resources, components, or properties in

abundcmce. or in excess. It is a feature of intelligent systems that information they deal with is intrinsically

redundant and the tools of processing tliis information are in excess of the minimally required set of tools.

It is a feature of intelligent s>'stems to cultivate this redundancy and it will be shown that intelligence is equipped by specific tools for doing this.

This property' of redundancy is ver>' important and very characteristic for intelligent systems. They should

be always read> to withstand uncertainty, and since the sur\'ival is at stake, the property of redundancy

helps to minimize the risk of failiu"e. E. Ruspini has mentioned: the systems should have more intelhgence

tlian it needs for soh ing tlie problem' . Obviously, the same problem can be resolved with different level of

intelligence. Then, tlie results of tills problem-solving process could be used for evaluating the level of

intelligence. Tliis level might depend on the level of redundancy.

Although redundancv' as a propert>' is considered negative (it should waste resources), ot only intelligent

systems do not fight redundanc\ . it explore, use. and even cultivate the redundancy. Redundancy is the tool

for combining and testing new alternatives of decisions. After evolving intelligent systems develop a mechanism of exploring tilings w itliin its "virtual reality," redundancy is becoming a tool for planning and

a tool for learning without actually having physical experiences.

Autonomous systems should acquire info in physical (realistic) and/or imaginar>' playgrounds. The

following factors are being displayed related to redundancy:

' In an exchange during the panel on Intelligent Control at I.TCNN'2000, E. Ruspini commented that probably such

creature as E.coli possesses all mtelligence it needs for functioning. A. Meystel proposed a paradoxical circular

definition for mtelligence that illustrates and further develops Ruspini's statement: "The system is intelligent iff it has more intelligence than it needs."

364
— Playfulness is a property observed in living creatures or linguistic systems that are characterized by a ver>' high level of intelligence. Playfulness of an intelligent system is to be

considered a part of tlie learning process. — Redundancy supports various manifestations of the property called "desire " including all known classical desires tliat detennine foraging and reproductive activities. — Certainly, speaking about "pla>ful ameba" might be a stretch however searcliing activities are observed even for amoebas [5] and E.coli's [6] (and this allows to talk about certain degree of

intelligence even in these classes of living creatures [7].

Intelligent systems are equipped by multiple tools of acquiring and increasing their redundancy. Learning is one of the tools that employs actual experiences or imagination.

2.2.2 Reduction ofcomplexity is a working technique ofintelligence

How is it possible to cultivate redundancy, and yet fight complexity ? Tliis paradoxical abilit\' is a hallmark of intelligence. Practically, it means tliat the tools of complexity reduction should not curb the

combinatorial capabilities of the system. Such tool exists, and tliis is organization of information in a muhiresolutional fashion (see [3. 4]). This organization of information actually determines appearance of

the levels mentioned in sub-section 1.2. The need to evaluate and reduce complexitv' was alvvays clear in computational mathematics and

this led to the concept of epsilon-entropy and techniques of its evaluation [8]. Many elegant matliematical

tecliniques of complexit\' reduction has been developed (e.g. like in [9]). The specifics of application

domain was appreciated (see [10] for the software complexity. [11] for syntactic complexitv. [12] for

complexity of information extraction. [13] for information of control system).

Howev er. the need to use multiresolutional organization of infonnation for complexity reduction was not immediateh acknowledged and considered an understandable and desirable tool even after

publication of [3. 4], FurtJier explanation of relations between multiresolutional tools of complexity

reduction can be found in [14. 15]. In all systems (technological, biological, psychological and linguistic) formation of

multiresolutional representation is a technique of complexity reduction. Even E.coli fights Uie complexity

by forming at least two levels of resolution (high resolution - single E.coli, low resolution - swarms

formed as a resuh of bacteria gathering in groups [7]).

2.2.3 Loop ofSemiotic Closure is the Primary Architecture ofIntelligence

The modules of (1) World, (2) Sensors, (3) Perception. (4) World Model. (5) Behavior Generation

and (6) Actuators, connected in a loop of closure, are forming an Elementary' Functioning Loop, or ELF.

The module of World is the ambient enviromnent including a source of information from the process

generated by Actuation to be observed by Sensors. This component of tlie World also consumes the energy

submitted by the module of Actuation. If one interprets Figure 2 as a general structure of an intelligent

vehicle, then die module of World is tlie couple Vehicle/Road. The energy is conveyed Uirough diis couple

to the body of the moving Vehicle, the vector of speed is measured for tlie Vehicle relative to die Road

within tliis couple. Sensors are transducing Uie information from die domain of physical realit> to the

information carrier accepted by the system of computation. In addition. Sensors are responsible for

complicated activities linked widi organization and coordination of testing. These activities are a part of

another loop of closure (see [17]).

The module of Senson,- Processing organizes the information and submits it to tlie World Model tliat puts

the units of acquired knowledge into a form appropriate for storing and utihzadon b>' the module of

Behavior Generation. The latter may var\' from the simple look-up table to the complex devices that

explore alternatives of plan and simulate tliem before submitting them to the module of Actuation. The

simple look-up table would contain the list of control functions f(t) together with previoush' experienced or expected measures of acliievement J (f x. x*) for the given goals x*(t) and present situations x(t) as couples

x*(t), x(t)^ f(t), J(f, x, x*). (2)

365
The concept of semiotic closure is not an obvious one. It exceeds the straightforward idea of feedback that can be formulated as follows. In a system, there exists a monitor (human or electronic/mechanical) that

compares what is happening at time t, x(t), with some standard of what should be happening x*(t). The

difference or error, A(t) = x(t) - x*(t), is fed to a controller for generating an action by a control function

f(t)=y(t+k), which can be taken only at a later time, t + k. Thus, the feedback equation presumes some

Including:

Receiving and organizing

Clustering

Generalizing

Pre-recognition

Pre-estimation

Alternatives Synthesis

PROCESSING

I

SENSORS

Including:

Transducing

Developing tests Sannpling generation

Sampling Integration

Including:

-Entity-Relational

Model Formation

-Knowledge Base

Maintenance

Estimation

Grouping

-Focusing Attention

Combinatorial

searcti

I

WORLD

MODEL

WORLD

Including:

- Alternatives Synthesis

- Task Decomposition

- Scheduling

- Forecasting

- Comparison

- Selection

- Execution

BEHAVIOR

GENERATOR

I

ACTUATION

including:

Motion Generation

IVIotion Coordination

Motion Integration

Figure 2. Semiotic Closure for a System With Motion

y(t + k) = f[A(t)]=f [x(t)-x*(t)] (3)

standard assigned, some variable compared with this standard, and some device that computes "feedback

compensation." The standard might be assigned as a goal externally, or stored in the module of World

Model. The device that computes "feedback compensation" can be associated with the module of Behavior

Generation. Sensors. Sensory Processing and World are meant but not explicated. Certainly, this concept

should be enhanced substantially to be transformed into the concept of semiotic closure.

Semiotic closure was anticipated in 1967 by L. von Bertalanfy [18] who considers feedback to be "a special case of general systems characterized by the presence of constraints which led the process interpretation

toward circular causality and thus making it self-regulating. This loop of "circular causality" was dubbed

"semiotic closure" by H. Pattee in 1973 [19]. It was introduced to analysis of intelligent systems in [20] and

[21], Semiotic closure can be constructed for any domain and any system that exhibit elements of

intelligence.

366
2.2.4 Entity-relational network (ERN) is a frequent form of constructing the

representation at a level ofintelligent system

It would be more prudent to say that we simply do not know any alternative to ERN. Of course, we can approximate ERN by a multiplicity of tables and approximate each of the tables by an analytical

fiinction. We do this for the variety of manual activities. However, as computer permeates our workplace,

we found that having ERNs even in a tabular form is the most flexible way of storing information.

Hhjs, a problem of generalization emerges as a problem of local substitution of large accurate tables by

small tables with larger but still acceptable error. Thus, instead of a global gigantic ultimately accurate

ERN, we receive a set of entity-relational networks {ERN,}, i=1.2...., n where 1 is the index (number) of

the level with highest resolution, n is the index of the level with the lowest resolution. The system does not

have all these levels in its storage because the amount of information in {ERN,} would substantially exceed

the amount of information in its level of highest resolution ERNi . The system remembers only levels with

middle (average) resolution and selected traces at the level of higher and/or lower resolution. If it requires more lower resolution information, it generalizes the middle level information as necessar\ . If it requires

higher resolution information, it instantiated (decomposes) the infonnation top down as requested. The

system {ERN,} is a nested system, i. e. the conditions of inclusion should be satisfied for the ontologies

constructed for the Worlds represented at each particular level of resolution. The same conditions should be

realistically satisfied for the objects and actions represented at the levels. Such a system can exist if it is supported by the operators of grouping, focusing attention (selection), searching for combinations of

interest (combinatorial search), and the operators that ungroup, defocus and eliminate the resuUs of search.

2.2.5 Constructing Multiresolutional Representation is a tool ofintelligence

Each level of representation has granularity that is a resuU of generalizing information from tlie lower level of higher resolution [16]. Both objects and actions of the real world have their representatives

at several (at least at two) levels of resolution and therefore are multiresolutional. The mechanism of

obtaining lower resolution objects and relationships out of higher resolution objects and relationships is called generalization.

The nature of generalization was envisioned by gestalt psychologists [22]. The need in the computational

theory of generalization was emphasized by J. McCarthy in [23]. One of the possible algorithms of

generalization is demonstrated in Figure 3. One can see in this example that the algorithm consists of

operators that perform Grouping (G), Focusing Attention (FA) and Combinatorial Search (CS) together

(the subscript means the level it works for). The joint set of operators G, FA, and CS we will call GFACS.

Using this set: computational procedures of grouping focusing attention and combinatorial search (GFACS)

is inevitable in an intelligent systems because the level of generalized information cannot be built

otherwise. GFACS generalizes information bottom up. Decomposition top down requires for an algoritlun

of instantiation (GFACS"'). There exist a vast multiplicity of algorithms belonging to tlie class of GFACS:

e.g. ARMA (auto-regressive moving average) as in [24, 25]; CMRA (convex multiresolutional analysis) as

in [26] and otlier. CFACS-1 has its prototypes, too, such as Sieve Decomposition algorithm [27].

Encoding of stored information is done in a muUiresolutional fashion too, and this leads to the further

reduction of complexity because instead of storing the body of the message (the file) we can store onle tlie code and apply to this code the mechanism of restoring the body. It is a legitimate mechanism of storing

informational entities by storing tlie code and regenerating (reconstructing) the information as necessary.

Storing infomiation in the form of DNA is an example of reconstructing the multiresolutional system of a

living organism.

2.2.6 Cost-functional

The need in a reduction of computational complexity would be easy to resolve by abandoning

computation. Yet, this cannot be done because the system has a goal to fight for reducing the time and

energy that are required to reach the target. This determines the conditions of the optimization process. The

latter should be performed in correspondence with the calculus of variations and Euler-Lagrange equation.

The central problem that emerges is to determine properly the Hamiltonian of the system, or its cost- fimctional.

367
As far as computational complexity is concerned, the results of optimization are driving the process of

forming levels of resolution. The optimization for an E.coli sounds like working under the heuristically

introduced cost-functional of foraging (see [6]):

STORING A UNIT

OF INFORVIATION

FOCUSING ATTENTION:

SELECTION OF ADMISSIBLE

UNITS OF INFORMATION

COMBINATORI.AL SEARCH FOR

ENHANCING THE UNITS

LTNDER CONSIDERATION

CONSTRUCTING SIMILARITY

CLUSTERS

SEARCH FOR HYPOTHESES

AMONG SIMILARITY CLUSTERS

STORING HYPOTHESES

ASSIGNING A STATUS OF THE

OB.IECT, ACTION, OR A RULE

TO SELECTED HYPOTHESES

ALGORITHM

OF GENERALIZATION

FOCUSING ATTENTION,

COMBINATORIAL SEARCH,

GROUPING,

SEARCH.

Figure 3. An Algorithm of Generalization and the Essence of its Operations

E + E

J

consumed lost

^ curr ^0

or E

J

consumed

^lost ' i^curr ~ ^.0 )

Using (4) and (5) for performance evaluation is a not a very simple matter. The system might actually have

many cost functionals pertaining to different levels of resolution. This can entail mutually conflicting

processes of optimization. Therefore searcliing for an optimum motion trajectory in the multiresolutional

state space would require recursive top-down/bottom-up algorithm of searching.

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2.2. 7 Ability to recognize and achieve goals

This ability should be considered an absolutely distinct feature of intelligent systems. In the simple

artificial intelligent systems, only the highest goal (belonging to the lowest level of resolution) should be

assigned to tlie ELF. The other goals will be obtained autonomously as a result of tlie planning process.

Searching for an optimum motion trajector\ at each level of resolution should be performed under a

particular goal assigned for tliis level of resolution. In the case of intelligence for mobile autonomous

vehicles a concept of horizon of goal assignment, or horizon of planning seems to eliminate many

difficulties in developing multiresolutional algoritlims of behavior generation. The concept of "horizon" is introduced because of tlie following conjecture:

Conjecture ofReducedAccuracyfor Remote Objects and Events

Under tlie same conditions and assumptions about the units of knowledge stored in the system of

representation, tlie units Uiat are remote spatially or temporally from the current state should be

assigned lower accuracy because the risk increases of being affected by the sources of uncertainty.

As a result, tlie higher the resolution is tlie smaller is the horizon of goal assignment. Thus, from the results

of finding the optimum trajectory of motion at low resolution, an intermediate state of this trajectory should

be chosen as tlie intennediate goal-state for the level of higher resolution.

2.2. 8 Emergence of "Self

Discussions about intelligence are permeated by the statements related to "consciousness.'" This

paper decouples the issue of intelligence and the issue of consciousness by introducing the concept of

representing "self" The need in representing self arises at some level of early learning processes because of

the need to increase tlie efficiency of plarming [28]. At the initial stages of development of the robot

intelligence, the whole World Model is being constructed relative to tlie robot. It is always situated in the

center of the state representation. As the knowledge gets more complicated, the need emerges in

representing tlie system in coordinates associated with the external system. This leads to a discovery similar

to that known as the Copernicus Revolution (apparently, Ptolemy failed to put the "self' on the map, and

this made using of his system less efficient).

The "self" emerges for an intelligent system (IC) after the representation of the IC itself becomes a part of

the World Model constructed by IC and thus, tlie model of IC is shown within its own system of

representation. Thus, tlie whole model of system (ELF. earlier shown in Figure 2) should emerge within the

World Model as shown in Figure 4.

INPUTT

Perception

BG

T

Behavior

Generation

Sensors World Actuation

SELF

OUTPUTT

Figure 4. ELF with "self

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If IC constructs its own ELF within its representation, it should have within its World Model a

representation of itself too. Thus, the idea of "self leads immediately to a paradoxical demand of having

within its system of representation an infinite system of nested models. Obviously, this is practically

impossible. Of course, in practice one or two nesting would be totally sufficient.

However, this is not the only paradoxical effect that can be listed for this phenomenon (called "reflexia" in

scientific psychology). Tlie situation gets more complicated when the World Model should also include the

model of anotlier intelligent system (IC) of a comparative level of intelligence. Then, the representation of

another IC should include its representation of the first IC. which contains in its representation the first IC

with its representation of both the first and the second ICs.

One of the important consequences of the emergence of "self is that a communication with this intelligent

system is possible as if it would be an external system. Since the differences in World Models are possible

between the initial ELF and the ELF of "self" this inner self might have subtle differences in the decision

making process. Algorithms of communications with "self seem to be an interesting part of introspection,

particularly, of "imagination."

2. 2. 9 Imagination

This "self can be considered a part of some more mundane processes that are known for many

animals: the processes of imagination. Creation of "virtual reality'" within our brains and supporting the

decision making process by exploring alternative mentally seems to be a very powerful mechanism of

intelligence increasing Uie efficiency of functioning. In artificial intelligent systems, "imagination" is synonymous with simulation of anticipated situations during the decision making.

WORLD MODEL BEHAVIOR

GENERATION

SIMULATED

SENSORY

PROCESSING

1

SIMULATED

SENSING

SIMULATED

WORLD

(ENVIRONMENT)

SIMULATED

ACTIATORS

TOWARD

ACTUATORS

IMAGINATION

Figure 5. The system of Imagination emei^ing in the ELF

As Figure 5 demonstrates, instead of submitting the decision to the real actuators, the module of Behavior

Generation submits it to the model of actuators, and simulates all consequences of this "WHAT IF" contemplation. IC simulates the events in the World, their development and simulates what will sensors

deliver, and how sensor>' processing will work, and what will happen after new information is delivered to

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the World Model. Searching with simulating the consequences is a powerful tool of the intelligence, it is

utilized for learning, planning, etc.

2.2.10 Autonomy

This propert>' is frequently considered a synonym of "intelligence" since both of them presume

each otlier. However, an objection is raised often tliat very autonomous systems can have low intelligence

while very intelligent systems can be deprived of autonomy. Further analysis shows that the latter statement

is not correct. If Uie system has low intelligence its autonomy is very limited within the world containing

many systems of high intelligence. On the other hand, if the system has a high intelligence, it would require a multiresolutional level of the effort to deprive it of autonomy. This means tliat it would require having

other highly intelligent systems to curtail the autonomy of another highly intelligent IC. Frequently,

introducing the autonomy constraints happens only for the one particular level of resolution.

The important implications for multiagent systems can be expected if this topic is pursued scientifically. At

the present time there are many groups that pursue the research on autonomy of multiple agents. However,

not too much research is conducted about multiple multiresolutional agents. One of tlie important issue is the following: how much should all agents-levels worry about cost-functions of each other taken in account

that they are nested within some of them while other agents-levels are nested witliin them.

3. Terminological Notes

3.1 Complexity

The term complexity is used in this paper in the following meaning: complexit>' is the property of a

situation to consist of excessively large number of a) objects, b) relations between tlie objects and c)

registered and modeled processes that include these objects and relationships as components, and d)

uiunodeled processes that depend on the stochastic factors and cannot be reliably modeled. The number

should be considered excessively large if as a result of its value the cost-function tliat evaluates the

goodness of the activities deteriorates. Evaluation of the number of components or connections, or

processes, or all of Uie above factors should reflect the following facets of the performance:

• time of computation.

• reliability of functioning, or

• probability of emergence of the phenomena unaccounted for in the logical analysis.

In many recent publications there is a tendency to associate the term complexity only with the latter phenomenon from the list above (uiunodeled processes). These references to something generated by

complexity but difficult to model are actually references to the lack of knowledge of what is going on. Thus, in this paper we refer only to the phenomena "a" through "c" from the definition above.

3.2 Reasoning

The term reasoning is understood as applying all or most of the rules consistently and directed

toward the goal. Consistency of applying signifies the absence of contradictions (paradoxes), and provides

for combining them in a proper sequence. Nevertheless, applications testily for existence of shortcoming in

many techniques of reasoning stemming from the predicate calculus of the first order. This is known for a

long time, and this is fly methods of fuzzy logic emerged together with the theories of belief and the

possibilistic approaches to determine preferences.

It became clear recentiy, that the substantial part of failing cases of reasoning happens because the

multiresolutional structure of representation is not taken in account by the process of reasoning, both in

living creatures and in computer equipped intelligent systems. What is true in one level is not necessarily

true in the adjacent levels. The temporal factor creates difficulties in a regular predicate calculus. Now, the

situation gets aggravated by the different time scales. These considerations can be illustrated by the

multiple examples. In many of them, reasoning is affected by the transformation of representation: while

tlie quantities change the list of objects is changing, too [29], and this affects the resuhs of generalization. It

371
was demonstrated that the motion of "pointing" in Hving creatures was affected by the different time scales

at the different level of abstraction in brain [30].

Finally, it has been found from many observations Uiat the logic of natural language is different from the one presented in the theory of predicate calculus of the first order. The inferences implied by the natural

language discourse to not allow to be easily transformed into statements of the predicate calculus while

their implications are eventually properly interpreted and understood by humans. It is tempting to develop

a) a theory' of natural language reasoning and b) an automated system that would allow to use the

advantages of natural language reasoning for artificial intelligent machines. The researchers of Drexel

University are working on these topics now.

3.3 Resolution

The term resolution related to the accuracy of detail in representation and sensor output is often

confused with the term resolution from the subsections of logic in artificial intelligence (resolutionrefutation). Resolution of the system's le\ el is determined by the size of the indistinguishability zone

(granule) for the representation of goal, model, plan and feedback law. Any control solution alludes to the

idea of resolution explicitly or implicitly.

Resolution determines the complexity of computations directly because it determines a number of

information units in a representation. In complex systems and situations one level of resolution is not

sufficient because the total space of interest is usually large, and the final accuracy liigh enough. So, if the

total space of interest is represented with the highest accuracy, the s-entropy (the measure of its complexity) of the system is very high.

The total space of interest is to be initially considered at a low resolution. Only one subset (or a limited set of subsets) of interest is further analyzed with higher resolution, and so on, until the highest resolution is achieved. This consecutive focusing of attention with narrowing the subsets' resuhs in a multilevel task

decomposition. The following terms are used with resolution intermittently: granulation, scale. "Granule" is another term of the distinguishabilit>' zone (pixel, voxel). Scale is considered to be equal to the inverted

value of the granule (or an "s-tile). When the space is intentionally discretized, we use the term tessellation,

and a single granule is called "tessellatum" or "tile."

3.4 Multiresolutional Representation

The term multiresolutional representation is defined as a data (knowledge) system for

representing the model of our system at several levels of resolution (or granulation, or scales). In order to construct a multiresolutional (muUiscale, muUigranular) system of representation, the process of

generalization is consecutively applied to the representation of the higher levels of resolution. As a result of

apph ing the algorithm of generalization to the modules of ELF emerge (Figure 2) with the new level of

Sensory Processing (SP). World Model (WM). and Behavior Generation (BG). These new. more

generalized BG-WM-BG sets are attached to the initial ELFs as the next '"floor" of this structure. If further

generalization is performed on the modules of the new level, an additional level of SP-WM-BG of the

structure would emerge.

Multiresolutional representation can be underlaid by an ERN principle of constructing the model. Objects,

relations, and actions of the ERN at the new level are different, and thus, the rules are different and the

results of searcliing for the best course of actions are presented in different terms. However, if necessary one can substitute it by other teclmiques of representing experimental knowledge, e.g. by using analytical

models with different accuracy of approximation.

3.5 Generalization

The term generalization is a formation of new entities (groups, classes, assemblies) where parts to be assembled are not prespecified. and new classes of properties can emerge. The synonym for the term

generalization is (in some cases) abstraction, however more frequently the meaning of generalization

exceeds the more narrow meaning of abstraction. Antonym - instantiation. Generalization usually presumes

372
grouping (clustering) of the subsets focused upon as a result of searcliing and consecutive substitution of

them by entities of the higher level of abstraction. This is why instead of term resolution levels we use sometimes an expression levels of abstraction, which means the same as levels of generalization, or levels

of granularity. Example; In most of the cases when humans encounter new situations they face the need to create groups. They make groups or assemble together components, which are not specified as parts

belonging to each other, and new classes of properties should be proposed on tlie flight.

From the definition of generalization, one can see that it can be performed tlirough applying tlie following

operators jointly: grouping, focusing attention, combinatorial search (or a simple search). There are many

operators that exhibit these functions: many algorithms of clustering tliat can be used to perfonn grouping,

many algorithm of choosing the subset of interest, e.g. windowing operators that perform focusing attention,

many algorithms of search, or search equipped with combinatorial generation objects among which the

search is done. To simplify further analysis of architecture we will call them operators of G. FA, CS, or about an integrated operator of GFACS.

It would be instructive to demonstrate how the term generalization differs from terms aggregation and

abstraction. Aggregation is formation of an entity out of its parts. Each of the parts can be also obtained as a

part of aggregation. Synonym - assembling. Antonym - decomposition. Example: The entity is formed out of

its parts. Information of belonging is contained in the description of the objects. We will consider this

process to be an example of a very simple group formation: we know what is the whole, and we know what

are the parts. Assembling of parts into tlie whole, or formation of an aggregate is detennined by

specifications.

Formation of a class of objects which is characterized by the same property, and labeling this class with the

name of this property is called abstraction. Synonym - class formation, sometimes, abstraction. Antonym -

specialization. Example: The properties, which characterized objects can be considered objects by

themselves. We won't be surprised if one calls kindness an entity. The fact that color is a property belonging

to tlie most physical objects of the real world makes it an important scientific and technological entity of the

system of know ledge. It is important to indicate that formation of such entity is possible only by grouping

together all similar properties of different objects. A red apple, red ink, red bird, red cheeks, tliey all belong

to the class of objects containing "redness".

So, generalization performs aggregation even when parts are not specified. This means that it subsumes the

aggregation. It subsumes die abstraction, too. In all cases concerning abstraction the term generalization is applicable. Generalization is typically applied when a similarity and observations are discovered and a

general rule should be introduced. The term abstraction is inappropriate in this case. Conclusion.-

generalization subsumes both aggregation and abstraction. This is a more general procedure for which

aggregation and abstraction are particular cases.

3.6 Nesting

Nesting is a property of recursiv ely applying the same procedures of multiresolutional knowledge

processing by using the operator of processing at a level for consecutively processing information of all

levels. The resuhs of Sensory Processing of all levels are nested one within another. World Models are nested one witliin another, and tlie decisions generated within the module of Behavior Generation are nested one within another. Levels of a multiresolutional ELF are nested one within another, while the levels

continue to function as separate independent ELFs. This separation of levels is a result of a need to reduce

the complexity of computations. Thus, instead of solving in one shot tlie whole problem with the maximum

volume of the state space and witli tlie amount of high resolution details one may choose to solve several

substantially simpler problems tliat are nested one witliin another.

3.7 Learning

The process of generalization upon tlie time-varying functions of a control system is called

learning. As a result of this process, the statements of experiences related to elementary objects,

relationships between objects, statements of actions merge together ito statements related to clusters of

objects, relationships and actions. These, generalized statements allow for construction of rules. Then, all

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this newly obtainted set of statements can be generalized again. This process that is being performed

recursively and is successively applied to its own output results in creating and constant updating of the

multiresolutional system of representation, and tlius, in improvement of plans and feedback control laws.

Learning is a component of this multiresolutional knowledge processing. Evolution of knowledge of the

system can be demonstrated as shown in Figure 6.

Obviously, learning is tightly linked with the property of Intelligent Systems of being equipped by the

systems of knowledge representation (e.g. tlie module of World Model in ELF). This module of

representation might not necessarily be physically lumped in one specific place; WM can be distributed over

a multiplicity of agents, or otherwise over tlie physical medium used in the intelligent system.

Updating of the World Model and enliancement of its multiresolutional system of knowledge representation

is done by the process of learning, wliich employs the set of GFACS operators that has been described

above. Levels of resolution are selected to minimize the complexity of computations. Planning and

determining of the beneficial feedback control laws is done also by joint using of generalization, focusing

attention, and combinatorial search (GFACS).

The operation of learning was associated wiUi layers: each layer learns separately. Learning experiences can

be organized only by using a multiresolutional structure. Levels are not hard-wired, they are constructed

TIME

Figure 6. Evolution of World Model as a Result of Learning

from the information at hand. As it is done in neural net, for example. Mathematics of various operators of

focusing attention, grouping and searching usually employed by GFACS algorithms can be found in [3 1].

One can see from Figure 7 that Learning in an intelligent system boils down to collecting experiences,

applying GFACS to them, and explicating objects, actions, rules and theories that might be used by the

module of Behavior Generation. Combining Figure 7 with Figure 5 gives an opportunity to learn not only

from real experiences of acting within the environment but also from the imaginary experiences of

simulating within the imagination of the intelligence.

3.8 Intelligent Control

Intelligent control is a computationally efficient procedure of directing to a goal of a complex

system with incomplete and inadequate representation and under incomplete specification of how to do

this in an uncertain environment. Intelligent control, typically, combines planning with on-line error compensation, it requires learning of both the system and the environment to be a part of the control

374
process. Most importantly, intelligent control usually employs generalization (G), focusing attention (FA)

and combinatorial search (CS) as their primary operator (GFACS) which leads to multiscale structure. In

all intelligent controllers, one can easily demonstrate the presence of the GFACS operators. It also is possible to demonstrate tliat using tlie set of GFACS operators is not topical for conventional controllers,

although tlie elements of GFACS are often utilized.

Not accidentally, at the dawn of intelligent control it was associated with using neural networks (NN),

ftizzy sets (FS) and generic algorithms (GA) for control purposes. (In some publications, these tree subjects are considered a must for intelligent control). In fact, neural networks is a tool for generalization

in the vicinity of the state space, fuzzy systems allow to expand the process of generalization to die larger

domains of the state space. GA is just a particular case of combinatorial search with some component of

internal generalization for learning purposes). In other words, NN+FS+GA is a particular case of GFACS.

Thus, the views presented above are confirmed.

RELATION .^L DATABASE OF CONCEPTS

Entities Database

Verbal

Entities Database

Iconic VALUES

Experiences Database

Rules Database

Theories Database PROCESES

OF THEORIES AND RULES

CONFIRMATION

Experiences

Recorded

Hypotheses

of Tlieories ^ and Rules ^ Knowledge

and

Learning

Confirmed

Theories

Behavior

Generation

Figure 7. ELF with Learning

4. More Formal Definition of the term "Intelligence"

Most of the literature on intelligence can be found within the stream of publications related to psychological sciences. Most often, this is not exactly the intelligence that is discussed in this paper: tliey are talking about human intelligence primarily (like in [32]). However, even the most fundamental

375
collections of sources do not define intelligence in the way other than listing of the mental abilities that are components of intelligence. We already spoke about immensity of abilities associated with intelligence. In the sources related to psychological science, intelligence is typically defined as a mental quality that

combines:

1. ability to learn from experience

2. abilit>' to adopt to new situations

3. abilit>' to understand and handle new concepts

4. ability to acquire and use knowledge

The following definition is based on a term thinking:

"[An] action exhibit intelligence, if, and only if, the agent is thinking what he is doing while he is doing it. and thinking what he is doing in such a manner that he would not do the action so well if he were not thinking what he is doing" ([1], p.29). Thinking is imderstood as a process of

mediation between iimer activities and external stimuli. It always alludes to the need in a specific

language of thought [33] and provide for a substantive link between the mechanisms of

intelUgence and a computational process [34].

Several definitions are presented in [35]. One of them belongs to J. Albus and can be found in [21]:

"An intelligent system has the ability to act appropriately in an uncertain environment, where an

appropriate action is that which increase the probability of success, and success is the

achievement of behavioral subgoals that support the system's ultimate goal."

This definition generalizes upon multiple abilities mentioned in the psychological definitions and

introduces a concept of a success associated with the behavioral subgoals that presume some hierarchy of

activities (with an inevitable multiresolutional representation). This definition is dominating since it is not

linked with a particular configuration, neither it alludes to any particular domain of application. Clearly, one can apply this definition for both hving creatures and artificial intelligent systems.

The operational definition introduced by A. Meystel and partially presented in [33] explains how the

intelligence works:

"Intelligence is a propert>' of the system that emerges when the procedures of focusing attention,

combinatorial search, and generalization are apphed to the input information in order to produce

the process of intelligent system functioning."

Focus in this definition is how information is processed so that it makes this mechanism intelligence.

Earlier in this paper, there was more about procedures involved (GFACS) and representations required

(World Model in one of the available forms of e.g. ERN).

More technologically inclined definition from [33] demands to concentrate on the issue

of uncertainty (that was already mentioned in Albus' definition):

"Machine intelligence is the process of analyzing, organizing and converting data into knowledge,

where machine knowledge is defined to be the structured information acquired and applied to remove ignorance or uncertainty about a specific task pertaining to the intelligence machine.'"

Focus in this definition: converting data into knowledge by removal of uncertainty.

All the above definitions can be supplemented by informative statements describing features typical for

intelligence but not reflected in the definitions. For example, a technological system with intelligence, i. e.

intelligent system, undoubtedly can deal with unanticipated factors due to the ability to learn: An intelligent system must be highly adaptable to significant unanticipated changes, and so

learning is essential. It must exhibit a high degree of autonomy in dealing with changes. It must be

able to deal with significant complexity, and this leads to certain sparse types of ftinctional

architectures such as hierarchies.

As a byproduct of all these abilities, a number of additional features emerge gradually in a developing

intelligent system. For example, a feature of autonomy is associated with intelligence although we still do

not know how. Dealing with complexity requires using multiple resolutions, because functional

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hierarchical architectures are declared. Thus, tlie feature of being based upon multiresolutional

representations is a fundamental one. Having in its core a semiotic closure is t>pical for an intelligent

system, too.

Now we try to create a synthetic definition absorbing tlie definitions and supplementary' statements above:

Intelligence is a control tool tliat has emerged as a result of evolution by rewarding systems with

increase of tlie probability' of success under informational uncertainty. Intelligence allows for a redundancy in its features of functioning simultaneously with reduction of computational

complexity by using a loop of semiotic closure equipped by a mechanism of generalization for the

purposes of learning. Intelligence grows through the generation of multiresolutional system of

knowledge representation and processing.

The multi-level systems fitting into this definition are not necessarily hardwired hierarchies. They are virtual

hierarchies of perception, of knowledge- representation about the v\orld model, and of decisions about

behavior generation. As a new concept of "knowledge" emerges, a new "node" of the representation ERN is being created.

From this effort to scan existing and create a new definition, new analytical and research tasks precipitate. It becomes clear that the system of intelligence should be equipped by a capability' to properly measure the

objects, relations, actions and behaviors. Thus, the problem of evaluating metrics of performance and

intelligence emerges. It becomes clear that intelligence can be evaluated by "a degree of intelligence". One

can see that the definitions explored in this sub-section allude to the need of measure and perform

quantitative ranking Uiat is supposed to end up with a choice of a decision making. The definition of

intelligent control should be based on tlie properties of intelligence as we understand them rather than the

virtue of using some particular hardware components.

There is tlie list of factors that are supposed to be measured for evaluating an intelligent system:

• Complexity Reduction.

Complexity should be evaluated and possibly the lowest level of complexity should be

preferred under other similar conditions. Reduction of complexity should not bind tlie capability

to develop redundancy.

• Redundancy

A measure of "exceeding" Hie immediate needs should be injtroduced: one can see that the

ability to evaluate the "iimnediate needs" is required.

• Increase in Functionality

The design specifications can be used to evaluate the measure for both "immediate needs" and

items of "functionality." However, in many cases, the specifications are not available.

Definitely, an ability to evaluate functionality quantitatively would be an advantage, but we

have to know how to restore their list. • Multi-level Systems.

The practice of intelligent system design demonstrates that the number of resolution levels is being selected based upon heuristics, not clear mathematical analysis of advantages and

disadvantages this number entails. Designers do not know how many levels should a system

have, and what are the other quantitative factors involved in assigning a number of levels to a

muUiresolutional system.

• Degree ofIntelligence.

A measure of intelligence is presumed to be known, at least a relative measure (which one

system is smarter if general parameters are the same but different mechanisms of sensory

processing, or different algorithms of planning are applied).

• Degree ofAutonomy.

A measure of Autonomy is presumed for tlie systems that are supposed to decide their own

course of actions for themselves.

• GFACS

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At the present time there is a multiphcity of the mechanisms (algorithms) of generaUzation. We

do not have any basis for comparing the results of their functioning. Even in more simple cases

(e.g. focusing attention in ARMA algorithms we do not have recommendation of comparing

different versions of ARMA.

• Increase in probability ofsuccess. How should success be evaluated depends on our ability to specify it. (Is this money, power,

knowledge, ability to live longer? Are these outcomes anticipated as a measure of success when

they are computed for the system imder consideration, or for the group, or for several

generations?)

5. Evolution of Intelligence

In nature, the evolution of intelligence can be demonstrated as the development of a tool of

survival. This tool evolved in living creatures (systems) as a control mechanism (a controller) to optimize

needs satisfaction in changing environment. As the complexity of needs was growing, in addition to creating ways of their satisfaction the duty was performed to accordingly develop the mechanism of

intelligence. The major destination of intelligence is to solving harmoniously the combined task of NEEDS

SATISFACTION + COMPUTATIONAL COMPLEXITY REDUCTION

Increasing functionahty for performing this task can and should be measured. The evolution of

intelligence presumes the evolution of both the system and the controller. The proper measure allows to judge results of evolution of intelligence. Evolution or development allows for increasing the functionality

of the system jointly with reduction of its computational complexity. This is why the ability to generalize

emerges, as the ability to lump entities of matter and/or information for more effective storing and

computation. Generalization is a tool of creating new. abridged systems and their representations. It is a

tool of creating representations in generalities, creating new levels (generalized)of lower resolution with

new metrics or granulation. At the lower level of resolution, the tools of intelligence can afford a larger

scope of attention, solve a problem of a larger picture, with a longer horizon of planning. So, the decision

making on any given resolution should be preceded by the preplaiming at a lower resolution level.

RANDOM MOTION GENERATOR:

[SEARCHING]

N

IS^

IS,

s —»^

E IS2 —

N

S IS3 —

BIO-COMMUNICATION

INFORMATION EXCHANGE

DNA-PROGRAM FORMATION <s^non-realizable "SELF"

[GROUPING]

SENSING

ACTUATION

PREPROGRAMMED

PREPLANNED

BEHAVIOR

ONLY SUCCESSFUL SURVIVE

[FOCUSING ATTENTION]

Figure 8. Architecture of E.coli's Intelligence

The biological models allow us to observe the growth of the degree of intelligence in the living forms

starting witli single cell organisms, through E.coli [6], via substantially more complicated living forms

from mollusks to mammals [36]. and concluding with a human being [32] (See Figures 8,9,10 developed

for E.coh level within the paradigm of [6] with [37]).

378
SENSORY

PROCESSING

WORLD

MODEL

BEHAVIOR

GENERATOR

t

SENSORS WORLD ACTUATION

Figure 9. Developing World Model

via reproduction

Figure 8 shows the group level of the E.coli intelligence architecture. Each individual E.coli is shown as a

separate intelligent system IS,. Random motion of all E.coli in this group (combinatorial search) leads to the situation that only those moving successfully survive (focusing attention). The sur\ived E.coli

individuals communicate and share their experience via reproduction (grouping). The successful behavior

is s a resuh of Uiis bio-information exchange and the results of GFACS operations are stored in the DNA,

and the group (as a level) changes its behavior, and produces a behavior which increases Uie fraction of

survived individual in tlie group. Thus, it can be considered "preprogrammed, preplanned behavior" at the

level of the group. The group ELF has actually as modified its World Model as shown in Figure 9.

DNA PROGRAM FORMATION

TEMPORARY CARRIERS

GENEIALIZATION

OBJECTS

AND

RELATIONS

G

F

A

C

s

TWO-LEVELS OF RESOLUTION

WORLD REPRESENTATION

SENSED

INFORMATION

PREPROGRAMMED

PLANS

COMPUTING OF

.4NTICIP.4TION ACT,

RANDOMIZATION

FOR SEARCHING

BEHAVIOR

Figure. 10 Evolution from group intelligence to individual intelligence

379
The architecture shown in Figure 8 can be developed into structure presented by Fig. 10, where learning is performed not through sacrificing unsuccessfiil individual but through abandoning unsuccessful

"theories" tested by internal simulation within the rudimentary just emerging system of representation.

Here, instead of the multiplicity of individuals we have ERN of objects and relations that are generalized

not by communication via reproduction but computationally by applying GFACS embodied as a set of

information processing procedures.

Similar evolution can be observed in the domain of technology and in the domain of Linguistics. The

processes of intelligence evolution extracted from these domains can be discussed in a generalized form

by using architectures similar to Figures 8 through 10. Some of the advanced technological architectures

(e.g. RCS) are described in [38].

Interesting temporal effect can be anticipated in the process of evolution. One can easily anticipate that the

evolution is a "punctuated" one" (in the sense of [36]) since the new blocks only occasionally emerge in

the architectures of intelligent systems.

6. Mechanisms of Intelligence

Analyses of the processes of structural evolution in the area of intelligence allow for discovering the

following mechanisms of intelligence.

• A semiotic closure is the basic structure of intelligence (see Figure 2). It differs from a simple feedback

loop because each element of the closure is a source of redundancy and a generator of the adjacent

resolution levels by the virtue of GFACS operation.

• Evolution of multiple-choice preprogrammed behavior into a muhiple alternative creation ends up with

multiple theories development (the latter is performed in the imagination).

• Through combinatorial search, focusing attention and grouping performed in Nature by tlie mechanism

of natural selection^ the discovery of more efficient techniques was done. It was discovered by the

intelligent agents that storing information about objects of the world, actions they encounter, and rules

entailed by the changes is more efficient. Indeed, it is less expensive than testing the same material (often,

living) samples again and again to receive similar results.

• Generalization and learning through natural selection fi^om the choices created by material alternatives

has demonstrated to be a waste of time, energy and matter. It is more efficient to learn by dealing with

information only, i.e. by theorizing (THEORY ^ THE RESULT OF GENERALIZATION UPON

RULES, RULE —>RESULT OF GENERALIZATION UPON EXPERIENCES)

• Mechanisms of generalization give a consistent explanation to the semiotic tools of evolution discovered

earlier in [39, 40]. The same mechanisms and tools determine that the ultimate methodology of analysis of

the mechanisms of intelligence can be defined and realized successfully in the domain of Natural

Language analysis.

• Any RULE discovered by an inteUigence is a statement of some generality: it cannot refer to all details

of realistic test cases. The selection of the proper details for the particular state of affair is performed by

the mechanism of focusing attention.

• Multiresolutional storage obtained via consecutive generalization turned out to be the most efficient

method of storing information.

^ Punctuated evolution demonstrate periods of changes with intervals of the absence of any development.

^ Actually, the reader should have already anticipated the conjecture that natural selection in the Nature played a role

similar to the algorithmic mechanisms of generalization and learning.

380
7. Intelligence of the Conventional Controller

It would be desirable to determine what is the relation between the conventional control and intelligent

control. The follov\'ing statements are based on the preceding materials.

1. Conventional control is about feedback. The goal formation is external to the problem. When we

include the goal formation the problem become IC-embedded because the goal for each level of the

higher resolution is created as a result of BG-module functioning at the level of lower resolution.

2. The structures of intelligent control are formed as semiotic closures, mostly the multiresolutional ones, which contain an element that can be called "feedback". But feedback is not the entire issue. The

transformations within the feedback loop are more important. The classical feedback does not need to have any redundancy in it. This is why Y.-C. Fu associated the concept of Intelligent Control with

"recognition" in the loop.

3. We would expect that the feedback of the semiotic closure contains GFACS as a rule.

4. Optimization as a part of functioning of the conventional controller presumes searching at a level but

stops short from recognition its embedding within the multiresolutional hierarchy of top-down constraint

propagation.

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382
Machine IQ with Stable Cybernetic Learning with and without teacher

Harold Szu, Ph.D. , Fellow IEEE

Digital Media Lab, ECE Dept. GWU, 22 & H St. NW, Wash DC 20052

1. MACHINE IQ

Lotfi Zadeh has raised an interesting and

philosophical question: what is the Machine

Intelligent Quotient (IQ) needed for intelligent

household consumer electronics and robots?

We wish to suggest a nonlinear but

monotonic scale similar to the logarithmic scale

adopted by C. Shannon information theorem based

on the logarithmic phase space in L. Boltzmann

entropy notion in Sect. 5. The justification is that

the human-like creativity is rare and difficult and

must be reached at the top 50% scale with

unsupervised learning in Sect. 2 & 3, and the

dumber machine near the low end of the scale. In between they are separated by dyadic basis.

However, for household convenience, after taking

the logarithmic nonlinear scale of human-like

intelligence, the net result is further measured by

taking the usual linear percentage scale. We

concede that these double scales may be sensible in

the operational definition but could not be

fundamental. For we are not absolutely certain

about what are the necessary and sufficient

ingredients for a humankind or machine to be

intelligent.

(1) After we have taken the logarithmic

scale, then MIQ=10% of human

being is loyal to human master and

its own survivability, say, the robot

having M1Q=10% is able to find

and differentiate the electric power

plugs having two porn's of 110

Volts or three porn's of 200 Volts.

(2) Then, MIQ=20% is able to understanding human conversation.

(3) In that direction we can extrapolate

MIQ=30% to be able to read facial expression and voice tone for the

emotional IQ to understanding

irrational emotion need of human

being.

(4) MIQ=40% is able to command and

control a team of other robots.

(5) MIQ=50% is able to "explore the

tolerance of imprecision," e.g. using

fuzzy logic to negotiate a single

precision path finding in an open

save terran. We divide MIQ into the supervised

learning with an open lookup table having the

extrapolation and interpolation capability up to MIQ c 50%. The key of human-like sensor systems is learning without supervision to be

scored MIQ beyond 50%. Such a learning

methodology is necessary, because, other than

factory robots, any indispensable need of robots

happens usually in an open, uncooperative and

hazardous environment with an unforeseeable

nonlinear dynamics interwoven with non-stationary

complexity. We believe that unsupervised learning

is necessary in building of human-like trial-and- error estimation systems, such that a major next

step toward the intelligent robot is visual and

natural language understanding needed for self- determination in uncontrollable environment. Thus,

laboratory simulations of robotic teams are necessary with the help of the wireless video

feedback control technology base (WaveNet video

communication devices on wheels).

Intelligent processing is a need common to

real world surveillance and control, especially in unmanned environment, namely the nucleus

reactor chamber, the undersea, and the outer space. To set the research direction of real world

applications, we consider a not-yet solved and

perhaps a milestone problem to design an

intelligent robot entering into a new challenging

situation (not unlike a newborn infant facing a bustling and hustling world). This is challenging

because the robot has not yet acquired any

pertinent internal knowledge representation. The

robot must learn how to process the unfamiliar

sensory, hearing and vision inputs and to travel through an uncharted environment (even if the

robot has been endowed with the past experience

and has had a man in the loop as the coach for the

remote guidance and control). In a distant and

novel situation, we anticipate the robot having some difficult in following the supervised learning

given that the robot has not yet learned what is the

desired output for the unfamiliar inputs. The

milestone problem is thus due to the impossibility

to specify all details ahead of the time, and

therefore it is important to develop unsupervised

sensory learning capability (which leverages

subsequently the self-supervised learning with the

gradually acquired experience).

A real world challenge happened recently

during the NASA Pathfinder exploring the Mar, of

which the round trip time for Control, Command,

& Communication (C3) is 5 minutes. A futuristic

383
suggestion would be using ANN for intelligent

pathfinder leader to control the rest of team

members, and then local control will be

instantaneous, while we control the leader with

non-real time commands (not unlike biologically a queen bee leading a hundred working bees).

Martian pathfinders would require a local adaptive and intelligent control because of timedelay of the ground station. Thus, instead of oneto-one, we have proposed a quarterback robot

(25% IQ) controlling a team of linemen robots (not

unlike a queen bee controlling a hundred working

bees). A team of Unmanned Marine Vehicles,

Dolphins, can parallel hunt for mines in the

extended littoral battle space. Using wireless video

feedback control (WaveNet via SINCGARS), a person as the coach may communicate with some

delay (because of out of the line of sight) the goal

with an intelligent robotic (quarterback of the

dolphins team). The quarterback is able to execute

locally and faster C3 of all linemen UMV's to identify objects in voting and going around local obstacles, using the prior GPS information from

the wide receiver scoutimng UMV.

2. LEARNING METHODOLOGY

Recently, Irwin has edited the Industrial

Electronics (IE) Handbook (CRC & IEEE Press, pp. 1-1 686, 1997) and devoted one thousand pages

to the intelligent electronics (IE) describing

comprehensively all enabling technologies. These

are expert systems and neural networks, fuzzy

systems and soft computing, evolution systems,

computational intelligence, and hybrid

applications, and emergent technologies. We

believe that some degree of human-like

intelligence is necessary for user-friendly

interaction with IE. On the other hand, the

classical artificial neural networks (ANN) with

supervised learning strategy have reached the

maturity and plateau with some mixed appraisals,

although the interdisciplinary studies have just

been bearing fruits (since the establishment of

international neural network society a decade ago).

For example, the neural physiological experiments

of human sensors have culminated a truly

unsupervised learning new paradigm. When a newborn baby faces the bustling and hustling

world, he/she cannot grasp the changing signals

Si(t) = Si(t)ai, from noisy inputs xi(t), X2(t). However, the intuition is that non-noises must be

signals. Thus, the child recognizes the fluctuating

noise that has zero correlation <vi(t)v2(t)>G = 0 of

the neuron outputs.

If one assumes a linear instantaneous inputs as X(t)=s,(t)ai+s2(t)a,4A]S(t) (1)

where S(t) and [ai, nj] = [A] are unknowns, then

the artificial neural network (ANN) seeks a weighted sum: V(t) = [W]X(t) which will produce

garbage outputs defined by

<V(t)V(t)^>G = [I]-[W][A]<S(t)S^(t)>[ A]^[W]^

which implies [W][A] = [I] (2)

because <S(t) S\t)> = [I] has a nontrivial higher

order statistics (HOS). Thus, the internal

knowledge [W] is discovered as [A]''. This math is called the Independent (i.e. joint probability

density factorization) Component Analyses (ICA).

For instance, after the external stimulus by light,

sound, and perhaps touch sensations, one hundred

millions of visual, hearing and tactile sensory neurons generate highly redundant collective

excitations, which can not and should not sustain

themselves. Local time scale complex nonlinear

dynamics will always yield to decaying in the

global time scale, according to Neurodynamics

Lyaponov-like Theorem, proved in Sect. 6. Unsupervised learning takes the advantage of the

necessary decay of those highly redundant

excitations, as the mean of memory toward

statistically independent components (IC) without

knowing precisely what they are. Therefore, this output state can not be specified ahead of the

learning in the truly unsupervised fashion. After a decade studies of neural nets, we have realized that the chief biological reason for a pair of sensors, eyes, ears, tongue sides, nose holes, hands, is to provide the robustness redundancy and the

instantaneous spatial temporal de-noise without

teacher together with a simultaneous recognition

with teacher (associative memory). To simplifying

the unsupervised portion of learning, two ears disagree must not be signal—a perfect de-noise

algorithm (called mathematically Independent

Component Analyses (ICA)), i.e. "pair of raw

inputs, garbage output" as opposed to a dumb PC:

"garbage in, garbage out". Since the output is garbage, no teacher is needed, and what's kept

inside the brain without being squeezed out as noise is useful feature Fig. \. Neuro Paradigm

384
Random EEC at

finishing of leanung

Maximum Etitmpy

Infonnadon is liept within memory Brain imaging experiments might support

the hypothesis that learning of a newborn child

might have various stages interwoven at ease. Advanced brain imaging, e.g. Functional Magnetic

Resonance Imaging (fMRI) or Positron Emission

Tomography (PET), can help substantiate the

unsupervised learning. For example, Positron

Emission Tomography (PET) image reveals all female subjects using both side of brains while all male subjects use only one-side to processing

speech. That radioactive-labeled glucose supplies

the nutrition needed in the brain processing can decay position rays, which, furthermore, decay into

two opposite Gamma rays onto all around films.

Image is synthesized like the Tomography, but the

PET radiation comes from selectively inside rather

than indiscriminately in traditional Tomography.

Should the collective neural activity randomizes

into the de-correlation, as stated, the intensity of

positions and associated 2 gamma rays that have

collectively lit up the brain imaging would fade

away as noisy-like. To carry the thought

experiment further, we consider instead adults an

infant just born. We conjecture that, without yet

any internal knowledge representation, a newborn

baby, who does not have anything

pain/pleasure/movement etc. to be associated with,

must utilize sensory input de-correlation process

toward noisy output, as a truly unsupervised

learning strategy to build up the internal knowledge

representation. Let us imagine that the first sight of

a face of mother composed of several millions of

collective excitations in human visual systems (6.1

millions color perception cones and 150 millions

dark light rods on retina). This first impression

must be decaying toward randomizing responses as lacking of any imagination ability of association

the infant can not sustain another concept or image

as the only reference point for feature extraction

stoppage criterion. This innate ability could be the

foundation for any artificial endowment of

machine IQ. Afterwards, the traditional ANN

supervised learning can be leveraged by the

existence of internal knowledge representation. In

this sense, the supervised learning may be called

the second stage of learning. This unsupervised

learning ability is amble demonstrated in blind demixing acoustic signals and images without

knowing what the original sources are, and how

they are mixed as an infant in a cradle.. This trait

is mathematically referred to be ICA or BSS. The

result is similar to the cocktail party effect that one

can de-mix, during a noisy drinking party, the

signals and detect one's name or other important

messages among cross talks. There are three stages

of learning as a new borne baby develops. The

initial stage is related to the decay of collective

excitation generated by millions of sensors (6.2

millions color perception cones and 150 millions

gray-scale rods) connected to millions of neurons

in the cortex area.

1. Initial Stage no details ( < 1 week/month old)

The first stage happens at the limiting case of

eye-opening first sight without knowing the

desired association memory and without

acquiring yet any internal knowledge

representation. This is characterized by the

merely decay of collective sensory excitations

to noise. "Mar, Bar, Dog, Cat, pleasure & pain,

etc." learned without feature definition not yet

having meaning of association; "(sensors) info

inputs = garbage output (randomized EEG

when CNS learning stopped)", namely the

unsupervised learning by maximizing the

output Entropy, (as if lemonade has been

squeezed and kept in the synaptic junctions

and the useless skin & junk is throw out).

2. The second stage is efficient by leveraging the

IC knowledge as the desired output

Intermediate State supervised learning sensory

inputs—desired outputs forming associated

pairs.

3. The third stage may be called the creative sate of thinking (< K12). The third stage takes the

advantage of both the internal knowledge

being internally generated as realistic excitafions, called active imagination, together

with the externally generated sensory

excitation (to seek again by the unsupervised

as new knowledge base). The external sensory

inputs plus internal imagination inputs

generate new thought.

These stages are alternatively going on

effortlessly, and may become not separable

subsequently. However, this initial condition of

learning should help robots with intelligent sensory

processing capability to deal with new

environment. In real world applications, we often

385
lack the precise knowledge of the desired output

features. Therefore, we cannot apply the

supervised ANN to associate the input data to the

output feature. However, without knowing the

desired output, the new unsupervised ANN can

extract the independent components of the input

data, which itself becomes the desired feature.

In summary, when a raw information

input through pairs of eyes, ears, nasal passages,

after been sieving through the synaptic weight

matrix for extracting IC features, the final neural

outputs become noise-like. The ANN unsupervised

learning changes the ANN weight matrix to sieve or squeeze anything useful (higher order

correlation information) from the input sequence

until the outputs are left with (nothing but

maximum entropy) redundant garbage or noise.

This strategy is on the contrary to the supervised one because in a truly unsupervised learning we

cannot assume any output goal but the garbageoutput for information-input. In paraphrase, after squeezing the juicy clean, ANN throws away the

trash. It does not matter whether the input is an orange or a lemon, the end product of the

unsupervised process is identical, being without a teacher the only logical choice is trash output

meaning no more useful covariance infoirnation,

1. e. the unique noise-like output state. While a

traditional computer has a motto to describe a dumb and do-nothing computers as "garbage-in,

garbage-out", we dramatize that a smart

neurocomputer has a motto that "raw informationin, garbage-out". Such a novel "information-input

and garbage/noise output" paradigm for the

neurocomputer has accomplished a machine 1/Q,

which is surely higher than a traditional computer

with the do-nothing motto. The new generation or

6'*' gen neurocomputer can at least do a sensor

feature extraction job without supervision.

2. UNSUPERVISED LEARNING

We present simple mathematical models

of unsupervised learning algorithms of artificial neural networks (ANN) as motivated by the

biological principle of redundancy reduction

(Barrow, 1953) via statistical decorrelation of

sensory mixtures, known in ANN as Blind Source

Separation (BSS) or Independent Component

Analysis (ICA). Different stages of learning are conjectured which could help robots acquire

additional knowledge needed in a hazardous and new environment. We begin with the familiar

formalism of auto-regression (AR) which is easily

generalized to the supervised back-errorpropagation ANN, and then to the unsupervised

sensory mixture decorrelation. This is initially

based on higher orders of statistics, e.g.

4'*' order

cumulant-Kurtosis, that is, furthermore, led to the

maximum entropy of all cumulants. These

generalizations have been illustrated with computer

simulations in a controlled setup. Real world

applications are given in Part II, such as remote

sensing subpixel composition, voice-dictation

phoneme segmentation by means of ICA dehyphenation, and cable TV bandwidth

enhancement by simultaneously mixing all Sport

and movie entertainment events. Such a truly unsupervised learning

strategy in terms of ANN is mathematically

elucidated in terms of a pair of sensory inputs vector X(t). Assume a linear piecewise-stationary

mixture model. The unknown but piecewise timeindependent feature matrix [A] consists of column

feature vectors [ai,a2] and the correspondingly

unknown vector source S(t) corresponds to the

percentage of feature vector composition, then

X(t) = [A]S(t) (1)

Through ANN feedback iteration unsupervised

learning of the synaptic weight matrix [W]. The

bipolar unitary output may be approximated at the

maximum entropy (cf Appendix A Proof)

V(t)= tanh([W]X(t)) = [W]X(t), (2)

which has a linear slope at the tnreshold value of

input for "may-be-yes may-be-no" no-information answer (proved to be at the maximum Shannon

entropy with the minimum of information; rather

than, the definite yes-or-no informative answers at the large input bipolar values). Instead of tanh one

can also adopt the positive sigmoid function,

whose outputs must be subtracted by the averaged

value to be bipolar fluctuations of mean-zero, in any case, the output is not the traditional desired

output, but must be reduced to be noise-like at the

end of unsupervised learning process in consistent

with the maximum entropy with no more output

information at the second moment level. The off- diagonal random components on the time average

vanish, while the diagonal elements are identically

squared and never vanish and can be normalized to

one. <V(t)V^(t)>= [I] (3)

To achieve it, a random permutation of a large

block of data is often recommended to avoid the

sampling inaccuracy. (The time order scrambled

for fear of sampling error will be preserved in data

X(t), once we have found [W] as the inverse matrix

of[A])

Specifically, the whitening of the second moment

of the output shows:

<V(t)V^(t)>=[W][A]<s(t) s^(t)>[Af [Wf =[I] (4)

This is equivalent to [W] = [A]"' provide that

statistical de-correlation of sources

386
<s(t)s^(t)> = [I] (5)

is true. If not, a whitening in the data domain is needed to get rid of the second order statistics,

namely eliminating Gaussian random process and

keeping high order information. In electrical engineering, this operation is known as the gain

normalization of different sensor inputs. U = [Wz]

X where the zero-phase or symmetric matrix[BS95]

[Wz] = [Wzf =<XAr^>""^ (6) can be derived by setting

<U U^> = [Wz] <xx^> [Wzf = [1], and post-multiply with [Wz]: [Wz]=[l][Wz]=[Wz]

<j£- jf^> [Wz] [Wz], and canceling the common

factor [Wz]: <xx''> [Wz]'^[Wz] = [1], where use

is made of the symmetry to arrive at the result of

inverse square-root of covariance matrix. Since

[W] is the statistical inverse of [A], one can use it

to obtain the unknown source point-by-point in the

identical time order

[W]X(t)=[W][A]S(t) = S(t)(7)

While an ill-posed deterministic problem

can not be uniquely solved, an ill-posed statistical problem has a lot more conditions in time to determine all those unknowns.

Deterministic: # of unknowns, S, A > # of known

X

Stochastic computing the covariance R

# of unknowns = # known; data Rxx' = source Rss' But if more than 2 sensors & de-correlated

then it's possible to gam more Rxi X] , RX2X2, Rxi X2 This is not unlike the human experience, which by

definition provides the statistics determination

based on past experience. Real world applications

are given in Part 11, such as remote sensing

subpixel composition, voice-dictation phoneme

segmentation by means of ICA de-hyphenation,

and cable TV bandwidth enhancement by

simultaneously mixing all Sport and movie

entertainment events. The visual cortical feature detectors

might be the end result of such a Redundancy

Reduction Process (RRP), in which the activation

of each feature detector is supported to be as

statistically independent from the others as

possible. Such as 'factorial code (of joint

probability density)' potentially involves

independence of all orders, but most studies have

used only the second-order statistics required for de-correlating the outputs of a set of feature

detectors. Field has observed that the early

learning algorithms are mainly based on the

second-order statistics, which might account for the

missing opportunity. Current understanding is that the need of high order statistics such as the 4*^

order cumulant called Kurtosis may be captured

completely by the information-Theoretical

approach of maximum mutual information entropy

underlying the Independent Component Analysis

(ICA). The fourth cumulant, the Kurtosis A^('m^, is

often used by Helsinki's Oja group to seek the

statistical matrix inversion.

K(V) = <V>-3 (<V^>)^ (8)

because <v | V2 V3V4>g = <v 1 V2> g <V3V4> g + <v 1 V3>

G <V2V4> G + <V2V3> g <V|V4> g are reduced for identical process to (8). One considers a single

weight vector update:

dw/dt =dK/dw. (9)

The other weight vectors are found by the

projection pursuits. If each voice and image

has its unique value of Kurtosis, then seeking a stationary Kurtosis yields the specific voice and

image, without knowing what is the desired output:

Gaussian, Laplacian, Multi-Modal Distribution &

each has a Super-Gaussian, K>0, or Sub-Gaussian.

K<0, Kurtosis value than Gaussian, K=0. Note that speech oscillation has Laplace distribution

decaying exponentially from the mean value, zero amplitude, which is faster than Gaussian quadratic

decay. Therefore, the Kurtosis of speech is called

super-Gaussian and by definition, has a positive

value (subtraction of smaMer variance than that of

Gaussian). On the contrary, an image histogram

has a bimodal distribution for most grey scale

images, then the variance is bigger than that of

Gaussian. Therefore, an image has a negative

Kurtosis value, so-called the sub-Gaussian.

Imagery edges occur naturally in human

visual systems as a consequence of redundancy

reduction towards "sparse & orthogonality feature

maps," which have been recently derived from the

maximum entropy information- theoretical first principle of artificial neural networks. Singularity

edge-maps are sparse and orthogonal for the

uniqueness & robust features necessary for pattern

recognition tasks. Sparseness of singularity edge

map needs more than second order statistics the

ICA to extract it. That decay of excitation patterns towards

noisy outputs results in the stored memory eight

matrix [W] among neurons. From the knowledge

representafion point of view, the more efficient and

robust representation, the better. Two principles

are the keys to achieve efficient representation:

orthogonality and sparseness in the hits frequency

of feature detectors leading to unique

identification. For example, an edge-map with

one's over zero background is clearly sparse, local,

and almost orthogonal. The IC notion may be

attributed first to Barrow in 1953 as the

redundancy reduction process (RRP). In fact, the

final IC State may be described by a factorized

387
joint-probability density function, and is sometimes

called as factorized code. This factor-code

corresponds to all sparse orthogonal edge maps in the early vision processing. The second moment of

IC must be by definition a diagonal matrix, which

appears like a Gaussian random process whose off- diagonal elements cancel one another. If the

learning of weight matrix [W] can achieves the

maximum entropy H(V) of the output V or the

linear slope portion of the maximum entropy

sigmoidal neuron output H(V)=H(o(U))=H(U)

which implies that all nth moments of the ANN

output components U={ui, U2} of two sensor neurons are independent in terms of the normalized

statistical histograms p(u) defined as: jointp.d.f.

p(xi,X2) = p(xi)p(x2)( 1 +h(xi,;^))

Factorized Code [Ati92] implies:

E{X,",X2"} = JJx,",X2"p(x,,X2) d

xid)^= Jxi"p(xi)dxil?^"'p)^) d;^ = E{x,"} E{X2"}

Example: Gauss Center of Limiting Theorem

p(^) = exp(-(^-<^>)2/2o); = <^> <^> - 0, if Gausissn iid where < u" >= ^u"p(u)du = E{u"}

Biological evidence is first due to Nobel

Laureats Hubel and Wiesel showing an oriented

edge-map in the several octave scale in cats, similar to 2-D oriented Gabor Logon (information

unit similar to a windowed FT or a WT without

affine parameterization). Such an unsupervised

learning methodology has been given in solving the

statistically Blind Source Separation (BSS), as first introduced by C. Jutten, J. Herault.

[w] =[[!] + [s]]-' where [s'] =[s] + a f(y)g(x)

is an odd function for Blind Source Separation

(BSS) of Super-Gaussian Laplacian distribution of

speeches A[W]=g(x)tanh(u ) in terms of some ad

hoc odd functions, in the first Snowbird ANN

Conference in 1986. Both ICA subsequently

coined by P. Comon [Com94], and BSS further

elaborated by Herault & Jutten were appeared in Signal Processing Journal in 1991. Oja elaborated

the nonlinear PCA learning, because neuron output

V = tanh(Ul = U - 2/3 + . . . has a similar Taylor

expansion as dK(V)/dw. The first principle of ICA

may have several forms, e.g. absolute entropy

versus mutual entropy, Neg-entropy— the distance

from the normality, Edgeworth versus GramCharlier expansions (of pdf in terms of moments)

which are related to the maximum Shannon

entropy H(V). The essential portion related to the

change of weight matirx is equivalent in achieving

the redundancy reduction toward independent

components which gives rise naturally to a sparse

orthogonal edge map (unfortunately only at one

wavelet resolution). The landmark

accomplishment of ICA is to obtain, by

unsupervised learning algorithm, the edge-map as image feature a , shown by Helsinki researchers

using fourth order statistics of V ~ Kurtosis K(V),

and derived from information-theoretical first principle of ICA by Bell & Sejnowski. Amari has

further contributed to the speedup of learning by

suggesting a natural gradient descent, rather than

the original entropy gradient involving a non-local

weight matrix inversion.

Fig. 4 Why do we have two eyes? They can

provide instantaneous spatial learning without

teacher, i.e. two eyes agree must be signal, and

don't noise. A perfect denoise is possible using two

eyes or two ears, two sides of tongue and two nose

passages, two hands, etc., but one sensor needs the

slower help from the brain memory itself. Sophisticate cross-talk de-mixing is in Sect. 5.

4. CYBERNATIC THEORY

Human intelligence can not yet be

mathematically defined and addressed here. Instead, the supreme manifesto of the human

intelligence might be the learning ability without

teachers, which is modeled by the thermodynamics

neural net learning theory. In so doing, we have

discovered that the parallelism between the

supervised associative recognition and the

unsupervised ICA de-noise [1,2] (e.g. the cocktail

party effect) is conveniently controlled by the

Gibb's free energy temperature [3]. In this paper, we identify the temperature as the cybernetic

temperature defined as a root-mean-square

fluctuation of synaptic transmission activity.

388
During the last Russian Academy of Nonlinear

Sciences Academician meeting (at St. Petersburg

June 1999), I have proposed the role of cybernetic

temperature for learning capability as warm

blooded man, mammals, versus cold blooded

dragons, reptile, lizards. Furthermore, I have

investigated the information content of an unsupervised learning by means of Independent

component analyses (ICA) with several students

(cf. Wavelet Applications Orlando, April 2000

proceedings). This is based on joint probability

density factorization for all independent moments

(cf Shannon's Principal Component Analyses

(PCA) information content based on the second

moment covariance only). Application to two eye

de-noise, and image blind de-mixing and seven

spectral band remote sensing are respectively given

in [3,4,5].

Those who know of the animal

intelligence having very little to do with brain sizes

will be critical about the homeostasis theory having

anything to do with the intelligence.

Mathematically, neural network models of both

learning seem to predict a constant temperature T

for the minimization of the thermodynamic

Helmholtz free energy, A = U - TS (equivalent

ANN notation Lyaponov L = E -T H), in order to achieve the synergistic learning balanced between

the supervised energy, E(vi), Hopfield-like

minimization of neuron firing rate v,, and the

recently breakthrough of the unsupervised sensory

pre-processing based on the output entropy H(wij)

Bell-Sejnowski maximization. Since some mammals have bigger brains than Einstein's

having a normal human being size, it implies not

the brain size rather the interconnectivity wrinkles

of the gray matter, which are responsible for associative memory. Again, it is not the degree of

temperature rather the constancy of brain body

temperature, which may be important to the kinetic

diffusion rate controlling the chemical reactions

that are vital to the healthy cellular functions (as

evident in the excess fever causing by fatal diseases).

First, we define the supervised learning to include self-taught (involving higher motivation

and intelligence) considered being equivalent to learning with a teacher either implicitly internally or explicitly externally. Secondly, we define the

unsupervised learning to be the pre-attentive pre- processing of all real-time and short-term memory

pairs of sensory inputs without conscience effort with associative recall. Neural network learning

models suggest a constant brain cybernetic

temperature, which balances the output energy

E(vj) for neuron firing rates for supervised

learning, and the maximum entropy H(Wij) of

synaptic matrix for unsupervised excitation decays

for redundancy reduction (to wavelet or singularity-map). While the supervised learning

(with implicit or explicit teaching) may be driven

by the internal energy minimization, the

unsupervised learning (sensory pre-processing)

may be driven by the relaxation decaying processes

by means of the maximization of local entropy.

For example, there are 6.1 millions cones for color

vision and 150 millions rods for dark light vision,

and any imbalance on the visual neural pathway

might cause the hallucination. This balance is achieved by the thermodynamics L = E- THata

constant temperature T to determine the internal

energy E(Vi) minimization and the entropy H(wij)

maximization. According to the theory of statistical mechanics, such a thermodynamic balance between

E & H is possible due to a constant temperature T. This natural thermodynamic equilibrium might be

useful to help develop fully the innate learning

ability of a mammal. Thus, it is conjectured that the homeostasis of mammals must have a profound

effect upon learning ability of the mammals, which

in turn affect the development of intellectual

capability. The cold blood reptiles can obviously

learn without such a thermodynamic equilibrium,

and it is interesting to notice a lower intelligence

associated with them.

These models suggest it may also be

important to the intercellular communicationmediated learning mechanism. Furthermore, based on recent breakthrough of sensory learning, the

minimization of Helmholtz free energy L =E-TH at a constant T involves the internal energy E and the

entropy H is believed to have maintained the

thermodynamic equilibrium of those intercellular

communication mechanisms useful for Hebbian

synaptic modification. This free-energy

minimization is mathematically shown to be

Lyapunov functions that control a proper balance

between the unsupervised sensory preprocessing

based on maximum entropy of synaptic weights

and the supervised learning based on minimization

of neuron firing rate energy.

5. INFO-THEORETICAL MODEL

Recently, the biological edge map

developed by the Nobel laureates, Hubel-Wiesel, was reproduced computationally by maximizing

the neuron output entropy of among 10"* images by means of maximum output entropy:

aH(V)/a[w] = a[w]/at. (lO)

Algorithmically, ANN adjusts |W] at the linear

output range, V(t) = tanh([W]X(t)) = [W]X(t), so

that <V(t) V(t)^>G = [I]. Note that no decision of

the sign of the tanh function is necessary in the

389
linear range, implying that the maximal output

entropy, and thus the input information is kept in [W]. There is a need to unify both the supervised

learning of Principal Component Analyses (PCA)

by Oja et al. and the unsupervised learning of ICA

(advanced by Jutten & Herault, Comon (1991),

Cardoso (1998) in France and by Bell-Sejnowski

(1995) in U.S.A., and by Amari-Cichocki (1996)

in Japan).

Baysian prob f(x,y) = flix|y)f(y)= f(y|x)f(x)

Shannon Entropy H(x,y)= - < Ln f(x,y) >

= - <Ln f(x|y)f(y)> = H(;^y) + H(y) = - <Ln f(y|x)f(x)> = H(y|x) + H(x) = H(x) + H(y)- I(x,y)

Mutual Info I(x,y) = <Ln [f(x,y)/f(x)fi;y)]>

=<Ln[f(x|y )f(y)/fl:x)f(y)]>= -H(x|y)+ H(x)

Statistical information content similar

geometricalinformation content of PCA

to

The associative recall by the associative memory

outer-product approach can determine the center of

training set clustered around each ICA basis, (11),

and only 30% of them are significant in the

direction cosine sense, and the rest ICA bases have

no significant alignment with the training set. This

is similar to PCA eigenvalues, which fall off

drastically after the principal components, and is called by Shannon as the degree of freedom of the

information content. We have generalized

information content to statistical information

content for those non trivial ICA bases.

Define ] = ^ (VV,. VV- )' (^2)

A fast estimation of the principal information

content of a normalized ICA basis is denoted

similarly by the eigenvalue that sums the

squared magnitude of all the projection of

normalized training data A',- upon k basis :

1 M 2

6. LYAPONOV CONVERGENCE PROOF

Szu has postulated the Helmhotz free energy [3]

I(V 1 ,. . . ,Vn Wi , . . .Wn,)=£'(v , „Vn)-T //( W, ,.. Wn)

as the Lyaponov function, and proves the

convergence dL/dt < 0 of both supervised energyE-minimization and unsupervised entropy-Hmaximization dynamics in synergism: Given local gradients:

Min. energy: du/dt| = - dE/dvi

Max. entropy: (3[w,]/9ti)= (9///9[wi]).

Proof:

Min. Lyaponov (namely Helmhotz free energy):

dL/dt = Zi OE/avi)( avi/dui) (aui/ati)(dti/dt)- T

li (aH/awi)(awj/9ti)(dti/dt)

=-{I,(3E/avi)^( avi/dui)+TIi(aH/awi)^ }(dti/dt) < 0 Q.E.D.

Here use is only made of stable cybernetic

temperature T and the local gradient dynamic

equations and positive firing rates to eliminate the

temporal derivatives by spatial derivatives to form

two real quadratic expressions, which by definition

are always positive.

7 CONCLUSION

Helmhotz-Lyaponov drives the

punctuated evolution for brain open systems at constant temperatures as opposed to less intelligent

cold blood animals. One must go beyond the least mean square (LMS) error energy, and apply HOS

to ANN. Applications are possible to multimedium computers & machine intelligence.

8. REFERENCES

1 H. H. Szu, I. Kopriva, A. Persin. Independent

component analysis approach to resolve the

multi-source limitation of the nutating rising- sun reticle based optical trackers, Optics

Communications, Vol. 176, Issue 1-3, pp. 77-

89, 2000

2 I. Kopriva, H.Szu, "Blind Discrimination of the

Coherent Optical Sources by Using Reticle

Based Optical Trackers is a Nonlinear ICA

Problem", Proc. of the2nd Int. Workshop on

Independnet Component Analysis and Blind

Signal Separation,ed. P. Pajunen and J. Karhunen, June 19-22, 2000, Helsinki,

Finland, pp. 51-56.

3 H. Szu, "Thermodynamics Energy for both

supervised and unsupervised learning neural

nets at a constant temperature," IntT J. Neural

Sys. Vol.9, pp. 175-186, June 1999.

390
4 H. Szu, "progresses in unsupervised artificial neural networks of blind image demixing, " New tech of IEEE Ind. Elec. Soc. Newsletter,

pp. 7-12, June 1999.

5 H. Szu, "ICA-an enabling tech for Intelligent

Sensory Processing", IEEE Circuits and

Systems Newsletters December of 1 999.

391
Properties of Learning Knowledge-Based Controllers

Edward Grant

Associate Professor

Director of the Center for Robotics and

Intelligent Machines

Electrical and Computer Engineering

Department

North Carolina State University

Raleigh, NC 27695

Gordon K. Lee

Professor

Assistant Dean for Research, College

of Engineering

Mechanical and Aerospace Engineering

Department

North Carolina State University

Raleigh, NC 27695

ABSTRACT

This paper addresses the field of knowledge-based

systems, and in particular the sub-field of knowledgebased control systems. The rule-based approach used

here, particularly in its machine learning or rule

induction mode, continues as a major theme in the emerging field of data mining - the extraction of usable

insights from large databases.

KEYWORDS: knowledge-based control, learning

control

1. INTRODUCTION

The core tenet of this paper is that rule frameworks

(i.e., directly programmed rule bases, rule bases

derived by rule induction over experimental data

and rule bases derived from induction over data

produced by rule-based qualitative modeling with

rule-based simulation) can be applied to achieve

successful control of diverse systems.

The results obtained show that the

underlying goals of the knowledge-based

approach are as valid as ever and are particularly

relevant to many of today's critical applications.

In certain specific areas, they remain superior to all

others. These areas include: (1) the inherent

ability of knowledge-based systems to make their operation transparent to computer experts, to subject domain experts and to their users - a consequence of the systems' representing their

knowledge directly in the form of symbolic rules;

(2) the ability of the technology to capture the knowledge of the best experts in the field, to refine

consistent and understandable symbolic rules from cases and empirical datasets; and (3) the

ability to generalize such rules to cover a much

larger set of possibilities than can feasibly be

detailed explicitly by the domain expert or empirical dataset by using knowledge-based

simulation, which is important in the field of

diagnostic systems.

This, at its highest level, is what is demonstrated in this study. The authors

methodology for designing knowledge-based (KB) systems will be described, along with

descriptions of its application to systems which

can be to produce designs that proved

successful in practice.

2. DEVELOPING KNOWLEDGEBASED CONTROL

The focus of this paper is on mechanisms and

technologies for implementing machine

intelligence. Nevertheless the ability to learn must

be one criterion for describing intelligent behavior.

In robotics terms, intelligence is the ability of a machine to act autonomously in the presence of

uncertainty. The ability of a robot to adjust its actions based on sensed information [1, 2, 3, 6, 12,

13, 14, 15] is another prerequisite for intelligence.

In this work, the actions taken by the machine are considered to be intelligent if the actions reflect

the action that a human would take, given the same conditions.

In advanced robotics systems [1, 2, 3, 6, 12,

13, 14, 15], robots are equipped with networked

sensors: vision, tactile, proximity, speech

recognition, voice synthesis, robot controllers,

conveyors, vision processing equipment, and

computers, an ideal domain for researching into

machine intelligence (see Figure 1). However, the interconnection of physical systems, or the task

392
undertaken by the system, does not make a machine intelligent. Intelligence comes from the manner in which the system is controlled or from

the reasoning and decision making that the machine

performs. In our terms, "intelligent control" is closely associated with "machine intelligence" [9,

10].

Schematic Contrail er Type

Human

MLC

Location

None

Type of

Controlled System

Simulator

0

^

Human

None Simulator

None Physcai

None Physcai

Figure 1 . Automatic or Human Control (MLC

machine learned control)

intelligent control systems must deal with sensor data and task specification and the taskstate derived from integrated sensor data. An

intelligent-control system must handle information

about its own state and also the state of the

environment; it must be capable of reasoning under

uncertainty. Intelligent control commonly involves

the use of both heuristic and algorithmic

programming methods.

First we review hierarchically ordered control

architectures for intelligent control. After this we

concentrate on controlling dynamic systems with a variety of rule-based and machine learned

programs. The final section deals with "human-inthe-loop" control as a knowledge-based controller.

3. ARCHITECTURES FOR

INTELLIGENT CONTROL

Saridis [11] states that intelligent machines require

the use of "generalized" control strategies to perform intelligent functions such as the

simultaneous utilization of memory, learning or

multi-level decision-making in response to "fuzzy"

or qualitative commands. His work proposes that

intelligent functions can be implemented using

"intelligent control".

Intelligent control combines high-level

decision-making, advanced mathematical

modeling, and synthesis techniques of systems

theory. These approaches along with linguistic

methods attempt to deal with imprecise or incomplete information from which appropriate

control actions evolve. The control functions in an intelligent machine have been implemented as

a hierarchy of processes [1, 2, 3, 7, 11]. The upper

layers concentrate on abstractions, decisionmaking and planning, while the lower levels concentrate on time-dependant sub-tasks, such as processing data from sensors or operating an

actuator. Hierarchical decomposition is applied to complex control problems to reduce them to smaller sub-problems.

In the hierarchical control architectures of

Albus [1, 2, 3] and Meystel [7], each layer

essentially possesses the same processing nodes.

These two architectures include a knowledgebase, sensory processing, task decomposition,

and communication. But Saridis [11] recognized

that each layer in a hierarchy need not perform the same activity over time and he and Albus [6]

recognized that middle layers are frequently

hierarchies of linguistic or heuristic decision

structures that handle imprecise or "fuzzy"

information. The National Institute of Standards

and Technology (NIST) implemented Albus'

architecture in manufacturing control (AMRF) [2]

and in the NIST/DARPA Multiple Autonomous

Undersea Vehicle (MAUV) [1]. Meystel [7] used

an autonomous undersea vehicle as a demonstrator and Saridis [11] applied his architecture to space station robot and control

applications.

4. REINFORCEMENT LEARNING

Since the mid-1970's, artificial intelligence (Al)

methods have been continuously developed and

applied by industry, business, and commerce.

Expert systems are the most successful

implementation of Al. However, the difficulties surrounding the development of the production

rules for expert systems, going from the general to the specific led to the development of a subdivision of expert system technology known as "machine learning". In this section, we will look at the how the production rules for "rule-based"

control can be produced manually and

automatically and we will discuss approaches for

achieving machine -learned control (MLC). We

will describe a controller based on the machine

learning algorithm BOXES [10], an algorithm that

393
uses a reinforcement learning approach and we will discuss the implementation of neural networks for

control. Both reinforcement learning and

competitive learning are considered [13].

g sin0 - cosG

6 =

F + mp I 9 sine

ni(.+ nip

4 nip cos

9

nic + nip -I

.. _ F + nip I [e sine - 9 cosG]

X

nic + nip

can adapt to varying system configurations.

Schematic

Q—a—•-5=^ —

Of:

Controller MLC

Type

Auto

TvDe of Control Ucation

Human Off-line Sfnulator

Off-line Sinulalor

Human Off-line Physical

Off-line Physical

Figure 2. The Pole and Cart Equations Figure 4. Machine-Learned Control (Passive

Learning)

Rule-Based Control

Humans are capable of deriving a set of control

rules through the process of interpretation. For

example, consider the equations for the pole and

if theta_dot > THRESHOLD theta_dot then push RIGHT

if theta_dot > -THRESHOLD theta_dot then push LEFT

if theta > THRESHOLD theta then push RIGHT

if theta < -THRESHOLD theta then push LEFT

if x_dot > THRESHOLD x_dotthen push RIGHT

if x_dot < -THRESHOLD x_dot then push LEFT

if X > THRESHOLD x then push RIGHT

if X < -THRESHOLD x then push LEFT

Figure 3. The Makarovic Rule Derived from

Interpreting the Equations of Motion

cart problem (see Figure 2). Makarovic [8] derived a

rule by examining the system's differential equations of motion (see Figure 3). The Makarovic

rule worked well when the parameters of the

system remained constant. When system

parameters changed, the Makarovic rule cannot

guarantee success. This showed that the arbitrary

choice of one set of threshold values is not ideal for

a system whose configuration changes. In

contrast, a rule derived from observing a physical

system's performance [18] can be written without

any threshold values placed on the observation.

Here the condition part of the rules only deals with

the sign of the errors and with the sign of the

variations of observed system state variables. This

approach reflects human control heuristics and it

Machine Learned Control

Machine learning as presented here is classified

into two areas: (I) artificial-intelligence type

learning based on symbolic computation and (2)

neural nets. These are chosen because we have

first-hand experience of applying them to realif (the»a(k) > THRESHOLD)

then

if ((thela(k) < theta(k-l)) and (|thela(k) - lhela(k-l)| > |lheta( k-l) - theta( k-2)|)) then

apply a RIGHT force

else apply a LEFT force

if (lhe(a(k)<-THRESHOLD)

then

if ((theia(k) > theta(k-l) and (|lheta(k) - Ihela(k-I) > ^hela(k-l) - the>a(k-2)[)) then apply a RIGHT force

elseapply a LEFT force

if (|lheta(k)|<= THRESHOLD)

then if(x{k)>=0)

then

if ((i(k)<x(k-i)) and (|x(k) - x(k-l) - x(k-2)[)) then apply a LEFT force

elseapply a RIGHT force if(x(k)<0)

then if((x(k)>x(k-l)) and(|x(k) -x(k-l)|>|x(k-l) -x(k-2)[)) then apply a RIGHT force

else apply a LEFT force

Figure 5. A Control Rule Derived from

Experimentation with a Pole and Cart Simulator

worid applications. An effective machine learning

system must use sampled data to generate internal

updates and also be capable of explaining its findings in an understandable way, e.g., symbolically. The learning system must also be

394
able to provide an explanation of its results to a human-expert. The findings should also improve

the human expert's understanding and verification.

Artificial-intelligence type learning originated from

an investigation into the possibility of using

decision trees or production rules for concept

representation. Since then, the work has extended

to the use of decision trees and production rules to handle most conventional data types, including

noisy data sets, and as a knowledge acquisition

tool (see Figures 4 and 5).

Reinforcement Learnins

Reinforcement learning, of the type produced by

Michalski [9] and Michie [10], is similar to feedback

for adaptation. However, unlike supervised

learning, reinforcement feedback learning only

gives an indication of the value of the system's

action. Reinforcement is a feedback on the correctness of an action; it is not information on what the correct action is. Reinforcement learning

is useful in cases where supervisory information is not available (see Figure 6). Also, reinforcement learning falls into two

categories: (1) non-associative type, which only

receives a reinforcement signal from the

environment and (2) associative reinforcement

learning, where the system receives both a reinforcement signal, and sensory information, on

the state of the environment. Sensors are used to discriminate between different situations. This we

considered more suited to our particular needs with

the pole and cart application. We will discuss the

theta_dot >0 : RIGHT

theta_dot <=0 : theta <= -2 : LEFT

theta>-2:

thata_dot <= -1 : LEFT

theta_dot > -1

x_dot<= -6: theta <= 1: LEFT

theta > 1 : RIGHT

x_dot > -6 : X <= 0 : LEFT

x>0 : RIGHT

Figure 6. A Control Rule for the Pole and Cart- Derived Using the BOXES Algorithm

application of rule-based (MLC) and neural network

controllers to control a pole-cart system by using

AI techniques.

ControJIer Type MLC

Location

^

-F^ MLC On-ine

Type of Controlled System

Physical

Figure 7. Machine-Learned Control

partitioned into boxes. The algorithm learns to set correct decisions for each box through trial-anderror [10, 13]. Unfortunately, state space

partitioning prior to experimentation is arbitrary

because it is reliant on human knowledge. If the

original partitioning is wrong, the algorithm can

not learn to correct it. In the following, we will show how our rule can be used to partition the

state space in the pole-cart application.

5. NEURAL NETWORK - BASED

REINFORCEJVIENT LEARNING

A learning controller consisting of a two-layered

neural network was used to implement the inputoutput transfer function and an evaluation

network, a look-up table, which provides the

necessary reinforcement signal for evaluative

feedback via a goal oriented performance index.

The high-level architecture for the teaching

controller is given in Figures 7 and 8.

Neural Networks

Neural networks implement information storage

with synaptic weights storing information and

distributed patterns acting as keys; they combine

the benefits of both the computational method

and look-up tables. With neural networks,

1 t t t t t

BOXES '

In Michie and Chamber's learning algorithm

'BOXES' [10, 13], the physical state space is

Figure 8. A Neural Network Controller for Controlling a Pole and Cart

395
Pattern Pattern Features Number ul u2 u3 u4 u5 u6

Actual Output Classification

0 0 0 0 0 0 0.0069 1 0 0

2 0 0 0 0 0 1 0.003990 0

3 0 0 0 0 1 0 0.002360 0

4 0 0 0 0 I 1 0.U01337 0

•

0 1 I 1 1 1 U.Udjy / /

•

0 33 1 0 0 0 0 0 0.9599 1 6 1

34 1 0 0 0 0 1 0.958932

•

64 1 1 1 1 I 1 0.952255

65 aso 0 0 0 0 0.819989 1 66 aso 0 0 0 1 0.616265 1

96 05 1 11 1 1 0.8244 66 Figure 9. A Rule-Based Table Look Up for Determining Neural Network Control Actions

information about control situations is coded in terms of distributed patterns; hence they can

support distributed representation and reduce the

storage requirements associated with control

surface dimension.

A neural network can specify control actions

for a given situation not visited during learning; it

specifies according to its similarity. This associated

structure automatically generalizes according to degree of similarity. The trade-off between

computation time and storage space is resolved

using neural networks.

Establishing the Look-up Table

In reinforcement learning, at every time step during

learning, control actions are evaluated with respect

to a sub-goal. The action that maximizes the subgoal is regarded as the optimal control action and is rewarded; all other actions are punished. In the

pole-and-cart, learning is difficult because the

effects on choosing different actions cannot be

tested. So, here we evaluate alternative control

actions with respect to a small region of the state space. We also assume that they have the same

reinforcement value (see Figure 9).

Decoding the State Variables

The term "decoder" describes the process of

accepting an input situation and transforming it

into one activity from a choice of a large number.

Hence evaluation signals are stored as a look-up

table where an input situation appears as an

activity on a single path-way to a storage location.

The storage location contains the appropriate

evaluation specification. This approach was

motivated by 'BOXES' [10]. Here, the four- dimensional state-space is divided into disjoint

regions ('BOXES') by quantizing the state variables. The evaluation of different control

actions was made with respect to a sub-goal. This

estimate provided the necessary reinforcement

signal for a reinforcement learning neural-network

(RLNN) for control [13].

6. LOUGHBOROUGH GLUE

DISPENSING WORKCELL

After the researchers at Loughborough University

of Technology had tried numerous methods to visually inspect a dispensed 'blob' (e.g.,

inspecting for the "blob volume" using striped

light) it was found that a simple feedback measure gave acceptable control. Tests using the "blob

area" as the measured variable showed that this parameter could keep the process within a desired

operating bandwidth. The measured variable

based on the blob area, termed "box-area-ratio"

(BAR), could distinguish between many of the

common faults associated with the process. Common faults observed in the process

include: (1) a blob collapsing; (2) stringy

(stitching) attachments; and (3) the presence of

entrapped air. The researchers used on-line data

recorded from the process to elicit information

from that data. The extracted information is then

presented as rules. After the process expert had

verified these rules, the rules became a "knowledge-based" controller. During this period

of interaction with the process expert, an

interesting observation was made; it appeared that the expert measured the performance of the

process through fault diagnosis (Williams, West,

Figure 10. A Schematic of the LUT Glue

Dispensing Workcell

396
and Hinde (1992) [12, 13]). This observation led to

the development of a knowledge-based controller, one that was tested on the process (see Figure 1 0). Before a knowledge-based controller can produce the rules inherent or embedded in the

process data, a model must be established. Such a model produces rules from process data (Shepherd

(1992) [12]). The model was constructed in three

parts: image capture, feature extraction, and

classification (see Figure 1 1). The data from the LUT process was first normalized, so that it matched the integer

requirement of the rule induction software. Thus, a blob area of 1 103 is equivalent to a normalized area of 1 . 1 03. Also, since the bubble threshold is set for

a 10% increase for the 'blob area', the data are

classified as bub_inc. When there is more than one

fault present in the data, each encountered fault is recorded as an attribute exceeding a threshold.

Multiple fault conditions are obtained when a combination of attributes has exceeded their limits

and an attribute-class vector is repeated for every

exceeded threshold. Every exceeded threshold is represented as new class vector and they are added

to the class list. Note that in order to be consistent,

these may also combine any original class.

IF BAR > threshold THEN

IF area_diff < bubble_threshold THEN

IF area outside control limits I HEN

apply rule-based control action ELSE

do nothing

IF risetime > risetime fault threshold THEN

flag air IFfalltime > falltime fault threshold THEN

flag pulse width and pulse height

Figure 11. Pseudo-code of Process Operators

Control Rule

Two knowledge-based controllers were tested on the LUT adhesive dispensing process: (1) a controller based on operator derived rules alone,

and (2) a controller based on the VACLS rule induction algorithm (control via the fault detection

rules derived from the process data). A simple

BANG_BANG controller was written in C; this was

used to maintain the dispensed blob area within 5%

of a target area of 30,000 pixels. The results were

remarkable in that the knowledge-based controller

using the VACLS rule induction

algorithm was highly successful in dealing with

this application environment that is inherently

difficult to control, and where knowledge

elucidated from the expert human operator proved

crucial, when combined with rule induction over

empirical data from the multiple sensors. These

results reinforced the value of human-in-the-loop

type controllers whereby information from the human along with results from simple

experimentation improves system performance.

6. CONCLUSIONS

Knowledge-based controllers (e.g., those

constructed using expert verified rules) were

tested on the various dynamic systems including

the LUT industrial process control system. The

control experiments tested overall system

performance based on data from dynamic system

and process parameters. To extract the

knowledge-based control rules experiments were conducted on simulators and physical systems.

Human control with simulators is achievable, but

difficult with physical plant with fast response

times. Passive learning proved useful but machine

learned control had limitations, particularly when

used with physical systems. In terms of the LUT

process this included the variation of area and

measured and programmed pulse height variation.

Two factors that have to be taken into

account when using rule induction algorithms are timing, e.g., what are the overheads associated

with implementing rules, and clashes. These

influence of these two factors is reduced if the

following procedure is adopted when preparing

data prior to submitting it to the algorithm being

used. First, divide the data set into two and train the algorithm on one half of the data set. Second,

build the rule-based controller and test its performance on the other half of the data set. Third, after refining the controller, install it into the

process and obtain test results. This was the

procedure adopted when working with VALCS

and it produced the richest amount of information.

Although this amount of information may not be wholly necessary for controlling a process, it does aid the expert in understanding the process

performance and focuses on important interrelationships. The importance of clashes is that they present a logical interpretation for fault monitoring and diagnosis. Clashes aid the expert

understanding of the flags that are set as the

process operates.

ACKNOWLEDGEMENTS

The authors would like to thank the faculty and

students of the College of Engineering at North

397
Carolina State University, the Department of

Computer Science at Strathciyde University, the

Turing Institute, British Aerospace (HOTOL).

Also, thanks go to Drs. David Williams and

Andrew West of the Dept. of Manufacturing Eng.,

at Loughborough University of Technology.

7. REFERENCES

[1] Albus, J. S., "System Description and Design

Architecture for Multiple Autonomous

Undersea Vehicles", NIST Report 1251,

Gaithersburg, MD, September 1988.

[2] Albus, J. S., McCain, H. G., and Lumia, R., "NASA/NBS Standard Reference Model for Telerobot Control System Architecture

(NASREM)", NIST Technical Note 1235,

1989.

[3] Albus, J. S., "A Theory of Intelligent

Systems", in Proceedings of the 5th IEEE

International Symposium on Intelligent

Control, Eds: A. Meystel, J. Hereth and S. Gray, 5-7 September, Philadelphia, PA, USA,

pp 866-875, 1990.

[4] Efstathiou, J., Davies, B., Razban, A., and

Harris, S., "Expert Systems for an Adhesive

Dispensing Robot", in Proceedings of the

Sixth International Conference on Industrial

and Engineering Applications of Artificial

Intelligence and Expert Systems

(IEA/AIE'93), Edinburgh, Scotland, pp 94-97,

1993.

[5] Grant, E., "Machine Learned Control", Final

Report to the NEL/BAe/BT/SERC, The

Turing Institute, Glasgow Scotland, UK,

1992.

[6] Grant, E., The Knowledge-based Control of

Robot Workcells and Dynamic Systems,

Ph.D. Dissertation, University of Strathciyde,

1999.

[7] Isik, C. and Meystel, A., "Pilot Level of a

Hierarchical Controller for an Unmanned

Mobile Robot", IEEE Journal of Robotics

and Automation, Vol. 4, June 1988, pp 241-

255, 1988.

Underlying Methodology and the

Description of Program AQll", Technical

Report: ISG 83-3, Department of Computer

Science, University of Illinois at UrbanaChampaign, Urbana, ILL, USA, 1969.

[10] Michie, D. and Chambers, R. A., "BOXES:

An Experiment in Adaptive Control",

Machine Intelligence 1, E. Dale and D. Michie (eds.), Oliver and Boyd, Edinburgh,

Scotland, 1968.

[11] Saridis, G. N., "On the Revised Theory of

Intelligent Machines", CIRSSE Report 58,

ECSE Department, Rensselaer Polytechnic

Institute, Troy, NY, 12180-3590, USA, 1990.

[12] Shepherd, B. A., High-level Programming

of Vision Guided Assembly Tasks, Ph.D.

Dissertation, University of Strathciyde,

1992.

[13] West, A. A., Williams, D. J., and Hinde, C.

J., "Experiences of the Application of

Intelligent Control Paradigms to Real

Manufacturing Processes", in Proc Instn Mech Engrs, Vol. 209, pp 293-308, 1995.

[14] Williams, D. J. West, A. A. and Hinde, C. J., "A Discrete Process Control Software

Testbed", Science and Engineering

Research Council Report, Grant GR/F 37101,

1992.

[15] Williams, D. J. West, A. A. and Hinde, C. J., "The Selection of Software Techniques for Discrete Process Control Applications",

Science and Engineering Research Council

Report, Grant GR/F 71973 1992.

[16] Zhang, B., Experiments in Learning

Control Using Neural Networks, Ph.D.

Dissertation, University of Strathciyde,

1991.

[8] Makarovic, A., "A Qualitative Way of

Solving the Pole-balancing Problem",

Machine Intelligence 12, J. E. Hayes, D. Michie and E.Tygu (eds.), Oxford University

Press, Oxford, England, 1989.

[9] Michalski, R. S. and Larson, J., "Incremental

Generation of the VLl Hypotheses: the

398
Assessing the Run-Time Performance of Artificial Intelligence

Architectures

S. A. Wallace, J. E. Laird, and K. J. Coulter

University of Michigan

1101 Beal Ave.

Ann Arbor, MI 48109-21 10

ABSTRACT

As intelligent systems are pushed forward to become more

autonomous, there is a tendency for the underlying software

architecture to grow in complexity to support these new behaviors.

However, with the addition of new features, two potential costs may

be incurred: increased execution time and additional memoiy

requirements. As architectures evolve, it is important to continually

evaluate the costs and benefits of each new change. Seemingly veiy

similar architectures may require significantly different resources: small changes to the features in a single architecture may have a

large impact on its performance. Thus, it is necessaiy to understand

and to quantify the resources consumed by different architectures

and by the components of a single architecture. Unfortunately, there

IS no standard method for evaluating features of an architecture or for comparing sets of architectures. In this paper, we begin by

discussing such a methodology. We then dissect the Soar architecture

into a core set of functionality and examine how incrementally

adding each of the features found in the original implementation

affects the overall performance and resource requirements. Next, we

show how the same methodology can be used to compare two

different architectures. Finally, we discuss initial results of a comparison that indicates both qualitative and quantitative

differences between the Soar and CLIPS architectures. KEYWORDS: Architecture evaluation. Soar, CLIPS

1. Introduction

As artificial intelligent agents become increasingly robust and

autonomous, the software underlying their behavior also

becomes more and more complex. Success with simple agents

in simple domains inspires research into the capabilities

required to operate more efficiently and effectively. This in turn causes the software architectures to evolve, as functionality is added to support the new demands. Because

this is a common process, many architectures have been

developed incrementally over the course of many years as they

become increasingly sophisticated.

Design decisions made at implementation time often play

critical roles in the efficiency (both in time and space

complexity) of the architecture. The impact is seen both when

the new features are used by an agent and in some cases even when the features are not used. Thus, after a feature is added

to an architecture, agents operating in complex domains and

relying heavily on the new feature may operate more

efficiently than was previously possible, while agents that do

not rely on the new architectural feature may become less

efficient. To properly assess the impact of an architectural

modification, it is necessary to quantify the resource consumption of that modification. In most cases, it is extremely difficult to draw meaningful conclusions using

analytical methods, although in some cases, a formula that

relies on prior knowledge of a relatively few variables may be

obtainable. Even in these instances, however, comparisons

between two such formulas are further hampered by the fact

that constant factor differences may have profound

implications on their relative suitability in real-world tasks. As

a result, we believe that empirical methods are currently the

most suitable way to evaluate the impact of design decisions.

Additionally, two distinct agent architectures are likely to yield agents with differing efficiencies even if the

architectures (and agents) appear otherwise similar. As

architectures become increasingly divergent, it may become

overtly obvious that the features of one architecture are better

suited to a particular task than are those of another. In many

cases, however, this is not necessarily clear a-priori. As a

result, designers of intelligent agents, and of agent

architectures may benefit from understanding the relative

differences in resource consumption between two or more

architectures. As in the single architecture case, empirical

methods can yield approximate answers to these questions.

Unfortunately, however, there are no standard methodologies

for evaluating the resource consumption of a particular

architecture or of components of a single architecture.

In this paper, we discuss a methodology that can be used

to examine the resource requirements of an architecture as a whole, or of particular aspects of that architecture. We present

a practical example by applying this methodology first to components of the Soar architecture and then to the standard

version of both the Soar and CLIPS architectures. Our results show both qualitative and quantitative differences between

these two architectures and show how components of the Soar

architecture contribute to its overall performance. Early

versions of some of the material and results in this paper

appeared in [15,16].

399
2. Architectures, Knowledge and Modularity

The class of AI symbolic architectures we are interested in are those that support the development of general, intelligent

knowledge—rich agents. Following Newell's description [10], an architecture is the fixed set of memories and processing

units that realize a symbol processing system. A symbol

system supports the acquisition, representation, storage, and

manipulation of symbolic structures. An architecture is analogous to the hardware of a standard computer, while the

symbols (which encode knowledge) correspond to software.

The role of a general symbolic architecture is to support the

representation and deployment of diverse types of knowledge

that are applicable to various goals and actions.

The basic functions performed by an architecture usually

consist of the following (from Newell [10] p. 83):

• The fetch-execute cycle

• Assemble the operator and operands

• Apply the operator to the operands using

architectural primitives

• Store the results for later use

• Input and output

Architectures are distinguished by their implementation of

these functions, and the specific set of primitive operations

supported. For example, many architectures choose the next operator and operand by organizing their knowledge as sequences of operators and operands, incrementing a program

counter to select the next operator. They also have additional

control constructs such as conditionals and loops, but depend

on the designer to organize the knowledge so that it is executed in the correct order. Other architectures, such as rule-based systems, examine small units of knowledge in

parallel, selecting an operator and operands based on

properties of the current situation. Some examples of these

architectures inlude: Atlantis [4], CLIPS [1], Soar [7] and PRS

[6]. Because the definition above leaves a fair amount of room

for interpretation, architectures can often be further

distinguished by the inclusion of additional functions, such as interruption mechanisms, error-handling methods, goal

mechanism, etc. The inclusion of such functionality illustrates the necessarily blurry distinction between knowledge and

architecture. Because most agent architectures are Turing

complete, features not supplied directly by the architecture can

often be emulated by the appropriate addition of knowledge,

but with additional execution time overhead. However, it is often unclear a-priori how different design decision will affect

future performance, and designers may choose to construct

architectures modularly.

Architectures are modular in so far as features can be

removed while still preserving the basic requirements of an

architecture. Potentially, modules can be added or removed in order to optimize the architecture for a particular situation. Note that this is different from simply being able to refrain

from using certain features because it suggests that the internal

design of the architecture with and without a modular feature

is different.

3. A Methodology for Agent Architecture

Evaluation

Our methodology begins with dissecting the architecture into

constituent modules, leaving a core set of features intact. In many cases, such as when an architecture is developed

incrementally, certain features may be naturally modular. In other cases, a great deal of thought may be required to determine what aspects of the architecture can be removed

while still allowing the core functionality to meet the design

goals of the researchers. In either situation, modifications to the source code will undoubtedly be necessary to construct a

set of architectural variants that combine different modular

features with the core functionality.

The second step in our methodology consists of

determining a class of situations in which to examine the

architectural variants. Particularly interesting problem classes

may be found at both ends of a spectrum from situations that do not rely on a specific architectural feature to those which

rely very heavily on such a feature. Although any single study

is likely to be limited to examining a relatively small problem

class, as the number of studies increases, it is anticipated that general trends will emerge indicating which architectural

variant is most suited for a particular class of problems.

The third step involves selecting an environment in which

to examine the problem class selected in the previous step.

Because there is no single environment that can be used to represent "environments" as a whole, selection must be made

with care, and equal care must be used to ensure that results are not over generalized. Understanding how the environment

fits within a typical taxonomy (e.g. from Russell and Norvig

[12]) may help moderate this problem.

Fourth, for each architectural variant, an agent must be

designed to solve the specific problem within the selected

environment. Agents solving the same problem form a group.

All agents within a group must utilize the same problem

solving methods. The effect of this constraint is that any two

agents within a group must not only have identical interactions

with the environment, but must also utilize the same internal

problem-solving methodology. Proper implementation of this

step is critical; otherwise there is a serious risk of confusing

the contribution of different architectural aspects and different

knowledge (i.e. problem solving methods) on the overall

results. However, in certain circumstances this pitfall is eliminated because all of the agents within a group can be

implemented using identical knowledge. This exceptional case occurs when architectural variants differ only in their

inclusion or exclusion of unused features. Once a group of

agents has been fully implemented, the performance of

agent/architecture pairs can be directly compared.

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4. Soar and Its Modular Components

The Soar [7] architecture is a forward chaining production

system based on the RETE matching algorithm [2,3]. It contains a long-term memory (LTM) that stores production

rules, and a short-term memory (STM) containing elements

that are matched by the rules. Short term, potentially volatile, knowledge is stored in STM in the form of a directed graph with labeled edges. Each

memory element can be thought of as an ordered triplet whose

slots refer to the parent node, the edge name and the child

node respectively. Because this structure is so generic, it can be used to represent a multitude of more complex data

structures. Long term, stable knowledge, is stored in LTM as a set of

productions. Productions are created explicitly by the

programmer, or may be generated automatically by Soar's learning mechanism. The condition of a rule may contain

either variables or constants, and variables may be bound to any of the three slots in a memory element's ordered triplet.

This ability allows a large amount of flexibility in terms of

how a rule is designed, but it can also greatly increase

matching costs when it is used indiscriminately. The condition

side of Soar's rules may also ensure that values bound to a variable satisfy one or more basic predicates (e.g. >, <, =).

Generic predicates, however, are not supported in a rule's conditions. The right hand, or action side of a rule, can be used

to modify the contents of STM. Additionally, it can propose

architectural-constructs called operators or preferences for

such operators.

In Soar, knowledge is deployed by rule firings. This

process begins as follows:

• First, determine which rules, if any, match the current contents of STM.

• Next, fire all matching rules m parallel, by executing the

instructions in their right hand side.

These two steps, called an elaboration cycle, are repeated until a quiescent state is reached in which no more rules can fire.

Parallel rule firings allow Soar to make use of all relevant

knowledge in a given circumstance. It also forces

programmers to explicitly encode control knowledge into rules

to select operators instead of relying on a potentially cryptic

architectural mechanism to determine which rule among the current matches should actually be fired.

In addition to the basic execution supported by the

elaboration phase, Soar also has an architecturally supported

decision-making phase that occurs immediately after elaborations have ceased. During the decision phase, operators

representing actions of higher-level goals, which have been

proposed during the elaboration phase, are examined. The

operators are ranked according to their relative preferences,

which have also been specified during the elaborations. At this point, Soar selects the operator with the highest preference to be pursued. Although the serial nature of pursuing operators

may seem similar to productions systems that fire rules

serially, this is not typically true. One important distinction is that in Soar, knowledge about proposed operators is explicitly

declared, and is available to be used for further reasoning,

whereas information about matched rules in a serial system is typically not available to be used in this way.

In certain cases. Soar may decide that it is no longer

making progress on the current problem (e.g. the elaboration

phase terminates without any rules being fired, or Soar cannot

select between two operators). In such cases, Soar will react by creating a sub-state in which further reasoning can take

place. Within this sub-state, operators can be proposed and

pursued just as in the super-state. The sub-state vanishes when

Soar has done enough reasoning to resolve the problem that triggered its creation. It is during the resolution of sub-states

that Soar's learning mechanism creates new search control

knowledge (in the form of a rule) and adds it to LTM so that similar sub-states (and the additional reasoning to resolve

them) can be avoided in the future. Although Soar has been developed incrementally over a number of years, the mechanisms needed to modularize the

architecture were not completely in place. Nonetheless, some

features were clear candidates for modularization, and these

are listed below:

• Detailed Timing Facilities - Soar has the ability to keep

track of the time spent on various aspects of execution,

but in many cases this information is not critical to the

task.

• Callbacks - Soar has the ability to call user-defined

functions during execution. Some of these callbacks are invoked many times per decision cycle, and even if no

functions are registered with the architecture, some

overhead is incurred due to looking up and testing one or more variables.

• Learning - Each time Soar completes reasoning within a

sub-state, the architecture has the ability to learn a new

rule. When using Soar in certain domains, however,

learning has not been employed because these forces have

been expected to perform at an expert level without

undergoing a potentially costly training phase.

• Backtracing Mechanism - Soar also has the ability to keep

(potentially elaborate) information as to how it reached a particular conclusion. The full power of this feature is used only during learning. Thus, as only a small portion

of this mechanism is required for other purposes,

significant amounts of source code can be removed or optimized when learning is also removed.

The four features we have identified above are only a subset of the features in the Soar architecture that could be

modularized. However, this partitioning of the architecture was particularly suitable for our initial exploration because in

certain testbed environments, a single set of knowledge could

be used to examine all of the resulting architectural variants.

401
Three variants of the Soar architecture were examined for our tests by including or excluding some or all of the modular

features described above. Variant 1, which we will also refer

to as the standard version of Soar, includes all of the modular

features. Variant 2, removes the Detailed Timing Facilities as well as the Callback module. Variant 3 removes all of the

modular features described above.

5. Decision-Making Strategies

The class of problems we have selected for the initial implementation of our methodology is what we refer to as decision-making strategies. Most, if not all, agents are similar

in that they must examine their current state and decide which

of the many possible options to pursue. This process can take

place in a variety of ways. In particular, one set of

methodologies that can be used by Soar (as well as by a potentially large set of agent architectures) focuses on the

individual pieces of knowledge which must be brought to bear

in order to make the most appropriate decision about the next

action. Some agents, for example, may use knowledge that directly ties a particular state or set of states to the most

appropriate action. If the preconditions for each action are

disjoint, only a single piece of knowledge will be brought to bear in any given situation, and the decision will essentially

make itself. This is analogous to the operation of a lookuptable. Other agents may bring multiple pieces of knowledge to bear in order to make their decision. As the knowledge

becomes hierarchically organized, the agent will go through an

increasing number of refinement steps (reflected by a path in the tree from the root to a leaf) before it is able to select the

most appropriate action for the circumstances. It is this general

process of refinement that we have used as the basis for this study. Below is a list of decision-making strategies in which

the refinement process is increasingly complex:

• Simple, Declared Actions - Actions are represented

declaratively to the system, in Soar this is done using

operators. The programmer supplies enough knowledge to guarantee that only one action is applicable at any given

time, thus no conflicts between courses of action can

arise.

• Three-Phase Decision - The decision takes place in three

distinct phases. In the first of these, actions are proposed,

in the second phase actions are ranked according to their

relative preferences and finally the most preferred action

is selected and pursued. This allows for multiple layers of

refinement in the decision making process, potentially

decreasing the size and complexity of the knowledge

base.

• Goal Directed - A goal is a subtask that requires the

application and pursuit of a sequence of one or more

actions. In this strategy, goals are selected the same

manner as primitive actions and may improve

performance by constraining the subsequent problem

solving. Soar expresses goals with high-level operators,

and uses sub-states to perform the reasoning needed to achieve these goals.

6. Towers of Hanoi

The Towers of Hanoi problem is well known to the AI

community and has an equally well-known optimal solution.

Although it is a relatively simple problem, it is complex

enough to examine the class of decision-making strategies

outlined in the previous section. Moreover, within this domain

it is possible to limit differences between the agents'

knowledge to exactly what is required to implement each

decision making strategy. It is important to remember that we

intend this environment to be used as a starting point for further investigation, and as a proof of concept. No single

domain can claim to be representative of all situations an

agent may face in general.

Table 1 shows the runtime performance of the Soar

architectural variants described in section 4. Across all problem-solving strategies, significant timesavings are achieved between variants 1 and 2 as unused features are removed from the architecture. Further savings are achieved in the Tower of Hanoi subgoaling agent because the differences

between variants 2 and 3 affect the efficiency of the

architectural subgoaling process in situations where learning is not employed. Based on these results, and knowledge of how

the architecture was modified, we expect that all Soar agents

that do not require learning will achieve some performance

savings by using the more streamlined architectural variants.

Moreover, we further expect that agents that solve problems

similarly to the Towers of Hanoi subgoaling agent above will

be most enhanced. That is, agents that use a large number of

subgoals, each of which requires relatively little reasoning to resolve on its own.

Variant Declarative 3-Phase Goal-Directed j Standard Soar 12.45 21.98 22.17

Variant 2 4.19 11.06 7.92

Soar LITE 4.13 11.42 5.64

Table 1. Soar Performance in Towers of Hanoi

7. Complex Real-Time Task: Quake II

The tests we conducted in Section 6 seemed to indicate that a

substantial savings could be gained in situations that do not

require learning. To substanfiate this belief, we looked for other previously developed agents that shared this attribute

and could be used for additional tesfing.

The agent we selected for this set of tests was an obvious

choice. Constructed by one of us (Laird) to run with the latest version of Soar, it is suitably complex (employing -600 rules)

402
and operates in the highly dynamic and complex environment

of the Quake II computer game. Although Quake II shares

few, if any, attributes with the Towers of Hanoi puzzle,

application of our evaluation methodology within this new

domain was straightforward. As in Towers of Hanoi, a single

set of knowledge could be used to test all of the Soar

architectural variants, and testing followed the same basic

procedure. The only significant difference resulted from the

fact that in the Quake II environment, exogenous events are possible. Unless the world's events unfold in exactly the same manner between tests of two architectural variants, it is impossible to determine whether the agents interacting with

the world underwent the same processes of reasoning. As a

result, whether or not the performance of the

architecture/agent pairs is comparable also depends on the

ability to ensure that the world's events unfold in a repeatable manner. To ensure that this did happen, the agent was initially

allowed to operate in the Quake II environment by competing

against a human opponent for a predetermined amount of

time. During this phase, the agent's sensory inputs were

recorded and stored in a file. During benchmarking, however,

agents did not actually communicate with Quake II. Rather,

their sensory input was replayed in exactly the same manner

as occurred during the initial recording phase. Not only did

this allow us to ensure that agents always performed the same

reasoning, but because agent inputs were read from disk and

stored in memory prior to benchmarking, it also guaranteed

that timing results would reflect Soar's true performance, and

not be skewed by a communication bottleneck with the

environment.

Figure 1 shows the run time performance in Quake II for

the standard version of Soar (variant 1) and Soar Lite (variant

3). The performance was measured by recording the time

required to complete each of 380 successive decision cycles.

The histogram in Figure 1 shows the number of cycles that were performed within specific time frames. The best behavior

is to have all of the decision cycles execute in the minimal

amount of time (to the left). As this behavior is difficult to achieve, a secondary goal is to have a low variance without

any outliers so that there are no decisions that disrupt the

overall system execution. In the figure, the standard version

of Soar does have a high variance and many outliers. In contrast, the Soar Lite version shifts the histogram to the left so that almost all of the decisions execute in .03 seconds or

less. There is one significant outlier at .08, but that is the first decision when working memory is initialized and it is irrelevant to the overall runtime performance of the bot. This

illustrates that Soar Lite not only improves the aggregate

execution time (in this case there is a factor of 3 improvement

in average execution time) but improves it at the level of

individual decisions in a such a way as to decrease the overall

maximum computational requirements of any single decision.

- LITE

Standard

1 .Ji-I,

^

0.01 0.03 0.05 0.07 0.09

Kernel Time / Decision Cycle

Figure 1. Execution cost in Quake II

8. Comparing Multiple Agent Architectures

The methodology described in section 3 and that we have

employed to examine the performance of the Soar architecture

and some of its variants can also be used to examine or compare two distinct architectures directly. The same steps are applied as outlined previously, but the architectures need not be split into modules. The most difficult aspects of using our method for distinct architectures are deciding what class of

problems to examine and how to implement the agents. The

difficulties stem from the fact that problem definitions must be

highly constrained so that each agent's knowledge is extremely

similar, if not identical. At the same time, however, these

problem definitions are likely to require more flexibility than

in the single architecture case, because perfect behavioral

analogues may not exist between two architectures. Thus, the

burden is on the research team to ensure that agents are appropriately similar and that they encode the same

knowledge. As in the single architecture case, once agents

have been created, their performance in the problem domain

can be measured and compared.

8.1 The CLIPS Architecture

As an initial choice of a second architecture with which to conduct our evaluation, we have selected CLIPS [1]. Like

Soar, CLIPS is a forward-chaining production system based on the RETE matching algorithm. In CLIPS, short term,

potentially volatile, knowledge is stored in STM in the form of

lists. Each list is given a name, or type, which is essentially the

first element in that list. The remaining elements are labeled

either explicitly or implicitly by referring to their position in the list. Each element is also a constant value, either numeric

or string, and there is no architectural mechanism for referring

to the contents of another list, or pointing to another slot.

403
As in Soar, long-term knowledge is stored as rules that

are defined by the programmer. The conditions of these rules match against the contents of STM. Conditions can contain

combinations of both constants and variables; however,

variables may not be bound to list names or to slot labels. CLIPS, however, is not limited to using simple predicates in the right hand side of a rule as is Soar. A large variety of

predefined predicates, as well as user defined predicates and

functions can also be used as conditions. The action side of a CLIPS rule is used to modify the contents of STM or to execute external procedures.

CLIPS deploys knowledge via serial rule firings. The

basic execution cycle consists of two steps:

• First, rule matches are calculated by comparing the

conditions of each rule to the contents of STM.

• Second, successfully matched rules are placed into an ordered list such that the instantiated rule at the top of the

list has highest priority.

Priority is defined using two methods. The first of these is a

rule level conflict resolution mechanism called salience, which

can either be a constant \alue. or a value calculated at run

time. Rules with higher salience are placed higher in the list.

In many cases, salience alone is not enough to determine a single highest priority rule. In these cases, CLIPS defers to one of a few user selected architectural mechanisms called

search strategies, which orders rules of equal salience. At this

point, the first rule in the list is fired and the entire process

repeats itself. When no more rules are able to fire, the system

halts.Although there are many similarities between the Soar

and CLIPS architectures, the differences are equally

significant. These differences occur in each of the three

architectural areas we have discussed: knowledge

representation, knowledge deployment, and execution cycle

Recall for example, that Soar stores short-term knowledge in a directed graph structure and can perform variable binding on any slot in a memory element. CLIPS, on the other hand,

stores short-term knowledge in lists, and cannot bind variables

to the list name or to the names of its slots. Moreover Soar

fires all matching rules in parallel whereas CLIPS fires only

the highest priority rule. An additional difference is that Soar

natively supports the decision making process within its execution cycle whereas CLIPS does not.

8.2 Towers ofHanoi revisited

We have examined CLIPS in the Towers of Hanoi domain

using the same parameters that were used in our earlier

evaluations of the Soar architecture. Note, however that the

absolute timing data is not the same as in the first runs. Previous runs in this domain were done on different machines

and measure total CPU time, not just Soar kernel time Below,

we briefly review the decision-making strategies of each agent

and discuss the particularities of the CLIPS implementation.

Notice that two additional categories have been added to further constrain the implementations and to examine areas

that may be more amenable to the CLIPS architecture.

• Mutually Exclusive Reactions - Action conditions are mutually exclusive, and no symbol is declared to represent the action being pursued. In both Soar and

CLIPS this is done by the construction of individual rules which specificity the preconditions of an action and its

effects. Actions are applied sequentially within the world,

and the programmer must ensure that no conflicts arise between two actions.

• Simple, Declared Actions - Similar to the first category,

but in this case the action being pursued is declaratively

represented. In Soar this is done using operators to represent the action. In CLIPS a fact is asserted which

describes the current action being pursued. Once again

however, the programmer must ensure that action

preconditions are mutually exclusive.

• Two-Phase Decision - Two distinct phases are used to make the decision. In the first phase, actions are proposed

via declarative symbolic representation. In the second

phase one of these actions is selected and pursued. Note

that this means that preferences corresponding to a specific action must be expressed simultaneous to the

creation of the action symbol (e.g. within the same rule).

In Soar, this is done using the architecturally supported

decision phase, and the same rule is used both to propose

an operator as to express its preference. In CLIPS,

partitioning knowledge into a salience hierarchy supports

the two phases. This guarantees that the first phase (action

proposal) completes before the second phase (selection)

begins.

• Three-Phase Decision - Three distinct phases are used to make the decision: proposal, preference and selection.

These distinct phases help support situation dependent

preference structures without an explosion of individual

rules. In CLIPS this is done using a three-stage salience

hierarchy.

• Goal Directed - High-level actions, possibly requiring more than one action to complete, are used to constrain

rule matching. In CLIPS, goals are maintained

declaratively and represented in a stack. Two Soar

implementations were examined, one using Soar's native

mechanism as demonstrated in the previous trials, and the

other using a declarative stack similar to that used in the

CLIPS implementation.

Figure 2 shows CLIPS and Soar performance in the Towers of

Hanoi domain. Qualitatively, performance is very similar

between the architectures except at the end points. On the left404
hand side of the graph, the Soar agent performs markedly

worse than the corresponding CLIPS agent. This performance

difference can liicely be explained by the fact that this Soar

agent does not use the operator construct. As a result, it does

not benefit nearly as much from constraining the rule matching as the other Soar agents do, and thus suffers an

increase in execution time. At the other end of the graph, Soar

and CLIPS behavior are once again divergent. In CLIPS we

can attribute the performance increase to the fact that the

problem's recursive nature allows the proper puzzle-solving

knowledge to be easily expressed with a goal stack, and results

in highly constrained rule matching. In the standard version of

Soar (point 2), however, we have already seen that the benefits

of subgoaling are dominated by the costs of Soar's architecturally supported subgoaling mechanism. However,

when performance is re-examined using architecturally

supported subgoals in the Soar-Lite variant (point 2') or when

using a declarative subgoal stack similar in nature to the

CLIPS implementation (point 1) the difference between the

Soar and CLIPS agent's performance is minimal. In all, the

similarity of performance between the declarative goal stack

implementations in both Soar and CLIPS, and the architectural

implementation in Soar-Lite, indicate that in simple

environments such as Towers of Hanoi, declarative subgoaling

provides a simple and efficient means of problem solving. As

tasks become increasingly complex, however, we expect that the rule-based techniques employed by these implementations

will become significantly less efficient than the lightweight

architectural counterpart of Soar- Lite

9. Related Work

Examining differences between agent architectures has

received relatively little attention compared to the

complementary task of examining how different agent

strategies are more or less suited to a particular problem.

Nonetheless, a variety of approaches have appeared in the

literature. The majority of architectural evaluations can be

placed into a single group that we refer to as categorical

comparisons [5,8,9,14]. Within this body of work,

architectures are evaluated at a high level, in a domainindependent manner, typically based on whether they natively

support certain features (e.g. backward or forward chaining, or

the ability to make real-time commitments). The benefits of

this approach are that the concise tabular data, representative

of these studies, may allow architectures to be quickly

assessed as having or not having the minimal necessary

capabilities to perform the task at hand. Categorical

evaluations are most useful when they examine aspects of the

architecture that are extremely difficult, or impossible, to emulate using additional, programmer supplied, knowledge.

However, the high-level approach of categorical

evaluations can also be a short-coming. In particular, the many

situations in which architectural features can, in fact, be

successfully emulated with addition knowledge are often not

explored. More over, because these studies rarely incorporate

^ 2

1

0.5

Soar CLIPS

l

9 2'

MutEx Declared 2-Phase 3-Phase Goal Reactions Actions Directed

Figure 2. Execution Time of Soar and CLIPS in Towers of Hanoi

benchmarks, there is often no indication as to the relative

performance of different architectures or their underlying

features.

In contrast to high-level categorical comparisons, the

Sisyphus-VT initiative examined the problem of implementing

a complex real-world problem on a number of different

architectures [13]. Although the pursuit of complex, real world, problems as test bed domains is a laudable undertaking,

the implementation overhead is extremely high. As a result,

independent teams of programmers, expert in one particular

architecture, carried out the implementations. A critical difference between the methodologies used in the SisyphusVT study, and the one we have presented is that we emphasize

that the problem solving methods used by two comparable

agents should be strictly specified and adhered to. SisyphusVT, on the other hand, allowed relative freedom in this area. Although this freedom allows programmers to use a problem

solving method which they feel is best suited to their

architecture, it also means that differences in two agents'

performances might be attributable more to differences in their

knowledge, than to differences between the architecture which

serve as their foundations. Plant and Salinas attempted to circumvent the problem of confounding the contribution of

knowledge and architecture to the overall performance rating

in their 1994 study [II]. Under their methodology, agents were constructed in a generic manner so that they had minimal

reliance on architecturally specific constructs. This allowed

them to create agents for each architecture based primarily on

syntactic transformation of a single, handcrafted, specification.

This methodology certainly adheres to our requirement of

strictly specifying the agent's underlying problem solving

methods. But it deviates from our requirements because it does not examine a range of these underlying methods. As a

result, it is less likely that the benchmarks will include near- optimal implementations for any architecture, especially since

405
reliance on architecturally specific constructs is purposely

minimized.

10. Discussion

The methodology we have presented allows the performance

of two architectures, as well as variations of a single

architecture to be compared directly. Our methodology is an

evolution of prior research, and emphasizes aspects of the

benchmark design (e.g. problem-solving specification), which

help ensure that agents built using two different architectures use equivalent knowledge. An initial application of our comparative approach has shown significant differences

between 3 variations of the standard Soar architecture when

Soar's learning capabilities are not required. This hypothesis was further supported by examining the performance of an agent in the complex, real-world, environment of Quake 11. The broader implication of this finding is that knowledge both

about the domain and about the implementation of the agent

should play a role in deciding what architecture (and what

architectural features) are most suitable for a particular

circumstance.

We have also shown that the same methodology used to compare variations of a single architecture can also be used to compare two distinct architectures. We have illustrated this application with an initial comparison of Soar and CLIPS

Results from this set of tests indicated both qualitative and

quantitative differences in their performance, and have also

illustrated the potential performance savings that can be

achieved by an architecture whose features are well suited to the current task. We believe that the work presented in this paper provides

a good foundation for addressing the question of what are the resource requirements of architectural properties, or, which

properties of an architecture are most suitable for a given

situation. Because the needs of intelligent agents often

simultaneously push architectures to support a wide array of

features and to be highly efficient in terms of run-time

performance, an improved understanding of the answers to these basic questions is important.

11. Acknowledgments

This research was funded in part by grant N61339-99-C-0104

from ONR and NAWCTSD.

12. References

[1] CLIPS Reference Manual: Version 6.05.

[2] R. B. Doorenbos. Production Matching for Large

Learning Systems. PhD thesis, Carnegie Mellon

University, 1995.

[3] C. L. Forgy. On the Efficient Implementation of

Production Systems. PhD thesis, Carnegie Mellon

University, 1979.

[4] E. Gat. Integrating planning and reacting in a heterogeneous asynchronous architecture for mobile

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on Artificial Intelligence, pp. 809-15.

[5] W. B. Gevarter. The nature and evaluation of commercial

expert system building tools. Computer, 20(5):24-41,

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[6] F. F. Ingrand, M. P. Georgeff, and A. S. Rao. An

architecture for real-time reasoning and system control. IEEE Expert, 7(6):34-44, Dec. 1992.

[7] J. E. Laird, A. Newell, and P.S. Rosenbloom. Soar: An

architecture for general intelligence. Artificial

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[8] J. Lee and S. I. Yoo. Reactive-system approaches to agent

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[9] W. A. Mettrey. A comparative evaluation of expert

system tools. Computer, 24(2): 19-31, 19§L1.

[10] A. Newell. Unified Theories of Cognition. Harvard

University Press, Cambridge, MA, 1990.

[II] R. T. Plant and J. P. Salinas. Expert system shell benchmarks: The missing comparison factor. Information

& Management, 27:89-101, 1994.

[12] S. Russell and P. Norvig. Artificial Intelligence: A

Modern Approach, chapter 2, pages 31-52. Prentice-Hall,

Upper Saddle River, NJ, 1995.

[13] A. Th. Schreiber and W. P. Birmingham, Editorial: the

Sisyphus- VT initiative. International Journal of HumanComputer Studies, 44(3): 275-280, 1996.

[14] A. C. Stylianou, R.D. Smith, and G. R. Madey. An

empirical model for the evaluation and selection of expert

system shells. Expert Systems With Applications, 8(1): 143-155, 1995.

[15] S. A. Wallace and J. E. Laird, Toward a methodology for AI architecture evaluation: comparing Soar and CLIPS, in N.R. Jennings and Y. Lesperance, editors, Intelligent

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Languages (ATAL-99), Lecture Notes in Artificial

Intelligence. Springer-Verlag, Berlin, 2000.

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Representation , pp. 73-83

406
A Metric For Monitoring And Retaining Flight Software

Performance

Chariya Peterson

Computer Sciences Corp. 7700 Hubble Drive,

Lanham-Seabrook, MD 20706, cpeters5(a)csc.com

ABSTRACT

The control of spacecraft dynamics are handled by on board flight

software which are typically based on sequential algorithm such as Extended Kalman filter (EK.F) to perform closed-loop automatic

control. This level of automation does not require any decisionmaking or learning capability. Decision-making capability comes

to play at the higher level of autonomy in task management, such

as mode/model selection, planning and scheduling. Most of these

functions are still performed on ground and are not fully autonomous. In this paper, we propose the concept of intelligent

flight software that is capable of learning, and improving its performance in the future based on information gained in the past. This capability will enable the software to appropriately deal with

uncertainty or incomplete knowledge of model or environment.

To be precise, we will focus on on-board Attitiude Determination

and Control software (ADCS). A typical ADCS is an automation

where attitude solutions are computed dynamically via a filter and

controlled by a closed-loop PID process. The performance of a

typical ADCS is maintained by Flight Dynamics ground personnel.

These tasks involve, among other things, attitude determination

and validation, and attitude sensor model calibration. in this paper, we propose an intelligent ADCS that is able to monitor its own performance and able to perform a self-calibration when

needed. KEYWORD: Control Theory, Intelligent software.

Uncertainty Management, Machine Learning

1. INTRODUCTION

The task of maintaining long-term performance and

accuracy of software onboard a spacecraft can be a major

factor in the cost of operations. In particular, the control and

maintenance of constellation or distributed spacecraft

undoubtedly pose a great challenge, since the complexity of

multiple spacecraft flying in formation grows rapidly as the

number of spacecraft in the formation increases.

Eventually, new approaches will be required in developing

viable control systems that can handle the complexity of the

data and that are flexible, reliable and efficient. These new

approaches will have to face the problems that are encountered during the development of a control system, in particular how to deal with uncertainties in the application

domain and how to balance between efficiency and

complexity of the system. The accuracy of control software

depends on how much information about the domain is modeled into the system. The more information taken into

account, the more complex the system becomes, leading to higher computational cost. Hence pure model-based

approaches will undoubtedly be too costly for a large

control system.

Most of the material covered in this paper is in the paper

presented at the SpaceOps Symposium, Toulouse, France

June 19-23, 2000 [7].

2. SOFTWARE PERFORMANCE

Typical flight software performs closed-loop automation

control without any high level decision-making, or learning

involved. On the other hand, autonomy are added to the

flight software in terms of flight or ground component that aims to increase operational range of the software, involving

model selection, performance monitoring and self- calibration and tuning. We identify the intelligence of

modem flight software with its decision-making capability,

which results in the autonomy level of the software. We

measure the intelligence of onboard software in terms of its

ability to learn from experience and its rate of success. In the lowest level, we define the performance of flight

software as a measure of the closeness between the

observed and the predicted state of the systems. These

quantities are usually referred to as residuals.

Understanding the uncertainty underlying these residuals,

identifying their controlling factors, and quantifying the

propagation of these factors through the model for the

system can lead to an improvement in the intelligence of the

software.

On-board ADCS generally reacts directly with input sensor measurements and thruster control via simple closedloop process. The typical operational range of such standard

ADCS is narrow, and as a result, the system may perform

poorly under uncertain conditions such as incomplete

knowledge of world model, or unanticipated changes in the

environment. To cope with this problem, the models used

in the software are parameterized. The model parameters

are adjusted regularly to maintain the accuracy level of the

software. These tasks are typically performed manually on

407
the ground in a regular basis. This suggests that, the

intelligence of flight software may be increased by enable

the software with self-monitoring and self-calibration

functionality. Recently, there have been a few research

efforts in increasing the intelligence of flight software: for instance, the Remote Agent Experiment (RAX) onboard

DS-1 spacecraft [1], and autonomous on-board dynamic

monitoring developed at Jet Propulsion Lab, [BEAM].

We propose to develop the Monitoring and Autonomous

Self-Tuning (MAST) system that aims to maintain the

efficiency of onboard software by dealing with uncertainty

in an appropriate way. MAST is an extension of a project at NASA/Goddard Space Flight Center (GSFC): Autonomous

Model-based Trend Analysis System (AMTAS) [2]. MAST

extends the objective of ASCAL from health and safety

management of hardware to dynamic applications. MAST

uses machine learning approach to handle uncertainty in the

problem domain, resulting in the reduction of over all computational complexity. The underlying concept of this technique is a reinforcement learning scheme based on

cumulative probability generated by the past performance of

the system. The success of MAST depends largely on the

reinforcement scheme used in the tuning algorithm and its

ability to remember and learn from its experience.

3. THE MONITORING PROCESS

MAST consists of two main pails: a monitor and a tuner. The monitor is a real-time dynamic system that monitors

relevant residual output of the software it is monitoring. The

step size of the sampling time varies depending on the

parameters being monitored. The state of the monitor is the

quantity representing software performance in real time.

When the state of the monitor approaches a given threshold,

the tuning process will be initiated. This process has no

intelligence i.e. it does not require any decision-making

capability.

Sensor input from spacecraft

Real-time Controller Control output

Running with small time step dt to thrusters

Residuals

Monitor

Running at a larger time step Dt

Prediction output

to tuner _L_

models

Figure 1. Monitonng Mode

Figure 1 demonstrates the monitoring mode, which

consists of the software being monitored and a monitor, both

running in real time. The detail description of the monitor

depends on the software being monitored. It is necessary that the monitor have sufficient knowledge of the software in order to make an accurate prediction and diagnosis of the

problems.

For intelligent ADCS where Kalman filter is used for real-time computation of attitude solutions, the performance

of ADCS is monitored by trending the attitude solutions and

the effective sensor measurements. In a nutshell, attitude of

spacecraft is continuously propagated through time using

angular rate measurements from gyroscopes. These attitude

solutions are not very accurate since gyroscope

measurements are usually erroneous. The accuracy of

attitude computation can be improved by occasionally

comparing the attitude with vector (directional)

measurements from available sensors on-board such as star

trackers, magnetometers, sun or earth sensors. Kalman filter

is an algorithm that performs such sequential process of

propagating and measurement updating. The size of the

residuals of sensor measurements reflects the performance of

ADCS.

Let;f denotes the state vector estimated by the software

and s denotes the vector of sensor parameters being

monitored and calibrated. Assume that an expected state

vector is given, may be obtained in various ways

depending on the software and on sensors and parameters

being monitored. Let the software be driven by the dynamic

system

k{t) = f{x{t))+u{t)

z^, =G(5,,x(/,)) + w(/J

where z^. ^. is the measurement for sensor r at time , and

is the parameter vector associated with the model of

measurements. The process noise u and measurement noise

w is assumed to be uncorrelated white Gaussian noise with

zero mean. During the normal mode of operation are

kept constant. The performance of (1 ) is observable from the

deviation of certain quantities, such as state residuals

X — x^ , and sensor residuals, z^. ^ — G{s^. , x^ {tf. )) . Let ^

represents the vector of the desired residual observations.

The monitoring process is then defined via a tracking

process, i.e. the linear dynamic of ^ and its slope ^ :

'^(^>,)=^(^) + A/-v(r,,)

408
where V is a zero mean white Gaussian uncorreiated

acceleration noise. The time step A/ = t ^.^^ — t ^ for residual

samplings may be larger than the time step of the input

system (1). Let ^ = [<^ <^]'- Then the state-space

representation of the predictor can be written as

(2)

where

1 At

0 1 V = [At'/2 At], H^[l 0].

Note that, the measurement f^, represents the residual

sampling while the state x{ti^ ) measures the level of

performance of (1) during the time t^ . A propagation of

x{tf^) predicts if and when the performance of (1)

approaches an acceptable threshold at a certain time in the

future. The system (1) and the predictor (2) connect as shown in Figure 1. Higher order derivatives of state

residuals can also be included in x{t^ ) in a similar way. In which case, we would have a higher order predictor. Higher

order derivative may be crucial for software systems that are

sensitive to uncertainties in measurement models, which is generally the case for a highly non-linear, chaotic or unstable systems.

4. TUNING PROCESS

The tuning process is a closed-loop learning algorithm

based on a reinforcement learning scheme. The goal of the

tuning process is to restore the performance of the software

by iteratively adjusting relevant model parameters in a "certain wa/' until a cost function is minimized. The tuner

possesses two types of intelligence:

1) During each cycle the tuner will select which parameter

to adjust. This selection is MAST's long-term

knowledge on its past tuning experience. This

intelligence is measured by the rate of success in software tuning.

2) The amount of adjustment for each parameter. This

selection is a short-term knowledge generated by the

reinforcement scheme of the learning algorithm. This

type of intelligence is measured by the rate of

convergence for each particular tuning process.

Note that, the learning approach does not give an

optimal solution, but it has a much wider operational range

than the conventional optimal batch least square or filter techniques. This is simply because; MAST automatically

accumulate and reuse its past activities in its long-term

memory, which will enable the system to react and adapt to changes in the environment. This approach is therefore

appropriate for problems with large degree of uncertainties.

Moreover, this technique is not critically dependent on the

detailed knowledge of the software being tuned. As a result, some of the technical restrictions generally required in conventional techniques such as linearity, or conditions on

process and measurement noises are not required if a learning algorithm is used. It should be noted that the tuner

is an off-line algorithm, or a process running in parallel and

isolated from the routine operation of the software. Not

until the tuning goal has been reached, that the software will

be updated with the new values for the model parameters.

Hence, the tuner may be performed on the ground or on an onboard computer.

Sensor input from spacecraft

Off-line estimator

with small lime step dt ^~"-~vAdjusted model

paTaflagters Residuals

Evaluator

with larger time step Dt

scores Tuner

models Reinforcement generator

Figure 2. Tuning Mode

Figure 2 demonstrates the tuning mode. In this mode,

there are three components connected in a closed-loop: an

off-line copy of the software being monitored, the evaluator,

and the tuner. The evaluator measures the convergence of the

tuning solutions and the tuner makes appropriate adjustment

to certain model parameters of the software guided by a reinforcement learning scheme, generated by an uncertainty

handling technique. Several techniques have been used by

various research projects in reinforcement learning. In MAST, the scheme is based on the Local Dempster-Shafer

theory (LDS) which is a modification of the Dempster-Shafer

theory of belief and evidence [4,5]. For the detail description

of LDS we refer to [2,6]. LDS is specifically developed to deal with systems with large number of variables. As

opposed to the monitor, the evaluator and the tuner are generic processes that do not require detailed knowledge of

the software being tuned. Their basic requirements are a set of software parameters to be tuned and an appropriate cost function that models the inaccuracies of the software. The

evaluator evaluates and scores the result of each cycle by

examining the effect of the parameter adjustment on the cost

409
function. Based on this score, the tuner continues to adjust

the parameters until the process converges.

Reinforcement learning is the type of learning that is popular among most current researches in machine learning

and statistical pattern recognition. Other popular type of

learning systems such as artificial neural network, requires a

priori training from examples provided by an experienced

supervisor. Such systems are not quite appropriate for problems involving learning from interaction. In interactive

problems it is often impractical to obtain examples of

desired behavior ahead of time, which are both correct and

representative of all the situations to which the system has

to react. In an unknown situation, where learning is most

beneficial, the system must be able to learn proactively from

its own experience.

During the tuning process, the parameter adjustment is based on the rate of convergence (or divergence) of the

residuals during the previous two (or more) cycles. Assume

there are n sensor parameters to be adjusted, i.e. the

dimension of the parameter vector is«. The parameters

can be increased or decreased by A/?^. . The set H of all

possible adjustments has I elements. Each 2'

i = 0

element is a set of parameters with a plus (+) or minus (-)

sign to denote if the parameter is being increased or

decreased. For instance, an increase in parameter a and a decrease in parameter b is represented by the "signed" set {a^,b_}. During each loop K, the step size A/»^is

computed, and the set H is constructed. An indexed by a cumulative probability distribution which generated by

LDS theory. The learning process in the tuner is precisely

the mechanism that adapts to obtain the new index

p for the next cycle. The original Dempster-Shafer

theory is defined on a set of n elements. Recall that, // is a

set of all possible ways of modifying model parameters

being tuned. A mass function on // is a probability function

that assigns a degree of belief to each of its element. The

mass function satisfies the following conditions

m(0)

I, forA^0 and

= 0

Two mass functions w, and on H can be combined

into a single mass function m, ® nij by the Dempster

composition rule:

m^(S)m2(A) =

B^C^A I BuC=0

for/\#0, m, (8) W2(0) = O.

These mass functions are used to generate the degree of

belief associated to each element of H. A belief function

generated by a mass function in is defined as: p:H^ [0,1]; biA)=Y,m(B)

where the union between two signed sets is obtained by

"adding" all elements in the two sets according to their sign.

This way, every subset of the form {a^,a_}will all be

cancelled out. In statistical terms, the belief function is a cumulative probability on H.

During a tuning cycle K, the belief function p^, is

evaluated and used as an index set for H. If the resulting

residuals are found to decrease with a faster rate or increase

with a lower rate, the tuner will re-compute the next belief

vector /»^^| by applying a positive learning algorithm

described in [1,9]. The new index will strengthen the

performance of the cycle K. Conversely, if the residuals

performed in the negative manner, then the negative

learning algorithm will be applied, resulting in lessening the

degree of belief on the failed action.

The learning process discussed above is the simplest

application of the (modified) DS theory to the tuner. In practice this algorithm can be enhanced in various ways to increase the performance and robustness of the tuner. First,

the localization of the DS theory on H defined in [1,9] will reduce the size of search space. Second, the size of

parameter increment may be decreased as the residuals

begin to converge. Third, the use of hierarchical or

multilevel learning systems accelerates the learning process

(more so for the initial rate of learning) and simplifies the

structure of the tuner in each layer.

5. TWO APPLICATIONS OF MAST

The attitude monitoring and self-calibration (ASCAL) [3],

and the maintenance of spacecraft formation. In the first

application, the accuracy of attitude software depends on, among other things, the accuracy of sensor models. These

models are generally a function with parameters

representing relevant uncertainties such as bias, scale factor or misalignment. In the beginning, these parameters are set

at certain pre-calibrated values and are manually tuned and

updated periodically throughout the life of the spacecraft.

Some tuning processes are routine activities, while others

are elaborated and performed on ground by attitude

specialists. In this proposed application, MAST will automatically monitor and tune a set of sensor parameters.

The second example is the maintenance of large

formation of spacecraft. The task of controlling a number

410
of spacecraft to fly in formation is more complicated than

controlling a single spacecraft. One problem that may be

encountered in the development of formation control

algorithms for large formation is the complexity that arises

from the high degree of freedom of the system. In practice,

the conventional approach based on state-space

representation is manageable only for formation of a small

number (2-3) of spacecraft. The complexity increases in a large formation, which makes the control algorithm

computationally intensive. Moreover, uncertainties in the

system models or from environmental disturbances can be

propagated and magnified. To correct these errors the

control system has to be tuned often and regularly to keep

the formation intact by continuously monitoring and

adjusting the position of each individual spacecraft. Ideally,

these tasks should be performed onboard, and hence

efficient and fast algorithms for the real-time solution of

such a large-scale optimization problem are needed.

6. REFERENCES

[1] N. Muscettola, B. Smith, C. Fry, S. Chien, K. Rajan, G.

Rabideau, and D. Yan, On-Borad Planning for New

Millennium Deep Space One Autonomy, Proceedings of

the IEEE Aerospace Conference, Aspen, CO 1997.

[2] C. Sary, C. Peterson, J. Rowe, K. Mueller, W.

Truszkowski, T. Ames, N. Ziyad, Automated

Multimodal Trend Analysis System, Proceedings of the

AAAI Spring Symposium 1998.

[3] C. Peterson, J. Rowe, K. Mueller, N. Ziyad, ASCAL:

Autonomous Attitude Sensor Calibration, proceedings of

the Flight Mechanics Symposium, NASA/GSFC May

1999. M. D. Shuster, D. M. Chitre, and D. P. Niebur,

Inflight Estimation of Spacecraft Attitude Sensor

Accuracies and Alignments, J. of Guidance and Control,

5,4, 1982.

[4] G. A. Shafer, Mathematical Theory of Evidence,

Princeton U. Press, 1976.

[5] A. P. Dempster, Upper and Lower Probabilities

Induced by Multivalued Mappings, Annals of Math.

Stat. 38, pp. 325-329, 1967.

[6] C. Peterson, Local Dempster Shafer Theory, CSCAMTAS-98001, C.S.C Internal Report

[7] C. Peterson and N. Ziyad, Autonomous Performance

Monitoring System: Monitoring and Self-Tuning

(MAST), Proceedings of the SpaceOps 2000

Symposium, Toulouse, France, June 19-23.

411
Flexible Robotic Assembly

David P. Gravel

Senior Technical Specialist

Ford Advanced Manufacturing Technology Development

Detroit, Ml 48239

Wyatt S. Newman

Professor EECS Dept., Case Western Reserve University

Cleveland, OH 44106

ABSTRACT

Application of robots in automobile manufacturing

plants is primarily limited to material handling and

spot welding operations. Only 3 percent of all robotic applications are currently performing tasks

related to assembly [1]. These assembly tasks often

have the characteristic that the positional

uncertainties in parts to be mated exceed the

assembly tolerances by many times. Humans are

particularly capable of part assembly under these

conditions for three reasons: (1) Humans can apply

compliant, goal directed forces and positions to the

assembly task. (2) Humans have a powerful vision

capacity that integrates well in the application of

these goal directed forces and positions. (3) Humans

are also quick to learn new assembly techniques and

can often perform complex assembly tasks easily

after a short training period.

This paper will detail the work done at Case

Western Reserve University' (CWRU) in Cleveland,

OH. and by Perceptron in Plymouth, Ml on a NIST

ATP for Flexible Robotic Assembly for Powertrain

Applications (FRAPA). FRAPA is a Joint Venture

comprised of the following companies: Ford Motor

Co., MicroDexterity Systems (MDS), ComauPlCO,

Perceptron. and the National Center for Manuf.

Sciences. Also participating as subcontractors in the

FRAPA project are Sandia National Laboratories,

the University' of Michigan, and CWRU.

This work will lead to the production of an autonomous system that exhibits all three

characteristics that humans are naturally endowed

with, that is: goal directed compliant forces and

positions applied to part assembly, visual feedback to reduce the part location uncertainty, and learning

algorithms that improve the performance of the

assembly task over time.

STATUS OF CURRENT TECHNOLOGY:

POSITION CONTROLLED ROBOTS

Robots used as position-servoed mechanisms are

ineffective as an assembly tool in cases where the

assembly tolerance is less than the positional

uncertainty. This can be illustrated using a simple

example of a peg-in-hole problem.

Suppose a position-controlled robot attempts to assemble a peg into a hole, but the hole position is not precisely controlled, and fiirther, the assembly

tolerance is comparatively small. Unless the centerlines of the peg and the hole are nearly parallel and

lie closer together than the assembly tolerance, the

robot will not be able to insert the peg into the hole. In a misaligned state, if the robot does attempt to perform the assembly, unacceptably high contact

forces will be generated as the robot attempts to push

the peg into place. Even if the peg is chamfered, a misalignment between the peg and hole would

produce large side loading forces as the robot

struggles to move the peg along the programmed

centerline of insertion. This misalignment of the

peg is likely to cause a jam if no means for

compliance, such as a remote center compliance

(RCC) device, is employed (see e g [2]).

In practice, the geometry of real parts is often more

complex than a peg and hole which, can further

complicate robotic assembly and decrease the

chances for a successful automated assembly using

position-controlled robots.

L A NEW ROBOT CONTROL PARADIGM

Wyatt Newman at Case Western Reserve

University in Cleveland, OH has implemented a

robotic control strategy called "Natural Admittance

Control" (NAC) [3,4,5], on several robot platforms.

Instead of having the robot stiffly track a precise

412
trajectory, it is guided by an attractor point with preprogrammed stimulus-response dynamics. This new

robot control strategy provides excellent rejection of

Coulomb friction, good force responsiveness, and

guaranteed contact stability.

platform. This configuration of robot meets the

criterion for low inertia, excellent mechanical

stiffness, and back drivability. The first generation

of Paradex, shown in Fig. 2, is currently undergoing

testing and development at CWRU.

A programmed point called the "attractor point"

controls the direction of the applied force. It is the

attractor point that is manipulated by the trajectory

generator in the robot. The attractor point pulls the

robot tool center point (TCP) towards it with

strength proportional to programmable spring

constants in the X, Y, Z, Y, P, R directions. The

damping of the robot TCP is accomplished in a

similar fashion with programmable damping

coefficients Bx, By, Bz, By, Bp, and Br that model a damping forces proportional to the velocity

components in the Vx, Vy, Vz, Vy, Vp, and Vr

directions.

Vir-tuat

/ ri /

' -

'i

^^^'^int ^^^^^^ ^

Fig. 1 Simplified schematic ofrobot TCP modeled as a programmable spring/mass/damper system.

Changes in the state of assembly can be

accomplished by either altering the attractor point

position or by changing the virtual spring or damping coefficients. In this manner it is possible to change the applied forces or endpoint dynamics as required for the assembly task.

In addition to the NAC robot control scheme, it is desirable to implement force controlled robotics on a

robot platform that has low inertia, low gear friction,

and high mechanical stiffness and is backdrivable.

To address these control concerns and to provide a high fidelity response to input forces, a robot called

Paradex has been created.

Paradex is a parallel robot structure comprised of six

linear motor axes joined at a common distal

Fig 2 FRAPA Paradex 1 robot

The NAC paradigm of robot control can be made

to act as a conventional position controller by using

very stiff virtual spring coefficients, e.g. for handling

parts that are precisely or for spot-welding

applications.

Given that NAC can be accomplished on a responsive robotic platform, the remaining task is to provide programmed strategies based on machine

assembly states that can be first identified and then

resolved by intelligently changing the virtual

parameters and endpoint dynamics. This will be

further discussed in section III.

NAC provides robots with the first human attribute

of actively controlled, goal directed forces and

positions being provided to parts to be assembled.

II. MACHINE VISION

Humans can easily visually locate and acquire

parts to be assembled. Machines however, do not

possess such a facility. Machine vision has

historically been plagued by a lack of robustness in

factory applications. Many of the problems related

to the robustness of grayscale vision processing are due to a lack of contrast of features of interest, and to

413
changes in lighting that cause object segmentation

errors in the image.

3-D range imaging technolog>' does not suffer from

the contrast and lighting issues of grayscale vision

processing, but many of the algorithms that can be

commonly found for grayscale vision processing do

not operate on 3-D range data. Under a NIST ATP

for Flexible Robotic Assembly for Powertrain

Applications (FRAPA). Perceptron in Plymouth. MI

is developing new vision processing algorithms that

w ill provide a robot with part pose information that a can be used to acquire randomly located parts.

One disadvantage of range imaging machine

vision s>'stems. howev er, is that the speed of image

acquisition is about 2 s compared to 0.016 s for a CCD grayscale camera. This limits 3-D range

imaging to applications to ones where the scene is

static or slowly changing in time. Fortunately, in

automotiv e applications, there are many examples of

static scenes that this technology can be utilized.

Fig. 3 Example ofparts delivered to an imprecise

location that need to be acquired prior to assembly.

Currently most of these operations are done

manually in automotive plants.

Another disadvantage of range images is that

distances measured from 0 -: show a periodicity.

This results from the measured phase shift reflected

from an object back repeating at a distance called an

"ambiguity interv al" due to the phase changing from

0 to 360 deg. and starting over at 0 deg. again.

Fortunately, the ambiguit\' interval is somewhat

large (~2m) so there are a number of plant

applications that satisfy the condition of using a

single ambiguit\' interval over the workspace.

The raw sensor data from the range vision system

is acquired in a spherical coordinate frame with more pixels spatially located in the central region of

the image than on the edge. An image region of

interest (ROI) is rectified into a regularly spaced

(X,Y,Z) 3-D array and processed by a new class of

operators [6] that can quickly perform part

identification and deliver part pose information as

well.

These new vision operators are model based and

yield a set of scalar values that represent both the

existence of an object and pose information about the

object. These operators are also relatively fast compared to correlation techniques and have full 6

degrees of freedom (DOF) isometry invariance.

Fig. 4 shows a comparison ofan intensity image(top)

and a range image (bottom). These two pallets

contain automotive torque converters. The bottom

left hand pallet is a brighter image because it is closer in the range image.

The methodology employed by Perceptron to locate

parts in roughly placed pallets follows these steps.

(1) Locate the boundaries of the pallet and calculate

ROI windows to isolate individual parts. (2) In each

ROI window transform the row-column-range raw

data into a uniformly spaced X-Y-Z representation.

(3) Use the new vision operators to locate the part

pose in each ROI. (4) Calculate robot coordinates in

the robot world coordinate frame so the robot can

pick up the parts from the pallet.

414
The capacity to locate roughly placed parts in 3-D

will be used to robotically acquire these parts. This

capacity provides automation with the second human

attribute of coordinated vision to perform robotic

assembly.

HL MACHINE LEARNING APPLIED TO

ROBOTIC ASSEMBLY

Humans are able to learn complex assembly tasks

relatively quickly. Machines, however, are notoriously difficult to "teach". Any ability to provide a machine with a learning capacity to perform a task will require decomposing the task

into subtasks that can be understood and modeled in

ways a machine can use.

Work at CWRU on machine learning of automotive

assembly tasks has focused on four areas. (1) Gain

an understanding how people perceive edges and

boundaries when attempting to mate two parts

together. (2) Understand strategies people invoke

under various force feedback conditions (3) Create a model based system that uses signal feedback to

direct the robot motion to the proper position for

assembly (4) Implement the knowledge gained in

steps 1-3 into a neural net robot controller.

At CWRU an idealized peg-in-hole virtual model was created for round and square peg-in-hole

applications. Moments can be shown to exist as a

portion of a peg passes over the region of a hole. A

peg-in-hole model is similar to a simplified set of

problems in transmission assembly. The goal is to extend this work to automotive assembly tasks such

as a sun gear insertion into a planetary gear set.

Moment

Fig. 5 Near the hole momem mjormation exists that

be exploitedfor determining the location ofthe

center ofthe hole for automated assembly.

Figure 6 shows the computed direction of the

reaction moment vector for a square peg and hole

when the peg z-rotation is close to the assembly

orientation. Looking for patterns in the robot

force/torque sensor signals to match these theoretical

moments may provide a robot with the ability to infer the location of the assembly position and

orientation as the assembly attempt proceeds.

yporilion(m) k poiNteo (nt)

Fig. 6 Theoretical moment plotfor a virtual square

peg-in-hole simulation (peg z-rotation nearly

aligned with hole)

CWRU researchers were able to show the dramatic

effect that moments play in the human perception of

assembly by feel. Experiments were conducted

performing virtual-reality assemblies with a Cybernet force-reflecting hand controller. Mobility was restricted to the equivalent of a 4-dof SCARA

robot (i.e., wrist pitch and roll were fixed).

Computed interaction moments about the x and y

axes (e.g., as shown in Fig 6) were reflected back to the hand controller as the operator attempted the

virtual assembly. Without the benefit of vision, the

operator had to search blindly in x and y (and zrotation, for square pegs) for the insertion location.

For a small-clearance assembly, a blind search

without sensory feedback was typically unsuccessful

within 2 minutes per trial. However, when the

moment information was displayed haptically,

human operators were able to learn how to interpret

these signals and guide the peg to the assembly

location. For roimd pegs, the mean assembly time

for a trained operator using moment-feedback cues was less than 7 seconds.

While we can conclude that humans can exploit

perception of reaction moments to dramatically

improve assembly performance, it is not obvious how

these signals are being interpreted and utilized. To

415
help expose the essential features of such feedback

cues, the moment information was deliberately

corrupted before being presented to the user. Lowpass filtering, high-pass filtering and nonlinear

transformations were performed to determine the

influence on usability of the signals by humans.

Figure 7 shows a record of high-pass filtered

computed interaction moments presented to the

operator during an assembly trial. This limited

feedback was also successfully interpreted by

humans to achieve rapid successful assemblies

feedback was even fiirther corrupted to display

(exert) pulsed moments of fixed amplitude and

duration when the rectified, high-pass filtered

moment data exceeded a threshold. Remarkably,

humans were just as adept at keying off of this

impoverished feedback to achieve rapid assembly

success. Such experiments are helping to identify

the essential features of sensory feedback for expert,

sensor-based assembly.

This

y-monwTt iJitafortVBh-piH-niter«(t

Fig. 7 Highpassfiltered moment data is sufficientfor

humans to perform edge identification

In addition to testing which sensory cues are most

effectively utilized by humans, we can also observe

human assembly strategies from records of virtual

assembly trials. Figures 8 and 9 show records of

novice and trained human operators attempting the

teleoperated virtual assembly of a round peg in a round hole. Both the experts and novices exhibit the

behavior of attraction towards a sharp discontinuity

in reaction moments (the vertical line segment

separating large negative moments from large

positive moments). The expert operator utilizes this

discontinuity in a purposefiil manner. The expert

behavior shows an initial scan (apparently seeking

the discontinuity boundary) followed by tracking the

moment discontinuity boundary towards its apex. If the assembly location near the apex is overshot, the

operator detects the loss of signal and loops back to

restart the search within a small neighborhood of the

solution.

-aw -0.01 -i.o'i D.H D.M S.Ca

Fig.8 The path taken during an untrained assembly

trial.

tniectonsB (or tmned naerbon

Fig. 9 The path taken during an trained assembly

trial. Notice that this path is much more ofan

optimal trajectory to achieve assembly.

Based on such observations of human strategies for

assembly, researchers at CWRU have been able to construct a neural-net based controller that exhibits

similar sensory interpretation to perform guided

assembly. Using an NAC-controlled AdeptOne

robot equiped with a JRRR 6-axis force/torque

sensor, a map of reaction moments vs displacement

errors was experimentally obtained for a peg-in-hole

assembly task. The acquired raw data was used to

train a neural net to recognize assembly coordinates

from X and y moment signals.

416
After training, the quality of the learned mapping

was tested. Over a large region of displacements, the

mapping was untrustworthy. In contrast, over a small region, the mapping was reasonably reliable

and precise. Significant additional regions were

capable of coarse but useful predictions. These

results are illustrated in Fig 10. The sparse x's

indicate regions of high-quality predictive capability,

the triangles and squares are regions of coarse but

useful mappings, and the regions of diamonds and

circles correspond to poor predictive capability.

Following the strategy of human operators, our

intelligent controller incorporates a sensory-driven

behaviors. When in the low-quality mapping

regions, the robot is controlled to perform a compliant raster search for the hole. When useful sensor information becomes available, the robot

alters its search direction as indicated by the vectors

in Fig 10. The coarse information is sufficient to guide the robot towards the region of high

information, after which the robot can follow a

reasonably precise path towards the goal.

The raster-search phase is like the approach phase

of the human operator illustrated in Fig 9. In the

region of the moment discontinuity, the operator

(and the robot) are drawn towards the discontinuity

boundary. Upon reaching the discontinuity

boundary, both the human operator and the robot

follow this narrow region of high-quality sensory

information towards the goal.

The intelligent control algorithm was tested on an AdeptOne robot controlled by NAC. It demonstrated

searching behavior like that of humans for a simple

(large-clearance, round peg-in-hole) task. For

tighter clearances, however, the algorithm was not

sufficiently robust. Subsequently, sensory

interpretation was based on sensory patterns

(moments vs time) rather than point-by-point

measurements. With this augmentation, it was much

easier to recognize key features (peaks and

discontinuities) from a sequence of measurements, as opposed to depending on fortuitous samples within

the slim region of high-quality information. It seems

likely that humans perform similar processing,

interpreting moment feedback in terms of temporal

patterns rather than processing of distinct, pointwise measurements. (This would be consistent with,

but not deducible from the recorded trajectories of

Fig 10).

1

0 9

0.8

07

06

0.5

0.4

03

0,2

0.1

' ^O000yVV<J OVOOOo

(^OgOOO - oooOOOOO<a<]<l<|<|0, °o-ooooooo<j<i<a<ifloo'

*%o^ooO° O04<l<]<a<l

oo . ooooOOO<IO<l<<<]<|<|,Q |-,n° 0<>oooo000<l<<l<i<l<f»aDpjji0D

^00000000<<<1<<I<I<<| y 0 0 n

° °ooooOOO<l<l ]<<IO«oddtdR]X1

OooooooOO<l*3<< o 0 o o oooOO<<10 1<] °°ooooo<

soooOoooO<]«L r"ooooooOO

> 0(sO 0 0 0 0 0 0* '°o00oo

0^000000000 ^JOooOOOOOOo

<J < < <<]«»„

< / 9^ O O a yis a"sira°.

o o a fa ^ ^

^ a • *• • ^ " " " . • o_ cPd a

o o o° o o o o o c ^*0«o%«o° v."" 0 « 0 0 ° ° o o o o 0 o o o 0

° "O000000o0Oooooooooo^>o

(vOOO OOOOOOOOOOOOOO 000^^0°

Fig. 1 0 NN calculated hole center direction vectors

based on actual sensorfeedback. Note: Where

diamonds and circles are indicatedsensory cues are

unreliable, whereas x's indicate high-information

sensory data.

The above methods are promising for generating

sensory-driven soft attractor trajectories. In addition

to the goal directed manipulation of an attractor

point, it is also possible to change the end-point

dynamics of the robot by altering the spring and

damper coefficients. The alterations of these

parameters during the assembly process enable

complex capabilities, such as tracking a trajectory

quite stiffly (e.g. to approach a high-confidence

target location) followed by a sudden relaxation of

stiffness or damping parameters (e.g. to accommodate contact constraints while searching for an uncertain assembly location), as required by a

particular application.

Such work in sensory-driven, behavior-based

control may provide the foundation for realizing the

third important quality of expert assembly exhibited

by humans: the ability to improve performance

through experience. It is our hope that our

intelligent robot controller will be capable of

autonomous data collection and autonomous neuralnet training for automatic generation of programs for

new assemblies. Such capability would eliminate the

need for expensive, time-consuming robot

programming and would enable robots to acquire

expertise through experience

417
CONCLUSION

Attributes once thought only to belong exclusively

to humans have now been demonstrated on the NIST

FRAPA project using NAG robot controllers, vision,

and neural networks. A large class of applications

that have resisted automation due to positional

uncertainty being greater than assembly tolerances,

and the neeed to control the forces of contact of

workpeices manipulated by robotics are now feasible.

In the near future robots will be commonly

available that have the capacity to control their

forces of contact, acquire and manipulate parts in

uncertain locations and poses, and use trained neural

networks to accomplish a predefined goal.

Acknowledgements:

We thank NIST for providing the FRAPA Joint

Venture with the ATP award. Matt Collins of

Perceptron for his work in 3-D vision. David

Kozlowski from Sandia National Lab. The other

members of the FRAPA Steering Committee: Connie

Philips of the NCMS; Bob Stoughton of

MicroDexterity Systems; Mike Yoshonis of PICO;

Valerie Bolhouse, Frank Maslar, Steven Sparkes of

Ford Motor Company. Contributors at CWRU are Ravi Hebbar and Dan Morris on low level controls,

Yonghong Zhao, Craig Birkhimer and Prof YohHan Pao on force-guided strategies, and Prof

Michael Branicky. Siddharth Chhatpar, Yuesong

Wang, and Ittichote Chuckpaiwong.

References

1. Robotic Industries Assoc. 1999

2. Whitney, D. 1987, "Historical Perspective and State ofthe Art

in Robot Force Control, "International Journal ofRobotics

Research, pp. 3-14, vol. 6, no.l.

3. Newman, W. S. and Mathewson, B.B., "Integration of Force

Strategies and Natural admittance Control", Proceedings from

1994 International Mechanical Engineering Congress and

Exposition, Vol. 1 of 2, Nov. 1994

4. Newman, W.S. and Y. Zhang, "Stable Interaction Control and

Coulomb Friction Compensation Using Natural Admittance

Control". Journal ofRobotic Systems, 1994. 11(1).

5. Newman, W.S., "Stability and Performance Limits of

Interaction Controllers, " ASMEJ. Dynamic Systems, Measurement and Control, 1992. 114(4): p. 563-570.

6. Pipitone, Frank and Adams, William, "Rapid Recognition of Freeform Objects in Noisy Range Images Using Tripod

Operators", Navy Center for Applied Research and Artificial Intelligence (NCARAI), AIC-93-037, 1993

418
PART II

RESEARCH PAPERS

7. MULTIRESOLUTIONAL PHENOMENA IN INTELLIGENT SYSTEMS

7.1 Heterogeneous Computing

A. Wild, Motorola, USA

7.2 A Hierarchical Framework for Constructing Intelligent Systems Metrics

R. R. Yager, lona College, USA

7.3 Exploratory Analysis Enabled by Multiresolution, Multiperspective Modeling

P. Davis, RAND CA, USA

7.4 Definition and Measurement of Machine Intelligence

G. Saridis, RPl, USA

7.5 Domain Independent Measures of Intelligent Control D. Friediander, S. Phoha, A. Ray, The Pennsylvania State University, USA

7.6 Choosing Knowledge Granularity for efficient Functioning of an Intelligent Agent

Y. Ye, IBM T. J. Watson Research Center, USA

J.K. Tsotsos, York University, Canada

419

Heterogeneous Computing

A. Wild

Motorola, Phoenix, AZ 85018

ABSTRACT

It is often considered, explicitly or implicitly, that constructed

systems with autonomy must reflect, and potentially duplicate, the

perceived capabilities of human intelligence, including having the

ability to handle heterogeneity, e.g. to perform numerical computing,

as well as various types of non-numerical computing. Since our knowledge is getting obsolete, no matter how much wisdom will be

included in an intelligent system, it will loose its sharpness in time,

unless it can improve itself This would require changing system

domains and internal interfaces, and selecting architectures that would

support self-change.

KEYWORDS: Heterogeneity, non-numerical computing,

domain, interface, architecture.

1. INTRODUCTION

Several authors pointed out capabilities that an intelligent

system should possess, in addition to its ability to handle

numbers. According to Merriam-Webster On-line Thesaurus,

"compute" has as etymology the Latin "computare", from com- + putare, meaning "to consider", a much wider meaning that the contemporary usage of the verb "to compute", listed by

the same source as being: 1 : to make calculation; 2 : to use a computer.

If anybody had any doubts, this historically wider

perspective confirms that non-numerical computation is no oxymoron, but a legitimate area of research. However, when

considering an intelligent system, it is easy to see that the

question is not so much whether the right way to go is numerical or non-numerical computation : quite obviously,

they should both be present among the system capabilities.

Furthermore, "non-numerical" is a simplification,

reducing the number of cases to two, "tertium non datur".

Actually, the generic "non-something" will spontaneously

split into any number of " somethings". An intelligent system

needs to be able to handle "all of the above", and more.

This paper lists some questions that have been partially

addressed, from this perspective, and/or might be woilh

pursuing. It does not contain a corresponding list of answers,

as most of them are expected from future research. It is rather

initiating a whish list.

2. EVOLUTION

At any point in time, our understanding of the world is imperfect, as it is: incomplete, not being able to explain all we observe

contradictory, containing different and mutually

incompatible explanations of the same facts

partially wrong, as many explanations currently accepted

are likely to be falsified in the future. We may try to incorporate all our knowledge at this point

in time, or any part thereof we think appropriate, into a constructed system. But, sooner or later, unavoidably, it will become hopelessly outdated, unless it would have a built-in

capability to progress. Obviously, this capability would be an

important feature even at lower levels of resolution, and for simpler tasks, than maintaining an internal image of the world.

Of particular interest would be whether the system could

evolve in synchronism with our understanding of the human

mind, as the theories about the human mind provide a major

source of inspiration for constructed systems. Their evolution

would surely add new content to be implemented in constructed systems. But, in general terms, this would be the

effect of any advancement, in any area of knowledge.

How can a constructed system evolve, without human

intervention ? Can it include domains that became relevant

after the system was built ? Can and should it modify or eliminate some of the domains implemented at its "birth", that turn out as being wrong or irrelevant ?

3. SELF-STRUCTURING

A particular aspect of the evolution is the capability to modify

the interactions between the parts of the system. In an evolving system, it is to be expected that the information

exchange between domains would have to be adjusted

continuously, "on the fly".

As a particular example, when evolving, the system must

be able to define new interfaces between its parts, be they old

or new. The changes may be initiated in response to different

needs: eliminate computational bottlenecks, add new

capabilities, eliminate useless parts of the system, etc. Even if no domains are added or eliminated, the system may determine

that a different structure would have desirable advantages, and

would take advantage of it by re-defining its own partitioning,

information flow etc.

Is there a way for a system to control interfaces among

its own sub-systems, e.g. define new ones, eliminate or modify

existing ones ? How can a system re-architect itself?

421
4. ARCHITECTURE, HIERARCHY

For some time, scientists pursued the idea that everything can be reduced to simple axioms, from which we would derive the

apparent complexity of our surroundings. Still useful in particular disciplines, this hope has been largely replaced by a view accepting complexity as a fundamental feature of the

world.

Usually, we handle complexity by introducing hierarchy.

At each level, simpler concepts can be used to describe

observations, while rules and procedures are established for

crossing the boundaries from one hierarchical level to the next

one.

In heterogeneous computing systems, each domain is

likely to have some hierarchy, but assembling domains

presents difficulties even in relatively simple cases. For

instance, the hierarchical design database of an integrated

circuit would not result automatically from merging hierarchical

sub-circuits. If the same objects are rooted at different levels in the sub-circuits, the assembly will have most objects present at most hierarchical levels. Connections between sub-circuits will cross hierarchy borders, if the inputs/outputs of sub-circuits

are at different levels. Various views of the objects in the

database, such as timing, physical construction, etc., would

not be propagated automatically up the hierarchy levels. To

make merging possible without diluting or destroying the

hierarchy, the designers of the sub-circuits must follow

common, rigid rules. Alternatively, the system should

automatically re-structure domain hierarchy. Today, systems

do that only for the trivial case of one single level (flat hierarchy, actually no hierarchy !). The problem is obviously more complicated when the system includes heterogeneous parts. Human programmers,

exploring ad-hoc possibilities for connecting different domains

of numerical computing, introduce transition domains,

implementing rules for connecting space, time and parameters,

e.g. by using interpolation/extrapolation rules in space, running the local times in lock step, and coding equations for

parameters.

In more general terms, the requirement for an intelligent

system would be to connect domains of various types of nonnumerical computation, both with each other, and with

domains of numerical computation.

How could a system be architected such that heterogeneous elements, like different types of computation

(reasoning ?), may coexist, interact and add value to each other

? What would the interfaces between its domains be looking

like ?

5. ONE, TWO, MANY

An intuitive way to build a system capable of acting upon

itself is to architect it as a two-part structure: the first part

addresses the tasks at hand, while the second part is optimizing the first part, acting like a conscience, or an ego of

the system. This architecture seems to be able to ensure

capabilities like evolving the first part of the system, or re- structuring it. Another principle, probably much easier to envision than

to implement, could build upon the paradigm of decentralization. Any domain, facing difficulties in solving a

task, would be entitled to start a browser, searching for useful

capabilities in other domains. If the answer seems positive,

interfaces would be put in place to connect the discovered

resource with the domain trying to solve the task. If the

browser does not provide a useful answer, a "generate

domain" function could be started to fill the gap.

is a non-hierarchical, self-configuring, heterogeneous

system at all possible ? If yes, are there any rules to follow, are there impossible situations to avoid, or, alternatively, anything

goes, and the solutions will be selected by trial and error ? Can

this happen across hierarchical boundaries without generating

unbearable chaos ?

6. IDENTITY, DREAMS

Allowing every domain to take the initiative in changing the system seems risky (yet democracy mostly works !). Clearly, in

a two-part architecture, one part remains untouched, and can assume the task to ensure the stability of the system. In a decentralized system, there may still be a need to define some

parts as "untouchable", and a boundary might be needed to separate them from the parts that can be changed.

For one thing, the decentralized process envisioned

above is likely to accumulate, over time, numerous useless

connections, unnecessary search results, and other byproducts. This suggests that the system would develop a need

for a cleaning procedure. The system would "go to sleep",

while running procedures that would tide it up. While

"sleeping", it would be going through a sequence of abnormal

states, strange and seemingly useless, that could be

metaphorically called "dreams". The control of the system

could be provided by a relatively simple and unintelligent

mechanism, forcing it to undergo circadian cycles.

This line of thinking, this model and this metaphor may

seem excessively anthropomorphic. Nonetheless, there may be

sufficient reasons to allow for it, among other representations,

in a system truly capable to handle all types of heterogeneous

computing.

422
A Hierarchical Framework for Constructing Intelligent Systems

Metrics

Ronald R. Yager

Machine Intelligence Institute

lona College

New Rochelle, NY 10801

ABSTRACT

The focus of this work is on the development of a tool

to enable the construction of performance metrics for

intelligent systems which allows for the expression of

intelligence in terms of high level concepts while allowing

for the evaluation in terms of more basic measurable

attributes.

1. Introduction

The measurement of performance of intelligent

systems is clearly a context dependent process. This

type of evaluation strongly depends upon the purpose

for which the system is being used and the types of

"intelligence" it is required to manifest.. However

independent of the context the construction of such

performance metrics requires the ability represent

sophisticated human concepts needed to describe the

various aspects of intelligence. While the expression of

what constitutes intelligent performance may involve

high level cognitive concepts the actual calculation of

performance must be based upon measurable attributes

associated with the system. The focus of this work is on the development of a tool to enable the construction

of performance metrics for intelligent systems which

allows for the expression of intelligence in terms of

high level concepts while allowing for the evaluation in terms of more basic measurable attributes. This

framework, which makes considerable use of the Ordered

Weighted Averaging (OWA) operator [1, 2], also

supports a hierarchical structure which allows for an

increased expressiveness.

2. A General Approach to Aggregation

Central to any tool used for construction of

intelligent systems metrics is the need for the

aggregation of scores. In order to provide a very general

framework to implement aggregations, we shall use the

Ordered Weighted Averaging (OWA) operator [1,2]. In

the following, we briefly review the basic ideas

associated with this class of aggregation operators.

Definition: An Ordered Weighted Averaging (OWA)

operator of dimension n is a mapping F which has an

associated weighting vector W such that its components

Wj satisfy the following conditions ' • wj e [0,1] and

" n

2. = 1 and whereF(aj, a2,..., a^) = X b: j=l j=l

with bj being the j^'^ largest of the aj

A key feature of this operator is the ordering of the

arguments by value, a process that introduces a nonlinearity into the operation. Formally, we can

represent this aggregation operator in vector notation as

F(ai, 32,..., a^,) = W^ B, where W is the weighting

vector and B is a vector, called the ordered argument

vector, whose components are the bj. Here we see the

nonlinearity is restricted to the process of generating B.

It can be shown that this operator is in the class of

mean operators as it is commutative, monotonic, and

bounded, Min[ai] < F(aj, a2,..., a^) < Max[ai]. It can

also be seen to be idempotent, F(a, a,..., a) = a. The great generality of this operator lies in the fact that by selecting the wj, we can implement many

different aggregation operators. Specifically, by

appropriately selecting the weights in W, we can emphasize different arguments based upon their position

in the ordering. If we place most of the weights near

the top of W, we can emphasize the higher scores, while placing the weights near bottom of W emphasizes

the lower scores in the aggregation.

A number of special cases of these operators have

been pointed out in the literature [3]. Each of these

special cases is distinguished by the structure of the

weighting vector W. Consider the situation where the

weights are such that W) = 1 and wj = 0 for all j ^^^^l

this weighting vector is denoted as W . In this case we

get F(a

J

, 32,..., a^) = Maxj[aj]. Thus the Max operator

is a special case of the OWA operator. If the weights

are such that Wj, =1 and wj = 0 for j n, denoted W*,

we get F(ai, 32,..., aj^) = Minj[3j]. Thus the Min

423
operator is a special case of the OWA operator. If the

weights are such that w; = -L for all j, denoted W^yg,

n

then F(aj, a.2,--, = — X Thus we see that the " j=l

simple average is also a special case of these operators.

If W = w[k] is such that wj^ = 1 and wj = 0 for j

k,then F(a|, a2,..., a^) = b^,, the kth largest of the aj.

The median is also a special case of this family of

operators. If n is odd, we obtain the median by

selecting Wj^_|^j = 1 and by letting wj = 0, for j ^

2

If n is even, we get the median by selecting wn

2 2 = w = J- and letting w: = 0 for all other terms, n + 1 2

J 2

An interesting class of these operators is the so- called Olympic aggregators. The simplest example of

this case is where we select wj = Wp = 0 and let wj - —J— for j 5i 1 or n. In this case, we have eliminated n - 2

the highest and lowest scores and we've taken the

average of the rest. We note that this process is often

used in obtaining aggregated scores from judges in Olympic events such as gymnastics and diving.

In [1], we introduced two measures useful for

characterizing OWA operators. The first of these

measures, called the alpha value of the weighting

1 ^ vector, is defined as a = , X (n - j) w;. It can be

n - 1 . , J

shown, a e [0, 1]. Furthermore, it can also be shown

that if W = W* then a = 1 , if W = W^vg then a = 0.5

and if W = W^ then a = 0.

Essentially a provides some indication of the

inclination of the OWA operators for giving more

weight to the higher scores or lower scores. The closer

a is to one, greater preference is given to the higher

scores, the closer a is to zero, the greater preference is given to lower scores, and a value close to 0.5 indicates no preference. The actual semantics associated with a

depends upon the application at hand. For example, in using the OWA operators to model logical connectives

between the and and or, a can be associated with a measure of the degree of oniess associated with an

aggregation. .

It can be shown that while a = 1 only if W W

and a = 0 only if W = W*, other values of a can be

obtained for many different cases of W. A particularly

interesting case is a = 0.5. It can be shown that for

any OWA operator having a W with Wp_j^j = wj for

all j, we get a = 0.5. Thus we see any symmetric

OWA operator has a = 0.5. Essentially these operators

are in the same spirit as the simple average.

The second measure introduced in [ 1 ] was Disp(W) = - —l—Wj In(wi).

n-1 ^

^

In [1] it was suggested that this measure can be used to measure the degree to which we use all the information

in the argument. It can be shown that for all W

0 < Disp(w) < ln(n).

We note Disp(w) = 0 iff W = W(]^) and Disp(w) = ln(n)

iff W =W^yg. It can be shown that of all the symmetric

implementations of W, those having a = 0.5 (W^yg)

has the largest measure of Disp.

3. Linguistic Description of OWA

Operators

Let us now consider a basic application of the

OWA operator. Assume Aj, A2,...., A^ is a collection

of measurable attributes useful in characterizing

intelligence in a system. For any given system d, let Aj(d) 6 [0, 1] indicate the degree it satisfies the property

associated with attribute Aj. Using the OWA operator

we can obtain a measure of satisfaction to this collection of attributes as Val(d) = F^(Ai(d), A2(d),

A^Cd)). Since the value obtained as a result of using the

OWA aggregation is dependent upon the weighting

vector, the issue of deciding upon the weighting vector appropriate for a particular aggregation is of great

importance. One of the beneficial features of the OWA

operator is the considerable number of different

approaches that have been suggested for obtaining the

weighting vector to use in any given application [4]. Of particular significance is the strong semantic

underpinning of these approaches. This strong semantic

connection allows users to easily translate their

requirements, which may be expressed in many different

ways, into appropriate OWA weighting vectors. Here

we shall describe an approach based upon the idea of

linguistic quantifiers.

The concept of linguistic quantifiers was originally

introduced by Zadeh [5]. A linguistic quantifier, more

424
specifically a proportional linguistic quantifier, is a term corresponding to a proportion of objects. While

most formal systems, such as logic, allow just two

quantifiers, /or a// and there exists, as noted by Zadeh,

human discourse is replete with a vast array of terms,

fuzzy and crisp, that are used to express information

about proportions. Examples of this are most, at

least half, all, about -. Motivated by this Zadeh [5]

3

suggested a method for formally representing these

linguistic quantifiers. Let Q be a linguistic expression

corresponding to a quantifier such as most. Zadeh

suggested representing this as a fuzzy subset Q over I =

[0, 1] in which for any proportion r e I, Q(r) indicates

the degree to which r satisfies the concept indicated by

the quantifier Q.

In [6] Yager showed how we can use a linguistic

quantifier to obtain a weighting vector W associated

with an OWA aggregation. For our purposes we shall

restrict ourselves to regularly increasing monotonic

(RIM) quantifiers. A fuzzy subset Q : I —> I is said to represent a RIM linguistic quantifier if: I. Q(0) - 0, 2. Q(l) = I and 3. if r| > r2 then Q(r| ) > Q(r2)

(monotonic)

These RIM quantifiers model the class in which an increase in proportion results in an increase in compatibility to the linguistic expression being

modeled. Examples of these types of quantifiers are at

least one, all, at least a %, most, more than a few, some.Assume Q is a RIM quantifier. Then we can

associate with Q an OWA weighting vector W such that

for j = 1 to n w - Q(i) - Q(J— ). J n n Thus using this approach we obtain the weighting

vector directly from the linguistic expression of the

quantifier. The properties of RIMness guarantee that the properties of W are satisfied. Let us look at the situation for some prototypical

quantifiers. The quantifier /or all is shown in figure

#1 . In this case we get that wj = 0 for j n, and w^, =

1 , W = W^ In this case we get as our aggregation the

minimum of the aggregates. We also recall that the

quantifier/o/- a// corresponds to the logical "anding" of

all the arguments

In figure #2 we see the existential quantifier, not

none. In this case W| =1 and wj = 0 for j > I, W =

W*. This can be seen as inducing the maximum

aggregation. It is recalled this quantifier corresponds to a logical oring of the arguments

I

1

Figure #1. Linguistic quantifier "for all"

1

Figure #2. Linguistic quantifier "not none"

Figure #3 is seen as corresponding to the quantifier at least a. For this quantifier wj = 1 for j such that

< a < J- and w: = 0 for all other

n n J

I

a

I

Figure #3. Linguistic quantifier "at least a"

Another quantifier is one in which Q(r) = r. Here

J we get Wj = ^

J n

j- 1 _ 1 for all j. This gives us n n

the simple average. We denote this quantifier as some. As discussed by Yager [3] one can consider

parameterized families of quantifiers. Consider the

parameterized family Q(r) = r^, where p g [0, °o]. If p =

0, we get the existential quantifier; if p = oo, we get

for all and when p = I, we are get the quantifier soine.

In addition for the case in which p = 2, Q(r) - r^, we get one possible interpretation of the quantifier most.

425
As a result of the ideas so far presented here we can

introduce the idea of a basic intelligence measuring

module (IMM): <A], A2,.... A^: Q>, consisting of a

collection of attributes and a linguistic quantifier

indicating the proportion of the attributes we desire.

Implicit in this module is the fact that the linguistic

expression Q is essentially defining a weighting vector W for an OWA aggregation.

4. Including Attribute Importance

In the preceding we have indicated an IMM as consisting of a collection of attributes of interest and a quantifier Q indicating a mode of interaction between

the attributes. Implicit in the preceding is the equal

treatment of all attributes. For the construction of some intelligent systems measures we may need to ascribe differing importances to the attributes [4, 6]. In the following we shall consider the introduction of

importance weights into our procedure.

Let Qj 6 [0, 1 ] indicate the importance associated

with attribute Aj. We assume aj = 0 indicates zero

importance With the introduction of these weights we

can now consider a more general metric:

<Ai, A2,...., Ap: M: Q>.

Here as before, the Aj are a collection of attributes and

Q is a linguistic quantifier, however, here M is an n vector whose component mj =aj, is the importance

associated with Aj.

Our goal now is to calculate the overall score of a system d as Val(d) = FQ/j^(Ai(d), A2(d),...., A^Cd)).

Here Fq/j^ indicates an OWA operator. Our agenda

here will be to first find an associated OWA weighting

vector, W(d), based upon both Q and M. Once having

obtained this vector we calculate Val(d) by the usual n OWA process) = W(d)T B(d) = X Wj(d) bj(d). Here

j=l

bj(d) is the j^^ largest of the Aj(d) and Wj(d) is the j^^

component of the associated OWA vector W(d)

What is important to point out here is that, as we

shall subsequently see, the weighting vector will be

influenced by the ordering of the Aj(d).

We now describe the procedure [4, 6] that shall be

used to calculate the weighting vector, Wj(d). The first

step is to calculate the ordered argument vector B(d)

such that bj(d) is the j'^" largest of the Aj(d).

Furthermore, we shall let )ij denote the importance

weight associated with the attribute that has the j*^*^

largest value. Thus if A^Cd) is the largest of the Aj(d),

then b|(d) = A5(d) and uj = o.^. Our next step is to

calculate the OWA weighting vector W(d). We obtain

Si Si 1 the associated weights as Wj(d) = Q(—) - Qi— ) J J ^

j

where S; = X ^""^ ^ ~ ^n- ^

k=l

importances and Sj is the sum of the importances of the

j''^ most satisfied attributes. The following example

will illustrate the use of this technique.

Example: Assume there are four attributes: Aj, A2,

A3, A4. The importances associated with these criteria

are uj = 1, U2 = 0.6, U3 = 0.5 and U4 = 0.9, giving us T= 3. We shall assume the quantifier guiding this

aggregation is most, which is defined by Q(r) = r^. Assume we have two system we are comparing, x and

y, and the satisfactions to each of the attributes are: Ai(x) = 0.7, A2(x) = 1, A3(x) = 0.5 and A^x) = 0.6

Ai(y) = 0.6, A2(y) = 0.3, A3(y) - 0. and A^y) = I We first consider the valuation for x. In this case the

ordering of the criteria satisfactions gives us:

^2 1 0.6

Al 0.7 1

0.6 0.9

A3 0.5 0.5

Calculating the weights associated with x, we get:

wi(x) = 0.04, W2(x) = 0.24, W3(x) = 0.41 and W4(x) =

4

0.31. Using this Val(x) = X Wj(x) bj = 0.609.

j=l

To calculate the score for y we proceed as follows. In

this case the ordering of the criteria satisfaction is

"j

A4 I 0.9

A3 0.9 0.5

Al 0.6 1 A2 0.3 0.6

The associated weights are wjCy) = 0.09, W2(y) = 0.13,

W3(y) = 0.42and W4(y) = 0.36 we then calculate

426
Val(y) = X Wj(y) bj = 0.567. Using this metric we

j=l

see that system x would be deemed more intelligent.

More details with respect to the properties of this methodology can be found in [4, 6], however here we

shall point out some properties associated with this approach. It can be shown that any attribute that has

importance weight zero no affect on the result. Consider the situation when all the attributes have

the same importance, aj = a. In this case Wj(d) =

J j-1 • • , Q(-L X a) - Q( — X a) = Q(-L) - Q(I^).

This is the same set of weights we obtained when we

didn't include any information with respect to importance.

Let us now look at the form of aggregation

function obtained for some special cases of linguistic

quantifiers. In the following we shall assume, without

loss of generality, that the indexing is such that Ai(d) >

Aj(d) if 1 < j. Furthermore we shall suppress the d and

denote A^{d) - aj. Using this notational convention

n Val(d) FQ/oj(ai, 32 a^) = X

j j-1

where wj = Q(l X «k) - Q(- X «k)

Consider first the quantifier some , Q(r) = r. For

a; 1 ^ this quantifier w; = ^

and hence Val(d) = ^ ^ ot; a;

1

J =1

This is simply the weighted average of the attributes. Consider now the case of the quantifier for all, Q(l) 1 and Q(r) = 0 for r 1. In this case Wj = 0

j j-1

unless X otk ~ ^ X ciC|^ < T. We see wj = 1 for

k=l k=l

the attribute having the smallest satisfaction and nonzero importance, hence Val(d) = Min [a:]. For the case

of the existential quantifier, Q(0) = 0 and Q(r) - 1 for

all r^O, we can easily show that Val(d) = Max [a;]

Another example of a quantifier is the median

quantifier. Here Q(r) = 0 for r < 0.5 and Q(r) = 1 for r >

0.5. In this case it can be shown that Val(d) can be

obtained by the following simple process. First

^ a; we normalize the weights, a: = —i. Next we order the

J J

attribute scores in descending order and associate with

each its normalized weight. We then, starting from the

top, the highest score, add the normalized weights until we first reach a total of 0.5, the score of that attribute at which this total is reached is the aggregated value.

An interesting example of an OWA aggregation is the Olympic aggregation. Here W| = w^, = 0 and wj = —!— for j ^\ or n. Using this aggregation we

n - 2

eliminate the highest and lowest scores and then take

the average of the remaining scores. We can provide a generalization of this type of aggregation using a

quantifier shown in figure #4.

1

P 1-p 1

Figure #4. Generalized Olympic Quantifier

For this case

Q(r) = 0 r<p

r-p

Q(r) =

Q(r):

1 -2p

1 r > 1

p < r< 1 - p

Here wj = 0 for all j for which

J1

k=I

"k < p

Similarly,

w: = 0 for all j for which Y —!^ > 1 - p . In the

^ T k=j

range in between wj =

a;

l-2p

Another interesting example of OWA aggregation, one that is in some sense a dual of the Olympic

aggregation, is the so called Arrow-Hurwicz aggregation

[7]. Here wj = a and w^ = I - a, and wj = 0 for all

other. In this case we just consider the extreme values

and eliminate the middle values. We can provide a

generalization of this type of aggregation, one that can

be used with importance weighted attributes, by

introducing the quantifier shown in figure#5.

427
a /

P 1-p 1

Figure #5. Generalized Arrow-Hurwicz

For this quantifier: Q(r) =^ r if r < p, Q(r) = a if p < r P < 1 - p and Q(r) = 1 - (1 - r) if r > 1 - p. It is P assumed p < 0.5. For tiiis quantifier the weights used

in the OWA aggregation are such that for the highest

scoring attributes, those accounting for p portion of the

importance, w; = ^, for the least satisfied attributes,

J p

those accounting for p portion of the importance,

w i = ^ " ^ and the middle scoring attributes w: = 0. In

J p J

this quantifier a can be seen as a degree of optimism

and 1 - p as an indication of the extremism of the

aggregation. A number special cases of this quantifier are worth noting. If p = 0 then we have wj = a and

Wji = 1 - a, the basic Arrow-Hurwicz aggregation. If a = p = 0.5 then we get the quantifier Q(r) = r. If a = 1 then we get the quantifier at least p and if a = 0 then

we get the quantifier at least 1-p.

5. Including Priorities

In the preceding we have described a method for

measuring the intelligence of a system based upon the

metric <Aj, A2, A^: M: Q>where the component

aj of the vector M indicates the weight associated with

the attribute Aj. Implicit in our formulation was the

idea that the weight aj was explicitly provided by the

user. This is not necessarily required. It is possible for

the weight associated with attribute Aj to be determined

by some property of the system itself. Thus let Bj be some measurable attribute associated with a system, and

let Bj(d) be the degree to which d satisfies this attribute. Then without introducing any additional complexity we

can allow aj(d) = Bj(d). Thus here the weight

associated with attribute Aj depends the value Bj(d).

Thus within this framework we have the option of

specifying the importance weights conditionally or nonconditionally or not at all. Typically the association of importance weights

with attributes reflects some measure of trade-off

between the worth of the attributes. For example,

consider the averaging operator where Val(d) =

nX Aj(d) a;. We see that a gain of A in Aj(d) results j=l

J

in an increase in overall evaluation of ttj A, while a

gain of A in Aj(d) is worth an increase of aj A. In

particular, if aj = 2 and aj = 1, then we are willing to

trade a gain of A in Aj for a loss of less than 2A in Aj.

In some cases where we desire two attributes, we

may not be willing to trade-off one of for the other.

For example, in evaluating the performance of an

"intelligent" car, while we would like both safety and

mileage efficiency, we are not willing to give up safety

for efficiency. Such a situation implies the existence of

a priority between the attributes, safety has priority over cost. In the following we suggest a mechanism

for the inclusion of priority type relationships.

Assume A| and A2 are two attributes for which

there exists a priority relationship: Aj has priority over

A.2- In order to manifest this priority relationship, we

require that the importance associated with A2 be

dependent upon the satisfaction of attribute Aj by the

system being evaluated. Here then a2(d) = A j(d). Let

us investigate this idea for the simple weighted average.

Assuming aj is fixed, we get

Val(d) a1 A1 (d) A 1 (d) A2(d) = A j (d)(a

j A2(d))

Here we see that if Aj(d) is low, the contribution of

A2(d) becomes small and hence it is not possible for a

high value of A2 to compensate for a low satisfaction

to A| . Thus if a system scores low on Aj it will get a low rating.

More generally, consider the quantifier Q and assume Aj has priority over Aj. To implement this

priority we make the importance associated with Aj

related to the satisfaction of Aj. In particular, we let aj = a Aj, where a e [0, 1]. Using this we get for the

weight Wj associated with Aj that

Wj = Q(Sk.i+"^)-Q(Sk.i)

428
J-1

where S|^. ] = . We see that as Ai(d) gets

smaller, the value wj will decrease.

6. Concepts and Hierarchies

Throughout the preceding we have assumed a

collection of attributes characterized by the fact that for

any d we have available A[(d) e [0,1], we say that the

value of attribute Aj is directly accessible, we call Aj

ground attribute. We shall now introduce a more

general idea which we shall call an Intelligence

Measuring Concept (IM Concept). We define an IM Concept as an object whose measure of satisfaction can be obtained for any system d. It is clear that the

ground attributes are examples of concepts, they are special concepts in that their values are directly

accessible.

Consider now the intelligence measuring module of

the type we have previously introduced, it is of the form

<A|, A2, Aq! M: Q>. As we have indicated the

evaluation of this for any system d can be obtained

using our aggregation process. In the light of this observation, we can consider this object to be a IM

concept, with Con = <Ai, A2, . ., Aq: M: Q> then its

evaluation is Con(d) = FQ^(Ai(d), A2(d), Aq(d)).

A special concept is an individual attribute. Con = <Aj

: M: Q> = Aj,we shall call these atomic concepts.

These atomic concepts require no Q or M, but just need

an Aj specification.

The basic componentsat these IM Concepts are the

attributes, the Aj. However, from a formal point of

view, the ability to evaluate these type of concepts is based upon the fact that for any d we have a value Aj(d).

As we have just indicated a concept also has this property, for any d we can obtain a measure of its

satisfaction. This observation allows us to extend our

idea of IM concepts to allow for IM concepts whose

evaluation depends upon other concepts. Thus we can

consider IM concepts of the form

Con = <Coni, Con2, Con„: M: Q>.

Here each of the Couj are concepts used to determine the

satisfaction of Con by an aggregation process where M

determines the weight of each of the participating

concepts and Q is the quantifier guiding the aggregation

of the component concepts.

The introduction of concepts into the intelligence

measuring results in a hierarchical structure for the

construction of metrics. Essentially, we unfold until

we end up with atomic attributes which we can directly

evaluate. The following simple examples illustrate the

structure. Example: Consider here the measure

(A I and A2 and A3) or (A3 and A4).

We consider this as a concept <Con j, Con2 : M: Q>.

Here Q is the existential quantifier and M = ' In

addition

Conj = <Ai, A2, A3: M) : Q|>

Con2 = <A3, A4 : M2: Q2>

1

Here Q] = Q2 =all and M] = and M2 =

L 1

This can be expressed in a hierarchical fashion, see

figure #6.

0©

Figure *6. Hierarchical Formulation

An often used construct is the logical if ... then

specification expressing the desire for some attribute if some other attribute is present. In a the following we

describe a method for modeling this type of structure within our our framework.

429
Consider (Aj and A2) or (if A3 then A4).

Figure #7 provides its hierarchical expansion.

In constructing this hierarchical implementation,

we used the fact that "if A3 then A4" is logically

equivalent to "not(A3) or A4." Thus in this framework

we interpret the concept "if A then B" as the concept

A or B. We note that A(d) = 1 - A(d). More generally,

the expression

if Aj and A2 and A3 then B

is seen as equivalent to the expression A| or A2 or A3

or B. This is represented as concept of the form

<Ai A2 A3, B: -.Or>. We note the importances have

not been specified and hence by default are all assumed

to be one.

Figure #7. Implementation of if ... then

Using the ideas presented in the preceding we have

a tool that can be used to construct complex measures

of intelligent system performance. Using this tool we

start with a high level expression of the appropriate measure. We then decompose this expression into the

aggregation simpler concepts. We proceed in this manner until we obtain a characterization of our desired measure in terms of ground attributes which can be

directly measured.

7. References

[1]. Yager, R. R., "On ordered weighted averaging

aggregation operators in multi-criteria decision

making," IEEE Transactions on Systems, Man and

Cybernetics 18, 183-190, 1988.

[2]. Yager, R. R. and Kacprzyk, J., The Ordered

Weighted Averaging Operators: Theory and

Applications, Kluwer: Norwell, MA, 1997.

[3]. Yager, R. R., "Families of OWA operators,"

Fuzzy Sets and Systems 59, 125-148, 1993.

[4]. Yager, R. R., "On the inclusion of importances in OWA aggregations," in The Ordered Weighted

Averaging Operators: Theory and Applications, edited

by Yager, R. R. and Kacprzyk, J., Kluwer Academic

Publishers: Norwell, MA, 41-59, 1997.

[5]. Zadeh, L. A., "A computational approach to fuzzy

quantifiers in natural languages," Computing and

Mathematics with Applications 9, 149-184, 1983.

[6]. Yager, R. R., "Quantifier guided aggregation using

OWA operators," International Journal of Intelligent

Systems 1 1, 49-73, 1996.

[7]. Arrow, K. J. and Hurwicz, L., "An optimality

criterion for decision making under ignorance," in Uncertainty and Expectations in Economics, edited by

Carter, C. F. and Ford, J. L., Kelley: New Jersey,

1972.

430
EXPLORATORY ANALYSIS ENABLED BY MULTIRESOLULTION, MULTIPERSPECTIVE MODELING

Paul K. Davis

RAND and the RAND Graduate School

1700 Main St., Santa Monica, CA 90407-2138, U.S.A.

ABSTRACT

The objective of exploratory analysis is to gain a broad

understanding of a problem domain before going into details for particular cases. Its focus is understanding comprehensively the consequences of uncertainty, which re- quires a good deal more than normal sensitivity analysis.

Such analysis is facilitated by multiresolution, multiperspective modeling (MRMPM) structures that are becoming

increasingly practical. A knowledge of related design

principles can help build interfaces to more normal legacy

models, which can also be used for exploration.

1 BACKGROUND

Strategy problems are typically characterized by enormous

uncertainties that should be central in assessment of alter- native courses of action—although individuals and organizations often suppress those uncertainties and give a bizarre level of credence to wishful-thinking planning factors

and other simplifications (Davis, 1994; Ch. 4; Davis,

Gompert, and Kugler, 1996). In the past, an excuse for downplaying uncertainty analysis—except for marginal

sensitivity analysis around some "best-estimate" baseline

of dubious validity—was the sheer difficulty of doing better. The time required for setup, run, and analysis made

extensive uncertainty work infeasible. Today, technology

permits extensive uncertainty analysis with personal computers.

A key to treating uncertainty well is exploratory analysis

(Davis and Hillestad, 2001). The objectives of exploratory

analysis include understanding the implications of uncertainty for the problem at hand and informing the choice of

strategy and subsequent modifications. In particular, ex- ploratory analysis can help identify strategies that are

flexible, adaptive, and robust. In successive sections, this paper describes exploratory analysis; puts it in context; discusses enabling technology and theory; points to companion papers applying the ideas; and concludes with some

technology challenges for modeling and simulation. The

paper draws heavily on a forthcoming book (Davis and

Hillestad, 2001) and builds on a much rougher preliminary

presentation of the same material (Davis, 2000).

2 EXPLORATORY ANALYSIS

2.1 What Exploratory Analysis Is and Is Not

Exploratory analysis examines the consequences of uncertainty. It can be thought of as sensitivity analysis done

right, but is so different from usual sensitivity analysis as

to deserve a separate name. It is closely related to scenario

space analysis (Davis, 1994, Ch. 4) and "exploratory modeling" (Bankes, 1993; Lempert, et al., 1996). It is particularly useful for gaining a broad understanding of a problem

domain before dipping into details. That, in turn, can

greatly assist in the development and choice of strategies.

It can also enhance "capabilities-based planning" by clari- fying when—i.e., in what circumstances and with what

assumptions about all the other factors—a given capability

such as an improved weapon system or enhanced com- mand and control will likely be sufficient or effective

(Davis, Gompert, and Kugler, 1996). This contrasts with

establishing a base-case scenario, and an organizationally

blessed model and data base, and then asking "How does

the outcome change if I have more of this capability?"

2.2 Types of Uncertainty

Uncertainty comes in many forms and it is useful (National

Research Council, 1997) to distinguish between input uncertainties (i.e., parametric uncertainties) and structural uncertainty. Input uncertainty relates to imprecise knowledge of the model's input values. Structural uncertainty

relates to questions about the form of the model itself: Does it reflect all the variables on which the real-world

phenomenon purportedly described by the model depends?

Is the analytical form correct? Some uncertainties may be

inherent because they represent stochastic processes. Some may relate to fuzziness or imprecision, while others

reflect discord among experts. Some relate to knowledge

431
about the values of well-defined parameters, whereas others refer to future values that as yet have no true values.

It is convenient to express the uncertainties parametrically. if unsure about the model's form, we can describe

this also to some extent with parameters. For example,

parameters may control the relative size of quadratic and

exponential terms in an otherwise linear model. Or a dis- crete parameter may be a switch choosing among distinct

analytical forms. Some parameters may apply to the deterministic aspect of a model, others to a stochastic aspect.

For example, a model might describe the rate at which Red

and Blue suffer attrition in combat according to a simplistic Lanchester square law:

dt dt

where the attrition coefficients for Red and Blue have both

deterministic and stochastic parts, each of which are subject to uncertainty, as in (illustrating for Blue only)

^^(0= Kt,^{\+ChNi^{t\\i,(5,,)].

Here the N term is a normal random variable with mean

|j, and standard deviation a. It represents stochastic processes occurring within a particular simulated war, e.g., from one time period to the next. The means and standard

deviations are ordinary deterministic parameters, as are the coefficients Kbo, Cr, and Cb. These have constant values

within a particular war, but at what value they are constant

is uncertain.

So far the equations have represented input uncertainty.

However, suppose there is controversy over using the linear, square, or some hybrid version of a Lanchester equation. We could represent this dispute as input, or parametric, uncertainty by modifying the equation to read

^ = -k;B\t)~R'\t) ^=-k;B\t)~R\t).

dt dt

Now, by treating the exponents as uncertain parameters,

we could explore both input and structural uncertainties in the model—at least to some extent. The fly in the ointment

is that nature's combat equations are much more complex

(if they exist), and we don't even know their form. Suppose, merely as an example, that combatant effectiveness

decays exponentially as combatants grow weary. We

could not explore the consequences of different decay

times if we did not even recognize the phenomenon in the

equation's form. In fact, we often do not know the true system model. Nonetheless, much can be accomplished by

allowing for diverse effects parametrically.

2.3 Types of Exploratory Analysis

Exploratory analysis can be conducted in several ways

(Davis and Hillestad, 2001). Although most of the methods have been used in the past (see especially Morgan and

Henrion, 1992), they are still not appreciated and are often

poorly understood.

Input exploration (or parametric exploration) involves

conducting model runs across the space of cases defined by

discrete values of the parameters within their plausible

domains. It considers not just excursions taken one-at-a- time as in normal sensitivity analysis relative to some pre- sumed base-case set of values, but rather all the cases cor- responding to value combinations defined by an experimental design (or a smaller sample). The results of such

runs, which may number from dozens to hundreds of thousands or more, can be explored interactively with modem

displays. Within perhaps a half-hour, a good analyst doing

such exploration can often gain numerous important in- sights that were previously buried. He can understand not

just which variables "matter," but when. For example, he

may find that the outcome of the analysis may be rather

insensitive to a given parameter for the so-called base case of assumpfions, but quite sensitive for other plausible as- sumptions. That is, he may identify in what cases the pa- rameter is important. To do capabilities-based planning for complex systems, this can be distinctly nontrivial.

A complement to parametric exploration is "probabilistic

exploration " in which uncertainty about the input parameters is represented by distribution functions representing

the totality of one's so-called objective and subjective

knowledge. I sometimes use quotes around "probability"

because the distributions are seldom true frequencies or

rigorous Bayesian probabilities, but rather rough estimates

or analytical conveniences.

Using analytical or Monte Carlo methods, the resulting

distribution of outcomes can be calculated. This can

quickly give a sense for whether uncertainty is particularly

important. In contrast to displays of parametric exploration, the output of probabilistic exploration gives little visual weight to improbable cases in which various inputs all have unlikely values simultaneously. Probabilistic exploration can be very useful for a condensed net assessment. Note that this use of probability methods is different from

using them to describe the consequences of a stochastic

process within a given simulation run. Indeed, one should

be cautious about using probabilisdc exploradon because one can readily confuse variation across an ensemble of

possible cases (e.g., different runs of a war simuladon)

with variation within a single case (e.g., fluctuation from

day to day within a single simulated war). Also, an un- known constant parameter for a given simulated war is no

longer unknown once the simulation begins and simulation

agents representing commanders should perhaps observe

and act upon the correct values within a few simulated time

432
periods. Despite these subtleties, probabilistic exploration can be quite helpful.

The preferred approach treats some uncertainties para- metrically and others with uncertainty distributions. That

is, it is hybrid exploration. It may be appropriate to pa- rameterize a few key variables that are under one's own

control (purchases, allocation of resources, and so on),

while treating the uncertainty of other variables through

uncertainty distributions. One may also want also to pa- rameterize a few variables characterizing the future context

in which strategy must operate (e.g., short warning time).

There is no general procedure here; instead, the procedure

should be tailored to the problem at hand. In any case, the

result can be a comprehensible summary of how known

classes of uncertainty affect the problem at hand.

Let me give a few examples of what exploratory analysis can look like. Figure 1 mimics a computer screen during a parametric exploration of what is required militarily to defend Kuwait against a future Iraqi invasion by interdicting the attacker's movement with aircraft and missiles

(Davis and Carrillo, 1997). Each square denotes the out- come of a particular model case (i.e., a specific choice of

all the input values). The model being used depends on 10

variables-those on the x, y, and z axes, and seven listed to the side (the z-axis variable is also listed there, redundantly). The outcome of a given simulation is represented

by the color (or, in this papei, by the pattern) of a given

square. Thus, a white square represents a good case in which the attacker penetrates only a few tens of kilometers

before being halted. A black square represents a bad case

in which the attacker penetrates deep into the region that contains critical oil facilities. The other patterns represent

in-between cases. The number in each square gives the

penetration distance in km.

To display results in this way for a sizable scenario space

RAND has often used a program called Data View, developed at RAND in the mid 1990s by Stephen Bankes and

James Gillogly. After running the thousands or hundreds

of thousands of cases corresponding to an experimental

design for parametric exploration, we explore the outcome

space at the computer. We can choose interactively which

of the parameters to vary along the x, y, and z axes of the

display. The other parameters then have the values shown

along the right. However, we can click on their values and

change them interactively by selecting from the menu of

values for cases that have been run. As mentioned above, in about a half an hour of such in- teractive work, one can develop a strong sense of how out- comes vary with combinations of parameter values. This is much more than traditional sensitivity analysis. Moreover, one can search out and focus upon the "good" cases. Fig- ure 1 is merely one schematic snapshot of the computer

screen for choices of parameter values that show some

successes. Most snapshots would be dominated by black

squares because it is difficult to defend Kuwait against a

large threat. Data View is not a commercial product, but

RAND has made it available to government clients and some other organizations (e.g., allied military staffs).

Speed (Km/day)

60 _r

3&

Vehicles to Kill Attacker Speed

D-Day Shooters

(fixed-wing) D-Day Shooters

(helos) Deployment

Rate SEAD Time

Effect, Factor for Tacair during SEAD

Effect, Factor for helos during SEAD

fyleasure of Outcome

(determines shading

of square)

16

Figure 1 : Display of Parametric Exploration

Other personal-computer tools can be used for the same

purpose and the state of the art for such work is advancing

rapidly A much improved version of Data View called

CAR^'^ is under development by Steve Bankes at Evolving

Logic (www.evolvinglogic.com). For those who

prefer spreadsheet modeling, there are plug-in programs

for Microsoft EXCEL® that provide statistical capabilities

and some means for exploratory analysis. Two of them are Crystal Ball® ( www . decisioneering . com ) and @Risk®

( www . palisade . com/html/risk . html

. For a number

of reasons such as visual modeling and convenient array mathematics, 1 usually prefer the Analytica® modeling

system (the exception is when one needs procedural pro- gramming). Analytica ( www . lumina . com ) is an out- growth of the Demos system developed at Carnegie Mellon

University (Morgan and Henrion, 1992).

Figure 2 shows a screen image from recent work with

Analytica on the same problem treated in Figure 1. In this case, we have a more traditional graphical display. Outcome is measured along the Y axis and one of the independent variables is plotted along the X axis. A second

variable (D-Day shooters) is reflected in the family of

curves. The other independent variables appear in the rotation boxes at the top. As with Data View, we change

parameter values by clicking on a value and selecting from

a menu of values. Such interactive displays allow us to "fly through the outcome space" for many independent

parameters, in this case 9. For this number, the display was still quickly interactive for the given model and computer (a Macintosh PowerBook G3 with 256 MB of RAM).

So far, the examples have focused on parametric exploration. Figure 3 illustrates a hybrid exploration (Davis, et

al., 1998). It shows the distribution of simulation out433
comes resulting from having varied most parameter values

"probabilistically" across an ensemble of possible wars, but with warning time and the delay in attacking armored

columns left parametric.

H

MKK (MM n.

Figure 2: Analytica Display of Parametric Exploration

The probabilistic aspect of the calculation assumed, for

example, that the enemy's movement rate had a triangular

distribution across a particular range of values and that the

suppression of air defenses would either be in the range of

a few days or more like a week, depending on whether the

enemy did or did not have air-defense systems and tactics that were not part of the best estimate. We represented this possibility with a discrete distribution for the likelihood of

such surprises. The two curves in Figure 3 differ in that the one with crosses for markers assumes that interdiction

of moving columns waits for suppression of air defenses

(SEAD). The other curve assumes that interdiction begins

immediately because the aircraft are assumed stealthy.

This depiction of the problem shows how widely the out- comes can vary and how the outcome distribution can be

complex. The non-stealthy-aircraft case shows a spike at the right end where cases pile up because, in the simulation, the attacker halts at an objective of about 600km.

Note that the mean is not a good metric: the "variance" is huge and the outcome may be multimodal.

These results have been from analyses accomplished in recent years for the Department of Defense. As we look to the future, much more is possible with computational tools. Much better displays are possible for the same information

and, even more exciting, computational tools can be used

to aid in the search process of exploration. For example.

instead of clicking through the regions of the outcome

space, tools could automatically find portions of the space

in which particular outcomes are found. One could then

fine-tune one's insights by clicking around in that much

more limited region of the outcome space. Or, if the model

is itself driven by the exploration apparatus, then the apparatus could search for outcomes of interest and then focus

exploration on those regions of the input space. That is, the experimental design could be an output of the search

rather than an input of the analysis process. These methods are at the core of the evolving tool mentioned earlier called CAR (for Computer-Assisted Reasoning).

Stif

Kwr

in Hi iu <et

IUt£r 'imt Utfi'-

Figure 3: Analytica Display of "Probabilistic" Exploration

3 EXPLORATORY ANALYSIS IN CONTEXT

Exploratory analysis is an exciting development with a long history with RAND's RSAS and JICM models. How

ever, it is only one part of a sound approach to analysis

generally. It is worth pausing to emphasize this point.

Figure 4 shows how different types of models and simulations (including human games) have distinct virtues. The

figure is specialized to military applications, but a more

generic version applies broadly to a wide class of analysis

problems.

White rectangles indicate "good;" that is, if a cell of the

matrix is white, then the type model indicated in the left column is very effective with respect to the attribute indi- cated in the cell's column. In particular, analytical models

(top left comer), which have low resolution, can be especially powerful with respect to their analytical agility and

breadth. In contrast, they are very poor (black cells) with

respect to recognizing or dealing with the richness of un434
derlying phenomena, or with the consequences of both

human decisions and behavior. In contrast, field experiments often have very high resolution (they may be using

the real equipment and people), and may be good or very

good for revealing phenomena and reflecting human issues. They are, however, unwieldy and inappropriate for

studying issues in breadth. The small insets in some of the

cells indicate that the value of the type model for the particular purpose can often be enhanced a notch or two if the

models include sensible decision algorithms or knowledgebased models that might be in the form of expert systems

or artificial-intelligence agents.

Richness of Reso- Analytical Decision Integra- Pheno- Human

Type Model Agility Breadth support tion

Analytical Human game

Campaign

Entity-level

Field expt.

lution mena actions

Low

Low

Med.

High

High

Figure 4: Virtues of a Model and Gaming Family

Figure 4 was developed as part of an exhortation to the

Department of Defense regarding the need to have families

of models and families of analysis (Davis, Bigelow, and

McEver, 1999), Unfortunately, government agencies often

focus on a single model such as the venerable TACWAR,

BRAWLER, or JANUS.

The niche of exploratory analysis is the top left hand cor- ner of the matrix in Figure 4, which emphasizes analytical

agility and breadth of analysis, rather than depth. However, the technique can be used hierarchically if one has a

suitably modularized system model. One can do top-level

exploration first and then zoom in. This is easier said than

done, however, especially with traditional models. Specially designed models make things much easier, as discussed in what follows.

4 TECHNOLOGICAL ENABLERS

Quite aside from setup-and-run-time issues, comprehending and communicating the consequences becomes very

difficult if M is large. Suppose someone asked "Under

what conditions is F less than the danger point?" Given

sufficiently powerful computers and enough time, we

could create a data base of all the cases, after which we

could respond to the question by spewing out lists of the cases in which F fell below the danger point. The list, however, might go on for thousands of pages. What would

we do with the list? This is one manifestation of the curse of dimensionality.

4.2 The Need for Abstractions

It follows that, even if we have a perfect high-resolution

model, we need abstractions to use it well. And, in the

dominant case in which the high-resolution model is by no means perfect, we need abstractions that allow us to ponder

the phenomena in meaningful ways, with relatively small

numbers of cognitive chunks. People can reason with 3, 5, or 10 such cognitive chunks at a time, but not with hundreds. If the problem is truly complex, we must find ways

to organize our reasoning. That is, we must decompose the

problem by using principles of modularity and hierarchy.

The need for an aspect of hierarchical organization is inescapable in most systems of interest—even though the sys- tem may be highly distributed and relatively nonhierarchical in an organizational sense. A corollary of our need for abstractions is that we need

models that use the various abstractions as inputs. It is not

sufficient merely to display the abstracts as intermediate

outputs (displays) of the ultimate detailed model. The rea- sons include the fact that when a decision maker asks a what-if question using abstractions, there is a l:n mapping

problem in translating his question into the inputs of a more detailed model. So also when one obtains macroscopic empirical information and tries to use it for calibration. Although analysts can trick the model by selecting a mapping, doing so can be cumbersome and treacherous. It

is often better if the question can be answered by a model

that accepts the abstractions as inputs.

4.1 The Curse of Dimensionality

In principle, exploratory analysis can be accomplished with

any model. In practice, it becomes difficult with large

models. If F represents the model, it can be considered to be simply a complicated function of many variables. How

can we run a computerized version of F to understand its character? If F has M inputs with uncertain values, then

we could consider N values for each input, construct a full factorial design (or some subset, using an experimental

design and sampling), run the cases, and thereby have a

characterization. However, the number of such cases would grow rapidly (as N"^ for full-factorial analysis),

which quickly gets out of hand even with big computers.

4.3 Finding the Abstractions

Given the need for abstractions, how do we find them and

how do we exploit them? Some guidelines are emerging

(Davis and Bigelow, 1998).

4.3.1 When Conceiving New Models and Families

With new models, the issue is how to design. Several options here are as follows:

• Design the models and model families top down so

that significant abstractions are built in from the start, but do so with enough understanding of the microscopies so that the top-down design is valid.

435
• Design the models and families bottom up, but with

enough top-down insight to assure good intermediatelevel abstractions from the start.

• Do either or both of the above, but with designs taken

from different perspectives.

The list does not include a pure top-down or pure bottom-up design approach. Only seldom will either generate

a good design of a complex system. Note also the idea of

alternative perspectives. For example, those in combat

arms may conceive military problems differently than logisticians, and even more differently than historians at- tempting a macro-view explanation of events.

4.3.2 When Dealing With Existing Models

Only sometimes do we have the opportunity to design from

scratch. More typically, we must adapt existing models.

Moreover, the model "families" we may have to work with

are often families more on the basis of assertion than line- age. What do we then do? Some possibilities here are:

• Study the model and the questions that users ask of the

model to discover useful abstractions. For example,

inputs X, Y, and Z may enter the computations only as

the product XYZ. Or a decision maker may ask questions in terms of concepts like force ratio. For mature

models, the displays that have been added over time

provide insights into useful abstractions.

• Apply statistical machinery to search for useful abstractions. For example, such machinery might test to see whether the system's behavior correlates not just

with X,Y, and Z, but with XY, XZ, YZ, or XYZ.

• Idealize the system mathematically and combine this with physical insight or empirical observation to guess

at the form of aggregate behavior (e.g., inverse dependence on one variable, or exponential dependence on another). Consider approximations such as an integral being the product of the effective width of the in- tegration interval and a representative non-zero value

of the integrand.

The first approach is perhaps a natural activity for a smart modeler and programmer who begins to study an

existing program, but only if he open-minded about the

usefulness of higher-level depictions. The second ap- proach is an extension of normal statistical analysis. The

third approach is a hybrid that 1 typically prefer to the sec- ond. It uses one's understanding of phenomenology, and

theories of system behavior, to gain insights about the

likely or possible abstractions before cranking statistical machinery.

4.3.3 The Problem with Occam's Razor

The principle of Occam's razor requires that we prefer the

simplest explanation and, thus, the simplest model. Enthusiasts of statistical approaches tend to interpret this to mean

that one should minimize the number of variables. They

tend to focus on data and to avoid adding variables for "explanation" if the variables are not needed to predict the

data. In contrast, subject-area phenomenologists may prefer to enrich the depiction by adding variables that provide

a better picture of cause-effect chains, but go well beyond

what can be supported with meager experimental data. My

own predilection is that of the phenomenologist, but with

MRM designs one can sometimes have one's cake and eat

it: one can test results empirically by focusing on the abstract versions of a model, while using richer versions for deeper explanation.

As an aside, a version of the Occam's Razor principle

emphasizes use of the explanation that is simplest enough

to explain all there is to explain, but nothing simpler! This

should include phenomena that one "knows about" even if they are not clearly visible in the limited data. I would add

to this the admonition made decades ago by MIT's Jay

Forrester that to omit showing a variable explicitly may be

equivalent to assuming its value is unity.

Competition among approaches can be useful. For ex- ample, phenomenologists working a problem may be con- vinced that a problem must be described with complex

computer programs having hundreds or thousands of data

elements. A statistical analysis may show that, despite the model's apparent richness, the system's resulting behavior

is driven by something much simpler. This, in turn, may

lead to a reconceptualizing of the problem phenomenologicaily. Many analogues exist in physics and engineering.

4.3.4 Connections Between New and Old Models

Although the discussion in Section 4.3.2 distinguished

sharply between the case of new models and old ones, the reader may have noticed connections. In essence, working

with existing models should often involve sketching what

the models should be like and how models with different

resolution should connect substantively. That is, working

with existing models may require us to go back to design

issues. Individuals differ, but I, at least, often find it easier

to engage the problem than to engage someone's else's idiosyncratically described solution. Furthermore, I then

have a better understanding of assumptions and approximations.

With this background, let me now turn to the design of

multiresolution, multiperspective models and families

(Davis and Bigelow, 1999). Although this relates most

directly to new models, it is relevant also to working with

legacy models in preparing for exploratory analysis.

4.4 Multiresolution, Multiperspective Modeling

4.4.1 Definition

Multi-resolution modeling (MRM) is building a single

model, a family of models, or both to describe the same phenomena at different levels of resolution, and to allow

436
users to input parameters at those different levels depending on their needs. Variables at level n are abstractions of

variables at level n+1. MRM is sometimes called variable- or selectable-resolution modeling. Figure 5 illustrates MRM schematically. It indicates that a higher level model

(Model A) itself has more than one level of resolution. It can be used with either two or four inputs. However, in addition to its own MRM features, it has input variables

that can either be specified directly or determined from the

outputs of separate higher-resolution models (models B

and C, shown as "on the side," for use when needed. In

principle, one could attach models B and C in the software

itself—creating a bigger model. However, in practice there are tradeoffs between doing that or keeping the more detailed models separate. For larger models and simulations, a combination single-model/family-of-models approach is desirable. This balances needs for analytical agility and

complexity management.

MRM is not sufficient by itself because of the need for different abstractions or perspectives in different applications. That is, different perspectives—analogous to alter- native representations in physics—are legitimate and important. They vary by conception of the system and choice

of variables. Designing for both multiple resolution and

multiple perspectives can be called MRMPM (pronounced

Mr. MIPM).

Model A

..-.^

Model B

Mo /

/

/ /

//

//

///

/

del C

<

Figure 5: Figure 5: A Multiresolution Family

4.4.2 Mutual Calibration Within a Model Family

Given MRMPM models or families, we want to be able to reconcile the concepts and predictions among levels and

perspectives. It is often assumed that the correct way to do

this is to calibrate upward: treating the information of the

most detailed model as correct and using it to calibrate the

higher-level models. This is often appropriate, but the fact

is that the more detailed models almost always have omissions and shortcomings. Further, different models of a family draw upon different sources of information—ranging from doctrine or even "lore" on one extreme

to physical measurements on a test range at the other. Figure 6 makes the point that members of a multiresolution model family should be mutually calibrated (National

Research Council, 1997). For example, we may use lowresolution historical attrition or movement rates to help

calibrate more detailed models predicting attrition and

movement. This is not straightforward and is often done

crudely by applying an overall scaling factor (fudge factor), rather than correcting the more atomic features of the

detailed model, but it is likely familiar to readers. On the

other hand, much calibration is indeed upward. For example, a combat model with attrition coefficients should typically have adjustments of those coefficients for different

circumstances identified in a more detailed model.

Low resolution

High Resolution

Figure 6: Mutual Calibration of Models in a Family

4.4.3 Design Considerations

So, given their desirability, how do we build a family of

models? Or, given pre-existing models, how do we sketch

out how they "should" relate before connecting them as software or using them for mutual calibration? Some

highlights are as follows.

The first design principle is to recognize that there are

limits to how well lower-resolution models can be consistent with high-resolution models. Approximation is a central concept from the outset. Several points are especially

important:

• Consistency between two models should be assessed

in the context of use. What matters is not whether

they generate the same final state of the system, but

whether they generate approximately the same results in the application (e.g., rank ordering of alternatives).

This ties into the well-known concept of experimental

frames (Zeigler, et al., 2000).

• Consistency of aggregated and disaggregated models

must also be judged recognizing that low-resolution

437
models may reflect aggregate- level knowledge not contained in the detailed model.

• Comprehensive MRM is very difficult or impossible

for complex M&S, but having even some MRM can be far more useful than having none at all.

• Members of an MRM family will typically be valid for only portions of the system's state space. Parameter

values (and even functional forms) should change with

region.

• Mechanisms are therefore needed to recognize differ- ent situations and shift models. In simulations, human

intervention is one mechanism; agent-based modeling

is another.

• Valid MRM will often require stochastic variables rep- resented by probability distributions. Further, valid

aggregate models must sometimes reflect correlations

among variables that might naively be seen as probabilistically independent.

With these observations, the ideal for MRM is a hierarchical design for each MRM process, as indicated in Figure 5.

4.4.4 Desirable Design Attributes

From the considerations we have sketched above, it fol- lows that models and analysis methodologies for exploratory analysis should have a number of characteristics.

First, they should be able to reflect hierarchical decomposition through multiple levels of resolution and from alter- native perspectives representing different "aspects" of a system.

Less obviously, they should also include realistic mechanisms for the natural entities of the system to act, react, adapt, mutate, and change. These mechanisms should reflect the relative "fitness" of the original and emerging

entities for the environment in which they are operating.

Many techniques are applicable here, including gametheoretic methods and others that may be relatively familiar to readers. However, the most fruitful new approaches

are those typically associated with the term agent-based

modeling. These include submodels that act "as the agents

for" political leaders and military commanders or—at the

other extreme— infantry privates on the battlefield or drivers of automobiles on the highway. In practice, such models need not be exotic: they may correspond to some rela- tively simple heuristic decision rules or to some wellknown (though perhaps complex) operations-research al- gorithm. But to have such decision models is quite differ- ent from depending on scripts.

Because it is implausible that closed computer models

will be able to meet the above challenge in the foreseeable

future, the family of "models" should allow for human

interaction—whether in human-only seminar games, smallscale model-supported human gaming, or distributed inter- active simulation. This runs against the grain of much

common practice.

4.4.5 Stochastic Inputs To Higher Level Models

The last item in the above list is often ignored in today's

day-to-day work. Indeed, too often models that need to be

stochastic are deterministic, with quantitatively serious

consequences (Lucas, 2000). Often, workers calibrate a high-level (aggregate) model using average outcomes of

allegedly "representative" high-resolution scenarios. For

example, a theater-level model's air model might be cali- brated to results of detailed air-to-air simulation with

Brawler, which treats individual engagement classes (e.g.,

1 on 1 , 1 on 2, ... 4 on 8). This may appear to establish the

validity of the theater-level model, but in fact the calibration is treacherous. After all, what kinds of engagements

occur may be a sensitive function of the sides' command

and control systems, strategies, and weather. The calibrations really need to be accomplished on a highly studyspecific basis. Furthermore, the higher-level model inputs often need to be stochastic. Figure 7 illustrates the concept schematically for a simple problem. Suppose that a process (e.g., one computing the losses to aircraft in air-to-air encounters) depends on X,Y, S, and W. But suppose that the out- come of ultimate interest involves many instances of that process with different values of S and W (e.g., different

per-engagement numbers of Red and Blue aircraft). An

abstraction of the model might depend only on X,Y, and Z

(e.g., overall attrition might depend on only numbers of

Red and Blue aircraft, their relative quality, and some command and control factor). If the abstraction shown is to be valid, the variable Z should be consistent with the

higher-resolution results. However, if it does not depend

explicitly on S and W, then there are "hidden variables" in the problem and Z may appear to be a random variable, in which case so also would the predicted outcome F be a random variable. One could ignore this randomness if the

distribution were narrow enough, but it might not be.

In the past, such calibrations have been rare because

analysts have lacked both theory and tools for doing things

better. The "theory" part includes not having good descriptions of how the detailed model should relate to the

simplified one. The tool part includes the problem of being able to define the set of runs that should be done (representing the integral of Figure 7) and then actually making

those runs.

Ideally, such a calibration would be dynamic within a simulation. Moreover, it would be easy to adjust the cali- bration to represent different assumptions about command,

control, communications, computers, intelligence, surveillance, and reconnaissance (C4ISR), as well as tactics. We

are nowhere near that happy situation today.

438
F(X, Y,S, W) ~ F{X, Y, Z) and weapon flight time. However, it can also accept more

aggregate inputs such as time from last update. If the input

variable Resolution of Time of Last Update Calculation is

set "low," then Time From Last Update is specified di- rectly as input; if not, it is calculated from the lower-level

inputs.

This design has proven very useful—both for analysis it- self and for communicating insights to decision makers in different communities ranging from the C4ISR community

to the programming and analysis community. In particular,

the work clarified how the technology-intensive work of

the C4ISR acquisition community relates to higher-level

^, , , r,^ , strategy problems and analysis of such problems at the

Figure 7: Input to Higher Level Model May Be Stochastic

theater level

5 RECENT EXPERIENCE AND CONCLUSIONS

MRMPM is not just idealized theory, but something usable. Over the last several years, my colleagues and 1 have

done considerable work related to the problem of halting an invading army using precision fires from aircraft and

missiles. The most recent aspects of that work included

understanding in some detail how the effectiveness of such

fires are affected by details of terrain, enemy maneuver

tactics, certain aspects of command and control, and so on. This provided a good test bed for exploring numerous as- pects of MRMPM theoiy (Davis, Bigelow, and McEver,

2000).

For this work we developed a multiresolution personalcomputer model (PEM), written in Analytica, to understand and extend to other circumstances the findings from

entity-level simulation of ground maneuver and long-range

precision fires. A major part of that work was learning

how to inform and calibrate PEM to the entity-level work.

There was no possibility, in this instance, of revising the

entity-level model. Nor, in practice, did we have such a good understanding of the model as to allow us to construct a comprehensive calibration theory. Instead, we had

to construct a new, more abstract, model and attempt to impose some of its abstractions on the data from runs of

the entity-level simulation in prior work, plus some special runs made for our purposes. The result is a case history

with what are probably some generic lessons learned.

Figure 8 illustrates one aspect of PEM's design. It shows

the data flow within a PEM module that generates the impact time (relative to the ideal impact time) for a salvo of

precision weapons aimed at a packet of armored fighting

vehicles observed by surveillance assets at an earlier time.

Other parts of PEM combine information about packet

location versus time and salvo effectiveness for targets that happen to be within the salvo's "footprint" at the time of

impact, to estimate effectiveness of precision weapons.

For the salvo-impact-time module. Figure 8 shows how

PEM is designed to accept inputs as detailed as whether

there is enroute retargeting of weapons, the latency time.

Time Offset

Resolution of Impact Time

Calculation

Descent Time

Time of Update

List Extra C2 Time

Latency of RSTA data

Flight Time

Figure 8: Multiresolution, Multiperspective Design

In other reports (McEver, Davis, and Bigelow, 2000a,b),

we describe a broader but more abstract model (EXHALT)

that we use for theater-level halt-problem analysis and ex- periments to deal with the multi-perspective problem. One

conclusion is that MRMPM work rather demands a building-block approach that empasizes study-specific assembly

of the precise model needed. Although we had some success in developing a closed MRMPM model with alternative user modes representing different demands for resolution and perspective (e.g., the switches in Figure 8), it proved impossible to do very much in that regard: the

number of interesting user modes and resolution combinations simply precludes being able to wire in all the relevant

( 439
user modes. Moreover, that explosion of complexity occurs very quickly. At-the-time-assembly from building

blocks, not prior definition, is the stronger approach. This was as we expected, but even more so. Fortunately, we were able to construct the models needed

quickly—in hours rather than days or weeks—as the result of our building-block approach, visual modeling, use of

array mathematics, and strong, modular, design.

We also concluded that current personal computer

tools—as powerful as they are in comparison with those in past years—are not yet up to the challenge of making the

building-block/assembly approach rigorous, understandable, controllable, and reproducible without unreal istically

high levels of modeler/analyst discipline. Thus, there are good challenges ahead for the enabling-technology community. Also, the search models for advanced exploratory

analysis are not yet well developed.

REFERENCES

Bankes, S. C. 1993. "Exploratory Modeling for Policy

Analysis," Operations Research, Vol. 41, No. 3. Bigelow, J. H., P. K. Davis, and J. McEver. 2000. "Case

History of Using Entity-Level Simulation as Imperfect

Experimental Data for Informing and Calibrating Simpler Analytical Models for Interdiction," Proceedings of

theSPIE, Vol.4026.

Davis, P. K (ed.). 1994. New Challenges in Defense Planning: Rethinking How Much Is Enough, RAND, Santa

Monica, CA.

Davis, P.K.. 2000. "Multiresolution, Multiperspective

Modeling as an Enabler of Exploratory Analysis," Proceedings ofthe SPIE, Vol. 4026.

Davis, P. K. and R. Hillestad. 2001. Exploratoiy Analysis

for Strategy Problems With Massive Uncertainty.

RAND, Santa Monica, CA.

Davis, P.K. and J. H. Bigelow. 1998. Experiments in Multiresolution Modeling, RAND, Santa Monica, CA.

Davis, P.K. and M. Carrillo. 1997. Exploratory Analysis of

the Halt Problem: A Briefing on Methods and Initial Insights, RAND, DB-232, Santa Monica, CA.

Davis, P. K., D. Gompert, R. Hillestad, and S. Johnson,

Transforming the Force: Suggestions for DoD Strategy,

RAND Issue Paper, Santa Monica, CA, 1998.

Davis, P. K., D. Gompert,. and R. Kugler, Adaptiveness in National Defense: the Basis of a New Framework,

RAND Issue Paper, Santa Monica, CA, 1996.

Davis, P.K., J. H. Bigelow, and J.McEver. 2000. Effects of

Terrain. Maneuver, Tactics, and C4ISR on the Effectiveness of Long Range Precision Fire, RAND, Santa

Monica, CA.

Davis,P.K., J. H. Bigelow, and J. McEver. 1999. Analytic

Methods for Studies and Experiments on Transforming

the Force, RAND, Santa Monica, CA.

Lucas, Thomas. 2000. "The Stochastic Versus Deterministic Argument for Combat Simulations: Tales of When

the Average Won't Do," Military Operations Research,

Vol. 5, No. 3. McEver,J., P. K. Davis, and J. H. Bigelow. 2000a.

EXHALT: an Interdiction Model for the Halt Phase of

Armored Invasions RAND, Santa Monica, CA.

McEver, J., P. K. Davis, and J.H. Bigelow. 2000b. "Implementing Multiresolution Models and Families of

Models: From Entity Level Simulation to PersonalComputer Stochastic Models and Simple 'Repro Mode/5'", Proceedings ofthe SPIE, Vol. 4026.

Morgan, G., and M. Henrion. 1992. Uncertainty: A Guide

to Dealing with Uncertainty in Quantitative Risk and

Policy Analysis, Cambridge University Press, Cambridge, Mass.

National Research Council. 1997. Modeling and Simulation, Volume 9 of Technology for the United States Navy

and Marine Corps, 2000-2035, National Academy

Press, Washington, D.C., 1997.

R. Lempert, .M. E. Schlesinger, and S. C. Bankes. 1996.

"When We Don't Know the Costs or the Benefits:

Adaptive Strategies for Abating Climate Change," Climatic Change, Vol. 33, No. 2. Zeigler, B. P.,T. G. Kim, and H. Praehofer. 2000. Theoiy

of Modeling and Design , Academic Press, San Diego.

AUTHOR BIOGRAPHY

PAUL K. DAVIS is a senior scientist and Research Leader

at RAND, and a Professor of Public Policy in the RAND

Graduate School. He holds a B.S. from the U. of Michigan

and a Ph.D. in Chemical Physics from the Massachusetts

Institute of Technology. Dr. Davis is a recipient of the

Wanner Memorial Award of the Military Operations Research Society. He is a member of the Naval Studies

Board under the National Research Council and has served on a number of studies for the Council, the Defense Sci- ence Board, and the National Institute for Standards and

Technology. His e-mail and web addresses are pdavisferand.org and www.rand.org/personal/pdavis .

440
DEFINITION AND MEASUREMENT OF MACHINE

INTELLIGENCE

GEORGE N. SARIDIS

Professor Emeritus RPI

1. DEFINITIONS OF MACHINE INTELLIGENCE

Recently, there have been a lot of arguments on the subject of Intelligence for Machines

that operate autonomously and knowledgeably in unfamiliar or hazardous environments.

A position view is presented herein, that represents the engineering point of view as the

so called "Intelligent Machines" that implement it are designed and built by engineers

(Task Force of the Control System Society of IEEE, chaired by P. Antsaklis1993}

In the last twenty or so years, a lot of discussions have taken place regarding the meaning

of Intelligence. The psychologists argue about human intelligence to be used as the

model, while the computer scientists suggest artificial intelligence for the job. All these

arguments are based on the intelligence that humans demonstrate in dealing with their

every day activities, a concept that is still nebulous and very little understood. The

engineers stress the concept of machine intelligence.

Human Intelligence is to general and poorly understood to be used as model for Intelligent

Machines. Artificial Intelligence, on the other hand, was created to deal with the effort to

make computers act like human beings, when making decisions and perform other human

like activities (Winston 1977). Finally, engineers developed the concept of Machine

Intelligence to represent the properties of autonomous machines created to perform

unsupervised anthropomorphic tasks (Saridis 1977).

The theory of Intelligent Machines may be thought of as the result of the intersection of the

three major disciplines:

• Artificial Intelligence,

• Operations Research,

• Control Theory.

The reason for this claim has been proven necessary is that none of the above disciplines

can produce individually a satisfactory theory for the design of such machines. It is also

aimed in establishing Intelligent Controls as an engineering discipline, with the purpose

of designing Intelligent Autonomous Systems of the future. It combines effectively the

results of cognitive systems research, with various mathematical programming control

techniques. The control intelligence is hierarchically distributed according to the Principle

of Precision with Decreasing Intelligence (IPDI), evident in all hierarchical management

systems. The analytic functions of an Intelligent Machine are implemented by Intelligent

441
Controls, using Entropy as a measure. Such an architecture is analytically implemented

using entropy as a measure. However, various cost functions expressed in entropy terms,

may be used to evaluate the generated design. Reliability, a property highly desirable for

systems functioning autonomously, is a very desirable measure of performance and can

also be expressed by entropy, and can be combined in the criterion of performance of the

system design.

Intelligence is defined according to the American Heritage Dictionary (1992) as :

• Intelligence (Human) is defined as the capacity to acquire and apply

Knowledge.

Such a statement implies that knowledge is the key variable in an Intelligent system.

The following two definitions, due to P. H. Winston and G. N. Saridis respectively are given

to clarify the subject (Winston 1977).

Artificial Intelligence is represented as a mapping of anthropomorphic tasks into the

analytic tools of the computer in order to study human behavior, while Machine Intelligence

is the inverse mapping of analytic tools imbedded in a machine into anthropomorphic

tasks.

• Artificial Intelligence is the study of ideas which enable computers to do the

things that make people seem intelligent. Its central goals are to make

computers more useful and to understand the the principle, which makes

Intelligence possible.

The key components of Artificial Intelligence are: interactive systems between man and

machine, heuristics and expert system exaustive programming (Saridis 1977).

Some definitions regarding Machine Knowledge and Intelligence are appropriate in order

to clearly define the field of Intelligent Machines. In order to establish the definition of

Machine Intelligence we revisite the American i-leritage Dictionary (1992) :

• Intelligence is defined as the capacity to acquire and apply Knowledge.

Such a statement implies that knowledge is the key variable in an Intelligent system. Since

we shall be dealing with Machine Intelligence an appropriate definition is necessary:

• Machine intelligence is defined as the process of analyzing, organizing and

converting data into Machine Knowledge.

The key components of Machine Intelligence are: computer mathematics, cognitive

engineering and Intelligent control.

442
Now it is well understood that:

• Knowledge is a form of structured Information.

This is very convenient because an analytic formulation of Intelligent Machines may be

developed, using Shannon's Information Theory. Therfore, Machine Knowledge is

defined as:

• Machine knowledge is defined to be structured information acquired and

applied to remove ignorance or uncertainty about a specific task pertaining

to an Intelligent Machine.

Similarly,

• The Rate of Machine Knowledge is the flow of Knowledge in an Intelligent

Machine.

Using the above definitions analytic expressions of Machine Knowledge and its Rate are

obtained.

Assuming that Machine Knowledge is Information:

K = -ln[p(K)] (1.1)

and the average Rate of Knowledge is also:

R = - a - iJ ln[p(R)] (1.2)

where p( ) is the probability density of the event. Solving for p(R) we obtain:

p(R) = exp (- a - pR) (1.3)

a = In f^^ exp(-pR) dx

Complexity is always imbedded in the design and execution of Intelligent Machines.

However, their performance is always prescribed by a certain level of detail required by the

task expected to be executed, which is defined as Precision. Such details are inversely

associated with the uncertainty of execution and thus are measurable with entropy. The

following definitions help to clarify this concept.

• Precision is the compliment of the uncertainty of execution of the various

tasks of an Intelligent Machine, and Imprecision serves as a measure of the

complexity of the process.

443
The concept of precision will therefore be associated with the Principle of Increasing

Precision and Decreasing Intelligence. Precision is required for the smooth and accurate

execution of tasks associated with world processes.

This generalization is found useful in order to accommodate unconventional systems that

are served by intelligent Machines, like biological, environmental etc (Prigogine 1980).

Intelligent Control is the main tool to implement Intelligent Machines. In order to properly

implement the Theory of Intelligent Machines the broader definition of Automatic Control

Systems is used.

• Control is making a Process do what we want it to do.

The Theory of Hierarchically Intelligent Controls has been recently reformulated by

Saridis(1996) to incorporate new architectures that are using Neural and Petri nets. The

analytic functions of Hierarchically Intelligent Machines are implemented using Entropy as

a measure. The resulting hierarchical control structure is based on the Principle of

Increasing Precision with Decreasing Intelligence (IPDI) which is discussed in the next

Chapter. Each of the three levels of the Intelligent Control is using different architectures,

in order to satisfy the requirements of the Principle:

The Organization level

modeled after a Boltzmann machine for abstract reasoning, task planning and

decision making;

The Coordination level

composed of a number of Petri Net Transducers supervised by a dispatcher for

command management, serving as an interface with the Organization level;

The Execution level . includes the sensory, navigation and control hardware which interacts one-to-one

with the appropriate Coordinators, while a VME bus provides a channel for

database exchange among the several devices.

This system was implemented on a robotic tele-transporter, designed for construction of

trusses for the Space Station Freedom, at the Center for Intelligent Robotic Systems for

Space Exploration laboratories at the Rensselaer Polytechnic Institute.

The basic concepts underlining the theory of Hierarchically Intelligent Machines like

Machine Intelligence, Machine Knowledge, Precision and Complexity were defined and

contrasted to Artificial and Human Intelligence. The basic difference being the search for

an analytic formulation that would lead to an engineering implementation. Further more it has been recently realized that other scientific disciplines have being using the same

concepts for an analytic representation of their subjects (Prigogine 1980). Such ideas will

be discussed in the next Chapter.

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2. THE ENTROPY CONCEPT

Entropy is a form of lower quality energy, first encountered in Thermodynamics. It represents an undesirable form of energy that is accumulated when any type of work is

generated. Recently it served as a model of different types of energy based resources, like

transmission of information, biological growth, environmental waste, etc. Entropy was

currently introduced, as a unifying measure of performance of the different levels of an

Intelligent Machine by Saridis (1 985). Such a machine is aimed at the creation of modern

intelligent machines which may perform human tasks with minimum interaction with

a human operator. Since the activities of such a machine are energy related, entropy may

easily serve as a cost measure of performing various tasks as Intelligent Control, Image

Processing, Task Planning and Organization, and System Communication among

diversified disciplines with different performance critena. The model to be used is

borrowed from Information Theory, where the uncertainty of design is measured by a

probability density function over the appropriate space, generated by Jaynes' Maximum

Entropy Principle.

Other applications of the Entropy concept are for defining Reliability measures for design

purposes and obtaining measures of complexity of the performance of a system, useful in

the development of the theory of Intelligent Machines.

Entropy is a convenient global measure of performance because of its wide applicability

to a large variety of systems of diverse disciplines including waste processing,

environmental, socio-economic, biological and other. Thus, by serving as a common

measure, it may expand system integration by incorporating say societal, economic or even environmental systems to engineering processes.

The concept of Entropy was introduced in Thermodynamics by Clausius in 1 867, as the

low quality energy resulting from the second law of Thermodynamics. This is the kind of

energy which is generated as the result of any thermal activity, at the lower thermal level,

and is not utilized by the process.

It was in 1872, though, that Boltzmann used this concept to create his theory of statistical

thermodynamics, thus expressing the uncertainty of the state of the molecules of a

perfect gas. The idea was created by the inability of the dynamic theory to account for all the collisions of the molecules, which generate the thermal energy. Boltzmann (1872)

stated that the entropy of a perfect gas, changing states isothermally, at temperature T is

given by;

S = - k Jx (qj-H)/kT exp{(qj-H)/kT} dx (2.1)

where ^J is the Gibbs energy, i|j = - kT In exp {-H/kT}, H is the total energy of the system,

and k is Boltzmann's universal constant. Due to the size of the problem and the

uncertainties involved in describing its dynamic behavior, a probabilistic model was

assumed where the Entropy is a measure of the molecular distribution. If p(x) is defined

445
as the probability of a molecule being in state x, thus assuming that,

p(x) = exp{(i|j-H)/kT} (2.2)

where p(x) must satisfy the "incompressibility" property over time, of the probabilities, in

the state space X, e.g.;

dp/dt = 0 (2.3)

The incompressibility property is a differential constraint when the states are defined in a

continuum, which in the case of perfect gases yields the Liouville equation. Substituting

eq.(2.2) into eq.(2.1) the Entropy of the system takes the form,

S = - k Jx p(x) Inp(x) dx (2.4)

The above equation defines Entropy as a measure of the uncertainty about the state of the

system, expressed by the probability density exponential function of the associated

energy.

Actually, the problem of describing the entropy of an isothermal process should be derived

from the Dynamical Theory of Thermodynamics, considering heat as the result of the

kinetic and potential energies of molecular motion. It is the analogy of the two formulations

that led into the study of the equivalence of entropy with the performance measure of a

control system. If the Dynamical Theory of Thermodynamics is applied on the aggregate

of the molecules of a perfect gas, an Average Lagrangian I, should be defined to describe

the average performance over time of the state x of the gas,

I = 1/ L(x,t) dt (2.5)

where the Lagrangian L(x,t) = (Kinetic energy) - (Potential energy). The Average

Lagrangian when minimized, satisfies the Second Law of Thermodynamics. Since the

formulations eqs.(2.1) and (2.5) are equivalent, the following relation should be true;

S = l/T (2.6)

where T is the constant temperature of the isothermal process of a perfect gas (Lindsay

and Margenau, 1957). This relation will be the key in order to express the performance

measure of the control problem as Entropy.

In the 1940's Shannon (1963), using Boltzmann's idea, e.g., eq. (3.4), defined Entropy

(negative) as a measure of the uncertainty of the transmission of information, in his

celebrated work on information Theory:

H = - Jo p(s) Inp(s) ds (2.7)

446
where p(s) is a Gaussian density function over the space O of the information signals

transmitted. The similarity of the two formulations is obvious, where the uncertainty about

the state of the system is expressed by an exponential density function of the energy

involved.

Shannon's theory was generalized for dynamic systems by Ashby (1965), Boettcher and

Levis (1 983), and Conant (1 976) who also introduced various laws which cover information

systems, like the Partition Law of Information rates.

The £-entropy formulation of the metric theory of complexity, originated by Kolmogorov

(1956) and applied to system theory by Zames (1979) is another use of entropy.

It implies that an increase in knowledge about a system, decreases the amount of £-

entropy which measures the uncertainty (complexity) involved with the system.

£ - H = In(n3) (2.8)

where ne is the minimum number of coverings of a set e. Therefore e-entropy is a measure

of complexity of the system involved. It may also be interpreted as a measure of precision

if it viewed as the number of points required to describe a line.

Since the latest major improvements in the average quality of life, major increases have

occurred in the production of waste, traffic congestion, biological pollution and in general

social and environmental decay(Bailey 1990, Brooks Wiley1988, Prigogine1996, Rifkin

1980), which can be interpreted as the increase of the Global Entropy of our planet

(Saridis 1998), an energy that tends to deteriorate the quality of our modern society.

According to the second axiom of thermodynamics this is an irreversible phenomenon, and

nothing can be done to eliminate it.

In an attempt to generalize the principle used by Boltzmann and Shannon to describe the

uncertainty of the performance of a system under a certain operating condition, Jaynes

(1957) formulated his Maximum Entropy Principle, to apply it in Theoretical Mechanics.

In summary it claims that

• The uncertainty of an unspecified relation of the function of a system is

expressed by an exponential density function of a known energy relation

associated with the system.

As an example of the use of Entropy as a measure of performance, is a modified version

of the Principle, as it applies to the Control problem, is derived in the sequel, using

Calculus of Variations (Saridis 1987). The proposed derivation represents a new

formulation of the control problem, either for deterministic or stochastic systems and for

optimal or non-optimal solutions.

The purpose of this work is to establish entropy measures, equivalent to the performance

447
criteria of the optimal control problem, while providing a physical meaning to the latter.

This is done by expressing the problem of control system design probabilistically and

assigning a distribution function representing the uncertainty of selection of the optimal

solution over the space of admissible controls. By selecting the worst case distribution,

satisfying Jaynes' Maximum Entropy Principle, the performance criterion of the control

is associated with the entropy of selecting a certain control (Jaynes 1957, Saridis 1985).

Minimization of the differential entropy, which is equivalent to the average performance of

the system, yields the optimal control solution. Furthermore, the Generalized HamiltonJacobi-Bellman equation is derived from the incompressibility over time condition of the

probability distribution. Adaptive control and stochastic optimal control are obtained as

special cases of the optimal formulation, with the differential entropy of active transmission

of information, claimed by Fel'dbaum (1965), as their difference. Upper bounds of the latter

may yield measures of goodness of the various stochastic and adaptive control algorithms.

In this section, the entropy measure for optimal control will be established.

The optimal feedback deterministic control problem with accessible states is defined as

follows: given the dynamic system;

dx/dt = f(x,u,t) ; x(to) = Xq;

and the cost function,

V(u;Xo,to) = /,o L(x,u,t) dt (2.9)

where x(t)eQx is the n-dimensional state vector u(x,t)sQuXTc:Q^XT, is the m-dimensional

feedback control law and t s ^ = [to.T].

An optimal control u*(x,t) is sought to minimize the cost,

V(u*;Xo.to) = Min /,o L(x,u,t) dt (2.10)

u

Define the differential entropy, for some u(x,t),

H(Xo,u(x,t),p(u)) = H(u) = -/oxo-^ox p(Xo,u)lnp(Xo,u) dudXo (2.11)

where XoeQ^o. ^^^^x the spaces of initial conditions and states respectively, and p(Xo,u)=p(u)

the probability density of selecting u. One may select the density function p(u) to maximize

the differential entropy according to Jaynes' Maximum Entropy Principle (Jaynes 1 957),

subject to E{V(Xo,u,t)}=K, for some u(x,t). This represents a problem more general than the

optimal where K is a fixed but unknown constant, depending on the selection of u(x,t).

For appropriate constants A and p, the worst case density is,

P(U) = Q-A-MV(u(x,t),xO,tO)

448
(2.12)

and the total Entropy is equivalent to the average cost function:

H(u) = A + /iE{V(u(x,t),Xo,to)} (2.13)

and the corresponding minimum value with respect to u(x,t) represents the optimal design.

In most organization systems, the control intelligence is hierarchically distributed from the

highest level which represents the most intelligent manager to the lowest level which

represents the worker, which is a manifestation of the Principle of Increasing Precision

with Decreasing Intelligence (IPDI),. On the other hand, the precision (complexity) or skill

of execution is distributed in an inverse manner from the bottom to the top as required for

the most efficient performance of such complex systems. This has been analytically

formulated as the Principle of Increasing Precision with Decreasing intelligence (IPDI),

by Saridis (1989). The formulation and proof of the principle is based on the concept of

Entropy in that report.

According to the IPDI Principle:

• Machine Intelligence (Ml) is the set of actions and/or rules which operates on

a Data-base (DB) of events or activities to produce flow of knowledge.

This principle suggests that for constant flow of knowledge through the machine less

intelligence more data (complexity) are required. Thus it provides an interesting definition

of Machine Intelligence that has been debated. This has been realized in the three level

architecture of Intelligent Machines discussed in the previous section. In the case of fixed

database DB, a measure of Machine Intelligence being the Entropy of Knowledge flow,

may be concluded:

3. CONCLUSIONS

A set of definitions leading to the concept of Machine Intelligence have been discussed

in this paper, and it is contrasted to Artificial and Human Intelligence. Entropy, defined

as a Universal Energy, resulting from the production of Work in a system, may successfully

serve as a measure of Machine Intelligence. The production of Entropy is irreversible

without the use of additional work, and may represent thermal energy in Thermodynamics,

Information in Communication systems. Performance in Control systems, as well as waste

and pollution in Ecological systems. Economic spending in Societal systems, or

Biodegradation in Biological systems ( Bailey 1990, Boltzmann 1872, Brooks Wiley1988,

Prigogine1996, Shannon 1963, Saridis 1998, Rifkin 1980). It represents an unifying

(Ml) : (DB) - (R) (2.14)

E{MI} = H(R) (2.15)

449
measure for globalization of many, up to now, disjoint sciences and may successfully be

used as measure for the development of Hierarchically Intelligent Machines with the use

of the Principle of Increasing Precision with Decreasing Intelligence.

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452
Domain Independent Measures of Intelligent Control

David Friedlander

Shashi Phoha

Applied Research Laboratory

The Pennsylvania State University

University Park, PA 16802

ABSTRACT

There is no standard method for measuring intelhgence in

artificial systems. One reason for this is that no single definition of

intelligence exists. Another is that many of the definitions of

intelligence are not appropriate for artificial systems based on the

current level of scientific understanding. This includes introspective,

as opposed to behavioral, measurements. This paper explores

quantitative, domain independent measures of intelligence for

discrete event control systems. It is motivated by traditional measures of effective control such as controllability, and robustness,

and includes original work on robustness and permissiveness.

Keywords; intelligence, artificial systems, control systems,

hierarchical control, intelligent control

1. Introduction

1.1. Intelligence of Artificial Systems

A single Intelligence Quotient, as generated by a standardized IQ test, has been used as a measure of general

human intelligence. More recent work suggests that there are multiple types of intelligence. This concept is essential to the

development of intelligence measurements for artificial systems for two reasons. First, current systems have not

reached the level where they can display behavior indicative

of a nontrivial understanding of the general representations of

human knowledge such as language or mathematics. Second,

current techniques in constructing artificial systems result in a sharp trade-off between the quality of performance and the

breadth of the domain. In order to perform at a reasonable

level, most artificial systems are restricted to a relatively narrow domain.

The current criteria for intelligent systems tend to look at either how well the system performs its assigned tasks, or to what extent can the system behave in ways that are characteristic of human intelligence. The former criteria tend

to be domain specific, implementation independent, and

relatively easy to quantify and measure. The latter criteria tend to be domain independent, implementation dependent,

and are difficult to quantify and measure.

Asok Ray

Mechanical Engineering Department

The Pennsylvania State University

University Park, PA 16802

Domain independent intelligent behaviors that can be

approximated in current artificial systems include:

• reacting effectively to novel stimuli and situations,

• perceiving essential properties from a large, complex

world of sensory information,

• identifying and taking action on the essential problem

of a given situation, • making appropriate decisions in a variety of

situations, in complex environments,

• recognizing and exploiting opportunities within one's environment,

• recognizing patterns within the environment,

• manipulating symbols,

• overcoming obstacles,

• correcting for errors,

• handling uncertainty,

• and learning from experience.

These behaviors are difficult to measure in the general

case. This paper presents quantitative measurements of

intelligent behaviors for systems based on hierarchical

networks of discrete event controllers. Some of the metrics

used here are based on extending methods from continuous

control to discrete event control systems.

The methods are illustrated with two types of examples.

The first is a highly simplified control system for command

and control (C") of aircraft operations in battle management as

illustrated in Figure 4. The second is an abstract controller of

slightly greater complexity. The lowest level is shown in Figure 1 ; hierarchical versions are shown in Figures 2 and 3. This paper examines the following characteristics of

hierarchical control systems:

• Controllability, the ability ofthe system to accomplish its goals without reaching an error state from which it cannot

recover.

• Hierarchical Consistency, the ability of a higher-level

controller to achieve its goals indirectly, by controlling one or more lower-level controllers.

• Robustness, the ability of the system to operate under

uncertainty and novel situations, and to recover from

errors,

453
• Permissiveness, the ability to achieve goals through more

than one path.

• Aggregation, the ability to abstract essential properties

from lower level data, • Disaggregation, the ability to take effective specific

actions based on higher level abstractions, • Scalability, the ability to handle large, complex

environments, and

1.2 Hierarchical Finite Sta teAutoma ton

(fsa) control systems

Although, computational theory shows that finite state automata (FSA) are more restricted than more general

representations such as general finite state machines, it has

been shown that networks of FSAs do have the same

computafional power as general finite state machines [5] such

as digital computers.

Control systems have a history of successfully operating

large complex systems of interacting components in real-time.

For most of this history, control systems were continuous.

They used sensors to collect data from various points in the

system (often referred to as the plant), process the data, and

perform actions on the plant through the use of mechanical

devices. A number of metrics such as controllability and

robustness have been developed to measure the performance

of the control system in controlling the plant.

The concept of a continuous control system was extended

to discrete event systems (DES) by Ramadge and Wonham

[7]. Instead of processing continuous numerical data, DES

controllers process strings of symbols that form a formal

language. This has resulted in a synergy with work on

processing formal languages from the discipline of Computer

Science, and resulted in mathematical theorems that provide

ample insight to the strengths and limitations of the approach.

Artificial Intelligence approaches do not have the same extent of mathematical grounding.

In DES control, the plant can be considered as a machine

that processes symbols from a (finite) alphabet in a way that forms a formal language, L, over an alphabet of symbols,

S. The alphabet, Z, can be partitioned into symbols

corresponding to uncontrollable events, Z^, and symbols

corresponding to controllable events, Zc. These symbols

represent events in the system. An example of a controllable

event is a friendly (controlled) aircraft firing at an enemy

target, while an example of an uncontrollable event is the

enemy target firing on the friendly aircraft. The controller can

be thought of as a recognizer of uncontrollable events and a generator of controllable ones. In analogy to continuous

control systems, the controllable events are the plant input

(feedback), and the uncontrollable events are the plant output.

The association of DES controller actions with decisions

expands the notion of intelligent control towards general

intelligence. Performance measurements from confinuous

control theory have been extended to include DES control.

New measurements, which are mathematically grounded, have

also been developed for DES controller performance. DES

control systems can exhibit intelligent behavior in the same way as AI software or robots. The performance measures for

intelligent DES control are an indication of intelligence in the more generic sense. More recent work has extended the nofion of single DES

controllers to interacting hierarchies of DES controllers. This

includes mathematical work to extend the notion of

controllability to hierarchies, where it is called hierarchical

consistency [13]. The use of a hierarchy allows the controller

to make complex decisions while taming the explosion of the

number of states that would take place in a single controller

performing the same function. Lower level controllers

transmit an aggregated version of their formal languages to higher-level controller nodes. The higher-level nodes exert control on lower levels by enabling and disabling controllable events at the lower levels. Such systems exhibit behavior

analogous to forming conclusions and formulating and

executing plans.

2. Controllability and Hierarchical

Consistency

The controllable events ofthe system can be disabled, i.e., prevented from occurring, by the controller. It uses this ability to influence the evolution of the system by preventing certain

events from occurring at certain fimes.

The goals of the control system are given as a set of

specifications such as, "If the aircraft runs out of weapons, it returns to base." Given the uncontrolled language of the plant,

L, the specifications can be formalized as a sublanguage of L, K L . The controller attempts to enforce the specifications

by restricting the plant to operate within K, rather than L. The

plant is said to be controllable by the controller if it can

operate the plant in a way that satisfies the specifications.

A controllable system is able to follow its specifications

from any of the system states. Even when an uncontrollable

event moves the system in an unanticipated way, there is a path that satisfies the specifications. This shows some

characteristics of purposeful behavior.

FSA controllers can be organized in a hierarchy where

high-level controllers achieve high-level goals by observing

and controlling low-level controllers. Hierarchical

consistency, the hierarchical equivalent of controllability, is the ability of the high-level controller to achieve its goals in

this way, starting from any legal combination of states in the

high and low level controllers. This behavi or, when it can be

achieved, shows the ability of the system to achieve high-level

goals through low-level actions, suggesting an ability to formulate and carry out plans.

454
2.1. Controllability

The controllability definition can be formalized as: A" is

controllable if ^„ fl ^ £ ^ , where K is the prefix closure

language of K. That is, the occurrence of any uncontrollable

event at any time permitted by the dynamics of the plant, will

not violate the control specifications.

Figure 1 shows how a controller would be implemented in

practice. The controller has been implemented as a finite state machine with the specification that the plant should always

return to state So- The first event, a, moves the controller from

state So to state Si and the second event, b, moves it from state

Si to state S2. At state S2, the controller takes action, e, and

moves to state S4, where it takes action h, and returns to state

Sq. The system's performance is characterized by the string

a,c,e,h, which is in K if the plant is controllable by the

controller.

Controller

Uncontrollable events Plant

a, C, .„ i

Actions (controllable

events)

• Figure 1 . Discrete Event System Control

The example exhibits behavior that looks purposeful. It seeks to bring the plant back to a goal state Sq. It also gives

the appearance of overcoming obstacles, uncontrollable

events, in an uncertain environment. For example it could not

perform action e when it was in state Si, because event c

occurred. It then achieved its goal in another way, by taking

actions e and h. If the plant is controllable, there will always

be a path to the goal state. In a large and complex enough system, these behaviors

would appear intelligent. These types of systems have been

implemented to solve complex applications such as controlling an automated factory.

2.2. Hierarchical Consistency

A hierarchical structure is used in control of dynamic

systems for a variety of tasks. Control is divided between

higher levels, which process events of greater generality and

larger scope; and lower levels, which process more specific

events of lesser scope.

This notion has been formalized in a way that is illustrated in Figure 2. The higher level controller is designed

to control a virtual high level plant with the following

components: the low-level, i.e. actual, plant; the low-level

controller, M, a mapping from strings of the low-level plant

language to high-level events; and U, a mapping from highlevel, controllable events to low-level control patterns. A

control pattern is a set of low-level controllable events that are

to be disabled in the low-level controller.

We start with a low-level plant with language Lp, and a low-level controller with language Ar,„ c . We wish to implement further restrictions on the performance ofthe lowlevel plant to a language /^,„ c K,^ c . This is to be done

by translating strings from Lp to a high level language,

Li,i - M{Lp) , which has an alphabet that containing

controllable and uncontrollable symbols, l'" = Z^' UZ|" . In

the example, each state in the low-level controller is labeled

with either a high-level symbol or to. This implicitly

determines M. Whenever the low-level controller enters a

state marked with a high-level symbol, the symbol is added to the high-level translation of the low-level string. In order for this scheme to work, the high-level controller

needs to be able to control the virtual high-level plant via the

mechanism shown in Figure 2. This is called hierarchical

consistency. It is true when M{KiJ = AT;,, . Note that the example is hierarchically consistent because

Virtual high level plant

High level controller

B

A,C

ABACACACAC,

Low level controller

M

hfebgabgabgabga

, h'ebgabgabgabga.

Low Level

Plant

Figure 2. Hierarchical Control

455
the only controllable high-level event, B, can be disabled with

the its associated control pattern {d,f}, which blocks the lowlevel controller from entering the state which transmits B to

the high-level controller. This is done by disabling all of the

low-level (controllable) events leading to the low-level state marked B. The high-level controller adds the following requirement

to the behavior of the low-level controller: an eventfrom the

set {dj] can only occur once in any low-level string. This

translates to the high-level requirement that: the event B can

only occur once in any high-level string.

3. Robustness AND Permissiveness

Two additional concepts in DES control theory that relate to intelligent behaviors are robustness and permissiveness.

Robustness is the ability of the controller to handle

uncertainty. In this section we will look at robustness with

respect to uncertainty in the characteristics of the actual plant.

Permissiveness is the ability of the controller to allow a wide

range of behaviors while operating the plant within the control

specifications. This behavior gives the appearance of

resourcefulness. It may allow the system to satisfy its requirements in multiple ways and might improve

performance in the real world where a single path to a goal

may be blocked by an unanticipated event.

In this section, we first propose a measure for formal

languages and then use it to derive quantitative measures of

robustness and permissiveness.

3.1. Robustness

In reality, the actual plant is not completely known. It is represented by an FSA, called the nominal plant model, which

contains all of the available knowledge about the actual plant.

The language of the plant is approximated by the language of

the nominal plant model, and the controller is designed on this basis.

It is therefore important to determine whether the

controller can control languages (i.e., plants) other that the one

for which it was designed. This quantity is known as robustness. The more robust the controller, the more likely it

is to be able to control the actual plant.

We have determined a method to estimate robustness

through the development of two concepts, a population of

plant languages near the language of the nominal plant model,

and a measure for formal languages of a given alphabet, Z. The population of plant languages can be defined as the

languages of plants derived from the nominal plant model

through the application of a small number of primitive

operations such as the addition or deletion of a single state or

transition, under the restriction that the results define a deterministic FSA.

The measure of formal languages can be defined in terms

of a weighted partition of the set Z of all finite strings in the

;U(L)=XwA(^),

1

where Ay(L) = •

alphabet I. The countable set I* is partitioned into disjoint

n subsets, 5i, such that I* = U S. , where n is at most countable

infinity (i.e., either « is a finite positive integer or infinity).

n Each subset is assigned a weight, such that ^w, =1. The

measure of a given language/., is defined as:

(1)

[l i/ 3^ e L such that s g 5/

[ 0 otherwise

The robustness of a controller, C, can then be defined as: R(C)=^M{LiP^)-L{P))D(L{P^)), where R is the

P,eR population of plant models related to P, the nominal plant

model; and D(L) = 1 if C can control L, 0 otherwise.

3.2. Permissiveness

Given a set of specifications in the form of a language, E,

a controller, AT], controls a plant, G, if L(G | /C,) c £ . There

could be, however, another controller, K2, such that UG \ Kf) Q L{G \ K2) E . In this case, K2 is considered more permissive than AT, . Although both controllers control

the plant, K2 allows a greater range of behaviors in the closed

loop system.

Since L{G \ K)^ L(G) , for any controller defined on G,

it follows that, for any controller K which satisfies E,

L{G I K) c L{G) n E . Using the proposed language measure,

we can define the permissiveness of a controller, K, defined on

a plant, G, operating within the specification language, E, as UjLjGlK)) V

[n{L(G)nE)

J

' (2)

4. Aggregation, Disaggregation, and

Scalability

The technique for hierarchical control, described in Section 2, can be extended to allow a high-level controller to control more than one low-level plant. It can also be extended

to form multilevel hierarchies of arbitrary size. The number

of nodes in the entire hierarchy grows linearly with the

number of leaf nodes. Since the leaf nodes recognize events

and take actions on the plant (i.e., the outside world), the

coordination provided by hierarchical control networks is scalable.

There is a mapping from events in the low-level, child

controllers to events in the higher-level, parent controller, and

a corresponding mapping from controllable events in the

parent controller to control patterns in the child controllers. If the mapping from child to parent compresses the data, the

456
A, E1,A|C|A|. hif|e|b|gia,b,g|a,„

|d„f,)

Low-level

Controller I

h| e,b,g,a,b,g,a,„

Low-level Plant

Low-level

Controller 3

h; ejbigjajbigja,-

Low- level Plant 2

A,B,AjCjAj... hjfjCjbjgjijbjg.a,..

Low-le\«l

Controller 3

*>! =jbjgjai)jgja,..

Low-level Plant 3

Figure 3. Hierarchical Control of Multiple Low-level Controllers

control structure will exhibit some additional intelligent

behaviors. It will appear to draw conclusions from lower

level events, make decisions based on abstractions, can carry

out the decisions at the lower, possible physical, level. There

is currently no technique to synthesize or measure "good"

aggregations from lower to higher level strings in formal

languages. We will therefore use the data compression ratio as an indication of this ability.

Figure 3 illustrates how a high-level controller can control more than one low-level plant. The events being recognized

by the high-level controller is the asynchronous product of the

high-level symbols being produced in each low-level

controller, i.e., they are sent to the high-level controller in the

order of their occurrence. Multilevel hierarchies are formed

when every non-root node sends higher-level symbols to the

level above it, and every non-leaf node sends control patterns

to the level below it.

a. Single

Controller Three Level

Hierarchy

f

<CJ ^ ^ MIf-.i @ ® '§ ?

V'

b. Two

.J,/ i \,, Level

, t <j Hierarchy ^

1

1

1 ^

^ 1 /li 1 f

Figure 4. Scalability of DES Controller Hierarchies

The scalability of control hierarchies is shown in Figure

4. The size of the hierarchy increases in proportion to the size

of the battlespace. In 4a, a single controller/plane is attacking

a single enemy target located in a given area, A. In 4b, a two

level controller hierarchy with three planes is attacking three

enemy targets in an area of 3A, and in 4c, a three level

controller hierarchy with nine planes is attacking nine enemy

targets in an area of size 9A.

5. Conclusions

Intelligent control, using a hierarchy of discrete event

controllers, is a good application for deriving quantitative measures of intelligence because these systems are complex

enough to exhibit intelligent behavior, but simple enough to allow for a relatively thorough mathematical analysis.

Domain independent, quantitative measures of controller

performance derived from the analysis are correlated with

intelligent behavior by the system. Controllability and

hierarchical consistency are correlated with goal -seeking,

purposeful behavior; robustness is correlated with the ability to handle uncertainty, and permissiveness is correlated with

resourcefulness. The process of aggregation and

disaggregation in hierarchical control suggests the ability to make abstractions, plan, and carry out plans.

6. Acknowledgment

Some of the concepts of intelligence metrics presented in

this paper were initially developed on the JFACC project,

sponsored by the Defense Advanced Research Projects

Agency (DARPA) and Air Force Research Laboratory, Air

Force Materiel Command, USAF, under agreement number

F30602-99- 1-0547 (JFACC). The U.S. Government is authorized to reproduce and distribute reprints for

Government purposes notwithstanding any copyright

annotation thereon. The views and conclusions contained

herein are those ofthe authors and should not be interpreted as necessarily representing the official policies or endorsements,

either expressed or implied, of the Defense Advanced

457
Research Projects Agency (DARPA), the Air Force Research

Laboratory, or the U.S. Government.

7. References

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[2] Baum, A., Newman, S., Weinman, J., West, R. and

McManus, Cambridge Handbook ofPsychology Health

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[3] Miele, F., "Skeptic Magazine Interview With Robert

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[4] Hamad, S., "Minds, Machines and SeaxXc" Journal of

Theoretical and Experimental Artificial Intelligence 1

:

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[5] Delorme M. and Mazoyer, J., Cellular Automata, A

Parallel Model, Kluwer Academic Publishers, The

Netherlands, 1999, pp. 32-35.

[6] Ramadge, P.J. and Wondham, W.M., "Supervisory

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[7] Ramadge P.J. and Wondham, W.M., "The Control of

Discrete Event Systems," Proc. IEEE, Vol. 77, No. I, January, 1989.

[8] Ray, A., Xi, W., Zang, H.and Phoha, S., "Hierarchical

Consistency of Supervisory Command and Control of

Aircraft Operations," Proc. 2"^ Symp. on Advances in Enterprise Control, Minneapolis, MN, July 10-11,

2000, published by IEEE.

[9] Searle, J. R., "Minds, Brains and Programs,"

Behavioral and Brain Sciences 3: 417-424, 1980.

[11] Takai, S. "Synthesis ofRobust Supervisorsfor PrefixClosed Language Specifications," to be published.

[ 1 2] Wondham, W.M. Notes on Control ofDiscrete-Event

Systems. Systems Control Group, Dept. ofElectrical

and Computer Engineering, U. ofToronto, April 1999.

[13] Zhong H. and Wondham, W.M. "On the Consistency of

Hierarchical Supervision in Discrete-Event Systems,"

IEEE Transactions on Automatic Control, Vol. 35, No.

10.

458
Choosing Knowledge Granularity for Efficient Functioning of An Intelligent Agent

Yiming Ye" and John K. Tsotsos^

IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA''

Department of Computer Science, York University, Toronto, Ontario, Canada M3J 1P3'

(1) 914 784-7460" (1) 416 736-2100^

yiming@watson.ibm.com° tsotsos@vis.toronto.edu^

Abstract

In this paper we introduce the concept of knowledge granularity and study its influence on an agent's action selection process. The goal is to provide a guideline for an agent to

select a reasonable knowledge granulsirity for a given task. Finally we present an idea of using an adaptive mesh method

for uneven granularity representation.

1 Introduction

An agent is a computational system that inhabits dynamic,

unpredictable environments. It has knowledge about itself and the world. This knowledge can be used to guide its ac- tion selection process when exhibiting goal-directed behaviors. Here we address the following question: "How much

detjiil should the agent include in its knowledge representation so that it can efficiently achieve its gOcJ?" There are two extremes regarding granularity of knowledge representation. At one end of the spectrum is the purely reactive

scheme which requires little or even no knowledge representation. At the other end of the spectrum is the purely

planning scheme which requires the agent to meiintain as much detEiiled knowledge as possible. Experience suggests

that neither purely reactive nor purely planning systems are capable of producing the range of behaviors required by in- telligent agents in a dynamic, unpredictable environment.

This paper offers an alternative point of view of the spec- trum of knowledge abstraction based on the grsinularity of

knowledge representation. The goal is to find the proper

balance in representing an agent's knowledge such that the

representation is detailed enough for the agent to select rea- sonable actions, and at the same time it is coarse enough

that it does not exhaust the agent's resources when select- ing those reasonable actions. At the end of the paper, we

propose an idea of using adaptive mesh to represent knowledge within a domain.

2 A Case Study

Here we use object search as an example to study the in- fluence of knowledge granulcirity on the performance of an

agent. Object search is the task of searching for a given

object in a given environment by a robotic agent equipped

with a pan, tilt, and zoom camera [13]. The goal of the

agent is to intelligently control the sensing parameters so

as to bring the target into the field of view of the sensor and to make the target in the image easily detectable by

the given recognition algorithm. To efficiently detect the

target, the agent uses its knowledge about the teirget position to guide its action selection process. This knowledge

is encoded as a discrete probability density that is updated

whenever a sensing action occurs. To perfectly encode the

agent's knowledge, the size of the cube should be infinitely

small - resulting in a continuous encoding of the knowledge.

But this will not work in general because an infinite amount

of memory is needed. In order to make the system work,

the agent is forced to represent the knowledge discretely - to use cubes with finite size. This gives rise to an interesting question: how we should determine the granularity of

the representation (the size of the cube) such that the best

effects or reasonable effects can be generated. The granularity function G can be defined as the total memory used by

the agent to represent a certain kind of knowledge divided

by the memory used by the agent to represent a basic ele- ment of the corresponding knowledge. In this case, G equals

to the total number of cubes in the environment.

D i 10 IS a) !S 30 0 S 10 li 9 U JO 10 100 lOD lOLO lOmO Icttt lOD lODGO lam) \:M

(a) (b) (c) (d)

Figure 1: Experimental results for object search agent.

We have performed experiments to study the influence

of knowledge granularity on the performance of the agent.

Usually the higher the value of the knowledge granularity,

the longer the time needed to select an action. This is simply

because the planning system has more data to be processed.

The approximations involved in discretization wiU cause er- rors in calculating various values. In general, the higher the

value of the knowledge granularity, the less the error caused

by discretization. Figure 1(a) shows the errors caused by

459
granularities 40 x 40, 50 x 50, and 60 x 60. The error associ- ated with knowledge granularity may influence the qucility of

the selected actions, and thus influence the performance of

the agent. As shown in Figure 1(b), the higher the granularity, the less the number of actions are needed for the system

to reach its detecting limits. Figures 1(c) (d) show the per- formance of the agent for action execution time 1 second (c) and 1000 second (d), respectively. We can see that a higher

granularity may not always beneficial, especially when the

action execution time is long.

(aa) (ab) (ac) (ad)

(bd) (be) (ca) zh)

(cc) (cd) (ce) (da)

Figure 2: The influence of knowledge granularity on the

performance of an agent.

3 Knowledge Granularity in General

In this section, we study in general the influences of knowledge granularity on an agent's action selection performance.

For a task oriented agent, a finer granularity usually results

in a better selected action. However, the action selection

time for a finer granularity is usually longer. Thus, a finer granularity requires more time for selecting actions, and has

less time in executing actions. On the other hand, a coarser

granularity requires less time for action selection, thus has more time for action execution. In other words, with respect

to a fixed time constraint, an agent can usually execute more

low quahty actions for a coarser granularity, and less high

quahty actions for a finer granularity. It is thus very in- teresting to study how the performance of a task oriented

agent is influenced by the degree of knowledge granularity.

and how the agent should choose a reasonable granularity

from the spectrum of knowledge abstraction.

Different agents use different kinds of knowledge and dif- ferent kinds of action selection procedures. Because of the

complexity and diversity of the world of agents, it is impossible to provide a general conclusion or solution with re- gard to knowledge granularity. What we can do is to group

agents into different categories and study the behavior with

respect to each category. It is obvious that the performance

of an agent is influenced by the action execution time, te, the action selection time, ts, the total time constraint for the given task, T, and the quality Q of the selected and

executed actions, ts and Q is influenced by the knowledge

granularity adopted by the agent. Suppose for a granularity

g, the average time needed in selecting an action is ts{g),

the average contributions of a selected action to the task

is Q{g)- Assuming that the total contributions U made by

an agent within the time constraint T can be represented

by the sum of the average contributions of all the actions

that is executed within T. Then, U can be represented cis follows.

U{9) = I te + tsig) \Q{9)

In the following, we study how U{g) is influenced by dif- ferent ts{g) and Q{g). We assume T = 100 and g G [1, 150].; The following functions are used in our empirical study:

faig) = 5; fb(g) =ln[g)\ fc(g) =5 + 1; fd{g) = g*g*9 + l; fe{g) = exp{g) + 1. These functions represent different rela- tions between the knowledge granularity g and the entities to be discussed. Function fa{g) means that the entity is a

constant, and thus is not influenced by granularity. fb(g),

fc{g), fd{g), fe(g) refer to different degrees of the influence

of granularity on the entity. Figure 2 shows how the granularity influences the performance of the agent under different situations. The graphs are indexed by the above functions.

For example. Figure 2(ab) corresponds to the situations that

ts{g) = fa{g) and Q{g) = U[g).

From Figure 2, we can notice that tg is a very important factor that influences the selection of the knowledge

granularity. Figures 2(aa)(ab)(ac)(ad)(ae) show the situa- tion when ts is not influenced by the granularity. In this special case, the finer the granularity, the better the per- formance, except for the first one (aa). From Figures 2 (ca)(cb)(cc)(cd)(ce) we can notice that for a large execution time (> 50), g should be low in order to guarantee that

at least one action can be executed. Figure 2(ca) shows the

situation that the benefit of each action is not influenced

by the granularity, thus a smaller graniilarity is preferred no

matter what the action execution time is. From Figure 2(cc) we can notice that the situation becomes complex. For ex- ample, for a small te, there are several granularities that can

generate satisfactory results. These reasonable granularities are different for different tg.

4 Selecting Knowledge Granularity

The experiments in the above section show that the level of knowledge granularity has a big impact on the quahty

and speed of the agent's behavior. It is thus important for an agent to adapt its knowledge grainularity based on en- vironmental and task-specific demands. In this section, we

address the following interesting question: how can we select the knowledge granularity G(k) for a given representation

scheme k such that the best agent performance or a rela- tively good agent performeince can be achieved?

460
In some situations, we are able to select the best knowledge granularity in the sense that it maximizes the perfor- mance of the agent. Here is an example. Suppose we have

an agent whose task is to collect food from a region of length

L within a time limit T. The agent can use different rep- resentation lengths A = {Zi , . . . , ig} to represent the region,

(suppose

Jis integer, where 1 < * < If the agent selects

I G {Zi , . . . ,lq\ as its representation scheme k for the cor- responding knowledge, then the corresponding knowledge

granularity for this scheme will be G(k) = y. The total region is thus divided into j units. The process of food

collection is as follows. Before the collecting process, all the units of the region will be in the status of "not ready"

When the collecting process begin, one of the luiits becomes

"ready". The agent wiU then search for this unit. The time,

ts{l), used by the agent to locate the unit is the time for the

agent to select an action under the current representation

scheme. Suppose ts{l) = y. After the unit is located, the

agent wiU collect food from this imit. The total time needed

for the agent to coUect food is the time needed for the agent

to execute the selected action. Suppose it is te(l) = CI

(where C is a constant). The total amount of food that

is collected is B{1) = j. When the agent finishes its food

collection process at the selected unit, the status of another

unit will become "ready". The agent will search for this new unit and coUect food again from this new unit. This

process will contmue until the total time T is used up. If the total time T is exhausted when the agent is locating a

unit or when the agent is collecting food within a unit, then

the amount of collected food from the corresponding unit

will be zero. It is obvious that the number of units that can be processed by the agent within T is and the

^ (i)-t-t (i) ' number of units available is j.

The performance P of the agent is measured by the total amount of food collected by the agent and is given by the

following formula:

tB{1)

I 1

if >

Jfl(Z)

t,(i) + t^(!) - !

if X <• k (1)

This is actually

P =

CL

(2)

CL

The problem is to find a Z in A = {Zi , . .

. , Z,} such that

P is maximized. The set A can be divided into two parts

A^ = {li, . . . and As = {Z^+i , . . . , Z,}, such that aU the

elements in A^ are less than

Y

T-CL

Ab are greater than or equal to .^^/^

and all the elements

^-jj. It is obvious

that for elements Z £ Aa, the smallest one has the best per- formance because p- is a decreasing function. For elements

Z G Ab, we can calculate the value of LcFTT-ll ^° identify

the best element. Then we compare the smallest element

in Aa and the best element in Ab to identify the one that

maximizes the performance of the system.

The above example shows that in some situations, ein agent is able to identify an optimum knowledge granularity

based on the task requirement (here T) and the environmental characteristics (here L). The basic method is to try to represent the performance of the agent as a function of the

agent's knowledge granularity, and then to find the granularity that maximizes the performance.

In general, it is very difficult or even impossible to find a best knowledge granularity for an agent, because the per- formance of the agent might be influenced by many other

factors in addition to the knowledge granularity. For ex- ample, there does not exist a best knowledge granularity

for the object search agent, because its performance is also influenced by the initial target distribution. A granularity

that is best for one distribution might not be the best for another distribution. Thus, in general, we need to relax our requirements. Instead of finding the best granularity, we search for a reasonable one such that a relatively good

performance can be achieved. Because of the variations of

different agent systems, it is impossible to provide a detailed

procediure to select the acceptable granularity that can be

applied to aU the agent systems. However, we can provide

a general guidehne for the selection of the knowledge granularity.

5 Selecting Reasonable Granularity in Complex Agent En- vironment

In an agent environment where the relationships among the

task constraints, the environments, and the knowledge granularity are very complex, the "demand-environment-granularity"

(DEG) Hash Table can be used to select a reasonable granularity. The DEG Hash Table is a Hash Table such that the

"key" is the combination of different factors and the "value"

is the granularity that is appropriate for the corresponding

factors. When an agent is informed of task requirements,

it first transforms the task requirements and the environmental factors into a key. Then it retrieves the granularity

from the DEG Hash Table based on the key. This granularity wiU be used by the agent to represent the corresponding

knowledge.

For a complex agent environment, it might have more

than one task constraints Ti , . . ., T„.j, . Each Ti forms one component in the "key" of the DEG Hash Table. It can

be divided into severed groups Ti i, . . ., ^ based on cer- tain criteria. For example, the task constraint for an object

search agent is the total time available for the search. This

time constraint can be divided into groups hke "from 1 sec- ond to 30 seconds", "from 30 seconds to 100 seconds", etc.. In addition to the task constraints, we should also consider the influences of the environmental factors when se- lecting the granularity. Suppose Ei, . . ., En^ are the en- vironment factors that need to be considered. Like above,

each Ei can be divided into several groups Tj i , . . ., Ti^k based on a certain criteria. The DEG Hash Table is then looks hke following:

T El EnE G

^1 ei 9

Table 1 Where each row in the table, except the first one, gives a "key" (ti, . . .

, tnrp , ei , . .. , e-n^ ) and the corresponding granularity value g. Here, 6, is a category (group) for the task

constraint factor Ti and Ct is a category (group) for the en- vironmental factor Ei. Term g is the knowledge granularity

value corresponding to the "key" and should be obtained

461
by conducting various simulation experiments or theoretical

analysis before the agent performs any task. When an agent

is informed of a task, it first determines the key based on

the current situations, and then uses this "key" to locate

the knowledge granularity.

6 The Adaptive Mesh Approach

The above analysis and methods assumes that an agent

maintains exactly the same graniilarity for the whole re- gion. This may not be necessary. For example, for the case

of object search, the search agent may only need to have

a detailed representation of the surrounding area or cireas with high density. Because these areas are the most important areas that need to be considered during the sensor planning process. Thus, the granularity should be differ- ent for different areas. Actually people seldom maintain the same granularity when they perform tasks. They dynamically adjust the granularity at different time and iinder dif- ferent situations. For example, when a person travels from

his university to another city to attend a conference, the

representation of the geographical situations or maps wiU

be different at different times and context. Before he left for the conference, he will have more detailed representation of

his office and less detailed representation of the conference

site. However, when the plane is about to arrive at the

destination, his representation of the conference site will be

much more detail and he will intentionally use more crude

representation for his office. The above discussion suggests that it might be beneficial for an agent to maintain a non-imiform representation

of its knowledge and dynamically adjust the granularity dis- tribution based on context. In the following, we propose a method of achieving this. We illustrate our approach under the scenario of object search. Our goal is to adjust the

representation density based on the target distribution, the

higher the density, the more detail the representation.

Our approach is to first obtain the highest granularity

map as represented by little cubes. Then, we combine those

cubes whose probabilities are not big enough into a larger

blob. This process wiU continue until the probability within

the blob is big enough. This effort wiU result in an mesh

representation of the knowledge.

Here is the algorithm:

1. TesseUate the region into small cubes corresponding to the highest granularity.

2. Assign all the small cubes as unconsidered.

3. The process terminates when all the cubes are considered.

4. Find the unconsidered cube with the smallest probability, and mark it as considered.

5. If the probabihty is smaller than the threshold, then

find a neighbor cube that is unconsidered with the

smallest probability. Mark this cube as considered.

If there is no such cubes, goto 3.

6. Combine the two together and form the blob. If the

probabihty of the blob is bigger than the threshold,

goto 3.

7. Find a neighbor imconsidered cube of the blob with

the smallest probabihty. Mark it as considered and

goto 6.

7 Conclusion

The message derived from both the case study and the general analysis is that knowledge granularity has a big impact on the performance of an agent. Thus, an appropriate

knowledge granularity should be selected by an agent in or- der to guarantee a satisfactory result. In complex situations,

the selection of granularity depends on many factors. In general, we can construct graphs like Figure 2 to analyze the

effects of granularity on the performance of the agent under

different factors and constraints, and then select a favorable

granularity. We may also use an adaptive mesh to represent

knowledge in some situations.

References

[1] R. Bajcsy. Active perception vs. passive perception. In Third IEEE Workshop on Vision, pages 55-59, BeUaire,

1985.

[2] R. A. Brooks. InteUigence without representation. Artificial Intelligence, (47):139-160, 1991.

[3] I.Ferguson. Touring Machine: An Architecture for Dynamic, Rational, Mobile Agents. PhD thesis, University

of Cambridge, UK, 1992.

[4] T. D. Garvey. Perceptual strategies for purposive vi- sion. Technical Report Technical Note 117, SRI Inter- national, 1976.

[5] M. Georgeff and A. Lansky. Reactive reasoning and

planning. In Proceedings of the Sixth National Confer- ence on Artificial Intelligence, pages 677-682, Seattle,

WA, 1987.

[6] A. Meystel. Theoritical foundations of planning and

navigation for autonomous mobile systems. Interna- tionaljournal of Intelligent Systems, 2(2):73-128, 1987.

[7] A. Meystel. Multiresolutional architectures for au- tonomous systems with incomplete and inadequate

knowledge representation. In Artificial Intelligence in

Industrial Decision Making, Control, and Automation.

Eds. S.G. Tzafestas and H.B. Verbruggen. Kluwer Academic, pages 159-223, 1995.

[8] A. Meystel, R. Bhatt, D. Gaw, P. GragUa, and S. Waldon. Multiresolutional pyramidal knowledge representation and algorithmic basis of imas-2. In Proc. of Mobile Robots, SPIE Vol. 851, pages 80-116, 1987.

[9] J. MuUer and M. Pischel. Modelling interacting

agents in dynamic environments. In Proceedings of

the Eleventh European Conference on Artificial Intel- ligence, pages 709-713, Amsterdam, The Netherland,

1994.

[10] S. Sen, S. Roychowdhxiry, and N. Arora. Effects of local information on group behavior. In Proceedings of Sec- ond International Conference on Multi-Agent Systems,

pages 315-321, Kyoto, Japan, 1996.

[11] T. TyrreU. Computational Mechanisms for Action Selection. PhD thesis, Center for Cognitive Science, University of Edinburgh, England, 1993.

[12] M. Wooldridge and N. Jenning. InteUigent agents: the- ory and practice. The Knowledge Engineering Review,

10(2):115-152, 1995.

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[13] Y. Ye. Sensor Planning for Object Search. PhD thesis,

University of Toronto, Toronto, Canada, January 1997.

[14] Y. Ye and J. K. Tsotsos. Sensor planning in 3d object search: its formulation and complexity. In The 4th

International Symposium on Artificial Intelligence and

Mathematics, Florida, U.S.A., January 3-5 1996.

[15] Y. Ye and J. K. Tsotsos. Knowledge difference and

its influence on a search agent. In First International

Conference on AUTONOMOUS AGENTS, Marina del

Rey, CA, January 1997b.

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action selection. In Eighth International Conference on

Artificial Intelligence: Methodology, Systems, and Applications, pages 475-489, 1998.

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463

PART II

RESEARCH PAPERS

8. EXPLORATORY ISSUES

8.1 FIPER: An Intelligent System for the Optimal Design of Highly Engineered Products

M. W. Bailey, GEAE, USA, and W. H. VerDuin, OAl, USA

8.2 Towards an Objective Comparison of Stochastic Optimization Approaches

J. C. Spall, S. D. Hill, and D. R. Stark, The John Hopkins University, USA

8.3 Some Measurable Characteristics of Intelligent Computing Systems

C. Landauer, K. Bellman, The Aerospace Corporation, USA

8.4 Generalizing Natural Language Representations for Measuring the intelligence of Systems

A. Meystel, Drexel University, USA

8.5 Toward Measures of Intelligence Based On Semiotic Control

C. Joslyn, Los Alamos National Laboratory, USA

8.6 Selected Comments on Defining and Measuring of Machine Intelligence

P. K. Davis, RAND, USA

8.7 Hierarchic Social Entropy: An Information Theoretic Measure of Robot Group Diversity

T. Balch, Carnegie Mellon University, USA

8.8 The Development Approach to Evaluating Artificial Intelligence A Proposal A. Treister-Goren, J. Hutchens, AI NV, USA-Israel

8.9 General Scientific Premises of Measuring Complex Phenomena

H. M. Hubey, Montclair State University, USA

8.10 Evaluation of System Intelligence via Pictorial Data Visualization V. Grishin, View Trends, Ltd, USA, and A. Meystel, Drexel University, USA

465
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FIPER: An Intelligent System for the Optimal Design of Highly Engineered Products

Michael W. Bailey, GE Aircraft Engines, Cincinnati, OH 45215

and William H. VerDuin, OAI, Cleveland, OH 44142

Abstract

This paper outlines the development of an advanced

design environment that invokes a new intelligent system

paradigm for the design of highly engineered products. The

paradigm of the CAD Master Model (MM) is extended with

the introduction of the Intelligent Master Model (IMM). The

use of knowledge based engineering tools captures why and

how of the design in addition to the what.

Turbine engine development is a highly coupled

disciplinary process. With ever increasing demands in life cycle costs, environmental aspects (noise, emissions and

fuel consumption) and performance, the availability of

accurate analytical tools during the design process is a

given and ceases to be a discriminator between competitors.

The application of these tools and their automated

interaction in a robust computational environment may

determine the success or failure of a project by reducing

design cycle time and avoiding costly rework.

This paper describes pilot projects at GE Aircraft Engines (GEAE) and the productivity metrics that justified

broader implementation within GEAE. Developed using the UniGraphics CAD system for the design of aircraft engines,

^ "this system is applicable to any highly engineered product.

This approach will, with the support of a four year $21.5M

NIST ATP (National Institute of Standards and Technology

Advanced Technology Program), be generalized in FIPER

(Federated Intelligent Product EnviRonment), a web based

environment that will support multi-disciplinary design and

optimization.

The Problem

The development of robust and optimal, highly

engineered products and processes in today's environment

of step-function reductions in cycle time, cost take-out, and

improved performance seriously tax the capabilities of

today's design systems. Further exacerbating the problem is the need to improve and control quality, for both internally

manufactured parts and materials and parts produced

through supply chains. Since products are now designed,

manufactured and serviced at geographically disparate

locations, the ability to share relevant product data is critical.

The Solution

FIPER presents a solution in the form of an Integrated

Multidisciplinary Design System which

• Exploits the concept of the IMM, permitting context

specific views of the MM

• Seamlessly integrates relevant technologies to enable

rapid instantiation and simulation-based evaluation of

products and processes

Vision: Integrated Multidisciplinary Design Environment

The integrated multidisciplinary design environment

under development will enable users to define process maps

and rapidly integrate their own proprietary product-specific

design and simulation tools through visual programming

techniques. It will automatically provide access to a set of

technologies including CAD systems and low and high

fidelity analysis modules, as well as Multidisciplinary

Optimization (MDO) and Robust Design technologies. It will exploit Knowledge Based Engineering to capture rules and

best practices that can drive product definition through the (IMM)

Intelligent Master Model

The Intelligent Master Model (Figure 1) is a major

enhancement to the Master Modeling concept. Knowledge

Based Engineering (KBE) is fused with Product Control

Structure (PCS), conventional MM and Linked Model

Environment (LME) to collectively render it an Intelligent

Master Model. The IMM captures the intent behind the product design by representing the why and how, in addition to the what of a design. The geometric description

is only one view of the information associated with the total product model. The IMM can also contain part

dependencies, geometric and non-geometric attributes,

manufacturing producibility and cost constraints. IMM can provide access to external databases, and can be integrated

with proprietary and commercial codes through the LME.

Intelligent Master Model

Engineering

Unted Model

Envdrofwnent

CAO Sf9t9m mt^ropormblltty

The IMM can

Figure 1. Intelligent Master Model

467
capture and archive corporate design practices as well as design and manufacturing engineering expertise. This

knowledge can enable less experienced engineers to

consistently produce correct first time designs.

The IMM captures the process for generating the PCS

at the conceptual and preliminary design level, which then

flows the critical information to the detail design and

manufacturing. The IMM uses its knowledge base to enable

parametric scaling of designs in a top down fashion. When

parameters must be computed by execution of simulation

codes, the IMM manages this execution by working with

process integration tools.

The Master Model

The Master Model captures the requisite information,

geometric and non-geometric, to enable context-specific

views of necessary design, manufacture, test, and service

data. A product design system that supports early

requirements definition and flow-down demands that the underlying representation be flexible to geometric, attribute,

feature and knowledge-based changes. The traditional CAD

representation is flexible only in a geometric sense. The Master Model (Figure 2) at the lowest or geometric

level consists of parametric geometry features such as

primitives, extrusions, holes, etc., which form the basic

product description. Parameters associated with these geometric features are a subset of the key characteristics which are manipulated to define the product. At this level,

the key characteristics include the traditional concepts of

dimensionality (length, radius, angle, etc.), as well as those

concepts that follow from knowledge-based solid modeling

such as offset, spatial alignment, and perpendicularity

constraints. Additionally, the existence of a feature is itself an attribute which may be turned on or off as needed to represent the part to varying fidelity levels. For example a

bolthole is typically present during a stress analysis but

omitted during a computational fluid dynamics analysis. This

simplification would be part of the context model, thus

creating a context-specific view of the geometry using

feature suppression.

Figure 2. The Master Model supports Feature based

Modeling

Figure 3. Feature Based Modeling

Using parametric feature-based technology, models are constructed by initially creating simple parametric block

shapes to which features (e.g. flanges) are attached. Compound blends are then created and added to the model

together with standard features such as radii and chamfers,

to create the axisymetric solid. Finally, non-axisymetric

features such as holes and slots are then added as shown in Figure 3. This feature-based approach is consistent with

feature based analytical model building and cost estimating,

while also providing feature suppression functionality.

The initial approach to KBE was the encapsulation of

product rules within UniGraphics XESS spreadsheets. These

spreadsheets are linked to the geometry such that design

rules and practices are parameterized to drive geometry.

External codes such as those for disk design could also be

executed. Thus an increase in flow thorough the compressor

would initiate an aerodynamic resizing of blades and vanes

resulting in a blade platform and attachment resizing

combined with a disk redesign due to increased centrifugal

loads. The whole compressor would thus "rubber band" or

parametrically expand to accommodate increased flow.

The Product Control Structure

The PCS facilitates top-down control of the design,

allowing the engineer to layout the system configuration and

control changes in a top-down fashion. It facilitates what-if

analysis at the conceptual, preliminary, and detailed design

levels by allowing the designer to make parametric changes

or to evaluate alternate configurations. This encourages

design reuse and enforces standardization in the design

process. The PCS is a hierarchical decomposition of the product

into its systems, subsystems and components (Figure 4). These are represented by high-level product attributes and

key datum planes and axes to capture their spatial location

and orientation. Once the top-level datums have been

established and referenced by the subsystems, each

subsystem can be designed independently in a distributed manner and later be automatically assembled. Within the PCS, components may be represented by preliminary,

simplified geometry (e.g., 2-D cross-sections) or just datums.

The cross-sections are picked fi-om a library of cross-section

468
types based on rules. The values for the parameters that define a cross-section are determined using rules

captured in the knowledge base. The leaf nodes of the

PCS become the seed parts for the bottom-up design of

the product into a 3-D assembly. The parts contain 3-D

features to capture additional design and manufacturing

intent. Everything is fully associative, and thus all changes to the PCS propagate throughout the model.

The Linked Model Environment

Disciplines such as stress analysis, heat transfer

analysis, fluids or combustion analysis, and

manufacturing and cost prediction each use their own

abstraction of the physical model of the product. Within one discipline, several context-specific views may exist as the design evolves. For example, 2-D axisymmetric

stress analysis models and detailed 3-D stress analysis

models of various levels of refinement for the

individual components of a jet engine are required.

Each of these analysis models is associated with one or more simulation tools or codes, from simple response

surfaces or performance maps during the conceptual

design phase, to more complex analysis codes for

detailed design, manufacturing process simulation, and

cost modeling. This provides the promise of geometric

zooming. Historically,

469
SImutation Cube

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Figure 5. Linked Model Environment

470
Amiysis "ZoomuVjg " Cube

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Of Sequence of Simulation Tools

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Figure 6. The Simulation Engine

these models exist in a heterogeneous environment, without

explicit connections between them. Thus, a design change

demanded by one disciplinary group has to be manually

incorporated into all the various models of the product that

co-exist; a process that is both tedious and error prone. Within the LME (Figure 5) a product's analysis and process models are linked to the Master Model so that all models are automatically synchronized to a single Master Model. Thus,

a process is established by which design changes caused by

one discipline are fed back to the Master Model. A Product

Data Management (PDM) system tracks the design revisions

and the associated analysis views or context models of the product.

Simulation Engine

An integral part of the LME is the simulation engine

where the analysis tools themselves are wrapped for ease of

reuse in a plug-and-pay architecture. To achieve robust and

optimal designs, iterative analysis is required. Therefore,

ready access to the requisite analysis codes and process

maps is essential. The Simulation Engine (Figure 6) provides:

• a programmable mechanism to specify and control the execution of the analysis process

• a mechanism to enable users to easily wrap codes

• an ensemble of pre-wrapped multidisciplinary, variablefidelity, product-specific analysis tools • Individual codes and process maps to be linked to the IMM for either manual or automatic execution under

program control. The NASA Glenn Research Center's Numerical Propulsion

System Simulator (NPSS) has used a similar cube

representation to show the interconnectivity of functional

codes, multiple levels of analysis, and zooming to represent

their computer-based engine in a test cell. The Simulation

Engine is a generalization of this concept for generic

products.

Design For Six Sigma

The goal of Design For Six Sigma (DFSS, 3.4 defects per

million opportunities) is to create products and processes which are at Six Sigma levels of performance,

manufacturability, reliability and cost. DFSS is based on an

orderly process which identifies and flows down Critical to Quality (CTQ) characteristics for the product, process or

service. This enables quality measures to be driven into the product during the early design phases where the cost of

implementing changes is relatively low in comparison to fixing the problems later in the product life cycle. Key design

factors for each CTQ are identified and statistical performance models are developed. Modeling, simulation.

Design of Experiments (DoE) and analysis are usually

employed to develop the statistical models. The essence of

DFSS is to migrate from a deterministic to a probabilistic

design approach. DFSS is generally focused on shifting means for CTQ's and reducing variances about means so

that customer expectations are met at minimum cost. Robust Design is an intrinsic part of DFSS. Traditionally

optimal design and robust design were viewed as independent technologies, but in fact there is great

synergism and common core concepts that can be exploited

to achieve

471
FIPERRole

Performance &

Technical Requirements

Integration

and

Optimization

-vy3 = 0

Key Control Robust Parameter

Design (KCP)

Figure 7. The True Meaning of DFSS

optimal and robust designs for products and processes. Optimality and robustness often have competing objectives.

The focus of the robust optimization problem is to simultaneously optimize the performance (mean of the response) and minimize the variation. In other words, a maximization problem would not merely strive for the highest

peak, but would strive for a high plateau. In practice this represents a trade-off between Performance and Technical

Requirements, Reliability and Producibility (Figure 6). This

represents a paradigm shift in design methodology.

Background

There are many definitions of a Master Model. At GEAE the

definition is a single geometric representation, ideally 3-D,

created at concept using feature based parametric modeling

techniques in a linked associative environment, and utilized through manufacturing. In addition there is an evolution of a

tight integration of all elements of a product creation,

manufacturing and support permitting true concurrency for analysis and manufacturing since updates can be flowed

down to the individual activities from the MM. An additional

requirement is the management of all types of data or metadata within the Common Geometry environment. The

fusion of a conventional MM with PCS, LME and KBE

results in an IMM, the next logical step in CAD's evolution.

Historically analysis codes were coupled together with

input and output files; geometry was provided as an output

as necessary, probably as an IGES file. The new approach is

to have geometry central or common to all processes and to use it as a design integrator. This facilitates CAD integration

with analysis and manufacturing. Four years ago GE Aircraft

Engines started its Common Geometry initiative, based on

UniGraphics and commercial code to the extent possible. The

first year focused on strategy. Historically at GEAE

conceptual and preliminary design are accomplished using

simplifying assumptions in a unique set of tools. Changes in the underlying assumptions and the lack of a rigorous

handoff to detail design often meant that the preliminary

design was repeated. Since business commitments are made

based on preliminary design this increased the risk of

meeting customer CTQ requirements. It is well understood

that 70 to 80% of a product's cost is locked in during

conceptual and preliminary design. Previous efforts had

focused on productivity tools that relied heavily on

automation. The discovery of UG/WAVE with its top down

approach using a Control Structure meant it was possible to drive the design using requirements providing functional

and spatial integration thereby making it possible to create 3-D solid models at the Conceptual/Preliminary Design

Phase. This combined with a tight integration of CAD with

analysis and manufacturing in LME would provide a truly concurrent design environment.

During the second year three pilots were conducted to demonstrate the technical feasibility and generate metrics for the return on investment analysis necessary to move to a broader implementation across the business. These pilots

focused on Conceptual/Preliminary design, Detailed design

and Manufacturing. Although these pilots addressed

different sections of the engine, success in individual areas would provide confidence to proceed to a broader

implementation.

472
Heat Transfer Context Model

Turbine Rotor Assembly

Figure 8. LME Engineering Pilot SOLID Pilot

The puqDOse of the SOLID (System Oriented Layout

with Integrated Design) pilot was to build a 3-D solid geometry model of a compressor. This was constructed

using the UG/WAVE PCS. Model construction is of

paramount importance if productivity gains downstream are

to be realized. Constructing the model from features enables

suppression of selective features by downstream users using context models or "views of geometry". Traditional 2- D axisymetric cross sections can still be generated from the 3-D solid. These would be completely associative to the

solid and would constitute an output instead of an input.

Thus the parameters that drive the 3-D solid would also drive the 2-D

cross section. Time invested in constructing the 3-D models

facilitates updates as the design evolves. By segregating out

the work that would be eliminated using the SOLID model

from the charging data from a recently completed program, it was estimated that 34% would be saved at the Conceptual/Preliminary design phase and 7% at the Detailed

Design phase.

A key element in the Integration of CAD with Analysis,

or any geometry dependent activity, is the creation of

context models. A Context model uses the concept of CAD

Assemblies to create a "view" of geometry. Just as it is conventional CAD practice to combine parts into assemblies

building up into the complete system, it is possible to combine geometry with context information in the form of an

assembly. Context in this application means the attachment

of information necessary to create a structural, thermal or Computational Fluid Dynamics (CFD) model to geometric

entities. The rotor assembly could also be regarded as a

context model. This information could be boundary

conditions such as pressures, temperatures, loads and the meshing strategy such as mesh seeds or mesh densities. These attributes are applied to the geometric entities in the CAD package.

This context information or "Tagging" should be robust

to parametric or non-topological changes and have some

robustness to topological changes. A longer term goal is to apply these "Tags" as the analysis model is built in the meshing software, then export these to the CAD software for

storage. Currently they are applied in the CAD software. The

CAD assembly context model is imported into the meshing

software such as PATRAN, ANSYS or ICEM CFD to create

the application model. The heat transfer context model is shown in Figure 8. From data accumulated during the pilot, it was estimated that savings of 25% in Detailed Design were

possible.

In the manufacturing pilot the focus was using

manufacturing context models in conjunction with the 3-D

Master Model to generate in process planning and shapes,

tooling and Computer Numerically Controlled (CNC)

machining tapes. A Low Pressure turbine disk currently in production was used. Note that in the manufacturing

environment the modeling works in the opposite sense to

detail design. In the detailed design features are added to the model as the design progresses from conceptual through

preliminary and detail design; in manufacturing features are removed consistent with manufacturing operations until the raw material remains. Figure 9 shows the associated in- process models and tooling together with the engineering

analysis, results and drawing creation. The pilot

demonstrated a 1 5% reduction in process development time

and an 80% reduction in process regeneration for parametric

changes. In addition the associated tooling was updated

when the model was changed.

Computer Measuring Machine (CMM) inspection

programs can also be generated from the process models.

This is another key context model use of the linked

associative environment. Aircraft engine manufacture

involves the machining of complex shapes from high

temperature alloys that "move" during the manufacturing

process. Thus it is important for process control to inspect

the process shapes to know what the dimensions are so adjustments can be made to future machining operations.

This offers the possibility of a "real time" machining

feedback loop.

473
I

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Figure 9. LME Manufacturing Pilot

1

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474

!

1
Tools Strategy^,

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(Productivity

V tools

• Network Enabled

• Automating Serial Processes

• Part Specific Bottom-iift

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' Visual collaborative environment

• Incorporates Digital Mockup

• Spatial Integration & Analysis Product Data Management

Web Enabled

Engineering Management

Manufacturing Management

Supply Chain Management

Services Management

• Visualization Plus

• Federated Intetllgent Product EnvlRonmaflt(FIPER)

top-Down

• f'l/ncf/ona/integration & Analysis

Figure 10. Incremental Approach to Development and Deployment

475
Incremental Approach to Development and Deployment

GEAE's incremental approach to development and

deployment is shown in Figure 1 0. Productivity Tools/Common Geometry was network enabled

and automated serial tasks such as mesh creation on

individual parts. This can be described as the "run faster" approach and is sub-optimal since it optimizes individual

Management, Manufacturing Management, Supply Chain

Management and Services Management with databases,

parts lists, process flow, etc. It would provide the infrastructurefor subsequent development.

e-Visualization represents an enhancement of e-PDM in that

it provides a visual collaborative environment incorporating

a digital mockup. It thus provides a visual representation of

the engineering assembly permitting spatial integration and

rows«f

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parts in bottom-up design as opposed to the system design.

e-PDM focuses on Product Data Management (PDM) and is Web enabled. PDM typically provides Engineering

is with functionality such as interference clearance and

removal envelope assessment.

Figure 11. Federated Integrated Design EnviRonment

e-Engineering builds on the benefits of e-PDM and e- Visualization to provide an environment that supports

functional integration and analysis providing a top-down or "run smarter" design environment.

The recent PIPER project award by NIST ATP will provide such an environment. Drawing on the experience

and qualifications of the PIPER team members and

leveraging GE's Corporate commitment to Design For Six

Sigma methodology and products, the proposed program

will result in the development, demonstration and transition of advanced tools and technology. Key elements of the NIST ATP include:

• Development of an extensible, standards-based plug

and play. Web-based architecture to enable the creation

of Six Sigma products and processes.

• Development and major enhancement of a set of

advanced core technologies necessary to realize Design

For Six Sigma, most notably Intelligent Master Modeling, Knowledge Based Engineering, Robust

Design, Multidisciplinary Design and Optimization, Cost

Modeling and Producibility.

• Demonstration of FIPER on a diverse set of demanding

applications, which span conceptual design, through

manufacturing for systems, subsystems and

components.

• Dissemination of the technology through a well

founded commercialization plan, complimentary teaming.

Web-based access, publications, educational programs

and the creation of an early adoption program.

Thus FIPER represents a paradigm shift for product

development through the introduction of a standards based

product development environment Conceptually the FIPER

environment is described in Figure 1 1 and in more detail in Reference 1

.

The team was chosen for their complimentary roles in achieving the overall FIPER objectives. GEAE is a complex

engineering system developer and manufacturer and a Unigraphics CAD system user. Parker Hannifin is a complex

aircraft engine and aircraft subsystem and component

supplier and a ProEngineer CAD system user. BFGoodrichAerospace is a complex aircraft sub-system

and component supplier and CATIA CAD system user. Thus with CAD interoperability being one of the major

FIPER initiatives, three out of the four major CAD systems is

represented. The fourth, SDRC IDEAS Master Series will be

addressed at a later stage, possibly through the early

476
adopter program. GE Corporate Research and Development

(CR&D) has been developing the technology associated

with IMM, KBE, MDO and DFSS for a number of years. Engineous Software Inc. is the commercializer for the FIPER

software and their current product is iSIGHT, an engineering

analysis process integration and optimization tool. Ohio

University is providing computer system integration

software wrapping tools and is developing a cost model that

will be integrated with the IMM. Stanford University is creating producibility models that will be integrated with the IMM. OAl (Ohio Aerospace Institute) is the sponsoring

organization and provides program administration. The

complimentary teaming are key to the technical and

commercial success of the FIPER project.

Acknowledgements

This research is jointly funded through the National

Institute for Standards and Technology Advanced

Technology Program (NIST ATP) and the General Electric Company. The authors would like to acknowledge this support as well as the valuable input from the whole FIPER

team. References

Ref 1: Rohl, P. J., Kolonay, R. M. et al. A Federated

Intelligent Product EnviRonment AIAA-2000-4902, 8* AlAA/USAF/NASA/ISSMO Symposium on

Multidisciplinary Analysis and Optimization, Long Beach,

CA, September 6-8, 2000

477
Towards an Objective Comparison of

Stochastic Optimization Approaches*

James C. Spall, Stacy D. Hill, and David R. Stark

The Johns Hopkins University

AppHed Physics Laboratory

1 11 00 Johns Hopkins Road

Laurel, Maryland 20723-6099 U.S.A.

ABSTRACT

This paper is a first step to formal comparisons of several

leading optimization algorithms, establishing guidance to practitioners for when to use or not use a particular method.

The focus in this paper is four general algorithm forms:

random search, simultaneous perturbation stochastic

approximation, simulated annealing, and evolutionary

computation. We summarize the available theoretical results on rates of convergence for the four algorithm forms and then use the theoretical results to draw some preliminary

conclusions on the relative efficiency. Our aim is to sort out some of the competing claims of efficiency and to suggest a

structure for comparison that is more general and transferable

than the usual problem-specific numerical studies. Work

remains to be done to generalize and extend the results to problems and algorithms of the type frequently seen in

practice.

KEYWORDS: Rate of convergence; random search;

simultaneous perturbation stochastic approximation;

simulated annealing; evolutionary computation.

1. INTRODUCTION

To address the shortcomings of classical deterministic

algorithms, a number of powerful optimization algorithms

with embedded randomness have been developed. The

population-based methods of evolutionary computation are only one class among many of these available stochastic

optimization algorithms. Hence, a user facing a challenging

optimization problem for which a stochastic optimization

method is appropriate meets the daunting task of determining

which algorithm is appropriate for a given problem. This

choice is made more difficult by the large amount of "hype"

and dubious claims that are associated with some popular

algorithms. An inappropriate approach may lead to a large

waste of resources, both from the view of wasted efforts in implementation and from the view of the resulting suboptimal

solution to the optimization problem of interest. Hence, there is a need for objective analysis of the relative

merits and shortcomings of leading approaches to stochastic

optimization. This need has certainly been recognized by

others, as illustrated in the recent 1998 IEEE International

Conference on Evolutionary Computation, where one of the

major subject divisions in the' conference was devoted to comparing algorithms. Nevertheless, virtually all comparisons have been numerical tests on specific problems.

Although sometimes enlightening, such comparisons are severely limited in the general insight they provide. On the

other end of the spectrum are the "No Free Lunch Theorems"

(Wolpert and McReady, 1997), which simultaneously

considers all possible loss functions and thereby draw

conclusions that have limited practical utility since one

always has at least some knowledge of the nature of the loss

function being minimized.

Our aim in this paper is to lay a framework for a

theoretical comparison of efficiency applicable to a broad

class of practical problems where some (incomplete)

knowledge is available about the nature of the loss function.

We will consider four basic algorithm forms—random

search, simultaneous perturbation stochastic approximation

(SPSA), simulated annealing, and evolutionary computation

via genetic algorithms—in the context of continuous variable

optimization. The basic optimization problem corresponds to

finding an optimal point 0*:

e* =arg minL(9),

eeD

where L(9) is the loss function to be minimized, D is the

domain over which the search will occur, and 9 is a /?-

A more complete version of this manuscript is available upon request (james.spall@jhuapl.edu). This work was partially

supported by the JHU/APL IRAD Program and U.S. Navy contract N00024-98-D-8124.

478
dimensional (say) vector of parameters. We are mainly

interested in the typical case where 9 is a unique global

minimum.

Although many stochastic optimization algorithms other

than the four above exist, we are restricting ourselves to the

four general forms in order to be able to make tangible

progress (note that there are various specific implementations

of each of these general algorithm forms). These four

algorithms are general-purpose optimizers with powerful

capabilities for serious multivariate optimization problems.

Further, they have in common the requirement that they only

need measurements of the objective function, not requiring

the gradient or Hessian of the loss function.

2. NO FREE LUNCH THEOREMS AND

THEIR RELATIONSHIP TO RATE OF

CONVERGENCE

Wolpert and Macready (1997) present a formal analysis of

search algorithms for optimization, the most popular of which

are evolutionary computation, simulated annealing (SAN)

and random search. This work results in several "No Free

Lunch Theorems," stating, in essence, that no algorithm is universally better than other algorithms. The full version of

this paper goes into some detail on the implications of these

theorems.

3. SIMPLE GLOBAL RANDOM SEARCH

We first establish a rate of convergence result for the simplest

random search method where we repeatedly sample over the

domain of interest, D q R'^ . This can be done in recursive

form or in "batch" (non-recursive) form by simply laying

down a number of points in D and taking as our estimate of

9* that value of 9 yielding the lowest L value. It is well

known that the random search algorithm above will converge

in some stochastic sense under modest conditions (e.g., Solis

and Wets, 1981; Spall, 2000b):

To evaluate the rate of convergence, let us specify a

"satisfactory region" 5(0 ) representing some neighborhood

of 0 providing acceptable accuracy in our solution (e.g.,

S(9 ) might represent a hypercube about 9* with the length of

each side representing a tolerable error in each coordinate of

9). An expression related to the rate of convergence of

Algorithm A is then given by

/'(0^eS(0*)) = 1 -[1 - /'(9new(A:)e 5(9*)]* (3.1)

We will use this expression in Section 7 to derive a convenient formula for comparison of efficiency with other

algorithms.

4. SIMULTANEOUS PERTURBATION

STOCHASTIC APPROXIMATION

The next algorithm we consider is SPSA. This algorithm is designed for continuous variable optimization problems.

Unlike the other algorithms here, SPSA is fundamentally

oriented to the case of noisy function measurements and most

of the theory is in that framework. This will make for a

difficult comparison with the other algorithms, but Section 7

will attempt a comparison nonetheless. The SPSA algorithm

works by iterating from an initial guess of the optimal 9,

where the iteration process depends on a highly efficient

"simultaneous perturbation" approximation to the gradient

g(9) = dueydQ

The SPSA procedure is in the general recursive SA form:

^^k+\=^k-ak8ki^k) (4-1)

where gk^^k^ is the SP estimate of the gradient g(0) =

3L/30 at the iterate 6^ (Spall, 1992) based on the

measurements of the loss function and > 0 is a "gain"

sequence. This iterate can be shown to converge under

reasonable conditions (e.g.. Spall, 1992; Dippon and Renz,

1997). The essential basis for efficiency of SPSA in multivariate problems is due to the gradient approximation,

which uses only two measurements of the loss function to estimate the p-dimensional gradient vector for any p. This

contrasts with the standard finite difference method of

gradient approximation, which requires 2p measurements.

Most relevant to the comparative analysis goals of this paper is the asymptotic distribution of the iterate. This was

derived in Spall (1992), with further developments in Chin

(1997), Dippon and Renz (1997), and Spall (2000a).

Essentially, it is known that under appropriate conditions,

k^'\ 6^-9') > N{\i, I) as A: oo , (4.2)

where |3 > 0 depends on the choice of gain sequences {a^^ and

Ck), |i depends on both the Hessian and the third derivatives

of L(9) at 9* (note that in general, |li 0 in contrast to many

well-known asymptotic normality results in estimation), and

S depends on the Hessian matrix at 0* and the variance of the

noise in the loss measurements. Unfortunately, (4.2) is not

directly usable in our comparative studies here since the other

three algorithms being considered here appear to have

convergence rate results only for the case of noise-free loss

measurements. Recent results by Gerencser (1999) and

Gerencser and Vago (2000) on noise-free SPSA may

ultimately be useful.

479
5. SIMULATED ANNEALING

ALGORITHMS

The simulated annealing (SAN) method (Metropolis et al., 1953; Kirkpatrick et al., 1983) was originally developed for

optimization over finite sets. The Metropolis method

produces a sequence that converges in probability to the set of global minima of the loss function as 7)^ , the temperature,

converges to zero. Geman and Hwang (1986) present a SAN

algorithm for continuous parameter optimization. Their

algorithm produces a continuous-time stochastic process—

a

diffusion process—whose probability distributions converge

weakly to the uniform probability distribution concentrated on the (global) minima of the loss function, as the

temperature decreases to zero.

More recently, Gelfand and Mitter (1993) obtained

discrete-time recursions for Metropolis-type SAN algorithms

that, in the limit, optimize continuous parameter loss

functions: Suppose that {6^^ } is a Metropolis SAN sequence

for optimizing L and assume that the gradient g of L exists (it does not have to be actually computed).

Furthermore, like SPSA, SAN has an asymptotic

normality result (but unlike SPSA, this result applies in the

noise-free case). Let H{Q*) denote the Hessian of L(9)

evaluated at 6 and let denote the p x p identity matrix.

Yin (1999) showed that for bk = {b/{k'^\og (A:'"^ + 5„) f\

[log(A:'"^ +fio)]""(9i- 9*) MO, S) in distribution,

where I// + H^L + (bla)l = 0.

6. EVOLUTIONARY COMPUTATION

There are three general approaches in evolutionary

computation, namely Evolutionary Programming (EP),

Evolutionary Strategies (ES) and Genetic Algorithms (GA).

All three approaches work with a population of candidate

solutions and randomly alter the solutions over a sequence of

generations according to evolutionary operations of

competitive selection, mutation and sometimes

recombination (reproduction). The fitness of each population

element to survive into the next generation is determined by a selection scheme based on evaluating the loss function for

each element of the population. The selection scheme is such

that the most favorable elements of the population tend to survive into the next generation while the unfavorable

elements tend to perish.

The principle differences in the three approaches are the

selection of evolutionary operators used to perform the search

and the computer representation of the candidate solutions.

EP uses selection and mutation only to generate new

solutions. While both ES and GA use selection,

recombination and mutation, recombination is used more

extensively in GA. A GA traditionally performs evolutionary

operations using binary encoding of the solution space, while

EP and ES perform the operations using real-coded solutions.

The GA also has a real-coded form and there is some

indication that the real-coded GA may be more efficient and

provide greater precision than the binary-coded GA. The

distinction among the three approaches has begun to blur as new hybrid versions of EC algorithms have arisen.

Global convergence results can be given for a broad

class of problems, but the same can not be said for

convergence rates. The most practically useful convergence

rates for EC algorithms seem to be for the class of strongly convex fitness functions. The following result due to Rudolph (i997b) is an extension of a more general result by

Rappl (1989). The theorem will be the starting place for the

specific convergence rate result that will be used for

comparison in Section 7. A more complete discussion of the

relevant EC theory is in the full version of the paper.

An EC algorithm has a geometric rate of convergence if

and only if E[ L* -L(e')] = 6>(c^) where c g (0, 1) is called

the convergence rate. Under conditions, the convergence

rate result for a ( 1 , X)-ES using selection and mutation only on a strongly convex fitness function is geometric with a rate of convergence

c = (l - M^^^,/Q') where M^^^ = E[B^.,x]>0

and where Bxx denotes the maximum of X independent

identically distributed Beta random variables. The

computation of Mx,,, is apparently very complicated since it depends on both the number of offspring X and the problem

dimension p. An asymptotic approximation for the

convergence rate for the (A^, A,)-ES where offspring are only

obtained by mutation is c < [1 - (2p"'log(?l/A0)/Q^].

480
7. COMPARATIVE ANALYSIS Simulated Annealing

7.1 Problem Statement and Summary of Efficiency

Theoryfor the Four Algorithms

This section uses the specific algorithm results in Sections 3

to 6 above in drawing conclusions on the relative

performance of the four algorithms. There are obviously

many ways one can express the rate of convergence, but it is expected that, to the extent they are based on the theory

outlined above, the various ways will lead to broadly similar

conclusions. We will address the rate of convergence by

focusing on the question:

With some high probability 1- p fp « small number), how

many L{ ) function evaluations, say n, are needed to achieve a solution lying in some "satisfactory set" 5(0 ) containing

e'?

For each of the four algorithms, we will outline below an

analytical expression useful in addressing the question. After

we have discussed the analytical expressions, we present a comparative analysis in a simple problem setting for varying

PRandom Search

We can use (3.1) to answer the question above. Setting the

left-hand side of (3.2) to 1 - p and supposing that there is a

constant sampling probability P* = P{Qnew{k) e 5(0*)) V k, we

have

logp

n =

\og(\-P*)

(7.1)

Simultaneous Perturbation Stochastic Approximation

From the fact that SPSA uses two L(0*) evaluations per

iteration, the value n to achieve the desired probability for

0^e5(0*) is then

\3

n = 2

2d{p)cy

65

The value n to achieve the desired probability for 6^ eS(9*)

is

logn^ ^ =

Evolutionary Strategy

\2

J

The full version of the paper employs Markov's

inequality and the bound in Rudolph (1997b) to show that for

each generation k, there are X evaluations of the fitness

function so that n = A,^:, where

logp-log(l/e)

log 1 PQ'log(k/N)

7.2 Application of Convergence Rate Expressions

for Varying p

We now apply the results above to demonstrate relative

efficiency for varying p. Let D = [0, 1]'' (the /^-dimensional

hypercube with minimum and maximum 0 values of 0 and 1 for each component). We want to guarantee with probability

0.90 that each element of 0 is within 0.04 units of the

optimal. Let the (unknown) true 0, 0*, lie in (0.04, 0.96f.

The individual components of 0* are 0^ . Hence,

5 (6 * ) = [0 ; - 0.04, 0 ;+ 0.04] X [9 ; - 0.04, 0 ;+ 0.04] X

... X

[0*^ -0.04,0*^ +0.04] c D.

Table 7.1 is a summary of relative efficiency for the setting

above for = 2, 5, and 10; the efficiency was normalized so

that all algorithms performed equally at p = I, as described

below. The numbers in Table 7.1 are the ratios of the number

of loss measurements for the given algorithm over the

number for the best algorithm at the specified p; the

highlighted values 1.0 indicate the best algorithm for each of

the values of p. To establish a fair basis for comparison, we

fixed the various parameters in the expressions above (e.g., a

in SPSA and SAN, p for the ES, etc.) so that the algorithms

produced identical efficiency results for p - I.

481
Table 7.1. Ratios of loss measurements needed relative

to best algorithm at each p for 1 < p < 10

p = 2 p = 5 P=10

Rand. Search 1.0 11.6 8970 2.0x10''

SPSA 1.0 1.5 1.0 1.0

SAN 1.0 1.0 2.2 4.1

ES 1.0 1.9 1.9 2.8

Table 7.1 illustrates the explosive growth in the relative (and

absolute) number of loss evaluations needed as p increases

for the random search algorithm. The other algorithms

perform more comparably, but there are still some nonnegligible differences. For example, at p = 5, SAN will take

2.2 times more loss measurements than SPSA to achieve the

objective of having 9^. inside 5(9*) with probability 0.90.

Of course, as p increases, all algorithms take more

measurements; the table only shows relative numbers of

function evaluations (considered more reliable than absolute

numbers).

This large improvement of SPSA and SAN relative to random search may partly result from the more restrictive

regularity conditions of SPSA and SAN (i.e., for formal

convergence, SPSA assumes a unimodal, several-timesdifferentiable loss function) and partly from the fact that SPSA and SAN work with implicit gradient information via

gradient approximations. The performance for ES is quite

good. The restriction to strongly convex fitness functions,

however, gives the ES in this setting a strong structure not

available to the other algorithms. It remains unclear what

practical theoretical conclusions can be drawn on a broader

class of problems.

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483
Some Measurable Characteristics of Intelligent Computing Systems

Christopher Landauer, Kirstie Bellman

Aerospace Integration Science Center

The Aerospace Corporation, Mail Stop M6/214

P. O. Box 92957, Los Angeles, California 90009-2957, USA

cal@aero.org, bellman@aero.org

Abstract

We discuss the following measurable characteristics of intelligent behavior in computing systems: (1) speed and scope of adaptibility to

unforeseen situations, including recognition, assessment, proposals,

selection, and execution; (2) rate of effective learning of observations,

behavior patterns, facts, tools, methods, etc., which requires identification, encapsulation, and recall; (3) accurate modeling and prediction of

the relevant external environment, which includes the ability to make

more effective abstractions; (4) speed and clarity of problem identification and formulation; (5) effective association and evaluation of disparate information; (6) identification of more important assumptions

and prerequisites; (7) use of symbolic language, including the range and use of analogies and metaphors (this is about identification of similarities), and the invention of symbolic language, which includes creating effective notations. We make no claim that these are all the important characteristics; discovering others is the point of our research program.

Key Phrases: Intelligent Autonomous Systems, Measuring Intelligent Behavior, Constructed Complex Systems, Reflective Infrastructure

1. Introduction

This paper will describe some characteristics of intelligent conm- puting systems, describe how to make measurements of those

characteristics, and discuss what they might mean, though we

know that they do not cover the full spectrum of what is com- monly considered to be intelligent behavior. We extract these

mesaurements from several different viewpoints about what is important for intelligent behavior, and explain their most popularly expected implications.

Intelligence is difficult to measure, because it is thought

to be an intrinsic property of systems, like a potential capability or competence, whereas the only things that can be measured are actual performances under various kinds of conditions. This problem has plagued the evaluators of human intelligence since the beginning, to the point that they have generally concentrated on measuring some postulated corresponding

performance characteristics [8].

Therefore, metrics can only be based on observed system

behavior (though the observations can, of course, measure internal processes from an internal perspective, since we can have some kinds of internal access), since we have no direct access

to how internal organization and structure affect intelligence.

Even if we assume that intelligence is entirely intrinsic, we cannot evaluate it separately from its corresponding behavior (even

if the behavior is only observable introspectively). Measuring

performance to infer competence, even of externally observable

behavior, is also very difficult and time-consuming, since we in- tend to use the measurements over a range of situations in order

to evaluate the intelligence of different systems.

Success in a particular task is not by itself the right criterion (even if success were well-defined). Many intelligent decisions founder on the rocks of poor information and / or unexpected events, and brute force can make up for a lack of intelli- gence (e.g., Deep Blue's defeat of Kasparov relied on very fast special-purpose hardware).

Computer programs that play combinatorial games or

search the web are not very interesting to us from an intelli- gent systems point of view, because their domain is so limited and their goals are provided from the outside. Even so, we're interested in computer programs as creative entities (coinvestigators, so to speak, instead of just tools), and we think

that a careful study of what we can make programs do will be

helpful in understanding what the issues are [2] [4]. In order

to study these possibilities, we want to define a set of measure- ments that can be used to differentiate and understand the relationships among different kinds of behavioral characteristics.

We consider autonomy to be more than choosing methods

to satisfy goals. A system is autonomous to the extent that it

also chooses those goals. In fact, there are really only two

classes of (difficult) requirements for effective autonomy: ro- bustness and timeliness. Robustness means graceful degradation in increasingly hostile environments, which to us implies

a requirement for adaptability, and timeliness means that situations are recognized "well enough" and "soon enough", and

that "good enough" actions are taken "soon enough". There is

484
almost never any optimization here (that almost always takes

too much time and requires too much information).

For the purposes of this paper, we concentrate on the measurement problem instead of the construction problem, though

we have some definite ideas about how to build these interesting programs, based on our Wrapping infrastructure for Constructed Complex Systems [17] [21] [22].

2. System Behaviors

We'll start with the assumption that a computing system is designed to help its users -do_ something [9]. That something is a problem in some subject area, such as, for example, copy a

file in a computer system, produce a document in a legal office,

kill monsters and collect treasures in a computer game, retrieve a web page for a user, solve an equation in a mathematical subject area, find patterns in noisy data in a scientific field, coordinate a distributed simulation for a military application, launch

a spacecraft in the aerospace business, collaborate remotely on

a design problem for space systems, etc.. We'll use these cases

as illustrations in the rest of the discussion.

In all of these cases, there is an application domain, which

provides a certain context of use and corresponding terminology. Actually, this is more of a domain-specific language, since

it includes more than just vocabulary terms. It also has a set of abbreviations and conventions about what can remain implicit, and a set of simplifications (which are fruitful lies about

the entities and behaviors in the domain). It is important to note

that these languages might or might not be written symbolically,

since, for example, a computer game is often commanded us- ing a joystick instead of typed commands, and some immersive

Virtual Environments are commanded by user movement and

gesture.

What the user wants to do is called the problem, which only

makes sense within the context of interpretation provided by the

domain-specific language of the application domain. These lan- guages are used to define the problem context or problem space,

which is a specialized context within the application domain, in which it makes sense to state a problem.

In other words, it is our opinion that a problem cannot be

even stated properly or sensibly without an agreed upon (more

often, merely assumed) application domain and problem context. Very often, it is mistakes in the common understanding of

this problem context that leads to unexpectedly bizarre or constricting behaviors on the part of the computing system.

So now we have a well-specified problem defined in a problem context. We are purposely setting aside creativity for now,

though we believe that this framework can also be applied in

that case, with a problem statement of finding the appropriate

well-defined problem (this approach is part of our Problem Posing paradigm [20]). Explicitly identifying the problem, and separating it from the possible solutions or required user actions, is an important aspect of our approach. It allows many different

possible solution methods to be considered. Since NO one analysis or problem-solving method can deal with all problems in a complex domain [6] [1], it is important to have many methods

available.

These form the resource space, which contains the computational and information resources that are available to address

the problem. It is usually implemented as a large set of in- dependent methods, but we think that more structure here can

help (which is why we call it a space).

A certain configuration of those resources is needed to address the particular problem that the user has specified. This

collection is usually much smaller than the total resource space,

so we call it the solution space. Since it contains only those

resources required to solve the problem, we would ideally like

to have the computing system find this space quickly.

However, in order to find a solution space, very often a much larger examination space or discovery space must be

searched.

For example, in trying to prove a theorem (in geometry,

say), the problem space is one in which the assertion can be

made, the solution space is one in which the proof can be made,

and which often involves extra elements constructed just for the

proof. The resource space is the collection of lemmas, theorems, inference rules, problem-solving methods, and previously

solved problems, and the solution search space is much wider,

since it has to include many different kinds of construction and

proof discovery methods.

3. Characteristics

In this section, we discuss the following measurable characteristics of intelligent systems (it can be seen that there are nontrivial overlaps among them, which we try to unravel later on):

1 . adaptibility,

2. learning,

3. predictive modeling,

4. problem identification,

5. information association,

6. assumptions, and

7. symbolic language.

In each case, we offer an approach to at least one way to compute a measurement value for the characteristic, which we hope

will stimulate others to invent and provide better ones.

485
We make no claim that these are all the important characteristics; discovering others is the point of our research program.

3. J. Adaptibility

By far the most commonly expressed attribute of intelligence

is adaptibility, which for us means the speed and scope of

adaptibility to unforeseen situations, including recognition (of

the unforeseen situation), assessment, proposals (for reacting to

it), selection (of an activity), and execution. Accurate prediction of effects is even better (and more successful), but we save

that one for a later section.

A common example of adaptibility is flexible planning, in which a system can react quickly to situations by changing its plans. It seems clear that flexibility in plans is partly the result of their incompleteness: if the detailed goals remain partly unspecified, then there are more possible steps to take. This phenomenon shows up in programming as "late binding", in which

a resource used to address a problem is often not selected until

just before it is used (as in our Wrapping approach to heterogeneous system integration in Constructed Complex Systems

[19]). The delaying of these decisions does, of course, conflict

with rapid execution, and the resulting tradeoff is important and

depends essentially on rapid elaboration and evaluations of the

choices.

To measure adaptability of a system, we have to present it with different kinds of variability in its environment, and measure its performance, then average that performance over some

variability measurement of the environment. The variability in the environment can be static (many different kinds of slowly

changing environment), dynamic, (rapidly changing phenomena within the environment), and in both cases, we can describe

the degradation in performance as a function of the variability

in the environment.

3.2. Learning

Another common attribute of intelligence is learning, which for us is the rate of effective learning of observations, behavior patterns, facts, tools, methods, etc. [27]. There is an enormous

literature on learning in humans and animals, but our interest

here is mainly on the measurements for computing systems that can learn. Learning is about improving performance, so in a sense all of our proposed measurements can be improved by

learning. Part of this learning includes concept formation and

formulation, which is a way to summarize different structures and processes compactly. We return to this point later on, in the

section on symbol systems.

It is important to note here that there are some fundamental limitations on the kinds of symbol systems that can be used

in the expressive tasks above. One of the limitations of any

discrete symbol system is the "get stuck" theorems [18] [23],

which show that unless a system can change its own basic symbols, and re-express its knowledge and behavior in new symbols, new knowledge gradually becomes harder and harder to incorporate, leading to a kind of stagnation.

Measuring learning is a little easier than measuring adaptability. We have long made a distinction between a smart sys- tem, which has a lot of knowledge about its domain of applicability, and an intelligent system, which can learn new knowledge quickly about its domain of applicability. Smartness is a performance characteristic that is relatively easy to measure,

and the ability to learn, which is about improving that performance, is easy but time-consuming to measure.

3.3. Predictive Modeling

An important way to be less surprised at environmental phenomena is predictive modeling, which for us means accurate modeling and prediction of the relevant external environment.

This kind of modeling includes the ability to make more effective abstractions (which is treated below in a later section).

Since a system cannot know everything about its environment,

we assume that there will be multiple models carried in parallel,

with new data interpreted into information using the model as an interpretive context, and each model adjusted, assessed, and

ranked for likelihood continually. This kind of modeling makes

the computing system an anticipatory system in the sense of

Rosen [33], since it can make current decisions on the basis of

its models of future effects of its decisions. It is therefore ex- pected to be much more capable than a merely reactive system,

since it can be preparing responses to its environment before

anything important happens in the environment.

A concrete example of this kind of modeling is trying to distinguish trends from fluctuations at different time scales in a complex environment. In such an environment, activity occurs

at many time scales, so the only viable approach is multiresolutional [31] [32], that is, the system must maintain several differ- ent filtering processes that examine the environment at different

resolutions (time, space, and even conceptual), and look for local stadonarity.

There are three kinds of models to be considered: empirical

models, which are computed according to the observed data, a

priori models, which are provided up front, and fitted to the

data (we think these are much less important than the others),

and deduced models, which are derived from other models and

knowledge available.

In addition, analyses of these models requires several different kinds of reasoning, both mathematical and linguistic [16].

These methods include case-based reasoning, in which the sys- tem tries to match the current situation with one it has encoun486
tered before, deductive reasoning, which can be illustrated as having statements "A" and "A implies B" and concluding state- ment "B", and inductive reasoning, which can be illustrated as having statements "A" and "B" and concluding statement "A

implies B". The best-known example of inductive reasoning is exploratory pattern analysis, which is a way of extracting properties of mostly unknown data. The last style of reasoning is abductive reasoning, which can be illustrated as having state- ments "B" and "A implies B" and concluding statement "A". This style of reasoning is the one corresponding to explanation,

since it follows the deductive chains backwards.

Measuring the modeling capability is not about comparing

the resulting models with the processes underlying the environmental phenomena, but rather, it is about measuring the correct- ness or appropriateness of the predictions. Some predictions

take the form "this phenomenon is unimportant", while some

must be much more definite, such as "the moving ball will be

there at that time" or "the closing door will be open enough for a few seconds". Once explicitly formulated, these predictions can be compared, and the results plotted against the complexity

of the prediction task (which we as evaluators must assess).

3.4. Problem Identification

The best way to respond to problems quickly is to identify them

quickly, which requires speed and clarity of problem identification and formulation. In our opinion, speed of problem solution

is secondary. Even if we seem to specify a problem as a constrained search, we seem to construct search spaces that are very

problem-specific, often extremely intricate, constructed using

the constraints directly (i.e., not by searching a large encompassing space, and ignoring the parts outside the constraints).

This problem identification problem is a special case of

the situation identification problem, in which acceptable per- formance is often dependent on recognizing that a situation is similar to one encountered before, and that, in turn, depends on

identifying the "right" set of features of the situation to explicitly notice and recall.

Naive models of situated computing systems assume that

all of the important data that defines a situation is contained

in the sensor values for that instant. Humans don't do that; we

seem to extract information from the data, based on a number of

continual, particular, and only occasionally goal-directed models, and retain only a small part of the actual sensor data. There

is also some reason to believe that we only keep interval aver- ages, not instantaneous pictures, of a situation (even a mental

image is the result of a lot of processing, for object separation

and identification, etc.).

The ability to identify important situation features quickly

and correctly depends on having at hand the right specification

spaces to determine and describe the features.

Very often, the application domain and problem context

that allow a problem or even a situation to make sense must

be inferred from the observable environmental behavior. This

process is also part of good problem identification, a kind of

recognition or noticing.

Good problem identification is an intermediate stage between goals and solutions, so it must in part depend on the re- sources available to a system.

Criteria for good problem identification are still difficult to

describe. We will take speed of problem formulation, succinct- ness of problem statement, and accuracy of problem statement

to be the main criteria. Here, we can only assess the accuracy

of the problem statement using knowledge of the potential solution methods, since the effectiveness of the problem statement

depends on which resources can address it.

3.5. Association

One of the clearest signs of intelligence is the wide scope and

effectiveness of associations, and the corresponding evaluation

of disparate information for inclusion into a decision process.

Discovery and explanation of new associations is even fre- quently associated with creativity.

This includes several different kinds of reasoning, from

analogies and use of metaphors, through the connection of facts

to inference rules. It includes ways to use complex relationships summarized numerically (as we so often do when we implement these systems), and it must include a very flexible rea- soning system [16]. There is some argument to the effect that

all of these can be viewed as similarities in conceptual spaces

[10], as long as we make the class of spaces large enough (i.e., not just numerical ones).

These abstract associations are also part of the mysteri- ous phenomenon of "noticing", which can occur when repeated

or anomalous environmental effects are pushed into awareness,

seemingly without any prior attention. Similarly, we seem to be

adept at noticing correlations in temporal sequences (this ability clearly has some evolutionary advantages), even when they occur in distinct sensory or conceptual spaces.

The simplest version of these processes uses empirical statistical techniques, such as the use of co-occurrence measure- ments in natural language information retrieval. These and related methods work surprisingly well for this case [26], and we

have shown that they can be used in other areas as well [12]

[24].

On the other hand, what allows these methods to work well

is the explicit representations for words and phrases in the kinds

of documents used. In our case of Constructed Complex Systems, the system has to make the representations explicit first,

after which the analyses are relatively easy. In particular, it

487
is important to have a representational mechanism that allows

comparisons in many different conceptual spaces, so that differ- ent kinds of associations can be computed and analyzed.

Since we discuss in other sections the choices of representation and the difficulties of appropriate ones, we consider in

this section only the problem of computing associations. We

could posit that the wider the associations range, i.e., the more

conceptual spaces are involved, the better the association process, but that width of scope has to be traded off against the

speed of use of the associations, since we are actually only

able to measure performance, not competence. This ability will

manifest itself as an improved ability to recognize similarities

in difficult problems, and an improved ability to use unlikely resources to address problems in a useful way.

3.6. Assumptions

A perennial problem with reasoning in systems, and particularly with deduction, is the mis-identification and conflation

of assumptions. It is important that a system can identify its more important assumptions and prerequisites, which includes

the ability to widen a context (by removing some of the assumptions).

This problem is a special case of Computational Reflection [28] [11], which is the ability of a Constructed Complex

System to analyze its own behavior [15]. Having access to internal data structures and reasoning processes in an explicit and

analyzable way allows a system to monitor its own behavior,

short-circuit unsuitable lines of reasoning, and perform "whatif" studies of itself, which can eliminate some errors before they occur [21] [24]. We have shown that it is relatively straightforward to implement systems with this kind of Computational

Reflection [17], but the general case is much harder.

We can consider systems that identify the prerequisites of an action, since identification of prerequisites is abductive rea- soning (also called "backward chaining" in the Artificial Intel- ligence literature), but designing a system that can determine a

context limitation, which is a kind of prerequisite of representation, and then move outside that limitation, is much harder.

Identifying assumptions is a kind of creative reasoning, that examines reasoned arguments and transform them into an identification of the assumptions and inference rules required to ac- complish the arguments. Since we expect the system to perform

these operations itself, it must have a mechanism for reasoning

within a system, about the boundaries and limitations of that system. We think that this ability is both hard and essential for

intelligent systems.

We can measure how well a system identifies its own as- sumptions by placing it into environments where many com- mon assumptions fail, and checking how well the system performs. We can also use environments in which the basic as- sumptions change with time, to see if the system can react sufficiently quickly. These measurements are subtle, and disentangling them from the other possible reasons for performance

failures will be difficult. We need much better measurements

here.

3.7. Symbolic Language

Perhaps the most important property of all, in our opinion, is the use of symbolic language for explicit representations, in- cluding the range and use of analogies and metaphors (this is about identification of similarities), and the invention of symbolic language, which includes creating effective notations for

internal representation. This property is not altogether unchallenged, but despite the "behavior-based" intelligence work [29],

we believe representation to be essential at all levels of intelli- gence [3], especially for computing systems.

We repeat here that we don't care particularly whether liv- ing systems (and in particular humans) have all of these models

explicitly represented or implicitly embodied. Our Constructed

Complex Systems will have them all explicit.

This property should be unraveled into several different

characteristics, but there doesn't seem to be an appropriate analysis of it yet, though there are some promising or at least interesting approaches [34] [7], and we have proposed an architecture that emphasizes the symbol systems [22].

Such an approach to the use of symbols in Constructed

Complex Systems must account for the semantics of representation [35], at many different levels, and for the processes that

change those representation methods (our conceptual categories

are an example representation style [13] [14], and our computational semiotics research is about changing the symbol system

when it becomes necessary [18] [23]).

It turns out that human expertise often correlates with

better-organized knowledge, and not just with more knowledge,

so that problems are recognized more quickly [8].

Since, in our opinion, appropriate abstraction requires a

repertoire of conceptual spaces, so that the important properties

of the situation at hand can be matched to many more choices

of analysis space, and evaluative assessments can become part

of the matching process, we think that a very large repertoire

is needed, together with some very flexible and fast indexing

methods.

Following our own symbol system studies here [13] [14],

we measure the use of symbol systems via an efficiency notion:

the total size of the representations used compared to the scope

of what is represented. This comparison can be estimated using

the analysis described in the papers cited: a fixed symbol system

has a fixed finite set of basic symbols, and a fixed finite set of

488
symbol structure combination methods. These sets strictly limit

the number of distinctions that can be represented within the

symbol system with each size expression. If the system can

also change the combination methods, then the numbers can be

much larger (though they are still computable).

This measurement is, of course, an intrinsic one (i.e., it

is a competence measure), not an extrinsic one (i.e., a per- formance measure), but we think that it will help us develop more performance measurements. In addition, we want some

other performance measurements, such as the speed of representational encoding, measured in some units independent of

machine-hardware, and the speed of interpretation of those representations (which is about determining the appropriate action

to take). There are many other possible measures here.

4. Intelligent Systems

In this section, we discuss how these issues affect the design of

Constructed Complex Systems [15], which are artificially constructed systems that are managed or mediated by computing

systems. We are concerned with issues of autonomous and intelligent behavior in such systems, which for us, at least means

that the system takes a major role in selecting its own goals [ 1 7]

[25]. When we expect Constructed Complex Systems to operate

autonomously, whether out in the real world or in cyberspace,

we need to incorporate a great deal of flexibility and adaptability into their design and implementation. We have shown one way to implement such a system [21] [22], one that also helps

avoid the most common difficulties found in complex computing systems: rigidity and brittleness.

Biological systems have much more flexible and powerful

adaptation properties than most constructed systems [5], and a

careful consideration of their properties provides stringent re- quirements for the kind of Constructed Complex Systems that would be able to act autonomously. It also gives us some hints

about the design structures that are needed [30] [17].

Our approach is to define a new kind of architecture [22]

that includes both our Wrapping integration infrastructure [19]

and our Problem Posing interpretation [20], that provides a

declarative interpretation of all programming languages, so that

posed problems can be separated from applicable resources, and

our conceptual categories [13] [14] to provide a flexible repre- sentation mechanism that separates model structures from the

roles they play.

Our Wrapping architecture provides the required flexibility by supporting systems that are variable as far down as we

choose to make them (even all the way down through the operating system to the hardware) [15]. One reason that we want this

variability is that we expect to study many different approaches

to any given problem area, and our infrastructure has to support

alternatives for almost every part of every process. In fact, one

of the principles we have highlighted in our architecture investigations is that NO one model, language, or method suffices for a complex system (or environment), so the variability is not just

convenient; it is necessary [6] [1].

In addition, we take the hypothesized common origin of

language and movement [3] as a hint, since the implied layers

of symbol systems can be implemented easily in Constructed

Complex Systems using a meta-level architecture [17].

In addition to the data and processes, we also need a third

style of computation, that of "re-expression", which allows a system to re-organize itself when its current organization is not

adequate. What this means for us is that the system can some- how detect when its own representational mechanisms are not

adequate, and it can use the failures to help invent new ones.

To make things even more interesting, we also want to have the system decide for itself when it needs to be re- organized, because its fundamental symbol systems are not expressive or powerful enough, and then carry out for itself the

re-organization automatically, by defining new symbol systems

and re-expressing itself in the new terms. This behavior is hard

to implement usefully, but we have made some progress in identifying the important issues.

The Wrapping processes give the process structure and the

Wrappings and conceptual categories give the data structure. The re-expression criteria are implemented as resources that

monitor the system. We describe each of these technical issues

in turn, and then show how they can be used to help construct

the kind of system wc want to build.

The essence of computation is interpretation of symbol sys- tems. The only operations that a digital computer can perform

are copying and comparison. All arithmetic in digital computers is via limilccl-precision explicit models of the corresponding integer or real arithmetic. Therefore, we cannot construct

computing systems to do complex or otherwise interesting tasks

without many explicit models of the kinds of computation, deduction, or analysis required. All of these models must then be

expressed in terms of the operations that we can implement on

these (very) limited computers.

The theorems of Turing, Go:del, and others show that there

are fundamental limits on the expressive and computational

power of computing systems, but ALL of the theorems assume

that the symbol system remains fixed (that is a basic assumption in all of the mathematical proofs), and that the parallelism can be mapped into interleaved events. Systems that are not restricted in either of these ways might escape the bounds of these

theorems. This is one of our current direction of research [18]

[22] [23].

489
5. Conclusions

We care about measuring intelligence because we want to build

such devices, and without some better measurement processes,

we will have no repeatable way to evaluate and compare differ- ent designs.

We have described some properties that we think are important, that have driven our research in Constructed Complex

Systems, including a few that have not been extensively used or

identified in the literature. We do not think that they completely

cover the spectrum of what is commonly considered to be intelligent behavior, but they do cover more of the scope than simply

"adaptability" or "intellect".

We have examined these properties to determine what they

require as fundamental enabling capabilities, and described an

architecture that includes all of these enablers, as a way to test our assertions about the connection between them and intelli- gent behavior. We expect that as we build systems with more of

these enablers, the systems will exhibit more of the important

properties we have identified, and at the same time they will seem more intelligent.

We think that this problem is hard, and that we are on

a right track (we make no assumption about how many right

tracks there may be; the more we collectively explore, the more

likely it is that we will get some of the right answers). We

think that fundamental investigations like these are necessary;

we hope that they are sufficient.

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491
Generalizing Natural Language Representations

for Measuring the Intelligence of Systems

A. Meystel

Drexel University, Philadelphia, PA 19104

Abstract. In the core ofthis method of intelligence evaluation, there is a concept of using natural language as the

least damaging medium for representing knowledge of the systems. The goal of all existing methodologies of

knowledge representation boils down to performing generalization of this knowledge in one of the coasting forms:

analytical representation, automata theory, predicate calculus of the first order. Connectionist schemes are not on

this list because the problem ofgeneralization upon the entity-relational net^vork (ERN) have not been addressed

consistently. In this paper, the concept of constructing a nested mubiresolutional system of ERNs by consecutive

generalization ofthem bottom-up and consecutive instantiation ofthem top-down. It is demonstrated that given a set ofproblems to be resolved, one can learn which one the nested ERN alternatives is more appropriatefor solving this set Finally, a problem ofevaluating ERN 'for any set ofproblems" is discussed

Conceptual Paradigm. This theoretical

paradigm is related to intelligent systems for text

processing. Although, it has a broad practical

application by itself, we would be interested in

considering it as a symbolism for any intelhgent

system. The goal is to obtain a structure of text

organization, elements of which can be used upon the

initial narrative for the subsequent processing in

order to generate a variety of different texts that have

various degrees of compression and/or enhancement.

It is anticipated that by constructing a proper

organization of the text representation, obtained from

the original document, different st'^jctures of the text

could be constructed, for example, the one that would

allow to encode its meaning as a set of nested and

interrelated generahzations. In turn, this should allow

for generating the narrative text from each of these

structures. These texts should be different in their

level of generalization, focus of attention, and the

depth of detail. Theoretical premises of this paper

have been applied for commercial products'

.

General Vision. As soon as the automated

analysis starts, the whole texts changes its initial

shape and demonstrates a multiplicity of potential

interpretations at each level of resolution. The text

subjected to the process of multiresolutional analysis

demonstrates its semantic fuzziness, and zones of

combinatorial possibilities emerge around each unit

of the texts^. These zones characterize the

interpretational ambiguity which should be

eliminated (or at least, substantially reduced) as a

result of text processing. The frizzy and not totally

disambiguated imits have frequently an emergent

property of sticking together, forming new

generahzed units that precipitate from the fuzzy

intermediate structure. Eventually a new text emerges

which is shorter or longer than the initial one

depending on the algorithm applied. The merger of

the text units happens in a strictly multi-granular

fashion. Each text has a potential to several rounds of

compression by generalization as well as to several

rounds of enhancement by instantiation. Construction

' Cognisphere, Inc., URL http;//www.cognisphere.com

The metaphor "combinatorial cloud" alludes to the semantic fields of potential meanings that will depend on

our wiUingness and preparedness to combine various

groups of words as potentially salient units of the text.

492
of such a multigranular structure can be performed

for each text, or a group of texts.

The fuzzy zones for combinatorial

exploration can be obtained from the text, or can be

assigned if the need arises. The process of

combinatorial fuzziness generation includes the

formation of the links of nestedness, and precipitation

of the multigranular text structure (together with

levels of enhancement and/or compression),

interpretation of an unknown document. However, if

the assignment contains the description of a specific

customer's interests, this combinatorial fuzziness

generation can be guided by this assignment. It does

not necessarily need to be guided. In the latter case, a

summary of the general (non-goal-oriented) form is

created.

Domain of Application. This metliod of

analysis and the computational algorithms are used to

obtain a structure of text organization, elements of

which can generate a variety of different texts that

have different degree of compression.

Each text has a potential to several rounds of

compression by generalization as well as to several

rounds of enhancement by instantiation. Construction

of such a multigranular structure can be performed

for each text, or a group of texts (see Figure la). The

hierarchy of compression by generality can be

considered (see Figure lb) as a set of equally

available outcomes.

The expectation is that by moving the

representation from narrative to tlie multiresolutional

system of knowledge representation several goals can

be achieved simultaneously:

a) evaluation of complexity by determining

parameters of the architecture of knowledge

Enhanced Text

Abbreviated Text

Extended Abstract

Ahsttrart

Summaries

Annotation

summary —extended abstract

abbreviated text

original text

enhanced text

Figure 1. Text and its versions (compressed and enhanced)

493
representation (number of levels, branching, size of

the fuzzy vicinity of the node, etc.).

b) evaluation of the degree of generalization,

c) evaluation of the depth of instantiation,

d) characteristics of the algorithms of

focusing attention, clustering and combinatorial

search that has generated the architecture.

By using a set of standard text processing

routines we succeed in unifying the formal structures

of various problems and contexts. A limited number of standard routines of

text processing are used in the proposed method:

frequency analysis evaluation of the association

strength of the text units and their associations

construction of the tentative groups and syntactic

parsing are applied as a part of the software package,.

These tools play a supportive role in the process of

constructing the multiresolutional structure of text,

the user can use any of existing routines.

Description of processing. Extracting the

multiresolutional (multigranular, multiscale) structure

(nested hierarchical architecture) of text units

(entities) from the Text is a prerequisite to

transformation from the narrative representation into

the relational architectiu-e of knowledge. The main

dictionary is used for the initial interpretation of the

units of Text, and the new domain dictionaries are

formed for the text-narrative, or Original Text (OT)

together with its Structure of Text Representation

(STR) as a part of the text analysis. The

muhiresolutional hierarchy of STR consists of the

units, which lump together elements of the text, that

has emerged due to the "speech-legacy" grammar.

Since the transformation of OT into STR can be done

through incremental generalizations within OT,

building the vocabulary of the OT is a prerequisite

for the subsequent STR construction.

The vocabulary is a list of "speech-legacy"

words and muhi-word expressions that are symbols

for encoding entities of the real situations and can be

represented by single words as well as groups of

words. An entity of the reality is anything that exists,

important for registering and memorizing, has a

meaning as a part of some functional description and

is (or should be) assigned a separate word (or a group

of words) no matter whether we use it as a part of

"speech-legacy" representation, or an element of the

STR. The first problem to be resolved is finding

entities that are represented by single words, then test

groups of interrelated words, as phrases that denote

entities. Therefore, fimctioning of STR requires

understanding how entities are discovered within

Reality (the World): similarly, they will be

discovered within the text.

Entities are extracted from Reality as a

result of consecutive two-stage testing of the

available information (these Stages of processing are

universal and are used in all discipUnes):

Stage Rl. Browsing (searching) the

perceived and stored signals and testing the ability to

justifiably group them. Creation of hypotheses about

groups of the perceived signals (data) that can be

interpreted as unified messages that could be put in

cause-effect correspondence with other hypotheses

about possible groups.

Stage R2. Labeling this hypotheses so that it

could be stored and manipulated as a unified group

(as an entity) in fimctioning of the information

processing system. Collecting cases of confirmation

or rejection for these hypotheses so that for a

multiplicity of cases a final decision could be made

whether this hypothesis should be confirmed, or

rejected.

494
These stages are performed at any particular

granularity, and if there exist more than one

granularity, these stages are performed for each of

these granularities. If only one level of granularity

existed initially, the adjacent level above emerges.

The Reality is presumed to contain:

a) entities that we have already learned as a

result of prior experiences, and

b) the rest of the Reality that we have not

yet learned (whose entities, therefore, cannot be

listed); the rest is considered provisionally to be a

"uniform" background.

It would be prudent to regard anything

unlearned as a continuum, and the process of learning

as a process of discovering entities within this

continuum. (Continuum is defined as a thing whose

parts can't be separated or separately discerned.

Initially, the entities hidden in the continuum are

indistinguishable). The described approach fits within

the scheme of a "scientific method." From various

sciences we know that physical laws work in such a

way that singular entities are formed from the

initially uniform media, and thus, separated from the

continuum. These entities are assigned symbols

(labels), and they become words in vocabularies.

For each text, the resuhs of structuring can

be organized in a hierarchy. Knowing in advance the

expected results, let us form and apply the 2-stage

procedure.

Text processing oriented toward

multigranular structuring is organized in the similar

two- stage fashion. We will describe it in more detail.

The stages are performed at a particular granularity,

and if for the particular text more than one

granularity is registered, the stages are performed at

each of them. Given a Text, the following operations

are performed:

Stage Tl. Browsing the text so that all

single units of this particular level of resolution could

be tested for a variety of the group-forming

phenomena. Among the group forming phenomena,

the following are of a practical interest as examples

of grouping: ~ natural division that provides easily detectable

tokens of structuring starting with "Chapters" and

ending with "Sentences." ~ frequent spatial adjacency in the text testifies for a

possible carrying a particular meaning together; if

two words are adjacent, their adjacency might be

meaningful, and it would be interesting to see

whether these particular two words can be found in

this text together again and again; a similar interest

might appear about groups of three, four and more

words; if a large group of words repeats as a

combination many times, it can be considered a

possible carrier of meaning "object" or "subject"

relevant groups of words should be spatially

distinguished from the "action" relevant groups of

words; if "nouns" and "adjectives" are swarming

together linked with particular rules of grammatical

parsing, they can be unified into an ACTOR, or OBJECT-OF-ACTION related groups; on the

contrary, if "verbs" and "adverbs" fit within

grammatical rules of teaming for Action description -

the action-related groups could be detected.

Stage Tl*. Creation of hypotheses about groups

of the perceived signals (data) that can be interpreted

as unified messages that could be put in cause-effect

correspondence with other hypotheses about possible

groups. We consider them to be a hypotheses about

meaningful unit at a particular level of resolution.

There are many schools of thoughts that suggest

different rules of forming groups because of various

grammatical rules and features. They are never 100%

495
reliable. We will give several examples of using

particular rules. However, they should not be

considered as a dogma, and for a particular

application of Uie algorithm of structuring multiple

additions, subtractions and modifications of rules of

grouping should be considered and tested.

"Text" is at Uie input of the processing.

WINDOWING and NEIGHBORHOOD ANALYSIS

are performed. First allows for finding all meaningful

(frequent) N-word combinations part of which are

called here "M-seed." The second serve to analyze

with the help of syntactic rules such grammatical

couples as "adjective-nouns/pronouns," "nouns-

(preposition)-subordinate nouns" and helps to

disambiguate difficult cases. As a result, we receive a

relational structure. Some of the areas in this

structure still remain unclear, however (it will be

demonstrated later) similar processing at adjacent

levels allows to reduce the amount of unclear labels

on words and their relationships.

The process of "groups hypothesizing" is

relevant to each level of granularity. Given

explanation above, this diagram is interpreted here

for die level of "Words." As a result of groups

formation, a new level of generalization is received

where the units of the level are "pair of words" and

"triplets of words" and/or "M-seeds" (or the "Seeds

of Meaning" that differ by the value of their

"significance"). The next level of generalization is

combined by further lumping tiie groups into

formations like: chunks Uiat have a meaning "Being

an Actor," or "Action Description," or "The Object

Upon Which die Action Was Directed", etc.

Naturally, "group hypothesizing" should be

performed at this level too, and the result of this

grouping should be simple sentences that are sets as

reflected in tiie actual set of rules. At the next level,

we will have grouping into compound sentences.

Then, the levels of paragraphs. Sections, Chapters,

etc. are going. Running the algorithm of "group

hypothesizing" always creates a level above. In

Section 4 of this disclosure, this issue is addressed in

more detail.

Stage T2. Collecting cases of confirmation

or rejection for a particular hypothesis so that for a

multiplicity of cases a final decision could be made

whether this hypothesis should be confirmed, or

rejected. Labeling of the hypotheses is performed

provisionally, however after statistical confirmation,

the hypothesis is included into the domain

dictionaries permanentiy. After this it can be

legitimately manipulated as an entity and participate

in fimctioning of the information processing system.

All obtained groups are tested by using the rule-base

that detects N-word groups having relationships of

the type: "adjective with its noun," main nounpreposition-subordinate noun", and others. This

refinement plays a decisive role in finalizing the text

structure.

Units of representation generated within

texts. The decomposition of the uniform chaotic

informational medium takes place driven by the

irutial goal and a set of criteria that might determine

different kinds of uniform media. Thus, the results of

developing the linguistic world representation

depends on the aspect of interest submitted and

encoded by the user. As a result of recognition

processes, a variety of singular information units

(entities) emerges, which fit within a natural

categorization that is impbcitly influenced by the

observer. Formation of singularities (as entities) can

be metaphorically described as a result of clustering

processes in which the elementary units of the

primordial Text gravitate to each other in the areas of

496
higher informational density (where the elementary

units are more in quantity, more interrelated, and

more important for the user. For clarifying the

gravitational metaphor, we should emphasize that for

the further discussion it is irrelevant whether the

density is increased as a result of the gravitation, or

gravitation starts prevailing because of an initial

increase in density. In our disclosure, these processes

are to be understood in computational terms. At this

point, the observer will legitimately appear in our

presentation as a carrier of the interrelated concepts:

scale, resolution, and granulation. ("Resolution" is

determined in the same way as the "granule" by the

size of the smallest distinguishable zone, a pixel, or a

voxel^ or even a "word" of the space in which we

describe our system. Scale is a value inverse to the

resolution.)

The concept of scale allows for introduction

of a formidable research tool that can be applied for

each couple of adjacent levels of knowledge

organization obtained by the method described in this

Section of the disclosure. This tool is related to the

specifics of a different interpretation of units in

higher and lower levels of resolution (HLR and

LLR). The units of the HLR emerge as a result of the

process of forming singularities at the previous, even

higher resolution level (which is not a part of our

couple levels of resolution under consideration).

After these singularities have been formed, they

receive an interpretation, a meaning, a separate word

of a vocabulary at this HLR. For the LLR of the pair

of levels that we discuss, these particular singularities

have no meaning at all—not yet!

^ "Pixel" is the smallest indistinguishable unit of a twodimensional surface. "Voxel" is the smallest spatial

indistinguishable unit of a three-dimensional volume.

Frequently, the term "voxel" is used in the N-dimensional

case. In the single-dimensional case we are talking about a

unit of the scale.

The meaning will emerge after these entities

of the higher level of resolution (HLR) will assemble

together into a singularity which can be recognized

by the user at the lower resolution level (LLR) as a

meaningful entity. Before grouping of these entities

into meaningful singularities happens, they are just

nameless units with a tendency to gravitate to each

other, expressed in the set of their relations. This

phenomenon is similar to physical gravitation

although the gravitation "force" depends on the text,

context, goals, and other details of the situation. So,

the process of entity formation for LLR recognizes

the entities of a HLR just as a set of anonymous

units. Their "gravitational" field leads to clustering of

features and can give a birth to a new entity of LLR.

Uniform (chaotic) medium is always a

collection of some non-uniform units at a finer scale

(at higher resolution), and uniformity of the medium

is a parameter that we obtain from characterizing the

medium at a coarser scale (at lower resolution). In

order to compute this parameter (the degree of

uniformity, or density) different techniques can be

used. All of them work as follows.

Phenomena of Attention: Scope and

Focus. Windowing is a result of the need to focus our

attention within a specific scope. Let us consider a

particular zone of the medium that we use to

evaluate; we will call it the scope of interest. An

imaginary large window (the scope of attention) is to

be imposed upon the medium (scope of interest).

Then, the smaller window is sliding within the scope

of interest to evaluate the information density. Thus,

the size of the scope of attention is presumed to be

substantially smaller than the scope of interests.

Density of non-uniform units is to be computed

within this window which allows evaluation of the

continuum quantitatively. Then the window slides

497
over the whole scope of interest, and in each position

the density is again computed.

The sliding strategy of moving the window

of attention over an Image and/or Text is assigned in

such a way that all scope of interest can or will be

investigated efficiently. This strategy can be different

for constructing different models: we can scan it in a

parallel maimer; we can provide a very unusual law

of scaiming; we can make random sampling from

different zones of the scope of interest. The strategy

selection should depend on needs, hardware tools,

and resources available (for example, time). If values

of density are about the same everywhere (with small

variations within some particular interval) then the

medium is considered to be uniform.

Notice, that

a) that in order to introduce the concept of

uniformity we used a sliding window which is

one of the techniques offocusing attention;

b) tJiat in order to form entities of a particular level

of resolution we should group the entities of the

higher level of resolution;

c) that to find candidate units for grouping we

should search for future members of these

groups or otherwise combine them together

Later we will return to these operations as

components of the elementary unit of

intelhgence.

More details on the nature of our approach

can be found in [1-6].

References

1. A. Meystel, Semiotic Modeling and Situation

Analysis: An Introduction, AdRem, 1995

2. A. Meystel,"Multiresolutional Semiotic Systems",

Proc. 1999 IEEE Int'l Symposium on Intelligent

Control, Boston, MA 1999, pp. 198-202

3 . A. Meystel, "Multiresolutional Text Interpretation

System", In the collection. Sign Processes in Complex Systems, publ. by the 7* International

Congress of lASS/AIS, Dresden, Germany, October

6-11, 1999

4. A. Meystel, "Multiresolutional Umwelt: Toward

Semiotics of Neurocontrol", Semiotica , No. 3-4,

1998, pp. 343-380

5. "Semiotics: The Toolbox of Intelligence", in Proceedings of the 1997 International Conference on Intelligent Systems and Semiotics, Gaithersburg,

MD, 1997, pp. iii-vi

6. A. Meystel, S. Uzzaman, "Multiresolutional

Decision Support System, " US Patent 6102958,

2000

498
Towards Measures of Intelligence

Based on Semiotic Control *

Dr. Cliff Joslyn

Computer Research and Applications Group (CIC-3)

MS B2C5, Los Alamos National Laboratory

Los Alamos, NM 87545

j oslynOlanl . gov , http : //www . c3 . lanl . gov/~ j oslyn

July, 2000

Abstract

We address the question of how to identify and

measure the degree of intelligence in systems.

We define the presence of intelligence as equivalent to the presence of a control relation. We

contrast the distinct atomic semioic definitions

of models and controls, and discuss hierarchical and anticipatory control. We conclude with

a suggestion about moving towards quantitative

measures of the degree of such control in systems.

1 Introduction: A Control

Theory Framework for Intelligence

We consider some of the challenges presented in

the white paper designed to prepare for this conference [13]. I take the fundamental question to

be "How can we as external observers measure

the degree of intelligence in a target system?"

One approach is to invoke the typical lists

which can characterize intelligent behavior, including adaptability, complexity of internal models, problem solving ability, etc. But what is fundamental to each of these? For example,

adaptability is the ability to adjust responses

to make them appropriate under variable conditions. Problem solving is the ability to come to

'Prepared for the 2000 Workshop on Performance

Metrics for Intelligent Systems.

a correct choice about actions to achieve a particular goal, hereby solving the problem. And

finally, complexity of internal models must al- ways be considered as relative to their ability to

predict the outcome of future behaviors.

Thus can see that fundamental to all of these

is the idea that intelligence requires the ability

of a system to make appropriate decisions given

the current set of circumstances [1, 2, 3]. On

analyzing this a bit further, we can identify the

following necessary components:

Measurement: The ability to know the current

set of circumstances.

Decision: The freedom to choose between one

of many posibilities.

Goal: The possibility that the choice made will

be either appropriate or inappropriate relative to a goal state.

Action: The ability for the decision to aflfect ex- ternal and future events, in order for them

to be either closer to or further away from

the goal.

2 Intelligence as Semiotic Control

We note the similarity to the scheme of an intelligent system as outlined in the conference White

499
Paper [13]. This requires a "loop of closure" consisting of six modules: a world interface, sensors,

perception, a world model, behavior generation,

and actuation. We understand this situation as the existence of a semiotic control system. We

know briefly outline the theory of semiotic systems.

2.1 Semiotic Models and Controls

There is a rich literature (eg. [5, 15, 17, 18, 19]),

traceable back to the founders of systems theory

and cybernetics in the post-war period [4], which

has tried to construct a coherent philosophy of

science based on two fundamental concepts:

• Models as the basis not only for a consistent epistemology of systems, but also as an

explanation of the special properties of liv- ing and cognitive systems.

• Control systems as the canonical form of

organization involving purpose or function.

While controls and models are distinct kinds of

organization, what they share is a common basis in semiotic processes, in particular the use of a measurement function to relate states of the

world to internal representations. Perhaps for

this reason there has been some ambiguity in

the literature about the specific nature of controls and models, and more importantly how the

interact. This has led to confusion, for example, about the role of feedback vs. feedforward

control, and endo-models within systems vs. exo- models o/ systems.

Consider first a classical control system as shown in Fig. 1. In the world (the system's en- vironment) the dynamical processes of "reality"

proceed outside the knowledge of the system.

Rather, all knowledge of the environment by the

system is mediated through the measurement

(perception) process, which provides a (partial)

representation of the environment to the system.

Based on this representation, the system then

chooses a particular action to take in the world,

which has consequences for the change in state

of the world and thereby states measured in the

future.

Environment

^ z ^ System

Representation Decision— Action U

Measurement Consequences

I i

World' M Dynamics World V J

Figure 1: Functional view of a control system.

To be in good control, the overall system must

form a negative feedback loop, so that disturbances and other external forces from "reality"

(for example noise or the actions of other external control systems) are counteracted by compensating actions so as to make the measured

state (the representation) as close as possible to

some desired state, or at least stable within some

region of its state space. If rather a positive

feedback relation holds, then such fluctuations

will be amplified, ultimately bringing some critical internal parameters beyond tolerable limits, or otherwise exhausting some critical system re- source, and thus leading to the destruction of the

system as a viable entity.

Now consider the canonical modeling relation

as shown in Fig. 2. As with the control relation, the processes of the world are still represented to the system only in virtue of measure- ment processes. But now the decision relation is replaced by a prediction relation, whose responsibility is to produce a new representation which

is hypothesized to be equivalent (in some sense)

to some future observed state of the world. To

be a good model, the overall diagram must commute, so that this equivalence is maintained.

As outlined here, models and controls are distinct and atomic kinds of organization. We have

argued [8] that this capability begins with living

systems, and perhaps defined the necessary and

sufficient conditions for living systems.

500
Environment

System

Representation —Prediction-^ Representation'

-4

Measurement li/leasurement ^

World Dynamics World' V y

Figure 2: Functional view of the modeling relation.

2.2 Hierarchical Control

Of course, all of the relations described here are a great deal more complex in real intelligent systems. In particular, usually controls and models

are considered together. This concept is fully developed elsewhere [7, 9]. We now summarize the

primary results of these considerations.

First, the classical view of linear control systems theory [14] is recovered by introduced a

"computational" step which plays the role of cognition, information processing, or knowledge development. Typically, extra or external knowledge about the state of the world or the desired

state of affairs is brought to bear, and provided

to the agent in some processed form, for example

as an error condition or distance from optimal

state. So now measured states are manipulated

and compared to a goal state. In particular, we are impressed by Bill Powers system for hierarchical control [15, 16, 6],

which he has succesfully generalized to explain

the architecture of neural organisms. As shown

in Fig. 3, he views the computer as a comparator between the measured state and a hypothetical set point or reference level (goal) . This then

sends the second representation of an error signal

to the agent. He also explicitly includes reference

to the noise or disturbances always present in the

environment, against which the control system

is acting to maintain good control. For us, these

are bundled into the dynamics of the world.

Another great virtue of Powers' control theory

Environment

System

Set Point ~1

Representation

Comparator^

Error

Sensors Agent

(Decider)

Measurement Action

^^^World'

Disturbances

Figure 3: A Powers' control system.

model is its hierarchical scalability. Fig. 4 shows

such a hierarchical control system, containing an

inner level 1 and the outer level 2. The first key

move here is to allow representations to be combined to form higher level representations. In

the figure and ^2 are low distinct level sensors providing low level representations Ry and R2 to

the inner and outer levels respectively. But Ri

is also sent to the higher level S3, and together

they form a new high level representation R3.

The second step is the ability for the action of

one control system to be the determination of the

set-point of another, thus allowing goals to decomposed as a hierarchy of sub-goals. In the figure, the outer level uses R3 to generate the action

of fixing the set point of the lower level. Note

how this recovers Meystel et a/'s "Feature 10" of

multiscale knowledge representation where the

action of a lower level system is actually the goal

of an upper level system [13].

Notice also that the overall topology of the

control loop is maintained. While ultimately the

lower level is responsible for taking action in the

world, it is doing so under the control of the comparison of a high-level goals against a high-level

representation. Neural organisms especially are

501
Environment

Level 2

Set Point,— Comparator, / \

Error^ / \

Figure 4: Hierarchical nesting of Powers' control

systems.

systems of this type, low-level motor and perceptual systems combining to accomplish very highlevel tasks. And of course, determination of the

outermost goal is not included within Powers'

formal model.

2.3 Anticipatory Control

While familiar to us as a standard engineering

discipline, a number of researchers are pursuing

the applicability of this kinds of semiotic control

[12]. It is also being generalized to a number of

other engineering [2] and scientific domains.

However, our normal sense of control combines

it with models, which are used to aid in decisionmaking by predicting future states of anticipated

actions, using prediction of future events to guide

actions. This is what Ashby refers to as '"cause

control" [4], or Rosen as "anticipatory" [17], or

Klir as feedforward [10]. In this architecture an

endo-model embedded within a control system is used to make a decision as to which action to

take, and thus acts in the role of the agent. It is

this view which most dominates our conception

of the nature of control in general.

However, this architecture is actually highly

complex and special. It is shown in Fig. 5, where

now the agent is replaced by an inner system

which is both a model and a control system (the arrows have been reflected diagonally to make

the graph planar and ease the drawing). This

inner system is a control system in the sense that

there are states of its "world", its "dynamics",

and an "agent" making decisions.

However, it is also a model in that the states

of its "world" are in fact representations, and

its "dynamics" is actually a prediction function.

The inner system is totally contained within the

outer system, and runs at a much faster time

scale in a kind of modeling "imagination" . The

representation R from the sensors is used to instantiate this model, which takes imaginary ac- tions resulting in imaginary stability within the

model. Once this stability is achieved, then that

action is exported to the real world.

Note that the outer control loop here is simple,

lacking computation. In Powers' terms, there is no set point which the state of the internal model

is being compared to. But this could be present

in a slight elaboration where an imaginary measurement is taken from "world'" and compared

to some set point. The outer error signal would

then be fed to change the imagined actions inside

the model until stability is achieved.

3 Tests for the Presence of

Control

Thus we have now transformed the original question of "how do we measure intelligence?" to

"How can we as external observers determine

whether a target system manifests control relations with its environment?" and "How can we

then measure the degree and modalities of that

relation?" I would then offer some ideas based

on the work of Powers and his colleague Rick

Marken [11, 15, 16].

502
Environment

System

Sensors

Endo-Model

world" "dynamics" "world"'

consequences "measurement"

"action"-^— "decision" "representation"

State of Consequences

Affairs y

brid

Figure 5: Anticipatory control.

They address the question from the following perspective. Control relations, in virtue of

the stability of the controled variables in the en- vironment, have many of the characteristics of

other equilibrium phenomena. Both the thermostat and the ball rolling to a stop at the bottom

of a hill evidence this kind of stability behavior.

In the first case, the ball does not want to roll

down the hill, but in a very leal sense, the thermostat does want to regulate its "perception" of

the state of the room temperature.

So how can we distinguish a complex dynamic

equilibrium from a control relation? Powers and

Marken do this distinguishing on the basis of

what they call The Test. It involves the system acting in a way which is counter to physical

law: if the ball failed to roll down the hill, we'd

be surprised, thus we hypothesize that such a

ball is manifesting a control relation. Similarly,

we would normally expect a room to come to

equilibrium with its environment. When it does

not, and we believe our dynamical model, then

we would hypothesize the presence of a control

device, and we might investigate and discover a

thermostat. The "intelligence" of such systems

is based on their manifesting a semiotic relation

which has been selected by evolution or by designers, allowing the system to "choose" to act

counter to physical law.

Now the rub is that this Test thereby requires

the prior presence of a model of what the system should be doing, so that we can be surprised

when it fails to do so. Thus our recognition of a

control relation in an exogenous system requires

of us an exogenous model of reality, whether or not the system has any endogenous model itself.

4 Towards a Measure of

Control-Based Intelligence

So now, given this semiotic control-based view

of intelligence, we wish to go on and attempt to

quantify and characterize the degree and kind of

control relations present. Thus the problem of

measuring intelligence revolves around our ability to meaijure:

503
• The amount of phenomena imder control;

• The number of environmental distinctions

measured by the system;

• The complexity of modalities of measure- ment and control;

• The complexity of the environmental variety

available to the measurement and control of

the system;

• If hierarchical control is present, what is the

depth of the hierarchy of control; and

• If anticipatory control is present, what is the

complexity of the internal, endogenous models?

No doubt in both real and designed systems

these are all related to each other in complex

ways. However, each of these quantitative terms

is effectively a statistical information measure,

a measure of variety or freedom. Thus th are ammenable to information-theoretical measures

like entropies, based on quantities of variety, distinctions, and constraints which a control system

can recognize in its environment and then act on

in appropriate ways.

References

[1] Albus, James S: (1991) "Outline of a Theory

of Intelligence" , IEEE Transactions on Systems,

Man, and Cybernetics, v. 21:3, pp. 473-509.

[2] Albus, James S: (1999) "The Engineering of

Mind", Information Sciences, v. 117, pp. 1-18

[3] Albus, James S: (2000), discussion on email list iab-listOicompsol . com

[4] Ashby, Ross: (1956) Introduction

to Cybernetics, Methuen, London,

http : //pep . vub . ac . be/books/IntroCyb . pdf

[5] Cariani, Peter A: (1989) On the Design of

Devices with Emergent Semantic Functions,

SUNY-Binghamton, Binghamton NY, PhD Dissertation

[6] Control Systems Group,

http : / /www . ed . uiuc . edu/csg

[7] Joslyn, Cliff: (1995) "Semantic Control Systems", World Futures, v. 45:1-4, pp. 87-123

[8] Joslyn, Cliff: (1998) "Are Meaning and Life Coextensive?", in: Evolutionary Systems, ed. G. van de Vijvier, pp. 413-422, Kluwer

[9] Joslyn, Cliff: (2000) "The Semiotics of Control

and Modeling Relations in Complex Systems",

under review for Biosystems

[10] Klir, George: (1991) Facets of Systems Science,

Plenum, New York

[11] Marken, Richard S: (1988) "The Nature of Behavior: Control as Fact and Theory", Behavioral Science, v. 33, pp. 196-206

[12] Meystel, Alex: (1996) "Intelligent Systems: A

Semiotic Perspective", Int. J. Intelligent Control and Systems, v. 1, pp. 31-57

[13] Meystel, Alex, et al: (2000) "Measuring

Performance of Systems with Autonomy:

Metrics for Intelligence of Constructed Systems", white paper for 2000 Workshop on Performance Metrics for Intelligent Systems,

http : / /www . isd . mel . nist . gov/ conferences/

perf ormance-metrics/white4)aper . html

[14] Nise, Norman S: (1992) Control Systems Engineering, Benjamin-Cummings, Redwood City

CA

[15] Powers, WT: (1973) Behavior, the Control of

Perception, Aldine, Chicago

[16] Powers, WT, ed.: (1989) Living Control Systems, CSG Press

[17] Rosen, Robert: (1985) Anticipatory Systems,

Pergamon, Oxford

[18] Rosen, Robert: (1991) Life Itself Columbia U

Press, New York

[19] Turchin, Valentin: (1977) Phenomenon of Sci- ence, Columbia U Press, New York

504
Selected Comments on Defining and Measuring of Machine Intelligence

Paul K. Davis

RAND and RAND Graduate School

1700 Main Street

Santa Monica, CA 90407-2138

June 1 1, 2000

ABSTRACT

This paper records some thoughts about defining and measuring

machine intelhgence. It touches on ( i) the shortcomings of any

scalar metric; (2) the power of having mixes of inleliigence types in a population of machines; (3) the special issues related to "common

sense;" (4) the need to broaden discussion beyond nonnally

understood intelligence; (5) consistent with that, the need in a population to assure for exploration and "mutation;" (6) some

technical issues in modeling reasoning in agents; and (7) a methodology (exploratory analysis) for measuring intelligence that emphasizes a diversity of contexts. KEYWORDS: agents, exploratory analysis, miiltiresolution modeling, machine intelligence, robot intelligence

1. INTRODUCTION

This is an informal think piece recording a number of thoughts

generated by a lengthy discussion, accomplished by e-mail, in preparation for a conference on defining and measuring

machine intelligence. Section 2 argues against the search for

any simple metric for intelligence. Section 3 draws upon

experiences in other domains to suggest that intelligent

machines might be assessed as groups exhibiting a good mix

of intelligences types. Section 4 asks what we might seek in such groups of machines, notes that narrow concepts of

intelligence do not obviously allow for wisdom, and relates the search for wisdom with meta-knowledge subjects such as

ethics. Section 5 touches upon the issue of learning and ties

this to the need, in some populations of intelligent machines,

for attributes encouraging exploration and discovery. Section

6 discusses the need for intelligent machines to have internal

models of their external worlds (and perhaps themselves); it then summarizes briefly some potentially relevant lessons

learned from my own work with multiresolution,

multiperspective modeling (MRMPM) of decision making.

Finally, Section 7 suggests a new approach or measuring

intelligence, an approach that would use emerging concepts

and techniques for "exploratory analysis" to assesses a machine's (or group's) effectiveness over an enormous range

of conditions.

2. NO SINGLE METRIC MEASURES

INTELLIGENCE

2.1 The Multiple Dimensions of Intelligence

One of the many lessons learned from a century of work on human intelligence is that intelligence is multifaceted [1],[2].

This, of course, accords with our everyday observations as

well, but the focus—by scientists and organizations—on a

single metric of intelligence (IQ, or related items such as college-board scores), has arguably done a great deal of

mischief and interfered with what might otherwise have been

natural: recognizing and honoring the richness of human

capabilities that we refer to collectively as "intelligence." It appears wise to define and measure machine intelligence as a multidimensional concept from the start. To say that the functionality of intelligence is multidimensional (and multifaceted) does not necessarily mean that the underlying capabilities manifesting themselves

as functional intelligence are fundamentally different. Indeed,

it has been argued that a single set of abstractly-characterized

capabilities (e.g., search) applies across the range, and that

intelligent behavior—across domains—can be seen as a common set of nested behaviors [3], [4]. That may well be the

case; if so, it is a matter of enormous significance. However,

my point here is that in seeking ways to measure the

intelligence of machines we will often be looking at functional

behaviors (e.g., the accomplishment of tasks) and we should

not should not make the mistake of imagining that functional

intelligence can usefully be reduced to a single metric'

2.2 Connections with Multi Attribute Utility Theory

Some may quarrel with this conclusion. After all, in many

endeavors it has proved feasible to combine various factors

into a single scalar quantity that reasonably measures what we

are interested in and proves quite useful. We see this in the

applications of multi-attribute utility theory (MAUT) [5], [6]

and in countless modeling problems where people introduce

' Gardner's types are linguistic, logical-mathematical, bodilykinesthetic, spatial, musical, interpersonal, and intrapersonal.

505
abstractions that combine various factors in ways that appear

adequately sound.

Based on some decades of experience, we now know a

great deal about the usefulness and shortcomings of MAUT,

although the subject is still one that divides people of good

will. Personally, I urge students of policy analysis to savor

the multiple attributes of strategies and avoid combining them

until and unless it is necessary. The paradigm for displaying

results of policy analysis is, for most of my colleagues and

me, a "scorecard' in which one views the ratings of options in each of a number of aggregate categories. We may or may not add up the scores for the purpose of having a single, simpleminded, result (e.g., for making cost-effectiveness

comparisons), but if we do it is only after we have adjusted

assumptions so as to assure that the aggregated result is

"right." By that I mean that decision-analysis methods are often most useful when used iteratively: we try to be logical

and explicit; we try to do things by the numbers; we look at the results; we then observe that they are "wrong" (meaning

that we don't like them). We go back to the assumptions and

either fiddle the input scores or muse a bit until we discover some hidden variables that are bothering us, and affecting us

implicitly. We then iterate. And so on.' At the end of the process, the algorithms may work and

we may have a sense that we understand the problem, but this was due to the disaggregated process of getting to that point.

At the trivial level, I like to challenge students with a car- buying problem, the purpose of which is to demonstrate that the usual hard-headed approach does a terrible job in representing our real values. Some individuals, for example,

really do want a red Mercedes sportscar, and it's hard to get

that answer when looking at mileage and repair costs. Even if one has a category for prestige or some such, it is very

difficult to get the red Mercedes as the answer unless one

essentially zeros out the other categories or recognizes the

shortcomings of the MAUT methodology with its assumptions

of linearity and related substitutability.^

" See [7] for an example of scoreboard methods applied in one

of my recent projects for the defense department. This

methodology includes what amounts to multi-attribute utility

theory in providing the opportunity to calculate the randordered goodness of options as measured by a composite cost- effectiveness, but the central focus is a more disaggregated

scorecard with multiple measures. Further, the methodology

emphasizes exploratory analysis across different assumptions

and values. This is in contrast with more usual decisionanalysis methods that seem to emphasize getting the inputs

"correct."

^ In principle, MAUT theory (e.g., [5], [6]]) allows for

nonlinear combining rules and includes methods for inferring

values systematically. However, these add a good deal of

complexity and tediousness.

The value of not abstracting early to a single number is evident to all of us who use Consumer Reports or other

commercial reviews of competing products. The approach has

won in the marketplace. It is therefore a bit puzzling to me

why the allure of the single metric continues to be so high in some technical domains, and why reductionist versions of

decision analysis are so commonly taught (with only lip

service to the caveats).

2.3 The Persistent Tyranny of IQ and SAT Scores

Despite the shortcomings of single-metric methods, the fact is that IQ scores, SAT scores, and GRE scores are ubiquitous in assessments ranging from the personal ("Wow, Marcus is

really smart: he got a double 800.") to hard-nosed decisions by

admissions committees at universities and managers in industry. We all know that intelligence is a complex issue, but

most of us nonetheless use the simple metrics—at least to some extent. Moreover, they are more than pure crutches or

scientific astrology; i.e., they actually do correlate, at least to some extent, with things we care about (e.g., performance in classes or in the business environment). And shorthands are

useful.This said, the quality and depth of any discussion of

machine intelligence and its measurement would likely be

greatly restricted by having a reductionist goal such as finding

a single "IQ." Talking in shorthand is an excellent way to "dumb down" conversations and inquiries.

Consider the following as part of an indictment [see also

[2]):

• The correlation of IQ and SAT scores with subsequent

performance in graduate school and life is modest.

Indeed, it is so modest that one can only puzzle about

why so much fuss is made over the related tests. The

answer appears to be that, bad as the scores are as predictors, they are the best available. [8]

• The predictive power of the simple metrics is particularly

poor in explaining, for example, the effectiveness of top

executives.''

• We probably all know individuals who would flunk tests of mathematics, but who are brilliant in other

ways—whether verbally, or, for example, in the arts.

• Most of us know individuals who scored very highly on

intelligence tests and yet lack the capability to excel in

" It is perhaps of interest that the SAT scores of both

Presidential candidates in 2000 were bandied about in the

press. Neither candidates scores appear outstanding when

compared to those of top-half applicants to graduate schools.

Given the achievements of the individuals to date, doesn't this

tell us something?

506
various higher level activities. Perhaps they lack common

sense; perhaps they lack creativity; perhaps they are so obsessed with numbers that they cannot deal with fuzzier

aspects of life. Or, on the other end, we can all think of "geniuses" who

behaved in ways that can only be regarded objectively as

stupid. My favorite example is Napoleon, who marched on Moscow in the winter and predictably lost nearly his entire

army.

In summary, we should avoid with prejudice the goal of

finding a simple-minded metric for robots such as IQ.

Multiattribute utility theory will not suffice either—except in highly controlled circumstances—because reality is much too nonlinear and complex.

3. THE POWER OF MIXES

Once we recognize that intelligence is a multifaceted concept,

and that society places a high value on all aspects of

intelligence, broadly construed, then we are also ready to recognize the value of healthy mixes:

• Instead of optimizing the average "IQ" of a robot

community, we should instead seek to "optimize" the

effectiveness of the community (perhaps omitting those

items we expect or want humans to continue to do).

Moreover, in "optimizing," we should apply nonlinear

schemes that assure that we don't end up with able

mediocrities. For example, in human society most of us believe that we benefit from having at least some people who

are extremely good at mathematics, physics, written verbal

matters, spoken language, the arts, and even the difficult human skills associated with the very best of leaders on the one hand, or the best of clinical psychologists on the other. But we don't require all of these skills from everyone.

To use a different analogy, consider how we go about

dealing with medical issues. Perhaps some readers have a

single physician who "does everything" from delivering

babies to extracting brain tumors, but the rest of us seek to have a mixture that includes top diagnosticians (the best of

whom are very smart in the traditional sense), very good

internists who deal more with quantity than the with the

hardest cases, and various and sundry specialists. Some of the

specialists may be superb at some skills (e.g., microsurgery),

but poor at others. Whether fair or not, the stereotype of

surgeons is one of blockheadedness, arrogance, and inability to deal with human issues or even medical subtleties that are not "mechanical." Many surgeons even kid about this,

describing themselves as world-class plumbers. Now,

suppose that we wanted to choose a mix of doctors for a community on the moon. Would we look for some metric,

test everyone, and then optimize, or would we instead identify

many attributes and assure that all were adequately

represented?

4. THE CHALLENGES OF WISDOM AND

COMMON SENSE

Despite familiarity with the hilarious (or infuriating)

shortcomings of some artificial intelligence programs, I am

not particularly mystical about issues such as wisdom and

common sense. Intuitively, I believe that they have to some

extent been over-rated as a reaction to failures of the

straightforward rule-based approaches in AI. I suspect that with large enough computers and sufficient emphasis on and

time spent in training with neural nets and other technologies,

machines will eventually have moderately good skills that include what look like wisdom and common sense over

significant domain areas. On a sliding scale, I probably place

myself closer to Raymond Kurzweil in this regard, than, say

Herbert Dreyfus.

Nonetheless, this remains a frontier area for research.

Measurements would depend not just on the intelligence

"wired in," but the intelligence developed by experience and

the data bases provided initially and built up over time. As we

know from discussions in many forums, it is notoriously

difficult at present to measure the information, knowledge, or value in data bases. This, then, is just a warning of a different

type.

As a related matter, we should avoid the error of focusing

exclusively on aspects of intelligence distinct from ethics,

morality, or spiritualism (broadly construed). It is of interest to note that this mistake was not made by the late, great Isaac Asimov. It was not accidental that Asimov, rather than more

pedestrian writers, took on these issues directly.

We know that one of the special characteristics of

intelligent people is that they learn, taking on knowledge and

skills that go beyond what they were "programmed for." However, without some kind of principles to act as filters, what machines (or, for that matter, people) choose to learn and

experiment with may prove dangerous. Again, we can look to science fiction for examples

As a hypothesis, it seems to me that

• It will continue to prove impossible to achieve top-notch

"intelligent performance" across a wide range of

situations without having principles that look more like

ethics than electrical engineering.

5. ASSURING EXPLORATION AND

MUTATION

Although we may differ among ourselves about the meaning

and existence of "progress," it is clear that the processes of

evolution such as mutation and natural selection have

profound effects. Suppose that there were no mutations, or

that there were no means by which to select. What might then

507
have happened? In a sense, we know. For example, we know

of the extreme vulnerability of populations when they

encounter a disease that is new to them. And we know of the

extreme vulnerability of overly "nice" communities when they

become prey to "bad guys." What implications does this have

for defining and measuring machine intelligence? Well, the answer would differ if we had in mind only specialist

machines such as window washers, rather than colonizers of some hostile planet. However, for some purposes at least, I would think that what we would seek to define and measure—perhaps under the rubric of a generalized notion of

"intelligence—would include attributes such as audacity,

curiosity, and the ability to "mutate" (in a sense to be defined).

As a corollary, I would think that:

• Otimizing a mix of machines for complex autonomous

tasks, the requirements for learning and adaptation would

require that some of the machines be designed to experiment and "mutate," rather than always following

what was thought by designers to be optimizing logic.

Much is known about dealing with uncertainty and much

can be accomplished with sufficiently rigorous prior thinking

about the circumstances that one may encounter. However, a

principle in much of the work on uncertainty is that one

cannot anticipate everything. Adaptation is essential. A great

deal of adaptation can be accomplished if one has the

appropriate building-block routines to try in different

combinations. This said, some adaptations require new forms

of organization, new processes, or both. One of the marvels of

nature is self-organizing systems that have the "talent" to reorganize to deal with new circumstances.

6. SOME TECHNICAL ISSUES IN

THINKING ABOUT BUILDING

INTELLIGENT MODELS

Much has been written about artificial intelligence modeling.

I would add here only a few observations based on personal

experience. Some of this involved building a massive analytic war gaming system during the cold war, one in which we had

Red, Blue, and Green agents representing the Soviet

UnionAVarsaw Pact, United States/NATO, and various third countries. These agents made decisions about war, strategy,

escalation, deescalation, and termination amidst the events generated by a simulation [9]. The first observation is that such models may prove more

useful if they reflect a strong design rather than, e.g., a more

unstructured approach such as lots of miscellaneous rules and

an inference engine. Even if performance in particular tasks

might be high with the latter approach, credibility and

understandability tend to go with structure and with the ability

to trace rationales.

Machines will need models of other machines, and highly

simplified models of the other machines' modeling. There is

no infinite recursion here because—if for no other reason—uncertainties in key inputs to judgments are

sufficiently large that fine-tuning doesn't work well. In our work. Blue's decisions were based on a model of Red, which

in turn had a highly simplified model of Blue. Both Red and

Blue could learn to some degree as the simulation proceeded,

although this was wired-in learning such as changing planning

factors based on events in the simulation and assessing which

opponent model seemed best given observed behavior.

The second observation is that multiresolution modeling

(MRM) is extremely important in such work (and in other

types of modeling as well). By MRM I mean modeling that provides alternative levels at which to make inputs, as distinct

from modeling that merely provides intermediate—and highly

aggregated displays, but that does all calculations from the

lowest level upward.^

MRM is important for many reasons, but one of them is relevant here. Higher level intelligent behavior depends on

higher-level models, not on calculations from incredible

depth. The reasons relate to the enormous uncertainties that exist at lower levels (higher resolution)—not only in "data,"

but also in algorithms. This is part of the celebrated "bounded

rationality" problem explained by Herbert Simon. As a result,

real people (and at least some intelligent models) must be able

to reason and decide at the level of abstractions. Abstractions

often get built into models willy-nilly, but there is great

benefit in designing them in from the start. Ideally, models

would also be able to infer their own abstractions on the basis

of experience. That is surely plausible with newer technology,

but we have a long way to go, to say the least. In the

meantime, good design can be quite helpful. I believe that one

of the best ways to "measure" the intelligence of machines

will probably be to review the hierarchical concepts it uses and the processes used to move up and down those

hierarchies. That is, just as we assess unintelligent computer

programs not only in terms of sampled behavior, but also in terms of inputs, structure, etc., so also for intelligence.

Some of this has interesting linkages to common sense, understandability, cause-effect relationships, and learning. As

a rule of thumb, I believe that a model intended to work at

level n of resolution should be accompanied by models at levels n-i-1 and n-1. The more abstract version may be needed

for planning functions such as screening, and the more

detailed version may be needed to provide "explanations" (a

highly relative concept) and the potential for a kind of learning

that would adjust the level-n model. Experiences that may

^ See [4],[10],[1 1],[12] for related discussion. Some authors use "multiresolution modeling" to mean only that a given

model describes different objects or processes at different

levels of resolution, without necessarily requiring that the

model user can specify inputs at alternative levels. They

sometimes fail to understand that truth does not always reside

at the level of most detail or that much of our best information

often comes at intermediate, or even low, levels of resolution.

508
appear magical at the intended level, level n, may be

explainable at level n+1 of resolution and it may be possible to use the experiences to recalibrate lower-level assumptions and

generate new abstractions. However, if the more detailed

model doesn't even exist, then it would seem that the only

recourse would be for the machine to use various and sundry

techniques such as statistical analysis to infer what are additional variables. There are severe shortcomings to such an approach—if, at least, it is feasible to do better. This said, it

is clear that humans do have the capability—with considerable

effort—to see new things and find new ways to reason without

them having been wired in our software. But we all know

how useful it is to have analogies, metaphors, or theories to

help.

It follows that one measure of intelligence might be the

structural richness of reasoning models: is it sufficient to accommodate a good deal of experience-based learning?''

Merely as an example here, consider the choices one has

in designing how to accomplish a disaggregation (i.e., moving

from a relatively more to relatively lesser degree of

abstraction, or from fewer to more variables). Economical

reasoning, such as a maximum entropy principle, might

suggest that the disaggregated view be as minimalist as possible given the explicit information available. However,

one might instead want to consider disaggregations that "speculate" about much richer detailed structures. This, in a

sense, is what happens in vision, where our mind "sees" far more than it has a right to see when viewed from a purely

biological perspective: the mind draws on general knowledge

to infer the presence of patterns. This process becomes

evident primarily when the mind's first guess is wrong and we

discontinuously shift to another one, perhaps noticing (if we

try) that a shift is taking place.

Suppose, now, that our minds had been designed only to infer by an entropy maximizing principle. Our functionality

would be substantially less. Instead, we have the ability to infer more than we see and the ability to do so iteratively,

experimenting with different inferences, until the result makes

sense. This is very different from what someone with a clockmaker's view of nature would have designed. This

should tell us something about the design of intelligent

machines.

7. EXPLORATORY ANALYSIS AS A KEY TO

MEASURING INTELLIGENCE IN AN

UNCERTAIN WORLD

^ Mystel [4] argues passionately for a multiresolution view of

systems generally. As he emphasizes throughout, and as James Albus articulates in the preface, the point is not to have

an MRM design so that one can move inexorably from the

realm of detail to the level of abstractions in a pyramid-like

structure, but to be able to move upward and downward at will—according to immediate needs.

One of the principles of our discussion of intelligence should

be that the intelligence of a machine cannot usefully be judged

independent of context. "Performance" measures exist, of course (e.g., processing speed), but

• How "intelligent" something is needs to be measured in relationship to both tasks to be done and contexts in which to do them.

These tasks and contexts, of course, may be extremely

uncertain. This is obvious enough, but by analogy with my

work in policy analysis I would argue that special methods are needed to make use of this notion. In particular, we should

plan to construct what I have variously called "scenario

spaces" or "assumptions spaces" in which to test our

behaviors. Not only is it insufficient to pick an allegedly

representative context, and work away at measurements for

that, it is also not sufficient to do sensitivities around that context. Key reasons are as follows. First, there may not be a meaningful best-estimate or representative context; instead,

there may be massive uncertainties that make any of many

possible and very different contexts plausible. Second, the

effects of contextual variables may be highly interactive, so

that any linear approach to sensitivity testing would fail. The approach my colleagues and I have used in this regard involves "exploratory analysis," which emphasizes

studying the problem (e.g., assessing behavior's effectiveness)

in a vast scenario space that is designed for

comprehensiveness rather than detail. I refer to both

parametric and probabilistic explorations. In the first, one

discretizes the context's defining variables, and creates experimental designs that consider all (or a cleverly sampled

subset) of the many combinations. In simple cases, we can do

the full factorial design. In the second approach, one

represents the defining variables' uncertainty with uncertainty

distributions. Ultimately—after initial exploration—one

settles on a hybrid approach in which some key variables are parameterized (so that one can see cause-effect relationships

in output displays) and the others are treated probabilistically

and convolved. This leads to a suggestion:

• Multiresolution, multiperspective exploratory analysis

could be the basis for measuring the effectiveness of a machine over an enormous range of conditions (and with

different measures). The results could be used to characterize intelligence—in multiple dimensions, and in different resolutions and perspectives, as appropriate.

Fortunately, recent technology makes a great deal of this type of thing feasible—even with PCs on our desktop at home.

We are already at the stage where much can be learned by

"flying through the space of outcomes" using clever graphics,

and thereby seeing what regions (what combination of

variable values) are most important (e.g., acceptable or unacceptable outcomes).

509
These exploratory analysis methods could prove quite

powerful in the task of assessing the intelligence of machines.

REFERENCES

[I] Gardner, Howard, Frames of Mind: the Theory of Multiple

Intelligences, Basic Books, New York, 1983.

[2] Steinberg, Robert J., The Triarchic Mind: a New Theory of

Human Intelligence, Viking, New York, 1988,

[3] National Institute of Standards and Technology,

Measuring Performance of Systems with Autonomy: a White

Paperfor Explaining Goals of the Workshop, final draft, Alex

Mystel, July, 2000.

[4] Meystel, Alex, Semiotic Modeling and Situation Analysis: an Introduction, AdRem Inc., Bala Cynwyd, PA, 1995.

[5] Keeney, Ralph and Howard Raiffa, Decision with Multiple

Objectives: References and Value Tradeoffs, John Wiley and

Sons, New York, 1976,

[6] Kleindorfer, Paul R., Howard C. Kunreuther, and Paul J. H. Schoemaker, Decision Sciences: An Integative Perspective,

Cambridge University Press, New York, 1993.

[7] Hillestad, Richard and Paul K. Davis, Resource Allocation

for the New Defense Strategy: the DynaRank Decision

Support System, RAND MR-996, 1 998.

[8] Klitgaard, Robert, Choosing Elites: Selecting the Best and

the Brightest at Top Universities and Elsewhere, Basic Books,

New York, 1985.

[9] Davis, Paul K. Davis, Paul K., Some Lessons Learnedfrom

Building Red Agents in the RAND Strategy Assessment System

(RSAS), RAND, N-3003-OSD. Available also in Military

Operations Research Society, Mini-Symposium Proceedings:

Human Behavior and Performance as Essential Ingredients in Realistic Modeling of Combat-MORIMOC II, 1989.

[10] Davis, Paul K. and James H. Bigelow, Experiments in Multiresolution Modelling, RAND, 1998.

[II] Davis, Paul K., "Multiresoluton, Multiperspective

Modeling (MRMPM) as an Enabler of Exploratory Analysis,"

Proceedings of the SPIE. Vol. 4026, 2000.

[12] National Research Council, Post Cold War Conflict

Deterrence, National Academy Press, Washington, D.C.,

1997.

510
Hierarchic Social Entropy and Behavioral Difference: New Measures of Robot

Group Diversity*

Tucker Balch

The Robotics Institute

Carnegie Mellon University

Pittsburgh, PA 15213-3891

Abstract

As research expands in midtiagent intelligent systems, in- vestigators need new tools for evaluating the artificial societies they study. It is impossible, for example, to correlate

heterogeneity with performance in multiagent robotics with- out a quantitative metric of diversity. Currently diversity is evaluated on a bipolar scale with systems classified as ei- ther heterogeneous or homogeneous, depending on whether

any of the agents differ Unfortunately, this labeling doesn 7

tell us much about the extent of diversity- in heterogeneous

teams. How can it be determined if one system is more or

less diverse than another? Heterogeneity must be evaluated on a continuous scale to enable substantive comparisons

between systems. To enable these types of comparisons, we

introduce: (1) a continuous measure of robot behavioral

difference, and (2) hierarchic social entropy, an application

of Shannon's information entropy metric to robotic groups

that provides a continuous, quantitative measure of robot

team diversity. The metric captures important components

of the meaning of diversity, including the number and size

of behavioral groups in a society and the extent to which

agents differ The utility of the metrics is demonstrated in the experimental evaluation ofmultirobot soccer and multirobotforaging teams.

1 Introduction

Heterogeneous systems are a growing focus of robotics

research [FM97, GM97, Par94, Bal99]. Presently, diversity

in these systems is evaluated on a bipolar scale; systems are

classified as either heterogeneous or homogeneous depending on whether any of the agents differ. This view is limiting because it does not permit a quantitative comparison

of heterogeneous systems. A principled study of diversity

'This is an abbreviated version an article published in Autummwus

Robots, vol 8, no 3.

requires a quantitative metric. Such a metric would enable

the investigation of issues like the impact of diversity on

performance, and conversely, the impact of other task factors on diversity. To address this, we propose social entropy

(computed using Shannon's information entropy formulation [Sha49]) as an appropriate measure of diversity in robot teams.

In this paper we briefly introduce the mathematical for- mulation of individual robot difference and robot societal diversity. More details and examples are provided in [BalOOj.

2 The meaning of diversity

What does diverse mean? Webster [MW89] provides the

following definition:

di.verse adj \: differing from one another: unlike 2: composed of distinct or unlike elements or qualities

Clearly, difference plays a key role in the meaning of diversity. In fact, an important challenge in evaluating robot

societal diversity is determining whether agents are alike or

unlike. Assume for now that any two agents are either alike or not. Now consider what diverse means for societies composed of several distinct subsets. To make the discussion more concrete, suppose the "society" under examination is a collection of four different shapes: circles, squares, tri- angles and stars. Figures 1 and 2 illustrate several sets of

shapes as examples of ways the groupings can differ. The

goal is to develop a quantitative metric that captures the

meaning of diversity illustrated in these examples.

First, how should the number of distinct subsets in a society impact the measured diversity? Consider Figure 1: four

sets of 1 2 shapes. Each set has a different number of homogeneous subsets; from one homogeneous subset in Figure la (all circles) to four in Figure Id. This example suggests that the number of homogeneous subsets in a society

is an important component of measured diversity.

511
• • •

•

••

• •

•

• •

•

* •

***

9^

**

a b c d

Figure 1. Several collections of shapes. The number of homogeneous subsets in each collection

grows from one in a to four in d.

a

Figure 2. In both of these groups there are the

same number of shapes and the same number of homogeneous subsets, but the proportion of elements in each subset is different.

Now consider Figure 2. Which group of shapes is more

diverse? In both cases there are exactly 12 shapes and exactly two different types. In Figure 2a however, there is a much higher proportion of circles than in 2b where there is an equal number of circles and squares. This example suggests that the relative proportion of elements in each subset

is an important component of diversity.

These examples highlight the fact that the distribution of

the agents between homogeneous subsets is at the core of

the meaning of diversity. In light of this observation, we

make the following commitment: the measured diversity

of a multiagent society depends on the number of homogeneous subsets it contains and the proportion of agents in each subset.

3 Simple social entropy

How should diversity be quantified? The properties

Shannon sought in a measure of information uncertainty

are also useful in the measurement of societal diversity

[Sha49]. In fact, researchers in a number of disciplines have

adopted information theoretic concepts of diversity. Infor- mation entropy is used by by ecologists as a means of evaluating species' diversity [LVW83, LW80, Mag88], by sociologists as a model of societal evolution [Bai90], and by

taxonomists as a tool for evaluating classification methodologies [SS73, JS71].

Before proceeding we must introduce some notation:

• 72. is a society of A'^ agents with Tl - {ri , rg, r3...ri\?}

• C is a classification of Tl into M possibly overlapping subsets.

• c, is an individual subset of C with C = {ci, 02,03. ..cm}

• p,= is the proportion of agents in the ith subset;

Ic.l

and J2p, = 1.

In the last section we argued that the measured diversity of a system should reflect the number of groups in the

system and the distribution of elements into those groups;

diversity should therefore be a function of M and the p,s

as defined above. Assume that a diversity metric exists and

call it H. The diversity of a society partitioned into M homogeneous subsets is written H{p\,p2,P3, ,pm)- So, for instance, the diversity of the group of shapes depicted in Figure 2a is

( jV, while the diversity for the group of

shapes in Figure 2b is (| , |) . The diversity of a particular robot society IZa can also be expressed H(7la).

Shannon prescribed three properties for a measure of in- formation uncertainty [Sha49]. With slight changes in notation, they are equally appropriate for a measure of societal

diversity:

Property 1 continuous: H should be continuous in the pi.

Property 2 monotonic: If all the p, are equal, (i.e. p, =

jj), then H should be a monotonically increasing

function of M. In other words, if there are an equal

number of agents in each subset, more subsets implies

greater diversity.

Property 3 recursive: If a multiagent society is defined

as the combination of several disjoint sub-societies, H

for the new society should be the weighted sum of the

individual values of H for the subsets. This property

is important for the analysis of recursively composed

societies (e.g. [MAC97]).

512
H(5/6, 1/6)

H(l/2, 1/2)

H(l/3,2/3) + 1/3 H(5/6,l/6) + 2/3 H(l/2,l/2)

Figure 3. A new society (riglit) is generated

by combining two others (left). Tlie diversity

of the new society is a weighted sum of the

individual values of H for the subsets.

In addition to Properties 1 , 2 and 3, H has a number of

additional properties that further substantiate it as an appropriate measure of diversity. First, as we would expect, H

is minimized for homogeneous societies; these groups are the least diverse. Also, for heterogeneous groups H is maximized when there are an equal number of agents in each

subset. More precisely:

Property 4: i7 = 0 if and only if all the pi but one are

zero. In other words H is minimized when the system

is homogeneous. Otherwise H is positive.

Property 5: For a given M (number of homogeneous subsets), H is maximized when all the pi are equal, i.e.

Pj = jj. This is the case when there are an equal

number of agents in each subset.

Property 6: Any change toward equalization of the proportions pi , po. • • ,Pm increases H. Thus if pi < p2

and we increase pi , decreasing po an equal amount so

that they are more nearly equal, H increases. An important implication is that there are no locally isolated

maxima.

The meaning of the requirement that H be recursive is

illustrated in Figure 3. The two groups on the left are combined into a new society on the right. In general, for a society TZc composed of two societies, TZa and 7v6, the recursive

criteria ensures that:

where q is the proportion of agents in 7?. a, /? is the proportion of agents in Kb and a + l3 = I.

Shannon's information entropy meets all three criteria [Sha49]. The information entropy of a random system X

is given as':

H{X) = -A'J] p, log2(p,) (1) !=1

where K is a positive constant. Because A' merely amounts

to the choice of a unit of measure. Shannon sets A' = 1 [Sha49]. Equation 1 (with A' = 1) is adopted for the measurement of multiagent societal diversity. H{7la) is the

simple social entropy of agent society TZa-

' H(X ) is used in coding theory as a lower-bound on the average number of bits required per symbol to send multi-symbol messages. The ran- dom variable X assumes discrete values in the set {x i,x2,X3 ...xm} (the alphabet to be encoded) and p , represents the probability that {X = xij.

Even if these properties are desirable in a diversity metric, why choose information entropy over another function

possessing the same properties? Because, as it turns out,

information entropy (Equation 1) is the only function sat- isfying Properties 1, 2 and 3. Shannon proved this result using the mathematically equivalent properties he required

of an information uncertainty metric [Sha49].

The entropy of a number of example systems using this metric is given in Figure 4.

4 Classification and clustering

The discussion of diversity left open the question of how

agents are classified into subsets. It was assumed that any

two agents are either alike (in the same subset) or unlike. In

actuality, the robotic agents to be classified are distributed

in a multi-dimensional space where the dimensions cor- respond to components of behavior and difference corre- sponds to the distance between agents in the space. Difference between agents is likely to vary along a continuous

spectrum instead of in the binary manner assumed previously.

The challenge of finding and characterizing clusters of elements distributed in a continuous multi-dimensional

space is exactly the problem faced by biologists in building

and using taxonomic systems. In the case of biology the

dimensions of the space represent aspects of morphology

or behavior that distinguish one organism from another. In

this research the dimensions are the components of behavior

that distinguish one robot from another.

513
•

•

• • •

• • • •

• • •

T • *

• • •

*• *

*•*

•

*

*

•

• T

*•

0.00 0.41 0.82 1.00 1.59 2.00

Figure 4. A spectrum of diversity. In the diagram above, each of the six squares encloses a multiagent

system, from least diverse (homogeneous) on the left, to most diverse (most heterogeneous) on the

right. The simple social entropy, a qualitative measure of diversity, is listed underneath each system.

The aims of taxonomic classification are distinct from

other types of classification in that one goal is to arrange the

elements in a hierarchy reflecting their distribution in the

classification space. Conversely, many classification tasks

only require a simple partitioning of the space (e.g. cate- gorizing e-mail into folders). Taxonomic trees (the end result of the taxonomic classification process, e.g. Figure 5) are potentially more useful in the analysis of diversity than

simple partitionings because they provide more information

about the society's spatial structure. Biology offers a rich literature addressing this problem.

In fact, an entire field — numerical taxonomy — is devoted

to ordering organisms hierarchically using principled numerical techniques [SS73, JS71]. Many of the approaches

in numerical taxonomy are directly applicable to the problem of robot classification. They include mechanisms for

building and analyzing classification structures (e.g. taxo- nomic trees) and for identifying organisms on the basis of

these structures. Techniques from numerical taxonomy address the problem of how to classify organisms, or groups of organisms,

at various levels. At the lowest level in biological classifi- cation for instance, humans and gorillas are more likely to be classified together than, say, humans and dogs. But at a higher level, primates are in fact grouped with canines in

the class mammalia. Dendrograms provide an orderly hier- archic view of the these classifications. While dendrograms

perse are not necessary for the evaluation of diversity, they

are useful visualization tools and their construction provides

clues for the evaluation of overall societal diversity.

Dendrograms are constructed using a clustering algorithm parameterized by h, the maximum difference allowed

between elements in the same subset. The notation D(a,b)

is used to refer to the difference between the elements a and

b. In most applications the difference metric is normalized

so that taxonomic distance between any two elements varies

between 0 and 1 . When h = I all elements are grouped

together in one cluster (see the cluster at the top right in Figure 5 for example). As h is reduced from 1 down to 0

Figure 6. The branching structure of the dendrograms for these two societies is the same.

However, the more compact distribution of el- ements in the system on the upper right is

reflected in the branches being compressed

towards the bottom of the corresponding dendrogram (lower right).

514
Figure 5. Example classification using numerical techniques. The top row shows how the system

is clustered at several levels, parameterized by taxonomic level h (h is distinct from information

entropy H). The classification is summarized in a taxonomic tree, or dendrogram (bottom). Strong

similarities between elements are indicated by grouping near the bottom of the dendrogram; weaker

similarities between groups are reflected in converging branches at higher levels.

cluster boundaries change; the number of subsets increases

as they split into smaller clusters. The splits are reflected

as branches in the dendrogram. Finally, when h = 0 each

element is a separate cluster; a "leaf at the bottom of the

dendrogram "tree."

Dendrograms can reveal subtle differences in societal

structure. Figure 6 for example, shows two societies with

the same relative arrangement of elements, but one grouping is compact while the other is spread out over a larger

area. The difference in scale is reflected in a compressed

dendrogram for the spatially compact society (Figure 6

right). Can these differences be accounted for in the evaluation of diversity?

The spatial extent of elements in a taxonomic space is a

reflection of the degree of difference between agents. Note

that sensitivity to the degree of difference between elements

in hierarchic clustering depends on h. Because /i is a parameter of the clustering algorithm, it can be varied to ex- amine clusterings at any scale. Hierarchic algorithms are,

in effect, variable power clustering microscopes. For values

of h near zero the tiniest difference between elements will cause them to be classified separately, while the clusterings

at large values of h reveal societal structure at a macroscopic level. This feature is exploited in the development

of a diversity measure sensitive to differences in the spatial

size of societies.

5 Hierarchic social entropy

Now consider how these tools from numerical taxonomy

can be applied to the measurement of diversity. The discussion of hierarchic clustering algorithms above described

how the number and size of clusters depend on h. But how

is simple social entropy impacted by changes in /i? Since

the partitioning of a society is based on h the entropy also

depends on it. An example of the relationship is illustrated

in Figure 7. Entropy changes in discrete steps as h increases. Note that points where change occurs correspond

to branch points in the dendrogram.

Compare the dendrograms and entropy plots of the two

societies in Figure 7. As in the earlier example, the two

groups have the same relative structure, but the society rep- resented on the right is more compact, resulting in branching compressed towards the bottom of the tree. The differ- ence in scale is also readily apparent in the plots of entropy.

Entropy drops to zero much more quickly in the plot cor- responding to the compact society. Because the value of

simple entropy depends significantly on h when hierarchic

clustering is used, we augment the notation to account for

this:

H{'R,h) = // (71) for the clustering of VI at taxonomic leve(2^

515
Figure 7. Entropy depends on h. A comparison of entropy versus h for for two societies.

For clarity, the dendrogram is rotated 90 degrees.

H is a function of Tl and h because the classification of

agents into subsets, and therefore the entropy, depends on them both. This highlights the fact that the entropy of a

particular clustering is only a snapshot of the society's diversity. A comprehensive evaluation of diversity should ac- count for clustering at all taxonomic levels. This is easily accomplished using the area under the entropy plot as a measure of diversity. This augmented metric, called hierarchic social entropy, is defined as:

' H{n,h)dh (3)

0

where % is the robot society under evaluation, /i is a pa- rameter of the clustering algorithm indicating the maximum

difference between any two agents in the same group and

H(Ti, h) is the simple entropy of the society for the cluster- ing at level h. Note that as /» -> oo a point is reached where

all elements are clustered in the same subset (the maximum

taxonomic distance). H{7i, h) drops to 0 at this point. In the behavioral difference measure used in this work, the

maximum possible difference between elements is fixed at

1.0, so the upper limit of the integration is 1 rather than oo

as in the general case. Hierarchic social entropy is a continuous ratio measure;

it has an absolute zero (when all elements are identical) and

equal units. This enables a total ordering of societies on the

basis of diversity. It also provides for quantitative results of

the form "7?.{, is twice as diverse as Tla" This is a signifiFigure 8. Hierarchic social entropy (bottom) is

computed for three societies (top). The values are 0.71 5 for the system on the left and

1.00 for the system on the right. The calculated value increases as the element on the

upper right is positioned further away from

the group. Dendrograms for the groups are also displayed (middle row).

cant advantage over the categorization of systems as simply

"homogeneous" or "heterogeneous." Three example calculations of hierarchic social entropy are provided in Figure 8.

6 Behavioral difference

To summarize: hierarchic clustering is a means of dividing a society into subsets of behaviorally equivalent agents

at a particular taxonomic level. Diversity is evaluated at each taxonomic level based on the number of subsets and

the number of robots in each subset at that level. Integrating

the diversity across all taxonomic levels produces an overall measure of diversity for the system. Previous sections

have described the overall diversity metric and algorithms

for clustering the agents into subsets. This section focuses on the difference metric used for clustering.

How should the behavior of two agents be compared?

The technique advocated here is to look for differences

in the agents' behavioral coding. In many cases (e.g.

[BBC+95, Mat92, GM97]) robot behavior is coded statically ahead of time, thus individuals may be directly compared by evaluating their behavioral configuration. Learning multirobot systems (e.g. [Bal97, Mat94]) pose a chal516
lenge because their behavior evolves over time. To avoid

that problem in this research, the policies of learning agents

are evaluated after agents converge to stable behavior.

This approach depends on three key assumptions:

Assumption 1: At the time of comparison, the robots'

policies are fixed and deterministic.

Assumption 2: The robots under evaluation are substantially mechanically similar: differences in overt behavior are influenced more significantly by differences in

policy than by differences in hardware.

Assumption 3: Differences in policy are correlated with

differences in overt behavior.

If these conditions are not met in a particular multirobot

system, the approach may not be appropriate. But the as- sumptions are reasonable for the conditions of this research,

namely: experiments conducted on mechanically similar

robots built on the same assembly line. Control systems

running on the robots differ only in the data specifying each

agent's policy. The comparison of these policies is the crux of the approach.

To facilitate the discussion, the following additional

symbols and terms are defined:

• « is a robot's perceptual state.

• a is the action (behavioral assemblage) selected by a robot's

control system based on the input i.

• 7r^ is r/s policy; a = 7rj(!).

• p] is the number of times » j lias encountered perceptual state

i divided by the total number of times all states have been

encountered. Experimentally, p] is computed postfacto.

The approach is to evaluate behavioral difference by

comparing the robots' policies. The two foraging robots in- troduced earlier, for example, exhibit behavioral differences

that are reflected in and caused by their differing policies. In

the terminology introduced above, i represents the perceptual features an agent uses to selectively activate behaviors.

Definition 1: /„ and ?•{,, are absolutely behaviorally

equivalent iff they select the same behavior in every

perceptual state.

In complex systems with perhaps thousands of states and hundreds of actions it may also be useful to provide a

scale of equivalence. This would allow substantially similar

agents to be grouped in the same cluster even though they

differ by a small amount. The approach is to compare two

robots, 7'a and 7*6, by integrating the differences between

their responses, | na(i) - nh(i) | over all perceptual states ?.

If the action is a single-dimension scalar, as in a motor current for instance, the difference can be taken directly. However, complex actions like wander and acquire are treated as nominal values with response difference defined as 0 when

Taii) = 7r(,(?) and 1 otherwise. This approach is often used

in classification applications to quantify difference between

nominal variables (e.g. eye color, presence or absence of a

tail, etc.). Using this notation, a simple behavioral differ- ence metric can be defined as:

D'(r,,ro) =

^ I

- 'r^l*) I (4)

or for discrete state/action spaces:

D'{ra,ro) = -V I 7ra(«) - 7r(.(2)

I (5)

where - is a normalization factor to ensure the difference

n ranges from 0 to 1 . In the case of the discrete sum, n corre- sponds to the number of possible states. If Va and select

identical outputs (naii) = in all perceptual states (/), then D'(ra, I'b) =0. When and rt, select different out- puts in all cases D'(ra ,77,) = 1 . In the numerical taxonomy

literature, this difference is called the mean character dif- ference [SS73]. The calculation parallels the idealized evaluation chamber procedure introduced earlier (Figure ??).

Equations 4 and 5 weigh differences equally across all perceptual states. This may be problematic for agents that spend large portions of their time in a small portion of the

states. Consider two foraging robots that differ only in their

reaction to blue attractors. If, in their environment, no blue

attractors are present the agents would appear to an observer

to have identical policies.

There may be other important reasons certain states are never visited. In learning a policy, for instance, the robots

might discover in early trials that certain portions of the

state space should be avoided due to large negative rewards.

Because these portions of the space are avoided, the agents

will not refine their policies there, but avoid them entirely.

It is entirely possible for the agents to differ significantly

in these portions of the space even though they may appear

externally to behave identically.

To address this, the response differences in states most

frequently visited should be emphasized while those that are infrequently experienced should be de-emphasized. This

is accomplished by multiplying the response difference in each situation by the proportion of times that state was vis- ited by each agent (p'^^ + p^). Formally, behavioral differ- ence between two robots ra and Vb is defined as:

D{ra,ro) = I

I rr^i) - ttUi) \ dt (6)

or in discrete spaces

D(r.,r,) = J2

'^"^^''^

I ir^z) - | (7)

When I'a and I't, select differing outputs in a given situation,

the difference is normalized by the joint proportion of times

they have experienced that situation.

517
7 Conclusion

This work is motivated by the idea that behavioral diversity should be evaluated as a result rather than an initial condition of multirobot experiments. Previously,^ researchers

configured robot teams as homogeneous or heterogeneous a priori, then compared performance of the resulting teams

[FM97, GM97, Par94]. That approach does not support

the study of behavioral diversity as an emergent property

in multirobot teams. Defining behavioral diversity as an independent rather than dependent variable enables the examination of heterogeneity from an ecological point of view. How and when

does diversity arise in robot teams interacting with each

other and their environment? This work provides the necessary quantitative measures for this new type of investigation.

In this paper we introduce a mathematical definition of

agent difference that can be used to group agents according to similarity. The grouping (or clustering) of agents is parameterized by /?, a limit on how different agents can be,

yet still be grouped in the same cluster. An overall diversity

metric, hierarchical social entropy may then be computed

using the difference metric, /;, and clustering algorithms

originally developed by biologists for taxonomic classification.

References

[Bai90] K. Bailey. Social Entropy Theory. State University of

New York Press, Albany, 1990.

[Bal97] Tucker Balch. Learning roles: Behavioral diversity

in robot teams. In AAAI-97 Workshop on Multicigent

Learning, Providence, R.I., 1997. AAAI.

[Bal99] T. Balch. The impact of diversity on performance in multirobot foraging. In Proc. Autonomous Agents 99,

Seattle, WA, 1999.

[BalOO] T. Balch. Hierarchic social entropy: An information theoretic measure of robot group diversity. Au- tonomous Robots, 8(3), July 2000. to appear. [BBC+95] T. Balch, G. Boone, T Collins, H. Forbes, D. MacKenzie, and J. Santaman'a. lo, Ganymede and

Callisto - a multiagent robot trash-collecting team. Al

Magazine, 16(2):39-5I, 1995. [Dem92] L. Demetrius. The thermodynamics of evolution. PhysicaA, 1 89(3-4):4l7-436, November 1992. [FM97] M. Fontan and M. Mataric. A study of territoriality; The role of critical mass in adaptive task division. In From Animals to Animats 4: Proceedings of

the Fourth International Conference of Simulation of

Adaptive Behavior, pages 553-561 . MIT Press, 1997. [GM97] D. Goldberg and M. Mataric. Interference as a tool

for designing and evaluating multi-robot controllers.

In Proceedings, AAAI-97, pages 637-642, July 1997.

[JS71] N. Jardine and R. Sibson. Mathematical Taxonomy.

John Wiley & Sons, 1971. [LVW83] D. Lurie, J. Vails, and J. Wagensberg. Thermodynamic approach to biomass distribution in ecological systems. Bulletin of Mathematical Biology,

45(5):869-872, 1983.

[LW80] D. Lurie and J. Wagensberg. Information theory and

ecological diversity. In L. Garrido, editor. Systems Far from Equilibrium, pages 290-303, Berlin, West Germany, 1980. Sitges Conf. on Statistical Mechanics, Springer- Vedag.

[MAC97] D. MacKenzie, R. Arkin, and J. Cameron. Multiagent mission specification and execution. Autonomous

Robots, 4{iy.29-52, 1997.

[Mag88] A.E. Magurran. Ecological Diversity and Its Measurement. Princeton University Press, 1988.

[Mat92] M. Mataric. Designing emergent behaviors: From

local interactions to collective intelligence. In Pro- ceedings of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats

2, pages 432^4 1, 1992.

[Mat94] M. Mataric. Learning to behave socially. In Proceedings of the International Conference on Simulation of

Adaptive Behavior: From Animals to Animats 3, 1994. [MW89] Merriam-Webster. Webster's ninth new collegiate dic- tionary. Merriam- Webster, 1989.

[Par94] Lynne E. Parker. Heterogeneous Multi-Robot Cooperation. PhD thesis, M.I.T. Department of Electrical Engineering and Computer Science, 1994.

[Sha49] C. E. Shannon. The Mathematical Theory of Communication. University of Illinois Press, 1949.

[SS73] P Sneath and R. Sokal. Numerical Taxonomy. W. H. Freeman and Company, San Francisco, 1973.

[Wil92] E.O. Wilson. The Diversity of Life. Norton, 1992.

518
The Developmental Approach to

Evaluating Artificial Intelligence—A Proposal

Anat Treister-Goren* and Jason Hutchens

Artificial Intelligence NV

ABSTRACT

We propose a developmental evaluation procedure for artificial in- telligence^ that is based on two assumptions: that the Turing Test

provides a sufficient subjective measure of artificial intelligence, and

that any system capable of passing the Turing Test will necessarily

incorporate behavioristic learning techniques.

KEYWORDS^ artificial intelligence, human-computer

conversation, Turing Test, child machine, verbal behavior, Markov

modeling, information theory

1. INTRODUCTION

In 1950 Alan Turing considered the question "Can machines think?" Turing's answer to this question was to define the meaning of the term 'think' in terms of a conversational scenario, whereby if an interrogator cannot reliably

distinguish between a machine and a human based solely on their conversational ability, then the machine could be

said to be thinking [1]. Originally called the imitation

game, this procedure is nowadays referred to as the Turing Test. The field of artificial intelligence (Al) has largely ignored this strict evaluation criterion. Today Al encompasses topics such as intelligent agents, chatterbots, pattern recognition systems, voice recognition systems and

expert systems, with applications in medicine, finance, en- tertainment, business and manufacturing. It could be said

that the field is currently in a contentious state. Even

though important work has been conducted in terms of the

sophistication and expertise of programs, the vision which

motivated the birth of Al has not yet been fulfilled: there

is neither sufficient cooperation nor agreement amongst its researchers. The unfortunate result of this trend is that

true advancement is inhibited. We believe that a new approach is required. In this paper we shall demonstrate that the Turing

Test is a sufficient evaluation criteria for artificial intelligence provided that the expectation level of the interrogator is set appropriately. We propose to achieve this

by complementing the Turing Test with objective developmental evaluation. The logical flow of this paper reflects the necessary steps one must take when trying to establish

*A11 correspondence should be emailed to anatSa-i.com.

^We use the term artificial intelligence to refer to machine intelli- gence which exhibits human-like conversational capability. To avoid

ambiguity, we shall refer to the field of artificial intelligence as Al

throughout.

evaluation standards for artificial intelligence: we begin

with a definition of artificial inteUigence, we continue with

a discussion of the theory and methods which we believe are an essential prerequisite for the emergence of artificial intelligence and we conclude with our proposed evaluation

procedure.

2. THE TURING TEST

The Turing Test is an appealing measure of artificial intel- ligence because, as Turing himself writes, it . .

. . . has the advantage of drawing a fairly

sharp line between the physical and the intellectual capacities of a man.

The Loebner Contest, held annually since 1991, is an

instantiation of the Turing Test [2]. The sophistication

and performance of computer programs entered into the

contest, or lack thereof, bears out our introductory re- mark that the Turing Test has been largely ignored by the

field. In a recent thorough review of conversational sys- tems, Hasida and Den emphasize the absm-dity of performance in the Loebner Contest [3]. They assert that since

the Turing Test requires that systems "talk like people",

and since no system ciuTently meets this requirement, the

ad-hoc techniques which the Loebner Contest sul)sequently

encourages make little contribution to the advancement of

dialog technology.

Although we agree wholeheartedly that the Loebner

Contest has failed to contribtite to the advancement of artificial intelligence, we do believe that the Turing Test is an

appropriate evaluation criteria, and therefore our approach

equates artificial intelligence with conversational skills. We

further beheve that engaging in domain-unrestricted conversation is the most critical evidence of intelligence.

2.1. Turing's Child Machine

Turing concluded his classic paper by theorizing on the

design of a computer program which would be capable of

passing the Turing Test. He correctly anticipated the limitations of simulating adult level conversation, and proposed that . .

. . . instead of trying to produce a program

to simulate the adult mind, why not rather try to prodxice one which simulates the child's? If this were then subjected to an appropriate course of

education one would obtain the adult brain.

519
Turing regarded language as an acquired skill, and rec- ognized the importance of avoiding the hard-wiring of the

computer program wherever possible. He viewed language

learning in a behavioristic light, and believed that the language channel, narrow though it may be, is sufficient to transmit the information which the child machine requires

in order to acquire language.

It is indeed unfortunate that this promising line of

work was mostly abandoned by the field. Today we find

ourselves at a crossroads—a paradigm shift is in the air,

and many AI r&searchers are returning to the behavioristic

approach that Turing favoured.

2.2. The Traditional Approach

Contrary to Turing's prediction that at about the turn of

the millennium computer programs will participate in the

Turing Test so effectively that an average interrogator will

have no more than a seventy percent chance of making the

right identification after five minutes of questioning, no

true conversational systems have yet been produced, and none has passed an unrestricted Turing Test. This may be due in part to the fact that Turing's

idea of the child machine has remained unexplored—the

traditional approach to conversational system design has

been to equate language with knowledge, and to hard-wire

rules for the generation of conversations. This approach

has failed to produce anything more sophisticated than

domain-restricted dialog systems which lack the kind of

flexibility, openness and capacity to leam that are the very

essence of human intelligence. As far as human-like conversational skills are concerned, no system has surpassed

toddler level, if at all. Since the 1950's, the field of child language research

has undergone a revolution, inspired by Chomsky's trans- formational grammar [4] on the one hand and Skinner's

behaviorist theory of language [5] on the other. Computational implementations based on the Chomskian philosophy are the norm, and have yielded disappointing results.

It is our thesis that true conversational abilities are more

easily obtainable via the currently neglected behaviorLstic

approach.

3. VERBAL BEHAVIOR

Behaviorism focus&s on the observable and measurable as- pects of behavior. Behaviorists search for observable environmental conditions, known as stimuli, that co-occur with

and predict the appearance of specific behavior, known as responses [6] . This is not to say that behaviorists deny the

existence of internal mechanisms; they do recognize that

studying the physiological basis is necessary for a better

understanding of behavior. What behaviorists object to are internal structures or processes with no specific physical correlate inferred from behavior.

Behaviorists therefore object to the kind of grammatical structures proposed by linguists, claiming that these

only complicate explanations of language acquisition [7]. The)' favour a functional rather than a structural approach, focusing on the function of language, the stimuli

that evoke verbal behavior and the consequences of language performance. We believe this to be the right approach for the generation of artificial intelligence.

Skinner argues that psycholinguists should ignore tra- ditional categories of linguistic units, and should instead

treat language as they would any other behavior. That is, they should search for the functional units as they naturally occur, and then discover the functional relationship

that predict their occurrence. Behaviorism focuses on reinforced training. Since language is regarded as a skill that is not essentially different

from any other behavior, generating and understanding

speech must therefore be controlled by stimuli from the

environment in the form of reinforcement, imitation and

successive approximations to mature performance. Skin- ner takes the extreme position that the speaker is merely a passive recipient of environmental pressures, having no

active role in the process of language behavior or development.

According to behaviorists, changes in behavior are ex- plained through the association of stimuli in the environment with certain responses of the organLsm. The procasses of forming such associations are known as classical

conditioning and operant conditioning.

3.1. Classical Conditioning

Classical conditioning accounts for the associations formed

between arbitrary verbal stimuli and internal responses or

reflexive behavior. In classical conditioning, for example, the word 'milk' is learned when the infant's mother

says "milk" before or after feeding, and this word becomes

associated with the primary stimulus (the milk itself) to eventually elicit a response similar to the rasponse to the

milk. Once a word or a conditioned stimulus elicits a conditioned response, it can become an unconditioned stimulus

for modifying the response to another conditioned stimulus. For example, if the new conditioned stimulus 'bottle' frequently occurs with the word 'milk', it may come to

elicit a response similar to that for the word 'milk'. Words

stimulate each other and classical conditioning accounts for

the interrelationship of words and word meanings. Classical conditioning is more often used to account for the

receptive side of language acquisition.

3.2. Operant Conditioning

Operant conditioning is used to account for changes in

voluntary, non-reflexive behavior that arise due to environmental consequences contingent upon that behavior. All

behavioristic accounts of language acquisition assume that

520
children's productive speech develops through differential

reinforcers and punishers supplied by environmental agents

in a process known as shaping. Children's speech that most

closely resembles adult speech Ls rewarded, whereas productions that are meaningless are either ignored or punished. Behaviorists believe that the course of language

development is largely determined by the course of training, not maturation, and that the time it tak&s children

to acquire language is a consequence of the limitations of

the training techniques. Operant conditioning is used to account for the productive side of language acquisition.

Imitation is another important factor in language acquisition because it allows the laborious shaping of each

and every verbal response to be avoided. The process

of imitation itself becomes reinforcing and enables rapid

learning of complex behaviors.

Behaviorists do not typically credit the child with in- tentions or meanings, the knowledge of rulas or the ability to abstract important properties from the language of the

environment. Rather, certain stimuli evoke and strengthen

certain responses in the child. The sequence of language

acquisition is determined by the mast salient environmental stimuli at any point in time, and by the child's past

experience with those stimuli. The learning principle of

reinforcement is therefore taken to play a major role in

the process of language acquisition, and is the one we believe should be used in creating artificial intelligence.

4. THE DEVELOPMENTAL MODEL

We maintain that a behavioristic developmental approach

could yield breakthrough results in the creation of artificial

intelligence. Programs can be granted the capacity to imitate, to extract implicit rules and to learn from experience,

and can be instilled with a drive to constantly improve

their performance. Language acquisition can be achieved

through succ&ssive approximations and positive and negative feedback from the environment. Once given these

capabilities, programs should be able to evolve through

critical developmental language acquisition milestones in

order to reach adult conversational ability.

Human language acquisition milestones are both quantifiable and descriptive, and any system that aims to be

conversational can be evaluated as to its analogical human chronological age. Such systems could therefore be

assigned an age or a maturity level beside their binary Turing Test assessment of "intelligent" or "not intelligent''

4.1. Success in Other Fields

Developmental principles have enabled evaluation and

treatment programs in fields formerly suffering from a lack

of organizational and evaluative principlevS [8], [9], and have

been especially useful in areas which border on the quastion of intelligence. Normative developmental language

data hrus enabled the establishment of diagnastic scales.

evaluation criteria and treatment programs for developn

mentaJly delayed populations. In other areas, such as schizophrenic thought disorder, in which clinicians often

found themselves unable to capture the communicative

problem of patients in order to assess their intelligence

level or cognitive capability, let alone to decipher medication treatment effects, the developmental approach has

proven to be a powerful tool [10].

5. LANGUAGE MODELING

We are interested in programming a computer to acquire

and use language in a way analogous to the behavioristic theory of child language acquisition. In fact, we believe

that fairly general information processing mechanisms may

aid the acquisition of language l>y allowing a simple language model, such as a low-order Markov model, to bootstrap itself with higher-level structure.

5.1. Markov Modeling

Claude Shannon, the father of Information Theory, was

generating quasi-English text using Markov models in the

late 1940's [11]. Such models are able to predict which

words are likely to follow a given finite context of words,

and this prediction is based on a statistical analysis of

observed text. Using Markov models as part of a computational language acquisition system allows us to minimize

the number of assumptions we make about the language itself, and to eradicate language-specific hard- wiring of rules

and knowledge.

Some behaviorists explain that language is processed

as word-sequences, or response-chains, with the words

themselves serving as stinuilus for their successors [12]. Information theoretic measures may be applied to Markov

models to yield analogous behavior, and more sophisticated techniques can model the case where long-distance

dependencies exist between the stimulus and the response.

To date, conversation systems l)ased on this approach

have been thin on the ground [13], although the technique

has been used extensively in related problems, such as speech recognition, text disambigiiation and data compression [14].

5.2. Finding Higher-Level Structure

Information theoretic measures may be applied to the predictions made by a Markov model in order to find se- quences of symbols and classes of symbols which constitute higher-level structure. For example, in the complete

absence of a priori knowledge of the language under in- vestigation, a character-level Markov model inferred from

English text can easily segment the text into words, while a word-level Markov model inferred from English text may

be used to 'discover' syntactic categories [15].

521
This structure, once found, can be used to bootstrap

the Markov model, allowing it to capture structure at even

higher levels. A hierarchy of models is thus formed, each

of which views the data at a different level of abstraction.

Although each level of the hierarchy is formed in a purely

bottom-up fashion from the data supplied to it by the level below, the fact that each model provides a top-down view

with r&spect to the models below it allows a feedback process to be applied, whereby interaction between models at adjacent levels of abstraction serves to correct bad generalisations made in the bootstrapping phase.

It is our belief that combining this approach with positive and negative reinforcement is a sensible way of real- izing Turing's vision of a child machine.

6. EVALUATION PROCEDURE

Our proposal is to measure the performance of conversational systems via both subjective methods and objective

developmental metrics.^

6.1. Objective Developmental Metrics

The ability to converse is complex, continuous and incremental in nature, and thus we propose to complement our

subjective impression of intelligence with objective incremental metrics. Examples of such metrics, which increase

quantitatively with age, are:

Vocnbulary size: The number of different words spoken.

Mean length of utterance: The mean number of word

morphemes spoken per utterance.

Response types: The ability to provide an appropriate

sentence form with relevant content in a given conversational context, and the variety of forms used.

Degree of syntactic com,plexi.ty: For example, the ability to use embedding to make connections between sentences, and to convey ideas.

The rise of pronominal and referential forms: The ability to use pronouns and referents appropriately and

meaningfully.

These metrics provide an evaluation of progress in conversational capability, with each capturing a specific as- pect. Together they enable an understanding of the nature

of the critical abilities that contribute toward our desired

goal: achieving a subjective judgement of intelligence.

The challenge in creating maturational criteria is in

combining these metrics meaningfully. One might expect

discrepancies in the development of the different aspects of

conversational performance. For example, some systems

may utter long, syntactically complex sentences, typical of a child aged five or above, but may lag in terms of the use

of pronouns expected at that age. Weighting the various

developmental metrics is far from trivial.

-We use the term metric in its non-mathematical sense of relating

to measurement.

6.2. The Subjective Component

We do not claim that objective evaluation should take

precedence over subjective evaluation, just as we do not

judge children on the basis of objective measures alone.

Subjective judgement is an important if not determining

criterion of overall evaluation. We believe that the subjective evaluation of artificial intelligence is best performed

within the framework of the Turing Test. The judgement of intelligence is in the eye of the beholder. Human perception of intelligence is always influenced by the expectation level of the judge toward the

person or entity under scrutiny—obviously, intelligence in

monkeys, children or university professors will be judged

difi"erently. Using objective metrics to evaluate maturity

level will help set up the right expectation level to enable a valid subjective judgement to be made.

Accordingly, we propase that suitable developmental

metrics be chosen in order to establish a common denominator among various conversational systems so that the

expectation level of these systems will be realistic. Given

that subjective impression is at the heart of the perception

of intelligence, the constant feedback from the subjective

evaluation 1 o the objective one will eventually contribute

to an optimal evaluation system for perceiving intelligence.

By usuig the developmental model, computer programs will be evaluated to have a maturity level in re- lation to their conversational capability. Programs could

be at the level of toddlers, children, adolescents or adults

depending on their developmental assessment. This approach enables evaluation not only across programs but

also within a given program.

7. CONCLUSION

We submit that a developmental approach is a prerequisite to the emergence of intelligent lingual behavior and to the assessment thereof. This approach will help establish

standards that are in line with Turing's understanding of

intelligence, and will enable evaluation across systems.

We predict that the current paradigm shift in understanding the concepts of AI and natural language will result in the development of groundbreaking technologies

which will pass the Turing Test within the next ten years.

8. REFERENCES

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Ince, Ed., chapter 5, pp. 133-160. Elsevier Science Publishers,

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[2] Stuart M. Shieber, "Lessons from a restricted Taring test," Available at the Computation and Language e-print server as cmp-lg/9404002., 1994.

[3] K. Hasida and Y. Den, "A synthetic evaluation of dialogue

systems," in Machine Conversations, Yorick Wilks, Ed. Kluwer

Academic Publishers, 1999.

(4) Noam Chomsky, Syntactic Structures, Mouton, 1975.

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[5] B.F. Skinner, Verbal Behavior, Prentice-Hall, 1957.

[6] R.E. Owens, Language Development, Macmillan Publishing

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[7J G. Whitehurst and B. Zimmerman, "Structure and function: A

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[8] J.B. Gleason, The Development of Language, Charles E. Merrill Publishing Company, 1985.

[9] A. Goren, G. Tucker, and G.M. Ginsberg, "Language dysfunction in schizophrenia," European Journal of Disorders of Communication, vol. 31, no. 2, pp. 467-482, 1996.

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(llj Claude E. Shannon and Warren Weaver, The Mathematical

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[12] O.H. Mowrer, Learning Theory and Symbolic Processes, Wiley,

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[13] Jason L. Hutchens, "Introducing MegaHAL," in NeMLaPS /

CoNLL98 Workshop on Human-Computer Conversation, ACL,

David M. W. Powers, Ed., January 1998, pp. 271-274.

[14] Eugene Charniak, Statistical Language Learning, MIT Press, 1993.

[15] Jason L. Hutchens, "Finding structure via compression," in NeMLaPS / CoNLL98: New Methods in Language Processing and Computational Language Learning, ACL, David M. W.

Powers, Ed., January 1998, pp. 79-82.

523
General Scientific Premises of Measuring Complex Phenomena

H.M. Hubey, Professor

Computer Science

Montclair State University, Upper Montclair, NJ 07043

ABSTRACT

General scientific and logical premises lurking behind the art of measuring complex phenomena, specifically intelligence, are explored via fuzzy logic, probability theory, differential equations,

thermodynamics, generalized dimensional analysis, philosophy and

psychology.

KEYWORDS: generalized dimensional analysis, path functions,

fuzzy operators, fuzzy logic, thermodynamics, extensive variables, intensive variables

0. THERMODYNAMICS, OSs, AND TURING

Thermodynamics is probably the classical and ideal example

of a system-theoretic point of view, and one that is built on the

twin concepts of state and process. Furthermore, it is probably

the only link from physics to the study of living things, which

are most likely the most complex things which humans will ever have to study. The physical sciences are the easy sci- ences; it is the life sciences that are the hard sciences.[l]

Unfortunately, physical scientists work with powerful tools,

and life sciences have restricted themselves to working with

much less powerful tools[l].

Thermodynamics is a perfect example of a science

whose development lead to the improvement of the measure- ment of a fundamental dimension of physics. It was not until Lord Kelvin saw some inconsistencies that the concept of an

'absolute' temperature scale was created. In measurements of

things such as length, mass, or time we can easily envision the

concept of 'zero'. But it is not so with temperature. Nobody

knew what the lowest obtainable temperature was. In the argu- ments in the philosophy of science there exist data-first and

theory-first schools. Here we have a case in which both are

iteratively used. The problem of intelligence is most likely to follow this pattern of development. If the problem is in an area

that has a well-developed theory, we must try to explain the

phenomenon in terms of the developed theory. It is only when we cannot that we can start thinking about a new theory, and

this requires datamining techniques.

An Operating System (OS) is a very complex object. It has been said that "I may not know what an OS is but I can rec- ognize one, when I see one!". The same thing may be said

about intelligence, (or cognitive ability or any of the other

related words such as awareness, consciousness, or autonomy,

or even life.) The Artificial life newsgroup (ahfe) skipped trying to define life or artificial life. The only serious effort in this direction was made by Alan Turing. He essentially formalized

the saying about the OS into intelligence. We may not know

what 'intelHgence' is but we know how to recognize one when we see one. Apparently when we talk about intelligence, we

are talking about 'human kind' or 'human type' or 'human

level' intelligence, or at least 'living thing' kind (type/level) of

intelligence. We can say things about this without being able to define it precisely. It is precisely about this intelligence that Turing was referring to when he wrote about what is now

referred to as the 'Turing Test'. He understood all the problems that involve discussions of this thing called intelligence

many decades ago and offered his 'Gordian Knot' solution.

Sometimes thinkers are unable to break through the boundaries

of what has been created. Whitehead claims that Aristotle hindered the development of science for 2,000 years because

nobody was courageous enough to break through the boundaries of the box for the sum total of all knowledge for human

kind.

1. MEASUREMENT THEORY I

Normally, in the physical sciences, the possibility that an instrument may be capable of high precision while not being

able of high accuracy does not occur to people. It can only occur if the instrument is broken. If the instrument is a very

simple one (such as a ruler) we'd see immediately if there was

something seriously (or obviously) wrong. If the instrument is a highly complex one, then there would be various self-tests. However, in the social/life sciences creation of 'instruments' is an art. It is quite possible for the instrument to be reliable (precise) but not valid (not accurate) or vice versa. For example, a psychologist might decide to create a questionnaire which he

claims measures 'hostility'. The same person taking this test

(the questionnaire) might obtain different scores at different

times. So habituated are we to measuring things in this modem

age that we scarcely give thought to the possibility that what is being represented as a number may be meaningless. That is the

validity of the measurement i.e. that the measurement or metric

actually measures what we intend to measure. In physical measurements there is usually no such problem. Validity also comes in different flavors such as construct-validity, criterionrelated validity, and content-validity. Reliability refers to the

524
consistency of measurements taken using the same method on

the same subject. (Please see Figure 1)

Reliable

(precise)

Not Valid

(inaccurate)

Not Reliable

(imprecise)

Valid

(accurate on average)

Reliable

(precise)

Valid

(accurate)

Figure 1 : Reliability and Validity Analogy: One normally expects accuracy

to increase witii precision. However in the social sciences they are independent.

2. MEASUREMENT THEORY II

We often need to make things comparable to each other. We

call this normalization. That is most easily done if we use numbers. For example, one way to normalize test grades is simply to divide every grade by the highest grade in class. This

guarantees that the highest grade in class is 1.0 In order to be

able to compare one boxing match to another a standard scor- ing system is used in which the same number of referees are used to score the bout, and for each round at least one boxer

must be given 10 points. In Rasch measurements, we use

\-P

= e a- 6

(1)

where P=Prob{ answering correctly}, a=ability, and 8=difficulty of question. However, this is not scale-free. It would

probably be better to use something like Paa P

\-P=8 \-P = I + In (!)

(2)

In this case it is only necessary that both a and 5 be measured on the same scale (somehow). Obviously, it would be best for

all purposes to use numbers in the standard interval [0,1].

3. MEASUREMENT THEORY III

Before we try to normalize quantities we should know what

kinds of measurements we have. They determine if we can multiply those numbers, add them, or can merely rank them etc. Accordingly measurements are classified as: (i) Ratio scale, (ii)

Interval scale, (iii) Ordinal scale, or (iv) Nominal scale. Absolute (Ratio) Scale : The highest level of measure- ment scale is that of ratio scale. A ratio scale requires an absolute or nonarbitrary zero, and on such a scale we can multiply

(and divide) numbers knowing that the result is meaningful.

Interval Scale: The Fahrenheit and Celsius scales are interval scales. The differences on these scales are meaningful but

ratios are not. That is what Kelvin found out, and that is what

the absolute temperature scale is about. When measuring things

such as intelligence, consciousness, awareness, or even autonomy, or hostility, we have no guarantee that we are measuring

any of these on an absolute scale. There must be some other

guidelines. One of the guidelines is obviously the study of var- ious scales. In the intelligence game, psychologists have mainly

relied on the central limit theorem in 'hoping' that intelligence

is a result of many many different things adding up to create a Gaussian density. Thus they have contrived to make sure that

test results are Gaussian.

Ordinal Scale: The next level on the measurement scale

is the ordinal scale, a scale in which things can simply be ranked

according to some numbers but the differences of these numbers are not valid. In the ordinal scale we can make judgements

such as A>B. Therefore if A>B and B>C, then we can conclude

that A>C. In the ordinal scale there is no information about the

magnitude of the differences between elements. It is possible to obtain an ordinal scale from questionnaires. One of the most

common, if not the most common is the multiple-choice test, called the Likert scale, which has the choices: extremely likely/

agreeable, likely/agreeable, neutral, unlikely/disagreeable, and

extremely/very unlikely/disagreeable.

Nominal Scale: The lowest level of measurement and the

simplest in science is that classification or categorization. In categorization we attempt to sort elements into categories with

respect to a particular attribute. It ranks so low on the scale that

it was added to the measurement scales later. Even an animal

that can tell food from nonfood can be said to have learned or can be said to know about set operations instinctively.

The most basic and fundamental idea underlying these

scales which is not even mentioned, and which is extremely important for measurement of complex phenomena in the life sceinces, is that in the final analysis, it is the human sensory organs

that are the beginnings of all measurement. In the measurement

of temperature, although a difference scale was easy to set up

via the human sensory organs (and induction), it took theory

and scientists to obtain an absolute scale for temperature. To obtain a difference scale the only thing necessary was for humans

to note that the liquid in the glass went up when it was hotter. There was no way to know which was more hot and which less hot except via our naked senses. This is/was as basic as knowing the difference between

which of two sticks is longer than the other or which of two

weights is the heavier one. Similarly in the measurement of intelligence, the final arbiter is still the naked human senses. Hu- mans must make up the tests and decide which is more

intelligent, say a chimpanzee or a dog. There can be no other

way to proceed. The genius of Turing was that he realized this immediately. Therefore, Turing's basic intuition is correct. We

might not know what intelligence is but we can recognize it when we see it. Secondly, we should probably turn to nature to

find examples and a hierarchy or scaling of intelligences. It would not be off the mark to accept that all Hving things are intelligent to a degree, and that EI (Encephalization Index) is ba525
sically a good scale on which to compare the intelligences of at

least some living organisms. [2]

4. MEASUREMENT THEORY IV

Before we can even think about whether our measurements are on an absolute or difference scale we have to make sure that

the objects that we deal with are quantifiable in some way and

that we can measure them (with numbers naturally). Our handle on the problem is that the things we measure in physics

(and hence engineering) come \r\ fundamental dimensions. For

example, dimensions of that particular branch of physics called

mechanics consists of M {mass}, L (length), and T (time).

For electrical phenomena we need one more dimension, Q

(charge), and for thermal phenomena we need 9 (temperature).

Then we can entertain the thought of using dimensional

analysis for complex phenomena which is a method of reducing the number and complexity of experimental variables

which affect a given physical phenomenon, using a sort of

compacting technique. If a phenomenon depends upon n dimensional variables, dimensional analysis will reduce the problem to only k dimensionless variables, where the reduction

n-jt = 1,2,3 or 4 depending on the problem. Since these new

dimensions are products/ratios of the old variables to various

powers, the new dimensionless space has nonlinearly twisted

and compacted the old problem in a way in which we can see

regularity.

These ideas have been put to good use in biology [3]. For example, the mass of an animal grows proportional to L

but its surface area is only proportional to L". Thus, as animals

get larger they have to have larger cross-sections of bones to support all that weight. So an elephant does not look just like a

large sheep. These ideas have to be taken into account when

prototypes, say, airplanes are tested in wind tunnels. Many

other things having to do with scaling of living things such as metabolism, oxygen consumption, heat exhaustion, cooling

etc. can be found in Schmidt-Nielsen[3]. For example, one way to make different animals's brains comparable is to compare not their brain capacities but the ratio of their brain mass,

b, to their body mass B. Until recently, there was no method

that could cluster the variables in similar ways as above so that nonlinear dimensional compaction was not available, but now

there is a generalized data-driven method. [4]

5. PHILOSOPHY

Why do we do philosophy? One reason is because we do not want to 're-invent the wheel'. If philosophers have already

thought about this topic, we should at least be aware that thought has been expended and results have been achieved.

OperatioiiaUsm

:

The problem of what is being measured in quantum mechanics was solved during the early part

of this century by 'operationalism' an idea (by Bridgeman) that

the operations that are being executed define what is being

measured. As long as everyone does the same thing, we are guaranteed that we all measure the same thing. In the measure- ment of something like intelligence, obviously, the problem of

validity remains.

Quality vs Quantity: Thermodynamics, gave us the

concept of extensive and intensive variables. It is often

remarked in narratives that a fundamental difference exists

which can be characterized by the words 'quantitative' vs.

'qualitative'. Often what is meant by the word qualitative is "intensive" since concepts often characterized as a quality can

also be quantified. If a system consisting of a lot of 10,000 TVs

is split into two sets at random, the quality of the two subsystems will equal each other and the quality of the TVs of the

whole original system. A state of a system is characterized by a

set of parameters. If we split a thermodynamic system (say a container of gas) in half some of the parameters will obey

X^+X^ - and others will obey x^ = jc, = x^. The former

(upper case) are extensive parameters, and the latter intensive

parameters.

Qpen vs Closed: The concepts open vs closed (endoge- neous vs exogeneous) are obviously very closely related to each other In a closed system there can be no such thing as an exogeneous variable. At the same time, in general there is

really no accurate or clear definition of what an open system is.

In thermodynamics from where these ideas are probably borrowed, an open system is one which exchanges mass with its surroundings. A closed system may exchange heat, and do

work on its surroundings, or have work done on it by its sur- roundings. Additionally, heat and work are processes. In other

words, they are not point functions, but path functions.

In general in mathematical modeling via differential equations, the surroundings {forcing or source term) is everything that does not have the system variable in it and usually

put on the rhs. However, when these concepts are specifically

applied to intelligence, we have to clarify what it is that the

system exchanges with its surroundings. The concept can

apply to both exchanging data and or information with its sur- roundings. At the same time, the word "open" may be used to

refer only to the problem at hand (i.e. if the problem is "openended"), but then it is not about generalized intelligence but

about a specific problem. To generalize it we will then be

forced to think about what little we know about how the brain

does its work or how to generalize from the mathematical

methodology that presently exists (i.e. logic, probability theory, etc). [1]

Munv-as-Qiie: The most fundamental such concept

according to modem math is 'set' and forms the basis of logic,

where philosophers are at home. This idea is the building block

of all systems. A body is not just a parts list although it is comprised of many subsystems thus is not merely a set. We have

many ways in mathematics of treating many things as one. A

tensor is a general object of any degree. A zero dimensional

526
tensor is a scalar. A one dimensional tensor is a vector or an ntuple. A two dimensional tensor is called a matrix. In addition

to this, from computer science we have the latest, and more

flexible concept of hierarchical ordering via OOP (obje^ t-ori- ented programming) in which an object is a set of parameters

without necessarily being merely a set or a vector. Parallel iw Serial {sequential): This is one idea that occurs quite often. Some problems are parallelizable. For

example, to dig a large ditch if we hire 100 workers as long as they do not interfere with each other, the ditch-digging will go

at a rate 100 times as fast as before. However, if I want to send

a message with a messenger, it does not matter if we use 100

messengers. The increase in the number of messengers might

increase the reliability but will not affect the speed of the delivery. But parallelity also has to do with simultaneity (not always

in time), choices, and substitutability, and logic. [7]

Trade-offs and Louie: We can sometimes trade-off

something for something else in which case these things are

substitutes of some kind. This idea shows up in logic as a logical-OR (co-norm). In the psychology and cognitive science literature, many different components of intelligence are posited.

It is quite possible that some of these intelligences are composed of other more primitive types. If so, then are some of

these substitutes for each other?

6. PSYCHOLOGY & COGNITIVE SCIENCE

Obviously throughout most of the century those who have

worked on the nature and measurement of intelligence (almost

always human intelligence) have been psychologists. They

have had recourse to and benefited from methods and argu- mentation in both philosophy and physics. The kinds of questions with which they have toiled can be summarized in modem (and mathematical) terms as:

i) What kind of a quantity is intelligence? Is it binary or measurable on some scale? What kind of a scale is appropriate?

Is it an ordinal, interval, or an absolute (ratio) scale?

ii) Is it an additive function of its constituents, the most

important ones for purposes of simplification being hereditary

(nature) and environmental (nurture)? Or is it a multiplicative

function! Is it logarithmic function, an exponential function or

a polynomial function of its variables?

iii) Is it a vector/tensor or a scalar (Spearman's g)? In other words, can a single number be produced from many numbers which is meaningful? Is there a hierarchy of intelligences, some of which subsume some of the others?

iv) Is it a state or a process ? In other words is it a point

function, or a pathfunctionl Is it a quality or a quantity! In other

words, is it an extensive variable or an intensive variable?

v) The nature vs nurture problem: Are the differences in intelligence among humans due mo.stly to heredity or environment?

There is a related (and incorrectly staled) version of (v)

which is "Is inlelligence mostly genetic?" The answer is quite

plainly that intelligence is mostly g6netie if intelligence is dis- cussed in its most general form, that is including machine intel- ligence and animal intelligence. However the answer to (v) is much more complicated. [5]

An almost perfect example of a vector of cognitive sci- ence is color. We all know what colors are but they would be

virtually impossible to explain to someone who was congenitally blind. If we did attempt to "explain" colors by explaining

that "black is the absence of color and white is a mixture of all the colors" it is likely that the blind person would think of col- ors as what we call "gray scale". The analogical question is whether the components of intelligence that psychologists

have posited are like colors in that they 'seem' as if they are 'unique' objects or is there a single number which we may

obtain from the components. [8] Is this single number like col- ors or is it like the gray-scale?

7. COMPLEXITY AND HIERARCHY

The concept of layering or hierarchy is one of the most basic in the universe. Whereas hierarchy requires more detailed explanation the concept of layering is easier to envision and observed

all over the world, at a very coarse-resolution. We use pictures

of all sorts (as in Figure 2).

V = A X

(A -XI) = 0 Higher Levels

y = fU) ^ + = T+F=T Level 3 2 2 (sin(O)) +(cos(*)) = 1 Algebra

1/3=0.3333... *

1 Level 2

13+5=18 1+1=2 Arithmetic

I 5 ^

Level 1 12 2

Small Integers

Sets? Logic ? Level 0

Figure 2: Highly-suggestive Layering in MathiiT^atics: Knowledge is

built-up in layers. New knowledge is built on top of o'd knowledge. This has

significance for intelligence testing.

What better example than knowledge? Data is raw. Information is data that is meaningful to an intelligent entity. Knowledge must be compressed information. The only way to compress information is via exploiting regularities and pat527
terns. Since mathematics is the study patterns, and regularities

of all kinds, it is clearly the best tool with which to do science.

Many more examples of layering can be found [ 1 ],[5],[6].

Thus the scientificity (intensity) of knowledge must be mathematics. Is it possible to measure intelligence separate and apart

from knowledge? Do we want to weight some kinds of knowledge more heavily than others?

8. DISTANCE & MEASUREMENT

The main problem here is whether, after having gone through

the problem of identifying the various components of intelli- gence, we should multiply them or add them to create a single

number called intelligence. Therefore two prototypical choices

for distance are

/ n

d{x,y) =

V/ = 1

lin

2 m

(3)

d{x,y) - na, p,

./ = 1

a

(4)

Obviously, in Eq (4) every component must be nonzero. There

are good reasons why it is so. If normal functioning of a human

depends on having absolutely no genetic defects, and if the intelligence of a human is determined by n genes, then if any of

them is defective it should effect the score in the same way that the reliability of a composite is the product of the reliabilities of its components. In this sense, then the factors are analogous

to probabilities.

This is also how we humans apparently tend to evaluate

intelligence, as can be seen in the schizoid labeling of the condition known as idiot-savant. Being apparently superhuman in one aspect of intellectual activity is not sufficient to escape the

label 'idiot'. It is said that an expert knows everything about

nothing whereas a generalist knows nothing about everything.

In an extension of this, then, today's experts (i.e. engineers) are

idiot-savants. Their social IQ is said to be low. Programs like Maple, then, are also idiot-savants.

9. AVERAGE-IZATION

Consider the problem of being a juror in a beauty pageant. We

will be forced to use a kind of scale in Eq. (5) (below)

B{x) = 1 n

J =

{x.-\i.}

a-,

J

(5)

where the |i. are the means. For example, the features/properties (of the vector x) may be nose length, skin color, lip thick- ness, fatness, etc. We will not want to vote for those with lips too thin or too thick, with noses that are too long, or too short,

legs too thin or too thick, skin too pale or too dark. In other

words, we are not looking for the minimum or the maximum

but rather the most perfect average there is (with some caveats). This is a different kind of logic, triage logic [10].

Then, the human-kind of intelligence, if it is going to resemble what we humans normally think about perfection

(apparently) should be measured via

1

lix) = 1

a.-

I

li = I

(6)

where the {x} are the various attributes of intelligence. The

Turing test is probably for this kind of intelligence. For example, a machine that can solve differential equations and multiply 20 by 20 matrices in a jiffy (such as Maple, a Computer

Algebra System) would flunk the Turing test. A human would

know that a normal human (or maybe even an abnormal

human) cannot do that. Therefore, the machine that could pass

the Turing test would either have to be designed dumbed-down

or it would have to learn to deceive. There are other things

machines can do very quickly that humans cannot accomplish.

Thus the 'measure' above would show that such an

entity could not be human (ceteris paribus, of course). In other

words, as long as the machine is able to do the other things more or less as a human, then overachieving (outdoing

humans) in one of the dimensions of the vector space would

mark it as a machine.

Exactly the same would apply in some other capability

such as being able to lift a few tons, swimming or running at superhuman speeds etc. For machines, then locomotion, would

also be treated as part of intelligence. However, since even

lower animals (less intelligent than us) can move around, it should not contribute much to the measurement of intelligence.

There are some pyschologists who want to include

many human capabilities, such as physico-kinetic intelligence

(i.e. physical ability) in the intelligence equation. Therefore,

this 'autonomy' capability of animals/machines may also be

considered to be a part of intelligence. We may take those that have been posited by psychologists as a starting point keeping

in mind that some of them may really be substitutes for each

other so that the measurement might be more complicated.

528
10. MORE SOPHISTICATION

Consider the simple problem of nutrition. Suppose we can create a balanced diet from the few foods available from three

separate food groups; meat (protein), carbohydrates, and vegetables as shown below.

Figure 3: Parallel or Serial Choices. The problem is actually about multiplication vs addition. Diagrams such as this occur in electrical circuits, Bool- ean circuits [9], or choice making.

In terms of circuit analysis (which can be thought of in terms

of Boolean algebra, [9] it is clear that the parallel lines are about choices (and thus lack of constraints) and therefore represent logical-OR (disjunction), whereas the seriality/sequentiality denotes a logical-AND (conjunction). Probably the first

thing a statistician would do if faced with the problem of determining the relationship between food groups and a balanced

diet would be to try correlation-regression analysis which

would be nothing more than

N = a^, + a t + a.ym + . . . + a c (7) 0 1 2 n

where t=tuna, m=mutton, c=com etc. This is really the same

kind of valuation of the problem as a weighted average. However, if we think logically then we should be considering a function of form;

N = MCV (8)

since we need to ingest food from all the groups. Furthermore,

since these food groups may be instantiated via specific examples, then using fuzzy logic, we should be regressing one of

= (t + m + d + v){b + a + k + c){z + p + r) (9a)

N = (t + m + d + v)^{b + a + k + c)^(z + p + ry^ (9b)

Obviously, the latter form (Eq. 9) is not only correct but will result in many products (possibly to various powers). It is exactly

this kind of products that dimensional analysis produces however it works only for problems with physical dimensions.

However, there are methods that will produce similar equations

for any problem if sufficient amount of data is available [4]. If intelligence-measurement is at least as complex as that of proper nutrition, then the simple weighted average kind of methods

which are additive will not work. In other words the regression

in Eq (7) is something like a combination of logical (or fuzzy)

ORs and ANDs. A question that comes to mind is if there are fuzzy operators which are neither OR nor AND but something

like both and exactly like neither. The special functions [11]

H^{x,y) = --(x + y)

M {x,y) = 2

m (x-y)

2 1/2

K2Ux-y) ] ;

m + 1

(10a)

(10b)

or others similar to these can be used in cases in which we are not sure if additive or multiplicative models should be used.

One can show that [11]

Max(x,y) = H^(x, y) + M^(x, y)

Min(x,y) = H^(x, y)-M^(x, y)

Therefore the operator (fuzzy t-co-norm)

F{x,y) = H^{x,y)^{2%-\)M^{x,y)

(11a)

(lib)

(11c)

is neither a norm (intersection) or conorm (union) but a fuzzy

operator or a fuzzy norm since it is a norm for ^ = 0 and a conorm for ^ = 1 . Some of the present day attributes of

intelligence posited by psychologists probably are substitutes

for each other and thus Eq (6) might distort the measurement.

Therefore, something like Eq (9) where the additions are fuzzy

unions and fuzzy intersections will probably give better

results. The equations are readily and intuitively

comprehensible in terms of theory of reliability based on

probability. Fuzzification of the norm-conorm can be done for

any fuzzy logic. For example, the simple product/sum logic

given by

i{x,y) = xy (12a)

u(x,y) = x + y-xy (12b)

can be easily fuzzified via

F'(.x,y) = pxy + (^\ - p)ix + y - xy)

11. HUlVIAN INTELLIGENCE

(12c)

The main problem today in human intelligence tests (and

genetics) is calculating how much of intelligence is 'inherited'

and how much of it is learned. There are several ways in which

the model for this may be derived. One way would be to point out general conditions which the 'intelligence function' must

satisfy. It should be multiplicative. It should display the

increase of intelligence in time from the time of birth. It should

converge on some limit on average for the people while being

529
allowed to fluctuate about the average rate of increase and the

limit of human intelligence. The equation

^ = X{a-x) (13) at

increases exponentially, and converges to a limit which is a good approximation. We need to know what the parameters

mean, and this can be gleaned from the behavior of the solution. In Fig (4a) we see several trajectories. Some converge to above average intelligence, and some to less than average.

Obviously the coefficient a determines this limit.

I ^

Figure 4a : Variations in a of the Intelligence Model

.

In Fig (4b) we see a fluctuation in the rate of increase of intelligence, and this is controlled by the coefficient X .

A

Figure 4b: Fluctuations in A, of the Intelligence Model

Logically both of these parameters then should be a function of

both genetics and environment. Since we have determined that

multiplicativity is important, the model should be

—I(t) + XG^E(t)l(t) = \aG it) (14)

dt

Integrating it once and rearranging terms we obtain the integral

equation

J e

/(r) = K(t)-XG^\ E ia)I(a)do (15a)

0

, J e + £ with K(t) = alG ^^j £ {s)ds (15b)

0

which is exactly what most researchers claim, that is, intelli- gence at time t, that is I(t), is a function of the past interaction

of intelligence with environment summed up over time from

time zero (birth) to the present time t. The interaction is multiplicative as it should be, and the equation is a reasonably good

approximation over time of how living things (especially

humans) learn. The solution is

where F = XaG which in the limit goes to

/ = aE^G^ (17)

If one day robots which learn from their environment are created, similar equations will be good first order approximations.

Same probability techniques can be used on these equations,

and statistics such as 'heritability' can be calculated. If the

multiplication above is treated as some kind of a fuzzy intersection, then we can see quite clearly that the same kind of an

equation can easily 'explain' the existence of natural language

among living things. At the limits the equation must reduce the

crisp logic, and we can see that it does. Only in the case when

both genetic capability is there and when there is proper environmental stimulation, does language exist. If one or the other

is missing there is no language. We can show how this equation explains what psychologists have said (in words) for a long time. Computing the virtual variation, we obtain for the

special (and simpler case) of e = = a = 1

dl = EdH + HdE (18)

If the environment is enriched, the corresponding increase in

intelligence depends on the genetic capability. Thus putting a dog in school cannot give it human level intelligence. Similarly, if there is a change in the genetic make-up (e.g. the dif- ference between a chimp and a human) the change in the

intelligence depends on the environment. A human brought up

without human contact cannot walk or talk or dress up.

530
APPENDIX

Exact Differentials and Path Functions

The distinction between the related concepts state and process

is an important one. There are mathematical definitions and

consequences of these ideas. A state (or property) is a point

function. The state of any system is the values of its state vector

(a bundle of properties which characterizes a system). If we use

these variables as coordinates then any state of the system is a point in this n-dimensional space of properties/characteristics.

Conversely each state of the system can be represented by a single point on the diagram (of this space). For example for an ideal gas the state variables are temperature, pressure, volume, etc. Each color can be represented as a point in the 3-D space

spanned by the R, G and B vectors. Intelligence is commonly

accepted to be a state variable, i.e. a point. The scalar. Spearman's g, (single number, not a vector) can be obtained from this vector by using a distance metric. The argument that the values

of the components cannot be obtained from the scalar, g, may

be valid depending on the distance metric however, the distance

metric may be devised in a way in which the components can be

obtained from the scalar. Distance on a metric space is a function only of the end points i.e. between two states. However, the

determination of some quantities requires more than the knowledge simply of the end states but requires a specification of a

particular path between these points. These are called pathfunctions. The commonest example of a path function is the length

of a curve. Another example is the work done by an expanding

gas. So is Q, the heat (transferred). In that sense work and heat

are interactions between systems (i.e. processes), not characteristics of systems (i.e. state parameters/variables). Intuitively,

when we talk about small changes or small quantities we use the

differentials dx or 5x. However the crucial difference is that al- though there may exist a function such that

/ 2 2

ds = ^Jdy + dx (A4)

we cannot integrate ds to obtain

S(b)-S(a) = jds = ^ds (A5)

a a

but instead first the curve y=f(x) must be specified. Equivalently, if z is a function of two independent variables x and y, and

this relationship is given by z=f(x,y) then z is a point function.

The differential dz of a point function is an exact differential

and given by

dz dy (A6)

dz Mdx + Ndy is

(A7)

Consequently if a differential of form

given, it is an exact differential only if dM ^dN

dy dz

Therefore in the mathematical function used for the simple twofactor (nature-nurture) Intelligence Function the environmental

path taken does make a difference in the final result which is as- sumed to be a state function (although computed from mental

processes).

jdF = jf(x)dx = F(x)f^ = F(a)-F{b) (A.l)

there is no function Q, (heat) such that

\hq ^ Q{x)^^ = Q(a) - Q{b) (A2)

Instead we write

J59 = Qab (A3)

meaning that is the quantity of heat transferred during the

process from point a to point b. Similarly because the infinites- imal length of a curve in the plane is given by

531
i

i

REFERENCES

1 . Hubey, H.M. ( 1 996) Topology of Thought, CC-AI: The Journal for the Integrated Study of Artificial Intelligence,

Cognitive Science, and Applied Epistemology, vol 13,

No.2-3, 225-292.

2. Eccles, J. (1989) Evolution of the Brain: Creation of the Self,

Routledge, New York

3. Schmidt-Nielsen, K. (1984) ScaHng: Why is Animal Size So

Important?, University of Cambridge Press, Cambridge.

4. Hubey, H.M. (2000) A Complete Unified Method for Taming the Curse of Dimensionality in Datamining and

Allowing Logical-ANDs in ANNs, submitted to Datamining and Knowledge Discovery.

5. Hubey, H.M. (1994) Mathematical and Computational Linguistics, Mir Domu Tvoemu, Moscow, Russia.

6. NIST (2000) White Paper

7. Hubey, H.M. (2001) Evolution of Intelligence:Direct Modeling of Temporal Effects of Environment on a Global

Absolute Scale vs Statistics, accepted by Kybemetes:

The International Journal of Systems and Cybernetics.

8. Hubey, H.M. (1996) Logic, physics, physiology, and topology of color. Behavioral and Brain Sciences, vol 20:2,

pp.191-194.

9. Hubey, H.M. (1999) Mathematical Foundations of Linguistics, Lincom Europa, Muenchen, Germany.

10. Hubey, H.M. (2000b) Fuzzy Logic and Calculus of Beauty,

Moderation, and Triage, The Proceedings of the 2000

International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences

(METMBS2000), June 26-29, Las Vegas.

1 1 . Hubey, H.M. ( 1 999) The Diagonal Infinity, World Scientific, Singapore.

I

532
Evaluation of System Intelligence

via Pictorial Data Visualization

V. Grishin*, A. MeysteP

* View Trends, Ltd., Cleveland, OH

° Drexel University, Philadelphia, PA

Extended Abstract

1. Introduction: Aspects of the research

The concept of evaluating tlie intelligence of systems presented in this paper is based upon the

model of intelligence outlined in [1] and the advancements in visuahzation described in [2]. Since the

main mechanism of intelligence is the mechanism of generalization, it would be prudent to judge the

degree of intelligence by the ability of the system to generalize. This abihty can be detected by the

means of visualization. Visualization of the system and/or the situation allows us to use the primary

orientation of our visual capabilities to the situations and/or modes of functioning based upon "gestalt"

i.e. capabihties to form a harmonious and consistent entity out of details.

We will explore the unique ability of the visualization systems to diagnose the system and/or its state by

discovering the syndrome: a group of symptoms, or diagnostic featiu-es that collectively indicate or

characterize a disease, a disorder, or another abnormal condition which has some unity within itself We

will use the terni syndrome for technological cases eitlier to characterize some psychological situation

based upon an intrinsic or other unit}'. For example, a multiplicity of unfortunately coinciding factors

can lead to a catastrophe. Thus, for this particular catastrophe, the combination of these factors is a

syndrome.

Thus, our approach is pursuing two major goals. First, our intention is to solve the unsolved yet a fairly

complicated problem of data mining and interpretation. This is a central problem of intelligence

functioning: how knowledge can be extracted from raw data via visualization. Solving this problem

would require analysis of the real world situations and constructing their models by effectively

combining formal verbal and even non-verbalized models of analyzed knowledge. It turns out that

visualization can help the human decision maker to associate these diversified models and to formalize

the new knowledge for the subsequent use in both manned and unmaimed intelligent systems.

To accomplish this goal a human-computer dialog has to be constructed at each step of visualization.

This dialog is aimed into restoration and analysis of the hierarchy offeatures and descriptions for

states, situations, and scenes. It should provide for fast and accurate discrimination, description and

understanding of known and new situation and their reasons. A visual-verbal language is created as a

533
part of this dialog for each case of analysis individually. Tliis approach provides effective selection of

new models for discovered singularities, observed changes of situation, detected structures, etc. An effective interpretation of visual-verbal results in terms of data properties and known models is the

result of this approach. Altliough the human participant caimot be replaced at this point: there is no

automated procedures to rely upon. A number of important advantages can be registered in comparison

with automated systems neural networks, pattern recognition, etc. To get the good interpretation we use

the simple data mapping into pictures and the human natural "gestalt-skills" for determining entities in

these pictures.

2. Principles of the Human-Computer Dialog for Picture Analysis

The following main components of our dialog realization distinguish our approach from others by more

effective use of hmnan cognition:

1. Constructing holistic images of exhaustively represented data about situation. If the number of

variables in a situation is more than 20-30, tlie matrix N*M is to be analyzed where each row displays a

time series of one variables or each cell represents a current value of separate variables or others. A

variable value is mapped into color-brightness. The ability to simultaneously represent more than

1000*1000 numbers and to see some "general image" of situation is considered to be an advantage of

our realization.

C. After picture smoothing and sharpening of edges.

Fig. 1 Coloi matrix di.splay of dynamics of 114 parameters

of nuclear powei- unit for a small leak of steamgenerator.

( 220 time samples)

534
This starting image allows for understanding a holistic structure of the situation, for detection some of

the available skeletons, and for mapping its different properties into levels of some hierarchical

organization that will direct the subsequent informative feature search (see Fig 1 A).

2. Combinatorial Searching in the Matrix by permutations of rows, columns and cells. For each

permutation the following is performed: sharpening the edges where required, smoothing where

possible, value-to-color mapping adjustment where beneficial, etc. to get more informative, more

interpretable image or at least to improve of its quality ( Fig. 1 B,C).

3. Mapping from matrix to entities: individual patches, group patches, clusters ofgroups. Informative

variables and features found as a result of matrices permutation can be visualized by grouping the

elementary units of image together. Frequently, change of the coordinate system (e.g. from Cartesian to

polar) can lead to new useful avenues in interpretation (as shown in Figure 2).

Fig. 2. Polar contour representations of 15 variables - "stars".

Thus, only tens of variables will be displayed at the levels of lower resolution but with complete

mapping of their relationships within the image. This allows to determine shapes and more general

forms that are more effective for visual analysis than color variation in the primary high resolution

crowd of elementary patches. The dialog with generalized levels consists of a searching group variables

and relationships among them.

4. Selecting appropriate criteria ofdecisions. It is important to underline that if no a priori knowledge

and/or hypotheses exist, then forming a syndrome is done based upon human gestaU skills and

experiences. If there is some knowledge of situation and its evaluation criteria, the process of

interpretation will synthesize this knowledge with the gestah intuitions.

5. Synthesis ofartificial representation ofobject - has to show selected sindroms in a maimer allowing

effective apphcation of above criteria.

3. Pattern Discovery by Human Vision: Can It Be Automated?

Human vision has an ability to quickly and efficiently compare several images in parallel with

hundreds of local attributes and features related to the shapes, textures, colors, or brightness of the

535
images. A standalone spot on a picture or a spot cluster has certain boundaries shapes which can be segmented using simple local features such as "straight line", "concave", "convex", "angle", "hole", etc.

These features have attributes as "sizes", "orientation", "symmetries", etc. and are connected by means

"upper-lower", "left-right", "inside-outside ' adjacency. Local features are visually unified into more

complicated shapes as "wave", "leaf', "a face profile" and others associated with real world objects and

also have above mentioned and more complicated attributes.

Fig. 3 State change.^ of nnclear power during 62 tiourts are clearly vblblc on this artificial pictorial representatfon but they were not detected by standard control system of power plant

( dates shifted ).

This process of feature generalization continues up to holistic image of a spot including also its

integral features as "complexity", "symmetries", "elongations" besides usual sizes, orientations, and

position on the picture and others. In addition, the color and textural properties can be described. These

attributes and features are tlien organized into a multilevel (multiresolutional) hierarchy that can be

partially verbalized, or at least, tagged with symbols. If a picture contains many different separate

shapes, such liierarchy can be constructed for tliese shapes clusters and clusters groups up to all picture.

In addition, the combinatorial and statistical features could be visually detected and estimated. Vision

rapidly moves through this hierarchy, searching for more details or generalizing the attributes allows for

the simultaneous examination of many facets of the image by means of a variety of attributes and

features.

536
4. Automated Description of Visual Patterns

Combinations of disjunctions and conjunctions of features Qi and their attributes Ai can be applied

for formalizing human representation of patterns. So called conjunctive normal form (CNF) describes

some pattern with variation of features attributes, e.g. ["middle size"[ Qa AND Qb [which is

"symmetrical" around axis Ac] AND Qk [known to be "small concave" and located in Ai (k) (e.g.

position)]. Disjunctive normal forms (DNF = CNF, OR (Qg v A^) OR CNFn) from separate features or

complicated patterns describe picture classes with supplemental patterns. These descriptions are

invariant for global rotations, shifts and projective transformations of whole shapes as well as tlieir parts

(with some limits). Similar formal tools can be applied with the purpose to formalize many other

elements of the human-computer dialog. The transfer of knowledge from a human to a computer can be

performed by using a subsystem of learning.

The analysis suggests that automated visualization is efficient in discovering entities, syndromes, and

singularities. The following phases of the analysis can be focused upon

Phase 1. Development of Automated Visualization System for Decision Making.

Usually data visualization is a human-computer dialog with the following general structure :

Stage 1. Entering data into the system and their consecutive processing in subsystems 1-4

Subsystem 1 . Data gathering, transformation, filtration.

Subsystem 2. Mapping Data into visual paradigm, e.g. pictures.

Subsystem 3. Computer supported human visual analysis of the visualized data: features

selection and transformation of situations into a visual relational map.

Subsystem 4. Comparison with a priori knowledge related to the features and the multifeature formations and search of new ones.

Stage 2. Change of the chosen set of variables and parameters for tlie analysis and repetition of the

cycle of consecutive running of Subsystems 1-4.

Stage 3. Estimation of resuUs, hypothesizing entities (syndromes), testing it through Subsystems 1-4

again, formalization and decision-making.

These three stages are run presently as a human-computer dialog that can have cycles between these

stages in any order assigned by a human. We intend to automate this process by equipping the hiunancomputer dialog processes by learning subsystem.

The strategy and the techniques of implementing the subsystem of learning and subsequent conducting

the interpretation of results will be determined by the following factors:

- goals are pursued within a particular domain and assignment

- limitations ofcombining human and computer capabilities

537
- available algorithms ofgeneralization and instantiation

- metrics acceptedfor evaluating the performance and intelligence ofthe system.

Phase 2. Application of Automated Visualization System for evaluating performance and Intelligence

of Intelligent Systems.

In this case, the resuh of learning from the human during the human-computer dialog will be

used for both: a) automatic analysis of data and b) for evaluating the performance and intelligence of

intelligent systems.

Assume, an intelligent computer vision system has performed image processing. As a result of this, a

particular image underwent a multiple generalization and the resuUs of this are presented as the result of

image analysis and interpretation. Let us consider another case: an intelligent system has planned a

motion trajectory for an unmanned vehicle. In order to evaluate the intelligence of these systems, their

problem solutions are presented to the automated system of visualization. The structure of the image

and the structure of the motion trajectory are visualized and the prospective syndromes are obtained.

The results of visualization are compared with the results of processing by the system undergoing

testing. This comparison serves as the estimate of performance and intelligence.

5. Visualization can be used not only for states but for the state-space

trajectories

It seems natural to expand the process of visualization from evaluation of states and situations to

evaluation of state space trajectories as a whole. This would allow for comparison of different system

behaviors by means of visualization of appropriate data. The results of visualization in this case are not

the images, or pictures but rather movies. There is plenty of evidence that the gestalt abilities can be

appUed not only to static images but also to their consecutive strings that represent processes. Finding a

temporal unity of a process is the problem that has never be proposed before as a problem for the system

of automatic visualization.

InteUigence is defined as a faculty of a system that increases the probability of successful fiinctioning in

a variety of problem solving situations and under imcertainty of the conditions of the environment.

When systems function, the resuhs of their functioning reflect not only changes of the envirorunents and

the goals assigned but also the results of their control system generating decisions and shaping

processes. The consistency of control system functioning will be reflected in a temporal gestalt of

processes that are generated as a result of control. It is oiu hypothesis that one can judge the control

system by observing the output and not only measuring how close it is to the output specifications but

also how satisfactorily the system responds to all changes. Since, the construction of a metric that

538
evaluates responses to all changes is a problematic one (H-infinity is one of the efforts) and since the

combination of uncertain circumstances has unlimited number of possible combinations, we assume that

using the natural ability of human vision to register and recognize singularities of external images, the

ability to distinguish differences in response can be detected via visualization.

6. Existing Experience of Using Visualization for the Purposes of

Recognizing Singularities

To the extent we analyzed the results of our experiences in Functioning Systems our hypotheses

can be considered confirmed. Our experiences in visualization system development for human decision

making support has shown that appropriate data visualization can :

- drastically enhance efficiency in comparing different approaches of intelligence,

- specify the most effective field of each approach application and combine many of them to built an

intelligent system for wide diversity of environment variations and control tasks ( or whatsoever. . .),

- extend tliis system capabilities for some set of important but uncertain (unpredictable) situations by

means their holistic visualization and recognition in real time.

Gas-turbine engine diagnostics in airplanes, nuclear power unit monitoring and search for the

cardiology diagnostic syndrome demonstrate capabilities of visualization techniques (see Figures 3 and

4 that illustrate the capabilities that arise during the analysis). Analysis of existing experimental data has

allowed to expect that the proposed method of intelligence evaluation can be successful. Pictorial

visualization has allowed to analyze the transition modes of equipments and temporal processes of

human heart functioning. As a result the effect of much earlier symptoms of many malfunctions in the

transition modes of operation were discovered to be different from the static modes, and more reliability

of conclusions was achieved.

In our interpretation of the cases of successful use of visualization, the following subjects were taken in

account:

-What was special in the way we have arranged the process of visualization

-What does it imply for the future organization of visualization

-What are the "Hypotheses of Visualization" that can be formulated

-What are the new concepts that should be introduced: temporal gestalt, dynamic syndrome,

visualization of transition modes.

The recommended use of visualization for intelhgence testing mclude:

-The specifics of intelligence testing

-The similarities of the case of intelligence testing and examples

-Restatement of the Hypothesis of visualization for the case of intelligence evaluation.

- How it will be applied for the cases of

539
• planner/controller for industrial crane

• autonomous xmmanned vehicle

23.09.99 10:40 27.09.99 10:47 30.09.99 10:3S

Fig. 4 Visibie methaboUc sblfts ' ti>p«R9lleinia.

Features 'attd d}^ of left atrium and ventiicle eUlargitle^t

Arterial h^ertensiDn of l enovascular genesii^.

References.

1. A. Meystel, "Evolution of Intelligent System Architectures: What Should be Measured?",

Proceedings of the NIST Workshop on Metrics for performance and Intelligence of Intelligent

Systems," Gaithersburg, MD, 2000

2. V. Grishin, "Multivariate Data Visualization for Qualitative Model Choice in Learning System," in

Proceedings of the 1998 IEEE International Symposium on IntelUgent Control, NIST, Gaithersburg,

1998, pp. 622-627

540
PART III

SUMMARY OF PLENARY DISCUSSIONS

541

Compendium of the Minutes

of Plenary Discussion

Panelists: T. Balch, K. Bellman, M. Cotsaftis, P. Davis, W. J. Davis, R. Fakory, R.

Finkelstein, E. Grant, J. Hernandes-Orallo, C. Joslyn, L. Reeker, E. Messina, A. Meystel, R.

Murphy, C. Peterson, L. S. Phoha, Pouchard, T. Samad, A. Sanderson, A. Schultz, W. C.

Stirling, G. Sukhatme, S. Wallace, A. Wild, J. Weng, T. Whalen

These notes follow the order of the papers presentation at the Workshop. Their significance is linked to the

ideas and generalizations that were noticed and recorded by the paneUsts. Themes of these notes follow the

concepts reflected in the session titles of the Workshop. Different tliemes generated notes of different depth

and originality. Of course, this is a result of papers presented, attendees, and how stimulating the

discussion was at the end of the session.

Theme 1. Features of the Industrial Intelligent Systems

The nature and embodiment of machine intelligence were discussed based upon papers:

(1) on the description of NIST ATP-flmded technology development and demonstration

project

(2) on the use of SOAR and CLIPS architectures to solve Towers of Hanoi and Quake II problems

(3) on the definition of Task Oriented System Intelligence

The challenge was to find the consensus among these three very different aspects of the

overall problem of distinguishing salient features of industrial intelligent systems. The

following statements of consensus were recorded:

1. An intelligent system was initially defined as one that works to achieve goals and to

survive. (The separation of the goals belonging to different time horizon is obvious).

2. The specifics of the present situation in the area of intelligent systems is in the fact

that we strive to measure system effectiveness and efficiency, not intelligence

(primarily, because we do not have the ability to meaningfully define and measure

intelligence). Also, we are under the impression that we are capable of determining

the effectiveness of a system. (Questions are not usually asked about the time horizon

and the scope of attention in which the effectiveness and/or efficiency are evaluated).

3 . Discussion was conducted on whether intelligence is inherently embodied in

hardware, not software. The consensus was reached that "Hardware is one constraint

on the range of a system's admissible tasks." (A dissenting point of view: hardware is

not just a constraint but rather a carrier of intelligence, or the knowledge that is

required for functioning of the intelligence).

543
4. An intelligent system was defined as the one that exhibits flexibility, generalization,

and innovations, as limited by availability of information and ability of applied

algorithms.

Theme 2: Metrics and Comparison of Alternatives: Case Studies

Paper 1 ; Rule-based learning can be applied successfully. Rules are derived from

data from simulation or experimentation with participation of a human expert.

Frameworks for knowledge-based controllers provide useful platforms for

alternatives comparison.

Paper 2: Knowledge extraction from raw data can be done by using visualization with

a human participating. The activities of the human can be learned by the intelligent

system. Using the human-computer dialog and the visual-verbal approach allows us

to extract data, properties, and models. Thus, visualization can be used for

intelligence testing.

Paper 3: Intelligence can be understood as the ability to make the appropriate choices, or decisions (e.g. for robots). Intelligence makes the process of choosing simpler

because of the structure that is imposed upon the decision making processes.

Learning can be understood as the ability to adapt to environment (i.e. at different

time scales, we will have different learning processes). Use analytic hierarchy

process to define weights for IQ for robots.

Paper 4: Intelligence must be measured by looking at several abilities of the system;

these abilities can be integrated by using the Additive Evaluation Method for

simulating the absent metric (intelligence). One of them is based upon the idea of

barter exchange and boils down to transforming all evaluations to a dollar-value.

Uhimately, only the human has the best sense of each value of the intelligent

function.

All papers of this Theme have something in common: the human must be kept in the

loop

(a) to measure intelligence, and

(b) to use metric for improving control, for analysis of tools, etc.

Also, intelligence metric is still subjective and the cost-function requires human

participation for evaluating the variables and assigning the weights.

Theme 3: Measuring Performance

The following salient issues were formulated:

1 . Although Life and Intelligence have many similarities, they are intrinsically different

in their evaluation: we can tell what is alive from what is dead. It is much more

difficult to distinguish what is intelligent from what is not intelligent.

544
2. Formal Requirements Specifications are needed for any system at hand. Both

performance and intelligence should be evaluated against the known set of their

specifications. ("Ask not how much our computers can do for us, ask what we want

them to do!" and "If you can specify intelligence, I can implement it!")

3. Some of the features of intelligent systems are frequently omitted at the present time.

Among them, the following should be taken into account in all cases:

a) disambiguation,

b) self-verification, and

c) automated synthesis (including self-synthesis)

4. The recommended approach to the system evaluation should combine the constraintbased specification with taking into account the temporal dynamic behaviors: timed

vs non-timed automata. But the hard problem is the specification: it is a part of

determining the dynamic behavior, too

5. Focusing upon performance measurements can be deceptive. Indeed, the system with

the best performance need not be the most intelligent. If the best performance can be

pre-programmed, the effort of arriving at the system with intelligence is excessive.

We need intelligence only if the best performance is not available otherwise.

6. Thus, measuring performance without measuring the level of intelligence is not

sufficient. Focus on information measures for intelligence is required, as distinct from

performance. This is why the standardized tests of performance do not say anything

about future functioning of a system as an intelligent system.

7. It would be desirable to find a simple measure of intelligence. One of the suggested

measures is: intelligence is inversely proportional to the minimum length of

descriptionfor the tasks performed by a system.

8. On the other hand, the proper functioning of the intelligent system requires

satisfaction of the optimum conditions for the subsystems that support the system of

intelligence, for example: minimize total representation size:

R=i;[Rm + Ra + Rr]

Rm - model representation

Ra - representation of the algorithm

Rr - representation of the residual part of the system

9. An example was discussed of a well described system that confirms the above

projections: dexterous manipulation with multi-fingered robotic hand

545
10. The concept of minimizing the state description can be seen from the known

importance of distinction between explicit (iconic) and implicit (abstract) state

representations

11. Evaluation of the degree of automation is one of the factors that can help us in

formalizing the way of evaluating intelligence. Successive technology generations

characterized by increasing automation from a non-automated system to the

autonomous system.

12. An opinion was presented that autonomy can be defined as the ability of a system to

react appropriately to unforeseen situations [following its own determination of how

to react]. Thus, the intelligent autonomy will be a subset of autonomy cases that leads

to a success.

13. Nevertheless, one can demand for autonomous and semi-autonomous systems being

evaluated on the scale (continuous) of the degree of autonomy observed.

14. Black Box Metrics was suggested in the White Paper. According to it the output

Vector of performance was considered varied by the input Vector of Intelligence.

Actually, that are other factors that affect the output of the black box:

a) the number of Human Operators of Complex System

b) the number of Loops/Operator

c) Size of Operational Space Automated

15. A transparent Glass Box Metrics can be introduced that allows for taking into account

not only the input and output but also what is going on within the box including:

• Richness of models implemented, e.g. using Multi-models

• Efficiency of applied algorithms, e.g. using Anytime Algorithms

• Sophistication of planning algorithms, e.g. using Dynamic Resource

Allocation

16. Among the productive examples that can be recommended for exploring the

comparative importance of the Black Box and Glass Box concept: unmanned

autonomous vehicles.

17. Performance Metrics for Intelligent Systems can be analyzed by formulating

"Intelligence Measuring Modules" (IMM). Their calculation is based on ordered

weighted aggregation operator F, and the decision maker. The basic IMM is a set

<Ai, A2, . .

. , An : Q> where

• Ai are relevant measurable attributes, or features of the system

• Q are linguistic quantifiers (such as "Most", "At Least," etc.)

• Fw(Q)(A, An) = E Wjbj

• Bj is jth best (largest) of available Aj

18. A more general metric incorporates importance factors for A^'s <A, ,. . ., An : M : Q>

546
Theme 4: Modeling and Measuring Machine Intelligence

The common issue of the papers related to this Theme was to observe a "scorecard"

or multiple capabilities and behaviors as characteristics of a single or muUi-unit

system and of the task environment are varied.

The attention was drawn to exploratory design for a community of what's important

over a suite of problems. Rather than conventional sensitivity analysis varying on

robot or task parameter one at a time, visualization is used to enable the researcher to

discover which combination of variables matter in which circumstances.

The concept of neuromorphic architectures is concerned with systems that mimic

brain architecture to implement action perceptual systems which focus their attention

in a closed loop interaction with the environment, an essential feature of intelligence.

Behavior of such systems is systematically studied as 2 compared with that or system

with "software lesions" to see if the effect of deactivating part of the simulated brain

parallels the effect of lesion to the corresponding part of a real brain.

The "metric" nature of comparison can be seen in the brain organization. Survival of

the organism is too slow and admits too many ahernate solutions to do the job. A

possible mechanism may be task completion and minimum energy matrices driving

competition between incipient sets of connections during ontogeny and learning.

A program of systematic observation and development of a robot can be a part of a

natural history museum, designed to be a rich social participant in interaction with

humans.

In addition to a systematic qualitative research program, quantitative metrics included

who followed the robot to look at aquatic dinosaurs, how long they stayed with the

robot, and how well they performed on a quiz compared with these who had not

interacted with the robot.

The chief bottleneck to ride application of mobile robots is not computational speed,

but interactive capability, such as vision and social intelligence.

These issues can be visualized at a different resolution level. The following four

languages are underlying human behavior: DNA, brain mechanisms, natural

language, and written and spoken language. Research in bio-informatics is a powerful tool for understanding the lower levels in order to achieve the goal of

computing with words, in which words are the input, words are the output, and the

intermediate computing remains in the background.

Some highlights can be stated as follows:

547
• Biological systems are inspiring, they encompass the richness of the

evolutionary process (the primary research already performed by Nature).

• Simulation is increasingly feasible: as the complexity of mathematical

models is growing, the futility of analytical approaches gets more explicit. • This leads to the situation when controlled experiments are easier to

conduct.

• One should not expect that all types of simulation are feasible. An

apprehension was expressed concerning proposals to simulate the

modular-brain concept.

• One should be very careful about metaphors used in the present days

terminology. One example: Social robots are different from software

agents! Awareness and expressiveness make robots social, and this can be

measured or estimated. This is not the case with software agents.

• Apparent intelligence matters; it depends on "socialness." It is not clear

presently how to judge upon this feature.

• An example can be suggested for the "socialness" evaluation: A Robot

Serving as the Museum Guide.

• Man-machine interactions are primary issues in robots with "socialness"

feature.

• The following list contains other factors that affect the ability to measure

the level of intelligence: DNA as a part of genetic algorithms observed

and/or applied, symbolic representation laws applied at each level of

resolution, speech and culture of intelligent systems, physical expressions

and codes of communicating intelligent systems.

• Need much more work on natural-language (NL) interfaces and

computing. Eventually, the NL issues might be the key into evaluation of

intelligence.

Recommendation:

1. One should systematically observe the "scorecard" of quantitative and qualitative

measures of performance as one varies the capabilities of a single or multiple robot

system and confronts it with a rich suite of environmental challenges (for example,

groups of aduhs and children visiting a museum with a Robot Guide.)

2. The camouflaging of the description of processes of intelligence by gratuitous use of

scientific and computational phraseology should be avoided. Let words and actions speak - keep special terminology in the background!

Theme 5: Evaluating Factors of Intelligence in Systems

The highlights of this discussion:

• Intelligence is gradual (continuous function of the features of interest) and multidimensional (depends of many variable factors-coordinates).

548
It is preferable to assign a numerical value depending on a variable than to rank in a

list that hides the dependence on the particular variables.

The difficuhy of tasks can and should be precisely measured. Thus, the evaluation of

performance and intelligence might depend on prior evaluation of the "objective"

complexity of tasks.

Information-theoretical tools are especially usefiil for presenting the resuhs of

evaluating performance, intelligence, and the complexity of tasks..

Factors should consider incomplete, contradictory and partially wrong information

handled by intelligent systems.

Different types of reasoning are the inherent part of the system of intelligence.

The need for self-structuring/self-organization demonstrates itself as a component of

normal learning process of the system of intelligence.

As the process of learning develops, the system improves its own efficiency by

generalizing upon similarity among multiple units of information. New, lower

resolution objects emerge as a resuh of generalization. As this process evolves,

different levels of granularity form multiresolutional hierarchies of representation.

Standard techniques from behavioral sciences (psychology, psychometrics), biology,

ecology are very useful (ANOVA, dependency analysis).

Quantitative measures turn out to be better for efficiency of computations than

qualitative/discrete ones.

Large number of experiments are needed for Intelligent Systems if the high variance

of results does not allow for forming a reliable rule.

Sharing the results of multiple experiments is crucial for increasing the group

efficiency of intelligent systems (a website and/or repository would facilitate the

sharing).

Measurement and experimentation do not provide the fully reliable value of certainty

but give useful information that helps statistically the overall population of intelligent

systems.

Thus, social behavior is fundamental: it compensates for the lack of perfection of the

individual intelligent system.

Agents in a group are not totally identical, we have to find how to evaluate the

optimum diversity of characteristics in the group of agents.

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• There are many useful results in the intuitive approaches of the past, such as sociology, ecology, but they should be combined with contemporary informationtheoretical, statistical, clustering techniques.

• Penahy-reward approach of reinforcement learning is useftil for training systems as well as for measuring them without the exactly predetermined goal.

• Behavioral definitions of intelligence (Albus) can and should be put in

correspondence with feature-based metrics of intelligence.

• More simple systems may behave more properly or even more "intelligently" for

particular success criteria or particular environments.

Theme 6: Measuring Intelligence of Multiagent and Autonomous

Networks

The major challenge for this group of intelligent systems is dealing with complexity, in

particular, with exponential complexity typical for many practical cases.

Approaches:

1 . Using biologically inspired systems

2. Extrasensory intelligence permissiveness

3. Metrics for embedded collaborative intelligent systems that are based on:

• Graphical Assessment Tools

• Various "orders" of Intelligence

• Both applications pull and tech push

4. Domain independent measures

5. Negotiation mechanisms and coordination protocols

Theme 7: Measuring Intelligence of Distributed Systems

Four papers were presented containing a treatment of intelligent distributed systems. The

following issues were highlighted:

• There is a need for highly reliable systems capable of dealing with extremely

complex situations (like air traffic control. . .)

• These systems are typically formed of subsystems that perform specific tasks that

solve some larger problem/task/or control - The process of decomposition is one of the key issues of analysis. An understanding

should be achieved concerning the following issues: what is the principle of

decomposition, how it is performed in the cases of spatial, temporal, functional, and

other special cases. The possibility should be verified to aggregate the decomposed

system.

550
- Functional aggregation of the subsystems is a separate issue because the problem of

coordination emerges which should be a part of behavior generation.

• As a result of decomposition/aggregation, the problem of intelligent control can

evolve: usually, it is required to modify the actions and translate these modifications

into subtasks, i.e. it is required to re-optimize the system.

• The problem of optimization is resolved at the stages of planning and control.

However, the system sometimes cannot implement the optimal solution. In these cases the "satisficing" contingency should be applied.

• The problem of symbol grounding has the following practical incarnation: simulating

the resuh of planning is frequently inadequate because a lot of underrepresented

information is lacking. Indeed, the Planner envisions the desirable and even probable

future, but it does not affect this future: the actuators of the system that enable and

activate the process do.

• Multi-resolution representation of the system should allow for evaluating the

performance and intelligence at all levels of resolution.

• Muhiple independent agents are different from a consolidated system with a

hierarchical implementation. The rules and laws are different of applying

multiresolutional methodology to multi-agent distributed systems.

• The following features are characteristic of Key Monitor Expert Systems that start from the model/role based Expert System (e.g. for Automated Monitoring):

Capturing Knowledge is equivalent to creation of rules; this is a difficult

issue

Hierarchical fault tree should be carefully constructed to distinguish the

branching by resolution from the branching by decision making

Using intelligent systems in these cases is expensive

It would be prudent to anticipate the human-operator resistance

A carefully collected information about constraints should precede the

process of action selection

• The system needs supporting "Intelligent" Agents to monitor the data

• In most of the practically known cases, the intelligent system cannot capture the

knowledge of experts in full detail

• Learn the optimizing strategy has limited capabilities in practice

Theme 8: Competitions: Test Beds and Metrics

1. The following observations were made:

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Test beds are good

* USAR test beds are hard to design and even harder to design performance metrics

because they are so multifaceted — finding victims/perception=>victims found — Interface => bandwidth used (AI is not limited to full autonomy) — Navigation==>coverage

* Performance based metrics which take into account the number of robots

collaborating (P/N) penalize multiple robots systems (except when Illah runs the

competition) — Tasks factor into this, e.g., 2 robots needed to pick up heavy box

Some other non-performance metrics are costs (monetary, energy consumption, etc.) and

meeting constraints during execution (e.g., formation control)

2. The following unanswered questions were detected:

* What are the metrics for mixed initiative/adjustable autonomy vs. flill autonomy?

[Including HCI, adaptation to drop outs]

* Does P/N really discriminate against muhiple robots in all tasks? — Can we compare intelligence versus cost? — How do we factor control strategy?

* Are competitions inherently flawed because they don't have the right scale/scope?

Do we have any metrics/taxonomy for task complexity?

Theme 9; Measuring Intelligence of Systems with Autonomy and

Mobility

Papers stressed metrics of utility, which were argued to be more useful to designers than

abstract intelligence. Two task-based metrics were combined into one task determined

for the process of navigation.

The architectures discussed were constructed for different goals and applications. The

system developer can only evaluate a system based on his or her own goal.

Some papers were focused on the issue of graph-based searching algorithms. The goal

is to optimize the creation of the graph based on the computation resource limit.

552
An analysis was presented, based on their work on mental development, that a fundamental criterion is not really what a machine can do in a special setting, but its

capability of developing mental skills.

The works presented in this session represent well the current status of the field: there

are three areas:

1 . Those that address system problems: construct a system to perform some

challenging tasks. Works in this category tend to use task-specific criteria. It is

not always the case that the same criteria can be used for other applications, as the

presenter argued.

2. Those that address a tool that can be used for many different systems. Those tools

cannot be directly used in the system until a designer has done a mapping from a

practical problem that he wants to solve to the tool. This kind of work

concentrates on an abstraction of a particular tool from a class of problems and

thus it studies an abstract tool.

3 . Another direction of the work, represented by the last presentation, addressed the

automation of the developmental process.

In area 1 the human is in the loop of system design, and may choose a tool in area 2 in his or her design. In area 3, the human is not in the loop of task-specific programming.

Instead the human designs a program that potentially can accomplish area 1 and area 2

autonomously, at the highly developed "adult" stage.

The field has a lot of work in area 1, which has achieved some limited success. The

difficuhies that face us in this area are very challenging. Although area 2 can provide

some useful tools for area 1, the fundamental problem in area 1 is not the problem of

tools, but rather something much more fundamental: systems are task specific and thus

there are no uniformly acceptable criteria at the task level. You simply use different

criteria to measure performance for different tasks.

Developmental paradigm in area 3 aims at a very different dimension. Its goal is to

design a system that can develop autonomously, including learning to perform many

different tasks, including such as tasks that the programmer does not know at the time of

programming. Then, the capability of development becomes a universal capability,

independent with what tasks that the system ends up learning to perform. In other words,

it is the autonomous learning capability that the area 3 is measuring, not how well the

system performs each task.

If a system has a powerful capability to autonomously learn, it will do well for many

tasks it learns to perform, not just for a particular task. Interestingly, human intelligence

does have a uniformly accepted set of tests for different age groups. This field is called

psychometrics. These tests do not test what a human child can do, but rather whether the

child can learn during the test. Thus, what is tested is the autonomous learning

capability.

553
With this autonomous learning capability, the system can learn to perform various tasks, as long as the teaching process is well designed. This new dimension is motivated by

human mental development from conception time through infancy to adulthood.

Another issue is whether it is necessary for an intelligent system to learn. This is a

subject that was discussed during the workshop among some participants. We seemed to

reach a consensus that if the tasks are static and are easy enough to directly program, one

does not have to use machine learning. However, if the environment is unknown or

partially unknown at the programming time, or the environment changes significantly

during the task execution, then learning is a must. Fully autonomous learning is a new

dimension known as development, which enables not only machine learning, but also

automation of the learning process. Since this subject is very new, the power of this new

research field is yet to be demonstrated.

Theme 10: Measuring Intelligence Taking into Account Linguistical,

Biological and Psychological Factors

• Many interesting ideas are being proposed related to using language and psychological

testing for measuring the intelligence, but they are not sufficiently fleshed out (at least, not

yet).

• Natural language encompasses much that is important in intelligence, and certain aspects of

natural language processing in the intelligent systems could even indicative the degree of

intelligence (though even fairly retarded people and computer equipped intelligent machines

are able to learn basic human languages).

• Some of the ideas related to Natural Language were presented in terms of the Turing test, and

the Turing test is certainly a test that has something to do with intelligence. However, until

now we are not sure what and how this relationship works and can be interpreted. Not

surprisingly, Turing Test has been criticized fi^om a lot of points of view, and our cautious

view on using it as a technique for measuring intelligence seems to be justified.

• As far as Natural Language acquisition, it was not clear whether the proponents wanted to

model language development or just measuring the stage of development; the first is very

hard, as all of us who are interested in modeling. However, it is clear that mere measurement

of "degree of development" may not tell not much, and certainly won't help with the Turing

test.

• Analysis of generalization processes by using Natural Language examples (summarization)

can be considered illustrative of other algorithms of generalization working in living and

computer-based creatures. It seems promising to explore similarities pf linguistic and

pictorial generalization, and eventually extend it toward symbolic generalization.

554
Theme 11: On the aspects of Projects related to Governmental Agencies

General observations

The amount and the diversity of issues presented at the Workshop exceed the

capabiHty of a single specialist to encompass the situation: the parable about six blind

sages analyzing an elephant: some see the trunk, some the tail, some the tusks of

"intelligence"

A taxonomy of natural and artificial intelligent systems should help to illuminate (but

hopefully not eliminate!) these differences in perspective

Ask the question: "how is the measure of a specific system's intelligence actually

going to be used?"

Decompose the system into its constituent subsystems. But what if the "intelligence"

is emergent at the system level?

Taxonomy of Intelligent Systems

These are some examples of "Intelligent Systems"

Human

Dog

Cat

Mobile robot

Industrial manipulator

Process controller

• These are some Factors of "Intelligence:"

Sensing/perception

Planning/reasoning

Effecting/skills

• Interface/language

Need some sort of matrix of Factors vs Types: Competencies? Requirements? . . . ?

• Possible uses for the measure of a specific system's intelligence...

Answer the question "Can system A perform task X?"

Help determine where to spend R&D money

"Raise the bar" by establishing an "expected" level of achievement

Serve as an advertising bullet for an intelligent product

555
Can a System "A" Perform the Task "X"?

DARPA supports the research and technology development in areas where the risk is

high but the payoff would be significant. One aspect of this policy is to fund

generously but abandon further support if it seems as though success is unlikely.

DARPA program managers are strongly urged to show meaningful evidence of

progress at yearly intervals. The evidence of success for the program on intelligence

could be given by demonstrating that by using this approach the reliability factors

could be increased.

In the past, the evidence of success has usually been in the form of demonstrations of

utility that are sometimes of questionable value in convincing potential service users of the technology that it has utility but tend to consume a significant fraction of the

allocated funds. This program can result in developing flindamental techniques for

testing that would be impossible to question and give them a voluntary interpretation.

If one or more metrics could be devised for each existing governmental program,

that are:

Directly relevant to the area being funded.

Related to the potential for a successful outcome, and

Measurable at reasonable cost,

it would be easier for DARPA management to evaluate progress and potentially

increase the fraction of program funding that is devoted to improvement of

technology.

Since most of the advanced governmental programs are based on or include systems

that can be said to embody or include "intelligence" as part of their design, flinding

support for the Workshop was a logical action to take.

556
PART IV

DECISIONS OF ADVISORY BOARD MEETING

557

Decisions

of the Advisory Board Meeting

conducted on August 14, 2000

1. The meeting of Advisory Board was conducted to provide an opportunity of a personal encounter

and communication among the Advisory Board Members. The importance of the regular e-mail

communication was noted. Members agreed to continue the effort of maintaining the Multidisciplinary

Community for Intelligent Systems measurements and analysis.

2. The Board continued the discussion of the issue what should be labeled "intelligent system" and

how to productively define "intelligence," since measuring something not clearly defined might not be

relevant. There was a consensus that Intelligent Systems can be distinguished by their ability to

a) generalize

b) build representation

c) make choices

d) formulate goals

These abilities are demonstrated by intelligent systems in different degree and they, probably, should be

used for establishing the Vector of Intelligence.

3. The ability to make choices should be regarded as a central property of intelligence. Other

properties of interest are linked with intelligence, too. Yet, other properties might and probably should be

considered separately from the ability of making choices, e.g. the ability to process, represent, and

communicate knowledge, as well as the ability to formulate the goals and determine their own behavior.

4. The consensus was that the effort should continue to be directed towards modeling the intelligence

focusing specifically upon systems that a) make choices (suitable, or appropriate ones), b) form their own

goals. Formafion of goals is linked closely with mechanisms of intentionality. Part of the discussion was

focused upon the place of learning in the mechanisms of intelligence: whether it should be considered a

separate "ability'" or it is built-in within all other abilities, e.g. as in the list in p. 2 of this document.

The opinion of the Board was that (at least initially) we should be interested in Systems for Making

Appropriate Choices.

5. The Board decided to coordinate the activities of the research community interested in Intelligent

Systems around the Systems Capable of Making Appropriate Choices, and their crifical experimental

and analytical characteristics that allow for evaluation of their performance in a particular environment.

Within these systems a subdomain should be recognized of systems that form their own goals. Probably,

other subdomains can be delineated, too. It would be important to discover and formulate these subdomains

as well as to demonstrate the relationships among them.

6. A part of the discussion was concentrated upon linkage between the concept of "success" and the

concept of "choice." The concept of "success" is the actual measure of performance of the intelligent

system. This measure is ingrained within the present definifion of intelligence (by J. Albus). The

phenomenon of "choice" seems to be the tool that serves the "ability to act appropriately." The importance

of the issue of "choice" was underlined by the members of Board and the decision was made to analyze the

situations where the success in not the matter of chance but rather the matter of choice.

7. A rough scale of the degrees of intelligence was agreed upon:

Degree III — Self-deciding systems

Degree II — Self-targeting systems, that implicitly incorporate their goal in their decision

Degree I — Self-deciding and self-targefing systems that are educable (W. Freeman's suggestion).

Educable systems are those that autonomously formulate the goals for their learning subsystem.

559
8. The Board has agreed upon the short term focus of research in the area of intelligent systems and

measuring their performance and intelligence. As a result of our short term research activities, the

research community should learn how to predict the IS performance if the system will be considered within

a different environment (new but related to the previous one). The last focus of research was proposed by

C. Weisbin, and the meeting decided to concentrate around this focus at least during the upcoming year

("Weisbin's Challenge").

9. The Board outlined how the work on Weisbin's Challenge should be initiated. Meeting decided to formulate 10-12 research problems collectively related to measuring the performance and the active

characteristics of the intelligent system (their "intelligence") for the cases of systems that make appropriate

choices, form their own goals, or both.

For each of these systems the preferability should be compared of two technical solutions:

a) one of them based upon a single, general purpose machine

b) another based upon utilization of multiple limited capabilities systems

The (a) and (b) would allow to understand what is preferable: to focus upon universal (broad) or specialized

(narrow) types of intelligence in developing IS.

10. The Board agreed that there is an urgent need of developing a draft of the Vocabulary in the

Area of Intelligent Systems. The draft of the Vocabulary should be distributed among the members of

Advisory Board for collecting comments and issuing a corrected and improved version.

Among the terminological issues that demand for urgently resolving them are the following terms:

• state space

• variables

• goal

• gestalt

• autonomy

• complexity

• intentionality

• representation

• learning

• behavior.

The goal of this iterative work on the Vocabulary is to achieve a consensus within the community on how

to discuss the issues in the area of Intelligent Systems.

The Board decided to distribute these decisions via e-mail and to dedicate the next meeting to the topics

bounded to the solution of these problems.

560
APPENDICES

561

APPENDIX A

THE PRELIMINARY DISCUSSIONS

563

In this part, the excerpts from the PreHminary Discussion of the Advisory Board

Members are given. Several months of active exchange helped to clarify many issues

that precipitated into the Workshop Agenda and its panel discussions. The discussion

was conducted by Alex Meystel.

DEFINING INTELLIGENCE

LET US CLARIFY WHAT THIS PHENOMENON IS

Dear Advisory Board Member,

As a working definition of intelligence, we use the following statement (proposed in

1991 by one of the Advisory Board members):

"Intelligence is the ability of a system to act appropriately in an uncertain environment, where

appropriate action is that which increases the probability of success, and success is the

achievement of behavioral subgoals that support the system's ultimate goal.

Another member of the Advisory board stated:

"We regard as "intelligent system with autonomy" only a system that can function in a self

sustained manner, i.e. has information from the World of what is going on, updates its

representation of the World, checks it with the goal, evaluates the situation, develops behavior

that is appropriate in this situation and executes (actuates) this behavior, and again, receives the

information from the world, and so on."

Finally, an opinion was voiced by the third member of the board that:

"The intelligence is incorporated in the mechanisms of inferring decisions and/or selfgenerating new rules from existing ones in combination with external data. These properties

might exist as a potentiality, they should not be associated with really successful functioning."

In other words, we have three platforms proposed about intelligence:

No. 1: success in achieving goal means "intelligence"

No. 2: self-sustaining functioning is "intelUgence"

No. 3: abilities to infer and learn mean "intelligence," nothing else matters!

What do you think about this trichotomy?

A. Meystel

NOT EVERYTHING IS ADDRESSED IN THE WHITE PAPER...

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1. You didn't come anywhere near covering the spectrum of philosophical views of

intelligence (just start to read the mind/body literature!) I would scale back your

analogies to human intelligence and testing to something more pragmatic. Your a-y

classification of measurable characteristics goes in the right direction but seems too

constrained by existing systems and ways of representing information.

2. You don't talk much about learning which is a critical characteristic of intelligence. It's

there, but it's primarily implicit.

3. I would define intelligence relative to a domain of application. Even in the human

cases there are people who are "car intelligent" but "literature ignorant" - different

domains, different abilities. Also in the human domain you have different types of

intelligence (Gardner's 7, Sternberg's 3, etc.) - do you want to try something similar in

the autonomous systems?

4. What's the goal of these metrics? Are they do be used in a TREC type environment?

John Cherniavsky, NSF March 16, 2000

INTELLIGENCE AND THE REQUIREMENT OF BEING SELF

SUSTAINED

Interestingly enough, we regard as "intelligent system with autonomy" only a system that can

function in a self sustained manner, i.e. has information from the World of what is going on, updates its

representation of the World, checks it with the goal, evaluates the situation, develops behavior that is

appropriate in this situation and executes (actuates) this behavior, and again, receives the information

from the world, and so on. No matter whether this is a human, a robot, a manufacturing system, an e-commerce system - it

is a full cycle of activities oriented toward being "self sustained".

I wonder whether a behavior can be regarded as "intelligent" without this component of being

used by a "self sustained" creature.

566
Maybe, this is why some of our intelligence-oriented endeavors fail: we considerjust a

component and do not take in account the whole creature? If this is correct, one should be suspicious of

any set of criteria that are not derived from the goals and means of the overall functioning.

Would you agree with this? (I hope, I am not intrusive!)

From the letter by Moderator

These are difficult and controversial questions. Which is why I like limited tests such as TREC

or the DARPA speech understanding. Other researchers (Gelemter for example) dismiss the

notion of intelligence (human that is) as nonsensical in machines and would feel the search for

intelligence metrics as doomed from the beginning.

John Chemiavsky March 20, 2000

... These are difficult and controversial questions. Which is why I like limited tests such

as TREC or the DARPA speech understanding. Other researchers (Gelemter, for example)

dismiss the notion of intelligence (human that is) as nonsensical in machines and would feel the

search for intelligence metrics as doomed from the beginning.

John Chemiavsky April 3, 2000

KNOWLEDGE IS NOT SUFFICIENT TO QUALIFY FOR

INTELLIGENCE

Alex,

I'm a big fan of Allen Newell's definition of intelligence (see "Unified Theories of

Intelligence"), which is essentially that the intelligence of a system/agent is its ability to use its

available knowledge to select appropriate actions to achieve its goals.

Available knowledge is important because a system that does not have knowledge

available is just ignorant, whereas a system that has knowledge but doesn't use it is stupid (not

intelligent). This also provides an entry for learning because available knowledge can be

constmed as what should be expected to be learned from experiences with the world.

Selecting actions is critical because intelligence without action is meaningless.

Goals are critical because action without purpose is also meaningless.

567
There is another dimension as to the generality of the intelligence of a system/agent

based on the breadth of knowledge it can acquire, encode, and use, the breadth of actions it has

available, and the breadth of goals it can attempt. All of these are related to the types of

environments that the agent will be successful in.

This definition fits pretty well with 1, although the emphasis in the above definition is

on selecting actions that the system thinks will achieve its goals (and not on some probabiUty).

I also think that adding uncertain environments in unnecessary. Chess has no uncertainty and

requires intelligence.

Autonomy is probably another dimension, but is related to the generality of intelligence

- what environments the system/agent can be successful in.

Hope this helps. These are interesting issues.

John Laird April 3, 2000

FINDING THE UNIFIED TECHNIQUE WILL INCREASE EFFICIENCY

OF TESTING

The problem with all these contests is that the measures are very task oriented and thus,

specialized. Each system is approached individually and its individual performance is measured.

Our intention is to standardize the measures of performance of intelligent systems so

that one could judge the level of intelligence of the system separately from its present concrete

application. It is not as far-fetched as it might seem. Indeed, the problem of software reuse will

release huge amounts of funds because we will stop developing "new" pieces of software just

because the application is different. (And this is just one of many justifications for standardizing

the measures of performance of intelligent systems.

Look, if we determine that the success of functioning depends on a particular set of

abilities: for example, the ability to search in the large set of data, or the ability to generalize

upon action rules, or upon object descriptions, or depends on other abilities, then, we will be

able to recommend a particular intelligent control system, or a particular pattern discovery

system, or another system which contributes to the overall intelligence, for a variety of

applications, where these abilities are critical.

568
Consider the examples that you've mentioned in your letter: data mining software,

information retrieval software, speech understanding software. Obviously, they contain reusable

sub-systems. These subsystems cluster information, search for patterns, recognize the

anticipated pattern, and perform several more typical tasks. These typical tasks performance

could be evaluated in terms of objective measures of the particular abilities (that are

components of their intelligence and determine the level of their intelligence).

But we do not know anything about their level of intelligence in terms of objective

measures of the set of their abilities. All we know is how this or that software package was

working with a concrete task assignment, and this does not allow to say anything how good it

might be for another task assignment. Its subsystems for control, recognition, etc., might be

quite dumb and I should avoid their reuse. Or they might be very powerful and I should look

forward to their reuse!

As you've suggested earlier, I studied TREC results as much as they are available. This

is a great work. But it does not allow me to make a judgment of how much intelligence these

systems rely upon and/or demonstrate. Maybe, they are very powerful, and I can use them for

dealing with large knowledge bases of the intelligent mobile vehicles, or for information

processing in the autonomous vehicle computer vision system. However, I do not have this

information and must invent these systems anew.

Maybe, my questions about defining intelligence are more pragmatic in their essence

than it seems. Maybe, by asking you I am asking a right person. Maybe, your vision and

experience will be extremely helpful in PROPER FORMULATION of these really new

problems.

Alex Meystel

FOLLOWING THE BIOLOGICAL MODELS MIGHT BE HELPFUL

[From the very beginning of this discussion, clear division of participants in several clusters

affected the style and the first results of the exchange. Participants belonging to different

clusters had different initial premises of "how are we supposed to approach our thinking about

intelligence." The groups belonging to the "different schools of thought" were using slightly

I 569
different vocabularies and probably different underlying models for describing functioning of

"intelligences." Two very prominent groups can be mentioned including:

a) researchers thinking about intelligence in the terms of biological models where processes of

evolution meant to be a component, and another - thinking in terms of computational

intelligence

The question was:

In other words, we have three platforms about intelligence:

No. 1: success in achieving goal means "intelligence"

No. 2: self-sustaining functioning is "intelligence"

No. 3: abilities to infer and learn mean "intelligence, " nothing else matters!

What do you think about this trichotomy?

Model 3 is the weakest - doesn't distinguish intelligence from the performance of any

existing high-grade adaptive control system.

Model 1 is better - but it doesn't specify that a system must be enabled to create its own

subgoals in the context of the ultimate goal prescribed by the agent that built it and released it,

and to evaluate 'appropriateness' and its own 'success' by criteria of its own design. These

functions in biointelligence are subsumed under intentionality. An intelligent device must have

this.

Model 2 is best of the 3 - incorporates the action-perception cycle that characterizes

biological systems, which is the mechanism of intentional action, but fails to address the

complementary property of assimilation, by which organisms construct and maintain a fully

integrated life-long store of information through learning through actions into the World, or the

mechanisms of reafference by which biosystems determine the information that is to be taken

from the World, as the basis for making their decisions.

Walter Freeman March 3 1 , 2000

570
INVARIANCE OF INTELLIGENCE

DEFINING SOMETHING MIGHT BE A POWERFUL THING (SOMETIMES)

May 16, 2000

Dear Dr. Kanayama:

1 . Let us try to discover what is the substance of our argument; then, we will try to

address it. You quoted my understanding of the problem: defining the intelligence of the system

so that you could judge how it affects functioning of the vehicles? Then, you are trying to

explain that you do not see the problem of finding how the intelligence affects fiinctioning, but

rather you see the problem in making these systems function well by equipping them with a

custom made intelligence.

Please, understand that the problem that I have formulated is not INSTEAD of the

problem that you are solving, it is a DIFFERENT problem. The solution of architectural

problem should make easier searching for a solution of your problem. You will see it from

positions 2 and 3 of my letter.

This is what you wrote:

First I want to define ttie problem I work on. Only after then, you are able to evaluate the performance of

the system as a problem solver Possible problems for groups of autonomous unmanned vehicles could

be: playing soccer, playing football, clearing a land-mine field, clearing a devastated city area by a

tornado, chasing a fleeing prisoner, standing-off against a criminal with hostages, driving themselves in a

row in a highway, placing themselves in a museum to watch if someone hurts the masterpieces, serving

people in a reception with drinks and hors d'oeuvres, line-dancing, ballet-dancing, fighting against an

enemy, and so forth. A specific team of autonomous unmanned vehicles may be good at one of the problems, but

may not be good at another.

I would like to compliment you on an impressive list of possible intelligent robots that

will surround us very soon.

2. Now, let me ask you two questions:

a) Can you see that all of these proficient robots (each in its domain) will have

something in common architecturally?

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b) Can you imagine that one might be interested in selhng the box of "system's

intelligence" to all your companies that manufacture these skillful, agile, and cunning creatures?

The whole issue is hidden in your belief that any system must be its own specifications

oriented, while I believe that the system's intelligence is beyond particular specifications.

It is the INVARIANCE for all these intelligent robots that can be manufactured

separately, have capabilities of all of the above robots, will be more reliable and cost less.

3. 1 would like to remind you one important thing. About 40 years ago, the systems for

control and automation of metalcutting machines were designed and manufactured individually.

Depending on the skills that a particular machine was supposed to demonstrate, we were used to

design very sophisticated electrical schemata, and the machines were successfully functioning.

Then, people realized that all of these machines could be controlled from the same

stereotypical "intelligence" and this is how the CNC systems emerged. Programmable

controllers turned out to be another solution for the problem. Now, each complicated automated

machine is controlled from the same PC computer that embodies its intelligence. The

technology develops as usual: from individualized custom-made solutions to a typical

architecture.

The same will happen with intelligent systems as soon as we understand the nature of

intelligence better.

1

Moderator

May 25, 2000

WHICH SIDE OF THE ARGUMENT... j

Dear Advisory Board Members:

I found this question in the recent mail: !

Prof, i The notion that rocks have consciousness is just as counterintuitive as your and Albus' notion that a

thermostat is intelligent. i

Which side of this argument are you on?

j Cheers, Mike -

I

!

! 572
Help me to answer this question. As a moderator, I would suggest to read Hans Moravec s letter

about John Searle's review of RayKurzweil, April 8, 1999 (see

http://www.frc.ri.cmu.edu/~hpm/project.archive/general.articles/1999/NYRB.99Q325.html) .

Excerpts from this letter are given below.

Which side are you on, indeed?

Letter re. John Searle's review of Ray Kurzweil, April 8, 1999

Subject: Re: "I Married a Computer" by John R. Searle, April 8, 1999

To the Editor, New York Review of Bool<s:

In the April 8 NYRB review of Raymond Kurzweil's new book, John Searle once again trots out

his hoary "Chinese Room" argument. So doing, he illuminates a chasm between certain intuitions in

traditional western Philosophy of Mind and conflicting understandings emerging from the new Sciences

of Mind.

Searle's argument imagines a human who blindly follows cleverly contrived rote rules to conduct

an intelligent conversation without actually understanding a word of it. To Searle the scenario illustrates

machine that exhibits understanding without actually having it. To computer scientists the argument

merely shows Searle is looking for understanding in the wrong places. It would take a human maybe

50,000 years of rote work and billions of scratch notes to generate each second of genuinely intelligent

conversation by this means, working as a cog in a vast paper machine. The understanding the machine

exhibits would obviously not be encoded in the usual places in the human's brain, as Searle would have

it, but rather in the changing pattern of symbols in that paper mountain.

Searle seemingly cannot accept that real meaning can exist in mere patterns. But such

attributions are essential to computer scientists and mathematicians, who daily work with mappings

between different physical and symbolic structures. One day a computer memory pattern means a

number, another it is a string of text or a snippet of sound or a patch of picture. When running a weather

simulation it may be a pressure or a humidity, and in a robot program it may be a belief a goal, a feeling

ore state of alertness. Cognitive biologists, too, think this way as they accumulate evidence that

sensations, feelings, beliefs, thoughts and other elements of consciousness are encoded as distributed

patterns of activity in the nervous system. Scientifically-oriented philosophers like Daniel Dennett have

built plausible theories of consciousness on the approach.

Searle is partway there in his discussion of extrinsic and intrinsic qualities, but fails to take a few

additional steps that would make the situation much clearer, but reverse his conclusion. It is true that any

machine can be viewed in a "mechanical" way, in terms of the interaction of its component parts. But

also, as Alan Turing proposed and Searle acknowledges, a machine able to conduct an insightful

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conversation, or otherwise interact in a genuinely humanlike fashion, can usefully be viewed in a

"psychological" way, wherein an observer attributes thoughts, feelings, understanding and

consciousness. Searle claims such attributions to a machine are merely extrinsic, and not also intrinsic

as in human beings, and suggests idiosyncratically that intrinsic feelings exude in some mysterious and

undefined way from the unique physical substance of human brains.

Consider an alternative explanation for intrinsic experience. Among the psychological attributes

we extrinsically attribute to people is the ability to make attributions. But with the ability to make

attributions, an entity can attribute beliefs, feelings and consciousness to itself independent of outside

observers' attributions! Self-attribution is the crowning flourish gives properly constituted cognitive

mechanisms, biological or electronic, an intrinsic life in their own mind's eyes. So abstract a cause for

intrinsic experience may be unpalatable to classically materialist thinkers like Searle, but it feels quite

natural to computer scientists. It is also supported by biological observations linking particular patterns of

brain activity with subjective mental states, and is a part of Dennett's and others' theories of

consciousness.

Elsewhere Hilary Putnam and Searle independently offered another kind of objection. If real

thoughts, feelings, meaning and consciousness are found in special interpretations of the activity

patterns of human or robot brains, wouldn't there also be interpretations that find consciousness in less

traditional places, for instance (to use their examples), in the patterns of particle motion of arbitrary rocks

or blackboards? Putnam, once a champion of the interpretive position, found this implication impossibly

counterintuitive, and turned his back on the whole logical chain. To Searie, it simply bolsters his

preexisting opinion. But counterintuitive implications do not refute an idea. The interpretations required in

Putnam's and Searle's examples are too complex for us to actually muster, putting the implied beings

out of our interpretive reach, thus unable to affect our everyday experience. The last chapter of my

recent book "Robot: Mere Machine to Transcendent Mind" explores further implications, and uncovers

no self-contradictions nor contradictions with reality as we know it. Rather, the interpretive position sheds

light on mysteries like the unexpected simplicity of basic physical law. It does predict many surprises

beyond our immediate observational horizons, and offends common metaphysical assumptions. But

today, when millions of 3D videogame players immerse themselves in increasingly expansive and

populated worlds found in very special interpretations of the particle motions of a few unimpressivelooking silicon chips, is the idea of whole worlds hidden in unexpected places still beyond the pale?

Hans Moravec, January 7, 1999

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May 26, 2000

A METHODOLOGY OF MEASURING THE PERFORMANCE OF

INTELLIGENT SYSTEMS

Dear Advisory Board Member:

Everybody would benefit from the insights into the problem of Performance Measures

gracefully submitted to us by Dr. Larry Reeker from NIST (a Member of our Advisory Board).

Interestingly enough, the recommended techniques of measuring the performance could be

applied to testing most of the Intelligent Systems with the elements of Autonomy.

This is what Larry wrote in his letter:

/ thought you might be interested in Teasuro's discussion of evaluation of bacl<gammon play. I remembered he had done some, andjust ran into it as I reread his paper for an entirely different reason.

If you are interested in the paper, you can read it at

http://www.research.ibm.eom/massive/tdl.html#hl:temporal_difference_leaming

It reflects three methods that have wider applicability: contests against other programs (particularly

benchmark programs), contests against humans (coupled with subjective evaluation), simulation of the

outcome of decisions made.

PERFORMANCE MEASURES

There is a number of methods available to assess the quality of play of a backgammon program;

each of these methods has different strengths and weaknesses. One method is automated play against

a benchmark computer opponent. If the two programs can be interfaced directly to each other, and if the

programs play quickly enough; then many thousands of games can be played and accurate statistics

can be obtained as to how often each side wins. A higher score against the benchmark opponent can be

interpreted as an overall stronger level of play. While this method is accurate for computer programs, it is

hard to translate into human terms.

A second method is game play against human masters. One can get an idea of the program's

strength from both the outcome statistics of the games, and from the masters' play-by-play analysis of

the computer's decisions. The main problem with this method is that game play against humans is much

575
slower, and usually only a few dozen games can be played. Also the expert's assessment is at least

partly subjective, and may not be 100% accurate.

A third method of analysis, which is new but rapidly becoming the standard among human

experts, is to analyze individual move decisions via computer rollouts. In other words, to check whether

a player made the right move in a given situation, one sets up each candidate position and has a

computer play out the position to completion several thousand times with different random dice

sequences. The best play is assumed to be the one that produced the best outcome statistics in the

rollout. Other plays giving lower equities are judged to be errors, and the seriousness of the error can be

judged quantitatively by the measured loss of equity in the rollout.

In theory, there is a potential concern that the computer rollout results might not be accurate,

since the program plays imperfectly. However, this apparently is not a major concern in practice. Over

the last few years, many people have done extensive rollout work with a commercial program called

"Expert Backgammon, " a program that does not actually play at expert level but nevertheless seems to

give reliable rollout results most of the time. The consensus of expert opinion is that, in most "normal"

positions without too much contact, the rollout statistics of intermediate-level computer programs can be

trusted for the analysis of move decisions. (They are less reliable, however, for analyzing doubling

decisions.) Since TD-Gammon is such a strong program, experts are willing to trust its results virtually all

the time, for both move decisions and doubling decisions. While computer rollouts are very computeintensive (usually requiring several CPU hours to analyze one move decision), they provide a

quantitative and unbiased way of measuring how well a human or computer played in a given situation.

Larry Reeker

May 27, 2000

Dear Advisory Board Members,

Attached, you will find, a document developed by a fellow Advisory Board Member,

Dimitar Filev from Ford Corporation. Let me know if you have any comments.

Moderator.

AN IMPORTANT SUBSET OF INTELLIGENT SYSTEMS

This comment is focused on one special group of intelligent systems - the intelligent control

systems. What makes a control system intelligent and is there a clearly defined border between

intelligent and other (nonintelligent) control algorithms? The trivial answer to this question usually is

576
determined based on the control methodology used. Commonly, the soft computing based control

algorithms (neural / fuzzy / genetic) are considered intelligent by default because of their knowledgebased content. Such a determination, however, is opposed by the control theohsts who claim that

modern (conventional) control methods with their strong mathematical foundations are not less intelligent

than the above mentioned soft computing technologies.

Strictly speaking, all robust control algorithms (conventional and soft) fit the first part of the

definition ofAlbus since they are targeted to work in uncertain environment and if properly designed they

generate approphate actions to increase the probability of success with respect to a given

criterion. In a broad sense, however, there are very few, if any, control algorithms that satisfy the

definition of system intelligence. While evaluating the level of intelligence based on this definition (to

avoid the confusion of introducing a new one) we have to take into account:

• type of uncertain environment

• strategy of achieving the goals

• capability of the system to automatically create and update its subgoals.

Most of the well-established methods for robust control design provide the capability to deal with

small parametric and structural uncertainties and therefore include a basic level of intelligence in the

control system according to the definition ofAlbus. Situational uncertainty, e.g. drastic changes in the

environment that are due to completely different operating conditions, severe and unpredictable

disturbances, etc., completely alter system dynamics, and therefore require control systems with much

higher level of intelligence.

These strategies of achieving the goals that deal with analysis of the situation, selection of

alternative control actions in accordance with the identified environment, and subsequent adaptation

convey more than basic intelligence to the control system. In this scheme the gain scheduling, adaptive

control and hierarchical control are only special cases of an intelligent control mechanism that brings in

the elements of perception of situation and decision making.

The flexibility of the structures offered by the fuzzy and neural models and the natural

granulation of the information that is associated with these models provide some of the basic building

blocks for development of intelligent control systems. In my view, we have seen only some of the

advantages of these methods over the conventional (equation based) control paradigm. So far, the gain

of using these technologies comes mostly from using them as universal approximators and as tools for

granulation (i.e. partitioning the space and natural decomposition of the system - typical example are the

so called neuro-fuzzy systems where the fuzzy/neural model is used as a powerful tool for approximation

of the plant model). Fuzzy/neural systems that are introduced in such environment are an alternative

and powerful tool that enriches the control toolbox but does not automatically generate a higher level of

intelligence. We are about to see more intelligent control strategies, e.g. task oriented control and

577
hierarchical control with dynamically created and updated subgoals when we start fully utilizing the

knowledge-based content and decision making capabilities of the soft computing technologies.

Dimitar Filev

May 27, 2000

OUR EVALUATIONS OF MACHINE INTELLIGENCE SHOULD BE

COMPATIBLE WITH OUR EVALUATIONS OF HUMAN INTELLIGENCE!

Dear Advisory Board Members,

The following Hans Moravec's thoughts will be interesting for you:

[THE PROPERTY OF INTELLIGENCE IS ASSIGNED BY US]

Perhaps the most unsettling implication of this train of thought is that anything can be interpreted as

possessing any abstract property, including consciousness and intelligence. Given the right playbook,

the thermal jostling of the atoms in a rock can be seen as the operation of a complex, self-aware mind.

How strange. Common sense screams that people have minds and rocks don't. But interpretations are

often ambiguous. One day's unintelligible sounds and squiggles may become another day's meaningful

thoughts if one masters a foreign language in the interim. Sometimes we exploit offbeat

interpretations: an encrypted message is meaningless gibberish except when viewed through a

deliberately obscure decoding. Humans have always used a modest multiplicity of interpretations, but

computers widen the horizons. The first electronic computer was developed by Alan Turing to find

"interesting" interpretations of wartime messages radioed by Germany to its U-boats. As our thoughts

become more powerful, our repertoire of useful interpretations will grow. We can see levers and springs

in animal limbs, and beauty in the aurora: our "mind children" may be able to spot fully functioning

intelligences in the complex chemical goings on of plants, the dynamics of interstellar clouds, or the

reverberations of cosmic radiation.

[THE CRUCIAL ROLE OF SELECTION]

There is no content or meaning without selection. The realm of all possible worlds, infinitely

immense in one point of view, is vacuous in another. Imagine a book giving a detailed history of a world

similar to ours. The book is written as compactly as possible: rote predictable details are left as homework for the reader But even with maximal compression, it would be an astronomically immense

tome, full of novelty and excitement. This interesting book, however, is found in "the library of all

578
possible books written in the Roman alphabet, arranged alphabetically"—the whole library being

adequately defined by this short, boring phrase in quotes. The library as a whole has so little content that

getting a book from it takes as much effort as writing the book. The library might have stacks labeled A

through Z, plus a few for punctuation, each forking into similarly labeled substacks, those forking into

subsubstacks, and so on indefinitely. Each branchpoint holds a book whose content is the sequence of

stack letters chosen to reach it. Any book can be found in the library, but to find it the user must choose

its first letter, then its second, then its third, just as one types a book by keying each subsequent letter.

The book's content results entirely from the user's selections; the library has no information of its own to

contribute.

The set of all possible interpretations of any process as simulations is exactly analogous to

the content of all the books in the library. In total it contains no information, yet every interesting being

and story can be found within it.

[WHO PERCEIVES AND WHO INTERPRETS?]

If our world distinguishes itself from the vast unexamined (and unexaminable) majority of

possible worlds through the act of self-perception and self-appreciation, just who is doing all the

perceiving and appreciating? The human mind may be up to interpreting its own functioning as

conscious, so rescuing itself from meaningless zombiehood, but surely we few humans and other biota-

-trapped on a tiny, soggy dust speck in an obscure corner, only occasionally and dimly aware of the

grossest features of our immediate surroundings and immediate past—are surely insufficient to bring

meaning to the whole visible universe, full of unimagined surphses, 10^40 times as massive, 10^70

times as voluminous, and 10^10 times as long-lived as ourselves. Our present appreciative ability seems

more a match for the simplicity of Saturday-morning cartoons.

[NEW MODELS ENHANCE OUR ABILITY TO CREATE POSSIBLE WORLDS]

Although our eyes and arms effortlessly predict the liftability of a rock, the action of a lever, or

the flight of an arrow, mechanics was deeply mysterious to those overly thoughtful ancients who

pondered why stones fell, smoke rose, or the moon sailed by unperturbably. Newtonian mechanics

revolutionized science by precisely formalizing the intelligence of eye and muscle, giving the Victorian

era a viscerally satisfying mental grip on the physical world. In the twentieth century, this common-sense

approach was gradually extended to biology and psychology. Meanwhile, physics moved beyond

common sense. It had to be reworked because, it turned out, light did not fit the Newtonian framework.

In a one-two blow, intuitive notions of space, time, and reality were shattered, first by relativity,

where space and time vary with perspective, then more seriously by quantum mechanics, where

unobserved events dissolve into waves of alternatives. Although correctly describing everyday

mechanics as well as such important features of the world as the stability of atoms and the finiteness of

heat radiation, the new theories were so offensive to common sense, in concept and consequences, that

579
they inspire persistent misunderstandings and bitter attacks to tiiis day. The insult will get worse.

General relativity, superbly accurate at large scales and masses, has not yet been reconciled with

quantum mechanics, itself superbly accurate at tiny scales and huge energy concentrations. Incomplete

attempts to unite them in a single theory hint at possibilities that exceed even their individual

strangeness.

[COMMON SENSE OF MEASURING]

In principle, if not practice, the point of collapse can be pinpointed: before collapse, possibilities

interfere like waves, creating interference patterns; after collapse, possibilities simply add in a commonsense way. Very small objects, like neutrons traveling through slits, make visible interference patterns.

Unfortunately, large, messy objects like particle detectors or observing physicists would produce

interference patterns much, much finer than atoms, indistinguishable from common-sense probability

distributions because they are so easily blurred by thermal jiggling.

Because, for humans, common sense is easier than quantum theory, workaday physicists take

collapse to happen as soon as possible—for instance, when a particle first encounters its detector. But

this "early collapse" view can have peculiar implications. It implies that the wave function can be

repeatedly collapsed and uncollapsed in subtle experiments that allow measurements to be undone

through deliberate cancellation at the experimenter's whim.

Einstein was troubled by the implications of quantum mechanics, and he devised thought

experiments with outcomes so counterintuitive he felt they discredited the theory. Those counterintuitive

outcomes are now observed in laboratories and utilized in experimental quantum computers and

cryptographic signaling systems. Soon, more advanced quantum computers will allow the results of

entire long computations to be undone.

Common sense screams that measurements are real when they register in the experimenter's

consciousness. This thinking has led some philosophically inclined physicists to suggest that

consciousness itself is the mysterious wave-collapsing process that quantum theory fails to identify. But

even consciousness is insufficient to cause collapse in the thought experiment known as " Wigner's

Friend. " Like the more famous " Schr dinger's Cat, " Wigner's friend is sealed in a perfectly isolating

enclosure with a physics experiment that has two possible outcomes. The friend observes the

experiment and notes the outcome mentally. Outside the leakproof enclosure, Wigner can only describe

his friend's mental state as the superposition of the alternatives. In principle these alternatives should

interfere, so that when the enclosure is opened one or another outcome may be favored, depending on

the precise time of opening. Wigner might then conclude that his own consciousness triggered the

collapse when the enclosure was opened, but his friend's earlier observation had left it uncollapsed.

580
[MANY-WORLDS INTERPRETATIONS]

In a 1957 Ph.D. thesis, Hugh Everett gave a new answer to that question. Given a universally

evolving wave function, where the configuration of a measuring apparatus, no less than of a particle,

spreads wavelike through its space of possibilities, he showed that if two instruments recorded the same

event, the overall wave function had maximum magnitude for situations where the records concurred

and canceled where they disagreed. Thus, a peak in the combined wave represents a possibility where,

for instance, an instrument, an experimenter's memory, and the marks in a notebook agree on where a

particle alighted—eminent common sense. But the whole wave function contains many such peaks,

each representing a consensus on a different outcome. Everett had shown that quantum mechanics,

stripped of problematical collapsing wave functions, still predicts common-sense worlds—only many,

many of them, all slightly different. The "no-collapse" view became known as the "many-worlds"

interpretation of quantum mechanics.

[INTELLIGENCE DETECTS SINGULARITIES WITHIN THE CHAOS]

No complete theory yet explains our existence and experiences, but there are hints. Tiny

universes simulated in today's computers are often characterized by adjustable rules governing the

interaction of neighboring regions. If the interactions are made very weak, the simulations quickly freeze

to bland uniformity; if they are very strong, the simulated space may seethe intensely in a chaotic boil.

Between the extremes is a narrow "edge of chaos" with enough action to form interesting structures,

and enough peace to let them persist and interact. Often such borderline universes can contain

structures that use stored information to construct other things, including perfect or imperfect copies of

themselves, thus supporting Darwinian evolution of complexity. If physics itself offers a spectrum of

interaction intensities, it is no surprise that we find ourselves operating at the liquid boundary of chaos,

for we could not function, nor have evolved, in motionless ice nor formless fire.

[INTELLIGENCE DEVELOPS THE IMAGES OF POSSIBLE WORLDS ]

The similarity between Everett's "many worlds" and the philosophical "possible worlds" may

become stronger yet. In "many worlds" quantum mechanics, physical constants, among other things,

have fixed values. Gravity, in objects like black holes, loosens the rules, and a full quantum theory of

gravity may predict possible worlds far exceeding Everett's range—and who knows what potent

subtleties lie even further on? It may turn out, as we claw our way out through onion layers of

interpretation, that physics places fewer and fewer constraints on the nature of things. The regularities

we observe may be merely a self-reflection: we must perceive the world as compatible with our own

existence—with a strong arrow of time, dependable probabilities, where complexity can evolve and

persist, where experiences can accumulate in reliable memories, and the results of actions are

predictable. Our mind children, able to manipulate their own substance and structure at the finest levels,

will probably greatly transcend our narrow notions of what is.

581
[WE ARE SELF-INTERPRETING BOTH OURSELVES AND THE EXTERNAL WORLD]

Like organisms evolved in gentle tide pools, who migrate to freezing oceans or steaming jungles

by developing metabolisms, mechanisms, and behaviors workable In those harsher and vaster

environments, our descendants may develop means to venture far from the comfortable realms

we consider reality into arbitrarily strange volumes of the all-possible library. Their techniques will

be as meaningless to us as bicycles are to fish, but perhaps we can stretch our common-sense-hobbled

imaginations enough to peer a short distance into this odd territory. Physical quantities like the speed of

light, the attraction ofelecthc charges, and the strength of gravity are, for us, the unchanging foundation

on which everything is built. But if our existence is a product of self-interpretation in the space of all

possible worlds, this stability may simply reflect the delicacy of our own construction—our biochemistry

malfunctions in worlds where the physical constants vary, and we would cease to be there. Thus, we

always find ourselves in a world where the constants are just what is needed to keep us functioning. For

the same reason, we find the rules have held steady over a long period, so evolution could accumulate

our many intricate, interlocking internal mechanisms.

Our engineered descendants will be more flexible. Perhaps mind-hosting bodies can be

constructed that are adjustable for small changes in, say, the speed of light. An individual who installed

itself in such a body, and then adjusted it for a slightly higher lightspeed, should then find itself in a

physical universe appropriately altered, since it could then exist in no other. It would be a one-way trip.

Acquaintances in old-style bodies would be seen to die—among fireworks everywhere, as formerly

stable atoms and compounds disintegrated. Turning the tuning knob back would not restore the lost

continuity of life and substance. Back in the old universe everything would be normal, only the

acquaintances would witness an odd "suicide by tuning knob. " Such irreversible partings of the way

occur elsewhere in physics. The many-worlds interpretation calls for them, subtly, at every recorded

observation. General relativity offers dramatic "event horizons": an observer falling into a black hole

sees a previously inaccessible universe ahead at the instant she permanently loses the ability to signal

friends left outside.

See more in URL:

http://www.frc.rixmu.edU/~hpm/project.archive/general.articles/l 998/SimConEx.98.htm^

Be patient to get to the last quarter of THIS DOCUMENT. It raises important issues

concerning world representation. As you know, H. Moravec was one of the first creators of

vehicles with elements of autonomy.

Moderator

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May 29, 2000

Dear Advisory Board Member,

Dr. Eric Horvitz (a member of our Board) has suggested to distribute the attached paper

among the Advisory Board Members. This paper is related to deahng with uncertainties as a

part of the process of decision making when the imprecisely computed Metrics are used.

Since any introduction of Metrics is linked with determining preferences under

uncertainty, it would be meaningful to be prepared to the non-trivial situations of decision

making using any proposed

Metrics.

It would be presumptuous to expect that our University education has prepared us to

these situations exhaustively (partially? maybe).

I found this paper enlightening (to the extent I could understand it). All of you are

expected to achieve some level of understanding of the peculiarity linked with introduction and

use of Metrics for Decision Making.

Moderator

May 30, 2000

THOUGHTS AFTER READING THE WHITE PAPER

BYTOMWHALEN

The "white paper" draft seems to me to be a real gold mine, but like any mine it requires

digging, sifting, and refining.

1. INTELLIGENCE AND AUTONOMY

I really like the idea of the autonomous climate control system being "motivated" to increase its autonomy by reducing the need for human inten/ention. (p. 3) I think this could be the kernel for a better

definition of what is meant by intelligence in autonomous constructed system.

Here's my stab at some global definitions:

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Def.1

"A constructed system is autonomous if there is a lil<elihood that circumstances will arise in

which no-one can predict in advance what it will do. This need not imply randomness, just complexity.

[This probably can imply both: complexity AND randomness. Moderator.]

Def.2

"An autonomous constructed system is intelligent if we can be reasonably confident that

whatever unpredictable thing it does do will be something that tends toward success in the goals for

which the system was constructed in the first place.

2. HOW TO FALSIFY THESE DEFINITIONS?

Thus, a claim that a system is autonomous and intelligent can be falsified in two ways: showing

it is not autonomous by predicting all of its behavior in advance, or showing it is not intelligent by

demonstrating that its behavior is stupid.

What an end user wants is a system that is trustworthy. If all behavior can be specified in

advance, there is no need for autonomy; the intelligence and autonomy reside in the design team and

not in the delivered system. If behavior can't be prespecified, then intelligence is necessary for

trustworthiness; if it is lacking, the system needs to be monitored by a human operator and thus, again,

lacks autonomy.

Note the statement on page of the White Paper that an intelligent system was "designed by

humans (engineers and programmers)" is not true in machines that learn and self-organize except in a

broadened sense of the word "designed. " Even very large and complex programs that have no learning

or self-organizing features need to be studied in much the same way we study social phenomena like

economics or natural phenomena like weather, since no one person will ever know the program in its

entirety. (For example, the Windows operating system.)

The paper has the beginnings of a structure for measuring the components of machine

intelligence based on the six-box semiotic loop, but it's not very consistent.

3. MIND-BODY PROBLEM

The discussion of the "mind-body" problem crops up several times in the White Paper; I suggest

making it specific by assigning perception, knowledge, and decision (behavior generation) as "mind" and

assigning sensation and actuation as body. The sixth box, "world, " is not part of the constructed

autonomous system.

[Here, we should think twice: should the work-piece that we drill be considered a part

of the drilling machine or not? The part of the world that immediately interacts with a system

584
under consideration might be legitimately considered a part of this system. When we write the

equations of the system, the "torque on the shaft" is a part of the equations of the system.

Moderator.]

4. ABOUT CHINESE ROOM

Note: my personal view of the Chinese room is that performing the task without understanding

Chinese is not in principle impossible, but the number of rules that would have to be written ahead of

time and searched in real time by the occupant of the room is far beyond the trillions. Learning to

understand Chinese is a much easier task, already mastered by over a billion people!

This is quite relevant to the issue of intelligent autonomous machines; there are tasks for which it may be within our grasp to produce a successful machine without autonomy, but it is actually easier to

achieve the same level of success using an intelligent autonomous machine. A simple and very familiar

example is the inverted pendulum, which is quite challenging to do with differential equations but a

beginner's exercise to do with fuzzy control.

[I would like to emphasize this significant Tom's statement by capitalizing:

THERE ARE TASKS FOR WHICH IT MAY BE WITHIN OUR GRASP TO PRODUCE A

SUCCESSFUL MACHINE WITHOUT AUTONOMY, BUT IT IS ACTUALLY EASIER TO

ACHIEVE THE SAME LEVEL OF SUCCESS USING AN INTELLIGENT AUTONOMOUS

MACHINE. Moderator]

5. WHA T DOES IT MEAN TO BE "INTELLIGENT"

My initial impression is that while human intelligence testing does rely heavily on response time,

my online thesaurus lists the following synonyms for "intelligent" (see the White Paper). Only three of the

sixteen involve the idea of "quick-witted. " Machines routinely do almost anything they can do at all more

quickly than humans can do them, and they also are incapable of doing at all many things humans do

quickly. There is only a small middle ground of things machines do roughly as fast as humans or that

they do well but more slowly than humans.

I'm more interested in the prospects for machines that are canny, percipient, perspicacious,

astute, and discerning.

Tom s insights are great!

Moderator.

585
May 30, 2000

THOUGHTS AFTER READING THE WHITE PAPER

by Sukhan Lee

/ would like to congratulate (...) the effort to formalize the intelligent system research by

establishing the measure of system intelligence. Establishing the measure (...) should not only be able to

turn the intelligent system into a formal academic discipline but also provide a means of designing better

and more powerful intelligent systems in practice.

I am generally impressed by the breadth as well as depth of [the proposed] measure of

intelligence for a constructed system with autonomy. (...) various aspects of intelligence [are considered]

including the need to learn as well as to generalize by an intelligent system. (...) a list of system

specifications [are proposed] as well as the vector of intelligence as features representing intelligent

functions of a system.

The list of features presented is very comprehensive. However, it is not clear how the measure

of intelligence can be formulated out of such a list or a vector Too many functional features may

obscure the essence of how intelligence is generated, as they may not represent the engine but the

expressions.

Having said that, I would like to pay attention to the following questions:

1) Should the intelligence measure be goal-dependent or goal-independent?

2) Should the intelligence measure be time varying or time-invariant?

3) Should the intelligence measure be resource-dependent or resource-independent?

1)

(...) a question [emerges] whether there exists a universal measure of system intelligence

such that the intelligence of a system can be compared independently of the given goals. A goalindependent measure may be more difficult to define, (if not impossible), and [it will be] more

controversial.

A goal-dependent measure, however abstract the goal may be, can allow [for a] clear

comparison among the systems of different architecture but with the same goal. For instance, for the

latter case, an intelligence can be represented as how efficiently, and how optimally a system reaches

the given goal by itself i.e., the power of automatically solving problems defined as the discrepancy

between the goal and the current state.

2) (...) [We should decide whether] the intelligence measure of a system should solely be based

on problem-solving capability at time t or it should contain the potential increase of problem-solving

capability in the future based on learning. My opinion is that we need both. But, it is better to define the

two separately before integrating them together into one measure.

586
3) (...) [Finally, it is an important] issue whether the resources required for building systems and

system operation should play a role for defining the measure of intelligence. As mentioned above, the

efficiency in problem solving, I think, should be included in the measure: for instance, the time and

energy required to reach a solution should be taken into consideration together with the optimality of the

solution. But, I am not sure whether we should or should not include the cost of building a system.

[I WOULD APPRECIATE SENDING TO ME YOUR THOUGHTS ABOUT THE

INTERESTING POINTS INDICATED BY S. LEE. Moderator]

May 31,2000

THOUGHTS AFTER READING THE WHITE PAPER: C. WEISBIN S

QUESTIONS

Would it be appropriate for you to specify for the workshop a SMALL number (~5) questions

which the workshop (perhaps within working groups, or abstracted from position papers) would try to

answer with some degree of specificity? The field is so broad and the interests are so varied that I am

puzzled how (whether?) tangible conclusions will emerge? The field is so broad and the interests are so

varied that I am puzzled how (whether?) tangible conclusions will emerge?

I WOULD LIKE TO DISCUSS WITH ALL MEMBERS OF THE ADVISORY

BOARD THE LIST OF QUESTIONS THAT I PROPOSED IN THE RESPONSE LETTER TO

C. WEISBIN.

LET ME KNOW WHAT DO YOU THINK, THIS IS VERY IMPORTANT:

This is the list of questions that the Workshop will try to answer:

1 . What is the vector of intelligence (VI) that should be measured and possibly used as a

metric for systems comparison?

2. Should VI be measured in addition, or instead of measuring the vector of performance

(VP) determined by the regular specifications?

587
3. If two systems have the same VP, what is implied by the difference in their VI

values? Can this difference be represented in $ units?

4. Is it possible (and meaningful) to have different VI measures: a) goal-invariant, b)

resource-invariant, c) time-invariant?

5. What should be recommended as a test of VI and how to normalize VP so that

comparison be performed at the same normalized value of VP.

These are the five questions that you have asked about. As a reminder, I would like to

formulate a set of other questions that are ingrained (directly, or indirectly) in the main five

questions:

6. These are the less profound ("secondary") questions that should be addressed at the

workshop and possibly unequivocally answered:

a) how to form VI for various architectures?

b) should the questions 1 through 5 be related to intelligent systems, or autonomous

systems, or both?

c) what is the protocol of dealing with uncertainty when the uncertain metric is to be

applied in the procedures of decision making? for example how the uncertainty of planning

affects the cost of goal achievement?

d) what are the guidelines in constructing the world model and determining its scope in

the variety of applications? how the scope of "world model" affects the sophistication of

intelligent behavior?

e) how are the questions 1 through 5 related (and the answers applied to) the systems

that are working under a hierarchy of goals.

f) should a competition between intelligent systems be considered a valid method of

judging VI value?

Moderator

588
May 31,200

SOME COMMENTS ON THE PROBLEM OF METRICS

OF INTELLIGENCE

GEORGE BEKEY HAS PROPOSED THE FOLLOWING:

1 . The selection of benchmark problems on which to measure the degree of autonomy

and intelligence of a system, and

2. Something orthogonal to the previous discussions: A discussion of the moral and

ethical implications of building increasingly intelligent systems.

For more details on these issues see his attachment. Moderator

Attachment: G. Bekey s comments:

1. Benchmarks

I am a strong believer in simplicity, so my definitions and metrics are very simple. I believe that

the fundamental attributes of intelligence involve:

• Ability to perform tasks in unstructured environments

• Ability to learn from experience

• Ability to transfer knowledge from one domain to another

• Ability to solve complex problems, requiring deductive and inductive reasoning

(While stated differently, these issues are similar to Jim Albus s definitions). I suggest that the

following simple measures can be used as metrics for such abilities in machines:

1 . Size and complexity of programs required

2. Memory requirement

3. Solution time

Clearly, such measures are useful only if (a) they are applied to benchmark problems, (b) all

contestants use the same type and model of computer, and (c) all programs are written by comparably

competent programmers, so that the programs are optimal in some sense.

589
Given these constraints, we could test intelligent systems A and B on the same benchmarks.

The one that accomplishes the task more quickly, and does so with the least complex programs and

least memory will be declared more intelligent . What could be simpler than that?

If anyone agrees with me, I would be interested in leading a discussion on the selection of

benchmarks on which we can all test our systems.

(There are several hidden questions here. One of them is the question of emergent behaviors.

As a system learns more and more, and is able to transfer knowledge to other domains, and solve

increasingly complex problems, it may begin to do so in totally unexpected ways. The emergence of

such new behaviors will be appear in its ability to solve problems more rapidly, but will not be directly

measurable).

2. On the ethics of building intellipent machines

Several recent books have dealt with this subject, such as the latest books from Hans Moravec

or Ray Kurzweil ( The age of spiritual machines ). Kurzweil predicts that by 2025 computers will be more

intelligent than people (but does nor provide metrics to measure this result!). Perhaps the most

thoughtful analysis was published by Bill Joy (the chief scientist of Sun Microsystems), in the April 2000

issue of WIRED. In an article entitled Why the future doesn t need us , he speculates that

developments in robotics, nanotechnology and genetic engineering will inevitably lead to selfreproducing machines with increasing intelligence, whose behavior will be not only unpredictable but

uncontrollable. Such machines may find human beings largely superfluous.

I would be interested in presenting a position paper summarizing current thinking on this matter

and leading a discussion. It seems to me that if we are concerned only with measuring the intelligence

of machines without any concern for the social implications of such intelligence, we are not fulfilling our

responsibility to society.

George Bekey

June 3, 2000

WINTER S CONJECTURE

(FROM MY CONVERSATION WITH VICTOR WINTER, MEMBER OF THE

ADVISORY BOARD)

Victor Winter (V. W.):

The ability to learn is generally considered a "sophisticated" behavior. Given this ability, a

machine can positively change its behavior over time thereby exceeding the sum of its initial input. By

this metric, a more a sophisticated system can "do more" with less initial input than an unsophisticated

system.

590
Alex Meystel (A. M.):

I believe that intelligent system should learn. By I am hesitant to say that anything that

can learn is necessarily intelligent. Having an increased degree of sophistication? Probably!

V. W.

For example, in theory, a theorem prover can discover all of mathematics from its

axiomatization. Of course there are some severe limitations to this in practice.

A. M.

I have already addressed this issue in the previous part of our conversation. No, a simple

set of axioms is insufficient to prove these two theorems that I have mentioned above. (I worked

this out with students).

V. W.

Nevertheless, we can think of an axiomatization of a closed system as a very compact model of

that system. (We are talking about the need to supply knowledge of the initial general laws that pertain

to a concrete environment; we call them "axioms").

A. M.

Compact - maybe. But sufficient - hardly.

V. W.

To the person writing the axioms the system need not be fully understood (e.g., knowledge of all

theorems in mathematics is not required).

A. M.

This is an unwanted surprise: your definition of "understood" means "having known all

theorems related." I think that as a label, you can use any word you want. But in reality

"understood" means much more, for example: "having future behaviors anticipated".

(The term "theorems" should be understood as the provable rules that hold in the

environment of the subsequent functioning).

V. W.

However, this compact representation of the system can be utilized by a sophisticated machine

to solve a large collection of problems-problems that may not even have been initially foreseen.

A. M.

You are talking about "sophisticated machine" that has not been defined. However even

if your "sophisticated machine" is equivalent "human intelligence", I doubt that having just

compact representation (you have defined it as based only upon the axioms) we will be able to

solve the unforeseen problems.

591
V. w. An application of this idea is in a space system where there may be multiple ways to achieve a

certain behavior (e.g., pitch or rotation of a spacecraft). If one possibility fails due to a malfunction of a component another possibility can be discovered - provided a sufficiently complete system model has

been given. In this example, a space bound system can be equipped with a computer that has

(1) a non-algorithmic specification of the behavior of the system, and

(2) an axiomatization of that system.

A sophisticated system would then be able to satisfy its specification whenever possible, even in

the presence of unforeseen circumstances.

A. M.

We have to add the word ALL into your last sentence; then my refutation will be

stronger: not "even in the presence of ALL unforeseen circumstances."

The reason 1 insist on this "ALL" is because you allowed for only limited variability in

the space of searching the strategies of solution.

There is a threshold, a level of variability that is required to cover the space of future

behaviors sufficient for, say, survival with a definite probability.

V. W.

The presence of such a specification would to some extent address the concerns/issues raised

by Searle (as described in your White Paper). For example, a robotic system having a defective (e.g.,

bent) arm would be able to sense (through "sensors") that the behavior of the arm - in its current state -

is not satisfying the specification. Alternate means of satisfying the specification could then be generated

and considered.

A.M.

Of course, many realistic cases of robots functioning in Space presently do not require

and do not exercise any sophistication not to speak of intelligence.

V. W.

The notion of learning can be taken one step further and one can consider a system, which has

the ability to dynamically generate axioms.

A.M.

I agree with you ecstatically.

V. W.

Given what I have mentioned above, I believe that an interesting metric would be the "size" of

knowledge required or the amount of information that must initially be provided to a system in order for it

to demonstrate a certain behavior - the smaller is the required initial information, the more

I

592
"sophisticated" tine system is. These kinds of systems (if practical) could effectively utilize the increases

in computing power that will occur in the future.

A. M.

Victor, this is a great idea. I will call it Winter's Conjecture, and will circulate it among

our Advisory Board Members. Prepare yourself for proving mathematically a number of

theorems that entail your hypothesis. For example, the fundamental theorem that you should

introduce is related to evaluation of the "amount" of this information based upon complexity of

the system that is supposed to be equipped by "intelligence."

This theorem is on you. I would suggest to use a measure of variability of this minimal

information and evaluate the probability of success in applying this minimum of information

with a given measure of variability.

CLASSES OF INTELLIGENT SYSTEMS

June 3, 2000

Dear Advisory Board Member,

Attached, you will find, an important contribution by Steve Grossberg (see his paper in

the next part of this book). This view focuses upon CONSTRAINTS that will undoubtedly

make more difficult our road toward Metrics for Intelligent Systems in a particular class of

intelligent systems.

The abstract also reminds about interesting problems that emerge in autonomous

intelligent systems and make them different from the classical control systems. I would suggest

to compare the class of systems that is described in S. Grossberg's paper and the class of

systems that is described in D. Filev's statement (see my message on May 27).

Both classes belong to super-class of "intelligent systems."

But look how different they are!

I would not be surprised if their Metrics will have the same properties: belonging to the

same super-class but very different.

Yours,

Alex Meystel

593
June 5, 2000

Dear Advisory Board Members,

I RECEIVED AN INTERESTING SET OF ANSWERS AND I WANT TO

FAMILIARIZE YOU WITH THEM.

THIS LETTER I RECEIVED FROM WALTER FREEMAN, A MEMBER OF THE

ADVISORY BOARD. IT CONTAINS SEVERAL FAR REACHING SUGGESTIONS,

IMPORTANT FOR ALL OF US.

FIRST QUESTION:

1 . What is the vector of intelligence (VI) that should be measured and possibly used as a metric

for systems comparison?

W. FREEMAN S ANSWER:

In my view, we don't measure intelligence; we infer it from measurements of performance.

[ACTUALLY, ANYTHING MEASURED IS CONCEPTUALLY INTRODUCED IN

THE BEGINNING. IN OTHER WORDS, ALL THINGS THAT WE MEASURE WE INFER

FROM MEASUREMENTS OF OTHER RELATED THINGS. WALTER IS MORE

INTERESTED IN THE QUALITATIVE CHARACTERIZATION OF INTELLIGENCE AND

HE ASKS:]

W. F. A better question is: What kinds of intelligence do we propose to emulate?

MY ANSWER TO THIS QUESTION IS:

I doubt that there are different kinds of intelligence. I know that multiple intelligences is

a faith (very similar to the polytheism of ancient people). It is easy to declare any manifestation

of perceptual and cognitive activity to be an intelligence. It is more difficult to find what do they

have in common, maybe, absolutely the same.

594
We all can easily demonstrate that all known phenomena of intellect and intelligence are

linked with a limited number of special computational algorithms including

—combining N-tuples

—searching

—focusing attention

—grouping (which includes "combining tuples")

—evaluating and ranking the results of grouping.

SECOND QUESTION: 2. Should VI be measured in addition, or instead of measuring the

vector of performance (VP) determined by the regular specifications?

\N. FREEMAN RESPONDS:

Hence, what kinds of performance do we choose as benchmarks for measurement?

THIRD QUESTION: 3. If two systems have the same VP, what is implied by the difference in

their VI values? Can this difference be represented in $ units?

W. FREEMAN SUGGESTS:

Instead: How might we choose and construct benchmark tasks with graded difficulty, so that we

can establish the competence of new systems and then challenge them with tasks of increasing

complexity.

[I THINK, THIS IS AN EXCELLENT SUGGESTION]

FOURTH QUESTION: 4. Is it possible (and meaningful) to have different VI measures: a)

goal-invariant, b) resource-invariant, c) time- invariant?

W. FREEMAN PROPOSES:

In that there are different kinds of intelligence, it follows that there are different VPs. For VI, in

analogy to IQ, I would suggest using a ratio of performance VP to cost of construction of a system in

time and money VC, giving a scalar value that will assign due place to building a machine gun to kill a

fly:

Vl[n] = VP[n]A/C[n], n = 1,N [number of classes of benchmark]

595
[I THINK THAT THIS PROPOSAL IS MEANINGFUL: NORMALIZING IS A

PROPER

THING TO DO. BUT I DISAGREE TO CONSIDER THESE DIFFERENT MODES

OF EVALUATION OF ONE INTELLIGENCE TO BE DIFFERENT INTELLIGENCES.

WHY?]

FIFTH QUESTION: 5. What should be recommended as a test of VI and how to normalize VP

so that comparison be performed at the same normalized value of VP.

WALTER FREEMAN EXPLAINS AND SUGGESTS THREE BENCHMARKS:

This question would be answered under #3 and #4. I suggest N = 3 benchmarks, relating to

comprehension through perception, planning action, and dynamic reasoning through decision:

n = 1. Pattern classification - for example, detection of chemical explosives of increasing

variety in increasingly complex background odorant environments. We have excellent chromatographs,

but there is a need here for the artificial dog behind the artificial nose.

n = 2. Spatial navigation - for example, foraging for fuel in natural environments of graded

complexity. Gray Walter's autonomous tortoises are still [in my opinion] the best of breed in this respect.

n = 3. Comprehension of instructions in a natural language — for example, accomplishing

sequences of operations, each conditional on the steps preceding. Ross Ashby's 'homeostat' might offer

a suitable early benchmark.

[I THINK THAT THIS GIVES AN ANSWER TO MANY QUESTIONS RELATED

TO METRICS OF INTELLIGENCE]

WALTER BELIEVES THAT THIS MY SIXTH QUESTION IS PREMATURE, THAT

WE ARE NOT AT THE STAGE THAT QUESTION No. 6 COULD BE APPROACHED

RIGHT NOW. WELL, I DISAGREE.

Moderator

596
EMPHASES IN RESEARCH OF BUILDING INTELLIGENT MACHINES

AND MEASURING THEIR INTELLIGENCE: THE ISSUE OF ETHICS

by J. Albus

June 6, 2000

/ agree with George Bekey that we should spend some time discussing the ethics of building

intelligent machines. I feel that the concerns voiced by Bill Joy should not go unanswered, and the

predictions of Moravec and Kurzweil should not remain unchallenged.

I, for one, feel that the most important characteristic of intelligent machines is that they have the

capacity to perform useful work, i.e., to create wealth. I also fervently believe that the biggest problem in

the world today is poverty, i. e. , the lack of wealth. (I see poverty is the fundamental cause of hunger,

disease, ignorance, pollution, intolerance, oppression, lack of medical care, and lack of education.) I

therefore would argue that we should focus on how to make it possible for intelligent machines to

eliminate poverty world wide within 50 years. In my opinion, to do anything else is unethical.

As for the concerns of Joy, I believe they are largely unfounded and overblown, and the

predictions of Kurtzweil, and Moravec are for the most part wildly exaggerated. However, these kinds of

sensational concerns fan the flames of the Frankenstein myth in the popular imagination and thereby

create a major distraction. They divert attention from real problems that intelligent machines could solve

by inciting fears of scenarios that are highly improbable. And they divert attention from what realistically

could be done to alleviate human suffering in the near future.

Jim Albus

June 6, 2000

MODERATOR S RESPONSE:

I support Jim's letter emphasizing the utter importance of ethics in the area of InteUigent

Systems. I also agree that one of biggest problems in the world is "lack of wealth."

However, there are two kinds of wealth: Material and Intellectual ones. Intellectual

wealth is almost always an asset especially for those in poverty, while material wealth can't

frequently help even those who is rich intellectually. Material wealth is not a universal remedy.

While curing known problems it creates new. It is a mixed blessing.

597
I consider intelligent systems to be our helpers not in creation of material wealth as the

first priority, but in elimination of Ignorance. Ignorance produces and maintains poverty both

material and intellectual. Ignorance maintains unethical environment. Ignorance is the major

adversary of Intelligence, so it becomes an adversary of the carriers of Intelligence.

Material wealth is frequently created by developing sophisticated and sometimes even

elegant but unethical methods (including algorithms and even software packages) whose only

goal is "to separate a client with his/her money." Intelligent Systems can become a powerful

tool of this unethical process. It is especially terrible to be robbed by an Intelligent System

tuned to create somebody's material wealth in an unethical way.

Sometimes, we give up on students that cannot figure out how things are associated with

each other, and graduate them anyway ~ this is when we make a step down even if we

succeeded in helping them to receive a position with a major insurance company developing a

huge material wealth. And this is also unethical, too.

We must think ethics before we construct anything intelligent. We should ask: hey,

what is this for? whose material wealth will it increase?

But understanding how the intelligence works — we must in all cases! This is why the

analysis of Metrics for Intelligence should lead us in the right direction unmistakably.

But as far as applying this Intelligence in practice, we should ask firmly: hey, what is

this for?

(Unless they offer us very good money...)

Moderator

598
WE DO NOT NEED JUST ANY KIND OF INTELLIGENT

SYSTEM!

By John C. Cherniavsky

I'll comment on this though it 's a bitfar out ofthe "measuring machine intelligence"

category.

First Joy is just raising questions that ethically should be raised by all scientists in the

performance oftheir research. During a recent DARPA/NSF talk hefully acknowledged the

benefits ofNGR (Nanotechnology, Genetics, Robotics - I'm not sure I've got it in his order) in

feeding the hungry, increasing llifespan, and generating general welfarefor all peoples. Contrary

to what Jim asserts, he is not ignoring the benefits ofNGR. He is pointing out potential dangers.

His main immediate concern, rightfully so in my opinion, is the possible creation ofbiological

and/or nano-biological organisms that replicate and that have no natural controls on their

replication. He is concerned that there are no regulatory bodies that oversee this sort ofresearch

which he sees as possible in the next 10 years or so. He is also very concerned about the possible

low cost ofentryfor this research leading to possible inexpensive terrorism on an unprecedented

scale (eg. the atomic bomb built in the garage scenario).

We certainly have regulatory bodies overseeing research on biological warfare agents, yet

none on similar research that could accidentily (or deliberately) release replicating organisms

into the environment. How concernedyou are depends upon your view ofhow likely this is to

occur and how likely it is to be low cost and easy, but it should be thought about and not dismissed

out ofhand. This particular danger has very little to do with intelligence and controls on research

could be very similar to controls on research for other potentially harmful biologicals (Ebola virus

for example) with a twist that these controls won 't work if the technology becomes too cheap, too

easy, and too ubiquitous.

Joy 's concern about intelligent robots is more long term and speculative and based a lot on

Kurzweil and Moravec 's writings. He again advocates serious study andperhaps safety controls

on research by oversight scientific bodies. Ifyou don 't believe that a truly intelligent robot will be

built, then ofcourse you have no concerns.

599
Ifyou do believe that such robots will be built, you should be concerned about rights for

that robot - after all it 's intelligent - and the possibility that such robots wouldpose dangers. Long

term speculation true - but not too early to be discussing in an open forum which is just what Joy,

Kurzweil, and Moravec are doing.

Will these sorts ofconcerns chill research? Quite possibly. Just look at the fairly benign

research on genetically altered crops and thefuror in the European Community. Is that

necessarily bad? Again it 's a question ofrisk perception. All ofsociety should be involved in

such debates, notjust the knowledgeable scientists and the cost/benefits ofsuch work befully

debated.

After all, there is another solution to worldpoverty and that is reducing the world's

population. Maybe we don 't need Moravec style robots.

John C. Cherniavsky, Ph.D.

Senior Advisorfor Research, EHR

National Science Foundation June 7, 2000

June 8, 2000

WHAT MEASURES INTELLIGENCE: A COMPETITION OR A SPECIAL

TEST?

A MEMBER OF ADVISORY BOARD R. GARNER SAYS:

The question was: f) should a competition between intelligent systems be considered a

valid method ofjudging VI value?

I would want to argue that a competition measures performance, but a

standardized test might measure intelligence.

WHAT DO YOU THINK? Moderator

600
June 8, 2000

HOW TO MEASURE INTELLIGENCE?

A MEMBER OF THE ADVISORY BOARD MARVAN JABRI SAYS:

1. A list of tasks/conditions can be defined.

2. Learning (on-line or off-line) can be included.

3. The generalisation capability is obviously critical.

4. The resources utilised are important.

5. Speed of learning (lapsed time of a trial and number of trials) would be important.

In every community there are some benchmarks. Maybe the workshop can come up with a list of

benchmarks that try to cater for various levels or dimensions of VI.

The difference between intelligent systems and autonomous systems is very vague. In some

sense A! is like shooting on a moving target, what systems can aspire at doing today could be simple in

the future. So ideally one would have a spectrum of tasks with various scales of difficulties. In other

words something like a MIQ test.

WHAT DO YOU THINK ABOUT THESE STATEMENTS?

Moderator

June 13,2000

ON THE UNIVERSALITY OF MECHANISMS OF INTELLIGENCE

By Thomas Whalen

/ doubt that there are different kinds of intelligence. I know that multiple intelligences is a faith

(very similar to the polytheism of ancient people).

It is easy to declare any manifestation of perceptual and cognitive activity to be an intelligence.

It is more difficult to find what do they have in common, maybe, absolutely the same.

We all can easily demonstrate that all known phenomena of intellect and intelligence are linked

with a limited number of special computational algorithms including

--combining N-tuples

-searching

601
-focusing attention

-grouping (whicfi includes "combining tuples")

-evaluating and ranking the results of grouping.

There are many humans who are superb at carrying out these five activities in one area but only

average or occasionally even below average in applying the same activities in other areas. By 'areas" I mean things like math, music, human relations, mechanics, visual images, etcetera.

It may well be that to be considered "intelligent" in any field one has to be good at every one of

these five within the context of that field, which would make the list a good universal definition of

intelligence. But I suspect that the details of creating a constructed system that's good at these five

things in one area will not be "plug and play" compatible with the details of doing so in another area.

It might be very instructive to look at just what is really measured by IQ tests and so on.

MODERATOR S COMMENTS

Tom Whalen agrees that probably the skill of intelligence consists of these five

intertwined components: combining N-tuples, searching, focusing attention, grouping (which

includes "combining tuples"), evaluating and ranking the results of grouping.

But he is worried that in many bright people these five components work in one context

(domain) and do not work in another. Therefore, he asks: "Why people having this mechanism

OK in one area cannot apply it within other areas"?

Probably, we all agree that these five activities constitute the body of the "mechanism of

intelligence". Then, we are all surprised that it does not make a genius in literature to be a

simultaneous genius in discrete math.

I think that we all are mistaken about it. The literature genius maybe is closer than we

think to the discrete math genius (and vice versa).

Tom gave a hidden answer to this question: ~ because this mechanism works "within the context of that field".

The keyword here is "context":

[

1 . CONTEXT - The part of a text or statement that surrounds a particular word

or passage and determines its meaning.

2. CONTEXT - The circumstances in which an event occurs; a setting.

(from American heritage Dictionary)].

The mechanism of intelligence is here but it works only in the language of a particular

domain (thus, can read only the context submitted in this particular language).

602
How can we escape this predicament? Either we should translate the problem and the

context into the language that the mechanism of intelligence understands, or from the very

beginning, we should be able to operate in a "metalanguage" and translate contexts from all

languages into the "metalanguage" (was proposed by E. Messina).

It seems reasonable to expect that the mechanism of intelligence working with

excellence in a particular language 1 of a domain 1 can be easily retrained into working in a

"metalanguage". Some humans have problems with this because they are enslaved by their

prejudices about multiple intelligences. Machines are more advanced creatures: they to not have

software prejudices unless one put them into an operating system.

Then, translation of a problem from languages 2, 3, etc. into the metalanguage ~ is just a

technicality. This is why we can expect that the box of "intelligence" can be context and even

domain independent: it will work in metalanguage. Just put at the input a translator from the

domain language into the metalanguage.

Do I expect that this is simple? Taken in account a thick bark of prejudices that the

problem is (and we are) covered, probably not.

Can we help this process? Probably yes.

June 14, 2000

INTELLIGENCE AS A GOAL-BUILDER FOR THE CONTROL SYSTEM

AND THE PARAMETRIC EVALUATION OF INTELLIGENCE

Cliff Joslyn (C. J.):

A. Meystel (A. M.) :

A. M.

1 . If the goal is somehow obtained (constructed), then we should build a model of the system

and apply Hamilton/Jacoby (H/J) and Euler/Lagrange (E/L). Actually, this is a reference to

Calculus of Variations that allows to derive the laws of motion, dynamics, physics without any

need to refer to experimental data. (In textbooks, you can find derivation of Newton's Laws, for

example, F=ma by applying E/L equations to the cost-function assigned as the expression for

energy).

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C. J.

OK, like a generalized least action principle? It also sounds similar to Jaynes' derivation of

thermodynamic laws from an entropic maximization constraint.

A. M.

Then, we can introduce planning of the future motion as finding the minimum cost motion

trajectories by assigning ANY form for the COST. This means that cost is the primary factor,

and since assigning COST depend on the goal, then the goal becomes the primary factor.

C. J.

OK, I think I follow you here.

A. M.

Of course, the goal presumes that there exists a source of the goal, and in many cases, this

source exists as the carrier of INTELLIGENCE. For example, for a single level in the hierarchy

of intelligence the adjacent lower resolution level (level "above") can be considered a source of

the "goal".

C. J.

This is what emerges from this line of reasoning. A definition of the amount of intelligence in a system

might involve a quantitative measure

of:

*) the amount of phenomena under control;

*) the number of environmental distinctions measured by the system;

*) the complexity of modalities of measurement and control;

*) the complexity of the environmental variety available to the measurement and control of the system.

These are all related to each other in complex ways, but the nub of it is there.

A. M.

I appreciate your compliance with the option of considering intelligence as a player. However, I am not sure that I can accept your FOUR SUGGESTED PARAMETERS that you consider a set

of quantitative measures for intelligence. To me, these four factors are rather characteristics of a

system that is associated with the use of intelligence.

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C. J.

What's the difference? The "intelligence" of systems (and I do NOT advocate the use of this term in this

context) is based on their manifesting a semiotic relation which has been selected by evolution or by

designers, allowing the system to "choose" to act counter to physical law.

A. M.

Semiotic or non-semiotic - it does not matter if you do not define the PLAYER who WANTS

and the PLAYER who PAYS THE COSTS (they might be the same). The temi "semiotic"

might obscure the essence of the situation that can reveal the phenomenon of intelUgence. It

looks like the essence is in an existence of a source of INTENTION.

C. J.

In attempting to reconcile your usages of terms with mine, I would say the following prerequisites

necessary to find an intelligence in the control system. First, a goal state is necessary, provided from an

external source, call it "a want" (an intention) provided by a player. The action of the control system is to

maintain the system aware from its natural equilibrium, and this requires action and work, which can be

identified as costs. And yes, the goal (ends) constrains the possible actions (means), and vice versa.

A. M.

We are interested in understanding the phenomenon of INTELLIGENCE and thus decided to

model the system with the factor of INTELLIGENCE taken in account. Therefore, we should

determine what plays the role of COSTS, what is the source of GOALS (WANTS), and

naturally, I call this source a PLAYER.

I state this again and again because you (in your statement above) said: The

"intelligence" of such systems is based on their manifesting a semiotic relation, and this

statement mutes the emergence of a player with his/her WANTS<=>GOALS=>PLAYER.

C. J.

Apologies: the (perhaps implicit) presence of a goal state is, of course, necessary.

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A. M.

I thought that this is pretty obvious that Powers/Marken do not want to introduce the concept of

intelhgence. They were not interested in this, they have other goals. The strive toward

minimahsm is not a new phenomenon. But there is a Hmit to our possibility to minimize the

number of factors to be taken in account. When I refer to your state of "have overgrown" I refer

to the FACT that initially we all are trying to cut the number of factors involved. One should

notice that at some point of system's complexity it becomes detrimental.

C. J.

OK, I understand the admonition. My problem is always that until we can agree on these fundamentals, I

have little faith that we can or should move onto the complexities.

A.M.

OK, I would agree with addressing an example of the Inanimate World with similar

complexities.

C. J.

Then, consider the flipping coin. In this context Representation plays the role of the causal forces acting

on the coin, and Will (Intention) is an abstraction of whatever it is which resolves the uncertainty as to

whether heads or tails will turn up (call it Chance, or Chaos, or Statistical Physics).

A. M.

I would not start with this example. It is very complicated because we have two Wills here: the

Will of the Man who is flipping the coin, and the Will of the Physical Law that we are not equip

to compute since we do not know well enough the point that the force has been applied, the

value of this force, the angle under which it was applied, the air resistance and so on.

C. J.

In the problem as set up, we ignore the will of the flipper. Thus in a sense the coin "wants" to come up

heads or tails.

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A.M.

Cliff, this is not so. The want of the coin is determined by the physical laws that are not well

determined

C. J.

Since the chaotic flipping process is unpredictable, we cannot resolve their will into a physical

explanation, but rather must resort to a statistical description. The complexity of those chaotic physical

processes we simply bundle into the "will" of the coin: that which resolves the uncertainty, chance.

In your usage you can extend control, and thus intelligence, to any physical process. You are free to do

this, but I find it unparsimonious, extending the term beyond any useful boundary. Instead, we need a

principled way to distinguish control from other processes.

A. M.

Still, some terminological issues will remain blinking on the screen and demanding for future

clarification.

C. J. My conclusion is that on strictly denotational grounds, every control system can be seen as a

semiotically closed system (NOT that "any closure is a semiotic system"), but that this is not the sense

that H. Pattee intended.

A.M.

It does not matter as soon as it is true, constructive and useful. I would say even more (and this

is what H. Pattee probably intended to say) that ANY INTELLIGENT SYSTEM IS

SEMIOTICALLY A CLOSURE.

C.J.

Rather, Pattee is referring to the situation where the selection of the semiotic (coding) relations present

in the system is itself a referent of that very semiotic system. This is thus not "simple" closure between

a system and its environment, but between a system and its own construction or creation.

A. M.

Cliff, I have no qualms about it. It is not related to our discussion of intelligence. I would like

to focus upon the set of issues that leads us to discovery, clarification, and better understanding

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of the phenomenon of intelligence. Obviously, the area of CONTROL SYSTEMS has its inner

issues.

C. J.

I agree, I was only trying to distinguish between a literal sense ofsemiotic closure and H. Pattee's sense.

June 14, 2000

IS THERE AN ALGORITHMIC INVARIANCE WITHIN ALL KINDS OF

INTELLIGENCE?

Walter Freeman has doubts about it and he responds to my discussion with T. Whalen in the

following way:

Tom Whalen agrees that probably the skill of intelligence consists of these five intertwined

components:

—combining N-tuples

—searching

—focusing attention

—grouping (which includes "combining tuples")

—evaluating and ranking the results of grouping.

W. F. These are pretty simple-minded, things MLPs can do.

YES, THIS LOOKS PRETTY SIMPLE-MINDED. BUT WE SHOULD NOT FORGET (I

REPEATEDLY STRESSED IT) THAT THIS IS THE SET OF ELEMENTARY

ALGORITHMS THAT FORM INTELLIGENCE OF A SINGLE LEVEL. AFTER

GROUPING HAPPENS WE RECEIVE ANOTHER LEVEL OF RESOLUTION, A LEVEL

WITH GENERALIZED OBJECTS. HERE THE SAME SET OF ALGORITHMS WORKS IN

A SIMILAR WAY. AS A RESULT, WE RECEIVE ANOTHER LOWER LEVEL OF

RESOLUTION, AND SO ON.

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WALTER FREEMAN SUGGESTS TO TEST THE CONCEPT RELATED TO A

SINGLE LEVEL. THIS CONCEPT THAT SEEMS TO BE TOO SIMPLISTIC SHOULD BE

CAPABLE OF RESOLVING SOME SERIOUS EXAMPLES:

Try the following:

• Abstracting figures from undefined backgrounds

• Creating adaptive images of what to search for

• Prioritizing conflicting demands for mental workspace

• Generalizing and classifying items that are not linearly separable in n-space

• Translating between natural languages

I WOULD MEET THIS CHALLENGE WITH AN OPEN VISOR. LET US TRY TO SOLVE:

Test No. I

Abstracting figures from undefined backgrounds

~I scan the image with a sliding window [SEARCHING] and store properties of the image [e.g.

average intensity, color, etc.] at regularly selected coordinates.

—I hypothesize clusters based upon both properties similarity and adjacency [e.g. FOCUSING

ATTENTION and COMBINING N-TUPLES]

~I promote clusters that I have discovered into a rank of objects [GROUPING]

~I am browsing my memory looking for similar objects [SEARCHING]

and so on.

Before I start browsing my images, I allow for some combinatorics upon created objects: the

hypotheses of strings are considered together with their vicinities, and within the vicinity a local

SEARCHING is executed (testing of combinations). This combinatorial freedom depends on

the uncertainty of the results of clustering. When I perform browsing of my memory together

with exploring combinatorial multiplicity of choices that comes from uncertainty.

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(If the complexity of all this is too high, the problem distributes itself to other levels of

resolution. This will reduce the complexity drastically).

What I have described in the previous two paragraphs is actually a solution of the second

problem from W. Freeman's list:

Test No. 2

Creating adaptive images of what to search for

It would not be proper for me to go trough all examples of the list. But if one wants to

do it, one will easily find that the solution for most of these problems can be represented by the

five elementary algorithms that together are sufficient for modeling what some might call a

"generic intelligence".

All cases of "gestalt" known from the literature allow for doing this.

I would suggest to all of you to make this and/or similar experiments. I am sure that if one have

not resolved many similar problems earlier, it was only for the reason that one knew for sure

that this is impossible. Sometimes, the expectation of futility of the possible effort is even more

frightening than the complexity of problem.

It is really chilling to read something like: ...if you try to do this "you might wind up

with is a collection of Turing Machines, that can talk to each other, but nobody else."

Sure, better even not to try...

In the meantime, if this collection will be a hierarchy of Turing Machines, the long term

outlook might be very promising.

Moderator

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June 14, 2000

WE CONTINUE TO DISCUSS THE ABILITY TO HAVE A CONTEXTINDEPENDENT MODEL OF INTELLIGENCE

Paul Davis wrote:

(AND I WILL COMMENT AFTER EACH STATEMENT. A. M.)

P. D. Reactions to the set of five:

1. We probably need multiple levels and perspectives of intelligence's components. Quantum

mechanics is beautiful, but it's not of much help to someone working at the levels of classical statistical

mechanics, thermodynamics, engineering laws like Navier Stokes, or even cruder engineering scaling

laws. The periodic table may be the essence of chemistry in some sense, but it doesn't take organic

chemists very far As the story goes in discussion of complex adaptive systems, different levels have

their own laws.

NO DOUBT ABOUT IT. THE ELEMENTS OF THE GENERIC INTELLIGENCE

(PAUL CALLS IT "THE SET OF FIVE" BUT IT MIGHT BE "SIX" OR "SEVEN") IS A SET THAT

IS PRESUMED TO WORK AT A SINGLE LEVEL OF RESOLUTION. AS ONE CAN SEE, A PART

OF ITS FUNCTIONING IS CREATION OF GROUPS, I. E. BUILDING UP A REPRESENTATION

FOR THE NEXT LEVEL OF LOWER RESOLUTION.

SO, MULTIPLE LEVELS EMERGE AS A RESULT OF NORMAL FUNCTIONING IF THIS

SET OF FIVE (OR SIX, OR SEVEN).

P. D. 2. Perhaps the set of five is a reasonable place to start discussion regarding ONE

level/perspective. I suspect that it is incomplete, and I note the comments here of Walter Freeman.

Beyond that, however, I wonder what empirical/theoretical basis exists for this or another set of

underiying components or mechanisms. I would be very interested in a related discussion, because I'd

learn a lot. But I don't believe that it would be nearly as useful as its proponents might hope (back to

item 1, above).

PAUL IS CONFIDENT THAT THE PHENOMENON OF BEING MULTIRESOLUTIONAL IS

MORE IMPORTANT THAN PROCESSES AT A SINGLE LEVEL. CERTAINLY! I AGREE WITH

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YOU, PAUL. BUT THE MULTIRESOLUTIONAL SYSTEM OF REPRESENTATION EMERGES

BECAUSE OF THIS "SET OF FIVE"!

P. D. 3. I would think that using Gardner's components of intelligence would not be a bad starting

point from the other end, although others may have better suggestions.

AS YOU KNOW FROM MY PREVIOUS MESSAGES THE PHENOMENON OF MULTIPLE

INTELLIGENCE IS EASILY TAKEN CARE OF BY INTRODUCING A TRANSLATION FROM

THE DOMAIN OF APPLICATION INTO A NEUTRAL (META) LANGUAGE. IS THIS

TRANSLATION IMPORTANT? OF COURSE! SHOULD WE DEVOTE ATTENTION TO THIS

PHENOMENON? YES, OTHERWISE WE WON'T BE ABLE TO HANDLE IT.

P. D. 4 . While some may have the OPINION that multiple intelligences is a myth or an expression of

"prejudice" (a rather inflammatory term), I have seen nothing in the e-mail to justify this opinion. The

periodic table wasn't postulated or asserted; it was built up from empirical observations and

minitheories.

PAUL, LET US GO TO AMERICAN HERITAGE DICTIONARY:

[Prejudice 1 . a. An adverse judgment or opinion formed beforehand or without

knowledge or examination of the facts, b. A preconceived preference or idea. 2. The act or state

of holding unreasonable preconceived judgments or convictions.]

NO, I DON'T THINK THAT THE TERM "PREJUDICE" IS OR SHOULD BE TAKEN AS

AN INFLAMMATORY ONE. THIS IS RATHER A TIMELY WARNING.

SPEAKING ABOUT PERIODIC TABLE: I WANT TO REMIND YOU, PAL, THAT THE

PHLOGISTON THEORY WAS BUILT UP ALSO FROM EMPIRICAL OBSERVATIONS

AND MINITHEORIES...

P. D. There is an extensive body of pyschological literature supporting-at that level of description-the

notion of multiple intelligences (and the failure of the single G-factor hypothesis).

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YES, BECAUSE THE IDEA OF INVARIANCE OF THE INTELLIGENCE (WITH AN

INPUT TRANSLATOR) MIGHT BE DIFFICULT TO BEAR FOR MANY. INDEED, ONE

MUST BE VERY RESPECTFUL OF THESE TONS OF SWEAT, BLOOD, AND TEARS

SHED TO GAIN HIS/HER DOMAIN KNOWLEDGE. IT IS HARD EVEN TENTATIVELY

TO ASSUME THAT ALL THIS IS JUST A TRANSLATOR WHILE REAL GENIUS IS A

SYMBOLIC ALGORITHM! WOULD I VOLUNTARILY ADMIT THAT ALL HIDDEN

TRICKS OF MY DOMAIN OF NUCLEAR PHYSICS ARE REALLY RESOLVED IN THE

SAME WAY LIKE THE PROBLEMS OF CULINARY OR PLUMBING DOMAINS? NO

WAY!

AGAIN, THIS IS THE ESSENCE OF THE HYPOTHESIS AT HAND:

1) AN INTELLIGENCE AT A LEVEL IS THIS SET

[SEARCH*FOCUSING ATTENTION*GROUPING*SELECTION* (MAYBE SOMETHING

ELSE)]

2) TOGETHER ALL OF THESE PRODUCE THE NEXT LOWER LEVEL OF RESOLUTION

WHERE THE SAME ACTIVITIES ARE INITIATED

3) [AND SO ON]

4) TOGETHER, THE HIERARCHY OF THESE LEVELS IS EASILY COPING WITH

NP-COMPLETE PROBLEMS

5) ALL MECHANISMS MENTIONED ABOVE CAN WORK IN THE SPECIFIC

LANGUAGE OF A PARTICULAR DOMAIN AND EQUIP THEMSELVES WITH VARIOUS

AND THE NEAT CORNER-CUTTING TRICKS APPROPRIATE FOR THE DOMAIN

LANGUAGE.

6) AS FAR AS MACHINE INTELLIGENCE IS CONCERNED, ALL IT COULD BE DONE

SYMBOLICALLY (IN A METALANGUAGE) IN THE SAME WAY IN ALL DOMAINS;

JUST AT THE INPUT AND OUTPUT WE HAVE TO HAVE CORRESPONDING

LANGUAGE(i)-->LANGUAGE(meta) AND LANGUAGE(meta)->LANGUAGE(i)

TRANSLATORS.

IVIoderator

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June 14, 2000

FROM THE RESPONSES TO C.WEISBIN'S QUESTIONS

Thomas Whalen wrote:

I WOULD LIKE TO DISCUSS WITH ALL MEMBERS OF THE ADVISORY BOARD THE LIST

OF QUESTIONS THAT I PROPOSED IN THE RESPONSE LETTER TO C. WEISBIN. LET ME KNOW

WHAT DO YOU THINK, THIS IS VERY IMPORTANT

This is the list of C. Weisbin's questions that the Workshop will try to answer:

1 . What is the vector of intelligence (VI) that should be measured and possibly used as a

metric for systems comparison?

a) understanding instructions expressed in language convenient

for the human giving them. *This is sometimes natural language,

sometimes human-oriented technical language.)

b) understanding goal specifications and working independently

to achieve goals presented to it in a language and level of

detail convenient to the human whose goals they are.

c) generating (sub)goals in a useful but surprising way so as

to improve the well-being of the humans using the system.

2. Should VI be measured in addition, or instead of measuring the vector of performance

(VP) determined by the regular specifications?

/ think it comes to the same thing, just with a different emphasis

3. If two systems have the same VP, what is implied by the difference in their VI

values? Can this difference be represented in $ units?

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If VP does not include cost, then a more intelligent system would sometimes be more costly,

sometimes cheaper If VP includes cost, benefit, and risk, including all externalizes, then nothing else is

economically interesting.

Example: it might be possible to someday build two Chinese rooms, one that "really

understands" Chinese and the other which just follows stimulus-response rules. If so, the intelligent one

will probably be cheaper to produce.

4. Is it possible (and meaningful) to have different VI measures:

a) goal-invariant, b) resource-invariant, c) time-invariant?

I don't understand the question.

5. What should be recommended as a test of VI and how to normalize VP so that comparison be

performed at the same normalized value of VP.

While I don't think that VP and VI are identical, I don't see a sharp enough distinction to

be able to "normalize." If a human has VI>VP we attribute it to poor motivation or else to a

specific disability. If a human has VP>VI we attribute it to a fault in testing or to extraordinary

motivation.

June 14, 2000

Kirstie Bellman responds:

Those Weisbin questions are a reasonable start. It will be interesting t see how quickly

discussions emerge on the behavioral correlates of "understanding" within different

environments or artificial ecosystems. (Kirstie Bellman)

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June 15,2000

ARE THE CONTEXT AND DOMAIN INDEPENDENT

MODELS OF INTELLIGENCE POSSIBLE?

\N. F. What I want to say is that intelligence is to be found in the capacity for defining objects, which

requires action by the agent (robot, animal, human) in respect to goals that the objects are to make

achievable.

A. M. You admit that this is something we can understand, model, and simulate with the help

of computer. To make it clear you refer to the fact that:

W. F. This is straight-forward theory psychology from the pragmatist and gestalt schools, and it has

been incorporated by a number of avant-garde roboticists.

A. M. And, yes, you admit that this straight-forward theories can be fully understood and even

simulated with the help of computer: this is what is actually available

W. F. defining of 'objects' that are to be measured as 'n-tuples', sought, attended, grouped, and

evaluated precedes these operations.

A. M. However, you firmly believe that DEFINING OBJECT is what we still cannot fiilly

understand, and this is why computationally it cannot be done IN ALL CASES:

W. F. Once the objects are defined, Turing Machines will do fine, but Turing Machines can't do that

.

[defining the objects. A. M.].[I would add "in all cases" A. M.]

A. M. Yes, we have a problem with defining the objects if this is linked with our "wants". You

finger exactly in this direction:

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W. F. My only contribution is to show by modeling brain dynamics that biological brains have this

capacity,

[defining the objects. A. M.] and its exercise is well described by the theory of intentionality.

A. M. Yes, but in numerous domains, we start implementing "intelligent systems" that are

solving more simple problems of defining the objects. In many cases this operation is within our

reach, and we perform it successfully. The efforts continue, sophistication grows. Then, I hope,

that modeling of "intentionality" will be in our reach soon, too.

All of this was just an introduction to the expression of your big doubts concerning the

concept that "intelligence" in various domains might be modeled by the same computational

structure

W. F. I don't really object to proposing a common feature of 'generic intelligence', which may be in a

class with other ideals such as truth, beauty and justice,

A. M. Walter, the only thing that I propose is to have a multiresolutional model of knowledge

representation that will have at each level of resolution a model-set

[searching*grouping*focusing attention* *evaIuation*selection] that will do a definition of

objects at this level from the objects of the higher resolution defined at the level beneath.

It is my conviction that this system can work both in the domain language and in

metalanguage. In the latter case, it can be considered a context- independent algorithm (model)

of intelligence.

I am far from a desire to talk about spiritual and other hot air producing issues that are,

as you are saying, "in a class with other ideals". Some participants of the discussion, called this

context-independent intelligence: "generic intelligence".

I have no objections against any relevant term. For me, the essence of this is the most

important issue.

In conclusion, you said:

W. F. but I doubt that it could supportjudgments more compelling than to say "system A is smarter

than system B". To make it stick, you have to say what each can do, at what cost.

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A. M. We will be able to say: "system A is smarter than system B" ? Not bad! We

"have to say what each can do, at what cost" — no doubt about it!

Moderator

June 16,2000

TURING TEST, SUCCESS VS. LUCK, SUPERVISION AND AUTONOMY

Dan Repperger wrote:

Some comments after reading the white paper:

(1) Your efforts to quantify intelligence, especially from the perspective of a machine present a

difficult problem. Your example of a Chinese room negates the Turing test as a possible definition of

machine intelligence.

A. M. DON'T YOU THINK THAT IT IS A RIGHT TIME TO STOP JUDGING

INTELLIGENCE

BY A SIMPLE SKILL TO PRETEND BEING "INTELLIGENT" ? (A. M.)

D. R. (2) The definition of J. S. Albus seems comprehensive enough and you transfer the

responsibility to defining success, rather than being due to pure luck.

A. M. I HOPE THAT ONE COMPONENT OF THE VECTOR OF SUCCESS OF OUR

MEETING WILL BE A CONSENSUS ON MEASURING THE SUCCESS OF IS

FUNCTIONING

D. R. (3) Your vector of intelligence on page 5, I thought, would have a component of the speed at

which it accesses information. It did not have this component but you address it later on.

A. M. YOU ARE RIGHT: THE SPEED OF ACCESSING INFORMATION IS A MAJOR

ISSUE

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D. R. (4) I agree that supervisory control and defining autonomy in subordinate systems is a l<ey

problem to be addressed in the next 10 years or so. The Air Force is very interested in this problem in

the design of unmanned air vehicles.

A. M. SUPERVISORY CONTROL-->A DEGREE OF AUTONOMY-->AUTONOMY THAT

MAXIMIZES EFFICIENCY -- PROBABLY, THIS WILL BE OUR PROGRESSION IN

TIME.

Moderator

June 16,2000

A RESPONSE TO THE DRAFT OF THE WHITE PAPER: FROM

COMPUTING WITH WORDS ^ TO CHINESE ROOM IN REAL CHINA

LB. Turksen wrote:

6. T. I have read your white paper with interest and enthusiasm. I agree with you that metrics of

intelligence need to be developed. However, I would like to suggest that such metrics should be

developed not Just with "Computing with Numbers" paradigm in your reference to Lord Calvin, but as a

synthesis of "Computing with Numbers" and "Computing with Words" paradigm of Lotfi Zadeh.

A. M. CERTAINLY, IT WOULD BE SUPERFICIAL TO UNDERSTAND THE NEED IN

METRICS AS THE NEED IN A SOLELY QUANTITATIVE FORM, OR A FORMULA.

ULTIMATELY, THE NEED IN A METRIC IS DETERMINED BY THE NEED TO

COMPARE ALTERNATIVES, IN OUR CASE, TO COMPARE INTELLIGENT SYSTEMS:

"WHICH ONE IS MORE INTELLIGENT," OR "WHICH ONE IS PREFERABLE FOR OUR

NEEDS."

IF THIS PREFERENCE CAN BE FOUND WITHOUT NUMBERS AND RANKING CAN

BE DONE AS A RESULT OF SOME LOGICAL INFERENCE - SO BE IT!

619
B. T. Which you indirectly say but not clarify as one should. For example, you write: ...by living

creatures, and especially by humans: ability to work under a hierarchy of goals, ability to perceive the

external world and organize objects, actions and situations,... ( quoted from the first page in the last

paragraph). Note that humans at least do this with the use of their natural languages. Thus my point

about the Computing with Words ofLotfi. Note that he recently began to talk about Computing with

Perceptions

.

A. M. ...WHICH REMINDS US THAT DETERMINING PREFERENCES MIGHT BE

DONE NOT ONLY WITHOUT NUMBERS, BUT EVEN WITHOUT EXPLICIT LOGICAL

INFERENCE: "AH! I LOVE THIS LANDSCAPE (OR THIS BEAUTIFUL FACE, OR THIS

POWERFUL PAINTING). WELL, IN ALL THESE EXAMPLES THERE IS SOME

INTERPRETATION OF PREFERENCE, AND THIS INTERPRETATION MIGHT BE DONE

ON A PRE-LOGICAL LEVEL (IF IT EXISTS).

B. T. Let me put it in a different way. Recently I participated in a teleconferencing with some Italian

Colleagues. They have developed an artificial Nose . They want to use their electro-mechanical device

with novel sensor which provide lots of information. They have used principal component analysis,

neural networks, etc. all numerical based analysis. But they are aware that they cant represent humans'

ability, e.g., sense of smell, to detect variations in food, e.g. , cheese, and drinks, e.g., wine. These are

however expressed with linguistic variable that humans use. Clearly fuzzy set and logic approach is a

preliminary but effective way to begin and conceptualize such complex metrics of intelligence.

A. M. THIS IS ONE MORE ARGUMENT IN FAVOR OF NON NUMERICAL

EVALUATION OF THE DEGREE OF INTELLIGENCE

B. T. In page 6, item (g) of the White Paper, you talk about (CIRCLE) why not also include (ellipse)

and other more complex shapes?

A. M. YES, IN EVALUATION OF THE UNCERTAINTY FOR EACH COMPONENT OF

THE VECTOR OF INTELLIGENCE ANY CONFIGURATION OF THE UNCERTAINTY

ZONE CAN BE EXPECTED. I AM TALKING ABOUT "CIRCLE" BECAUSE IT SHOULD

MEAN EQUALLY LARGE UNCERTAINTY FOR EACH COMPONENT OF THE VECTOR

OF INTELLIGENCE.

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B. T. In page 7 of the White Paper, you talk about "gestalt" concept. Is there a relationship between

gestalt representation and its word representation and the potential semiotic representation and its

interpretation?

A. M. WHEN WE ARE TALKING ABOUT "GESTALT" TODAY, WE ALL AGREE THAT

THIS IS THE TERM THAT HAS BEEN INTRODUCED TO ACCOUNT FOR

RECOGNIZING ENTITY FROM THE MULTIPLICITY OF ITS SEEMINGLY

UNORGANIZED COMPONENTS. IT SEEMS REASONABLE TO EXPECT LINGUISTIC

GESTALT SIMILAR TO THE GESTALT IN VISUAL

PERCEPTION, AND GESTALT WORKING IN ANY SYSTEM OF SYMBOLIC

REPRESENTATION, I. E. SEMIOTIC GESTALT.

B. T. In page 8 of the White Paper, J Searle and Chinese room experiment are mentioned. Let me tell

you my personal experience in 1 982 in Taipei Taiwan. A friend and I tried to locate a Banl< with a map

with Chinese characters. We were able to locate the Bank by matching the street labels on the map to

the street labels on the street name plates. Even though we didn't know what the street names meant or how they were pronounced, we were able to find the Bank. Hence mission was accomplished and the

goal was achieved without knowing the Chinese language.

A. M. THIS IS A FASCINATING STORY! BUT IT DOES NOT SAY ANYTHING GOOD

ABOUT THE NOTORIOUS TURING TEST. INDEED, YOU DEMONSTRATED

MULTIDIMENSIONAL, MULTIFUNCTIONAL INTELLIGENCE. FIRST, YOU WERE

CAPABLE OF PUTTING IN CORRESPONDENCE THE NOISY 3D-REALITY OF THE

CITY WITH THE NOT VERYCONGRUENT SYMBOLICS OF THE MAP. THEN, YOU

DEMONSTRATED INTELLIGENCE BY UNDERSTANDING THAT THE CAPTIONS ON

THE STREETS SHOWN IN THE MAP ARE THE SAME AS CAPTIONS ON THE STREET

POSTS IN THE INTERSECTIONS. I APPRECIATE THE FACT THAT ALL OF THIS WAS

IN A HIEROGLYPHIC SIGNS AND FINDING SIMILARITY BETWEEN HIEROGLYPHS

IN DIFFERENT SOURCES REQUIRES INTELLIGENT OF SIMPLE SEARCH FOR

SIMILARITY.

YES, YOU'VE DEMONSTRATED YOUR INTELLIGENCE!!!

1

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BUT THE MAN SITTING IN THE CHINESE ROOM AND COMPARING WRITTEN

HIEROGLYPHS BY SIMILARITY DEMONSTRATES ONLY A LITTLE BIT OF IT (AT

LEAST HE UNDERSTOOD THE ALGORITHMS OF COMPARISON OF SIGNS AND

SEARCHING IN A TABLE).

Moderator

June 16, 2000

DEFINITION OF INTELLIGENCE AND SEPARABILITY HYPOTHESIS

by B. Chandrasekaran

Consciousness is usually treated as an intrinsic property. I experience my being, but I don't

experience your being. However, I usually hypothesize that you have the same property,

consciousness. I'll never know for sure about you, just as you'll never know about me for sure.

Except for this theoretical caveat, we pretty much attribute to each other the property of

consciousness and get on with our lives.

On the other hand, the term "intelligence" has both an extrinsic and an intrinsic connotation,

depending on context. I watch an agent's behavior, and based on certain characteristics of the behavior,

I may conclude that the agent's behavior is intelligent. In this sense, it is an extrinsic characterization.

On the other hand, sometimes the term has an intrinsic connotation, that of having a mind, an entity that

experiences being, experiences having thoughts, and so on. Thus, calling a thermostat intelligent is OK

as long as it is intended as an extrinsic characterization (and as long as you agree with the criteria that

were used forjudging the presence of intelligence in thermostats).

Claiming intelligence in the intrinsic sense for them is much more problematic. In A! and

cognitive science, people often slide from one sense of the term to the other without being aware that

they are doing so. That is because, until very recently, there was no reason to separate the extrinsic

and intrinsic senses of the term. The only entities that we called intelligent - biological agents of various

sorts, including humans - showed intelligence extrinsically, and we were reasonably confident of

attributing to them intelligence in the intrinsic sense.

But technology has made it necessary to separate the two senses. It now seems theoretically

possible to conceive of entities that *behave* intelligently - have intelligence in the extrinsic sense. But

it is much harder to be certain about their having intelligence in the intrinsic sense. We are missing the

full panoply of the evidential basis that allowed us to abduce intrinsic intelligence from evidence of

extrinsic intelligence in biological agents.

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The situation is not unique to ttie term "intelligence. " There are other biologically based concepts

that seemed pretty clear until recently, but now suddenly seem problematic. Consider the concept,

"mother." One normally thinks that whether A is a mother of B is a matter of fact, not point of view.

However, consider the case where woman A 's fertilized egg is implanted in woman B's womb, and the

infant that is born is immediately given to woman C, who adopts and raises the child.

There is no self-evident answer to the question, "Who is the *rear mother of the child?". That is

because contributing the egg, carrying the fetus in the womb and raising the newborn for several years

are all typically done by one woman, and thus we normally do not separate these three properties

associated with the concept of "mother " Depending on the purpose behind the question, however, we

can answer the question. Thus, if the question is asked from the viewpoint of finding a donor for kidney,

woman A is the mother From the viewpoint of finding a woman who can suckle the infant, woman B is

the mother From the viewpoint of finding someone to solace the child when crying, woman C is the

mother.

I think that similarly, because of its natural orgin, at least two properties, perhaps more, come

packed in one word "intelligence. " If we don't recognize this and argue about what "really" is intelligence,

and whether the thermostat is "really" intelligent, we will be like the people who argue about who "really"

is the mother of the child in my story above.

It is possible to argue that the criteria used by Albus to atthbute intelligence in the extrinsic

sense to thermostats were too weak. Someone making this argument would hold that a meaningful

characterization of extrinsic intelligence would seek to capture a much larger range of adaptation and

behaviors than thermostats possess. Such an argument would identify higher mammals perhaps as a

reasonable place to start, if not Just focus on humans. But this is not a debate that has a clear correct

answer either One can choose one characterization as more interesting, more productive, and so on,

but not as the one that is truly correct.

And, carrying this argument further, one might claim that the more complex forms of extrinsic

intelligence can only be generated by systems that also have intelligence in the intrinsic sense. One

way to interpret Penrose is that he is saying that the extrinsically intelligent behavior of a top-flight

mathematician is not possible without certain essential characteristics of intrinsic intelligence. According

to him, the mathematician directly "experiences" the truth of certain mathematical propositions. This

capacity of intrinsic intelligence is essential for his behavior of finding a proof of the theorem.

While it may turn out to be true as a matter of empirical fact that we will only solve the problem of

making artifacts that have a significant extrinsic intelligence only by making them have intelligence in the

intrinsic sense, the latter is not logically a prerequisite for the former At least, no one has shown it is. I

have proposed what I call the "Separability Hypothesis" as a good working hypothesis forAI, namely,

that it is not necessary to solve the problem of consciousness or intelligence in the intrinsic sense, to

produce artifacts that show intelligence in the extrinsic sense. Those who are curious, see:

http://www.cis.ohio-state.edu/~chandra/separability.pdf

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Chandra

B. CHANDRASEKARAN, OHIO STATE UNIVERSITY

June 17,2000

THE IMPORTANT POINTS OF W. FREEMAN'S MESSAGE

Dear Advisory Board Members,

All of you received W. Freeman's message. I would like to emphasize some of the further

developments that his message triggers.

1 . ABOUT THE CONSCIOUSNESS

VJ. Freeman wrote:

I agree with Chandrasekaran, that "consciousness" need not be considered as a goal in machine

intelligence, nor for that matter in biological intelligence and intentionality.

A. M.:

In other words, we have an additional support for the view that consciousness is a "GUI" for

monitoring functioning of the system and its Umwelt.

2. INTELLIGENCE WITH AND WITHOUT LEARNING

W. Freeman wrote:

[An] intelligent system learns through practice. This rules out ordinary thermostats.

A. M.:

Is decision-making without learning "intelligent"? We can agree and accept that we will call

"decision making without learning" a lesser degree of intelligence than "decision making with

learning." It is possible even to postulate that a system with learning is supposed to be better

performance, reliability, and so on. Of course, a cockroach learns not within a single generation.

It learns at a lower resolution, at a specie level.

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I

\N. Freeman continues:

A child learns to recognize a spoon when it sees one, because it has practiced eating with it. Similarly, a

machine can learn to recognize an electrical connector, if it can practice plugging itself in to recharge its

batteries. You might say that a smart machine knows what it is doing, and a dumb one does not, but that

invokes "knowing" of knowledge (or information), which is irrelevant to the design.

A. M.:

Walter, your last sentence would not be controversial if you rephrase it like this:

... a smart machine learns what it is doing, and a dumb one does not, and that invokes

"learning" of knowledge (or information), which is relevant to the design since the devices for

learning should be designed.

3. USING CHAOS AS A TOOL OF RANDOMIZATION

Then, W. Freeman said:

The learning in biological systems depends on chaos for hypothesis formation. This process is more

closely related to statistics than to logic,

A. M.:

Walter, the statistics does not exclude logic, neither logic is fully complete without the logic of

statistics. The logic of class formation, the logic of cause~>effect derivations, the associated

deductions, inductions, and abductions neither disappear not lose their strength.

Randomization for hypotheses formation is a legitimate tool of reducing the complexity

of computations. Chaos is a tool of randomization to collect more or less persuasive statistics. If

chaos is generated that does not help to randomize properly, the statistical results may happen to

be deceptive even if they look meaningful.

Therefore, biological system are not unique in using the tools of randomization for

complexity reduction. Yes, they learned about these fascinating tool before Neanderthals, and

before Cro-Magnons, and even before Haken s Research Institute in Europe and Santa Fe in US

started exploring these things.

But in the engineering, these methods are utilized without too much associating these

tools with mechanisms of intelligence.

625
W. Freeman:

[As] Johnny von Neumann wrote, brains "lack the arithmetic and logical depth" that we expect in

machines, so he concluded that whatever the language of the brain might be, if it has any, it is "not

mathematics, or at least not what we consciously and explicitly call mathematics" (1958). In other words,

brains don 't have numbers, but they do have a "way" of functioning which is highly successful in certain

domains.

A. M.:

All musings of great people sooner or later become interpretable. In this particular case, we

should not overestimate this "number-versus-another way" dilemma. In the previous letter, I.

Turksen commented on Computing With Words paradigm that opens room for any symbolic

system to be a language of the brain. I am sure that you would not reject the hypothesis that the

language of the brain is symbolic (proof: based on the definition of "language").

W. Freeman:

That "way" is simulated in my Kill model (Freeman 2000). I look on it as a "machine-in-embryo", but it

can already do useful work, such as reliable pattern classifications, that no other existing system can do,

at all. Of course, it is simulated in software using numbers, so it is 100-fold slower than the sensory

system it models, but it is realizable in hardware that could do the tasks in real time. The use of

stochastic chaos instead of deterministic dynamics is what I perceive to be missing in your suggested

approach. Am I wrong?

A.M.:

Walter, from my previous comments you could already deduce that randomization (and

"stochastic chaos" is just a useful tools of randomization) is a regular and legitimate

computational measure of complexity reduction. We probably are not aggressively propagating

the word Chaos, we limit ourselves with a milder term "randomization" but - we have plenty of

examples.

Today, NIST uses randomization at all levels of resolution of the Autonomous

Intelligent Vehicle control. My first use of randomization can be dated to 1982-1984. In the

system of Computer Aided Conceptual Design of robotic structures, I used it for hypothesizing

626
assemblies. If one don't use it, the amount of computations grows unbearably*. But, I would

agree, that "randomization" as a term would lose the contest with the term "Chaos".

Moderator

June 17, 2000

PAUL DAVIS ON THE DISCUSSION WITH B. CHANDRASEKARAN

Paul responds to my discussion with B. Chandrasekaran. Please, take in account that

some thoughts concerning consciousness belong originally to B. Chandrasekaran (the letter is

attached in the end).

Paul Davis wrote:

The term "performance" is associated with externally observable actions such as the maintaining a

temperature task.

A. M.

[ The specification of each target variable like "maintaining a temperature task" is

actually more complicated. The specification sounds rather like: "maintain the temperature in

the room within a particular interval of temperatures [from tl - to t2]" ] and in a more realistic

scenario:

"maintain the expectation of the temperature within a particular interval [from tl - to t2] while

maintain the variance [sigma-t] within a particular interval [from sigma-tl to sigma-t2]."

Then, each variable will be supplemented by the list of constraints like:

[as you maintain what I asked above, please, do not exceed

the total number of "on-off switching operations" higher

than N; do not exceed the total consumed power (energy per time)

higher than Pk, and so on].

' Paper by A. Meystel and M. Thomas on this subject was published in Proceedings of the

IEEE Conference on Robotics and Automation, Atlanta, GA, 1984, pp. 220-229.

627
Paul, you are talking about the temperature. In the room, we always have a nonstationary arbitrarily shaped field of temperature. Usually, the temperature sensor is a part of the

thermostat. It measures the value of temperature in the vicinity of the thermostat, and the

temperature in other parts of the room is just assumed.

I would presume that a thoughtful engineer will install 5-7 sensors in different locations

in the room and will require from this single thermostat to provide for, say, an average

temperature to be within some interval. Donald Trump or a governmental diligent lab might be

willing to distribute several thermostats in the room so that the particular temperature field be

provided.

Paul Davis continues:

when the white paper talks about intelligence, it includes things like memory, processing speed, etc. In

one frame of mind, these "sound like" other measures of performance. "Boy, that hummer really

performs: it's a gigaflop machine!"

A. M. This sounds like a measure of performance only if your real problem is not (or cannot

be) specified properly. There are many such problems: they are UNDERSPECIFIED, and we

are interested to have some measure of the system universality and/or smartness so that the

unexpected factors would not caught us unprepared!

BUT WHAT SHOULD BE CONSIDERED AN INDICATOR OF SMARTNESS: THIS

IS THE SUBJECT OF OUR INQUIRY!

P. D.

Thus, one might think that we are going down the path of saying that both performance and intelligence

are about doing tasks (e.g., crunching a billion arithmetic operations).

A. M. I hope that all we have already understood that

a) in a well specified case this is plain WRONG

b) in the underspecified case to rely on some buzzwords from commercials (for

laymen as well as for the scientists) is plain silly,

or as Paul is saying UNSATISFYING.

628
p. D. That is correct - unsatisfying. It would hardly help with the Chinese Room problem. Even

adding items such as number of objects discerned, or number of levels of resolution used, still don't

sound like intelligence. Hmm.

A. M. The greatness of the moment is in the fact that we have reahzed already that the

characteristics of the system can and should be looked at carefully. In our white paper we have

divided them into two groups:

VECTOR OF PERFORMANCE (that characterizes the output variables) and

VECTOR OF INTELLIGENCE (that characterizes the properties and features of the system of

control)

Paul whose language is a little bit different prefers to talk about

EXTERNAL (equivalent to OUTPUT, or PERFORMANCE set of variables), and INTERNAL

(equivalent to INTELLIGENCE set of variables).

Then, the picture looks for him more peaceftil:

P. D. There are two parts to the solution, I think. First, we need to distinguish between internal and

external. If a machine has Gigaflop capability, that is an internal capability related to potential

intelligence. To be sure, we might measure that capabilitiy by having the machine do a task. However,

it might also be possible to study the "anatomy" of a machine and infer that it has parallel processing

capability; if so, that would be another way to infer internal capability--one that doesn't require having the

machine "perform."

A. M. But, of course! To study the ANATOMY of our intelligence is probably the only way to

judge upon the future functioning especially if we are uncertain about it!

P. D. If it were possible to read some of the machine's programming, then we might infer that it has

the capability to "detect" objects of certain classes, or even to give names to patterns that it "discovers.

"

We might not know how the machine would perform at tasks, because we might not understand the

totality of the programming, but we could at least see potentialities.

A. M. Hurray! This is one of the best descriptions of the Vector of Intelligence ONE could

ever dream. This is how COMPUTER VISION people are talking about their systems: in the

terms of classes available for distinguishing and interpreting. This is how

PLANNING/CONTROL people are talking about their systems: in the terms of classes of

629
terrain they can handle, and classes of obstacles that they can efficiently avoid. In both cases:

vision and planning/control the description of classes can be general enough, yet adequately

presenting the properties of the future problems.

P. D. It seems to me that, so far, we are on good ground distinguishing between performance and

components of intelligence, or, perhaps, enablers of intelligence VI. Moreover, it seems to me that none

of this is mystical.

A. M. In other words, we are capable of specifying for all cases the relevant VP and VI.

Paul Davis goes on:

The other part to the solution requires doing something about the "emergent properties"

business. Some aspects of intelligence seem to demand this. I don't think that we can claim to have

tackled intelligence without at least building place holders in for capabilities such as world modeling,

including world modeling that adds inferred features that were not already lurking in the machine's data

base.

A. M. These are formidable observations! We, the people, all our life are doing one thing: (as

Paul describes it) searching for "inferred features that were not already lurking" in our memory.

Obviously, the contemporary audience has learned about the "emergent properties" first and

only after this it has noticed that it actually infers new features.

[The next paragraph is more related to positions from

the B. Chandrasekaran's letter (see a couple of letters back)]

P. D. And what about emotion and its machine analogues if there are some? Perhaps Crick's work is

relevant here (e.g., his book using vision as something we more nearly understand that may be related

to consciousness). Perhaps it would be possible to determine whether machines have and adapt

internal models of the external world by giving it certain tests. We wouldn't necessarily "see" the

machine's model (unless it was a simple program that we inserted in the first place), but we might be

able to have a strong basis for inferring the existence of a model. Going back to our furry friends for

examples, some of us believe that certain of their actions go beyond something explainable by

straightforward "behavior" we would argue that the animals are "thinking, " although we haven't a clue

what their mental "picture" or reasoning is like in any detail. We could be wrong (and, certainly, some

scientists are vociferous in insisting that other animals don't think), but our inferences have some basis.

630
[With this background, here are some P. Davis responses to the questions

earlier presented to Advisory Board Members. See Attachment 1]

1. Yes, I think there is a difference between VI and VP. One has to do with internal capabilities;

the other has to do with accomplishment of externally observable tasks. The first may be inferentially

measured by having the machine do tasks.

2. VI should me measured in addition to VP.

3. If two machines have the same VP, it might be because we only had a meager set of tasks

and, as a result, didn't make the distinctions we might have. It certainly seems unlikely to me that we

shall soon be able to infer VI from VP.

4. Some aspects of VI (the enablers of intelligence, if not intelligence itself) might be goal

invariant, such as processing speed, memory, etc. I'm not sure, however, what is meant by the several

invariances.

5. This is a really tough question and I don't understand yet how to do it, except in some very

simple respects. Getting at the existence and richness of internal models seems important here.

(the question was: 5. What should be recommended as a test of VI and how to normalize

VP so that comparison be performed at the same normalized value of VP. A. M.)

6. I would think that we could construct broad problem spaces, measure performances that give

us hints about intelligence components, etc., without focusing on any particular problem area. If we did,

however, the result would not be context independent, but rather information about how intelligence

varied with context! Unless, of course, we did some gross averaging. I am very skeptical about simple

measures in this business.

7. I think that resources are relevant, but shouldn't be allowed to dominate.

[This is the end of P. Davis' commented message]

Attachment 1

This is the list of questions that the Worlcshop is to answer:

1 . What is the vector of intelligence (VI) that should be measured and possibly used as a

metric for systems comparison?

2. Should VI be measured in addition, or instead of measuring the vector of performance

(VP) determined by the regular specifications?

3. If two systems have the same VP, what is implied by the difference in their VI

values? Can this difference be represented in $ units?

631
4. Is it possible (and meaningful) to have different VI measures: a) goal-invariant, b)

resource-invariant, c) time-invariant?

5 . What should be recommended as a test of VI and how to normalize VP so that

comparison be performed at the same normalized value of VP.

The subsequent supplementary questions are ingrained (directly, or indirectly) in the

main five questions:

a) how to form VI for various architectures?

b) should the questions 1 through 5 be related to intelligent systems, or autonomous

systems, or both?

c) what is the protocol of dealing with uncertainty when the uncertain metric is to be

applied in the procedures of decision making? for example how the uncertainty of

planning affects the cost of goal achievement?

d) what are the guidelines in constructing the world model and determining its scope in

the variety of applications? how the scope of "world model" affects the

sophistication of intelligent behavior?

e) how are the questions 1 through 5 related (and the answers applied to) the systems

that are working under a hierarchy of goals.

f) should a competition between intelligent systems be considered a valid method of

judging VI value?

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June 17, 2000

LEARNING, GOALS, INTELLIGENCE: COMPARATIVE AND

DEVELOPMENTAL PSYCHOLOGY

Thomas Whalen wrote:

I think another good way to get perspective on the questions we have been wrestling with is to

lool< at biological systems other than fully developed humans. There has been some discussion of

animals already, but I think it could be made more systematic.

All biological organisms manifest learning by their genome, and what the socio-biologists call the

"goals" of the genome. Whether this is per se a manifestation of "intelligence" is a metaphysical

question.

An insect's behavior shows only this kind of learning, like a thermostat's behavior manifests

learning and intelligence but not learning and intelligence of the thermostat's own, just the learning and

intelligence of the thermostat's designers.

Higher animals such as mice show learning of their own. The trained behavior of a simple

neural net or even regression equation also manifests learning of the system's own, but the goals and

intelligence of tne designers.

Carnivores like dogs and cats, and even more so primates, seem to have goals of their own

beyond the goals given by instinct. Do our current autonomous constructed systems have goals of their

own in this sense?

Does a gorilla or chimpanzee, especially one who has learned to use language or at least a

language-like systems, have intelligence of its own?

More to the point, what does the question mean? Is it the same question as when we ask it

about a constructed system?

Coming from another direction, a newborn infant's brain is physically immature as well as having

vast amounts of learning ahead of it. Very young babies already manifest rapid learning of their own,

overshadowing billions of years of genomic learning. But a newborn baby does not manifest intelligence

of his or her own, while a five year old certainly does. If we watch the emergence of intelligence in

children and collectively introspect about when we want to use the word "intelligent" we amy learn things

useful in answering the same questions about constructed systems.

As an example, does the intelligence of a little child emerge as a unified phenomenon or do

some "kinds" of intelligence emerge before others? Does the "vector of Intelligence" emerge in concert,

or one element at a time?

I hope to break loose some time to review current comparative and developmental psychology

looking for slues we can carry over into understanding constructed systems.

Tom Whalen

July 3, 2000

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Answer by the Moderator:

Tom,

It is my deep conviction that your questions, both legitimate and interesting, CAN be

addressed and answered within the formahsms that we use in the muhiresolutional nested

hierarchical planning/ control systems, when we introduce learning phenomena,

(see URLs: http://www.ee.umd.edu/medlab/papers/Final/Final.html

which serves to enable a structure similar to

http://www.ee.umd.edu/medlab/papers/trep/trep.html

or ask me about published references)

It is true, presently we do not build intelligent systems that have instincts enabling them

to get involved in efficient learning. Living creatures have an interesting distinction from

intelligent constructed creatures. Our robots are concerned with survival of

themselves

their team

their master

an assigned object.

Living creatures are concerned in addition with survival of their species, and we even

don't know how exactly this thing is implanted within their architectures. My robot will defend,

or bring information to his own team, to me and to an assigned subject because this is what I

have implanted explicitly into its architecture, or its knowledge base.

Some people are saying: We can implant the ability for evolutionary development of the

specie of my robot. We just cannot wait a couple of billions years to see how it will develop.

They are right. We will have a problem of funding if we propose a type of research that should

be even 100 years short.

Clearly, analyzing goals implanted into genome within the constructed unmanned

robots, seems to be impractical. It is much easier to manipulate with its software at the stage of

design. Thus, I cannot wait until he develops a feature, or a goal: I must predict what I want and

prescribe both the goal and the feature.

Saying this, I do not feel sadness. I feel joy.

634
Maybe, because I was constructed to be an engineer.

Moderator

ARCHITECTURES OF INFORMATION EMPLOYED BY INTELLIGENT

SYSTEMS

C. Landauer responds to A. Wild s Questions

C. L. We have been studying exactly the kinds of questions that Andreas Wild suggested, and

we have answers for some of them and approaches for others.

A.Wild's questions:

1- How could an information system be architected such that heterogeneous elements, like

different types of computation (reasoning ?), may coexist, interact and add value to each other ? What

would be the interfaces between sub-domains looking like?

2- Can such a system evolve by including domains that became relevant after the system was

built, or by modifying or eliminating some of the domains implemented at its "birth" ?

3- Is there a way for a system to control interfaces among its own sub-systems, e.g. define new

ones, eliminate or modify existing ones ? Can a system re-architect itself ?

4- Can this happen across hierarchical boundaries without generating unbearable chaos ?

5- Is a non-hierarchical, self-configuring, heterogeneous system at all possible ? If yes, are there

any rules to follow, are there impossible situations to avoid, or, alternatively, anything goes, and the

solutions will be selected by thai and error ?

C. L. These are our answers / approaches / expectations / hopes:

1 -We have been writing about Constructed Complex Systems for some time now,

providing them with a Knowledge-Based infrastructure that supports exactly this kind of

heterogeneity, and a further property called Computational Reflection, which means that the

^ K. Bellman and C. Landauer

635
system has a complete model of its own behavior (internal and external), to some level of detail,

so it can examine and change its own functions and behavior .

2-We have added domains that only became relevant after the fact, since the system can

defer until run time its search for relevant resources to apply to a problem (including the

problem of interpretation of the problem statements in a language not defined until later in the

system lifetime) - the system has no privileged resources at all, so anything can be changed, on

the fly — this is partly achieved by explicit and uniform separation of the problems posed during

system operation and the resources used to address those problems (the Problem Posing

Programming Paradigm), and flexible mappings from problems to resources (Knowledge-Based

Polymorphism), that together lead to a new approach to Generic Programming^.

3 -We have an approach to the creation and management of internal interfaces in a

system, based on a new knowledge representation structure we called a conceptual category ,

which separates the purpose of an interface from its appearence in code (we have found the term

used much earlier for something different, but we intend to keep it for this data structure

anyway, since it is the right term for what we are trying to model)^. We are even trying to

arrange that a system can change its own basic symbol systems, since we have shown that it

must, if it is to persist for an extended time in a complex environment^.

4-We think so, but have not proven it

5 - We think so and are trying to prove it - the key here is that not all of the computations

can be in the application domain - many of them have to be in the "organization of the

computing system" domain, that is, much more internal infrastructure needs to be available than

How this works with autonomous computing systems is discussed in the following paper: C. Landauer, K. L. Bellman, "Computational

Embodiment: Constructing Autonomous Software Systems", pp. 131-168 in Cybernetics and Systems: An International Journal, Volume 30, No.

2 (1999)

4 This architecture is described in C. Landauer, K. L. Bellman, "Problem Posing Interpretation of Programming Languages", Paper eteccO? in Proceedings of HICSS'99; the 32nd Hawaii Conference on System Sciences, Track 111: Emerging Technologies, Engineering Complex

Computing Systems Mini-Track, 5-8 January 1999, Maui, Hawaii (1999); C. Landauer, K. L. Bellman, "Generic Programming, Partial Evaluation, and a New Programming Paradigm", ibid.. Track III: Emerging Technologies, Software Process Improvement Mini-Trackibid;

revised and extended as C. Landauer, K. L. Bellman, "Generic Programming, Partial Evaluation, and a New Programming Paradigm", Chapter 8,

pp. 108-154 in G. McGuire (ed.). Software Process Improvement, Idea Group Publishing (1999)

^ The approach is partly described in C. Landauer, "Conceptual Categories as Knowledge Structures", pp. 44-49 in A. M. Meystel (ed.). Proceedings of ISAS'97: The 1997 International Conference on Intelligent Systems and Semiotics: A Learning Perspective, 22-25 September

1997, NIST, Gaithersburg, Maryland (1997)

^ The proof and discussion are in C. Landauer, K. L. Bellman, "Situation Assessment via Computational Semiotics", pp. 712-717 in Proceedings

of ISAS'98: the 1998 International MultiDiscipIinary Conference on Intelligent Systems and Semiotics, 14-17 September 1998, NIST,

Gaithersburg, Maryland (1998)

636
in most systems, and it needs to be much more capable and knowledgable than in most systems

(it is clear from AW's description that this was at least part of the problem for the example)^.

We think we are making progress on all of these fronts, though more slowly than we

would like.

Christopher Landauer

July 4, 2000

LARRY REEKER SHARES HIS THOUGHTS:

1. We Measure In More Than Numbers Alone

Though numbers are very useful, other entities are always involved in measurement, in

various ways. The most obvious entities that are involved are linguistic ones. Words are used

to indicate the dimensions of the measurement (mass, time, etc.) and the units used (kg., lb.,

sec, etc.). But there are other linguistic means by which caveats on the measurements are

made, and these may be both numerical and non-numerical.

Numbers were invented for purposes of measurement, either of size (cardinal numbers)

or of sequence (ordinal numbers). They were invented because they can provide a succinct,

precise means of expressing size or sequence, and that is why people like them and have faith in

them. But that can be a disadvantage, and qualifications are therefore necessary. One type of

qualification has to do with precision or possible error, and other numbers can be used to

measure such a qualification.

Additionally, there are descriptions of the data on which measurements are made. In

information storage and retrieval. Recall and Precision are measures made numerically, but the

text corpus over which the measurements are taken is important in interpreting these numbers.

Similarly, the terrain over which a robot's navigation abilities are measured has a lot to do with

the relevance of the evaluation to particular applications.

An overview of the approach can be found in C. Landauer, K. L. Bellman, "Architectures for Embodied Intelligence", pp. 21 5-220 in Proceedings of ANNIE'99: 1999 Artificial Neural Nets and industrial Engineering, Special Track on Bizarre Systems, 7-10 November 1999, St. Louis, Missouri (1999)

637
Despite qualifications, there is often a tendency to misuse numbers just because they are

so handy and seemingly clear, whereas the qualifications are tedious and boring, often a little

like reading an insurance policy or a legal contract. Thus one can be tempted to think that there

are "lies, damned lies, and measurements" (to rephrase a popular adage about statistics).

I guess all of that is pretty obvious. It is also obvious that there are in principle ways to

measure that are not numerical, as long as they involve lattice relations. One can define such a

relation on a vector of numbers, and thereby rank the vectors according to some criterion. One

is just ordinary normalization, but one can use weightings, too. For a particular application, a

system evaluation vector could be multiplied by a vector that characterizes the needs of the

system.

2. Lord Kelvin (A Historical Digression)

I have always had some reservations about Kelvin's famous statement on measurement,

since really numerical measurements are neither necessary or sufficient for a scientific theory.

Even as he wrote or uttered his famous statement,

"I often say when you can measure what you are speaking about and express it in

numbers, you know something about it, but when you cannot measure it, when you cannot

express it in numbers, your knowledge of it is of a meagre and unsatisfactory kind...", he had to

know that numbers are not a sine qua non for measurement.

He knew, of course, that line congruence was the equivalent of numerical comparison in

Euclid's geometry, but not expressed in numbers, so that one could clearly find ways to express

significant facts about significant domains without numbers. (Despite the breadth across which

his brilliance was spread, he was first a mathematician, and the son of a mathematician.) His

aphoristic claim that is sometimes cited, "To measure is to know", is probably a better

statement of his real feelings, for that reason. But Kelvin knew how to say things that would be

remembered (no wonder that he eventually became a dean!).

Kelvin also said "If you can not measure it, you can not improve it", which is certainly

true in the wider sense that the Workshop on Performance Metrics in Intelligent Systems is

about, even if we take as the measurement device contests, or even human judgments of

gradation. It would be very difficult to define "improve" in a sense that is not circular, without

638
expressing some measurement concept. "To enhance in value or quality" is a common

definition, and value and quality refer to measures, even if subjective.

It is unlikely, however, that Kelvin would have accepted any measures that were overly

subjective. He might well have asked for more than Turing's "imitation game", test if he had

been alive later. If we look at the context of the statement above, we see some expression of

qualifications that are also revealing:

"In physical science the first essential step in the direction of learning any subject is to

find principles of numerical reckoning and practicable methods for measuring some quality

connected with it. [Here follows, "I often say that", as cited above.] but you have scarcely in

your thoughts advanced to the state of Science, whatever the matter may be."

Here too, I would guess (for the reasons mentioned above) that "numerical" means

"mathematical" in a wider sense than just numbers.

I would argue that in the last part of the quotation, Kelvin is wrong, but that he was

making a valid point within the prevailing Anglo-American view of science in his time.

Though Kelvin had been bom almost 200 years after the death of Francis Bacon, he still lived

in an era where the predominant view of science was due to Bacon, with contributions of Hume

and other philosophers. Charles Darwin, working in roughly the same time frame that Kelvin

was growing up, tells us that he (Darwin) "worked on true Baconian principles, and without any

theory collected facts on a wholesale scale"

This collection of facts "on a wholesale scale" often lacked any more than subjective

observation, and Kelvin's notion that measurement was important was potentially, in that

framework, an important way of improving the inference of causal laws and disentangling

phenomena that are only superficially related. But (to return to his contemporary Darwin) Lord

Kelvin should certainly have recognized the contribution of the leap from collections (no matter

how large) of phenomena to models (of which Darwin's "theory of evolution" was a model that

advanced science materially). Had he done so, he would not have declared that such

contributions (others of which he himself had made, as well as Darwin and others of the period)

"scarcely advanced to the state of Science..."

639
3. Back to Measurement (After A Short Philosophical Digression)

Today, our view of scientific theory has changed from that held in the 19th Century.

The bare-bones version of a scientific theory is that it consists of a model composed of abstract

theoretical constructs and a calculus that manipulates these constructs in a way that can account

for observations and accurately predict the value of experiments. The theoretical constructs

have a relation with observed entities, properties and processes that may be quite abstract, not

necessarily readily available to human senses, but following directly from calculations based on

the theory. There are a number of principles applied to a model that give us increased

confidence in the theory, but the one most relevant here is that we can measure the observed

entities to confirm the predictions of the theories.

It is relevant to observe that the "calculus" mentioned above is used in the dictionary

sense "a method of computation or calculation in a special notation (as of logic or symbolic

logic)". That means that it may be numerical or non-numerical. In fact, as Herb Simon and

Allen Newell pointed out so strongly almost a half century ago, that calculus might be

expressed in the notation of a computer program, the better to speed its manipulation of the

theoretical constructs.

With respect to the field of Artificial Intelligence, the point has sometimes been made

that the value of individual research results is difficult to confirm, and this has been used to cast

aspersions on the entire AI enterprise. It is not uncommon for this criticism to be made, at least

implicitly, by people whose own knowledge of AI is "of a meagre sort", and then one suspects

that it is motivated by feelings of the sort that Herb Simon was describing when he wrote:

"I continue to marvel at the fact that, after 45 years, the naysayers can still be taken

seriously, when they deny that computers (sometimes) think, or place that happy possibility in

the distant future. I am afraid that at the outset of our adventure I greatly underestimated the

emotional need many members of our species have to believe in its uniqueness... Patience! All

that will pass. (Herbert Simon, E-mail, 26 Jun 1999).

On the other hand, there are well-informed AI critics whose views often reflect the fact

that systems have been described qualitatively in ways that cannot be backed up by objective

evaluation. There have indeed been system developers with simplistic ideas about scientific

theory, suggesting that their computational models were theories of actual human cognition

merely on the basis of surface resemblance. These suggestions have also been of concern to the

640
majority of scientists in the AI and Cognitive Science Communities, who have been in search of

solid theoretical concepts to underpin the field. The issue here is not necessarily the fact that

the measures are qualitative, however. It is the fact that they are not meaningful as part of a

scientific theory. They are therefore vague in a way that Kelvin would not have admired; but

more importantly, they cannot as easily lead to improvements, as Kelvin so aptly observed.

4. Some Implications for Intelligent Systems Metrics

Perhaps (and Lord Kelvin would like this idea, I hope) our emphasis on finding metrics

can solidify the theoretical constructs of the field, as well as providing a means of measuring

progress.

The key to doing this is not to think of evaluation only as measurement of some

benchmarks or physical parameters (which I will call "behaviors") that are manifested in the

operation of the systems being evaluated. We need to think in terms of the inner workings of the

systems and how the parameters within them relate to the measured externally manifested

behaviors.

Consider the measures of Recall and Precision as an example. Given a particular text

corpus, one can consider various weighting schemes, use of a thesaurus, use of grammatical

parsing that seeks to label the corpus as to parts of speech, etc., within a system and see how

these items (I realize they are more resources than theoretical constructs) relate to precision and

recall in the context of a particular corpus, or of a corpus with particular characteristics (these

might be theoretical constructs). I believe this sort of thing has been done, but it is not a field

that I have followed recently.

It may be more interesting in the Workshop to consider component systems for things

like reinforcement learning (RL). There are a number of different techniques within the RL, all

of which have many possible applications. The concepts include the states chosen, the

reinforcement function, and the policy. The area is becoming quite sophisticated, and there are

known facts about the relation of these to outcomes in particular cases.

Suppose that a reinforcement learning system constitutes a part of the intelligence of an

intelligent system. There should be some way of predicting how that system would do upon

encountering problems of a certain nature. By knowing how it chooses the concepts in its

system and how they react on problems of that type, one can provide a partial evaluation of how

641
effective the learning system would be. By obtaining such figures for all such subsystems, one

could relate them to the performance of the full intelligent system.

I am working on a general framework of this sort, and hope to discuss it further at the

Workshop. I hope that we will eventually be able use such a framework systematically relate

measures, whether numerical, non-numerical, or a combination thereof, at all levels of the

system, from internal capabilities to external performance, to, as Lord Kelvin might say,

"advance to the state of Science".

642
APPENDIX B

WORKSHOP SCHEDULE

643

Performance Metrics for Intelligent Systems

Workshop Schedule

General Chair — Elena Messina

Program Chair — Alex Meystel

August 14 - 16

The Workshop opens in Lecture Room A, BIdg. 101

Afternoon Plenary Lecture will be conducted at Green Auditorium

N 1ST

Gaithersburg, MD

2000

645

Advisory Board

• G. Adomi, University of Parma, Italy

• J. Albus, NIST, USA

• P. Antsalciis, University of Notre Dame, USA

• M. Asada, Osaka University, Japan

• G. A. Bei<ey, University of Southern California, USA

• K. Bellman, Aerospace Integration Science Corp., USA

• J. G. Blitch, DARPA, USA

• H.-H. Bothe, Technical University of Denmark, Denmark

• B. Chandrasekaran, Ohio State University, USA

• J. Chemiavsky, NSF, USA

• M. Cotsaftis, LTME/ECE, France

• F. Darema, NSF, USA

• P. Dario, Scuola Superiore, Italy • P. Davis, RAND Graduate School., USA

• G. Doeben-Henish, Knowbotic Systems, Germany

• J. Fetzer, University of Minnesota, USA

• D. Filev, Ford, USA

• R. Finkelstein, Robotic Technology, Inc., USA

• D. Fogel, Natural Selection, Inc., USA

• N. Foo, University of New South Wales, Australia

• W. Freeman, University of California at Berkeley, USA

• E. Fromm, Drexel University, USA

• T. Fukuda, University of Nagoya, Japan

• R. Gamer, Loebner Prize Winner for 1 998 and 1 999, USA

• G. Gerhart, US Army TACOM, USA

• E. Grant, CRIM, North Carolina State University, USA

• S. Grossberg, Boston University, USA

• R. Gudwin, State Univerity of Campinas, Brazil

• W. Hamel, University of Tennessee, USA

• W. Hargrove, Oak Ridge National Laboratory, USA

• E. Horvitz, Microsoft Research, USA

• M. Jabri, University of Sydney, Australia

• D. Jaron, Drexel University, USA

• R. Jordan, Lockheed Martin, USA

• C. Joslyn, Los Alamos National Laboratory, USA

• S. Kak, Louisiana State University, USA

• H. Kitano, Sony Computer Science Labs, Japan

• K. Kreutz-Delgado, University of California at San Diego

• F. Kurfess, Concordia University, Canada

• J. E. Laird, University of Michigan, USA

• C. Landauer, Aerospace Integration Science Corp., USA

• S. Lee, Samsung Advanced Inst, of Technology, Korea

• C. S. George Lee, Purdue University, USA

• D. Lenat, Cycorp, USA

• P. B. Luh, University of Connecticut, USA

• B. Mirkin, Birkbeck College, GB

• R. Morris, NIST, USA

• T. Parisini, Politecnico di Milano, Italy • K. Passino, Ohio State University , USA

• L. Perlovsky, Innoverity.Inc, USA

• L. Pouchard, Oak Ridge National Lab, USA

• J. Pustejovsky, Brandeis University, USA

• L. Reeker, NIST. USA

• D. Repperger, AFRL/HECP, USA

• E. H. Ruspini, SRI International, USA

• T. Samad, Honeywell, USA

• A. Sanderson, NSF, USA

• R. Sanz, University of Madrid, Spain

• A. Schultz, Naval Research Laboratory, USA

647
• T. Shih, Tamkang University, Taiwan

• R. Simmons, Carnegie-Mellon, USA

• M. Swinson, DARPA, USA

• M. Tilden, Los Alamos National Lab., USA

• 1. B. Turksen, University of Toronto, Canada

• C. Weisbin, NASA, USA

• T. Whaien, Georgia State University, USA

• A. Wild, Motorola, USA

• V. Winter, SANDIA, USA

• J. Xiao, NSF, USA

• R. Yager, lona College, USA

• A. Yavnai, RAFAEL, Israel • Y. Ye, IBM T. J. Watson Research Center, USA

• B. Zeigler, University of Arizona, USA

• L. Zadeh, University of California at Berkeley, USA

The Schedule of a Session

• Each Session is allotted 2 hours.

• It is expected that a speaker will use a slot of 25 minutes for his/her

presentation (20 minutes) and answering questions (5 minutes).

• The remainder of time (20 minutes) should be used for a general

discussion and combining the Final Recommendations of the Session.

• The Final Recommendations of all sessions will be integrated into

Final Recommendations ofthe Workshop.

• The results of Each Day are discussed at the evening Plenary Discussion

648
V' Day, Monday, August the 14'

First Day starts at 8.30 AM with introductory presentation:

J. Evans, E. Messina, Performance Metrics for Intelligent Systems

PLENARY LECTURE — 9 AM — 10 AM

H. Szu, Machine IQ with Stable Cybernetic Learning With and Without a Teacher

Coffee Break: 10 AM-10.30 AM

Sessions: 10.30 AM

^

12.30 PM

I Day, morning A: Features of Industrial intelligent Systems,

Co-Chairs: M. Cotsaftis, W. H. VerDuin

• M. W. Bailey, W. H. VerDuin, PIPER: An Intelligent System for the Optimal Design of Highly

Engineered Products

• S. A. Wallace, J. E. Laird, K. J. Coulter, Examining the Resource Requirements of Artificial

Intelligence Architectures

• C. Peterson, A Metric for Monitoring and Retaining Flight Software performance

• M. Cotsaftis, On Definition of Task Oriented System Intelligence

I Day, Morning B: Features of Living Intelligent Systems

Co-Chairs: K. Bellman, C. Joslyn

• K.. Bellman, Understanding and Its Behavioral Correlates

• C. Joslyn, Toward Measures of Intelligence Based On Semiotic Control

• H. Sarjoughian, B. Zeigler, Model-based Design and Measurement of Intelligence

• T. Chmielewski, P. Kalata, Biometric Techniques: The Fundamentals of Evaluation

i Day, Morning C: Special Issues of Evaluating Intelligence

Co-Chairs: R. Sanz, A. Wild

• R. Sanz, 1. Lopez, Minds, MIPs, and Structural Feedback

• A. Wild, Using the Metaphor of Intelligence

• R. Gamer, R. N. Bishop, Applied Applications for Mimetic Synthesis: The AAMS Project Summary

• H. M. Hubey, General Scientific Premises of Measuring Complex Phenomena

Lunch 12.30 PM — 2 PM

649
PLENARY LECTURE — 2 PIVI-3 PM

G. Saridis, Definition and Measurement of Machine Intelligence

Coffee Break: 3 PM-3.15 PM

Sessions: 3.15 PM — 5.15 PM

I Day, Afternoon A: Metrics and Comparison of Alternatives: General Issues

Co-Chairs: L. Pouchard, W. C. Stirling

• L. Pouchard, Metrics for Intelligence: the Perspective from Software Agents

• J. Spall, et al. Towards an Objective Comparison of Stochastic Optimization Approaches

• W. C. Stirling, R. L. Frost, Intelligence with Attitude

• S. Lee, W.-C. Bang, and Z. Z. Bien, Measure of System Intelligence: An Engineering Perspective

I Day, Afternoon B: Metrics and Comparison of Alternatives: Case Studies

Co-Chairs: R. Finkelstein, E. Grant

• E. Grant, G. Lee, Properties of Learning Knowledge Based Controllers

• V. Grishin, A. Meystel, Using Visualisation for Measuring Intelligence of Constructed Systems

• R. Finkelstein, A Method for Evaluating the IQ of Intelligent Systems

• L. Polyakov, In Defense of the Additive Form for Evaluating Vectors

Plenary Discussion- 5.15 PM — 6.15 PM

Panel: K. Bellman, M. Cotsaftis, R. Finkelstein, E. Grant, C. Joslyn, C. Peterson,

L. Pouchard, W. C. Stirling, A. Wild

8 PM — Meeting of the Advisory Board (at "Holiday inn")

650
2"° Day, Tuesday, August the 15

PLENARY LECTURE — 9 AM-IO AM

J. Albus, Features of Intelligence Required in Unmanned Autonomous Vehicles

Coffee Break: 10 AM-10.30 AM

Sessions: 10.30 AM — 12.30 PM

II Day, Morning A: Measuring performance

Co-ChaIrs: A. Sanderson, T. Samad

• T. Samad, Technologies for Engineering Autonomy and Intelligence

• A. Sanderson, Minimal Representation Size Metrics for Intelligent Robotic Systems

• J. Zhang, A Formal Method to the Performance Metrics for Engineering Systems

• R. Yager, A Hierarchical Framework for Constructing Intelligent Systems Metrics

II Day, Morning B: Modeling and Measuring Macliine Intelligence

Co-Chairs: P. Davis, T. Whalen

• P. Davis, Exploratory Analysis Enabled by Multiresolution, Multiperspective Modeling

• M. Jabri, Measuring intelligence: a neuromorphic perspective

• I. Nourbakhsh , Two measures for measuring the 'intelligence' of human-interactive

robots in contests and in the real worid: perceptiveness and expressiveness

• T. Whalen, What is the Value of Intelligence and How Can It Be Measured?

II Day, Morning C: Evaluating Factors of Intelligence in Systems

Co-Chairs: J. Hernandes-Orallo, C. Peterson

• J. Hernandes-Orallo, On the Computational Measurement of Intelligence Factors

• A. Wild, Heterogeneous Computing

• J. Bryson, et al, Hypothesis Testing for Complex Agents

• T. Balch, Hierarchic Social Entropy: An Information Theoretic Measure of Robot Group Diversity

Lunch 12.30 PM — 2 PM

PLENARY LECTURE — 2 PM —3 PM

S. Grossberg, Some Constraints on Intelligent Systems:

Autonomous Computation in a Changing World

Coffee Break — 3 PM — 3.15 PM

651
Sessions: 3.15 PM — 5.15 PM

II Day, Afternoon A: Measuring of Intelligence of Multiagent Networks

Chair and Organizer: S. Phoha

• R. R. Brooks, STIGMERGY: A measure of intelligence for emergent distributed behaviors

• S. Phoha, D. Friedlander, Goodness of Fit Measures for Intelligent Behaviors of Interacting Machines

• M. E. Cleary, M. Abramson, M. B. Adams, S. Kolitz. Metrics for Embedded Collaborative Intelligent

Systems

• D. Friedlander, S. Phoha, A. Ray, Domain Independent Measures of Intelligent Control

• S. Perraju Tolety, G. Uma, On Measuring Intelligence in Multi-Agent Systems

II Day, Afternoon B: Evaluating Intelligent Systems by Testing and Competition:

Benchmarks

Co-Chairs and Organizers: A. Schultz, R. Murphy

• A. Schultz, Evolution of Metrics for Mobile Robots

• A. Jacoff, E. Messina, J. Evans, A Standard Test Course for Urban Search and Rescue Robots

• R. Murphy, J. Casper, M. Micire, J. Hyams, Assessment of the NIST Standard Test Bed for Urban

Search and Rescue Competitions

• T. Balch, Performance/N is the Wrong Metric for Multirobot Teams

• S. K. Agrawal, A. M. Ferreira, S. Pledgie, Performance Evaluation of Robotic Systems: A Proposal for a Benchmark problem

II Day, Afternoon C: Measuring Intelligence of Distributed Systems

Co-Chairs: R. Fakory, W. J. Davis

• W. J. Davis, Evaluating Performance of Distributed Intelligent Control System

• R. Fakory, M. Jahangiri, Real Time Distributed Expert System for Automated Monitoring of Key

Monitors in Hubble Space Telescope

• X. Qin, A. E. Aktan, Distributed Internet-Based Multi-Agent Intelligent Infrastructure System

• D. P. Gravel, W. S. Newman, Flexible Robotic Assembly

Plenary Discussion- 5.15 PM — 6.15 PM

Panel: T. Balch, P. Davis, W. J. Davis, R. Fakory, J. Hernandes-Orallo, R. Murphy, S. Phoha,

T. Samad, A. Sanderson, A. Schultz, T. Whalen

Evening: COCKTAILS AND BANQUET

— 6.45 PM at "Holiday Inn"

L. Zadeh,

Banquet speech "The Search for Metrics of Intelligence — A Critical View."

652
3 " Day, Wednesday, August the 16

PLENARY LECTURE — 9 AM — 10 AM

W. Freeman, The neurodynamics of intentionality in animal brains provides a basis

for constructing devices that are capable of intelligent behavior

Coffee Break: 10 AM-10.30 AM

Sessions: 10.30 AM — 12.30 PM

III Day, Morning A: Measuring Intelligence Taking in Account Linguistic, Psychological and

Biological Factors

Co-Chairs: L. Reeker, A. Meystel

• L. Reeker, Theoretical Constructs for Measurement Performance and Intelligence

• A. Meystel, Generalizing Natural Language Representations for Measuring the Intelligence of Systems

• P. Wang, Machine Intelligence Ranking

• A. Treister-Goren, J. Dunietz, The AI Language Development Metric

III Day, Morning B: Measuring Intelligence of Systems with Autonomy and Mobility

Co-Chairs: G. S. Sukhatme, J. Weng

• G. S. Sukhatme, Measuring Mobile Robots Performance: Approaches and Pitfalls • L. E. Parker, Evaluating Success in Autonomous Multi-robot Teams: Experience of ALLIANCE

Architectures Implementation

• A. Lacaze, S. Balakirsky, Search Graph Formation for Minimizing the Complexity of Planning

• J. Weng, Automatic Mental Development and Performance Metrics for Intelligent Systems

Lunch 12.30 PM — 2 PM

PLENARY LECTURE — 2 PM —3 PM

A. Meystel , Evolution of Intelligent Systems Architectures:

What Should Be Measured

653
Coffee Break — 3 PM — 3.15 PM

Afternoon Session — 3.15 PM — 5.15 PM

III Day, Afternoon (Plenary Panel): Perspectives of Governmental Programs on Measuring

Intelligence

Panel organizers — J. Albus, JBIitch, J. Evans

• J. Albus, NIST

• J. Blitch, DARPA

• J. Evans, NIST

• C. Shoemaker ARL,

• C. Weisbin, NASA

General Discussion of the Workshop Results- 5.15 PM — 6.15 PM

Panel: J. Albus, J. Evans, E. Messina, A. Meystel, L. Reeker, G. S. Sukhatme, J. Weng

The Meeting is adjourned 6.15 PM

654
APPENDIX C

AUTHOR INDEX
I
A

Abramson, M 295

Adams, M.B 295

Agrawal, S.K 271

Aktan, A.E 172

Albus, JS 243

B

Bailey, M.W 467

Balakirsky, S 289

Balch, T 511

Bang, W.-C 112

Barrish, R 172

Brassart, E 273

Bellman, K 484

Bianco, B 343

Bien, Z.Z 112

Brooks, R.R 186

Bryson, J 233

C

Casper, J 260

Catbas, F.N 172

Chmielewski, T.A 335

Cleary, M.E 295

Clerentin, A 273

Cotsaftis, M 80

Coulter, K. J 399

D

Davis, P.K 431, 505

Davis, W.J 225

DeLahoche, L 273

Duchscherer, H 211

E

Evans, J.M 101,253

F

Fakory, R 218

Ferreira, A.M 271

Finkelstein, R 145

Freeman, W.J 314

Friedlander, D 167,453

Frost, R.L 60

G

Gamer, R.G 95

Grant, E 392

Gravel, D.P 412

Grimmelsman, K 1 72

Grishin, V 533

Grossberg, S 311

H

Henderson, S.B 95

Hemandez-Orallo, J 72

Hexmoor, H 211

Hill, S.D 478

Hubey, H.M 524

Hutchens, J 519

Hyams, J 60

I

J

Jabri, M.A 323

Jacoff, A 253

Jahangiri, M 218

Joslyn, C 499

K

Kak, S 331

Kalata, P.R 335

Kolitz, S 295

Kulcu, E 172

L

Lacaze, A 289

Laird, J.E 399

Landauer, C 484

Lee, G 392

Lee, S 112

Lopez, 1 86

Lowe, W 233

M

Mackworth, A.K 118

Messina, E.R 101,253

Meystel, A 361,492, 533

Micire, M 260

657
Murphy, R ...260

N

Newman, W.S 412

Nourbakhsh, I.R 164

o

p

Parker, L.E 205

Pervizpour, M 1 72

Peterson, C 407

Phoha, S 167,453

Pledgie, S 271

Polyakov, L.M 142

Pouchard, L 123

Q

Qin, X 172

R

Ray, A 453

Reeker, L 49

S

Samad, T 43

Sanderson, A.C 127

Sanz, R 86

Saridis, G 441

Sarjoughian, H.S 90

Schultz, A.C ....267

Shih, T.K 302

Simmons, R 1 62

Spall, J.C 478

Stark, D.R 478

Stein, L.A ...233

Stirling, W.C 60

Sukhatme, G.S 281

Szu, H 383

T

Tolety, S.P 201

Treister-Goren, A 519

Tsotsos, J.K 459

Tzafestas, E.S 193

u

Uma, G 201

V

VerDuin, W.H 467

W

Wallace, S.A 399

Wang, P.P 160

Weng, J 349

Whalen, T 68

Wild, A 39,421

X

Y

Yager, R.R 423

Yavnai, A 135

Ye, Y 459

Z

Zadeh, L.A 105

Zeigler, B.P 90

Zhang, Y 118

658 tJtU.S. government printing OFFICE:200 1-472-068/55651
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characteristics of building elements and systems.

Technical Notes—Studies or reports which are complete in themselves but restrictive in their treatment of

a subject. Analogous to monographs but not so comprehensive in scope or definitive in treatment of the

subject area. Often serve as a vehicle for final reports of work performed at NIST under the sponsorship of

other government agencies.

Voluntary Product Standards—Developed under procedures published by the Department of Commerce

in Part 10, Title 15, of the Code of Federal Regulations. The standards establish nationally recognized

requirements for products, and provide all concerned interests with a basis for common understanding of

the characteristics of the products. NIST administers this program in support of the efforts of private-sector

standardizing organizations.

Order the following NIST publications—FIPS and NISTIRs—from the National Technical Information

Service, Springfield, VA 22161.

Federal Information Processing Standards Publications (FIPS PUB)—Publications in this series

collectively constitute the Federal Information Processing Standards Register. The Register serves as the

official source of information in the Federal Government regarding standards issued by NIST pursuant to the Federal Property and Administrative Services Act of 1949 as amended. Public Law 89-306 (79 Stat. 1127), and as implemented by Executive Order 11717 (38 FR 12315, dated May 11, 1973) and Part 6 of

Title 15 CFR (Code of Federal Regulations).

NIST Interagency or Internal Reports (NISTIR)—The series includes interim or final reports on work

performed by NIST for outside sponsors (both government and nongovernment). In general, initial distribution is handled by the sponsor; public distribution is handled by sales through the National Technical

Information Service, Springfield, VA 22161, in hard copy, electronic media, or microfiche form. NISTIR 's may also report results of NIST projects of transitory or limited interest, including those that will be

published subsequently in more comprehensive form.
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