Intelligent Systems Division - Research Areas - Research and Engineering of Intelligent Systems - Performance Metrics

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Research and Engineering of Intelligent Systems

Performance Metrics

Measuring the Performance and Intelligence of Systems: Proceedings of the

2002 PerMIS Workshop

The proceedings are available in PDF format. You can view each paper below. If you do not have Adobe Acrobat

Reader you can download it for free from Adobe.

NIST Special Publication 990

August 13-15, 2002

Edited by: E.R. Messina and A.M. Meystel

The Proceedings are available in PDF format. You can view each paper or the entire proceedings below. If you do not

have Adobe Acrobat Reader you can download it for free from Adobe.

Session 1M1- Performance Metrics (Chairs: S. Agrawal, H. Yanco)

R. Gao, L. Tsoukalas, Performance Metrics for Intelligent Systems: An Engineering Perspective

S. Ananthakrishnan, S. Agrawal, R. Venugopal, M. Demeri, RCS Based Hardware-in-the-loop

Intelligent System Design & Performance Measurement

H. Yanco, Evaluating the Performance of Assistive Robotic Systems

J. DeLeo, Measuring Classifier Intelligence

V. Stefanuk, Problems of Performance Measurement in Locally-Organized Systems

Session 1M2 - Performance of Multiple Agents (Chairs: E. Grant, M. Fields)

A. Shirkhodaie, A Control Scheme for Measure of Performance and Efficiency of Tactical

Cooperative Robots

A. Nelson, E. Grant, T. Henderson, Competitive Relative Performance Evaluation of Neural

Controllers for Competitive Game Playing with Teams of Real Mobile Robots

M. Fields, Representing Ground Robotic Systems in Battlefield Simulations

E. Dawidowicz, NICCI: A Multi-agent Cognitive Formation

Plenary Lecture 1-1

J. Albus, Metrics and Performance Measures for Intelligent Unmanned Ground Vehicles

Session 1P1 - Performance of Mobility Systems (Chairs: E. Messina, Y. Zhang)

S. Balakirsky, E. Messina, A Simulation Framework for Evaluating Mobile Robots

H. Scott, S. Szabo, Evaluating the Performance of a Vehicle Pose Measurement System

D. Dufourd, A. Dalgalarrondo, Performance Evaluation of Road Detection and Tracking

Algorithms

Y. Zhang, C. Eldershaw, M. Yim, K. Roufas, D. Duff, A Platform for Studying Locomotion Systems:

Modular Reconfigurable Robots

Session 1P2 - Performance of Planning Systems (Chairs: A. Meystel, W. Van Wezel)

A. Meystel, Performance of Planning Systems

I. Kacem, S. Hammadi, P. Borne, Lower Bounds for Evaluating Schedule Performance in Flexible

Job Shops

W. Van Wezel, R. Jorna, Performance Characteristics of Planning Actors

J. Maryak, D. Chin, Global Optimization via SPSA

General Discussion Panel 1 - What is the Role of Ontology in Performance Evaluation?

E. Dawidowicz, Ontology in Performance Evaluation
Intelligent Systems Division - Research Areas - Research and Engineering of Intelligent Systems - Performance Metrics

http://www.isd.mel.nist.gov/research_areas/research_engineering/Performance_Metrics/PerMIS_2002_Proceedings/index.htm[8/12/2009 11:59:50 AM]

M. Gruninger, Using Ontologies to Evaluate Knowledge-based Systems

L. Pouchard, Toward an Ontology of Performance

C. Schlenoff, What Role Can Ontologies Play to Improve the Performance of Intelligent

Systems?

L. Welsch, Role of Ontologies in Performance Evaluation

A. Meystel, Ontologies OF and FOR Intelligent Systems

Reception

M. Asada, RoboCup: Humanoids as a Testbed for Measuring Performance

Plenary Lecture 2-1

D. Fogel, Evolving Solutions that are Competitive with Humans

Session 2M1 - Uncertainty of Representation I (Chairs: C. Landauer, J. Gunderson)

J. Scholtz, Evaluation Methods for Human-System Performance of Intelligent Systems

R. Cottam, W. Ranson, R. Vounckx, Lifelike Robotic Collaboration Requires Lifelike Information

Integration

J. Gunderson, L. Gunderson, Integrating Effective Planning Horizons into an Intelligent Systems

Architecture

A. Lytle, K. Saidi, W. Stone, M. Shneier, Mobile Robot Pose Tracking for Performance Analysis

A. Clerentin, L. Delahoche, E. Brassart, C. Cauchois, An Uncertainty Propagation Architecture for the

Localization Problem

Session 2M2- Performance of Robots in Hazardous Domains (Chairs: A. Jacoff, C. Norman)

T. Frost, C. Norman, S. Pratt, B. Yamauchi, B. McBride, G. Peri, Derived Performance Metrics and

Measurements Compared to Field Experience for the PackBot

D. Bruemmer, J. Marble, D. Dudenhoeffer, M. Anderson, M. McKay, Intelligent Robots for Use in

Hazardous DOE Environments

Plenary Lecture 2-2

W. Freeman, On Communicating with Semantic Machines

Session 2P1 - Modeling Intelligence (Chairs: L. Arata, S. Wallace)

L. Arata, Modeling Interactive Intelligences

L. Polyakov, Structured Approach to the Intelligent System Design

L. Perlovsky, Semiotic Fundamentals of Information Processing in Human Brain

S. Wallace, J. Laird, Intelligence and Behavioral Boundaries

Session 2P2 - Modeling of Mind (Chairs: S. Adams, R. Cottam)

N. Alvarado, S. Adams, S. Burbeck, C. Latta, Beyond the Turing Test: Performance Metrics for

Evaluating a Computer Simulation of the Human Mind

R. Schrag, M. Pool, V. Chaudhri, R. Kahlert, J. Powers, P. Cohen, J. Fitzgerald, S. Mishra,

Experimental Evaluation of Subject Matter Expert-oriented Knowledge Base Authoring Tools

N. Cassimatis, G. Trafton, A. Schultz, M. Bugajska, W. Adams, A Task Domain for Combining and

Evaluating Robotics and Cognitive Modeling Techniques

R. Sanz, A. Meystel, Modelling, Self and Consciousness: Further Perspectives of AI Research

General Discussion Panel 2 - Informantion Access Technology: Does it Concern Mechanisms of

Mind and Intelligence?

M. Herman, Information Access Technology: Does it Require Getting Involved in Mechanisms of

Mind and Intelligence?

J. Cugini, Information Access: Do You Mind?

J. Garofolo, Using Speech Technologies for Information Access: Does it Require Getting Involved

in Mechanisms of Mind and Intelligence?

Banquet Speech

L. Zadeh, In Quest of Performance Metrics for Intelligent Systems - A Challenge that Cannot be

Met with Existing Methods

Plenary Lecture 3-1
Intelligent Systems Division - Research Areas - Research and Engineering of Intelligent Systems - Performance Metrics

http://www.isd.mel.nist.gov/research_areas/research_engineering/Performance_Metrics/PerMIS_2002_Proceedings/index.htm[8/12/2009 11:59:50 AM]

J. Blitch, Robot Intelligence for Tunneling and Confined Space Search and Rescue

Session 3M1 - Measuring Intelligence (Chairs: J. Horst, C. Schlenoff)

B. Clough, Metrics, Schmetrics! How the Heck do you Determine a UAV's Autonomy Anyway?

C. Schlenoff, L. Welsch, R. Madhavan, N. Zimmerman, Towards Measuring the Performance of

Architectural Components of Autonomous Vehicular Systems

J. Horst, A Native Intelligence Metric for Artificial Systems

G. Berg-Cross, Dimensions of Intelligent Systems

Session 3M2 - Grouping: A Core Procedure of Intelligence (Chairs: S. Ramaswamy, R. Bialczak)

L. Reeker, A. Jones, A New Classification of Information: A Step on the Road to Interpretability

C. Landauer, K. Bellman, Refactored Characteristics in Intelligent Computing Systems

S. Ramaswamy, R. Neelakantan, Software Design and Testing using Petri Nets: A Case Study

Using a Distributed Simulation Software System

R. Bialczak, J. Nida, B. Pettitt, M. Kalphat, Comparison Methodology for Robotic Operator Control Units

Plenary Lecture 3-2

X. Hu, B. Zeigler, An Integrated Modeling and Simulation Methodology for Intelligent Systems

Design and Testing (Supporting Material)

Session 3P1 - Uncertainty in Representation II (Chairs: D. Chin, R. Maarfi)

D. Chin, A. Biondo, Multiple Neural Network Model Interpolation

R. Maarfi, E.L. Brown, S. Ramaswamy, A Three-Tier Communication and Control Structure for

the Distributed Simulation of an Automated Highway System

A. Meystel, A. Bathija, Tessellating and Searching in Uncertain State Spaces

K. Bellman, Autonomy and Socialization

Session 3P2-Towards Universal Planning/Control Systems (Chairs: A. Meystel, P. Davis)

A. Meystel, A Sketch of Multiresolutional Decision Support Systems Theory

P. K, Davis, J. H. Bigelow, Motivated Metamodels

H. Sarjoughian, On the Role of Quality Attributes in Specifying Software/System Architecture for

Intelligent Systems

S. Orr, Uncertain Predictions of Flow and Transport in Random Porous Media: The Implications for

Process Planning and Control

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Phone: 301-975-3510

Fax: 301-990-9688

E-mail: elena.messina@nist.gov

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Research and Engineering of Intelligent Systems

Performance Metrics

Thousands of institutions are working on intelligent systems, but without a definition of what that means in a

quantitative sense. There currently is no way to measure performance, inter-compare research results, or even to

define measurable program objectives. Therefore the need is for quantitative metrics which can be used to assess

the performance of a system or of its sub-components.

We are collaborating with academia, industry, and other government agencies to define and develop measures for

intelligent or autonomous systems. A series of workshops, bringing together leading researchers from multiple

disciplines, was inaugurated in 2000. On another front, we are developing testbeds for autonomous mobile robots.

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Fax: 301-990-9688

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Performance Metrics for Intelligent Systems

An Engineering Perspective

Rong Gao and Lefteri H. Tsoukalas

Purdue University

School of Nuclear Engineering

Applied Intelligent Systems Laboratory

W. Lafayette, IN 47907-1290

gao@ecn.purdue.edu, tsoukala@ecn.purdue.edu

ABSTRACT

This paper provides a general discussion on performance

metrics for intelligent systems drawing largely on our

experience with engineering applications. The experience has

led us to view machine intelligence as a type of machinefacilitated human intelligence. This view implies that the

locus of machine intelligence is to be found in relations

amongst humans vis a vis the machine (in the subject-tosubject-via-object relation). Hence, quantitative metrics for

intelligence may be sought as functions of the humanmachine and machine-machine interface; evaluating them

may be achieved through a conventional behaviorist-type of

approach where a system is characterized by observing its

response to given inputs. Some guidelines for this process that

we found to be useful are discussed in the paper.

Keywords: Intelligence, Performance Metrics, Fuzzy Logic,

Neural Networks

1. INTRODUCTION

Since the onset of industrial revolution machines have been

developed to relieve humans from tedious work and reduce

the cost of producing goods and services. With the advent of

the digital computer, the industrial notion of machines as

artifacts capable of mechanical work has been greatly

expanded to include sophisticated capabilities of information

processing, decision-making, communications, sensing,

coordination and control. Somewhat naively but, as we will

argue latter on, quite accurately, we tend to refer to this postindustrial sophistication as “machine intelligence.”

Equipped with control and computing units, machines can be

made to be very predictable and quite reliable. The reliability

is exemplified by the fact that machines follow given

instructions literally regardless of changes in their

environment. This characteristic is undesirable in complex

applications. Predefined instructions are usually coded by

human experts and make it nearly impossible to take into

account every possible scenario that might happen in realworld applications. Therefore, it is desirable that machines

possess capabilities for appropriately handling cases that they

have not been directly instructed. Although there have been

numerous definitions of intelligence, engineers tend to think

that a machine or a system in general is intelligent if it is

capable of handling exceptions properly. In this respect the

engineering view is somewhat similar to that of the cognitive

scientists who look at various agnosias (for example, visual

agnosia or failure to recognize objects seen) as opportunities

and tools for understanding the complex inner-workings of

the human brain (Gazzaniga, 1998). But, the engineering aim

is quite different. Intelligent systems are transforming the

way we design, fabricate, operate and even dispose complex

engineering artifacts such as airplanes and power plants. A

Boeing 777 airliner, for example, may have in excess of nine

million distinct parts, while the number for an advanced

boiling water reactor is the 109 range. Intelligent systems are

necessary to make such systems safe, economical and

manageable at all times.

It should be noted that the adjective “intelligent” gradually

fades away from designating any system that becomes

routinely available and widely familiar. Intelligence is an

attribution reserved for systems that are at a more nascent

level of development and more likely not proven or

established technology. Building intelligent systems is a goal

that often appears to be quite elusive. Hence, having a metric

for intelligence, any metric, is useful not only for comparing

system A to system B, but also, for comparing system A at an

early age of development to system A at more mature level of

development. In this respect an index for intelligence is not

different from any metric of performance that can be

consistently applied to assess the growth of a system. This a

very important issue for systems such as nuclear power plants

or passenger airliners whose lifespan may be comparable or

exceeding the lifespan of their designers and operators.

We have observed in numerous engineering applications that

a major difficulty giving rise to the elusiveness of machine
intelligence is due to our deeply held notions and assumptions

about machines. Humans seem to be so overwhelmingly

prepared to think of machines as quite independent entities,

separate from us; distinct and also distinguished; sitting

outside the boundaries of human boundaries (physiologically,

cognitively, and socially); and yet so intimately ours.

Machines are always adjuncts to humans. Although we

describe them in terms of objective qualities (such as power,

mass, volume) their most important attributes are the ones

relating to their functionality and purpose (interfacial

characteristics). Intelligence is a functionality not an

objective property. Yet, and because of that, machine

intelligence involves what the psychologists call reification,

that is, something appears to exist just because we have

word(s) for it.

The view we espouse is that machines, including the

sophisticated and computationally savvy artifacts of today and

tomorrow, are accessories to human intelligence. They have

no intelligence of their own (to have that they would have to

live the lives of humans). Intelligent systems are machines

functioning as a medium for playing out the drama of human

intelligence; principally activities for asking and answering

questions, a kind of generalized dialogue amongst humans

(not anymore constrained to be physically present). Our view

defines intelligent systems as virtual interlocutors, that is as

systems that function in a way that makes it possible for

humans and other machines to ask and answer questions

unconstrained by personal presence or awareness. In this

sense, machines can be viewed as “intelligent” although we

all know that they could not possibly come about on their

own, without human volition, know-how, design and material

implementation.

Viewing intelligent machines as “virtual interlocutors,” raises

the question of language. What is the right idiom for the

man-machine discourse we are talking about? It has to be a

language that its ultimate aim is to facilitate a virtual dialogue

amongst humans and as such it is desirable to have the

computational characteristics of natural language. For this,

we have to turn to fuzzy logic. It is extremely difficult to

capture within any formal language the complex and rich

attributes of natural language including, but not limited to,

flexibility, semantic depth, computational economy

(parsimony), and portability. We strongly believe that fuzzy

logic is a highly promising tool; its potential is largely

uncapped, its full power is still to be harnessed.

Additional frustrations with intelligent machines are caused

by the lack of a bridge that links any interpretation of

intelligence (such as the one put forward in this paper) to the

implementation of intelligence. Definitions of intelligence do

not often provide useful information needed by engineers in

the realization process. Engineers would like to have

quantitative performance metrics that could be used to

measure the degree of intelligence of a system. Defining a

performance metric is, however, not easier than building an

intelligent system itself. Theoretically, provided a quantitative

performance metric is available, methods can be developed to

optimize the system’s performance to reach a threshold that

this particular system is deemed intelligent. We have gathered

plenty of experience in designing optimization algorithms and

they are being used in a variety of applications involving

neural networks and fuzzy logic (Tsoukalas, 1997). The

research on performance metrics for intelligent systems is an

important focus. In the following sections we present a

general discussion and some guidelines for designing

performance metrics.

2. INTELLIGENT SYSTEMS

Researchers of artificial intelligence have traditionally

defined intelligence as an inherent property of a machine. An

intelligent machine (or system) is viewed as one that has

some computational capacity to act like a human, that is,

“think” humanly, or “act” rationally, or “think” rationally

(Russel, 1985). Hence, computational metrics of intelligence

are traditionally expected to measure how well a machine

performs like a human, for example, like a chess master, or

like an expert diagnostician.

We believe that such thinking-like-human approaches trying

to mimic the way a mind operates are not technically feasible

with current engineering capabilities. First, the complexities

of human brain make simulation impossible using existing

technology. The latest Intel Pentium IV processor integrates

55 million transistors, much less than the 100 billion neurons

and 100 trillion synaptic junctions found in a person’s brain.

Second, the basic processing unit in a computer system, the

transistor, is identical throughout a processor and can only

handle two numbers, 0 and 1. On the other hand, neurons are

diversified and are capable of processing subtle

electrochemical signals efficiently in numerous possible

ways. Third, humans as complex biological systems are the

result of millions of years of natural selection. Many species

coexist but only humans have emerged as intelligent creatures

(in a full sense of the word). And certainly part of their story

is found in language and their capacity to form complex

cultural and technical artifacts and social institutions.

We think of intelligence as an advanced functionality of a

system. Based on the discussion above it is quite clear that

this type of functionality should be independent of a

machine’s internal implementation details. We propose that

this functionality is a function of the interface (humanmachine, machine-machine); to be found in the subject-tosubject relation vis a vis the object, that is, the computer or

“intelligent machine.” In order for the intelligent

functionality to be something observable (measurable), the

intelligence of a system ought to be judged based on a

system’s response to provided inputs. The internal structure
and implementation may be not so important for the purpose

of observing it.

But, is it really possible to measure intelligence

quantitatively? We believe that the answer ought to be yes;

else, we run the risk of viewing machine intelligence as

something metaphysical, mysterious and therefore not

amenable to investigation. Methodologically, we know that it

is very hard to quantify machine intelligence. The reason is

that any intelligence measure has to be an overall

performance index involving many detailed measures. For

example, an intelligent person may be extraordinarily good at

math but very poor in music. For another person the opposite

may be true (capable in music but incapable at math).

Obviously there is no single number that can be used to

characterize the difference between these two persons.

Human IQ tests have been criticized for their non-typicality,

unreliability and inconsistency. The incommensurability of

intelligence is a barrier we have to face with intelligent

machines as well; and not only because they are used or they

are better at different things. Evidently, it is different humans

that make for very different intelligent machines.

Despite all these difficulties, defining performance metrics for

intelligent systems is a worthy goal. People realize that the

long lack of universally acceptable measures has seriously

hampered the process of intelligent systems development.

Science and technology have advanced by cooperation and

competition. The root of cooperation and competition is a

common ground with which results of different researchers

can be compared. Comparisons are impossible without

agreement on performance metrics.

Some philosophical and methodological barriers to intelligent

metrics can be overcome by adopting a pragmatic approach.

Such an approach focuses on the specifics of the problem and

calls for strategies for improvement (learning) and

development (maturation) within a given context and with

well-defined metrics. Thus, for the first step a pragmatic

approach is to identify the needs of the applications. What are

the situations that intelligent systems are designed for? Are

we going to develop an intelligent system that clones a human

being (in some way) or solve a specific problem (for the

benefit of well-defined user needs)? Undoubtedly, the efforts

involved in designing performance metrics for these two

different systems are rather incomparable.

3. GENERAL GUIDELINES

In this section we present five items we found important in

designing performance metrics. They are intended to be

general guidelines. A comprehensive performance metric

should take into account all five of the proposed items. The

first describes a questionnaire-based method similar to

approaches taken for knowledge solicitation in well-defined

application domains. The second identifies the capacity for

generalization. The third item identifies the need for

adaptation. The fourth captures the social or group

capabilities of intelligent systems. Finally, the fourth

identifies transitivity or how different intelligent systems

ought to be comparable.

3.1 General and Specific Metrics

Detailed quantitative metrics of general intelligence are

difficult to formulate and potentially not necessary.

Intelligence in general integrates so many parameters and is

not possible to have an objective general measure. However,

approximate and application oriented measures are possible.

It is a lot easier to develop a metric to evaluate the

performance of some system for a specific application like

chess playing or medical diagnosis. Therefore, if possible,

application-specific measures should be always considered

first. Application-specific measures can be constructed based

on a set of questionnaires. Techniques from knowledge

solicitation and web-based assessments can be used.

Questionnaires can be analyzed via statistical approaches or

fuzzy quantification (Tsoukalas, 1997).

A Computer that acts humanly A Computer that performs a specific job

V.S.

Figure 1. General intelligence and specific intelligence

3.2 Metric for Generalization Capabilities

The degree of intelligence ought to reflect in some fashion the

capacity for generalization. An intelligent system solves a

problem first by searching its previous experience for similar

cases. The first level of intelligence is looking for a direct

match. A higher level of intelligence is needed when a direct

match is unavailable. The higher level of intelligence appears

as the capability of maneuvering experience in part in order to

generate new unseen instances, which resemble the problem

to solve, as shown in figure 2. Suppose, for example, that the

problem is to classify some unknown shape. The direct match

is the first approach and essentially compares the given shape

against ideal geometrical shapes. The indirect match may do

the matching against generalized instances and more

generally against composite generalized instances. An

appropriate metric in this example ought to capture the ability

of the system to deal with the more general topological

transformations involved in the indirect matches. Although a

lot has been written for generalization, typically

generalization in many engineering applications is little
Seen Instances

Generalized Instances

Composite Generalized Instances

Direct Match

Indirect Match

Indirect Match

Figure 2. An illustration of different levels of generalizations

different from interpolation. But, that’s fine. Even a good

interpolation metric is adequate and very useful if applied

consistently as a measure of generalization.

3.3 Intelligence Metric should be Adaptive

The intelligence of a system cannot really be evaluated by a

fixed rule. Rather it ought to be a collective index that reflects

the overall performance of the evaluated system on a variety

of situations. Consequently, this metric should be dynamic

and adaptive. It should change to adapt to the new

information that has been gathered regarding a system’s more

recent performance. In this respect a simple neural network

can be very useful as means of adaptation of the metric (even

though the range of adaptation may be rather narrow).

3.4 Intelligence as a Social Characteristic

From an engineering viewpoint, an isolated system, no matter

how intelligent it is, is not of great interest. Measuring

intelligence should be performed in the context of a group or

society that includes other systems (computers or humans), as

shown in figure 3. Any intelligent ability ought to be

evaluated from the interrelations among multiple systems. In

a sense the kind of machine intelligence we are called to

quantify almost always involves network systems, be they

computers, sensors, robotic devices, controllers, expert

systems, or search engines in the Internet. The criteria for

judging the social abilities of interacting systems are the

correctness of interpretations of their inputs and the

effectiveness of presentation of the outputs.

d i gi t a l

Figure 3: System and its environment

If a system is put into a society, we have to consider its

relations with other parties. In a society, a system always

stores in its local database the profiles of other systems that it

knows. In order words, the intelligence profile of a system is

distributed to the society. The argument that an intelligence

metric should be dynamic and adaptive requires that a system

be constantly monitored. Assigning a dedicated agent to

perform this job will be biased and unreliable. The solution is

that any system ought to be examined by its peers (the rest of

the systems in the same society). This implies that an

intelligent system (human or computers) should posses the

ability to evaluate the intelligence of other systems. However,

it is impossible for one system to evaluate all other systems

directly, partly because of security reasons or simply because

of the exponentially growing communication overhead. In

such cases, one system needs to reach its own judgment

indirectly based on the judgment (which may be direct or

indirect one) of other systems. For example, in figure 4,

System 1 is about to evaluate the performance of System 3.

However, System 1 has no means to communicate with

System 3 directly. In such a scenario, it is possible for System

1 to reach a decision based on the evaluations obtained from

System 2 and System 4 (with which it has direct connections).

The third party information might be direct (such as System 2

that has direct connection with System 3) or indirect (such as

System 4, which in return relies on System 5 to make its own

decision). To achieve this type of feature, a system needs to

“trust” to some degree the capabilities of other systems.

3.5 Transitivity

Finally any intelligence metric should be used with care,

especially when comparisons are involved. The direct

comparison between two systems using an intelligence metric
System 1 System 3

System 2

System 5

System 4

Indirect

Evaluation

Direct

Evaluation

Exchange

Evaluation

Figure 4. Indirect evaluation of intelligence

remains questionable, except when this metric has been

defined in a very narrow sense such as for the performance of

a specific job such as medical diagnosis. Generally, we should

avoid the use of an intelligence metric in a chained fashion

because of its inherent uncertainty and incommensurability.

For instance, system A is more intelligent than system A,

which is more intelligent than system C. Yet, it is not correct

to imply that system A is more intelligent than C.

4. A PROTOTYPE FOR GENERAL MEASURES

An intelligence metric in general is difficult to be written in

an analytical form. However, an engineering construction

approach may be useful. The process of evaluating

intelligence itself is an intelligent process. To break this

infinite loop, we must start from some systems that are

canonically intelligent. Humans are the main option. Some

prototype machine systems are first constructed and are

approved to be intelligent by humans. These initial systems

are not necessarily perfect in terms of natural intelligence.

The criteria of intelligence are numerous and the most

important one is the capacity of judging the intelligence of

other systems. The intelligence evaluation process is not a

calibration process where a less precise machine is able to

calibrate a more precise one. A more pragmatic approach is

needed. The prototypes that have been evaluated by humans

now can evaluate other machines, as shown in figure 5. The

evaluated systems can be further used to evaluate other

systems. It should be noted that this is not a one shot

operation and the first round of evaluation is usually

inaccurate. In later iterations, every system (including the first

prototype) will have the chance to improve its evaluation

capability by comparing others’ evaluations with its own one.

A steady state for the system, if reachable, informs us that it

has achieved stable and more accurate evaluations for other

systems. The key of this approach is that an intelligent system

is able to talk to its neighbors.

Human Prototype

Propagation of Intelligence Measure

Figure 5. Construct intelligence metric in a networking

approach

5. CONCLUSIONS AND REMARKS

We have discussed several important guidelines for intelligent

metrics. The key is to focus on the interface (machinemachine, machine-human). A system’s intelligence is

reflected by the ways of processing inputs and presenting

outputs. The interface not only accepts problem-solving data

but only those “control” or “judgment” pieces of information,

such as evaluations by other systems.

Over the years there has been a great interest in constructing

intelligent systems not only because machines can potentially

solve problem more consistently and flexibly (with human

supervision) but also because intelligent systems are a great

metaphor for intelligent human activities. The activities of

posing and answering questions and of building knowledge

through a dialectic process are now greatly facilitated by

computer systems which we tend to view as “intelligent.” The

results are an unprecedented and much needed access to the

human mind. And, machines that not only surprise and

fascinate us but, most importantly, we cannot do without

them; in the sense that we cannot really manage the

complexity of exceeding complicated engineering artifacts

that come to be over several generations.

However, there is no free lunch. The associated cost is the so

called responsibility dilemma. A system is intelligent because

it is capable of handling exceptions that it was never taught.

The question then may be raised as to who should be

responsible for the consequence incurred by the “intelligent

actions.” The designer of the system should not be blamed

because the system does not follow instructions literally and

the designer can not foresee the direction that the system

evolves. These issues have to be addressed and if possible

reflected in future intelligence metris.
6. REFERENCES

Gazzaniga, M.S., Ivry, R.B., Mangun, G.R., Cognitive

Neuroscience: The Biology of the Mind, W.W. Norton &

Company, New York, 1998.

Tsoukalas, L. H, and Uhrig, R. E., Fuzzy and Neural

Approaches in Engineering, John Wiley and Sons, NY, 1997.

Russell, S. and Norvig, P., Artificial Intelligence: A Modern

Approach, Prentice Hall, New Jersey, 1995

7. BIOGRAPHY

Rong Gao obtained his bachelor’s and master’s degrees from

Tsinghua University, Beijing in 1994 and 1997 respectively.

In December 2000, he received his master degree in electrical

and computer engineering, Ph.D. in nuclear engineering, both

from Purdue University. He is currently working at the

Artificial Intelligence Systems Laboratory (AISL) as a

visiting Assistant Professor. His research interests include

human-machine interface, intelligent signal processing and

complex systems modeling.

Lefteri H. Tsoukalas is Professor and Head of the School of

Nuclear Engineering at Purdue University and the Director of

the Applied Intelligent Systems Laboratory (AISL). He has a

PhD from the University of Illinois (1989) and numerous

publications in neurofuzzy systems including the book Fuzzy

and Neural Approaches in Engineering, co-authored with

R.E. Uhrig (Wiley, 1997).
1

RCS Based Hardware-in-the-loop Intelligent System Design and Performance

Measurement

 Ananthakrishnan S., Agrawal, Sunil, Venugopal, Ravinder, Demeri, Mahmoud

 Chief Engineer, Professor, Consultant, Senior Tech. Specialist

 Pathway Tech. Inc., Univ. of Delaware, Pathway Tech. Inc., Ford Research Lab

 Blue Bell, PA Newark, DE Blue Bell, PA Dearborn, MI

Abstract:

In this paper, we present an approach to the design of intelligent systems based on RCS architecture that allows

seamless transition from modeling and non real-time simulation to real-time simulation and subsequent hardware-in-theloop testing. This methodology provides a unified, structured, hierarchical environment, so that “analytical design” of

intelligence can be seamlessly transferred to machine/manufacturing process intelligence. As part of the research, we present

two case studies wherein we demonstrate how RCS architecture and functionality can be incorporated using commercial

software and hardware environment. The enhancements to commercial software in the areas of (i) knowledge hierarchy, (ii)

open, modular, and structured programming using RCS architecture, (iii) minimal software programming, (iv) advanced

control design methodologies, and (v) efficient numerical schemes for optimization provide a framework for comparing

qualitative and quantitative measures of performance improvement over traditional industrial automation hardware that uses

PID cards, programmable logic controllers (PLCs), and other microprocessor based controllers with limited functionality.

In order to compete in the global market place, engineering organizations are under increasing pressure to design,

develop, and deploy products in the market place as quickly as possible with first time quality. In order to achieve these

objectives, it is necessary to streamline the design and development process, namely, “transfer of analytical design of

intelligence to mechatronics intelligence” in an efficient and expedient manner. Using Real-time Control System (RCS)

architecture that organizes the elements of intelligence to create functional relationships and information flow across levels

following principles of hierarchy and assigned responsibilities at each level [1, 2], we have implemented control systems for

two applications, namely, a cable robot, and electrohydraulic test system.

The objective of this research effort was to conduct case studies on how RCS architecture can be used in a

flexible automation scenario where traditional industrial control cards (hardware) do not provide adequate measures of

performance. In addition, industrial control hardware and real-time software primarily focus on embedded software with no

structured approach or methodologies for real-time simulation or hardware testing of intelligent system design. Further, in

commercial software used for hardware-in-the-loop testing, there is a general lack of well-defined intelligent infrastructure.

Hence, from the standpoint of intelligent system design and performance metrics, through these design examples, we

demonstrate the need for a unified environment for design and development of intelligent systems that combine knowledge

hierarchy, computational schemes, dynamic models, etc., so that platform configuration, and repetitive coding can be

minimized [3].

Intelligent control has been a focus of attention for researchers over the past three decades. Initially, it was

viewed as interaction of artificial intelligence and control systems [4]. Another major attempt to formalize the discipline of

intelligent controls includes theories of nested hierarchical information structures to address control of complex systems [2, 5,

6]. Nonetheless, all these approaches emphasize the importance of “analytical design” of intelligent machines and focus of

system functions pertaining to machine intelligence.

In summary, design of intelligent systems based on methodologies described in [2, 4, 5, 6] have the following

three common characteristics: (i) utilization and implementation of concepts and ideas from diverse disciplines, (ii)

“additional controllers” to accommodate intelligent system performance that utilize knowledge based techniques to meet

performance requirements, (iii) emphasis on the overall system coordination and integration as opposed to control specific

system components. In this paper, through the two case studies we demonstrate how commercial software can be adapted to

meet these objectives of intelligent system design through hardware-in-the-loop testing. The qualitative measures of

performance enhancement with this design approach are, (i) structured environment using RCS architecture (ii) system

models for rapid plug and play design, (iii) minimal platform configuration and coding. Quantitative measures of

performance enhancement are, (i) improved control design and implementation techniques over commercial control

hardware, (ii) seamless migration from simulation to hardware testing, (iii) cost effective intelligent system design modules

for commercial environment.
2

System Dynamics and Control

Cable Robot Dynamics and Control:

Figure 3. Cable Robotic System

In order to systematically explore the questions of configuration design, coordination, and intelligent control, we

have implemented a hardware-in-the-loop control design environment for a cable robot as shown in Figure 3. The system

consists of 6 degrees-of-freedom cable robot mounted on a two degree-of-freedom X-Y gantry structure. Cable suspended

robots have one unique property – they carry loads in tension but not in compression. Due to this feature, well-known results

in robotics for trajectory planning and control are not directly applicable to cable robots, but must be modified to reflect the

constraints of positive cable tensions [8, 9]. In this paper, we present RCS based control implementation of design algorithms

taking into consideration that the cables have to be in tension for effective control.

Based on Newton-Euler formulation, the equations of motion for the cable system without considering the gantry

motion can be written as

J q u

I I

mz g

my

mx

m T

m

m

( )

~

3

2

1

3

2

1

3

2

1

= −

























































×





















+





















−

w

w

w

w

w

w

a

a

a

&&

&&

&&

(i)

where in equation (i), m is the mass matrix, I is the moment of inertia of the end-effector about its center of mass with

respect to the basis vector [ ] b1 b2 b3 [8]. The above equation can be rewritten in the following form

D x x C x x x G x J q u

T

( )

~

( )&&+ ( , )& + ( ) = − (ii)

where [ ]

T

m m m

x = x y z y q f and it’s derivatives refer to the configuration of the end-effector plate and q is a

vector of cable lengths. The functional relation between wi ai, and (y ,q,f ) is given by
3









































−

−

=





















f

q

y

y y q

y y q

q

w

w

w

&

&

&

S C C

C S C

S

0

0

1 0

3

2

1

(iii)

with ai wi = & and ( ) [ ]

T G x = 0 0 − mg 0 0 0 .

Based on the dynamic equations described in equation (ii), a Lyapunov based controller was developed with the

candidate Lyapunov function in equation (iv)

V x x x D x x x K xP

T ~ T ~

2

1

( )

2

1

, )

~( = & & + (iv)

where dx = x − x

~ with d

x as the reference trajectory. In order to ensure asymptotic stability of equation (ii) about dx , a

control law was developed in [8], to ensure positive control. The control law given in equation (v)

[ ] ( )

~ ~ ~

G x

K

K

u

J

d

P

T

=





















− Χ Χ

&

(v)

has the structure of Ay = b such that y > 0 with Χ

~

having a dimension of (6 X 6) with diagonal entries of ix

~ . The

resulting optimization problem to ensure positive control results in minimizing 2Ay − b and was solved using lsqnonneq, a

nonnegative least squares problem solver. The computations of pseudo-inverse and Singular Value Decomposition (SVD) are

directly applicable to least squares optimization problems. The technique of lsqnonneg is an iterative process. Consider a

system of equations represented by Ay = b , the technique consists of computing the residue R = b − Aye, where ey is the

initial guess or estimate of the solution. The main process of lsqnonneg consists of iteratively computing w A R

T

= , and

improving the estimates ey in each solution step. The process iterates till all elements of w are less than the specified

tolerance, and the number of iterations reach an upper limit. In each iteration step, the pseudo-inverse of a subset of the

elements of matrix A is used to compute the new estimates ey . The pseudo-inverse is computed using SVD. As part of the

knowledge hierarchy, the intelligent system design involved developing a real-time S-function C-code API structure in

Matlab/Simulink that would allow simultaneous optimized gain selection using lsqnoneg and Lyapunov based controller.

This was implemented using RCS methodology in the cable robotic system at University of Delaware. The details of RCS

based implementation of this design and the results of this experimental study are discussed later.

Electrohydraulic Test System:

New tooling concepts and an advanced binder control unit with individually controlled hydraulic cylinders has

recently been developed to allow the local control of metal flow into the die cavity during a stamping operation. Forces are

applied on the sheet metal blank using a set of hydraulic cylinders mounted on the lower bolster of the press. In a hydraulic

press, the ram depresses the piston of each one of the hydraulic cylinders in the binder area, thus compressing the hydraulic

fluid and raising the pressure inside the cylinders. This pressure is transferred through the piston back up to the blank. To

obtain the desired force on the blank, the pressure within the hydraulic cylinder is regulated by modulating the flow of

hydraulic fluid out of the cylinder by means of a closed-loop control system. But, unlike a hydraulic press where the ram

maintains a constant velocity profile, the piston velocity of the mechanical press is a nonlinear function of time, and

therefore, the differential equation that relates rate of pressure increase to servo-valve opening is nonlinear. Hence, control of

pressure within the cylinder cannot be achieved with simple PID control in the case of a mechanical press, although this

would be possible in the case of a hydraulic press for which the piston velocity is constant during the stamping cycle. In this

case, standard off-the-shelf PID cards were not able to control the cylinder pressure in a mechanical press.

In this case, we demonstrate an RCS based nonlinear controller [10] using a single cylinder test stand as shown

in Figure 4. As can be seen, the hydraulic cylinder in the middle of the platform will be controlled as the table is actuated
4

using the crank assembly using motors shown in Figure 4. The schematic of the hydraulic system consisting of reservoir,

servovalve, and other hydraulic components is shown in Figure 5.

Figure 4. Hydraulic System Test Stand for Single Cylinder Test

Figure 5. Schematic of the Hydraulic Test System for Closed Loop Control

System Dynamics:

Figure 6 shows the mechanical ram and the hydraulic force control unit in a typical mechanical press. The

governing equations of motion for the system shown in Figure 6 consists of cylinder dynamics of the hydraulic system and

the dynamics of the mechanical crank drive as described in the following equations (1) and (2):

.

2( ( ) )

( ) ( )

( ( ) )

( )















 −

−

− +

=

r

a

e

b t

servo

P t P

Ad t t K

A s d t

P t

& &

(1)

where P(t) is the cylinder pressure, Ptis the tank pressure, A is the cross-sectional area of the cylinder, d(t) is the

displacement of the piston from top-dead-center (TDC), 0 ≤a(t) ≤ 1 is the amount by which the servo-valve is

Hydraulic 

Fluid 

Reservoir 

(~0.1 gal)

Safety 

Valve (600 

PSI)

ServoValve 

HR 

Textron 

27C20

Doubleacting 

Hydraulic 

Cylinder

Entran

Pressure 

Transducer

Transtek

LVDT

Shaker 

Table

Entran

Pressure 

Transducer

Transtek

LVT

Hydraulic 

Fluid 

Reservoir 

(~0.1 gal)

Safety 

Valve (600 

PSI)

ServoValve 

HR 

Textron 

27C20

Doubleacting 

Hydraulic 

Cylinder

Entran

Pressure 

Transducer

Transtek

LVDT

Shaker 

Table

Entran

Pressure 

Transducer

Transtek

LVT
5

Figure 6. Schematic of Hydraulic Force Actuation in Mechanical Press

opened with a(t) = 0 representing the valve fully closed anda(t) = 1, r is the density of the hydraulic fluid, Kservo is the

effective cross-sectional area of the valve orifice, s is the stroke length of the piston and e is the height of fluid in the

cylinder when the piston reaches bottom-dead-center (BDC) as shown in Figure 6. We now mathematically model a simple

mechanical crank press drive to obtain a relationship between the displacement of the piston and its velocity, assuming that

all the links is rigid as shown in equation (2)

( ) cos ( ) sin ( ) .

2 2 2

d t = R q t − l − R q t − R − l + s (2)

where q (t) is the crank angle, R is the crank radius, and l is the length of the coupler, as shown in Figure 7. Differentiating

Equation (2) results in Equation (3) as shown below.

,

2 sin ( )

sin 2 ( )

( ) sin ( )

2 2 2

2

l R t

R t

d t R t

q

q w

q w

−

= − −

&

(3)

The resulting nonlinear model of equation (1) and (3) describes the dynamics of the hydraulic actuation system used

in a mechanical press. The nonlinear nature of the equations clearly indicates that standard PID control will not be sufficient

for precise closed-loop pressure control.

Control System Design:

The controller design for the hydraulic force actuation unit is based upon equation (1) and (3) of the preceding

section. The nonlinear control technique used for designing the controller is feedback linearization. Feedback linearization is

used in the control of nonlinear systems in which the nonlinearity is known and invertible. It involves the use of additional

terms in the control signal to cancel out the nonlinearity, after which a classical linear controller is used on the effectively

linear system. Using the structure of the nonlinearity, we choose















 − +

−

−

= ( )

( ( ) )

( )

2( ( ) )

( ) v t

s d t

d t

K P t P

A

t

servo t b

r e

a &(4)

where v(t) is a linear control term to be determined subsequently. Substituting equation (1) into equation (4) results in
6

P(t) = v(t)

& (5)

Thus, the control law in equation (4) converts the nonlinear control problem in equation (1) into a first order linear ODE

involving the new control variable v(t). The first type of controller that we choose for the system given by (5) is a PI

controller of the form shown in equation (6), namely,

( ) ( ( ) ( )) ( ( ) ( )) ,

0

∫

= − + −

t

t

p ref i ref

i

v t K P t P t K P s P s ds (6)

where Kpis the proportional gain, P (t)

ref is the desired pressure command, and Ki

 is the integral gain. Substitution of

equation (6) into equation (4) results in the following nonlinear control law,

( ( ( ) ( )) ( ( ) ( )) ) .

( ( ) )

( )

2( ( ) )

( )

0

















− + −

− +

−

−

= ∫

t

t

p ref i ref

servo t

i

K P t P t K P P d

s d t

d t

K P t P

A

t s s s

b

r e

a &

(7)

Figure 7. Cylinder Pressure Comparison between Linear and Nonlinear Control

Figure 7 shows the comparison between pressure profiles using linear and nonlinear controllers. The pressure

profiles are given from bottom dead center (BDC) of the cylinder, and so must be viewed from right to left. As the ram

comes down and makes contact with the cylinder, the initial pressure spike to the right is noticed. Then, as the ram plunges

the cylinder, the nonlinear control is able to track the reference pressure trajectory, while the PI control causes significant

oscillations. So, a simple change from hydraulic ram to a mechanical ram rendered the conventional control cards inadequate

for effective control. The structure of nonlinear control used in this design can be generalized to a class of systems.

Essentially, since the structure of nonlinearity is known in pressure control of hydraulic systems, we can use feedback

linearization techniques.

A second approach to control of the cylinder pressure in the binder force control unit use estimates of plant

dynamics in a pole assignment controller leading to pole assignment adaptive control. A deterministic auto regressive moving

average (DARMA) model of the plant represented by equation (8) represents the plant dynamics

( ) ( ) ( ) ( )

1 ' 1 A q y t q B q u t− −d −

= (8)

where the coefficients of the polynomial ( ), ( )

−1 ' −1 A q B q are given by equation (9) and (10)

1

1

1

0 1

1

( ) ...

n

n A q a a q a q

− − −

= + + + (9)

0 1 2 3 4 5 6 7 8

0

500

1000

1500

2000

2500

Piston displacement from BDC (in)

Cylinder pressure (PSI)

Reference

Low bandwidth valve

Mechanical press, nonlinear controller 

0 1 2 3 4 5 6 7 8

0

500

1000

1500

2000

2500

3000

Piston displacement from BDC (in)

Cylinder pressure (PSI)

Reference

Low bandwidth valve

Mechanical press, simple PI controller 

 Nonlinear Control Simulation for a Mechanical Press PI Control Simulation for a Mechanical Press
7

m d

m B q b b q b q q

− − − −

( ) = ( + + ... + )

1

1

1

0 1

1

(10)

The plant dynamics can be estimated based on past measurements of y(t),u(t) by rewriting equation (8) in normalized form

where a0 = 1as follows:

f q

T

y(t) = (t −1) (11)

with

[ , ,...., , ,....]

( 1) [ ( 1), ( 2),...., ( 1), ( 2)...]

a1 a2 b1 b2

t y t y t u t u t

T

=

− = − − − − − −

q

f

The parameters of the system can be estimated using recursive least squares (RLS) algorithm and its variation such as RLS

with covariance resetting as follows:

( 1)) ˆ( ( ) ( 1)

1 ( 1) ( 2) ( 1)

( 2) ( 1)

( 1)

ˆ

( )

ˆ − − −

+ − − −

− −

= − + y t t t

t P t t

P t t

t t

T

T

f q

f f

f

q q

(12)

1 ( 1) ( 2) ( 1)

( 2) ( 1) ( 1) ( 2)

( 1) ( 2)

+ − − −

− − − −

− = − −

t P t t

P t t t P t

P t P t

T

T

f f

f f

(13)

For covariance resetting, equation (13) is reset periodically by a known value of covariance as follows

P t k I

i − = i

( 1) (14)

The control input u(t) in pole assignment controller is determined by solving the following equation

( , ) ( )

ˆ

( , )

ˆ

( , )

ˆ

( , )

ˆ

−1 −1 −1 −1 * −1 A t q L t q + B t q P t q = A q (15)

where ( )

ˆq t consists of estimates ( , )ˆ

( , ), ˆ

−1 −1 A t q B t q and ( , )

ˆ

( , ), ˆ

−1 −1

L t q P t q are unique polynomials of order

(k − 1) and ( )

* −1 A q is the desired polynomial selected based on intended closed loop behavior. The feedback control law

that combines the estimates of the plant dynamics and equation (15) is given by [12, 13]

( , ) ( )

ˆ

( ) ( )

ˆ

( , ) ( )

ˆ

1 * 1

L t q u t M t y t P t q y t

− −

= − (16)

where

...

ˆ

1 ...

( )

ˆ

1

*

1

+

+ +

=

b

a

M t (17)

with ( )

*

y t same as P (t)

ref , y(t) same as P(t) and u(t) same as the valve command similar to the nonlinear controller.

Figure 8. Cylinder Pressure Comparison for Adaptive Controllers

Adaptive Control with Cov. Reset Adaptive Control w/o Cov. Reset
8

Figure 8 shows the comparison between pressure profiles using adaptive controllers with or without covariance reset. As can

be seen, unlike, linear controllers, both the adaptive controllers show a stable response like the nonlinear controller. But, in

the case of adaptive controller with covariance resetting, in addition to the tracking, the response is well behaved in the

presence of plant and measurement noise, similar to the nonlinear controller. The initial spike in the response is due to the

initial transient as the ram comes down and plunges on the cylinder. So, from these analytical results, it is clear that the

nonlinearities in the system require linear control structure with adaptation or a nonlinear controller in order to get desired

response behavior.

RCS Implementation:

In a goal driven, sensory interactive, and system behavior based intelligent architecture, we have used the four

key paradigms and four key elements [1, 2] as guideline for design of intelligent systems. With simulation as the heart of the

development process whether it be non real-time or real-time or hardware-in-the-loop, our case studies support the entire

intelligent design process without having to transfer data, change design environment, or write extensive custom code.

Further, the design process allows models of physical systems, namely, kinematics and dynamics of cable robot and

mechanical press test fixture, to be used for non real-time simulation and subsequent hardware-in-the-loop testing of various

real-time control schemes, namely, linear, nonlinear, adaptive, and Lyapunov based controllers. In order to avoid repetitive

coding we have developed application programmers’ interfaces (APIs) that allow optimized integrated code generation, and

embedded system options that allow seamless transition of intelligent controllers from simulation to hardware testing.

Figure 9 shows the functional decomposition of the information flow for the cable robotic system. As can be

seen from the schematic, the design flow uses the four key paradigms, and four key elements of RCS control node to

decompose the design problem into a multi-layered hierarchical control problem. The tension sensor signals {TS1,...,TS6}

are fed back to the lowest layer of the hierarchy to close tension feedback control loops using control laws that receive

tension request from the Prim process and the filtered sensor data from the sensory processing {SP1, ...., SP6}. Based on

whether the tension request in within the desired threshold, the value judgment {VJ1, ....,VJ6} is used to limit the tension

request so that appropriate motor commands are generated by the behavior generation module {BJ1, ...., BJ6}. The motor

command to the servo drives that actuate the motor are thus assured to be within operational limits in order to achieve

effective tension control.

The commands to the tension control RCS nodes are generated by the Prim process, which also uses the

elements of the RCS node to implement the Lyapunov controller in order to ensure that positive control is achieved. The

optimization problem to ensure positive control results in minimizing 2Ay − b and was solved using lsqnonneq, a

nonnegative least squares problem solver. Since the basic Simulink structure does not support real-time optimization and

control, S-function C-code API structure had to be developed as part of the knowledge hierarchy to demonstrate seamless

transition from simulation to hardware testing. As part of this knowledge hierarchy, the computations of pseudo-inverse and

Singular Value Decomposition (SVD) are directly applicable to least squares optimization problems and hence the associated

algorithms were also developed. As discussed earlier, at each control step the lyapunov controller requires the solution of

lsqnonneq, which consists of iteratively computing w A R

T

= , and improving the estimates ey in each solution step. The

process iterates till all elements of w are less than the specified tolerance, and the number of iterations have an upper limit.

In each iteration step, the pseudo-inverse of a subset of the elements of matrix A is used to compute the new estimates ey .

The pseudo-inverse is computed using SVD. The Prim process, based on desired reference trajectories dx from the E-move

process computes the tension request u of equation (v) to the tension control RCS nodes based on this Lyapunov controller.

In this structure of the Prim process, lsqnonneq serves as the VJ module for the Lyapunov controller that acts as a BJ module

based on desired and actual trajectories that were generated using encoder feedback and associated SP computations that

involves computation of Jacobian at each control step.

Similar methodologies were used in design of controllers for the electrohydraulic test system used for stamping

process automation prototyping and for the sake of brevity not discussed here. In summary, the analytical methodologies,

knowledge hierarchy and algorithms developed for estimation, optimization and control of intelligent systems provide a

framework for software and associated real-time control development that will allow seamless transition from simulation to

hardware. Figure 10 shows the framework of software developed for the case studies. As can be seen, this process can be

streamlined for generalized development of knowledge hierarchy and algorithms to allow simulation and hardware testing of
9

intelligent systems. Figure 11 shows the response plots and GUI used in real-time testing of cable robot. As can be seen, the

Lyapunov controller works very well while maintaining positive tensions. Similar GUI was developed for design and testing

of the electrohydraulic test system.

Figure 9. Hierarchical Decomposition of Intelligent Control Design for Cable Robot

Figure 10. Real-time Estimation, Control, Optimization and System Model Software

Task

Task Planning

E-move

Trajectory Planning

Prim

Cable Length-Inv. Kinematics

dSPACE Controller Board

Enc1 Enc2 Enc3 Enc4 Enc5 Enc6

TS1

Motor1

TS2 TS3 TS4 TS5 TS6

Motor2 Motor3 Motor4 Motor5 Motor6

SP1 BJ1

VJ1

SP2 BJ2

VJ2

SP3 BJ3

VJ3

SP4 BJ4

VJ4

SP5 BJ5

VJ5

SP6 BJ6

VJ6

Task

Task Planning

E-move

Trajectory Planning

Prim

Cable Length-Inv. Kinematics

dSPACE Controller Board

Enc1 Enc2 Enc3 Enc4 Enc5 Enc6

TS1

Motor1

TS2 TS3 TS4 TS5 TS6

Motor2 Motor3 Motor4 Motor5 Motor6

SP1 BJ1

VJ1

SP2 BJ2

VJ2

SP3 BJ3

VJ3

SP4 BJ4

VJ4

SP5 BJ5

VJ5

SP6 BJ6

VJ6

Position

Velocity

Force

Pressure

....

Sensory Processing

Least Squares

Lsq w Cov. Reset

Lsqnoneg

....

Estimation/

Optimization

PID

PID+Comp

Lyapunov

Nonlinear

Adaptive

....

Control

Physical Systems

•Structural

•Mechanical

•Hydraulic

•Fluids

•

•...

Physical

•Structural

•Mechanical

•

•Fluids

•

•...

Position

Velocity

Force

Pressure

....

Sensory Processing

Position

Velocity

Force

Pressure

....

Sensory Processing

Least Squares

Lsq w Cov. Reset

Lsqnoneg

....

Estimation/

Optimization

Least Squares

Lsq w Cov. Reset

Lsqnoneg

....

Estimation/

Optimization

PID

PID+Comp

Lyapunov

Nonlinear

Adaptive

....

Control

PID

PID+Comp

Lyapunov

Nonlinear

Adaptive

....

Control

Physical Systems

•Structural

•Mechanical

•Hydraulic

•Fluids

•

•...

Physical

•Structural

•Mechanical

•

•Fluids

•

•...

Physical Systems

•Structural

•Mechanical

•Hydraulic

•Fluids

•

•...

Physical

•Structural

•Mechanical

•

•Fluids

•

•...
10

Figure 11. Response Plots and GUI for Real-time Testing of Cable Robot

In summary, we present research and industrial case studies that allow efficient design and development of intelligent systems

using RCS architecture in order to provide a unified, structured, hierarchical environment so that a designer can build

software and associated real-time control without undue focus on software programming and hardware interface during the

development process. This design approach allows real-time production code generation of the intelligent control design in a

cost effective manner, thus providing improvement over commercial PIDs and PLCs. Rapid plug and play design and

seamless migration from non real-time simulation to hardware testing provide qualitative measures of perfomance

enhancement while the improved system behavior through advanced control provide quantitative measures of performance

improvement.

Acknowledgements

The authors wish to acknowledge that this work was done with funding from NIST, USAMP/AMD Project 301, and

corporate funding from Pathway. We specifically, wish to thank Elena Messina, Harry Scott, Huang Hui-Min, and Jim Albus

of NIST, Dr. Mahmoud Demeri of Ford for their valuable comments during this project. In addition, we thank Soreyok Oh

and Dr. Viswanath Ananth for their help in conducting the experiments. The Mechanical System Laboratory of University of

Delaware under the direction of Sunil K. Agrawal developed the Cable Robot hardware for this project.

References

[1] Huang, H., Scott, H., Messina, E., Juberts, M., and Quintero, R., “Intelligent System Control: A Unified Approach and

Applications”, NIST Report, 1999.

[2] Albus, J. S.,” Outline for the Theory of Intelligence,” IEEE Transactions on System, Man, and Cybernetics, Vol. 21,

No.3, 1991, pp 473-509.

[3] Ananthakrishnan, S., Agrawal, S.K., “ Intelligent Software and Animation Tools for RCS Architecture Based

Development – Phase I Final Report”, NIST SBIR Project #276, February 2001.

[4] Fu, K.S., “Learning Control Systems and Intelligent Control Systems: An Interaction of Artificial Intelligence and

Automatic Control”, IEEE Transactions on Automatic Control, 1971, 70-72.

[5] Saridis, G.N., “ Self Organizing Control of Stochastic Systems ”, Marcel Dekker, New York, 1977.

[6] Meystel, A.M., “Intelligent Control in Robotics”, Journal of Intelligent and Robotic Systems, Vol. 5, No. 4, 1988.

[7] Agrawal, S.K., Gardner, G., Pledgie, S., “ Design and Fabrication of a Gravity Balanced Planar Mechanism Using

Auxiliary Parallelograms”, Journal of Mechanical Design, Transactions of ASME, Vo. 123, No. 4, 2001, 525 – 528.

[8] Basar, A. A., Agrawal, S. K., “ Cable Suspended Robots: Feedback Controllers with Positive Inputs”, Proceedings of

American Control Conference, 2002.

[9] Basar, A.A., Agrawal, S. K., “Cable Suspended Robots: Design, Motion Planning, and Control”, Proceedings of IEEE

Conference on Robotics and Automation, 2002.

[10] Venugopal, R., Ananthakrishnan, S., “ Closed Loop Simulation Study of Binder Force Control Unit”, Pathway

Technologies Incorporated, Report to USAMP Project 301, 2002.

[11] Venugopal, R., Ananthakrishnan, S., “ Open Loop Simulation Study of Binder Force Control Unit”, Pathway

Technologies Incorporated, Report to USAMP Project 301, 2002.

[12] Fullmer, R., Ananthakrishnan, S., "Experimental Adaptive Controllers for a Class of Electrohydraulic Servo Systems",

Int. Journal of Adaptive Control and Signal Processing, Vol. 7, 1993.

[13] Ananthakrishnan, S., et al., “ Role of Estimation in Contact Dynamics Enhancement of Space Station Automated

Integration and Assembly Facility, IEEE Robotics and Automation Magazine, Vol. 3, No. 3, 1996.
Evaluating the Performance of Assistive Robotic Systems 

Holly A. Yanco 

Computer Science Department 

University of Massachusetts Lowell 

One University Avenue 

Lowell, MA 01854 

holly@cs.uml.edu 

ABSTRACT 

When designing robotic systems for the disabled, it is necessary to 

demonstrate that the systems are safe. Beyond safety, it must also be 

shown that the equipment improves the quality of life for its user. 

This paper discusses methods for testing assistive robotic systems to 

assure safety, usability and usefulness. An example of a user test 

designed for a robotic wheelchair is presented and discussed. 

KEYWORDS: Assistive technology, assistive robotics, robotic 

wheelchair, user tests

1 INTRODUCTION 

Assistive technology enables people to do things that would 

be impossible without the technology. Assistive technology 

can range from smart homes for the elderly to robotic 

wheelchairs to voice control software for a computer. 

Robotic workstations can provide people with vocational 

assistance [Dallaway et al 1995, Kazi et al 1998, Wagner et al 

1999]. Robotic walkers can be used to allow elderly people 

with decreased vision to walk around their nursing home 

[Lacey 1999]. 

In this paper, we discuss the testing process for assistive 

mobile robots which either carry or lead their users, so that 

the user and the robot must travel together during the use of 

the assistive system. We will call these devices assistive 

navigation systems. We will use Wheelesley, a robotic 

wheelchair system developed for indoor and outdoor use, in 

our discussions to illustrate the evaluation methods [Yanco 

2000]. 

The target community for a robotic wheelchair system 

consists of people who are unable to drive a powered 

wheelchair using a standard joystick. This group includes 

people with cerebral palsy, stroke patients who omit stimuli 

from one side, and quadriplegics. The users vary in ability 

and access methods1

 used to drive the wheelchair. Some 

people can move a joystick, but are unable to make fine 

movement corrections using the joystick. Other people are 

able to click one or more switches using their head or other 

body part. Some potential users are unable to control a 

 

1

 An access method is a means for controlling a powered 

wheelchair, such as a joystick or a sip-and-puff system. 

powered wheelchair with any of the available access devices 

and must rely upon a caregiver to move them throughout the 

world. A robotic wheelchair will enable this population to 

better self-navigate through the world, increasing 

independence. 

Human-robot interaction must be considered when 

designing assistive travel systems. Designing a poor interface 

will result in an unusable system. Robotic wheelchairs must 

be able to connect to a variety of commonly used access 

methods. User tests must utilize the access methods to be 

used by the target population, even when testing able-bodied 

subjects. With a target population lacking the fine motor 

control necessary to move a joystick, user tests with ablebodied subjects using a joystick can not be extrapolated to the 

intended users. 

2 EVALUATORS FOR SYSTEMS 

When designing and evaluating assistive navigation systems, 

there are three groups of people that should be involved: 

providers, able-bodied test subjects and people in the target 

population of the system. 

Providers are the people who prescribe and deliver 

systems to a user. In the case of wheelchairs, physical 

therapists adapt a wheelchair to its user by creating custom 

cushions, determining the proper access method, and 

adjusting settings such as speed controls. Physical therapists 

also work with wheelchair users to teach them how to use the 

system. Since they are very involved with providing care to 

users, it is important to involve these providers from the early 

stages of development through the final product testing. 

People who work individually and daily with users will have 

an understanding of the needs of the population. 

Systems should be tested first on able-bodied subjects. 

Many members of the target population for robotic 

wheelchairs are non-verbal, making it difficult to do user tests 

since it is important to be able to tell if the user feels 

comfortable when testing the system. Walkers for the elderly 

infirm who have limited vision should first be tested on 

people who can see. The move to a target user should be 

made only after the safety and reliability of the system has 

been repeatedly demonstrated through able-bodied user tests. 
3 EVALUATION OF SYSTEMS 

The performance of an assistive navigation system must be 

measured through user tests. Tests should range from 

preliminary demonstrations of safety and usability to long 

term use by one or two subjects from the target population. 

Before testing the system on users from the target 

population, tests on a large number of able-bodied subjects 

should be undertaken. These tests usually involve a test 

course, where subjects must traverse the course using assisted 

navigation and unassisted navigation for comparison. Metrics 

to be collected include time required to travel the course, 

number of safety violations which may range from scrapes to 

bumper hits to more serious failures, and the amount of effort 

required to drive the course. 

A system’s performance should be measured by 

conducting user tests that compare the performance of the 

robotic system against a non-robotic solution. For example, a 

robotic wheelchair can be designed to allow it to be controlled 

with no sensor mediation (manual control) or with sensormediated (robotic) control. By designing a system this way, 

the same user interface and access methods can be used to 

compare user performance with assisted control and with 

unassisted control. The human-robot interaction must be 

duplicated even when the robotic control is not used for 

navigation. It would be impossible to directly compare a 

traditional powered wheelchair driven by a joystick to a 

robotic system driven with an access method such as single 

switch scanning. This difference is even more problematic 

due to the fact that the target community does not have the 

fine motor control necessary to drive a powered wheelchair 

using a joystick. Care must be taken to design user tests that 

change only a small number of variables (ideally one). 

Tests involving the target users should focus on long 

term use instead of runs on a test course. Before these long 

term tests of a small group of target users, researchers should 

consider undertaking long term tests of a few able-bodied 

subjects so that the test methods may be fully evaluated 

before proceeding to test subjects with reduced mobility. 

4 ABLE-BODIED USER TEST DESIGN 

To illustrate the design of able-bodied user tests, this section 

discusses indoor user tests designed for and performed with 

the Wheelesley robotic wheelchair system [Yanco and Gips 

1998]. An experiment to test the performance of able-bodied 

subjects under robotic assisted control and under standard 

manual control was designed to determine if robotic 

assistance improved driving performance using single switch 

scanning as an access method. Single switch scanning is the 

access method of last resort for powered wheelchairs, 

primarily because drift is a significant problem. To correct a 

drift to the left or the right, the user must stop going forward, 

wait for the scanning device to get to the arrow for the 

Figure 1: The indoor test course. A test run consisted of an 

up and back traversal of this course. 

direction of choice, click to turn the chair, stop turning, wait 

to scan to forward and then click to move forward again. 

Fourteen able-bodied subjects (7 men and 7 women), 

ranging in age from 18 to 43, were tested. All subjects were 

familiar with using computers and none had driven the 

wheelchair before. 

At the beginning of a session, the subject was shown the 

wheelchair. Sensors that are used in robotic assisted control 

were pointed out and explained briefly. Safety measures, 

such as the power button, were discussed. Then the two 

driving methods were explained to the subject. After this 

introduction, the subject was seated in the wheelchair and the 

user interface was connected to the wheelchair. The single 

switch scanning interface was explained to the subject, who 

then practiced using the interface with the motors turned off. 

Once the subject was comfortable with the interface, the 

session entered a practice phase in which the subject first tried 

robotic assisted control and then standard manual control. 

The subject practiced both methods until he expressed an 

understanding of each control method; subjects usually spent 

about two minutes trying each method. All practice was done 

off of the test course, so that the subject was not able to learn 

anything that would assist him during the test phase. 

The course (shown in Figure 1) was designed to include 

obstacles (several couches and chairs, a fire extinguisher 

mounted to the wall 30 cm (11.8 inches) above the ground, a 

trash can, and a table) and turns to the left and to the right. 

The course is 20 meters (65.7 feet) long. Three doors in the 
Manual Robotic 

First Run Second Run First run Second run 

Number of clicks 90.2 (16.3) 77.1 (9.8) 25.6 (4.9) 22.0 (3.3) 

Scanning time (sec) 93.6 (20.3) 81.1 (13.0) 30.9 (8.3) 25.2 (8.6) 

Moving time (sec) 311.6 (36.4) 316.6 (36.2) 268.2 (21.5) 277.1 (28.4) 

Total time (sec) 405.1 (42.1) 397.7 (43.7) 299.1 (18.4) 302.3 (32.5) 

Table 1: Results of the indoor user tests: the number of clicks, amount of time spent scanning for commands, amount of time 

moving and total time to complete the course. The first number for each method is the mean and the number in parentheses is the 

standard deviation. 

hallway on the course could be open or closed, determined by 

the office occupants. 

The test phase consisted of four up-and-back traversals of 

the test course, alternating between the two control methods. 

Half of the subjects started with robotic assisted control and 

the other half started with standard manual control. Each upand-back traversal consists of two parts: running the course 

from the couch area to the hallway and then the return trip. 

The turn in the middle of the course is not counted as part of 

the run, as turning completely around in the middle of the 

hallway is not a normal driving occurrence. The total session 

time for each subject was approximately 45 minutes. 

There were four experimental performance measures 

collected by the computer that was running the user interface: 

(1) the number of clicks required to navigate the course, (2) 

the amount of time spent scanning to get to the necessary 

commands, (3) the amount of time spent moving or executing 

the given commands, and (4) the total amount of time spent 

on the course (scanning time plus moving time). The 

researcher only recorded the number of scrapes made by the 

chair. At the completion of the test, the user was asked to 

rank standard manual control and robotic assisted control on a 

scale from 1 (worst) to 10 (best). 

Data for each experimental measure was analyzed using 

an ANOVA test. The differences between robotic control and 

manual control were highly significant with p<.0001 for all 

measures. On average, robotic control saved 60 clicks over 

manual control, which is a 71% improvement. Total time for 

robotic assisted control was 101 seconds shorter than manual 

control on average, which is a 25% improvement. 

The only performance measure not collected on the 

computer was the count of the number of scrapes. A scrape 

was recorded when the chair brushed along a wall or piece of 

furniture. Bumps with the bumper were also counted as 

scrapes. No subject hit a wall or an obstacle with great force. 

The average number of scrapes per run under manual control 

is 0.25. The average number of scrapes under robotic control 

is 0.18. These numbers are not significantly different. 

Finally, the subjects were asked to evaluate the two 

driving methods by giving a score from 1 (worst) to 10 (best). 

The average score for standard manual control was 3.5. The 

average score for robotic assisted control was 8.7. These 

scores are highly significant with p<.0001. No test subject 

preferred manual control over robotic control. 

Subjects drove more efficiently and preferred to drive 

with robotic assisted control. Robotic control automatically 

adjusts for drift where manual control does not. When 

traveling down a long hallway under robotic control, a user 

can click on forward at the beginning of the corridor and does 

not need to do anything more until he wishes to stop or turn. 

Under manual control, the user must make many adjustments 

to compensate for drift. 

The total time taken on a test run is a sum of the scanning 

time and the command execution time. Both scanning time 

and execution time improved from manual control to robotic 

control. As would be expected, if fewer clicks are issued, the 

scanning time required is shorter. Estimating that forward is 

clicked 50% of the time, left is clicked 25% of the time and 

right is clicked 25% of the time2

, with a scan time of one 

second and an estimated reaction time of one half second in 

our able-bodied subjects, each click would require an average 

of 1.25 seconds. As Table 1 shows, the scanning time is 

approximately 1.25 times the number of clicks. 

Each user executed two trials for each control method. 

The differences between the two trials were significant for 

clicks (p=.003) and for time spent scanning (p=.015), 

indicating that the subjects were improving due to learning.3

 

As the user became more comfortable with the system, he was 

able to judge more effectively when it was necessary to make 

adjustments to the current course. 

A single subject ran the course 10 times in manual mode 

to determine how learning could affect the number of clicks 

and scanning time. The subject was this researcher; a naive 

user is not required to test for optimal performance. Over the 

10 runs, the average number of clicks in a test run was 71.4 

with a standard deviation of 9.5. Over the last 5 runs, the 

average number of clicks was 68 with a standard deviation of 

4. Scanning time averages 73.7 seconds (standard deviation 

 

2

 Empirically, backwards commands are issued very 

infrequently. 

3

 There was no significant effect of learning on moving time 

and total time between trials; since the speed is held constant 

throughout the experiment, the user can not significantly 

reduce the amount of time required to travel the course 

between trials of the same control method. 
12.0) over all 10 runs and 68 seconds (standard deviation 5.3) 

over the last 5 runs. Optimal performance for this course in 

manual mode will not approach the average performance in 

robotic mode. 

5 COMPARABLE ABLE-BODIED TESTS 

The NavChair system was tested in an indoor environment 

using voice control as the access method [Simpson and 

Levine 1997]. Six able bodied subjects navigated through 

three different scenarios (room traversal, door passage and 

wall following), four times with navigation assistance and 

then four times with no navigation assistance. For each 

scenario, it took longer to navigate using navigation 

assistance, primarily because the chair slows down as it gets 

closer to obstacles. However, no collisions occurred with 

navigation assistance, while there were occasional collisions 

with no navigation assistance. Test subjects preferred driving 

with navigation assistance. 

The VAHM Project was also tested in an indoor 

environment using single switch scanning as the access 

method [Bourhis and Pino 1996]. Four able bodied users 

familiar with computers drove the wheelchair through a 

course simulating a kitchen and living room environment in 

manual mode and in an assisted mode which provided 

obstacle avoidance. Assisted mode resulted in a 13% 

improvement in the number of actions required on the 

interface screen (which we called the number of clicks 

required). Execution time also improved an average of 7.7%. 

The tests were executed at three scanning rates: 0.8, 2.5 and 

4.5 seconds. There was a 2.5% improvement in execution 

time for the 0.8 second rate,4

 a 7.7% improvement for the 2.5 

second rate, and a 13% improvement for the 4.5 second rate. 

One would expect to see a more dramatic improvement in 

total execution time for longer scanning times since fewer 

clicks result in a greater time savings. 

Indoor user tests of Tin Man II used a joystick and 

buttons for the access method [Miller and Slack 1995]. The 

test course was 50 meters long and included a hallway, a 

doorway and two rooms. Subjects were told to attempt to 

minimize their joystick movements in both the manual and 

assisted tests. Manual mode required 50% more joystick 

moves than the obstacle avoidance mode. The time required 

to traverse the course was less than 10% longer in assisted 

mode than in manual mode. 

6 CONCLUSIONS 

User tests are required for assistive navigation systems. 

These systems are designed for people to ride; it is important 

that a great deal of testing occurs from the early design stages 

 

4

 This is the closest to our 1 second scanning rate. 

to the end. Able-bodied test subjects should be utilized for all 

testing until the system is demonstrated to be safe, reliable 

and useful. Only after a long period of testing on able-bodied 

subjects should the testing proceed to the target population. 

When designing assistive technology, providers should 

be asked for comments on the system from the initial design 

stages through final testing of the product. The inclusion of 

providers will result in designs that better reflect the needs 

and desires of the target population. Providers can also 

facilitate safe testing of the target population. 

Performance metrics for assistive navigation systems are 

necessary to demonstrate the usefulness and usability of the 

system. Assistive navigation systems are medical devices; as 

such, they should be held to the same strict testing guidelines 

mandated by the FDA, even in the initial design phases. 

ACKNOWLEDGMENTS 

The Wheelesley user tests were performed while the author 

was a graduate student in the Artificial Intelligence 

Laboratory at the Massachusetts Institute of Technology. 

This research was funded by the Office of Naval Research 

under contract number N00014-95-1-0600 and the National 

Science Foundation under grant number CDA-9505200. 

REFERENCES 

[Bourhis and Pino 1996] G. Bourhis and P. Pino. “Mobile 

robotics and mobility assistance for people with motor 

impairments: rational justification for the VAHM project.” 

IEEE Transactions on Rehabilitation Engineering, 4(1): 7 – 

12, March 1996. 

[Dallaway et al. 1995] John L. Dallaway, Robin D. Jackson, 

and Paul H. A. Timmers. “Rehabilitation robotics in Europe.” 

IEEE Transactions on Rehabilitation Engineering, 3(1):35 – 

45, March 1995. 

[Kazi et al. 1998] Zunaid Kazi, Shoupu Chen, Matthew 

Beitler, Daniel Chester, and Richard Foulds. “Speech and 

gesture mediated intelligent teleoperation.” In Lecture Notes 

in Artificial Intelligence: Assistive Technology and Artificial 

Intelligence, edited by Vibhu Mittal, Holly A. Yanco, John 

Aronis, and Richard C. Simpson, Springer-Verlag, pp. 194 – 

210, 1998. 

[Lacey 1999] Gerard Lacey. Personal Adaptive Mobility Aid 

for the Frail Visually Impaired. Ph.D. Thesis, University of 

Dublin, Trinity College, April 1999. 

[Miller and Slack 1995] David P. Miller and Marc G. Slack. 

“Design and testing of a low-cost robotic wheelchair 

prototype.” Autonomous Robots, 2:77 – 88, 1995. 
[Simpson and Levine 1997] Richard C. Simpson and Simon 

P. Levine. “Development and evaluation of voice control for 

a smart wheelchair.” In Proceedings of the Rehabilitation 

Engineering Society of North America 1997 Annual 

Conference, pp. 417 – 419, RESNA Press, 1997. 

[Wagner et al. 1999] J.J. Wagner, H.F.M Van der Loos, N. 

Smaby, K. Chang, and C. Burgar. “ProVAR assistive robot 

interface.” In Proceedings of ICORR ’99: International 

Conference on Rehabilitation Robotics, pp. 250 – 254, 1999. 

[Yanco and Gips 1998] Holly A. Yanco and James Gips. 

“Driver performance using single switch scanning with a 

powered wheelchair: robotic assisted control versus 

traditional control.” In RESNA-98, pp 298 – 300, 1998. 

[Yanco 2000] Holly A. Yanco. Shared User-Computer 

Control of a Robotic Wheelchair System. Ph.D. Thesis, 

Department of Electrical Engineering and Computer Science, 

Massachusetts Institute of Technology, September 2000. 
Measuring Classifier Intelligence

Jim DeLeo

Department of Clinical Research Informatics

NIH Clinical Center

National Institutes of Health

 Bethesda, Maryland 20892

 jdeleo@nih.gov

ABSTRACT

Classifiers are seen here as systems in which input feature

values are used with fitted or learned functions that produce

output values which are interpreted as probabilities or fuzzy

degrees of class membership, or in which output values are

used with cut-off decision rules to choose bivalent class

membership. Two complementary measurements for

evaluating training, validation, testing, and deployment phase

performances in human, mechanical, and computerized

classifiers are proposed here. These measurements are

derived from samples of classifier output values paired with

their corresponding known probabilistic, fuzzy, or bivalent

classification values. The first measurement is the area under

the ROC plot. The second is the separation index newly

introduced here. Both of these measurements are easy to

understand and to compute. It is proposed that they be

considered standard metrics for evaluating and comparing

classifier intelligence.

Keywords: classifiers, intelligence, performance

metrics, intelligence metrics, area under the ROC plot,

separation index, knowledge discovery from data, ensembles

1. Introduction

The task of a human, mechanical, or computerized

classifier is to use a set of values, x's, for certain

particular attributes to classify an entity or event into

one or more categories or classes. Classification may

be bivalent, where the classifier output, y, is either

negative (y=0) or positive (y=1), probabilistic where

the output is a probability (0 y 1) that the entity or

event is associated with the bivalent positive class, or a

fuzzy degree of membership (0<y<1) reflecting partial

degree of membership in the positive class. As

classification tasks become increasingly non-trivial

with many attributes and highly complex nonlinear and

discontinuous relationships among attribute values

and classification outcome values, it may be said, in

the spirit of classical artificial intelligence, that

classifiers that perform well are demonstrating

intelligence. Metrics are needed to measure this

intelligence in order to describe and compare classifier

performances. Here, two such metrics that

complement each other are proposed. The first is the

fairly well known area under the ROC plot. The

second is a new index called the "separation index."

Both metrics may be employed for bivalent,

probabilistic, and fuzzy classifier outcomes. They

have immediate use with present day classifiers and

they have potential future use with anticipated large

ensembles of autonomous intelligent classifier agents

engaged in data mining for knowledge discovery

purposes by means of perpetual dynamic exploration

of large and expanding data bases.

2. Classifiers and Intelligent Metrics

A basic classifier is illustrated in Figure 1.

Figure 1. Illustration of a basic classifier that maps a

fixed finite set of input parameter values, x's, into an

output parameter value, y, where y is a measure of

bivalent, probabilistic, or fuzzy classification of an

entity or event with respect to some specific class, C.

 Its purpose is to map a fixed finite set of input

parameter values, x's, associated with an individual

entity or event, E, into an output parameter value, y,

where y is a measure of association of E with respect

to some specific class, C associated with the output

node that produces y. Basic classifiers may be

designed so that y-values reflect bivalent, probabilistic,
or fuzzy classification associations. Classifiers may

have more than one output node where each node is

associated with a different outcome class. The

intelligence metrics described here may be applied to

each separate output node. Experience suggests,

however, that in polyvalent or n-class classifier

applications it may be wiser to construct n basic

classifiers, each having a single output node, instead of

constructing one classifier with n output nodes. This is

because the performance of internal mathematical

"features" is reduced when these features must be

"shared" in the computation of multiple outputs.

 There are many mathematical methods for

developing classifiers. These range from simple

function fitting techniques to highly sophisticated

statistical and neural network ensemble modeling. The

intelligence performance metrics described here, the

area under the ROC plot and the separation index, are

applicable to all underlying mathematical models used

in classifiers. Classifiers undergo training, learning, or

fitting - terms generally used interchangeably.

Classifiers also undergo validation, testing, and

deployment phases. The intelligence metrics described

here are intended for use in all of these phases.

3. Classifier Output Interpretation

A classifier should be designed from the outset to

perform bivalent, probabilistic, or fuzzy classifications,

and used in the same way throughout training,

validation, testing, and deployment phases. The

fundamental epistemological and mathematical

differences among these three classifier types must be

clearly understood at the outset of designing a

classifier. These differences are based on the

understanding and interpretation of the output

parameter, y, and this interpretation is based on the

meaning given to the class assignment data used in

developing and using the classifier.

 If membership data is bivalent, meaning that

entities or events are perceived as belonging discretely

to one or the other of the positive or negative bivalent

classes, then y-values must also be interpreted as

bivalent, negative or positive, usually expressed as 0

and 1 respectively. Classifiers trained with bivalent

class data will often produce continuous output values

for y on these intervals during all classifier phases.

When this is the case, threshold decision rules are

needed to force bivalent classification. If membership

data is probabilistic, meaning that entities or events are

perceived as belonging probabilistically to the positive

pole of the bivalent classes, then output y-values may

be interpreted directly as probabilities of positive class

membership. For example, y= .8 could mean there is

a .8 probability that the patient is a member of the

bivalent set “bivalent diabetics.” If membership data is

fuzzy, meaning that individual entities or events are

perceived to be partly in the positive class and partly in

the negative class, then output values should also be

interpreted as fuzzy membership values [1]. In this

case, y=.8 could mean that the patient has a .8 degree

of membership in the fuzzy set "fuzzy diabetics."

 Again, once a classifier system is designed to be

bivalent, probabilistic, or fuzzy, it should be

considered that way during training, validation, testing,

and deployment phases. The interpretation of the

classifier output must remain consistent throughout all

of these phases.

4. The Area Under the ROC Plot

The first proposed metric of classifier intelligence is

the area under the ROC plot. It is derived from ROC

methodology which has origins in signal detection

theory [2,3]. ROC methodology addresses forced

choice bivalent classifications [4-7]. The "receiver" is

a human, mechanical, or computerized agent

performing the bivalent classification. "Operating

characteristic" refers to the performance of the

receiver. The central feature of ROC methodology is

the ROC plot constructed from bivalent frequency

distribution data specified as independent variable

values, y, paired with dependent known classification

values yk where yk=0 means full membership in the

negative class and yk=1 means full membership in the

positive class. A basic bivalent classifier developed,

for example, with neural network methodology, will

have an output variable, y, with continuous values on

the 0-1 interval. For purposes of applying ROC

methodology, this output variable y is the independent

variable which when coupled with the known

classification values, yk=0 and yk=1 provides the data

with which to compute a ROC plot. Figure 2 shows

simulated bivalent frequency distributions of output

values from a neural network classifier at the

completion of a successful training operation. In this

figure, the abscissa variable is y, the continuous neural

network classifier output variable. The ordinate is the

frequency at which various y values occur in both the

negative class where yk=0 (grey bars), and in the

positive class where yk=1 (black bars). It is apparent

from these contrasting distributions that there are

approximately the same total number of negative

cases as there are positive cases. This means that the

prevalence or incidence of positive events is

approximately .5 in the training data. Special

consideration needs to be given when there is a

mismatch of prevalence in data used in training and

deployment. Additional special consideration needs

to be given to misclassifications cost differences,

meaning differences in false positive and false negative

costs. Prevalence and misclassification cost issues are
very important. They have been addressed elsewhere,

however more work is needed [8-12].

Figure 2. Bivalent frequency distributions of output

values, y, from a classifier at the completion of a

successful classifier neural network training operation.

 The ROC plot is a plot of sensitivity

versus 1-specificity as the independent variable

traverses its full range. In the case of the classifier, the

independent variable is y, the output of the classifier,

and it ranges continuously from 0 to 1. Specificity is

computed as the normalized (scaled to 1) integral of

the negative distribution. Sensitivity is computed as

the normalized integral of the positive distribution

subtracted from 1. The ROC plot computed from the

data summarized in the frequency distributions in

Figure 2 is the ROC plot with an area of .97 hugging

the upper left corner of the grid in Figure 3. It is

displayed in this figure with 4 other ROC plots that

were constructed at earlier stages in the training

process. These ROC plots are empirical ROC plots

meaning that they are directly computed from the y, yk

paired data. Since sensitivity and specificity are

computed independently, ROC plots are, in a sense,

prevalence independent.

 The area under the ROC plot is readily computed

by numerical integration. This area is a statistic. It is

the probability that a randomly drawn event associated

with the positive class will have a higher value for the

independent variable, y, than a randomly drawn event

associated with the negative class. In statistics it is

computed as the Mann-Whitney version of the

Wilcoxan statistic. The ROC plot area ranges from 0,

which indicates full separation with positive cases

having lower y values than the negative cases, through

.5 which indicates the poorest performance (no

separation of bivalent classes), to 1 which indicates the

full separation of bivalent classes with positive cases

having the higher y values. Classifiers with high ROC

plot area values may be further differentiated in

performance by means of the separation index.

Figure 3. ROC plots from simulated neural network

classifier output data after 0, 400, 600, 800, and 1000

training cycles.

5. The Separation Index

The second measure of classifier intelligence is the

separation index introduced here. The separation

index is a measure of the difference between the

median y-values of the positive and negative frequency

distributions. It is computed by first determining the

median y-value for all negative cases, nmed, and the

median y-value for all positive cases, pmed. Subtracting

nmed from pmed yields a value on the -1 to +1 interval.

To map this value onto the 0 to 1 interval, 1 is added

and the result is divided by 2. The formula for the

separation index (SI) is as follows:

 SI = (pmed – nmed + 1) / 2 (1)

6. Index Complementarity

The ROC plot area and the separation index both

directly measure class separation whereas other

measurements used in developing classifiers generally

measure the fitness of the data to the underlying

function. For example, the root mean squared (RMS)

error measures the square root of the sum of the

squares of the differences between known, yk, and

fitted, y, outcome class values. This is clearly not a

direct measure of separation. Since the task of a

classifier is separation and since performing this task

well requires intelligence, the ROC plot area and the

separation index may be justifiably thought of as

measures of intelligence since they both directly

measure separation. Furthermore these indices

complement one another. For example, if a ROC plot
area of 1.00 indicating full separation is obtained, the

separation index may be used to further differentiate

training states or to compare classifiers. If poor ROC

plot areas are obtained, the separation index could

again indicate better or worse separation in comparing

different training states or different classifiers.

7. Indices for All Classifier Phases

The ROC plot area and the separation index may be

computed for the training, validation, testing, and

deployment phases of classifiers for purposes of

classifier evaluation and inter-classifier comparisons.

Plotting values of these indices after each training

cycle provides a graphical representation of the rate of

intelligence development (“learning curves”) during

training as well as other characteristics of training,

such as stalling, nonmonotonicities, and reversals.

Figure 4 illustrates training plots for 1000 training

cycles in the simulated experiment referred to earlier,

and Table 1 contains a partial tabular listing of the data

plotted in Figure 4.

Figure 4. ROC plot area and separation index values

in a simulated 1000 cycle neural network classifier

training operation. (These might be thought of as

“learning curves.”)

What is being simulated here is the training of a neural

network in which individual training cases are selected

by bootstrap sampling [13-16]. This random sampling

with replacement strategy is what gives rise to early

higher variances tapering off to later lower variances

for both indices over the training cycles. Experience

suggests that this kind of sampling provides a weight

jogging effect which aids in avoiding local minima

entrapment. Also of note in this simulation is the

observation that index values idealistically approach an

asymptotic maximum. Perhaps other new metrics for

intelligence could be devised for training plot features

such as variance tapering and asymptotic convergence.

 If validation is pursued concurrently with training,

perhaps use of the area under the ROC plot and the

separation index as new intelligence metrics will yield

new ideas about training termination. Perhaps the

intelligence metric values derived from testing data

evaluated after training and validation will be

considered the appropriate values to use for comparing

classifiers. Monitoring intelligence metric values

periodically during deployment operations would be a

good way to assure that the trained classifier is holding

up and that environmental data sources are not drifting

too far from the original data populations associated

with training, validation, and testing data sources

 TRAINING ROC PLOT SEPARATION

 CYCLE AREA INDEX

0 .58 .57

100 .56 .54

200 .62 .59

300 .57 .57

400 .62 .60

500 .65 .60

600 .74 .66

700 .80 .70

800 .88 .75

900 .97 .80

1000 .97 .80

Table 1. ROC plot area and separation index values in

a simulated 1000 cycle neural network classifier

training operation.

8. Fuzzy and Probabilistic Membership

ROC methodology can be easily extended to include

fuzzy and probabilistic classifications [17,18]. This is

done by simply considering every entity or event as

having relationship to both the negative and the

positive bivalent poles of the class associated with the

dependent variable, yk. Let the membership

association value be yk for the positive class, and 1-yk

for the negative class. Thus, a fuzzy or probabilistic

membership value of yk=.82 corresponds with a .82

association value in the positive class and a .18

association value in the negative class. This simple

generalization subsumes classical ROC methodology,

because yk=1.00 corresponds to an association value of

1.00 in the positive class and 0 in the negative class,

and yk=0 corresponds to an association value of 0 in

the positive class and 1.00 in the negative class. After

positive and negative class association values are

determined, sensitivity and specificity values are

computed from the resulting bivalent frequency
distributions and the ROC plot is computed from these

sensitivity and specificity values as before. The area

under the ROC plot is computed by numerical

integration as before or by a weighted Mann-Whitney

version of the Wilcoxan statistic with tied data [18].

Likewise, the separation index is derived from the

resulting bivalent frequency distributions as before.

9. Discussion

Using metrics such as those proposed here to measure

classifier intelligence for evaluating and comparing

classifier systems will become increasingly important

in environments where large cadres of automated

intelligent agents will be used in knowledge discovery

from data efforts by continuously data mining large

and expanding data bases. Efficient algorithms for

computing intelligence metrics will be needed in these

environments.

 Intelligence measurement could perhaps be only

one kind of measurement appropriate for evaluating

intelligent systems such as classifiers. Design

simplicity, computational ease, computational speed,

and the capability to map knowledge produced with

machine intelligence to human understandable

knowledge are other important features for which

metrics could be developed. Developing such

measures could be an important step foreword in the

progression of machine intelligence. Perhaps this step

will help expand human knowledge and understanding

in a more general way. By understanding intelligence

and related characteristics in machines, humans may

come to better understand these characteristics in

humans.

10. Conclusions

Two complementary metrics, the area under the ROC

plot and the separation index, have been shown to be

effective measures of intelligence in all phases of

classifier systems that produce bivalent, fuzzy, or

probabilistic classifications. It is proposed that these

metrics be standardized as measures of classifier

intelligence for purposes of evaluation and

comparison. The need for fast algorithms for assessing

intelligence has been suggested as well as the need for

measuring other attributes of classifiers and other

intelligent agents, specifically attributes related to

parsimony, and human knowledge derivation. The

need for more work on prevalence and

misclassification cost issues in classifiers has also been

mentioned. It has been suggested that understanding

anthropomorphized characteristics in machines may

promote understanding of related characteristics in

humans.

References

[1] Kosko, B., Fuzzy Thinking. New York: Hyperion,

1993.

[2] Green, D.M., Swets J.A., Signal Detection Theory

and Psychophysics. New York: John Wiley & Sons,

Inc., 1966.

[3] Lusted, L.B., “Signal Detectability and Medical

Decision-making,” Science, vol. 171, pp. 1217-1219,

1971.

[4] DeLeo, J.M. “Receiver Operating Characteristic

Laboratory (ROCLAB); Software for Developing

Decision Strategies That Account for Uncertainty,” in

Proceedings of the Second International Symposium

on Uncertainty Modeling and Analysis, IEEE

Computer Society Press, 1993, pp. 318-325..

[5] DeLeo, J.M. “The Receiver Operating

Characteristic Function as a Tool for Uncertainty

Management in Artificial Neural Network DecisionMaking,” in Proceedings of the Second International

Symposium on Uncertainty Modeling and Analysis,

IEEE Computer Society Press, 1993, pp. 141-144.

[6] Lusted, L.B., “ROC Recollected,” Medical

Decision Making, vol. 4, pp. 131-135, 1984..

[7] Zweig, M, Campbell, G., “Receiver Operating

Characteristic (ROC) Curves: a Fundamental Tool in

Clinical Medicine,” Clinical Chemistry, vol. 39, pp.

561-577, 1993.

[8] DeLeo, J., Dayhoff, J., “Medical Applications of

Neural Networks: Measures of Certainty and Statistical

Tradeoffs,” in Proceedings of the International Joint

Conference on Neural Networks (IJCNN’01) IEEE

Press, 2001, pp. 3009-3014.

[9] DeLeo, J., Rosenfeld, S., “Essential Roles for

Receiver Operating Characteristic (ROC)

Methodology in Classifier Neural Network

Operations,” in Proceedings of the International Joint

Conference on Neural Networks (IJCNN’01) IEEE

Press, 2001, pp. 2730-2731.

[10] DeLeo, J.M., Rosenfeld, S.J., “Important

Statistical Considerations in Classifier Systems,” in

Proceedings of the Fourteenth IEEE Symposium on

Computer-Based Medical Systems, IEEE Computer

Society, 2001, pp. 285-293.
[11] Dayhoff, J.E., DeLeo, J.M., “Artificial Neural

Networks: Opening the Black Box,” Cancer 2001 Apr

15; vol. 91 Suppl. 8, pp. 1615-1635, 2001.

[12] Remaley, A.T., Sampson, M.L., DeLeo, J.M.,

Remaley, N.A., Farsi, B.D., Zweig, M.H.,

“Prevalence-Value-Accuracy Plots: a New Method for

Comparing Diagnostic tests based on Misclassification

Costs,” Clinical Chemistry, vol. 45(7), 1999, pp. 934-

941.

[13] Efron, B., Tibshirani, R.J., An Introduction to the

Bootstrap. New York: Chapman and Hall, 1993.

[14] Efron, B., The Jackknife, the Bootstrap and Other

Resampling Plans. Montpelier, Vermont: Capital City

Press, 1994.

[15] Hall, P., The Bootstrap and Edgeworth Expansion.

New York: Springer-Verlag, 1992.

[16] Campbell, G., DeLeo, J.M., ``Bootstrapping ROC

Plots,'' in Computing Science and Statistics:

Proceedings of the Twenty-Seventh Symposium on the

Interface, 1995, pp. 420-424.

[17] DeLeo, J.M., “The Fuzzy Receiver Operating

Characteristic Function and Medical Decisions with

Uncertainty,” in Proceedings of the. First International

Symposium on Uncertainty Modeling and Analysis,

IEEE Computer Society Press, 1990, pp. 694-699.

[18] Campbell, G., DeLeo, J.M., “Fundamentals of

Fuzzy Receiver Operating Characteristic (ROC)

Functions,” in Computing Science and Statistics:

Proceedings of the Twenty-First Symposium on the

Interface, 1989, pp. 543-548.
Problems of Performance Measurement in Locally-Organized Systems

Vadim L. Stefanuk

Institute for Information Transmission Problems

Russian Academy of Science

Bolshoy Karetny per. 19,

Moscow, 101447, Russia

 

This research was partially supported by grant from the program “Mathematical modeling, Intellectual computer systems and

non-linear mechanical systems control” of Russian Academy of Sciences, project # 2.24, and by grant from Russian Fund for

Basic Research, project #02-01-00955.

ABSTRACT

It is shown that many of the modern intelligent systems belong to a

wide class of distributed systems. The external behavior of such a

system is governed with criteria which induce only partial ordering

among various systems. This partial order does not allow building an

analytical metrics in the space of such systems, the fact making

systems largely indistinguishable. This explains the existence of

many versions of AI systems made for the same purpose, like Expert

Systems shells, which are frequently differentiated only through their

secondary properties. The situation may be compared with that of the

use of Pareto sets in theory of games where all different solutions

belonging to a Pareto set are considered to be intrinsically similar.

KEYWORDS: distributed systems, performance metrics

1. INTRODUCTION

Practical considerations do require introduction of some

metrics to measure system performance. If a scalar value is

used to estimate the performance it is quite natural to define a

metrics based on this scalar. In some cases there are some

serious grounds for this. The information throughput [1], an

average income obtained in the system for Stock Exchange

trend forecast may be considered as examples of the scalar

valued system.

However when the performance is measured by a vector

value it obviously brings some additional problems. In the

simplest case a weighted vector is used reducing the problem

to the scalar type formulation. However in many cases it is

impossible to find weights uniformly suitable for the whole

performance space like the distance measure in an Euclidean

space. It is for this reason some other “optimality”

considerations are used like Pareto sets in Game Theory [2]

and in many other applications (in particular, in Mathematical

Economics.)

2. DISTRIBUTED SYSTEMS

Another wide area where the search for a performance metrics

is doomed to failure, and which will be discussed in this

presentation, is the area of distributed systems, referred here to

as the locally-organized systems (LOS) [3, 4]. We use the

latter term in order to emphasize the fact that in such systems

a unique common measure defining the whole system

performance does not exist. Instead, one has a collection of

local criteria for the system components [5], which are used

concurrently and individually and hence can not be reduced to

a unique scalar or vector value.

The exact meaning of notions, used in our description of

locally organized systems depends on the subject domain.

However it is possible to demonstrate some classes of models

for which the situation is known in advance:

• The subsystems are deterministic or probabilistic finite

automata with the deterministic or probabilistic

interaction (collective behavior, collective behavior of

automata, automata games )

• Subsystems are automata with the continuous sets of

actions with the interaction of deterministic type (multiple

access communication systems, sociological and

economics models).

• Subsystems are finite automata with a fuzzy interaction

(Expert Systems).

• Subsystems are the enterprises or individuals who are

involved in a complex monetary and commodity

circulation.

• Inhomogeneous technical systems, where people are also

involved (man-machine systems).

• Subsystems are interacting programming modules

(interactive high level languages, Artificial Intelligence

systems).

Anyway, probably due to some practical considerations,

many distributed systems of this kind are treated as centralized

ones allowing some metrics for the whole system
performance. Thus, in the majority of multi-agent systems [5]

there exists a central point collecting all the information from

agents and assigning tasks to the individual agents. It is

important to note that such a centralization is out of question

for LOS.

The proposed presentation will be organized in the

following way. First, we will introduce a formal definition for

LOS. Then we will list the reasons why LOS becomes a

necessity in many applications, later describing the methods

typically used for analyses and study of LOS.

We still are able to speak about “goals” for LOS.

However the goal of the locally organized system (LOS) is the

provision of a normal functioning of its subsystems, instead of

reaching a certain system goal. That is why the criteria for the

choice of an adequate system version may include such

considerations as expediency, survivability, openness,

elasticity and etc., which have been studied in many

applications and in many subject domains [6] and which will

be briefly reviewed in our presentation. Any of the above

criteria may take only one of two values Yes/No or 1/0.

Obviously, the induced partial order gives only limited

possibility to compare systems.

The mentioned criteria are subject to changes from one

group of problems to another still having the property that

they do not allow to compare systems by introducing any

reasonable metrics to supplement the above step-wise partial

order. A number of examples will be listed - from collective

behavior to manipulator control and interacting programming

modules of an Artificial Intelligence system. This list and

other considerations will demonstrate that the fraction of

systems belonging to the class of LOS will probably only

grow with time.

Finally some mathematical models of an intelligent

warehouse [7] are used to illustrate the whole approach in a

step by step manner.

3. CONCLUSION

As many of the modern intelligent systems are organized in a

local way, being collectives of interacting components each

having its own goal and behavior, our results show that it is

not simple to find a suitable metrics for measuring system

performance to be used for making a comparison among

intelligent systems with respect to each other. Probably this

explains the existence of many versions of AI systems made

for the same purpose, like Expert Systems shells, which are

frequently differentiated only through their secondary

properties.

Our analyses is formal and strict. However it is

supplemented with a practical example of an intelligent

warehouse which admit different type of organization of

storage place.

4. REFERENCES

[1] Fano R.M., Transmission of information. A statistical

theory of communication., MIT Press, N.Y., 1961.

[2] Luce R.D. & Raiffa H. Games and Decisions. Introduction

and critical survey, Harvard University, N.Y., 1957.

[3] Stefanuk V.L. Collective Behavior of Automata and the

Problems of Stable Local Control of a Large Scale System,

Second International Joint Conference on Artificial

Intelligence (IJCAI), London, pp. 51-56, 1971.

[4] Stefanuk V.L. “General Approach to Locally-Organized

Systems”, Proceeding of Advanced Summer Institute 1999

(ASI’99) of the Network of Excellence in Intelligent Control

and Integrated Manufacturing Systems, Leuven, Belgium. pp.

39-45, 1999.

[5] Proceedings of IJCAI-01, “Multi-agent systems”, pp.

1017-1224.

[6] Stefanuk V.L., Stability of Local Control in a System with

Contralinear Interaction of Subsystems, Proceedings of

European Control Conference (ECC'93), vol. 1, Groningen,

pp. 117- 119, 1993

[7] Stefanuk V.L., Algorithms to Optimize Retrieval in a

Distributed Memory System, Proceedings of RAN: Theory

and Control Systems, #4, 1999. - pp. 93-99.
A CONTROL SCHEME FOR MEASURE OF PERFORMANCE AND EFFICIENCY OF

TACTICAL COOPERATIVE ROBOTS

Amir Shirkhodaie

Intelligent Tactical Mobility Robotic Research Laboratory

Department of Mechanical & Manufacturing Engineering

 Tennessee State University

Ashirkhodaie@TNState.edu

ABSTRACT

We have discussed technical issues regarding supervised world

perception modeling and task planning of cooperative mobile

robots performing tactical tasks in unstructured environment.

We have introduced a hierarchy Supervisory Controller for

robust cooperative task deployment of heterogeneous semiautonomous robotic vehicles. Primarily, we have described

functional and modular architecture of the Supervisory

Controller and presented our strategies for separation of

supervisory functions according to their level abstraction,

complexity, precedence, and intelligence. Furthermore, we have

discussed control schemes for measure of performance and

measure of efficiency of our cooperative robots tested under

different task situations. Results indicate the hybrid Supervisory

controller performs satisfactorily in most simulated cases.

Keywords: Cooperative Robots, Supervisory Control,

Measure of Performance, and Measure of Efficiency.

1. INTRODUCTION

Cooperative robots are critically important for solving a number

of real-world problems. Their applications span from civilian

search and rescue missions to military battlefield reconnaissance

and surveillance covert operations to deep space scientific

research explorations. In very near future, potential commercial

applications of cooperative robots are foreseen to be ubiquitous.

The cooperative robots while benefit from inherent parallelism,

their robust control is both multi-folded and challenging to

achieve and sustain reliably. Many different techniques and

approaches for mobile robots control have been developed. The

proposed control techniques in literature can be, categorically,

classified as either deliberative, reactive, or hybrid in nature.

The control schemes that tend to be more deliberative require

relative more knowledge about the world. They use this

knowledge to predict the outcome of their actions, an ability that

enables them to optimize their performance relative to their

model of the world. Deliberative reasoning about task planning

of cooperative robots requires strong assumptions about this

world model. Primary knowledge upon which the reasoning is

based on, should be consistent, reliable, and certain. In a

dynamic world, where objects may have arbitrarily moves (i.e.,

in a battlefield or a crowded hallway), it is potentially risky to

rely on information that no longer be valid. Instead, world

representational models are generally constructed at run-time

using a combination of past knowledge gained about the

environment and exteroceptive sensory data.

At the other end of the spectrum, the reactive control systems

attempt to tightly couple perception and action in order to

achieve faster robot response in dynamic and unstructured

worlds while minimizing computational overhead. With a

purely reactive control system, it is rather difficult to achieve

planned deliberated tasks consistently. This is mainly due to

variability in world uncertainty and lack of robot’s knowledge

and ability in resolving conflicts between competitive world

perceptions in a given situation. In most cases, a wrongly

selected world perception may cause the robot an unrecoverable

deadlock situation or failure.

The temporal inconsistency and stability of the environment and

the robot’s immediate sensing inadequacy for a task are typically

coupled. Difficulty in proper localization of a robot and the way

that the robot perceives its surrounding world also yield possible

situations that typically grounds erratic conflicts in decision

making process of the robot. To demonstration this notion,

consider figure 1 that illustrates spatial configuration of three

cooperative robots. Should each robot presume the other two

robots as obstacles or as its teammates approaching it

unintentionally? One answer is it would be dependent on nature

of the task and how the navigational modes of the robots are

defined. If the robots had reactive behavior, they would

probably try to find a way out of the crowd or avoid the

deadlock situation. If the robots had deliberative behavior, each

robot might refer to its chronicle memory and try to reason why

the other robots are there in the first place before it makes any

decision. Situation like this example can occur frequently

during task execution of cooperative robots within a limited

work environment. Hence, it is responsibility of an intelligent

supervisory system to deal with such situations in a manner that

causes least perplexity to plan execution of robots.

An overview of common conceptions of the behavior-based

approaches is given by Mataric [1]. Brooks [2] describes four

key concepts that lead to behavior-based robotic: situatedness,

embodiment, intelligence, and emergence. The design of

behavior-based systems is often referred to as a "bottom up"

process, but this offers not so much to determination of the

structure of the system as to a basis in physical sensing and
action, and incremental development of sophistication from

simple to complex. Namely, there are structured in terms of

observable activity that they produce, rather than traditional

functional decompositions. The activity producing components,

behaviors, compete for actuator resources as well as share

perceptions of the world rather than any centralized

representation.

Recently, hybrid deliberative/reactive robotic architectures have

emerged that rely more heavily on explicit world representations

and tend to combine many aspects of traditional AI symbolic

methods with situated-based reasoning. They operate based on

abstract representation of the world in light of providing faster

response, better robustness, and more tractable than deliberative

and reactive systems. Hybrid architectures permit

reconfiguration of reactive control systems based on available

world knowledge through their ability to reason over the

underlying behavioral components. However, building of such

hierarchy systems requires compromise and full utilization of

reactive and deliberative systems to maintain the desirable

system performance and efficiency.

An overview of approaches and issues in cooperative robotics

has been also reported in [3,4,5]. Parker [6] has demonstrated

multi-robot target observation using the ALLIANCE

Architecture, where action selection consists of inhibition

(through motivation behaviors). As opposed to her architecture,

Pirajanian and Mataric [7] have developed an approach to multirobot coordination in the context of cooperative target

acquisition. Their approach is based on multiple objective

behavior coordination extended to multiple cooperative robots.

They have demonstrated a mechanism for distributed command

fusion across a group of robots to pursue multiple goals in

parallel. This technique enables individual robot to select

actions that no only benefit itself but also benefit the group as a

whole. A significant amount of work in this area is being

conducted by researchers at NIST. Their hierarchical control

architecture is shown to have capability in controlling several

unmanned mobile robotic vehicles in unstructured environment

using a hierarchy platoon level control scheme where the robots

follow a designated leader while maintaining a fair distance

apart [14].

Nonetheless, intelligent control of multi-agent robots is both

complex and challenging. The complexity of the task is

contributed to a number of compounding factors including:

multi-agent task decomposition, task distribution, resource

allocation, sensory world perception modeling and data sharing,

pattern recognition and reasoning, skill learning and adaptation,

communication networking, man-machine interaction, and

others. For intelligent strategic task planning, execution, and

monitoring of cooperative robots, one should be concerned with

many of above technical challenges.

In this paper, we will present a hybrid hierarchical

deliberative/reactive robotic architecture called, Supervisor

Mobility Controller - in short "Supervisor" for controlling a team

of cooperative robots. By combining reactive and deliberative

navigational schemes, we have created a set of group

navigational techniques assisting task deployment of the robots.

The Supervisor has been tested for localization and dynamic

cooperative task planning of robots. Performance and efficiency

of the system is measured on a physical robotic system

consisting of five cooperative robots.

The proposed Supervisor control system has been developed

under FMCell software [18]. FMCell provides tools for world

perception construction and sensors modeling in 3D virtual

simulation environment. Other features of the software include:

high-level object-oriented environment with embedded robot

behavioral modeling tools, fast image processing tools, AI-based

inference engines for knowledge processing and reasoning, and

soft computing developmental tools such as neural networks,

fuzzy logics, and genetic algorithms for modeling, simulation

and validation of control strategies.

2. SUPERVISOR ARCHITECTURE

The Supervisor was originally designed for control of semiautonomous robots operating under one central control unit.

The modular software implementation of the Supervisor is

presented in Figure 2. Supervisor has a hierarchical

architecture and designed to handle hybrid reactive/deliberative

task deployment of the cooperative robots. Detailed description

of different functions of this Supervisor is behind the scope of

this paper and can be found elsewhere [8,9]. The hierarchy

architecture consists of sensing, planning and acting

components. The system allows direct interaction of the human

operator at different levels of abstract task planning, execution,

and monitoring with minimum restriction. An exclusive

language allows for mission plans of the virtual robots with

concise details. The supervisor can be used for control of both

simulated and physical cooperative robots.. The same syntax

as used for programming of physical robots is used for

programming of the simulated robots. This feature

significantly reduces development and implementation times

Figure 1. Three Semi-autonomous Cooperative Robots in a 

Tactical Formation.
and makes it submissive to both software and hardware

requirements.

There are many good reasons for working with simulated

robots: “learning often requires experimenting with behaviors

that may occasionally produce unacceptable results,” [10].

Making mistakes on real physical robots can be sometimes

quite costly and dangerous. Nolfi et. Al. [17] suggests that

simulated robots be used in developing control systems for real

robots when certain special conditions are taken into account.

However, one should not expect control systems that are

evolved in a simulated environment to behave exactly the same

in a real environment. Environment has many degrees of

uncertainties, in particular in case of mobile robots where

skidding and slippage are inevitable. To reduce this ambiguous

problem, one can literally add certain degree of white noise to

parameters having anticipated uncertainties. For instance, in

dead-reckoning computation of robots, one may add random

noise to offset encoders and compass readings of the robots and

mimic irregularity in mobility behavior of the simulated robots

modeled as if they are operating in unstructured environment.

In our approach, we consider simple unknown world with

geometrically identifiable obstacles. Construction of the world

perception is achieved at three levels. Initially, fragmented

sensory data gathered by robots are filtered, analyzed, and

streamlined into a more geometrical representation, i.e., lines,

and curves. Next, robots are localized through using a

localization technique [15]. Figure 3 illustrates gradual world

perception construction of a robot navigating a simulated

indoor environment. Secondly, localization of robots within the

environment is performed to localize the robot via a target

tracking technique [16]. The refined world perception

information along with estimated global positions of the located

robots are registered in a lookup table for further processing.

From the world perception information, we extract world

geometrical features. A connectivity check is performed to

identify neighboring line segments that closely represent a

certain environment feature. The line segments belonging to a

detected environment feature are marked accordingly along

with a degree of certainty in another lookup table. To infer a

robot's task plan and the world model, we use the latter lookup

[16].

For deliberative task planning, cooperative robot team members

must be planned based on a certain governing behavioral

scheme. Some approaches in literature considered evolution of

such governing behaviors as a way of demonstrating the

cooperation among robots in an emergence way [10]. Some

others attempted to motivate robots into cooperation [6]. While

others have proposed consenting agents with mechanism for

negotiation among cooperative agents [11]. For practical

purposes, algorithmic techniques have shown good stability and

performance [12,13]. We have developed different cooperative

robots behavior-based algorithms for different applications [19-

21].

Supervisor supports different multi-agent robot platform, (see

Figure 4) and a number of cooperative navigational deployment

strategies. Each robot by itself can be assigned variety of

distinct behavior-based navigational schemes while engaging in

a cooperative task. Each subsumption-based navigational

behavior controls interaction of a robot with the environment

and arrives at common decisions regarding turning and steering

requirements of the robot for a given situation.

Supervisor handles task planning of cooperative robots using a

high-level object-oriented language that will be discussed

shortly. Supervisor recognizes two groups of task instructions.

The first group of task instructions deals with mobility

requirements of robotic vehicles and the other group deals with

sensory requirements of cooperative robots (i.e., an instruction

that activate the robot to perform an environment sensing

operation).

Supervisor handles the Deliberative tasks with the highest

priority while paying coarse attention to detailed of mobility of

robots. In coordinated task planning of cooperative robots,

coherence task planning of cooperative robots is very critical.

Commands

Dispatcher

Fuzzy Logic 

Mobility Controller

Obstacle

Obstacle

Obstacle Physical

Test Bed

Commands

Scheduler & 

Synchronizer

Motors/Actuators

Commands

Generation

Emergency

Detector

Sensory Data

Acquisition & 

Processing

Sensory Data

Analysis & Fusion 

World Perception

Model Construction 

World Perception

Recognition &

Learning 

Context-based

World Model 

Depository

Simulation Model

Calibration and

Synchronization

Parallel / Serial

Tasks Plans

Sequencing

Motion/Action 

Plans

Generation 

Robotic Vehicles

Mission Planner 

A Priori Knowledge

Multi-Agents

Coherence 

Scheme Selection

Single-Agents 

Navigational

Behaviors

Reactive 

Perceptive 

Deliberative

Mobility Control

Kinematics and

Dynamics Models

Emergency Control

Single- Agents

Mobility Schema

Composition

Physical/Virtual

World

Calibration

Command Control

Exception

Handler

Adaptive

Man-Machine

Interface

Virtual

Environment

Commands

Dispatcher

Fuzzy Logic 

Mobility Controller

Obstacle

Obstacle

Obstacle Physical

Test Bed

Commands

Scheduler & 

Synchronizer

Motors/Actuators

Commands

Generation

Emergency

Detector

Sensory Data

Acquisition & 

Processing

Sensory Data

Analysis & Fusion 

World Perception

Model Construction 

World Perception

Recognition &

Learning 

Context-based

World Model 

Depository

Simulation Model

Calibration and

Synchronization

Parallel / Serial

Tasks Plans

Sequencing

Motion/Action 

Plans

Generation 

Robotic Vehicles

Mission Planner 

A Priori Knowledge

Multi-Agents

Coherence 

Scheme Selection

Single-Agents 

Navigational

Behaviors

Reactive 

Perceptive 

Deliberative

Mobility Control

Kinematics and

Dynamics Models

Emergency Control

Single- Agents

Mobility Schema

Composition

Physical/Virtual

World

Calibration

Command Control

Exception

Handler

Adaptive

Man-Machine

Interface

Virtual

Environment

Figure 2. Modular Architecture of Supervisor Mobility Controller

Figure 3. (a) A Simulated Robot World, (b) Reflection of

Robot’s Range Sensors Data as the Robot Navigates its Path, (c)

Constructed Robot World Model Based on Sensory Data

Fusion.

robot

(a) (b)

(c)
Equally important, is the interaction between deliberative and

reactive behavioral arbitration of cooperative robots. At the

highest level of task planning, the mission plan can be designed

using structured syntax and semaphore directives. Refer to

Figure 5, for an example of cooperative robot task

programming scheme. Supervisor has a parser that interprets

abstract textual task commands as shown in the example. A

double linked list buffers all task instruction with a time stamp.

Def Task1

Channel: 1,2,3 // Assign Channel 1,2,3 For Comm

Vel: 5,5,5 // Assign Velocity 5 in/s to Robots 1,2,& 3

Acc: 3,3,3 // Assign Accelecration in/s2 to Robots 1,2,3

Loc: (10,30),(40,30),(60,-20) // Assign 3 Vectors for the

 // the robots to follow.

NavBeh: 1,2,1 // Set Navigation Behaviors of Robot 1, 2,

 //& 3 to Navigational Id # 1,2, 1 Respectively.

MC: 1 //Turn on Continuous Processing Mode

Go: 1,1,1 // Execution queued motion commands

 // for Robots 1, 2, and 3.

Wait t >4000 // Wait until 4000 ms time is elapsed.

CapImg: 1,,1 //Have Robots 1 and 3 capture images

 // of their direction.

LMScan: ,1, //Have Robot 2 Scan Using Laser

 //Measurement Range Finder.

Go: 1,1,1 //Now, have all 3 robots to perform their sensing.

WaitAck: 1,1,1 //Wait Until All Robots Acknowledge

 //Completion of Their Specific Tasks.

MC: 0 //Turn off Continuous Processing Mode

EndDef //Terminate Task Block.

Figure 5. An Example of Task Programming of Cooperative

Robots .

For synchronization purposes, all motion commands and

sensory data acquisition operations are buffered in a temporary

transit queue until a trigger statement is executed. Execution

of buffered commands is performed in the order that the

commands have been received. In the example below, for

instance, some preparatory commands set up communication

channel, and preset velocity and acceleration of robot. The

command Loc assigns three positional vectors to the robots to

follow. The parameters of the Loc function define position and

heading angle requirement of corresponding robot in inch and

degree respectively. Next, proper navigational behaviors are

assigned to individual cooperative robots. Instruction Go

causes execution all buffered task plans to begin with the

predefined task requirements, i.e., robots should presume a

velocity 5 in/sec and an acceleration of 3”/sec2. With

continuous processing mode on, execution of commands

proceeds right after executing Go statement without any delay.

Next, the task command processing is delayed for 4 seconds

before robots 1 and 3 are assigned to capture image along their

heading direction, while the robot 2 is assigned to scan its

heading for detection of obstacle. The last Go statement causes

transmission of proper sensory data acquisition instruction to

robots.

3. CONTROL SCHEMES FOR TASK PLAN

TESTING OF COOPERATIVE ROBOTS

In practice, a mission plan may comprise of many sub-task

plans – requiring cooperative to do many tasks either in

synchronization or independent but in harmony. Each sub-task

plan may consist of many symbolic notions of activities.

Figure 6 shows one such sub-task plan where four robots are

employed to rapidly create a consolidated world perception of

their unknown environment. In this scenario, each robot's

behavior is set to be reactive. Without explicitly defining

individual navigational task of each robot, we applied a simple

deliberative learning strategy. In this technique robots are

rewarded more for exploring unvisited area of the world. The

Supervisor performs two operations in this scenario - gathering

range data from individual robots and fusing the range data to

Figure 6. (a) A Simulated Scenario of Cooperative Robots

World. (b) World Perception Model Based on Range Sensor Data

Fusion of Cooperative Robots (c) World Perception of each of

four individual Robots.

Adaptive 

Fuzzy-Logic

Reflexive

Virtual

Potential-Field

UniformGuarded

Non-Uniform

Guarded

Adaptive 

Neural-Net-Based

Rule-Based

Laser-based

Gate-Ways

Following

Landmarks

Following

Search

 Targets

Group 

Marching

Group 

Formation

Terrain

Mapping

Targets

Tracking

Follow-The

Leader

NeighborReferenced

Tactical

Maneuvers

Terrain

Following

Turn

Cooperative Navigational

Deployment Strategies

Actuation

Subsumption-based

Navigational Behaviors

Multi-Agents

Assignment

Adaptive

Schemes

Steer

Priority

Figure 4. Cooperative Behaviors and Behavior-based Navigational 

Alternative of Multi-Agent Robots Supported by the Supervisor 

Mobility Controller. 

Adaptive 

Fuzzy-Logic

Reflexive

Virtual

Potential-Field

UniformGuarded

Non-Uniform

Guarded

Adaptive 

Neural-Net-Based

Rule-Based

Laser-based

Gate-Ways

Following

Landmarks

Following

Search

 Targets

Group 

Marching

Group 

Formation

Terrain

Mapping

Targets

Tracking

Follow-The

Leader

NeighborReferenced

Tactical

Maneuvers

Terrain

Following

Turn

Cooperative Navigational

Deployment Strategies

Actuation

Subsumption-based

Navigational Behaviors

Multi-Agents

Assignment

Adaptive

Schemes

Steer

Priority

Figure 4. Cooperative Behaviors and Behavior-based Navigational 

Alternative of Multi-Agent Robots Supported by the Supervisor 

Mobility Controller. 
construct a world perception model as shown in the upper right

hand corner of the figure 6. As the sonar range data become

available, the world model is progressively constructed. The

world model is partitioned into matrix of cells. Each cell will

have a certain potential depending upon total number of sonar

data point that it contains. The Supervisor creates a gradient

field based on potential of each cell. The robots are then

command to explore world environment with low gradient

slope. To encourage the robots to explore the entire area, they

are rewarded more for exploring the areas that they have not

visited before. By adjusting the rewarding weights, the

navigational behavioral of the robot are tuned. To prevent the

robots from over exhaustion in their search, a time-based

terminating condition is imposed that is if the new world

discovery slows than behind a threshold over a fixed period of

time, then the navigational search should stop. The algorithm

was tested on a team of five cooperative robots. One robot

from the group becomes as anchor and monitors operations of

its other team members using its on-board surveillance camera.

Localization of cooperative searching robots is performed using

an image processing technique that localizes the cooperative

robots in the image frame of the surveillance robot. The

physical robotic test bed is shown in Figure 7. Coordinates of

the robots in the image frame are next mapped to the world

coordinate with the center of the camera at the origin [22]. A

total of twenty tests were conducted to assess performance of

the cooperative robotic team in detecting a total of 10 cans of

cokes randomly located on the floor within an area of 20'x20'.

Figure 8 shows the results of experimentation. On average over

86.5 percent of time, all 10 targets were located with 99 percent

confidence level. Figure 9 indicates the total time efficiency of

the system in detecting all targets. While time efficiency of

the system can serve as a basis for evaluation of intelligence of

the robots, but some small adjustment in the control behaviors

of the robots can have significant effect on overall efficiency of

the system. Perhaps for reducing wondering time delay of the

robots.

4. CONCLUSION

Cooperative robots have many practical applications. In this

paper, we have discussed architecture of a Supervisory

Mobility Controller with capability to facilitate deliberative/

reactive task deployment of cooperative robots. Some of the

issues regarding robot's intelligence requirement at robot

platform level and at cooperative robots level were discussed.

To have fully functional cooperative robots, many research

issues need to be addressed and taken into consideration. At

present time, there is no single established standard or in testing

procedures of the cooperative robots' intelligence. Measure of

performance and efficiency of intelligent robotic system are

(a)

(b)

Figure 7. (a) Physical Cooperative Robot Test Bed.

Localization of Cooperative Robot Using Visual

Servoing Technique.

0

10

20

30

40

50

60

70

80

90

100

13579

1 11 31 51 71 9

Figure 8. Performance Measure oCooperative Robots in

Detecting Random Targets.

Trials

% Success

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 1 2 13 14 1 5 16 1 7 1 8 19 2 0

Trials

Time (min)

Figure 9. Time Efficiency of Cooperative Robots in

Detecting Random Targets.
very subjective and conditional. Minute adjustments in control

parameters of a system can have significant influence over

performance and efficiency of the system. Furthermore,

incompatibility and heterogeneity among the robots makes it

quite difficult to relate performance and efficiency of one

system to another - even from one robot to another in the same

platform and ranking.

5. ACKNOWLEDGEMENT

This work was partly funded by the US Army TACOM under

DAAE07-01-C-L-065 and Penn State Applied Research

Laboratory Under Prime Grant Number DAAD19-01-1-0504

from the US Army Research Office. The support from both

funding agencies is acknowledged.

6. REFERENCES

[1] Mataric, M., "Behavior-based Control: Examples From

Navigation, Learning, and Group Behavior," J. of

Experimental and Theoretical AI, 9, 1997.

[2] Brooks, R., "Intelligence Without Reason," Computers

and Thoughts - IJCAI 91, 1991.

[3] Arkin, R. C., Behavior-Based Robotics. The MIT Press,

Cambridge, MA, 1998.

[4] Cao, Y. U., Fukunaga, A. S., and Kahng, A. B.,

“Cooperative Mobile Robotics: Antenedents and

Directions, Autonomous Robots, 4(1): 7-27, 1997.

[5] Mataric, M.J., “Issues and Approaches in the Design of

Coolective Autonomous Agents. Robotics and

Autonomous Systems,” 16 (2-4), pp. 321-331, 1995.

[6] Parker, L. E., “Cooperative Robotics for Multi-target

Observation,” Intelligent Automation and Soft

Computing, 5(1): 5-19, 1999.

[7] Pirjanian, P., and Mataric, M. J., “Multi-robot Target

Acquisition Using Multiple Objective Behavior

Coordination,” in Proc. Of the IEEE International Conf.

on Robotics & Automation, San Francisco, 2000.

[8] Shirkhodaie, A., Goetz R., Patkar, A., Mansour M.

Aderogba, S., and Avalareddy N., “Behavior-Based

Tactical Navigational And Mobility Control of Tandem

Ground Robotic Vehicles”, in the Proc. of SPIE

Unmanned Ground Vehicle Tech. II, Orlando, FL, 2000.

[9] Shirkhodaie, A., Rangavajhala, S., "Supervised Tactical

Mobility Behavior Modeling And Control of Multi-Agent

Robotic Vehicles,” in the Proceedings of SPIE,

Unmanned Ground Vehicle Technology II, April 24-25,

2001, Orlando, FL.

[10] Liu, J., and Wu, J., Multi-Agent Robotic Systems, The

CRS Press International Series on Computational

Intelligence, 2001.

[11] Rosenschein, J. S., "Consenting Agents: Negotiation

Mechanisms for Multi-Agent Systems," in Proc. of Int.

Joint Conf. On AI, pp. 792-799, 1993.

[12] Miyata, N. Ota, J., and Aiyama Y., Asama, H.., and Arai,

T., "Cooperative Transport in Unknown Environment -

Application of Real-time Task Assignment," Proc. of the

2000 IEEE Int. Conf. On Robotics and Automation, pp.

3176-3182, 2000.

[13] Mansour, M., and Shirkhodaie, A., “Navigational

Behaviors Fusion for Multi-Agent Intelligent Tandem

Mobile Robots,” in the 2000 Japan-USA Symposium on

Flexible Automation International Conference on

Manufacturing Systems” Innovation for the 21st Century,

Ann Arbor, MI, July 23-26, 2000.

[14] Lacaze, A., "Hierarchical Architecture of Coordinating

Ground Vehicles in Unstructured Environments," in

Proc. of 2001 PERMIS Workshop on Measuring

Performance and Intelligence of Systems, September 4,

2001.

[15] Hajjawi, M., and Shirkhodaie, A., "Comparative Visual

Team Working and Target Tracking of Mobile Robots",

in the 34th IEEE Southeastern Symposium on System

Theory, March 17, 19, 2002.

[16] Rangavajhala, S., and Shirkhodaie, A., and "VisualServoing and Navigation of Mobile Robots For Target

Tracking Applications With Laser Sensory Data

Fusion," in the Proceedings of the IEEE SSST 33rd

Southeastern Symposium on System Theory, March

18-20, 2001.

[17] Nolfi, S., Evolutionary Robotics: Exploiting the Full

Power of Self-Organization, Connection Science, pp.

167-183, 1998.

[18] Shirkhodaie A., "FMCell: User Manual - Vol I and II,"

Tennessee State University, Department of Mechanical

and Manufacturing Engineering, April 2002.

[19] Adergoba, S., and Shirkhodaie, A., “Navigation and

Visual-Terrain Tracking of Multiple Mobile Robots

With Vision and Sonar Using Neural Network

Techniques,” in the IMCC’2000 International

Manufacturing Conference in China,“ August 16-17,

2000, China.

[20] Avalareddy, N., and Shirkhodaie, A., “Communicationbased Tactical Task Planning of Cooperative Tandem

Mobile Robot Vehicles Under an Intelligent

Supervisory Mobility Controller,” in the 2000 JapanUSA Symposium on Flexible Automation International

Conference on Manufacturing Systems”, Ann Arbor,

MI, July 23-26, 2000.

[21] Avalareddy, N., Shirkhodaie, A., “Cooperative Task

Planning, Execution, and Monitoring of Mobile Robots

Under a Supervisory Mobility Controller”, in the IEEE

Southeastcon 2000 Conference, Nashville, TN, April 7-

9, 2000.

[22] Hajjawi, M.,"Visual Servo Tracking and Localization of

Cooperative Robots," M.S. Thesis, Mechanical

Engineering, Tennessee State University, August 2002.
Competitive relative performance evaluation of neural controllers for

competitive game playing with teams of real mobile robots

A. L. Nelson and E. Grant

Center for Robotics and Intelligent Machines

Department of Electrical and Computer

Engineering

North Carolina State University

Raleigh, NC 27695-7911

T. C. Henderson

Department of Computer Science

School of Computing

3190 Merrill Engineering Building

University of Utah

Salt Lake City, Utah 84112

ABSTRACT

In this research, we describe the evolutionary training of artificial

neural network controllers for competitive team game playing

behaviors by teams of real mobile robots (The EvBots). During

training (evolution), performance of controllers was evaluated

based on the results of competitive tournaments of games played

between robots (controllers) in an evolving population.

Competitive tournament fitness evaluation does not require a

human designer to define specific intermediate behaviors for a

complex robot task. Intermediate behavior selection and

evaluation becomes an implicit part of winning or losing games

in a tournament. The acquisition of behavior in this evolutionary

robotics system was demonstrated using a robotic version of the

game ‘Capture the Flag’. In this game, played by two teams of

competing robots, each team tries to defend its own goal while

trying to ‘attack’ another goal defended by the other team. Robot

controllers were evolved in a simulated environment using

evolutionary training algorithms and were then transferred to real

robots in a physical environment for validation. Evolutionary

robotics makes use of several distinct types or levels of

performance evaluation. The work presented here focuses on the

competitive relative tournament ranking metric used to drive the

evolutionary process. After a population has been evolved, a

second metric is needed to evaluate the quality of acquired gameplaying skills. We use a post training evaluation method that

compares the evolved controllers to hand coded knowledge-based

controllers designed to perform the same task. In particular, a

very poor controller, and high quality controller give us two

points on a continuum that can be used to rank the evolved

controller quality.

Keywords: evolutionary robotics, performance metrics, mobile

robot colonies, evolutionary neural computing, behavioral

robotics

1. Introduction

1.1 Evolutionary robotics

Evolutionary robotics (ER) is a relatively recent addition

to the field of autonomous robot control research. ER

focuses on the automatic design of model-free robot

controllers using evolutionary computing methods. Over

the course of last decade, proof-of-concept research in the

field of ER has been conducted. Much of this work was

done using computer-based simulations only [1][2][6].

Examples of ER research conducted with real robots

include the evolution of walking behaviors in hexapod and

octopod robots [7][8], and the evolution of simple

behavioral controllers for small mobile robots[9][10]. The

later include the development of phototaxis behaviors

[11][12] and of simple object avoidance [10] and

navigation [13]. For recent reviews of the field of ER see

[14][15][16][13].

1.2 Intelligence performance metrics in ER

An ER application may make use of one or more types of

fitness or intelligence metrics. These include 1)

measurement of behavior quality for selection during

training, 2) measurement of quality of transference from

simulated training environments to the real world, and 3)

post training evaluation of acquired behaviors. In this

paper, we will focus mainly on development and

formulation of the first type of metric. In addition, a post

training metric will be applied to evaluate the quality of

the best member of an evolved robot controller population.

In evolutionary robotics, a training performance fitness

function is applied to provide selective pressure to an

evolving population of robot controllers. The goal is to

develop intelligent behavior with regard to a particular

task. The nature and implementation of a machine

learning application affects the way in which its

intelligence can be measured. Behavioral robotics

applications impose tight physical constraints on the

expression and evaluation of learned behaviors. In

particular, a subtle issue arises regarding the point of view

of the intelligent robotic system and the point of view of

the external observing human who is trying to evaluate that

system’s intelligence. These different points of view are

known as the proximal (local) and distal (external)

viewpoints respectively [3].

In most cases, fitness functions used in ER are formulated

by designers from the distal point of view. Designers

naturally develop fitness functions based on their own

understanding of the desired behavior and system
dynamics. In doing so, they implicitly incorporate

information from their own complex distal world model

into the fitness evaluation of the evolving agent(s). Since

the evolving agent has no such model, the evolved

behaviors tend to be very brittle. In the following section

we will discuss some of the properties of such distal

absolute fitness functions. As an alternative to absolute

fitness functions we will present the formulation and

experimental testing of an aggregate relative fitness

evaluation method for ER.

1.3 Absolute vs. aggregate relative fitness

functions in ER

Fitness in ER systems is often measured as an absolute

scalar function to be maximized or minimized during

training (see [4][5], for examples of this type of fitness

function).

Absolute fitness functions used in evolving behavioral

robotics applications are problematic for the following

reasons: 1) Often, a forced learning plateau arises when the

fitness metric is maximized or minimized, 2) Human

biases are incorporation into the metric, 3) Each new

robotic application requires a new and often difficult-toformulate metric, and 4) For many complex behaviors, the

knowledge required to specify an adequate absolute

training fitness function is equivalent to that that would be

required to design a rule/knowledge based controller by

hand.

In this work we study a relative aggregate fitness selection

metric for the evolution of behavioral robotics controllers.

In particular, we focus on behaviors that can be formulated

into competitive games played between two or more

mobile robots. The metric produces a relative ranking in

an evolving population of controllers, but does not give an

absolute measure of fitness with respect to an external

scale. Evaluation of evolving controllers based on their

relative abilities to perform a task has the affect of

aggregating evaluation of intermediate behaviors into one

simple performance measure.

Tournament ranking evaluation partially eliminates the

need to generate a fully domain-specific fitness function.

As long as the problem can be formulated into a game that

is either won or lost, other details about the game need not

be included in the fitness function definition. Agents in an

evolving population that receive higher relative rankings in

a tournament of games will be propagated preferentially

over agents receiving lower rankings. This reduces the

amount of human bias that is incorporated into the

performance metric. It also allows metrics to be specified

in cases where humans lack adequate information to

specify effective absolute fitness factors. This is important

to the long-term scalability of ER methods to

uncharacterized domains.

Implementing an aggregate competitive fitness function in

the domain of evolving robot controllers is qualitatively

different than similar implementations in pure computer

science domains. In [17] tournament selection was applied

to evolve neural networks to play computer Checkers with

impressive results. In that work, the game board

configurations were deterministically coded and fed

directly into the neural networks. In ER, training

environments must maintain an explicit I/O coupling

analogous the robot’s physical functional environment.

This coupling must enforce a realistic proximal view onto

the evolving agents. Modeled sensors must report only

information that could be produced by the real sensors.

Modeled motor actuators must in turn produce an

alteration in the robot’s frame of reference that

appropriately alters the modeled sensor values (i.e. after

the robot moves, it sees something new). This forms a

controller-actuator-sensor loop with relational dynamics

must be the analogous to those experienced by the real

robots. The temporal fidelity of this controller-actuatorsensor loop must be maintained.

Another factor that complicates implementation of

reinforcement learning of behavior in ER systems is that,

many potential controller configurations may not lead to

detectable expression of a desired behavior in a finite

amount of time. In such cases, the search space must be

restricted so that controllers will display detectable

differences in performance even when that performance is

measured at the aggregate level of win or lose. One way to

do this is to formulate the competitive evaluation

environment so that most controllers, even very poor ones,

will eventually win at least a fraction of the games if their

opponents are as poor or poorer than themselves.

2. The evolutionary robotics research testbed

Before presenting a formulation of the relative aggregate

fitness function used in this work, we will provide a brief

summery of the ER research testbed used here. This will

provide a context for the training fitness function

formulation. The ER research testbed consists of a

physical colony of autonomous mobile robots, and an

evolutionary neural network training environment. The

real robots use a vision-based range finding sensor

emulation system to locate them selves in a physical maze

environment. The evolutionary neural network training

application uses simulation of the robots and their

environment to evolve neural controllers to drive the

robots. Controllers are evolved in simulation and then

transferred to real robots for testing and verification.
2.1 The EvBot platform and environment

The physical verification and testing of evolved controllers

developed in this research was conducted using a colony of

small mobile robots named the EvBots (EVolutionary

roBOTs)[18]. These robots are computationally powerful,

fully autonomous and capable of performing all control,

computing and data management on board. The robots

move and steer using differential speed control of parallel

drive wheels.

A physical maze environment was constructed for the

mobile robot colony. Robots and objects in the

environment were fitted with colored skirts to aid in vision

based sensing of the environment. A fully assembled

EvBot and the physical maze environment are shown in

panels (a) and (b) of Figure 1 respectively.

(a)

(b)

Figure 1. Photographs of (a) a fully assembled EvBot and

(b) the physical robot maze environment containing

several robots.

2.2 Video range-finding emulation sensors

Each robot was fitted with a small video camera. Images

captured from the video cameras are processed into object

range date before being feed into the neural controllers.

The vision-based range-finding sensor systems on the

robots used fixed geometric properties of the physical

maze environment to calculate the ranges and angles of

materials. Using color and position, the vision system

could detect walls, robots, and goals. The goals are

stationary cylinders and were used in the robotic ‘Capture

the Flag’ game.

At each sensor update interval, and for each object type,

range and angle values were calculated over the horizontal

field of view of the robot’s camera. A vector of range

values was produced for each object type. Angular data

was implicitly encoded in the order of the range values

reported in each object range data vector. Object type

information was not explicitly given to the robot neural

controllers. Controllers were only given these resulting

numerical data vectors. All associations relating distances,

angles, and object types must be learned by the neural

networks.

2.3 The evolutionary neural network architecture

In this research, a generalized evolvable neural network

architecture capable of implementing a very broad class of

network structures was used. The networks may contain

arbitrary feed forward and feedback connections between

any of the neurons in the network. Networks contain

neurons with heterogeneous activation functions including

sigmoidal, linear, step-threshold, and Gaussian radial basis

functions. Neurons include a variable time-delay

associated their inputs. This give networks the potential to

evolve temporal processing abilities.

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Figure 2. An example robot neural network controller

from an evolved population of heterogeneous neural

networks. The inputs to the network (left) are supplied by

the robot’s video range emulation sensors. The outputs of

the network (right) are interpreted as wheel motor speed

commands.

The connectivity and weighting relationships in a given

network are completely specified by a single twodimensional matrix W of real valued scalar weights.

Additional Information specifying neuron types and time

delays is given in a vector structure N with one formatted
field per neuron. W and N form the basis of the genetic

encoding for each network.

Figure 2 shows an example of a graphical representation of

an evolved neural network.

2.5 Network mutation

The elements in the weight matrix and neuron information

structures are acted upon directly by the genetic algorithm.

Formally, the genome for a network C can be specified by

the two dimensional matrix of real numbers

C = [W : N′] (1)

where N’ is a matrix of scalars extracted from the

formatted structure N.

During evolution, networks are mutated in three ways.

First, connection weight values can be perturbed. Second,

connections can be added or removed. Finally, neuron

units can be added or removed. Mutation of a network can

be formalized by the compound relation

C' ( ( (C))) M s M c M w= (2)

where C is the chromosome of the parent network and C’

is the resulting mutated offspring network chromosome.

Mw Mc and Ms are genetic operators that mutate the

weights, the connections, and the neuron structure of the

network respectively. Any or all of the different types of

mutation can occur during propagation.

3. The fitness function and genetic algorithm

3.1 Fitness function formulation

In this section, we will define the fitness function used in

this research. It is designed to be useful for team games

that can be formulated to produce a win-lose outcome.

These would include games like soccer and robot tag.

Many useful real world behaviors such as mine sweeping

and group searching behaviors in unknown terrain can be

also formulated into scorable team robot games.

The training fitness function is comprised of two over-all

parts: 1) select for controllers that win more games, 2)

identify and select against pathological controller

morphologies.

Only pathological cases that were known to lead to

catastrophic stagnation of the evolutionary process were

explicitly selected against. Two pathological controller

behaviors were actively selected against. The first

behavior was the production of constant continuous

reverse wheel speeds in one or both wheels throughout the

course of a game. The second pathological behavior was

that of becoming stuck and remaining stuck for the

duration of a game. These cases will be represented by

Boolean functions B1 and B2 denoting the presence (1) or

lack (0) of expression of each of the pathological

behaviors, respectively.

A population P of evolving robot controllers consists of a

fixed number P of neural networks. At the beginning of a

tournament, a set of game starting positions for robot

teams and goals is quasi-randomly generated and used for

every game in that tournament (generation). In every

training generation, a full tournament of games is played:

Each controller in the population P plays against every

other controller in P. After a tournament of games, each

controller is given a score that depends on the number and

quality of wins it achieved. For every pair of controllers in

the population, two games are played. In the first game the

first controller is used in the first team of robots and the

second controller in the second team of robots. In the

second game, the controllers are switched. This eliminates

any advantage a controller may have incurred due to the

random initial conditions used in the games of that

tournament.

A generalized form of the fitness function for an individual

controller after a tournament has been played can be

written as

F( p) = w+ d + n (3)

Where w, d and n are functions evaluating the

contributions of games won, games played to a draw and

expression of pathological behavior respectively during a

tournament. F(p) gives the relative fitness of the pth

controller from the population P.

The relative fitness of the robot controllers playing in one

game is dependent on the outcome of a reciprocal paired

game in which the starting positions of the controllers are

reversed. We will denote these paired games as g and g’.

Using these paired games we break the game wins into

three classes. In Class 1, a particular controller wins both

games g and g’. Games of class 2 are those in which one

controller wins one of the games but plays the other to a

draw. In class 3, one controller wins one game but loses

the other. Let G1, G2, and G3 denote numbers of games

won during a tournament of each of the three classes

respectively. Then the number of points awarded to the

pth controller for games won in a tournament is given by:

w = a *G1 + b*G2 + c *G3 (4)
Where a, b, and c are scalar weighting factors. The values

of a, b, and c are generally set so that a>b>c>0. This

reflects the evaluation that a controller that can win from

both of the starting positions of g and g’ is better than one

that can only win one of the two games.

Points are also given in the case that both of the games g

and g’ are played to a draw. If the robot agents of a

particular controller are closer to their opponent’s goal in

both games, that controller is awarded points. The d subfunction of equation (3) becomes

d = d*D1 (5)

where D1 denotes the number game pairs played to a

better draw and d is a scalar weighting factor. d is set to be

much less than a, b, or c so that results related to numbers

of wins dominate the tournament selection process.

Similarly, The n sub-function of 3 selecting against

pathological behaviors can be expanded as

n = a*B1+ ß*B2 (6)

Where B1 and B2 are Boolean functions denoting the

presence (1) or lack (0) of expression of each of the

pathological behaviors in the current tournament (these

were defined above as continual backward motion and

becoming permanently stuck, respectively). a and b are

scalar weighting factors and are generally set to be large

negative values relative to a, b, and c so there is a heavy

selective pressure against these behaviors even if they

result in wins.

3.2. The evolutionary training algorithm

Populations of fixed size P were evolved using an

evaluation, mutation, and replacement scheme. After each

tournament of games, controller population members p

were scored relative to each other using the performance

metric F(p) defined in equation (3). The population P was

then reordered from fittest to least fit before propagation.

The next generation population P_next was then

constructed from the union of the following three sets

derived from the current (parent) population:

_ { } { } { } 1 m 1 m m+1 −2m= ′ ′ P P next p K p U p K p U p Kp (7)

Where pm ∈ P is the mth individual of the ordered current

(parent) population P, mp′ is a mutated version of pm, and

P is the fixed population size. Equation (7) produces a

next generation composed of the following sets: 1) m of

the fittest controllers are transferred un-changed to the next

generation, 2) m of the fittest members of the controller

population are mutated using equation (2) and added to the

next generation, and 3) The remainder of the next

generation population is made up of the remaining fittest

remaining members of the current population. Although

this algorithm is technically a form of greedy mutationonly (μ + λ)-ES with incomplete replacement [19], the

game environment initialization for each tournament

affects the outcome of the games to such a degree that the

fittest member of the population could be eliminated. This

adds a high degree of probabilistic selection to the

algorithm.

4. Results and Discussion

4.1 The game

In this section, we present initial results and tests of one

population of robot controllers evolved to play robot

‘Capture the Flag’. In this game, there are two teams of

robots and two stationary goal objects. All robots on team

one and one of the goals are of one color (red). The other

team members and their goal are of another color (green).

In the game, robots of each team must try to approach the

other team’s goal object while protecting their own. The

robot which first comes within range of its opponent’s goal

wins the game for its team.

Evolved controllers were able to play and win games both

in simulation and when transferred to real robots in the

physical world. The best evolved controllers acquired

several distinct and testable abilities. These included

avoidance of ones own goal, wall avoidance, goaltending,

blocking and chasing robots from the other team, and

homing in on an opponent’s goal. Evolved controllers

generally acquired two or three behaviors and exploited

those rather than developing many behaviors for individual

situations.

4.2. Experimental setup

We will focus on an evolved controller that displays two

identifiable sub-behaviors: wall avoidance and selective

avoidance of the robot’s own goal. The controller was

evolved in a population of size P=6 for 366 generations.

The population replacement rate was set to 50% per

generation. The parameters relating to the performance

metric F(p) of equation (3) used in this training evolution

are given in Table 1 below.

Table 1. Fitness function parameter settings used to evolve

the controller studied in these experiments.

Parameter Game case

(g, g’)

Points

awarded

a win-win 20

b win-draw 15

c win-lose 10
d best draw 2

a backward -10

b stuck -2

4.3 Performance of evolved controllers in the

real world

Here we will present experiments aimed at measuring the

quality, and indirectly the intelligence, of the evolved

robot controller. These post-evolution evaluation

experiments were done in the real world using real robots.

Two knowledge-base controllers were developed. The

first was designed to be a difficult opponent to beat and

made use of both temporal and spatial information to avoid

walls, extract itself from corners, avoid team mates, block

opponents and home in on the opponent's goal. The second

controller was designed to be a poor player and produced

random wheel speed commands at each time step. In both

cases, sensor input and motor output ranges were restricted

to those allowed in the evolved neural network controller.

The evolved neural controller competed in a series of 20

real games against both the good rule-based and the

random controllers. This was done to rank the quality of

the evolved controller on a continuum including a good

controller and a very poor controller. All games were

recorded by collecting sequences of video images from a

camera mounted directly above the maze.

Ten games were played between the evolved neural

network and the good rule-base controller. Game initial

positions may give one of the teams an advantage. For

this reason, the set of games contained two games for each

starting configuration used. In the first, each team was in

a particular initial position, and in the second, the two

team’s starting positions were swapped. For these 10

games, five initial game positions were generated based on

the random seed states 11 to 15 of the MATLAB random

number generator. The games were conducted completely

in the real robot maze environment using real robots. No

games were allowed to proceed for longer than 200

controller update cycles (time steps). In addition, games

were terminated if all robots became permanently stuck.

A similar set of ten real games was also played between

the evolved neural network and the random (poor)

controller. Again, the same set of five game initializations

was used to conduct a set of 10 paired reciprocal games.

Tables 2 and 3 give the results of the games involving the

good knowledge based and the random controllers

respectively. The evolved neural controller, the good

knowledge based controller and the random controller are

denoted as “neural”, “rule” and “random” respectively.

Table 2. Results of the set of ten games played between

the evolved neural network controller and the hand coded

rule-based (good) controller.

Game Random

Init. state

Team1 Team2 Winner

1 1 neural rule neural

2 1 rule neural rule

3 2 neural rule neural

4 2 rule neural rule

5 3 neural rule rule

6 3 rule neural rule

7 4 neural rule rule

8 4 rule neural neural

9 5 neural rule rule

10 5 rule neural rule

Table 3. Results of the set of ten games played between

the evolved neural network controller and the hand coded

random (poor) controller.

Game Random

Init. state

Team1 Team2 Winner

11 1 neural random neural

12 1 random neural neural

13 2 neural random neural

14 2 random neural (none)

15 3 neural random neural

16 3 random neural neural

17 4 neural random neural

18 4 random neural neural

19 5 neural random (none)

20 5 random neural neural

Summarizing these results, the neural network controllers

won 3 out of 10 games against the good rule based

controller, or 30% and the good rule base won 7 out of 10

or 70% of the games. All of the games between the neural

network and the rule-based controller were played to

completion. The neural network controllers won 8 out of

10 against the random controller, or 80%. The random

controller was not able to win any games. In this case two

of the games were not completed because all the robots

became stuck or the game proceeded for more than 200

moves.

Figure 3 shows two example game results from the above

tables. These are games 2 and 12 respectively. The robots

are shown in their final end-game positions. The dotted

lines indicate the courses of the robots during each game.

In the first game (Figure 3 (a)), neural network controllers

(green, lighter dotted lines) compete against good

knowledge-based controllers (red, dark dotted lines). In

the second game (Figure 3 (a)), neural network controllers

(green) compete against poor random controllers (red). In

the first game, the rule-based robots reach the green goal

before the neural network based controllers can find the
red goal. On the other hand, in the second game, the

poorer random controllers are not able to make progress

toward the green goal and the neural network based

controllers eventually find the red goal and win the game.

 Red Goal

 Green Goal

 Green Robots

 Red Robots

(a)

(b)

Figure 3. Examples games played between trained neural

network controllers (green robots, lighter dotted lines) and

knowledge-based controllers (red robots, dark dotted

lines). In (a) good rule-based robots beat neural network

controllers while in (b) neural controllers eventually beat

random controllers starting from similar initial conditions.

4.4 Discussion

These results imply that the functional quality of the

evolved controller is somewhat less than that of the hand

coded rule base. This is compared to the base line

negligible abilities of the random controller. The evolved

controller was able to beat the random controller in every

game played to completion. It should be noted that

identical or equally matched controllers would receive the

same number of wins when competing against one another

in a set of reciprocal games. For example, the rule-based

controller would receive 5 out of 10 wins when played

against a copy of itself, or 50%. Also, the rule based

controller wins against the random controller 100% of the

time (data not shown).

Evolved behavioral robotics control systems do not yet

rival well designed sophisticated knowledge based

controllers. Nonetheless, These results indicate that an

evolve controller can beat a hand coded controller a

fraction of the time.

The method of post-training controller evaluation does not

influence the functionality of the relative tournament

fitness function used during evolution of the controllers.

This means it is possible to use post training fitness

evaluation functions that are inadequate to select for the

behavior, but can still measure behavior after it has been

evolved. Also, we can evaluate the evolved controllers

using human biases without imbedding such biased into

the evolved controllers.

5. Conclusions and future research

In this paper a new evolutionary robotics testbed was

described. A tournament training performance evaluation

function was implemented. This fitness function was used

to evolve controllers for teams of robots to play a

benchmark competitive game, ‘Capture the Flag’. The

fitness function was not based on specific features of the

game and could be used to evolve behaviors for other

multi-robot tasks.

An evolved controller was experimentally tested using real

robots in the real world. The evolved controller competed

against a sophisticated hand designed knowledge based

controller in a tournament and was able to win a fraction of

the games.

This work will be extended by applying the competitive

relative performance metric to other related mobile robot

behaviors and by investigating the related training

dynamics. We will investigate the possibility of improving

training measures without adding more task-specific

information. Alterations of the training metric could

include the weighting of some tournaments more highly

than others. It is also of interest to investigate the affects

of game initialization on controller evolution. This work

used random game initializations for each tournament.

Another approach would be to select several game starting

configurations and use only these. This method would run

the risk of controllers learning environment specific

behaviors that would not generalize well but could reduce

the negative effects of poor game initializations that result

in equal relative scores for all controllers and thus generate

no selective pressure.
References

[1] F. Gomez, R. Miikkulainen, Incremental Evolution of Complex

General Behavior, Adaptive Behavior, Vol. 5, pp. 317-342, 1997.

[2] J. Xiao, Z. Mickalewicz, L. Zhang, K. Trojanowski, Adaptive

Evolutionary Planner/Navigator for Mobile Robots, IEEE

Transactions on Evolutionary Computing, Vol. 1, no. 1 pp. 18-28,

2000.

[3] S. Nolfi, Evolutionary Robotics: Exploiting the Full Power of SelfOrganization, Connection Science, Vol. 10, pp. 167-183, 1998.

[4] M. Quinn, Evolving cooperative homogeneous multi-robot teams,

Proceedings of the IEEE / RSJ International Conference on

Intelligent Robots and Systems (IROS 2000), Takamatsu Japan,

Vol.3, pp. 1798 –1803, 2000.

[5] J. Kodjabachian and J.-A. Meyer, Evolution and development of

neural networks controlling locomotion, gradient-following, and

obstacle avoidance in artificial insects, IEEE Transaction on Neural

Networks 9(5) (September 1998)

[6] M. Potter, L. A. Meeden, A. Schultz, Heterogeneity in the coevolved

behaviors of mobile robots: The emergence of specialists,

Proceedings of the Seventeenth International Joint Conference on

Artificial Intelligence, Morgan Kaufmann, 2001.

[7] D. Filliat, J. Kodjabachian, J.A. Meyer, Incremental evolution of

neural controllers for navigation in a 6 1egged robot, In Sugisaka and

Tanaka, editors, Proc. of the Fourth International Symposium on

Artificial Life and Robotics. Oita Univ. Press, 1999.

[8] N. Jakobi, Running Across the Reality Gap: Octopod Locomotion

Evolved in a Minimal Simulation, Proceedings of the First European

Workshop on Evolutionary Robotics: EvoRobot’98, 1998

[9] D. Floreano and S. Nolfi. Adaptive behavior in competing coevolving species. Mantra technical report, LAMI, Swiss Federal

Institute of Technology, Lausanne, 1997.

[10] D. Floreano and F. Mondada , Evolution of homing navigation in a

real mobile robot. IEEE Transactions on Systems, Man, Cybernetics

Part B: Cybernetics, Vol. 26, No. 3, pp. 396-407, 1996.

[11] N. Jakobi, P. Husbands, I. Harvey. Noise and the reality gap: The use

of simulation in evolutionary robotics. In F. Moran, A. Moreno, J.

Merelo, and P. Chacon, editors, Advances in Artificial Life: Proc.

3rd European Conference on Artificial Life, Springer-Verlag,

Lecture Notes in Artificial Intelligence 929, pp. 704-720. 1995.

[12] Richard A. Watson, Sevan G. Ficici, Jordan B. Pollack, Embodied

Evolution: Distributing an Evolutionary Algorithm in a Population of

Robots, Robotics and Autonomous Systems, Vol. 39 No. 1, pp 1-18,

2002.

[13] I. Harvey, P. Husbands, D. Cliff, A. Thompson and N. Jakobi,

Evolutionary robotics: the Sussex approach, Robotics and

Autonomous Systems, Vol. 20, No 2-4, pp. 205-224, 1997.

[14] M. Mataria, D. Cliff, Challenges in evolving controllers for physical

robots, Robotics and Autonomous Systems , Vol. 19, No. 1, pp. 67-

83, 1996.

[15] S Nolfi, D. Floreano, “Evolutionary Robotics: The Biology,

Intelligence, and Technology of Self-Organizing Machines”, The

MIT Press, Cambridge Massachusetts, 2000.

[16] D. Floreano, J. Urzelai, , Evolutionary Robotics: The Next

Generation. In T. Gomi (ed.), Evolutionary Robotics III, Ontario

(Canada): AAI Books, pp. 231-266, 2000.

[17] K. Chellapilla, D. B. Fogel, Evolving an Expert Checkers Playing

Program Without Using Human Expertise. IEEE Transactions on

Evolutionary Computation, Vol. 5, No. 4, pp. 422-428, 2001.

[18] John Galeotti, The EvBot A Small Autonomous Mobile Robot for

the Study of Evolutionary Algorithms in Distributed Robotics, MS

Thesis, North Carolina State University, 2002.

[19] I. Ashiru, C.A.Czarnecki, Evolving Communicating Controllers for

Multiple Mobile Robot Systems, Proceedings of the 1998 IEEE

International Conference on Robotics and Automation, Vol. 4, pp

3498-3503, 1998.
Representing Ground Robotic Systems in Battlefield Simulations

M. Fields

U.S. Army Research Laboratory

Aberdeen Proving Ground, Maryland 21005-5066

mafld@arl.army.mil

ABSTRACT

As the Army continues to develop robotic systems for combat and

combat support missions, it needs to also develop representations

of intelligent system performance for its battlefield simulation

tools. These simulation tools differ considerably in their level of

abstraction, flexibility, and scale. Constructing the actual

performance model requires the modeler to consider three factors:

1) the purpose of the particular simulation study, 2) the overall

fidelity of the target simulation tool, and 3) the elements of the

robotic system that are relevant to the simulation study. In this

paper, we discuss a framework for modeling robotic system

performance in the context of a battlefield simulation tool. We

apply this framework to a model of the Demo III robotic system

used in the OneSAF simulation tool.

1. INTRODUCTION

As the U.S. Army continues to develop concepts such as the

Future Combat System (FCS) which include robotic assets,

it needs to develop representations of intelligent systems for

its battlefield simulation tools. This is a formidable task.

Robotics systems currently being developed range from

human-portable systems to large tracked or wheeled

vehicles. The level of control required for these intelligent

systems ranges from full-time remote operation to

intermittent supervisory control. The simulation tools

themselves have different levels of fidelity, different time

scales, and different intended uses. These tools allow the

technology developers, the analysts and the soldiers to

experiment with robotic systems in readily available, reconfigurable virtual environments. Technology developers

can use simulations to investigate system design questions

such as payload composition and placement, vulnerability,

and the appropriate sensor mix for autonomous mobility.

The soldiers and military analysts can use simulations to

develop Tactics, Techniques and Procedures (TTP) and

requirements for robotic systems based on parametric

studies involving key scenarios run over several terrain

databases representative of the types of environments the

robot is likely to encounter. Finally, well-designed

simulations can be used to identify critical near term

technology problems and help prioritize research efforts.

The purpose of this paper is to present a framework for

modeling intelligent systems which applies to a wide range

of battlefield simulation tools and simulation purposes.

Table 1 shows a breakdown of the types of models and

simulations used to support weapon systems development

and acquisition. The table gives a level of detail for the

model and some examples of the types of evaluations and

model output that can be expected at each level. In general,

models that fall in categories near the top of the table

represent systems more completely than simulations in

categories near the bottom of the table. Traveling down the

table, the size of the simulated world and the number of

entities represented in a battlefield engagement increases.

The categories are somewhat artificial; there are models and

simulations that fall somewhere between categories given in

the table. Two of the battlefield simulation tools currently

being used to examine robotic systems are the Combined

Simulation

Category Level of Detail Modeled PerformanceData/Models Required Type of Evaluation Example Output

First Principal Physics Physical processes Not applicable Design Feasibility Electric Field Strength

Engineering Components, Subsystems Possibly Subcomponent

level

Subsystem

Performance LADAR elevation map

One-on-One Complete Weapon

Systems

Component level System Performance Probability of successfully

navigating a cross-country path

Few-on-Few Small Military Units

(Squads to Company)

Component level

System level System

Effectiveness

Specific Exchange Ratio (SER)

Red losses caused by a specific

blue system

Force-on-Force Large Scale Combat Unit

 (Battalion or Higher)

System level Combat Utility Loss Exchange Ratio (LER)

Ratio of red to blue losses

Table 1 A hierarchy modeling and simulation tools used to support weapons systems development and
Arms and Task Force Evaluation Model (CASTFOREM),

and OneSAF. OneSAF has been used to support the Demo

III robotics program. CASTFOREM will be used to

provide weapon systems analysis for the FCS program.

The U.S. Army Training and Doctrine Command uses

CASTFOREM to study force composition and system

effectiveness at the brigade and battalion level. It is

primarily a force-on-force event-driven simulation.

Processes such as detection and individual system damage

are modeled stochastically using performance data provided

for each weapon system. The actions of combat units are

controlled by expert systems. Human participation is

limited to preparing the data, rule sets for the expert

systems, and scenario design [1].

OneSAF is a real-time distributed interactive simulation

tool developed by the U.S Army Simulation, Training and

Instrumentation Command. It is used to training soldiers

and to examine weapon systems concepts in brigade and

below scenarios. It can be used to model engagements

ranging in size from one-on-one encounter to battalion level

exercises so it spans several of the categories given in Table

1. The actions of individual or aggregate units are

controlled by behavior algorithms or human participants.

Since it is a distributed simulation, it can be used in

exercises involving different types of simulations,

simulators, and actual systems [2].

Constructing a particular robotic system model requires

the modeler to consider three factors. First, it is important

to keep in mind the purpose of a particular simulation study.

Examining the contributions of a robotic scout to a

battalion-level movement-to-contact scenario requires a

much different model than examining the effect of a

planning algorithm on autonomous driving. The overall

fidelity of the model is also a major consideration. Higher

fidelity simulation tools are compatible with physics-based

models of robotic systems and subcomponents. Lower

fidelity simulation tools use simple mathematical functions,

often given as lookup tables, to represent subsystem

performance. The quality of these lookup tables depends on

the experimental data that can be collected for the robotic

system being modeled. Finally, in constructing a model of

the robotic system, a modeler needs to consider the

elements of the robotic system that are relevant to the study.

For instance, the overall performance of the driving sensor

suite is certainly important for evaluating the contribution of

robotic systems to a scout mission. The performance of an

individual driving sensor may be less relevant.

In the next section, we present a general framework for

robotic models, which identifies the critical elements of a

robotic system that need to be represented in any battlefield

simulation. In the third section, we present some of the

modeling and simulation tools that have been developed to

support the Demo III robotics program. We also discuss

how these tools can be used to guide the development of

the robotic system performance models required by the

Force-on-Force models such as CASTFOREM.

2. A SYSTEM-LEVEL DESCRIPTION OF A

ROBOTIC SYSTEM

It is useful to take a systems engineering approach and

define a simulated robotic system as a collection of

interlinked subsystems. It is important to note that this

definition of the robotic system includes both the robotic

vehicle(s) and the operator. Robots (even autonomous

systems) cannot operate for long periods of time without

human intervention. In terms of a battlefield scenario, the

operator receives a mission and employs his robotic assets

to complete the mission. The diagram given in Figure 1

shows a notional robotic system consisting of five major

subsystems: Navigation; Communications; External

Command and Control; Internal Command and Control; and

the Payload System. Each of the major subsystems has

elements relating to the mechanical and software

components of the system.

In this notional robotic system, the External Command

and Control System consists of the human operator, the

human-machine interface, and any command decision aids

External Command 

& Control

Operator

Control Device

Planning Algorithms

Decision Aids

Payload

Mechanical Systems

Algorithms

Navigation

Mechanical Systems

Perception System

Planning Algorithms

Communication

Mechanical Systems

Algorithms

Internal Command 

& Control

Planning Algorithms

Decision Aids

External Command 

& Control

Operator

Control Device

Planning Algorithms

Decision Aids

Payload

Mechanical Systems

Algorithms

Navigation

Mechanical Systems

Perception System

Planning Algorithms

Communication

Mechanical Systems

Algorithms

Internal Command 

& Control

Planning Algorithms

Decision Aids

Figure 1. A systems engineering representation of a

sample robotic system
the operator may use. Depending on the application, this

system could be situated near the robotic vehicle or much

farther away. The Navigation System contains sensors and

other hardware and software such as perception and

planning algorithms. It resides on the robotic vehicle. The

Communication System consists of the radios that link the

human operator to the robot and the associated software.

Components of this system are on the robotic vehicle and

are also co-located with the External Command and Control

system. In one sense, the Navigation and Communications

systems are support systems intended to allow the payload

system on the robotic vehicle to contribute to the tactical

mission. The Demo III robot carried a Reconnaissance

Surveillance Target Acquisition (RSTA) package; other

payloads such as weapons, storage containers, or smoke

generators could also be represented. In general terms, the

composition of the payload system consists of mechanical

systems and supporting software algorithms.

The arrows in the pictures indicate the data flow in the

system. The operator uses the communication system to

give commands to the subsystems on the robotic vehicle. In

this notional system, commands are passed through an

intermediary command and control system that resides on

the robot itself. All the subsystems interact with the

simulated exercise, at least to some degree. They are

subject to degradation or damage from elements in the

simulated environment or from other entities in the exercise.

Some systems such as the navigation system gather

information from the environment to be used by systems on

board the robot or at the external command and control

station.

Diagrams similar to Figure 1 describe most weapon

systems; we are using it to illustrate the types of models and

supporting data needed to represent robotic systems in a

battlefield simulation. There are two distinctions between

robotic systems and most other weapons systems. First,

mobility system performance must consider not only chassis

response but driver reliability as well. Second, the robotic

system is distributed. Many processes are semiautonomous, requiring some level of operator participation.

In a robotic system, time-dependent performance measures

such as “average target identification time” include

communication time and two types of processing time

(robotic processing time and human processing time).

For any study, we need to represent the performance of

the major systems shown in Figure 1. Within engineering

level simulations, the major systems may be represented by

collections of high fidelity models of each of the processes

contained within the system. Another engineering or quasiengineering approach is to embed system components into a

simulation tool. This allows researchers to “virtually” test

hardware and software during the development process. As

simulations become more abstracted, less detail can be

included in the performance models. As researchers

construct abstracted models of each of the systems, it is

important to keep the following questions in mind. First,

what is the purpose of the system? How reliably does the

system accomplish its purpose? Finally, how quickly does

it accomplish its purpose? The speed and reliability

questions depend on collecting and analyzing experimental

data

A model of command and control must consider the

types of decisions the operator makes, the speed of the

decision process and the reliability of the decision process.

Right now, data about the performance of the decision

process are sparse. We can collect data from “virtual”

exercises using embedded decision software or from field

exercises.

To represent communications between the operator and

the robot in a large-scale simulation, we need to measure the

size and frequency of the messages. We also need to

measure the speed and reliability of the system. We can

gather some of this information from high fidelity

communication models. Most of the information should

come from integrated field experiments, where the robotic

system has to accomplish mission similar to those used in

combat.

Representing the navigation and payload systems also

depends on gathering data to determine the speed and

reliability of the process.

In the next section, we describe some of our current

models of the Demo III robotic system. Since the emphasis

of the Demo III program is on developing autonomous

mobility technologies, most of our modeling efforts have

been directed at autonomous mobility as well. Many of the

models we have developed describe processes within the

navigation system. Recently, we have begun developing

models for the other systems as well.

3. MODELING THE DEMO III ROBOTIC SYSTEM

Under the Demo III robotics program, the U.S. Army is

developing a small survivable experimental unmanned

ground platform (XUV) capable of autonomous operation

on rugged terrain. Although the primary focus of the Demo

III program is to develop and demonstrate autonomous

mobility technologies, the research was focused on

providing a robotic system for platoon-level scout missions.

The Demo III XUV was designed in accordance to the

NIST Real Time Control (4D/RCS) Reference Model

Architecture which is a hierarchical structure designed to

support the development of autonomous systems. Each

level in the hierarchy is referred to as a node. A node

consists of a behavior generation element, a value judgment
element, a world model element, a sensory processing

element and a knowledge database. The level of detail and

dimensions of the “world” in the world model is a function

of the node’s position in the hierarchy; a node controlling

several vehicles needs less resolution over a larger region to

plan than a node that controls a single vehicle. Nodes

receive goals, priorities, and plans from superiors and

produce goals, priorities, and plans for subordinates.

The five levels of the 4D/RCS architecture are Section

Level, Vehicle Level, Subsystem Level, Primitive Level,

and Servo Level. The section level receives a general plan

generated at a higher level such at the platoon level. This

plan contains a general command such as “Conduct a

Tactical Movement” and a plan based on a priori

information such as digital maps and situational awareness

overlays. A section level plan is generally used to control

multiple robots. At the Vehicle Level, the vehicle refines

the Section Level command by developing a plan based on

its world model which contains digital map data, situational

awareness information and low-resolution information

gathered by the on-board sensors. At this level, the vehicle

refines the Section Level plan to avoid relatively large

problem areas. At the Subsystem Level, the robot plans

paths to avoid obstacles in its path. The Primitive Level

controls the steering, acceleration, and braking of the robot.

The lowest level in the architecture is the Servo Level; it

controls the actuators for each of the subsystems.

Most of the modeling work for the Demo III system has

focused on small scale battlefield experiments and

engineering level studies. The primary purpose of these

models has been to support the system design process.

Systems are represented by collections of models for each

of the major systems given in Figure 1. As the technology

matures, the community can begin to develop system level

performance models. The challenge is to capture the system

characteristics contained in the current collection of

engineering and quasi-engineering level models into one

model or mathematical function representing each of major

systems. In the rest of this section, we discuss some of the

existing OneSAF models representing processes and subprocesses within the major systems. We are beginning to

examine performance models

 One of the modeling and simulation tools used to

support the Demo III robotics program is OneSAF. It is a

real-time battlefield simulation tool with a time step of

~0.015 seconds (66 Hz). It is an entity-level simulation so

all units are represented by collections of individual soldiers

or vehicles. The baseline OneSAF represents hundreds of

U.S. and foreign weapon systems and units. It has many

pre-programmed behaviors to control the movement and

interactions of those systems.

OneSAF is suitable for studying the interaction of

robotic systems with other systems participating in small

battlefield engagements. Because it is designed to interact

with human participants, OneSAF is also appropriate for

developing potential TTP for the use of robotic systems.

However, because of it is a real-time simulation, it may not

be appropriate for parametric studies requiring several

replications. OneSAF’s time step and battlefield

environment are also too coarse for most engineering level

studies. For example, its environment is not detailed

enough to be useful in evaluating the driving sensors or the

perception algorithms involved in autonomous mobility.

However, it can be used as a quasi-engineering simulation

tool for aspects of autonomous mobility such as vehicle

level planning that have a relatively long cycle time. We

discuss this application further in Section 3.1.

3.1 Autonomous Mobility

The autonomous mobility model for the Demo III robotic

system consists of the three main elements – a movement

equation, a sensory processing suite, and a planning suite.

The movement equation is a simple point model that

determines the position, velocity, and acceleration of the

vehicle at the end of each time step. The sensory processing

suite builds a world model from inputs provided by the

driving sensor suite. The planning suite uses the world

model to determine a suitable path for the robotic vehicle.

In the next couple of paragraphs, we describe the models

that we used to represent each of these elements. In general,

we can relate our modeling strategy to the 4D/RCS

architecture. We can represent many of the processes at the

Subsystem, Vehicle, and Section levels as algorithms that

are executed in real time as a part of the overall simulation.

However, We must depend on data and mathematical

abstractions to represent processes on the Servo Level.

The time step for OneSAF is approximately 0.067

second. In this amount of time, the robot travels less than 1

meter (The maximum speed for the XUV is 40 kilometers

per hours). We could excite the movement equation with

sub-meter resolution terrain. Some high fidelity terrain

databases for OneSAF are available, but they require large

amounts of computer memory to use them efficiently. In

our research, we use primarily 100 m and 30 m resolution

terrain databases. We use a relatively simple equation of

motion to model the motion of the XUV that uses the

current position, velocity, acceleration and desired direction

as input and gives the new position, velocity and

acceleration as output. This equation is used in OneSAF to

describe the motion of many of the ground vehicles.

Building a world model of the environment requires the

driving sensor suite to gather information from the

environment, process it, and present it to the planning suite

in the form of a world model. The time step in OneSAF
does not permit us to model the activities of the sensors

themselves. Instead, we model the process of generating the

world model from the simulated terrain database. In our

simulation studies, we want the robotic vehicles to respond

to relatively small obstacles such as woody vegetation and

ditches that are not available on the a priori map. These are

not features of a typical OneSAF terrain database. In our

prior research [4], we developed techniques to add these

features to existing OneSAF terrain databases. Figure 2

shows a section of a OneSAF terrain database with two

types of mobility obstacles positive obstacles shown dark

gray and negative obstacles shown in light gray. Each of

these obstacles is a polygonal feature with associated

parameters used to specify a probability of detection

function. Right now, detection of a particular obstacle

depends on the type of the obstacle, its size (length, width,

height), and the distance from the vehicle.

We produce two types of world models from the terrain

database information. The first type of world model is a

two-dimensional obstacle map with three types of pixels

(clear, unknown, and blocked). Unknown pixels indicate

areas within driving sensor range that are blocked from line

of sight. Blocked pixels show the location of detected

obstacles. Clear pixels indicate regions of the terrain that

are visible to the driving sensor suite and are free from

detected obstacles. This is a useful representation of the

obstacle detection process, but it is not the best

representation of the world model used by the XUV. The

XUV uses an elevation map to plan its near-term

movements. Figure 2 shows a two-dimensional obstacle

map in the context of a large battlefield map. In the

obstacle map, green indicates clear areas, yellow indicates

blocked areas, and gray indicates unknown areas. An

elevation map can be produced from the same data. The

heights in the elevation map are derived from two sources.

The terrain skin provides the underlying ground plane

elevation; detected obstacle polygons add or subtract

elevation from this ground plane.

The planning process on board the vehicle consists of

two planners: a near-term planner operating at the

subsystem level of the 4D/RCS architecture and a midrange planner operating at the vehicle level of the

architecture. In our work, we have developed two different

models of the Demo III robotic planning process. In

collaboration with the National Institutes of Standards and

Technology (NIST) and Science and Engineering Services,

Inc (SESI), we developed one model designed to examine

the performance of the actual robotic planning software in

tactical missions. This model requires software components

internal and external to the OneSAF simulation code. The

actual vehicle level planner was linked to the OneSAF

simulation code using the NIST neutral message language

(NML) to pass plans from the planner to the simulated

XUV. World models were passed from the simulated entity

to the planner allowing it to use information gathered by the

driving sensors on the simulated robot.

This same technique of linking actual software to the

simulation system can be used to include the near-term

planning system. In this case, the three-dimensional

elevation map is passed to the near-term planner from the

simulated world. Paths are passed back to the simulated

entity.

The linked simulation is a good method to gather data

about planning algorithm performance and to support the

algorithm development process. We can experiment with

the planners in different situations varying the tactical

situation and the obstacle distributions.

In the context of a larger exercise, possibly using

another simulation tool, it may be impractical to link the

actual code with the simulation. In this case, we want to use

surrogate algorithms or mathematical models that perform

similarly to the actual planning algorithms. We have used

simpler algorithms to represent the near-term planning

process. These algorithms use the two-dimensional obstacle

map to plan the path of the vehicle.

3.2 The External Command and Control

The external command system for the Demo III robotic

system consists of the operator, the operator control unit

(OCU), and the associate planning software. There are two

ways to represent the external command and control. The

first method is to put a human operator in the simulation

Figure 2. A battlefield snapshot showing the

robot and its obstacle map
loop. The Mounted Maneuver Battlelab and SESI used this

approach to support the Demo III program. The OCU was

linked to OneSAF via NML to pass plans and other

information between the operator and the simulated robotic

entity. This approach of embedding hardware and software

components into a simulation study allows researchers to

collect data about operator activity and workload. Such

information can be used to guide the design of effective

control devices. Information from the embedded model also

provides some system performance information that can be

used to construct performance models for complex

battlefield simulations.

We are beginning to construct an abstract model of the

human operator. In its simplest terms, the human operator

controlling one or more robotic assets is a server with a

queue of heterogeneous tasks to service. As with any

queuing problem, it is the frequency and service times for

each type of task that determines the workload on the

operator. In our model, there are two types of service

requests: mobility assistance requests and RSTA assistance

requests.

3.3 The Communication System

We are beginning to address communication system

models. Our approach is to model the amount of time

required to transmit a message between the robot and the

operator based as a function of message size. We are using

this model in connection with our queuing theory model of

the operator to introduce delays into requests for service and

operator response time.

3.4 The RSTA Payload System

The RSTA system model uses existing models from the

OneSAF simulation package. These models can represent

many systems, including camera systems, forward looking

infrared devices, and radar systems.

4. CONCLUSIONS

In this paper, we presented a framework for developing

models of intelligent systems which applies to a wide range

of battlefield simulation tools and simulation purposes. The

framework consists of five major systems: External

Command and Control; Communications, Internal

Command and Control, Navigation and Payload. Each of

these systems needs to be represented in a battlefield

simulation, regardless of the level of simulation. In lower

level simulations, we are able to use detailed models and/or

components of the robotic systems to represent the robotic

system. As the scale of the combat model increases, we

need to develop abstract performance models of the systems

within the robotic system. The validity of these

performance models depends on the experimental data used

to construct them.

5. REFERENECES

[1] Army Modeling and Simulation Resource Library

.http://www.msrr.army.mil/index.cfm?RID=MNS_A_1000185,

1998.

[2] R. Witman and C. Harrison, “OneSAF: A Product Line

Appraoch to Simulation”, Technical Report, The Miter

Corporation, 2001

[3] J. Albus,”4D/RCS Reference Model Architecture for

Unmanned Ground Vehicles”, Proceedings of the SPIE Vol.

3693 AeroSense Session on Unmanned Ground Vehicle

Technology, Orlando FL, April 7-8, 1999.

[4] M. Fields, “Modifying ModSAF terrain Databases to

Support the Evaluation of Small Weapons Platforms in

Tactical Scenarios”, ARL-TR-1996, Army Research

Laboratory, Aberdeen, Maryland, August 1999.
NICCI: A Multi-agent Cognitive Formation

Edward Dawidowicz

U.S. Army Communications-Electronic Command (CECOM),

Research Development and Engineering Center (RDEC)

Fort Monmouth, NJ

mailto:edward.dawidowicz@mail1.monmouth.army.mil

Keywords: Network-centric, command and control, battlefield, behavior generation, entity-relational network, hierarchy of

organization, knowledge, habitat, knowledge management, messages, and semantic network

Abstract

The demand for effective and expediently made decisions is

always in vogue. This is not surprising since making

correct decisions is essential for successful operations in

both the military and business environments. Decisions

require data processed for quality, concept and context.

The time is spent to weigh information for quality, to fuse

information into concepts, and to package information for

contextual relevance. The goal of information gathering and

processing is focused on existing or arising problems. The

network-centric paradigm allows for access to additional,

previously unreachable, sources of information. While

there is a benefit of getting more information, the time spent

to weigh information for quality, to fuse information into

concepts, and to package for contextual relevance is also

increasing. Without a dramatic decrease in informationprocessing time, the network-centric paradigm1

 will not

achieve its full potential.

The purpose of this paper is to propose a potential solution

to the information overflow problem. The solution is

proposed in a form of a system of cognitive agents, where

cognitive agents are located at every informationprocessing nod. These cognitive agents we will call

Intelligent Nodes. The Intelligent Nodes [4] consist of

machines and human-machine hybrids. Military processes

are used in this paper to illustrate the application of a

multi-agent cognitive framework as decision-maker

assistants for making collaborative decisions better and

faster.

Introduction

 

1

 Command and Control are responsible for making

decisions in transforming goals into actions. The

Intelligence supplies the information in regard to enemy

positions and analysis of enemy courses of action (COA).

In 2001, Dr. John Salasin has coined an appropriate name

for the Joint DARPA program as Network-centric

Infrastructure for Command, Control and Intelligence

(NICCI).

The battlespace today and in the future is different from

what we know based on the history of Twentieth century

campaigns which manifested themselves as trench warfare

of World War I and blitzkriegs of World War II. While the

military is trained to fight twentieth century style

campaigns, they find themselves today involved more and

more in smaller conflicts where asymmetric warfare tactics

are playing a bigger role. Stability and support operations

(SASO) are the predominate type of operations conducted

by the United States and coalition forces today.

With the emerging new technologies and tactics of this new

type of warfare, 'bigger guns' do not always lead to a

decisive victory and the need for a faster and more agile

force has been recognized [3]. This new force, in addition

to conventional weapons, will be armed with the latest

information technology, where radios and computers are

only part of the solution.

In this new warfare, the warfighter is no longer an isolated

entity. The warfighter becomes a part of the operational

environment where socioeconomic and physical laws play a

greater role in the success or failure of a particular mission

or even the war itself. The Network-centric warfare

paradigm [2] is a new way of looking at this type of future

battlefield. The Network-centric Infrastructure for

Command, Control and Intelligence (NICCI) program is an

attempt to find an out-of-the-box solution to an out-of-thebox problem.

 Solutions to complex real world problems require looking

at the problem space as a whole. If the solution is focused

only on satisfying a few constraints, the solution will have

weaknesses, which will be exploited by the opponent. The

solutions emerge in a planning process as elements of a plan

[1]. The incoming information influences and transforms

earlier constraints, or produces new ones. This dynamic

aspect of dealing with the real world complicates the

planning process. Decisions are always focused on

producing solutions which best satisfy the goals of

achieving a certain effect based on current and anticipated
conditions or states. The exchange of information relevant

to the process therefore becomes very important.

NICCI introduces a novel concept called the habitat. The

habitat in the conventional warfare schema is a set of

combat, and combat support entities focused on achieving a

given mission. Today habitats are more complex clusters of

military and civilian entities; where information interchange

is influenced by cultural, political, and socioeconomic

realities. The military, paramilitary and civilian constituents

of a habitat need a network-centric infrastructure to

effectively communicate and collaborate in an attempt to

focus in finding mutually benefiting solution. Due to the

associated complexities, a multi-agent framework imbedded

into the nodes of a communication network can assist

members of a particular habitat with resolving issues such

as information transformation, security, policy management

and policy enforcement.

References

[1] A. Meystel, J Albus, "Intelligent Systems Architecture, Design and Control", John Wiley & Sons Inc 2002

[2] "Network-Centric Warfare: Its Origin and Future", Vice Admiral Arthur K. Cebrowski, U.S. Navy, and John J. Garstka

Published January 1998, http://www.usni.org/Proceedings/Articles98/PROcebrowski.htm

[3] http://www.dtic.mil/jv2020/jvpub2.htm

[4], E. Dawidowicz, Albert Rodriguez, John Langston, “Intelligent Nodes in Knowledge Centric Warfare”, Proceedings of 2002

Command and Control Research and Technology Symposium, Naval Postgraduate School, Monterey, CA, June, 2002
Metrics and Performance Measures for

Intelligent Unmanned Ground Vehicles

James S. Albus

Senior NIST Fellow

Intelligent Systems Division

Manufacturing Engineering Laboratory

National Institute of Standards and Technology

Technology Administration

United States Department of Commerce

Abstract

Metrics and measures for physical phenomena are precisely defined and widely accepted.

However, metrics and measures for intelligent systems are as yet vaguely defined and

controversial. Even the definition of intelligence is not widely agreed upon.

A number of metrics and measures have been developed for measuring human

performance in scholastic aptitude, athletic ability, and task performance. Some of these

suggest metrics and performance measures for intelligent machine systems. An example

of a set of performance measures for unmanned military scout vehicles is presented.

Keywords: metrics, performance measures, unmanned ground vehicles, intelligent

systems

1. INTRODUCTION

Webster defines a metric as a standard of measurement. Examples include the meter (a

standard of length), the kilogram (a standard of mass), the volt (electromotive force), the

ampere (electric current), the second (time), and degree Celsius, Fahrenheit, or Kelvin

(temperature.) A metric is what is used to make a measurement. For physical metrics

there is wide agreement on how the metric is defined and how it can be applied to

precisely measure a physical entity or temporal event.

However, when we come to a metric for intelligence, there is much less agreement and

much less precision. There is not even agreement on what intelligence is, much less on

how to measure it, or even what is the metric for measuring it. Almost any meeting can

grind to a halt over the attempt to define intelligence. I don’t want that to happen here, so

I am going to simply state my definition of intelligence, and move on.

Df: An intelligent system is a system with the ability to act appropriately in an uncertain

environment

where

appropriate action is that which maximizes the likelihood of success in achieving or

maintaining the highest level system goal. [Albus91]
The first thing to note about this definition is that intelligence has to do with a goal.

Somewhere a goal is defined, and somehow the system accepts this goal as its own. The

system then generates action that maximizes the likelihood that the goal with be

achieved.

Note that the appropriate action may, or may not, be to head directly toward the goal. At

the very lowest level, the proper action may depend not only on the current position

relative to the goal, but on the current velocity and inertia of the system being controlled.

At a higher level, a path planner may observe that the current vehicle position is in a culde-sac that blocks movement in the direction of the goal and plan a path away from the

goal to escape the cul-de-sac.

Note also the reference to the “highest level” system goal. This implies a hierarchy of

goals and subgoals with different planning horizons in time and space. At lower levels,

goals are short-term, near-by, and high-resolution in time and space. At higher levels,

goals are more distant and less precise in time and space.

Higher level goals may require that the system estimate the state of the world, gather

information, build maps, plan routes, predict the future, imagine possible situations,

weigh costs and benefits of alternative courses of action, and decide what behavior is

most likely to achieve or maintain goals. For very high-level long-range goals such as

rearing children, growing crops, or engaging in war, an intelligent system may need to

make short term sacrifices, invent tools, develop weapons, and engage in deception.

2. LACK OF METRICS AND MEASURES

A major barrier to the development of intelligent systems is the lack of metrics and

quantifiable measures of performance. There cannot be a science of intelligent systems

without standard units of measure. To do science, you must be able to measure what you

are doing and measure the results against some metric. This is something the field of AI,

robotics, and intelligent systems has largely ignored. Most research results are in the

form of demonstrations rather than experiments with data that that is quantitative and

referenced against ground truth. There are few benchmarks or standardized tests wherein

performance can be compared. That, of course is the subject of this workshop.

Perhaps the most common metric for human intelligence is the intelligence quotient (I.Q.)

The average human I.Q. is arbitrarily defined as 100. But there is great controversy what

I.Q. is and how it should be measured. There are, of course, many mental skills and

abilities that can be measured. These include the ability to read and write, the ability to

calculate with numbers, to reason with logic, to remember what was seen and heard, to

perceive patterns, to understand relationships, and perform geometrical transformations.

There are artistic skills and abilities. These include the ability to draw, paint, and sculpt,

to sing and dance, to perform music, to compose poetry, to create or act out stories.

There are manual skills and abilities to build or fix things, and athletic skills and abilities
to compete in sports or fight in battles. In each of these areas, performance can be tested

and scored.

Metrics for performance include speed (how fast?), precision (how accurate?), style (how

graceful or well formed?), success/fail (criterion met?), effectiveness (desired result

achieved?), efficiency (resources expended?), or cost/benefit (benefits worth the cost?)

Another type of performance metric is a benchmark. The performance of a system can be

measured by comparing it against some benchmark performance. A benchmark may be

an average over a population, or it may be a record of some kind, e.g., a world record, or

a personal best.

One possible standard of measure is human performance. Human performance has been

well defined and carefully calibrated in many areas. There are many existing measures of

human performance, and human subjects are widely available. But how should this

metric be applied? What should be measured? Perhaps the most fundamental measure is

effectiveness in achieving goals. Certainly it is possible to measure timeliness. Cost,

benefit, and risk are easily quantified for many tasks.

Typically a performance measure yields a score. The score may be some absolute

quantity such as total points1, or kilometers per hour, number of interventions per

kilometer. Or the score may be a relative quantity such as order of finish in a race, or

percentile in a distribution.

Sometimes the score takes into consideration the degree of difficulty of the performance.

For example in competitive diving or figure skating, the performance score is multiplied

by the degree of difficulty to decide the winner of a competition. In other cases, effort is

made to assure that all competitors experience the same degree of difficulty – a so-called

“level playing field.” For example, in basketball and football games the teams switch

goals at half-time. In races, all competitors are required to cover the same distance. In

competitions where a level playing field cannot be achieved, there may be a preliminary

competition to decide who gets the advantage. For example in automobile racing,

qualifying time determines the starting line up. In tournaments, preliminary competition

determines who meets the weakest competitor.

For autonomous driving, the degree of difficulty depends on the environment. On-road

driving is more difficult on crowded streets and at intersections than on deserted roads

and empty streets. The level of difficulty of off-road driving depends on the terrain and

ground cover. It also depends on the density of obstacles such as ditches, trees, and

rocks, and whether obstacles are hidden beneath tall grass or dense weeds.

In many cases, it is necessary to take into account the amount of training and preparation

that have preceded the testing process. To accommodate these variations, different

classes of competition may be established. Thus there are many issues with regard to

how performance measurements should be made and how the results should be scored.

 

1

 where total points = points-per-goal x number of goals
Finally, there is the issue of what the score means. Typically the competitor with the

most total points wins,2 and rank order is determined by the number of points scored.

However, not all competitions mean the same, and all wins or losses are not the same.

Winning a pre-season game is not the same as winning the Super Bowl. In measuring the

performance of intelligent systems, not all tests are equal. Passing a routine drivers test is

not the same as qualifying for the “Indianapolis 500.”

3. PERFORMANCE MEASURES

To address the issues of performance measures for intelligent systems, NIST has begun

work in three areas:

1) a test course for search and rescue robots,

2) a measurement procedure for evaluating run-off-road detectors, and

3) a set of performance measures for autonomous driving.

The NIST test course for urban search and rescue addresses the problems of searching for

human victims in buildings that have collapsed because of earth quakes, terrorist attacks,

or other disasters. The USAR test course has been used in several AAAI and RoboCup

competitions around the world. [Jacoff et al.00, 01, 02] This work is sponsored by the

DARPA Mobile Autonomous Robot Software (MARS) program.

The NIST measurement procedure for run-off-road detectors addresses the problem of

evaluating commercial products for effectiveness in determining when a vehicle is in

danger of running off the road and warning the driver in time to prevent an accident.

[Szabo et al.99] This work is sponsored by the Department of Transportation Highway

Safety administration.

NIST work on metrics, performance measures, and standard reference data for

autonomous driving addresses both off-road and on-road applications. This work is

sponsored by the Army Research Lab Demo III Experimental Unmanned Vehicle (XUV)

program. The Army is interested in measuring the state of readiness of autonomous

driving technology for unmanned military vehicles. [Bornstein02] Specifically, the tests

are designed to determine whether the Demo III XUVs have achieved technology

readiness level six (TRL-6). TRL-6 requires that a prototype be demonstrated in a

relevant environment.

For autonomous mobility, we assume that the relevant environment includes driving offroad through tall grass, weeds, and brush; through woods and fields, in desert and

mountain terrain. The relevant environment also includes driving on-roads of all types

including overgrown dirt trails, gravel roads, paved rural roads and highways, as well as

and urban paved streets and alleys that may contain piles of rubble, burning tires, and

abandoned vehicles. Relevant environmental conditions include day, night, rain, dry,

dust, smoke, mud, and possibly snow and ice. It will not include the ability to

 

2

 except in golf where the competitor with the lowest score wins
autonomously cope with on-coming traffic, pedestrians, animals, moving vehicles,

intersections, traffic signals, or road signs.

3.1 TRL-6 Test Procedures

The TRL-6 tests will proceed as follows:

For a chosen set of missions in a variety of environments (woods, fields, roads, trails,

urban, desert, mountains) and a variety of conditions (day, night, dry, wet, snow, mud,

dust, smoke):

• A manned scout vehicle will perform an assigned mission and its performance

will be measured and scored for military effectiveness in terms of mission

success, timeliness, resource expenditure, risk, and probable human casualties.

• A robot scout vehicle will perform the same mission and its performance will be

measured and scored by the same criteria.

• The performance and score of the robot scout vehicle will be compared against

that of the manned scout vehicle.

• A terrain characterization vehicle will exactly retrace the routes traversed by both

manned and robot vehicles and will characterize the difficulty of the terrain

covered in terms of slope, roughness, soil mechanics, and ground cover.

• Terrain characterization will help to define the operational envelope within which

the robot vehicles can be used effectively.

3.2 Terrain Characterization

At least one way to measure difficulty is to measure the surface attributes of the terrain.

The following is a set of terrain measurement techniques proposed for the coming TRL-6

experiments.

There will be a baseline and advanced set of terrain characterization measurements.

1. The baseline measurements will consist of one or more human observers riding in a

HMMWV and subjectively scoring the difficulty of the terrain.

2. A more advanced scenario will include roughness measurements from an inertial

navigation system, a TV camera, and an instrumented bumper on the XUV. These

measurements will be compared with similar measurements made by similar sensors

on the manned scout vehicle.

3. Still more advanced scenarios will include measurements made from the terrain

characterization vehicle by high precision high-resolution LADAR .

4. The most advanced scenarios will include measurements of soil mechanics made

from the terrain characterization vehicle.

5. If necessary, additional terrain characterization data will be obtained from overflights

using airborne stereo cameras or LADAR scanners.

The terrain will be characterized by the following method:

1. A terrain characterization vehicle (HMMWV) will be driven over the exact paths

traveled by the manned scout vehicle and the XUV during their respective missions.
2. The paths chosen by both vehicles will be scanned at regular intervals by high

resolution LADAR cameras. These range images will then be registered with color

images from color cameras. Data from an INS system will measure accelerations

produced by bouncing over the terrain. Data from an instrumented bumper will

measure the strength of the vegetation being driven through. Instrumentation to

measure soil mechanics may also be carried by the terrain characterization vehicle.

3. The point clouds from the high resolution LADARs will be stitched together and the

terrain will be characterized in terms of slope, roughness, obstacles, and ground

cover. The conditions during the respective missions will be characterized by one or

more of terrain characterization measurements described above.

4. Missions will be run under a variety of conditions including different times of day

and night, different lighting, wet and dry, and different amounts of smoke and dust.

The terrain attributes and conditions will be combined to provide a measure of

difficulty for each path.

5. The two paths selected by the manned and robot vehicles will be scored and

compared to determine which was the “better” path for the mission.

The difficulty of the terrain will also be analyzed to characterize the operational envelop

within which the robot vehicle can be expected to reliably perform.

3. 3 Scout Vehicle Test Scenarios

The performance of the manned scout vehicle will be measured by the following

procedure:

1. The Test Director will give a human commander a typical scout mission to be

accomplished. The human commander will issue orders to the driver of a manned

scout vehicle (a HMMWV) via a radio operator. The commander will be located in a

HMMWV following the scout vehicle at a prescribed distance. The commander will

have a map display overlaid with the manned scout location, the command vehicle

location, and mission objectives. The commander may or may not have visual contact

with the scout vehicle depending on the terrain and the separation distance between

commander and manned scout vehicle.

2. A human driver in the manned scout vehicle will drive the scout vehicle in a tactical

manner to accomplish the assigned mission objectives.

3. The manned scout vehicle will be scored by conventional methods used for

evaluating human scout performance.

4. The number and type of conversations between the scout vehicle, the radio operator,

and the commander will be measured.

5. The work load on the radio operator and commander will be measured.

6. The bandwidth and total amount of communications between manned vehicle, the

radio operator, and the commander will be measured.

7. The behavior of the human driver, commander, and ratio operator will be video taped

8. A panoramic camera mounted on the manned scout vehicle will be used to record the

scenes encountered by the human driver.
The performance of the robot scout vehicle (a XUV) will be measured by the following

procedure:

1. The Test Director will give the human commander of the XUV the same mission as

given to the commander of the manned scout vehicle. The human commander will

issue orders to the XUV via a radio operator. The commander will be located in a

HMMWV following the XUV at a prescribed distance. The commander will have a

map display with the XUV location, the command vehicle location, and mission

objectives. The commander may or may not have visual contact with the XUV

depending on the terrain and the separation distance between commander and XUV.

2. The Demo III autonomous mobility system will drive the XUV to accomplish its

mission objectives. The XUV may, or may not, choose the same path over the terrain

as the manned scout vehicle. Either the commander or radio operator can provide

intermediate way points to help robot get to goal point. Only the radio operator can

teleoperate the robot.

3. The XUV performance will be scored by the same methods used for evaluating the

manned scout vehicle.

4. The number of interventions by the radio operator will be measured and the type of

interventions will be classified and analyzed.

5. The work load on the radio operator and commander will be measured

6. The bandwidth and volume of communications between XUV, the radio operator, and

the commander will be measured.

7. A trace will be kept of critical state variables and world model representations during

and prior to operator interventions.

8. The behavior of the XUV, the radio operator, and the commander will be video taped

9. A panoramic camera mounted on the XUV will be used to record the scenes

encountered by the XUV.

4. SUMMARY AND CONCLUSIONS

Metrics and measures for physical phenomena are precisely defined and widely accepted.

However, metrics and measures for intelligent systems are as yet vaguely defined and

controversial. Even the definition of intelligence is not widely agreed upon.

There are a number of metrics and measures that have been developed for measuring

human performance in scholastic aptitude, athletic ability, and task performance. It is

suggested that these may provide guidelines for developing metrics and performance

measures for intelligent machine systems. An example of a set of performance measures

for unmanned military scout vehicles is presented.

5. REFERENCES

Albus, J. S. (1991) Outline for a Theory of Intelligence, IEEE Transactions on Systems,

Man and Cybernetics, Vol. 21, No. 3, pgs. 473-509, May/June
Bornstein, J. A. (2002) “U.S. Army ground robotics research program”, Proceedings of

SPIE Aerosense Conference Vol. 4715, Orlando, Florida, 1-5 April, 2002

Jacoff, A., Messina, E., Evans, J. (2000) A Standard Test Course for Urban Search and

Rescue Robots, Proceedings of the Performance Metrics For Intelligent Systems

Workshop, National Institute of Standards and Technology, Gaithersburg, MD, August

14-16

Jacoff, A., Messina, E., Evans, J. (2001) Experiences in Deploying Test Arenas for

Autonomous Mobile Robots, Proceedings of the Performance Metrics For Intelligent

Systems (PerMIS) Workshop, in association with IEEE CCA and ISIC, Mexico City,

Mexico, Sept 4, 2001

Jacoff, A., Messina, E., Evans, J. (2002) Performance Evaluation of Autonomous

Mobile Robots, Industrial Robot 29:3

Szabo, S., Murphy, K., Juberts, M. (1999) The AUTONAV/DOT Project: Baseline

Measurement System for Evaluation of Roadway Departure Warning System, NISTIR

6300, National Institute of Standards and Technology, Gaithersburg, MD, March
A Simulation Framework for Evaluating Mobile Robots

Stephen Balakirsky and Elena Messina

National Institute of Standards and Technology

Intelligent Systems Division

Gaithersburg, MD 20899-8230

Email: stephen@cme.nist.gov, elena.messina@nist.gov ∗†

Abstract

As robotic technologies mature, we are moving from

simple systems that roam our laboratories to heterogeneous groups of systems that operate in complex nonstructured environments. The novel and extremely complex nature of these autonomous systems generates a great

deal of subsystem interdependencies that makes team, individual system, and subsystem validation and performance measurement difficult. Simple simulations or laboratory experimentation are no longer sufficient. To assist

in evaluating these components and making design decisions, we are developing an integrated real-virtual environment. It is our hope that this will greatly facilitate the

design, development, and understanding of how to configure and use multi-robot teams and will accelerate the

robots’ deployment.

Keywords:

simulation, architectures, 4D/RCS, mobile robots, algorithm validation

1 Introduction

There have been many recent successes in the field

of mobile robotics. These range from single robot

systems such as MINERVA that has been designed

to give guided tours of museums [8], Predator and

∗No approval or endorsement of any commercial product by

the National Institute of Standards and Technology is intended

or implied. Certain commercial equipment, instruments, or materials are identified in this report in order to facilitate understanding. Such identification does not imply recommendation or

endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

†This work was sponsored in large part by grants from

the Army Research Laboratories and the DARPA Mobile Autonomous Robot Software Program.

Global Hawk that have been designed for military air

applications, and Demo III [7] [5] and Perceptor that

have been designed for military ground applications to

multi-robot systems such as the multiplicity of robot

teams involved in the Robocup soccer league [3].

As these systems become more complex and attempt to perform more ambitious tasks, the knowledge and resources (both hardware and software) that

are necessary to make contributions to the field dramatically increases. As a result, informal (or formal)

code sharing now takes place between many universities and research institutions. For example, the code

used in the Robocup competitions is published and

freely available to anyone who wishes to download and

use it, and the mobility and planning software used

by the Perceptor program is heavily based upon the

code from Demo III. While this code reuse allows researchers to gain quick entry into the various mobile

robot arenas, it raises some interesting questions. If

multiple sources of algorithms that provide a solution

to a particular problem exist, which one is better?

Will the given algorithm work in the new proposed

environment? Are there any unintended consequences

on the rest of the system (or systems) of integrating

in this new component?

Code and component sharing also allows researchers to perform research in a specific area of

robotics without becoming an expert in every aspect

of robotics. For example, it should be possible on today’s computer hardware to develop a planning system

capable of creating plans to navigate through complex

city traffic. However, in order to do this, an image processing system must exist that can detect and predict

the location of traffic, road lanes, and traffic signs.

It is feasible that a planning researcher can reuse a

base technology such as the image processing subsystem rather then creating one from scratch. However,

is there a solution to this problem when no such base

technology or algorithm is available? Will the new
Figure 1: Model for RCS Control Node.

planning approach have to wait to be proven because

of dependencies on as yet undeveloped algorithms or

hardware?

This paper will suggest a means to address all of

the above questions by describing the development

of an integrated real-virtual simulation environment.

The objective of this environment is to provide a standard architecture and set of interfaces through which

real and virtual systems may be seamlessly coupled

together. It will be shown that through this coupling, components ranging from individual algorithms

to groups of vehicles may be developed, debugged, and

evaluated.

2 An Architecture for Intelligent Autonomous Vehicles

One of the key decisions to be made in building

any kind of complex system is how to organize the

hardware and software. The Demo III Program and

some of the teams competing for the Future Combat

Systems contract have selected the 4D/RCS reference

architecture for their autonomous vehicles [1]. Rather

than starting from scratch, the simulation framework

will be built upon the existing 4D/RCS architecture

and will take advantage of existing interfaces and components.

The 4D/RCS architecture consists of a hierarchy of

computational nodes, each of which contains the same

elements. Based on control theory principles, 4D/RCS

partitions the problem of control into four basic elements that together comprise a computational node:

behavior generation (BG), sensory processing (SP),

world modeling (WM), and value judgment (VJ). Figure 1 shows the 4D/RCS control node and the connections between its constituent components. Figure

2 shows a sample 4D/RCS hierarchy for military scout

vehicles.

3 Requirements for a Simulation,

Modeling, and Development Framework

An architecture is a first step towards guiding and

facilitating the construction and evaluation of complex

single or multi-vehicle autonomous systems. Tools

that help automate the software development and

component integration are another important element.

NIST has been working with industry, other government agencies, and academia to investigate tools to

facilitate construction of the types of large and complex systems that will be represented in this simulation framework. We are developing a large-scale simulation environment that will enable us, along with

others, to design the control hierarchy, populate the

control nodes, run the system in simulation, debug it,

and generate code for the target host. The development and simulation environments are closely tied to

the eventual deployment platforms and are intended

to be able to operate with a combination of real and

simulated entities. The ability to enable human-inthe-loop testing and execution is also crucial, given

the novel aspects of human-robot interactions.

A high-level list of the requirements for such a development and simulation environment has been developed to help guide its creation. The requirements

are as follows:

• Full support of 4D/RCS architecture

• Graphical user interface for developing, integrating, testing, debugging the system under development
Figure 2: 4D/RCS Reference Model Architecture for an individual vehicle.

• Reuse support:

− Architecture elements

− Component templates

− Algorithms

− Code

− Subsystems

• Intuitive visualizations of the control system to

support design and development by providing an

understanding of what the system is doing and

why, and what it plans to do next. Examples of

visualizations include:

− Display of control hierarchy as it executes,

including commands flowing down and status flowing up

− Ability to “zoom in” on a particular node

and view states as the system executes

− Ability to view world models within the system

• Execution controls, including

− Single step through execution

− Breakpoints and watch windows

− Logging

• Simulation infrastructure supporting realistic execution scenarios, visualization, and debugging/evaluation experiments. This includes

− Population of the environment external to

the system with relevant features (such as

roads, other pieces of equipment, humans,

etc.)

− Controlled dynamic environment (with prescribed repeatable events)

• Modification capabilities so that the designer and

user can perform “what if” experiments. The

tools should allow interactive and intuitive modification of situations in the environment or within

the system. The modification capabilities should

work seamlessly with the visualization, simulation, and execution features. Examples of types

of modifications that should be allowed include:

− Changing world model data

− Importing datasets that represent what the

system’s sensors would receive

− Changing environmental conditions

• Support for real-time computing. All levels of the

4D/RCS control hierarchy must execute within
certain time constraints (e.g., the servo level may

have to respond at 60 Hz; whereas higher levels

may have several seconds or even minutes per cycle).

4 Proposed System of Systems

We are seeking to create an integrated environment that provides capabilities typically associated

with software development tools and those associated

with simulation environments. All of the pieces necessary for the construction of this environment may

exist to some degree as separate commercially available packages. However, whereas several commercial

tools exist to help design and construct software, these

tool-sets are typically disconnected from the overall

system architecture and execution environments (real

or simulated). Likewise, many sophisticated simulation systems exist. However, these systems tend to

work at either a very broad scope at low resolution, or

a very limited scope at high resolution. What we are

building is a coherent environment for designing, developing, and validating software components ranging

in size from a single module to a team of systems.

As shown in Figure 3 one will be able to take software modules from a repository and interface them

directly into either a real or virtual system. This ability will be supported through the decomposition of

systems and algorithms into the 4D/RCS architecture

and the use of standard interfaces between modules.

This decomposition will be supported through the

software design and development support tools that

provide the ability to work in a graphical environment

to sketch the control hierarchy, bring up partially instantiated 4D/RCS control nodes, easily create connections between nodes (or components within them),

and automatically generate executable code. The software support tools will also encompass capabilities

typically found under run-time debug tools, including single stepping and setting break points. Furthermore, sophisticated displays of variables and execution

states are being created. For instance, a ”strip chart”

view one or more variables can be displayed on screen

and techniques for helping developers visualize complex world models are being designed. This includes

display of the graphs that several path planning algorithms utilize to search for the best (least cost) path.

The graphs typically have thousands of nodes, which

are connected in a neighborhood to other nodes by

edges that have a cost associated with them. The

costs vary, depending on operation mode, or as environmental conditions change and are computed based

on the various layers in the world model (such as roads,

obstacles, and elevation). Therefore, the visualization

of relevant and salient aspects of the world model in

order to validate the model itself and the planning is

a challenging undertaking.

The software development tools will segue smoothly

into the simulation environment. Under this concept,

a virtual world is being created that brings together

existing multi-platform and single platform simulation

systems into a system of systems. Through the use of

well-defined interfaces that are supported on a wide

variety of computer platforms, the simulator’s internal

command and data flows will be able to be interrupted

and modified. This will allow researchers to “plug-in”

individual technology components that meet the interface requirements and override the default methods

that the simulators normally employ. As shown in Figure 3, interfaces will be provided that range through

the entire spectrum of the 4D/RCS hierarchy; from

a low-fidelity multi-platform configuration to a highfidelity single platform configuration, to the ability to

add real platforms into the virtual world.

Global variable resolution database resources will

also be available. These include a terrain database

that contains elevation data, a feature database that

contains annotated vector data for roads, signs, buildings, rivers, etc., and an annotated entity database

that contains information on all of the platforms participating in the simulation. The annotations include

items such as lane markings and names for roads, text

contained on a sign, and health status of other entities. Filters will be available to tune the database

outputs to the specific needs of each algorithm. For

example, specific sensor processing capabilities may be

simulated by querying these databases with a specified

sensor range and resolution.

In addition to serving a priori data, these databases

will be able to be modified in real-time. Any modifications made to the databases will be viewable by

all participants (both real and virtual) in the exercise.

These modifications may be related to sensed information from a real vehicle that is participating in the

simulation or may be injected by the user to alter the

environment that the simulation framework in operating in.

The final component of the system is a set of data

capture, analysis, and evaluation tools. The data capture tool will allow for any or all of the messages being

transmitted over the standard message channels to be

time-tagged and logged into a trace file. In addition,

raw simulation results may be logged, for example the

location and activities of each participating entity.
Battalion

o

o

o

Vehicle

o

o

o

Servo

Low

Fidelity

Simulation

High

Fidelity

Simulation

Standard

Interfaces

Repository of Modules, Designs, Algorithms

BG

BG

Path Map

PID

Stereo

X

BG

LADAR

A

OA

Comm

Data Capture, Analysis and Evaluation Tools

Event Log Control Analysis

Ground

Truth

Data Log

Figure 3: Hierarchy of simulators.

Data analysis tools are also being developed that

will allow for a variety of data presentation and analysis options. This will include both the tracking of

timings as well as the values of specific variables or

combinations of variables. For example, the distance

between two entities may be constantly tracked. These

data analysis tools will also be tied into ground truth

from the simulation systems. This will allow algorithm and system evaluation against known data and

the determination of such items as the time from a

sensor event to a system reaction or the accuracy of a

real systems road following algorithms. Through the

use of these standard interfaces and tools, researchers

will be provided with a low cost technique for evaluating performance changes due to their system’s or

algorithm’s integration into the overall framework.

5 Current Implementation

While the entire software development and simulation system has not yet been implemented, progress

has been made on developing prototypes and designs

for the overall system. No single simulation tool has

been found that meets all of the criteria discussed in

the requirements section. Therefore, a hierarchy of

simulators has been explored.

At the top of the hierarchy, a low-fidelity, long temporal and spatial duration, multi-platform simulator

is required. As designed, this class of simulator is capable of simulating the interaction, coordination, and

movement of large groups of platforms. While these

simulators do simulate down to the level of the individual platforms moving across the terrain, the terrain and mobility models are typically low resolution.

Therefore, this class of simulator is best utilized in

developing algorithms for group behaviors where precise platform mobility and sensing modeling is beyond

the scope of the experiment. A second class of simulator has been identified for situations where precise

modeling is required. These simulators will need to

share interfaces with the low-fidelity simulator, and in

fact may take commands from the low-fidelity simulators in order to precisely model one or more of the

platforms involved in a particular exercise. The highfidelity simulators will also be able to read the shared

databases and construct simulated sensor output (or

pass real sensor output) that may be used by external

sensor processing algorithms. Complex, dynamically

correct platform motion models and high resolution
terrain information will also available at this level.

Interfaces will be inserted into each simulator that

will enable the export and import of both world model

and behavior generation information at each level of

the 4D/RCS hierarchy. This will enable researchers to

implement a particular group behavior or algorithm at

a particular level of 4D/RCS. For example, a cooperative search algorithm could be implemented at the

“section” level of 4D/RCS. The algorithm would receive its command input from the platoon level of the

low-fidelity simulator and construct a plan based on

information read from the terrain, entity, and feature

databases. The planned course of action (individual

plans for several platforms) would then be passed back

into the simulator for execution. In this particular

case, the plans could be passed either back to the lowfidelity simulator or to the high-fidelity simulator. In

addition, one or more of the platform’s plans could be

passed to real systems. As designed, the source and

destination of these plans and the data utilized to construct them is transparent to the planning system and

totally controlled by the user. This will facilitate an

environment where a researcher can simulate as many

or as few subsystems and levels as desired.

Both a low-fidelity and high-fidelity commercially

available simulation package have been selected and

implemented into the prototype framework.

5.1 Low-fidelity Simulator

For the prototype system, we have chosen the

US Army STRICOM’s OneSAF Testbed Baseline

(OTB)1for both the low-fidelity simulation and

shared database server. We have worked closely with

the Army Research Laboratory and Science and Engineering Services Inc. to install the standard interfaces.

All of the interfaces communicate over NIST’s NML

communication channels [6] which provide a multiplatform solution to inter-process communication.

Distributed, shared databases are implemented as

part of the standard implementation of OTB. We

have added interfaces into the simulation system that

allow for simple outside access of this information.

These interfaces include hooks into the terrain elevation database, feature database, and entity database.

Additional channels that tie the basic information contained in these databases to a full relational database

for the storage of attribute information is currently

being investigated.

For the terrain elevation database, both all-knowing

(what is the elevation in this area, to this resolution)

1http://www.onesaf.org/publicotb1.html

and modeled (what is the terrain map as modeled by

vehicle x with its sensors) are available. Feature vector

data is available on an all-knowing basis that may be

filtered by distance from the vehicle so as to simulate

what sensors perceive. In addition to the standard features that are modeled by the simulation system, simulated traffic signals and signs are being implemented.

For entity data, filtered information (all friendly, enemy, detected, etc.) reports are available as well as

event detections. Events currently supported include

line crossings and anticipated line crossings with more

to be added shortly.

In addition to the database access interfaces, we are

able to interrupt the standard OTB command flow to

inject our own plans. This has been demonstrated by

having OTB section level plans sent out over an NML

channel to a stand-alone vehicle level planner. The

results of the vehicle level planner can then be executed on real robotic hardware, sent to a high-fidelity

simulator, or sent back into the OTB simulator for

execution.

Input from real robot platforms into the simulation

environment is also supported. This interface allows a

real robotic platform to influence OTB databases by

continuously updating their own location as well as

adding detected features and entities.

Work is continuing on developing further interfaces.

These will provide further breaks in the OTB command flows that will allow for planning systems that

compute group plans to be implemented and evaluated.

Another feature that is standard with the OTB distribution is a data logger. This logging facility logs all

entity movements and events that occur in the simulation. Logging facilities to log message channel traffic

are currently under development.

5.2 High-fidelity Simulator

For the high-fidelity simulation, SimRobot from the

University of Bremen2 has been selected for the prototype system. NML channels for low-level command

input, and position output have been implemented.

Currently, this simulator is only capable of simulations

on a flat earth. Therefore, work is being performed to

improve the simulators ability to operate in complex

3-dimensional terrain. Once this work is completed,

the low-fidelity simulator will operate from the same

terrain database as the low-fidelity simulator.

2http://www.informatik.uni-bremen.de/ ̃roefer/simrobot/

index e.htm
Figure 4: Example of features in controller software development tool.

5.3 Software Development Support

We have been experimenting with various representation techniques and development tools. These range

from commercial packages, such as Real-Time Innovation Incorporated’s ControlShell to novel formal languages, such as Stanford’s Rapide [4]. Recent work has

focused on the use of the Unified Modeling Language

to support 4D/RCS control system development [2].

A commercial development and execution tool for

building simpler versions of RCS-style controllers has

been developed by a small company (Advanced Technology and Research), but it is targeted at manufacturing systems that have minimal sensing requirements. This tool is being modified to support the

types of visualizations, modifications, and execution

controls desired for on-road and off-road vehicles. Figure 4 is a screen shot taken of the tool while running

and illustrates some of its features. The top left window shows an animation of the vehicle being controlled

as it moves through its environment, which can include other vehicles. The top right window shows 2

variables that have been selected by the user to be

graphed (current speed and y position). All other

variables in the world model are accessible through

the lower right window, where logging, charting, minimum, maximum, and other values are displayable.

The lower left window displays the state table for one

of the controller nodes (in this case, the Prim) for the

vehicle, with the current state highlighted.

6 Summary

This paper has presented an integrated simulation

framework that is capable of aiding researchers in developing, debugging, and evaluating algorithms, subsystems, systems, and groups of systems. While the

entire framework has not yet been implemented, a

prototype system does exist. This system allows the

seamless operation of both real and simulated systems

in an environment that contains both real and virtual

features. In addition, standard interfaces, data logging

facilities and ground truth exist to aid in the evaluation of the performance of systems and the comparison

of multiple systems under repeatable conditions.

While interfaces exist for the “section” and “vehicle” level of the 4D/RCS architecture, it is desired to

have interfaces that allow the testing and evaluation

of components that reside at any architecual level. It
is also desirable to be able to simulate additional environmental models for both the high and low fidelity

simulators. These include traffic signals and signs for

the low-fidelity simulator and a more realistic mobility

platform simulator for the high-fidelity simulator.

Therefore, future work on this framework includes

the development of additional interfaces, further development of the simulation environment, and the incorporation of the design and debug toolsets.

References

[1] J. Albus. 4-D/RCS reference model architecture

for unmanned ground vehicles. In G Gerhart,

R Gunderson, and C Shoemaker, editors, Proceedings of the SPIE AeroSense Session on Unmanned

Ground Vehicle Technology, volume 3693, pages

11–20, Orlando, FL, April 1999.

[2] H. Huang, E. Messina, H. Scott, J. Albus, F. Proctor, and W. Shackleford. Open system architecture

for real-time control using an uml-based approach.

In Proceedings of the 1st ICSE Workshop on Describing Software Architecture with UML, 2001.

[3] H. Kitano, M. Asada, Y. Kuniyoshi, I. Noda,

E. Osawa, and H. Matsubara. RoboCup: A challenge problem for AI. AI Magazine, 18(1):87–101,

1997.

[4] E. Messina, C. Dabrowski, H. Huang, and J. Horst.

Representation of the rcs reference model architecture using an architectural description language.

In Lecture Notes in Computer Science EUROCAST 99, volume 1798 of Lecture Notes in Computer Science. Springer Verlag, 1999.

[5] K. Murphy, M. Abrams, S. Balakirsky, T. Chang,

A. Lacaze, and S. Legowik. Intelligent control for off-road driving. In First International

NAISO Congress on Autonomous Intelligent Systems, 2002.

[6] W. P. Shackleford, F. M. Proctor, and J. L.

Michaloski. The neutral message language: A

model and method for message passing in heterogeneous environments. In Proceedings of the 2000

World Automation Conference, June 2000.

[7] C. M. Shoemaker and J. A. Borenstein. Overview

of the demo III UGV program. In Proc. Of the

SPIE Robotic and Semi-Robotic Ground Vehicle

Technology, volume 3366, pages 202–211, 1998.

[8] S. Thrun, M. Bennewitz, W. Burgard, A.B. Cremers, F. Dellaert, D. Fox, D. Hahnel, C. Rosenberg, N. Roy, J. Schulte, and D. Schulz. MINERVA: A second-generation museum tour-guide

robot. In Proceedings 1999 IEEE International

Conference on Robotics and Automation, volume 3, pages 1999–2005, May 1999.
Evaluating the Performance of a Vehicle Pose Measurement

System

Harry Scott

Sandor Szabo

National Institute of Standards and Technology

Abstract

A method is presented for evaluating the

performance of a vehicle pose measurement

system (e.g., GPS, inertial sensors, etc.). The

method supports evaluations of the system on a

vehicle moving at high speeds. An example is

provided to illustrate the method.

Keywords

Vehicle, pose, position, measurement, accuracy,

uncertainty, GPS, IMU

1 Introduction

Precise knowledge of the pose of a moving

ground vehicle is important in such applications

as navigation, safety, robotics, metrology and

surveying. Vehicle pose measurement (VPM)

system manufacturers typically state the

accuracy of their systems under static conditions,

that is, with the system stationary over time.

Latency and synchronization errors are difficult

to detect under static conditions. In this paper, a

method is described for evaluating the

performance of a VPM's ability to measure the

position of a moving vehicle. The method

requires that the VPM have a synchronization

interface, which we call a sync pulse interface.

This interface is often incorporated in a VPM to

support photogrammetry applications where a

camera's vertical sync latches a pose

measurement. In the method presented in this

paper, a photo emitter/detector sensor that senses

energy reflected off a reflective target generates

a sync pulse. By mounting the photo sensor on

the vehicle and placing a reflective target on the

ground, pulses are generated whenever the

vehicle drives directly over the target. Repeated

measurements of the target as the vehicle drives

by are used to determine the repeatability in the

VPM's measurement of the target location.

The National Institute of Standards and

Technology (NIST) Intelligent Systems Division

is pursuing work in several areas of intelligent

vehicles, including research in autonomous

mobility and development of performance

measurement techniques. Various testbed

vehicles are used in support of several projects,

and each has been, or will be, outfitted with a

VPM system. These systems are used in at least

two distinctive ways. For autonomous vehicles,

the VPM is a principal component of a real-time

navigation system, providing necessary

information for the vehicle to move through the

environment. A somewhat different need is met

by the ability of the VPM system to capture

appropriate information in real-time that can be

combined with other sources of information after

the fact to generate more precise pose

information than is available in real-time. This

meets a metrology need, providing improved

accuracy to evaluate performance of the vehicle

itself and its various sensors.

This paper describes a method to determine or

confirm the performance of an implemented

VPM on a testbed vehicle. Since the VPM

examined in this paper is intended for metrology

applications, steps are included in the method to

post process the data to obtain the highest

possible accuracy. The method described could

also be used to obtain real-time performance

information. This is accomplished by analyzing

the real-time solutions as opposed to the postprocessed solutions. The method consists of a

way to precisely trigger vehicle position

measurements, a procedure for collecting data

and a way to analyze the repeatability in the

vehicle position measurements. Although VPMs

are capable of providing a full position and

orientation solution, this current effort addresses

only the position measurement capability. In

the following sections, the method is described

in the context of evaluating a specific VPM,

though the described method is applicable to any

VPM that supports a trigger mechanism.
Figure 1 Note the downward looking photo sensor, the retroreflector on the ground and the vertical

rod to guide the driver.

2 Test Procedures

The vehicle used in the evaluation is a full size

passenger sedan. The installed VPM is a system

that integrates a dual frequency carrier phase

Global Positioning System (GPS), a secondary

GPS, an inertial measurement unit (IMU)

consisting of accelerometers and fiber optic

gyros, a wheel encoder, and a system control and

data collection computer. This system is

configured to capture and provide solutions at a

200 Hz data rate. In addition, it is configured to

capture all data required for later post

processing.

The system is augmented with an external photo

sensor connected to the sync pulse interface.

When triggered, the result is the notation in a file

of an event occurrence, along with the time of

the event with microsecond time precision.

Since triggers will be caused during vehicle

motion, latency in the assertion of this signal will

be reflected in position errors in the resulting

data. For this reason, a low latency photo sensor

(300 ms) was employed. During post

processing, pose solutions are determined for

selected events of interest. The sensor was

mounted slightly in front of the vehicle bumper,

in line with the vehicle driver position. A

vertical rod above the sensor assists the driver in

steering the sensor over a target while driving.

The target is a simple retroreflective surface,

constructed by affixing a layer of retroreflector

material (available in sheets) to an aluminum

disk. A disk of 15.24 cm (6 in) diameter is used.

This size was selected as large enough to enable

the driver to successfully steer the vehicle over it

at speeds of interest most of the time, even in a

grassy and somewhat bumpy field, but small

enough to keep the target detection points close

to a surveyed point. In this test a National

Geodetic Survey (NGS) survey marker, flush

with the ground, is used. The retroreflector is

simply centered on top of it.

The configuration of the photo sensor, its

mounting, and target is shown in Figure 1.

To evaluate the VPM's measurement

performance, the vehicle is driven over the target

from a variety of directions while collecting all

data necessary to compute, via post processing,

the position of the sensor at the time of target

crossing. In the analysis phase, the repeatability

of the measurements of the target's radius and the

location of the target with respect to the survey

marker produce a measure of performance of the

VPM system.

The position solutions are computed for the

location of the sensor (actually for a point on the

ground directly below the sensor). This requires

a transformation of coordinate frames between

the VPM system and the position of interest

below the sensor. This transformation depends

on knowledge of the translations and rotations

between the two reference frames. In the case of

this implementation, translation and rotation

measurements between the IMU reference frame

and the sensor reference frame are needed.

These measurements are entered into the VPM

system. Determination of the translation and

rotation parameters is performed in two steps.
The first is a best-effort measurement of the (x,

y, z) distances between the IMU and the point

below the sensor. Performing these

measurements is physically awkward because of

the location of the IMU in the vehicle trunk and

the sensor location at the front bumper. An

attempt was made to mount the IMU to align

with the major vehicle axes as well.

Since these measurements are difficult and

somewhat prone to error, data is also collected

during the test run which is used to calibrate the

system by adjusting the translation and rotation

parameters as required. Several events are

triggered by driving the vehicle sensor over the

target as slowly ("creep") as possible and as well

centered laterally as possible. These creep

events are collected for approaches to the target

from four directions. The slow speed is intended

to eliminate any significant data latency errors.

Positions of events collected in this manner

should conform to the edge of the target, and the

calibration parameters are adjusted slightly to

yield solutions consistent with the known target

diameter. This “creep” data also helps detect any

movement of the sensor that may have occurred

between tests.

After collecting the initial creep calibration data,

the vehicle was driven over the target at a

number of speeds and from a number of

directions. Speeds were limited to about 10.6

m/s ( 20 mph) due to the roughness of the grassy

field where these tests were performed. (The

sensor/retroreflector system has been tested

successfully at highway speeds on a roadway as

well.) Approximately 40 events and their

associated data were collected in this way,

including a second set of creep points after

completion of the vehicle at-speed runs.

3 Post Processing

Since the purpose of this test was to determine

the maximum accuracy possible from the VPM,

post processing was conducted on the data. If

the purpose of a test is to evaluate the real-time

performance of a VPM, then this step would not

be performed.

The data logged on the vehicle during the test is

retrieved, and the real-time navigation solution,

though not the subject of this paper, is examined

to confirm the existence of good data depicting

an appropriate vehicle trajectory. The raw data

is then post processed to obtain the more precise

solution for the events of interest.

To enhance the quality of the solution,

differential GPS post processing is used. A

detailed explanation of this processing is beyond

the scope of this paper, but the approach

essentially makes use of information collected

from another nearby GPS receiver (base station)

at an accurately known location to remove

(during post processing) certain kinds of errors

from the reported position of the rover (our

vehicle).

For these tests, we used a National Geodetic

Survey (NGS) Continuously Operating

Reference Station (CORS) located in

Gaithersburg, MD known as GAIT.

The NGS, an office of NOAA's National Ocean

Service, coordinates a network of continuously

operating reference stations (CORS) that provide

GPS carrier phase and code range measurements

in support of 3-dimensional positioning activities

throughout the United States and its territories.

(See http://www.ngs.noaa.gov/CORS/)

The reference station data for station GAIT is

downloaded from the NGS for the appropriate

period during which we performed the test, and

used by post processing software to enhance the

GPS solution. Further post processing is

performed to integrate this GPS solution with the

raw data from the VPM system sensors (IMU

and wheel encoder). Smoothing algorithms are

executed, and interpolation of navigation

solutions for the recorded events is performed.

The result is a file with a full navigation solution

for each event. The solution information in this

file is analyzed below.

4 Analysis

Two types of VPM measurement errors are

estimated: target (i.e., reflector) location and

target radius. The estimated location error is

derived as the difference between the surveyed

(static) location of the target and VPM measured

(dynamic) location of the target. The estimated

radius error is the difference between the known

radius of the target and VPM measured radius of

the target. The location error may be left

uncalculated if survey data is not available. The

following process is used to compute

uncertainties.
1. Put event points in a convenient local

coordinate system. First, the event points (VPMmeasured coordinates of target's edge) are

transformed from latitude and longitude

coordinates into UTM coordinates (see

velvet.tec.army.mil/access/milgov/fact_sheet/geo

trans.html) so that errors may be expressed in

meters. Second, if available, the known location

of the target is subtracted from each event point.

This places the event points in a coordinate

system whose origin is the surveyed location of

the target.

O

Figure 2. Example measurement points

surrounding the target's edge.

2. Determine the target location error. A circle

is fit to the event points. The fit produces an

offset vector, O, indicating the center of the

circle. Figure 2 shows an example set of data

points with the offset vector to the center of the

circle. If the origin of the coordinate system is

the surveyed location of the target, then the

magnitude of O, el, describes the estimated target

location error.

3. Determine the target radius error and

uncertainty. First, the offset vector of the circle

is subtracted from each event point. This places

the event points into a coordinate system with

the center of the circle at the origin (see Figure

3). Then the distance of each event point from

the origin, di, is computed. These distances are

the measured radii of the target. The mean and

standard deviation of the radius measurements,

r and Sr

, are computed. The estimated target

radius error, el, is the difference between the

mean of the target radius measurements and the

target radius measured by hand. Choosing a 95

% level of confidence, the component of the

expanded uncertainty due to data scatter in the

radius measurement is:

rr u =kS (1)

Where k = 2 for N 30 and k equal to a t-factor

obtained from a t-distribution for N < 30.

di

Figure 3. Measurements points

translated such that center of circle is at

origin. The distance of each point is the

measured radius of the target

Figure 4 shows a plot of the event points

collected when the vehicle traveled over a

circular target with a radius of 7.62 cm (3 in).

The origin of the plot coincides with the survey

marker's coordinates, in this case, the coordinates

of the NGS survey marker where the target was

placed. The large circle is the target drawn to

scale. Each event point is plotted as a small

circle with an attached velocity vector (origin of

vector is inside of small circle and points away

from circle in direction of travel). The vectors

are scaled to fit to the plot. The largest velocity

vector is labeled 10.6 m/s to provide a scale for

comparison (label is at end of vector opposite

small circle shown on left side of plot). The

results of the analysis of these measurements

indicate a location error of 0.3 cm, an estimated

radius error of 0.2 cm and a radius uncertainty of

1.3 cm (95 % level of confidence).
-0.081

-0.060-0.040-0.0200.0000.0200.0400.0600.082

-0.084

-0.075

-0.070

-0.065

-0.060

-0.055

-0.050

-0.045

-0.040

-0.035

-0.030

-0.025

-0.020

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

0.045

0.050

0.055

0.060

0.065

0.070

0.079

10.6 m/s

Easting (m)

el = 0.3 cm 

er = 0.2 cm 

ur = 1.3 cm 

k = 2.0

c = 95.0 %

V = 40

C:\downloads\nbs3a_8_2\sbet_nbs3a_all_adj2.out (8/6

Figure 4 Plot of event points (shown as small circles) triggered when vehicle is driven over the

edge of a circular target (large circle) with a radius of 7.62 cm. Vectors at each event point are

vehicle velocity at event point. The largest velocity was 10.6 m/s (left side of target).

5 Conclusions

Further work is needed in several areas. This

effort, an initial step in developing a

performance measurement capability, focused

only on vehicle position information in x and y.

Height (z) data was collected but the data has not

yet been analyzed. The testing methodology

needs to be expanded to include orientation (roll,

pitch, yaw) capabilities of VPM systems as well.

That information, along with position

information, is critical in registering the data

received from intelligent vehicle sensors such as

cameras and laser scanners, and directly affects

the correctness of the model of the world being

maintained by the vehicle computers. In

addition, the scope of the work here included

differential post processing of GPS data and

further post processing of all the navigation

sensor data. This is appropriate for metrology

purposes. For navigation of intelligent vehicles,

the real-time solution is important, and methods

for determining the quality of the real-time

navigation solution should be explored. Further,

a real-time navigation solution may make use of

real-time differential GPS corrections received

by the vehicle during operation. The method

described here can be used to examine position

determination performance for those types of
systems as well, and should be extended to

characterize orientation measurement

performance. Further, these tests were

conducted with good GPS satellite coverage.

While gaps in satellite coverage occurred, a test

specifically excluding satellite info for

prescribed periods would enable performance

analysis of the VPM systems when they rely

more on their inertial components.

We wish to acknowledge the National Highway

Traffic and Safety Administration's Office of

Vehicle Safety Research and the U.S. Army

Research Laboratories' Robotics Program Office

for their support of this work.
	

 	
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A Platform for Studying Locomotion Systems: 

Modular Reconfigurable Robots 

Ying Zhang, Craig Eldershaw, Mark Yim, Kimon Roufas, Dave Duff 

System and Practice Lab, Palo Alto Research Center 

Palo Alto, CA 94304 

Email: yzhang,celdersh,yim,kroufas,dduff@parc.com

ABSTRACT 

There are many fundamentally different mechanical motions that a 

system can use to achieve locomotion. Two standard examples are 

the wheels on a car or the legs of an artificial ant, but many others 

exist as well. As with all systems, there is an obvious desire to 

quantify how "well" each locomotion method performs. 

Unfortunately, as with many metrics, this is far from being a welldefined problem. Apart from the usual difficulty of deciding 

exactly what is the most important measure (peak speed, efficiency, 

etc), there is the question of divorcing the underlying locomotive 

concept from the particular implementation (just like a universal 

machine such as Turing Machine divorces hardware 

implementations from algorithms). 

This paper proposes a particular platform which the authors 

believe can be used as part of a standard system for evaluating 

many different means of locomotion. Since one of the 

fundamentally different aspects of each of these locomotive 

methods is the underlying mechanism, then any standard platform 

must be capable of changing its shape and fashion of moving so as 

to be able to faithfully perform the locomotion to be tested. The 

PolyBot system, developed at PARC, is capable of just this.

KEYWORDS: locomotion systems, locomotion gaits, 

modular self-reconfigurable robots, experimental platforms, 

performance measures

1. MOTIVATION 

Locomotion is an important attribute for many intelligent 

systems. All known intelligent species of life are capable of 

locomotion by some means or other. The focus of this paper 

will be a little narrower, focusing only on locomotion on 

solid surfaces (thus excluding swimming or flying). Over the 

last century human beings have invented various kinds of 

locomotion systems for motion over ground, mostly for fast 

and efficient transportation. Probably the two most 

widespread of these are as cars and trains. Most cars or trains 

cannot be considered intelligent systems, because (1) they 

are not autonomous to any significant degree, (2) despite a 

large internal sensor network, their perception of the outside 

world is very limited, (3) they are intended for use in very 

specialized artificial environments -- (cars on highways and 

trains on railways). 

The world is being constantly changed through the 

increasing availability of progressively cheaper and more 

powerful computation. Predictions have been made 

suggesting that in twenty years time, cars and trains will 

become intelligent robotic systems. Like animals, these 

vehicles will not only have a brain (central control) but also 

nervous systems (networking) connecting all sensing and 

actuation components. 

The majority of existing man-made locomotion systems is 

wheeled, since that is simple and efficient in a conveniently 

engineered environment (flat surfaces or rails). However 

natural locomotive systems (such as used by animals) have 

almost exclusively favored employing legs. The use of 

wheeled vehicles is largely limited to flat environments. 

Tracked vehicles tend to handle a wider variety of terrain but 

suffer in efficiency. Legged machines tend to be less efficient 

and harder to control but have the potential of traversing an 

even wider variety of terrains. While much research has 

been done on legged locomotion, little has been usefully 

commercialized. Even though legged locomotion is 

generally recognized to be more flexible, and has the 

potential to effectively traverse natural environments, as yet 

more knowledge and understanding of how to engineer such 

systems is needed. 

It is hard to compare two locomotion systems with 

radically different design, or two systems engineered for use 

in different environments. This paper proposes the use of 

modular self-reconfigurable robots as a standard platform for 

studying various types of locomotion and developing 

concrete performance metrics. By using this one platform 

for testing all locomotive ideas, the fundamental locomotive 

principle being tested is somewhat divorced from the specific 

physical implementation. 

A modular self-reconfigurable robot, named PolyBot, has 

been developed over the last three years at the Palo Alto 

Research Center (http://www.parc.com/modrobots). 

PolyBot consists of many component modules (possibly 

hundreds), each of which has sensing, actuation and 

computation. These modules can be configured into many 

different shapes, such as wheels/loops, snakes and 

centipedes. It is due to this versatility that PolyBot is able to 

implement a wide variety of different locomotive systems, 

allowing concrete performance metrics to be calculated and 

clear comparisons to be performed. 
With PolyBot, it is possible to develop various types of 

locomotion gaits for different types of configurations, and 

study the effectiveness of various control strategies. The 

results can be used to develop the performance metrics, 

which in turn allows quantitative improvements to be made 

in the quality of locomotion systems. 

This paper is organized as follows. Section 2 characterizes 

some initial concepts on locomotion systems and gaits, 

Section 3 discusses terrain evaluations, Section 4 presents 

locomotion performance metrics; Section 5 describes more 

completely PolyBot, the modular reconfigurable robot. 

Finally, there are possible directions for future research using 

PolyBot as a platform for studying locomotion systems. 

2. LOCOMOTION SYSTEMS AND GAITS 

A locomotion system is a powered system being able to move 

from one position/orientation to another. There is a 

considerable body of knowledge on animal locomotion [1] 

and vehicle locomotion [2]. The most typical classification of 

land locomotion divides locomotion into four types: 

wheeled, tracked, legged, and other. From authors’ point of 

view, this is unsatisfactory for several reasons. First, the last 

area is a catchall, and would include such dissimilar means 

of locomotion as snake-like sidewinding, concertina, screw 

locomotion, etc. Second, there are too many instances of 

ambiguity. For example, a child cartwheeling may be 

considered legged locomotion since the child has legs. 

Would a spoked wheel with no rim or partial rims also be 

considered legged locomotion? Tracked locomotion is 

defined as traveling on endless belts. Is a belt around a tire 

then tracked locomotion? What about a slightly flat tire? 

Yim [3] in his PhD thesis in 1994 studied various locomotion 

systems and first characterized locomotion gaits 

systematically. 

A locomotion gait is defined as one cycle of a pattern of 

motion that is used to achieve locomotion. There are simple 

gaits and compound gaits; compound gaits are combinations 

of two or more simple gaits. There are maybe finite classes 

of simple gaits, but combination of these can generate 

infinite number of compound gaits. For example as wheeled 

locomotion is one type of locomotion and bipedal walking 

clearly is another, the two can be combined as with a person 

wearing roller skates. 

A large portion of ground-based locomotion gaits can be 

characterized as statically stable gaits. To achieve statically 

stable locomotion in general, one has to repeatedly do three 

things in any order: 

1. remove ground contact points from the rear, 

2. place ground contact points in front, 

3. shift weight forward. 

Throughout all of these steps, maintain static equilibrium 

throughout all motions. A statically stable gait defines a 

cyclical pattern that achieves these steps. 

The simple ground-based statically stable locomotion gaits 

are characterized by three categories [3]: (R)oll/(S)wing, 

(D)iscrete/(C)ontinuous, (B)ig/(L)ittle Footed. For 

examples, a 4-wheel passenger car is RCL, a treaded tank is 

RCB, a cockroach is SDL, an earthworm is SCB, human is 

SDB, etc. 

Yim [3] also characterized three fundamental ways that a 

simple gait may be combined: articulated, hierarchical and 

morphological. Articulated combination is to unite more 

than one locomotion systems, e.g., track and trailer. 

Hierarchical combination is to add one locomotion system on 

the top of another, e.g., roller skating. Morphological 

combination is to merge locomotion systems with different 

axis, e.g., a rolling sphere. 

When deciding which gait would be most appropriate for a 

given situation, it would be useful to know the characteristics 

of each type of classification. For simple gaits, rolling 

systems tend to be simpler and more efficient. Continuous 

motion can be smoother over hard flat terrains. The larger the 

footprint, the better the performance in terms of speed, 

efficiency and mobility, etc. For compound gaits, single 

chain articulated gaits have several desirable features: the 

ability to travel in highly constrained areas, to fit between or 

cross large obstacles, with a large payload. Hierarchical gaits 

can achieve higher speeds than individual gaits, e.g., walking 

on a moving track belt is faster than walking on a ground. 

Morphological gaits add degrees of freedom to locomotion, 

which make the system more flexible. 

3. TERRAIN EVALUATIONS 

Simply comparing the locomotion capabilities of a horse to a 

wheeled car is meaningless, just like comparing apples and 

oranges. In nature, each form of locomotion exists in the 

environment that fits it best. Locomotion performance 

metrics will not be complete without terrain evaluations. Yim 

[3] defined the taxonomy of terrain effects (Figure 1). 

Figure 1: Taxonomy of Terrain Features 

Terrain Effects 

Static 

Quasidynamic 

Dynamic 

Elevation

Map 

Full 3D 

Slope 

Ditch 

Hangup 

Barrier 

Height 

Width 

Curvature 

Elasticity 

Plasticity 

Penetrability 
Static 2.5D terrain features include slopes, gradual 

elevation with height, ditches, holes in the ground, hang-ups, 

bumps in the ground, and barriers, a vertical object to cross 

like a wall. Full 3D terrain features include height

constraints, obstacles on the top, width constraint, obstacles 

on the sides, and curvature constraint, the radius to turn. 

Quasi-dynamic terrain features include elasticity, plasticity

and penetrability of ground surface, for example, a 

locomotion system will perform differently on soft mud 

terrain and hard wood floors. Dynamic terrain features 

include moving wind/current, moving terrain and obstacles, 

etc. 

4. LOCOMOTION PERFORMANCE 

METRICS 

The complexity inherent in intelligent systems means that it 

is rare for a useful metric to measure just one aspect of 

performance. In general, the evaluation function which 

serves as the metric will have multiple objectives which must 

be appropriately scaled and weighted. There are two main 

types of measurements for locomotion systems: system 

centric and environment centric. 

In system-centric measurements, the type of environment 

is fixed (e.g. a dirt road) and other aspects (e.g. speed, 

acceleration, efficiency, stability, payload) are compared. In 

an environment-centric measurement, the value is some 

measure of the terrain which can be traversed (e.g. how steep 

the environment can be, or how rough) in terms of metrics of 

slopes, ditches, hang-ups, barriers, height, width and 

curvature constraints etc. 

In addition to mechanical capabilities, computational 

capabilities can also be measured, such as adaptability, 

robustness, self-repair-ability, as well as the underlying 

computational components such as CPU speed, memory, 

communication rate, etc. 

In addition to these "hard" measurements of locomotion 

systems, there are also "soft" measurements reflecting quality 

of system design (both hardware and software); these include 

maintainability, modularity, scalability and reusability. 

Some of these attributes are not directly related to 

performance, but are relevant to the total cost of ownership. 

Others are even less of interest to an end-user, but are still of 

importance for decision-making by the developer. 

The authors do not claim to have yet developed a complete 

metric which satisfies all the conditions discussed above–this 

is work in progress. However they do put forward the idea 

of employing a uniform platform for locomotion testing. 

This, they argue, will simplify the measurement and 

comparison process, allowing effort to be directed towards 

refining the base metric. This universal platform is described 

in the next section. 

5. MODULAR ROBOTICS PLATFORM 

A modular reconfigurable robot is one that consists largely 

(or entirely) of identical components which can be assembled 

into many topological configurations. These different 

configurations generally equate to different physical shapes – 

each with different abilities and limitations. In this way the 

platform can be used to test many fundamentally different 

forms of locomotion. The platform proposed here is a 

modular self re-configurable robot: one that can change from 

one configuration to another autonomously. While this 

capability is not actually essential for its use as a universal 

platform as proposed here, the sensing, distributed 

computation, communications and control middleware 

required to support self reconfiguration will prove useful in 

carrying out the measurements for the performance metric. 

There are a growing number of modular self-reconfigurable 

robotic [4][5][6][7][8][9]. This paper focuses on one 

particular modular self-reconfigurable robot, named PolyBot 

[10]. 

PolyBot, is a modular reconfigurable robot system 

composed of two types of modules, one called a segment and 

the other called a node. The segment module has two 

connection ports and one degree of freedom (DOF) motion. 

The node module is a rigid cube with six connection ports 

but no internal DOF. PolyBot has been designed for 

applications including planetary exploration, undersea 

mining, search and rescue and other tasks in unstructured, 

unknown environments. PolyBot has been developed through 

its third generation at the Palo Alto Research Center. The 

latest design features smaller module size (5cm), more 

sensors (IR range, touch, force) and multiple actuators for 

locomotion, manipulation and reconfiguration, as well as 

bridged networks using CAN (Controller Area Networks). 

Each PolyBot module has a Motorola PowerPC MPC555

embedded processor with 448K internal flash ROM and 1M 

of external RAM. Software architecture has been developed 

for PolyBot, with a higher layer CAN protocol MDCN 

(Massively Distributed Control Nets) [11][12] and an 

Attribute/Service Model [11][13] for coordination of 

multiple tasks in multiple processes. 

PolyBot is a good platform for studying various forms of 

locomotion. The PolyBot systems have demonstrated 

versatility by showing multiple modes of locomotion with a 

variety of characteristics, distributed manipulation and the 

ability to self-reconfigure. PolyBot can be configured into 

various shapes (see Figure 2,3,4). Each configuration has 

pros and cons in terms of performance. Snakes can traverse 

terrains with narrow entrance, such as pipes, and is the most 

robust among other configurations. Loops or wheels are most 

efficient over flat terrains. With deformed loops 

(conformance to terrains) it can also traverse effectively over 

stairs. Centipedes or spiders are good for avoiding obstacles 

and traverse rough terrains. 
 

 

Figure 2: Snake Configuration 

Figure 3: Loop Configuration 

Figure 4: Spider Configuration 

6. FUTURE WORK 

There will be 100+ PolyBot modules built by the October 

this year. Various locomotion configurations and gaits will 

be tested and compared in the near future. A more complete 

understanding of and development of locomotion 

performance metrics will commence. 

ACKNOWLEDGEMENT 

This work is funded in part by the Defense Advanced 

Research Project Agency (DARPA) contract # MDA972-98-

C-0009. 

REFERENCES 

[1] Gray, J., “Animal Locomotion”, W.W. Norton & Co. 

Inc., New York, 1968 

[2] Bekkar, M. G., “Introduction to Terrain Vechicle 

Systems”, Ann Arbor MI, The University of Michigan Press, 

1969 

[3] Yim, M., “Locomotion with a Unit-Modular 

Reconfigurable Robot”, PhD Thesis, Stanford University, 

1994 

[4] Fukuda, T. and S. Nakagawa, “Dynamically 

Reconfigurable Robotic System,” Proc. of the IEEE Int. 

Conf. on Robotics and Automation, pp. 1581-1586, 1988. 

[5] Kotay, K., D. Rus, M. Vona, C. McGray, “The Selfreconfiguring Robotic Molecule,” Proc. of the IEEE 

International Conf. on Robotics and Automation, pp424-431, 

May 1998. 

[6] Murata, S., H. Kurokawa, S. Kokaji, “Self-Assembling 

Machine,” Proc. of the IEEE International Conf. on Robotics 

and Automation, pp441-448, May 1994. 

[7] Pamecha, A., C. Chiang, D. Stein, G.S. Chirikjian, 

“Design and Implementation of Metamorphic Robots,” Proc. 

of the 1996 ASME Design Engineering Technical Conf. and 

Computers in Engineering Conf., Irvine, California, August 

1996. 

[8] Unsal, C. and P.K. Khosla, “Solutions for 3-D Selfreconfiguration in a Modular Robotic System: 

Implementation and Path Planning,” Proc. of SPIE Sensor 

Fusion and Decentralized Control in Robotic Systems III, 

Vol. 4196. 

[9] Will P., A. Castano, W-M Shen, “Robot modularity for 

self-reconfiguration,” SPIE Intl. Symposium on Intelligent 

Sys. and Advanced Manufacturing, Proceeding Vol. 3839, 

pp. 236-245, Sept. 1999. 

[10] Yim, M., D. Duff, K. Roufas, “PolyBot: a Modular 

Reconfigurable Robot” Proc. of the IEEE Int. Conf. on 

Robotics and Automation, April 2000. 

[11] Zhang, Y., K. Roufas, M. Yim, “Software Architecture 

for Modular Self-Reconfigurable Robots”, IEEE/RSJ 

International Conference on Intelligent Robots and Systems, 

Hawaii, 2001.

[12] Zhang, Y., K. Roufas, M. Yim, “Massively Distributed 

Control Nets for Modular Self-Reconfigurable Ro-bots”, 

AAAI Spring Symposium for Intelligent Distrib-uted and 

Embedded Systems, 2002 

[13] Zhang, Y., M. Yim, K. Roufas, C. Eldershaw, “Attribute/Service Model: Design Patterns for Distributed 

Coordination of Sensors, Actuators and Tasks”, sub-mitted to 

Workshop on Embedded Systems Codesign, 2002. 
Performance of Planning Systems

A. Meystel

Drexel University, National Institute of Standards and Technology

Planning is learning from experience in the

domain of imagination.

From the Section 10 of this paper

The goal of this paper is to help in the organization of

further research in the area of Planning.

1. Emergence and Development of the Theoretical

Domain on Planning

The area of planning is a victim of linguistics:

professionals of different domains give different

interpretation to the phenomenon of "planning."

Traditionally, it was associated with human activities

and the help of science was expected

a) in a better organization of information for

planning supporting the way the humans plan

b) in proposing of techniques that help to

come up with "interesting" alternatives of planning

decisions

c) in structuring the process of planning so

that to make it more efficient

d) in modeling human activities during the

process of planning.

Simon and Newell were the first that

visualized planning as an element of each problem

solving process. However, AI treated planning in the

way it treated other problems - with the help of toyproblems (like Hanoi-Tower and block-world

situations.

Specialists in control did not realize and did

not appreciate the fact that feedforward control is

actually the result of planning. The elegant discoveries

of Nilsson and Fikes in their heuristic search methods

remained unnoticed by control community until

recently.

Specialists in automation of therapeutic

solutions did not realize that they plan the process of

disease development and plan the process of healing.

Specialists in education did not realize that

proposing a curriculum and/or a particular syllabus has

no difference from the processes of feedforward

control and process of disease development and

healing.

Specialists in optimization (re: operation

research) did not realize that their problems were just

elements of the planning theory.

Specialists in cognitive psychology seldom

saw anything in common between "planning" and

"imagination."

Thinkers of finding the goal did not have

anything in common with thinkers computing

schedules of goal achievement.

A common wisdom about planning was (and

is) that there are many ways of planning and each of

them has its place.

The following linkages were totally

neglected:

a) the linkage between off-line planning and

on-line control,

b) the linkage between specifying the goal

and finding a schedule of achieving the goal.

c) the linkage between the linguistics of

finding the image of "goal" and searching of the best

schedule.

d) the linkage between the methodology and

the performance of planning.

2. Planning for Behavior Generation

Robotics became the integrated domain that

provided for blending the goals and testing the means

of achieving them, i.e. a domain with a direct need for

planning. In 1983, T. Lozano-Perez has introduced the

idea of search in "configurations space". From the

experience of using this search, it became clear that the

exhaustive search would be computationally

prohibitive if the configuration space is tessellated with

the accuracy required for motion control. But his

theory made two important thing obvious: 1) planning

is an apogee of creating admissible alternatives and

searching for the trajectories entailed by these

alternatives. 2) planning is performed upon milestones

in the state space, and if they do not exist in the reality,

they should be created artificially (re: centers of the

tessellata in the configuration space).

This development helped to realize that

planning should combine the exhaustive (or

meaningfully thorough) search off-line, and an

efficient algorithm of an off-line control. It was about

of this time that we stopped talking about control of

actions and introduced a more balanced term of

Behavior Generation. The latter became a codeword

for the joint process of arranging and testing the

alternatives within the mechanism of "planning" (open
loop, feedforward control) blended with the on-line

finding the alternatives of feedback for error

compensation (closed-loop control, or "execution").

Behavior Generation alludes to many

mechanisms of planning and execution. At the present

time, these mechanisms cannot be considered as

known thoroughly, and the general theory of planning

can hardly be immediately attempted. There is a merit

in discussing a subset of problems in which the goal is

determined as attainment of a particular state.

The following are scattered notes on the

progress in the domain of planning.

• Most of the realistic problems can be

translated into this paradigm. Other types of

realizations can also be imagined: in chess the

goal is clear (to win) but this goal cannot be

achieved by achieving a particular position in

a space (even in a descriptive space.) Most of

the problems related to the theory of games

and linked with pursuit and evasion are

characterized by a similar predicament.

• Let us notice the following: no matter what is

the domain of decision making, the process of

planning can be performed only by searching

the state space and thus, determining both the

final goal, and the trajectory of motion

leading to this goal.

• In 1981 J. Albus has introduced the

methodology of task decomposition for

hierarchical systems which has grown into a

NIST-RCS methodology.

• In 1981 G. Giralt outlines the concept of

planning for mobile robots via tessellated

space.

• In 1983, T. Lozano-Perez has introduced

search in Configuration Space.

• In 1986, A. Meystel has demonstrated (CDC,

Athens) that the most efficient functioning of

a multilevel learning/control systems can be

provided by a proper choice of a ratio of

lower level/higher level of resolution. This

concept of planning/control becomes a strong

theoretical support for the hierarchical

architecture of intelligent system control

developed by J. Albus during the period of

1980-1998.

• In 1985-87 M. Arbib's school of control via

"schemata" came up with a numerous

schemes of "reactive" behavior. This gave

birth to a multiplicity of robot control

concepts which explore and exercise reactive

behavior generation.

• In the meantime, the primary focus of

robotics shifts to the area of systems which do

not require any planning (robotics with

"situated behavior"). Thus, the interest in

planning diminishes (R. Brooks, MIT, R.

Arkin, Georgia Tech) and the curiosity of

researchers shifts toward emerging

phenomena in non-intelligent robots.

3. Planning in a Representation Space with a Goal

This is an outline of the common

methodology of planning pertaining to most of the

disciplines and areas of application. The world is

assumed to be judged upon by using its State Space (or

the Space of Representation) which is interpreted as a

time tagged vector space with a number of important

properties. Any activity (motion) in the World (Space

of Representation) can be characterized by a trajectory

of motion along which the "working point" or "present

state" (PS) is traversing this space from one point

(initial, or state, IS) to one or many other states (goal

states, GS.) The goal states are given initially from the

external source as a "goal region", or a "goal subspace"

in which the goal state is not completely defined in a

general case. 

From the point of view of planning, state

space does not differ from the configuration space.

Indeed, the upcoming behavior is represented as a

trajectory in the state-space (and/or configuration

space). One of the stages of planning (often the initial

one) is defining where exactly is the GS within the

"goal region." In this paper, we will focus upon

planning problems in which one or many GS remain

unchanged through all period of their achievement.

Traversing from IS to GS is associated with consuming

time, or another commodity (cost). So, the straightforward exhaustive search is feasible which allows for

exploring all possible alternatives.

Researchers in the area of reactive behavior

introduced a method of potential fields for producing

comparatively sophisticated obstacle avoiding schemes

of motion. Reactive behavior is considered to be an

anti-thesis for planning. It is not so. Planning based

motion can be called reactive, too. The difference is

that in the papers on reactive behavior, we react to the

present situation. In the system with planning, we react

too: but we react to the anticipated future.

Thus, planning can be considered an

anticipatory reactive behavior. The difference is in the

fact that anticipation requires representation richer than

the simple reactive behavior requires. The philosophy

of the approach affects the performance of planning.

4. Types of Representation Available

All Representation Spaces are acquired from

the external reality by the processes of Learning. Many

types of learning are mentioned in the literature

(supervised, unsupervised, reinforcement, dynamic,

PAC, etc.) Before classifying a need in a particular
method of learning and deciding how to learn, we

would like to figure out what exactly we should learn.

Can the process of learning be separated into two

different learning processes:

• that of representation, and

• that of the rules of action,

or are these two kinds of learning just two sides of the

same core learning process?

The following knowledge should be contained

in the Representation Space. If no GS is given, any

pair of state representations should contain implicitly

the rule of moving from one state to another. In this

case, while learning we inadvertently consider any

second state as a provisional GS.

 We will call "proper" representation a

representation similar to the mathematical function

and/or field description: at any point the derivative is

available together with the value of the function; the

derivative can be considered an action required to

produce the change in the value of the function.

We will call "goal oriented" representation a

representation in which at each point a value of the

action is given required for describing not the best way

of achieving an adjacent point but the best way of

achieving the final goal.

Both "proper" and "goal oriented"

representations can be transformed into each other.

However, they differ in the productivity of planning.

5. Components of Representation Space

Representation (that of the World) can be

characterized by the following artifacts:

• existence of states with its boundaries

determined by the resolution of (each

state is presented as a tessellatum, or an

elementary unit of representation, the

lowest possible bounds of attention)

• characteristics of the tessellatum which is

defined as an indistinguishability zone

(we consider that resolution of the space

shows how far the "adjacent" tessellata

(states) are located from the "present

state" (PS)

• lists of coordinate values at a particular

tessellatum in space and time

• lists of actions to be applied at a

particular tessellatum in space and time

in order to achieve a selected adjacent

tessellatum in space and time

• existence of strings of states intermingled

with the strings of actions required to

receive next consecutive tessellata of

these strings of states

• boundaries (the largest possible bounds

of the space) and obstacles

• costs of traversing from a state to a state

and through strings of states.

In many cases, the states contain information

which pertains to the part of the world which is beyond

our ability to control it, and this part is called

"environment." Another part of the world is to be

controlled: this is the system for which the planning is

to be performed. We will refer to it frequently as

"self." Thus, a part of the representation is related to

"self" including knowledge about actions which this

"self" should undertake in order to traverse the

environment.

It is seen from the list of artifacts that all

knowledge is represented at a particular resolution.

Thus, the same reality can be represented at many

resolutions and the "multiresolutional representation"

is presumed.

The system of representation is expected to be

organized in a multiresolutional fashion. This will

invoke the need in applying a number of special

constraints and rules. The rules of inclusion

(aggregation/decomposition) are especially important.

6. Planning in Redundant Systems

Non-redundant systems have a unique

trajectory of motion from a state to a state. Redundant

system is defined as a system in which there is more

than one trajectory of motion from one state to another.

It can be demonstrated for many realistic couples

"system-environment" that

• they have a multiplicity of traversing

trajectories from a IS to a GS

• these trajectories can have different costs.

These systems contain a multiplicity of

alternatives of space traversal. Redundancy grows

when the system is considered to be a stochastic one.

The number of available alternatives grows even

higher when we consider also a multiplicity of goal

tessellata of a particular level of resolution under the

condition of assigning the goal at a lower resolution

level which is the fact in multiresolutional systems

(such as NIST-RCS.)

In on-redundant systems there is no problem

of planning. Since the trajectory of motion to be

executed is a unique one, the problem is to find this

trajectory and to provide tracking of it by an

appropriate classical control system.

7. Learning as a Source of Representation

Learning is defined as knowledge acquisition

via experience of functioning. Thus, learning is

development and enhancement of the representation

space under various goals. The representation can be

characterized in the following ways:

• by a set of paths (to one or more goals)

previously traversed
• by a set of paths (to one or more goals)

previously found and traversed

• by a set of paths (to one or more goals)

previously found and not traversed

• by a totality of (set of all possible) paths

• by a set of paths executed in the space in a

random way.

One can see that this knowledge contains

implicitly both the description of the environment and

the description of the actions required to traverse a

trajectory in this environment. Moreover, if some

particular system is the source of knowledge, then the

collected knowledge contains information about

properties of the system which moved in the

environment.

All this information arrives in the form of

experiences which record states, actions between each

couple of states, and evaluation of the outcome. The

collection of information obtained in one or several of

these ways forms knowledge of space, KS.

If the information base contains all tessellata

of the space with all costs among the adjacent

tessellata - we usually call it the “a complete

representation.” The tessellation can be a randomized

one: a factor strongly affecting the performance.

Thus, the representation is equivalent to the

multiplicity of explanations how to traverse, or how to

move. In other words: all kinds of learning mentioned

in p. 3 are equivalent.

Comments: a) Knowledge of the space (KS)

is realized via knowing states, and/or knowing the

"derivatives" (or actions) from a state to a state.

 b) Apparently, each state can be

characterized by some cumulative cost (value), while

each traversal from a state to a state can be

characterized by some incremental cost (goodness of a

move or a set of moves.)

8. Standardizing the Problems of Planning

Any problem of planning is associated with

• actual existence of the present state

• actual, or potential existence of the goal state

• knowledge of the values for all or part of the

states as far as some particular goal is

concerned.

From this knowledge the cumulative costs of

trajectories to a particular goal (or goals) can be

deduced. On the other hand, the knowledge of costs for

the many trajectories traversed in the past can be

obtained which is equivalent to knowing cumulative

costs from the initial state (PS) to the goal state (GS)

(from which the values of the states can be deduced.)

In other words, any problem of planning

contains two components: the first one is to determine

and/or to refine the goal (bring it to the higher

resolution.) The second one is to determine the motion

trajectory to this refined goal. These two parts can be

performed together, or separately. Frequently we are

dealing with them separately. In the latter case they are

formulated as follows:

a) given PS, GS and KS (all paths) find the

subset of KS with a minimum cost, or with a preassigned cost, or with a cost in a particular interval.

b) given PS and GS from the lower resolution

level and KS (all paths) find the GS with a particular

value (which is satisfactory for the system).

9. Performance of Planning Algorithms

Finding solutions for these problems is done

by a process that is called planning. In other words,

planning is construction of the goal states, and/or

strings of states connecting the present state with the

goal states. Performance of planning algorithms is

determined by the way these procedures are arranged.

The first component of the planning algorithm

is translation of the goal state description from the

language of low resolution to the level of high

resolution. Frequently, it is associated with increasing

of the total number of the state variables. In all cases it

is associated with reduction of the indistinguishability

zone, or the size of the tessellatum associated with a

particular variable.

The second component is the simulation of all

available alternatives of the motion from the initial

state, IS to one or several goal states, GS and selection

of the "best" trajectory. Procedurally, this simulation is

performed as a search, i.e. via combinatorial

construction of all possible strings (groups). To make

this combinatorial search for a desirable group more

efficient we reduce the space of searching by focusing

attention.

Thus, all planning algorithms consist of two

components: a) a module for exploration of spatial

distribution of the trajectory, and b) a module for

exploration of the temporal distribution. No algorithm

of planning is conceivable without these two

components.

The need in planning is determined by the

multialternative character of the reality The process of

planning can be made more efficient by using

appropriate heuristics which is not considered in this

paper.

10. The Relations Between Planning and Learning

Planning is learning from experience in the

domain of imagination. Planning is performed by

searching within a limited subspace

• for a state with a particular value

(designing the goal)

• for a string (a group) of states

connecting SP and GP satisfying
some conditions on the cumulative

cost (planning of the course of

actions)

The process of searching is associated ether

with collecting the additional information about

experiences, or with extracting from KS the implicit

information about the state and moving from state to

state, or learning. In other words, planning is

inseparable from and complementary to learning.

This unified planning/learning process is

always oriented toward improvement of functioning in

engineering systems (improvement of accuracy in an

adaptive controller) and/or toward increasing of

probability of survival (emergence of the advanced

viruses for the known diseases that can resist various

medications, e.g. antibiotics.)

Thus, this joint process can be related to a

system as well as to populations of systems and

determines their evolution.

11. Other Components of Planning

Planning algorithms consist of the procedures

of Job Assignment and Scheduling. Job Assignment

distributes the motion among the spatial coordinates.

Scheduling distributes the motion along the time axis.

Together, they contribute to the search process. Search

is performed by constructing feasible combinations of

the states within a subspace. (“Feasible” means:

satisfying a particular set of conditions.) Search is

interpreted as exploring (physically, or in simulation)

as many as possible alternatives of possible motion and

comparing them afterwards.

Each alternative is created by using a

particular law of producing the group of interest

(cluster, string, etc.) Usually, grouping presumes

exploratory construction of possible combinations of

the elements of space (combinatorial search) and as

one or many of these combinations satisfy conditions

of "being an entity" - substitution of this group by a

new symbol with subsequent treating it as an object

(grouping.)

The larger the space of search is the higher is

the complexity of search. This is why a special effort is

allocated with reducing the space of search. This effort

is called focusing attention and it results in

determining two conditions of searching, namely, its

upper and lower boundaries:

a) the upper boundaries of the space in which

the search should be performed, and

b) the resolution of representation (the lower

boundaries)

12. Planning Embodies the Intelligence of a System

Formation of multiple combinations of

elements (during the search procedure, S) satisfying

required conditions of transforming them into entities

(grouping, G) within a bounded subspace (focusing

attention, F) is a fundamental procedure in both

learning and planning. Since these three procedures

work together we will talk about them as about a triplet

of computational procedures which include grouping,

focusing attention and search (GFS.) Notice, that in

learning it creates lower resolution levels out of higher

resolution levels (bottom-up) while in planning it

progresses from the lower resolution levels out of

higher resolution levels (top-down.)

This triplet of computational procedures is

characteristic for intelligence and probably is the

elementary computational unit of intelligence. Its

purpose is transformation of large volumes of

information into a manageable form which ensures

success of functioning. The way it functions in a joint

learning-planning process explains the pervasive

character of hierarchical architectures in all domains of

activities.

The need in GFS is stimulated by the property

of knowledge representations to contain a multiplicity

of alternatives of space traversal (which is a property

of representations to be redundant.) Redundancy of

representations determines the need in GFS: otherwise

the known systems would not be able to function

efficiently (it is possible that redundancy of

representations is a precondition for the possibility of

Life and the need in Intelligence)

13. Planning is Inseparable From Control

Development of a plan is equivalent to

computing the "feedforward control." To compute

FFC, we have to have a model of a system

(representation) and apply an operation if inverse

(computing the required FFC control commands for

the motion preassigned). Even if a system

representation is in a not-invertible form, the inverse

can be found by a forward searching.

 Representations reduce the redundancy of

reality. Elimination of redundancy allows for having

problems that can be solved in a closed form (no

combinatorics is possible and/or necessary).

Sometimes, this ultimate reduction of redundancy is

impossible and the combinatorial search is the only

way of solving the problem). If the problem cannot be

solved in a closed form, we introduce redundancy

intentionally to enable functioning of GFS (grouping,

focusing attention, and searching).

At each level of resolution, planning is done

as a reaction for the slow changes in situation which

invokes the need in anticipation and active interference

a) to take advantage of the growing

opportunities, or

b) to take necessary measures before the

negative consequences occur.
The deviations from a plan are compensated

for by the compensatory mechanism also in a reactive

manner. Thus, both feedforward control (planning) and

feedback compensation are reactive activities as far as

interaction system-environment is concerned. Both can

be made active in their implementation. This explains

different approaches in control theory.

Examples:

a) Classical control systems are systems with

no redundancy, they can be solved in a closed form.

Thus, they do not require any searching.

b) Any stochastics introduced to a control

system creates redundancy and requires either for

elimination of redundancy and bringing the solution to

a closed form, or performing search.

c) Optimum control allows for the degree of

redundancy which determines the need in searching.

Recently, an area of "supervisory control" has

emerged as a partial introduction of the control theory

to the domain of planning.

14. Research for Planning: Topics For Exploration

and Discussion

The following research topics can be outlined:

a) development of the system of

representation for planning purposes; it should provide

for a multiresolutional organization of information

b) analysis of existing and potentially

beneficial techniques of synthesizing the goal

assignments (spatial plan distribution)

--by using combinatorial techniques

(computer and human-based)

--by analytical methods (e.g.

variational)

c) analysis of existing and potentially

beneficial techniques of determining preferable

clusters, or groups: determining the preferred

schedules for strings of the way points, or milestones

(temporal plan distribution)

--by using search in the state space

--by using game-theoretical methods

--by using self-organization of

multiple agents

d) quantitative evaluation of the tools for

narrowing attention: determining envelopes around the

trajectory of motion (string of the milestone events) for

the consecutive refinement (repetition of the planning

procedure at the higher resolution level)

e) construction of the state spaces for the

consecutive searching

f) analysis of the methodologies of state-space

tessellation for applying different 

methodologies of consecutive refinement

g) exploring the methods of search applicable

for determining the preferential strings

--by searching techniques induced by

dynamic programming

--by standard techniques of

exhaustive search

--by methods of heuristic guiding

during the search

--by searching via evolutionary

programming

--by searching in nonlinear problems

h) testing the results of planning via various

simulation methodologies

i) exploring the phenomenon of nestedness of

plans obtained at various resolutions (at various

levels of resolution)

j) dealing with uncertainties of information

--by decision-making procedures

when the values of alternatives are

uncertain and do not allow for an

unequivocal choice

--by development and maintenance

of contingency plans

k) analyze the role of prediction in planning,

develop a system of creating and using predictions

l) analyze the phenomenon of goal

m) determine methods of forming different

functionals of "cost", or "goodness"

n) explore planning under condition of

multiple criteria (costs)

o) test benefits and deficiencies of various

schemes of decision-making in planning

p) the computer aspects of planning are

virtually unexplored: do we need a language for

planning?

All positions of this list affect the

performance both of the system in the World and of

planning algorithms as a part of the Design process.
Lower Bounds for Evaluating Schedule

Performance in Flexible Job Shops

	







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PERFORMANCE CHARACTERISTICS OF PLANNING ACTORS

WOUT VAN WEZEL AND RENÉ JORNA 

Faculty of Management and Organization 

University of Groningen, P.O.Box 800 

9700 AV Groningen, The Netherlands 

E-mail: w.m.c.van.wezel@bdk.rug.nl; r.j.j.m.jorna@bdk.rug.nl 

ABSTRACT. Planning is a field of interest in many scientific 

disciplines. These scientific areas cover a multitude of planning 

approaches that at first sight do not have much in common: psychophysiological analyses, organizational science, linguistics, cognitive 

science, operations research, and spatial science, to name just a view. 

The differences in ontologies and methods used make it difficult to 

make statements that transcend the mono-disciplinary perspectives. 

Still, no matter the research field, planning always concerns 

anticipating on the future and determining courses of action. As a 

consequence, there must also be similarities between the various 

approaches that deal with planning. This paper proposes a number of 

characteristics that can be used to analyze the differences and 

similarities in performance of different kinds of planning actors. 

1. INTRODUCTION

Where will we go and how do we get there? This question is 

an inherent part of intelligent systems. The ability to anticipate 

and plan is usually seen as a required and perhaps even 

essential feature of such systems. It is the fundament of goal 

directed behavior; systems that pursue goals need to take the 

future into account. 

 Planning is not a nicely ordered and well-defined subject. 

Various disciplines with various scientific backgrounds deal 

with planning, such as (cognitive) psychology, mathematics, 

economics, operations research, artificial intelligence, and 

management and organization. In our opinion, comparing, 

combining, or even integrating the research efforts of each of 

the individual planning approaches can be a fruitful next step 

in planning research in general. Due to the sheer differences 

between these scientific areas, however, it seems difficult to 

make generic statements about the relation between the 

planning of an actor and its performance. But, the fact that 

planning always concerns anticipating on the future and 

determining courses of action might provide an opening. This 

notion is used in this paper to introduce a frame of reference 

for planning. In section 2, we provide a generic and abstract 

definition of planning, resulting in a discussion of four 

different scientific planning areas. Section 3 provides a 

number of generic characteristics with which the planning 

research areas are compared. In section 4, we draw the 

conclusions. 

2. PLANNING ACTORS

As stated in the previous section, many research areas 

somehow deal with planning. In this section, we will describe 

four of such areas. This description will be based around our 

conception of planning, which can be outlined by four topics. 

 First, it is important to acknowledge that some entity must 

make the plan. Note that all kinds of entities can make plans, 

for example, humans, robots, computer programs, animals, 

organizations, etc. 

Second, someone or something must execute the plan, i.e., 

the intended future must somehow be attained. Again, this can 

be done by all kinds of entities, and the planning entities need 

not necessarily be involved in plan execution themselves. 

Third, the planning entity needs some kind of model of the 

future, since the future is essentially non-existent. This model 

should include states, possible actions of the executing entities 

and the effect of actions on the state they reside in, constraints, 

and goals. Planning and anticipation presume that such a 

predictive model is available, otherwise the chance that a plan 

can be executed as intended becomes a shot in the dark. 

 The fourth element of planning is the plan itself. The plan 

signifies the belief that the planning entity has in the model of 

the future: the actions in the plan (which are performed by the 

executing actor) will lead to the desired or intended future 

state. 

 The first and second topics lead to four kinds of planning 

actors that have their own embedding in literature: 

1. Humans that plan and execute: (cognitive) psychology 

2. Humans that plan but do not execute: organizational 

science 

3. Artificial actors that plan and execute: artificial 

intelligence 

4. Artificial actors that plan but do not execute: operations 

research 

In the remainder of this section, we will discuss these four 

kinds of planning actors. The third and fourth topic can be 

used to analyze the performance of planning actors. This will 

be discussed in the next section. 

Humans that plan their own activities: To have a closer look at 

the execution of the task, we start with cognition, where the 

study of planning contains the study of human (intelligent) 

activities (tasks). In this perspective the old definition of 

Miller, Pribram & Galanter is used, saying that a plan is a hierarchical process within an organism that controls series of 
operations [1]. Jean-Michel Hoc elaborates this definition and 

says that planning always involves anticipation and schematization [2]. What he means is that planning is a two-line 

parallel process, in which a future state is taken into account 

(anticipation) and in which a (stored) mental scheme can be 

applied if a concrete planning problem arises. Therefore, Hoc 

talks about bottom-up and top-down processes that are always 

involved in making a plan. 

 Because of the terminology he uses, Hoc is implicitly 

taking a position in the complex cognitive debate about 

planning. This debate involves two closely related topics. The 

first deals with the question whether planning is a form of 

problem solving [3] or whether planning and problem solving 

only overlap [4]. The second is about the question whether 

human planners work hierarchically [3] or whether they plan 

opportunistically or even chaotically [5]. We discuss the issues 

successively in greater detail. 

 Planning and problem solving: Newell et al. [6] describe 

the planning method as a part of a general problem solving 

technique. It consists of a reformulation of the problem in 

more abstract and restricted terms, its solution in a simplified 

problem space with another level of resolution [7], its 

retranslation into the original problem situation and 

subsequently its solution. In later papers Newell & Simon 

rename the planning method as problem abstraction, necessary 

if the problem is not solvable within its original state space [3]. 

Because planning as well as problem solving means searching 

for routes (i.e., sequences of actions that lead to a solution or a 

goal state), the explicit distinction between planning and 

problem solving disappears in the later work of Newell & 

Simon. Planning is just one very interesting example of the 

general problem solving approach. Das et al. [4] argue against 

this “planning is a subset of problem solving” approach in 

saying that a difference exists in problems to prove and 

problems to find. According to Das et al. [4, p. 40] “planning 

is a more pervasive, general regulating process than problem 

solving, with problem solving being a part of a planning 

process.” Planning includes anticipation and overview and 

refers to future actions, whereas these components seem to be 

absent in problem solving. According to us this may almost be 

a game with words, because one could state that searching and 

trying to reach a goal and constructing a problem space with 

states and operators, imply future actions and anticipation. We 

will not settle the discussion, here. Das et al., however, may 

have a point in one aspect of this debate. An enigmatic 

element in the problem solving approach of Newell & Simon 

has always been the starting point of the problem solving 

process. How does a problem solver construct a problem 

space? Where does the choice for a particular problem space 

come from? Why does a problem solver constructs this special 

problem space and not another? In terms of Newell & Simon 

the question is how a task environment gets its representation 

in a state space description. It is easy to say that one has a new 

problem here, which requires a second order state space 

description. Although this might be true in the strict sense of 

the word, it does not solve the issue. Perhaps something like 

what Das et al. called “overview” or “having a higher 

perspective” is necessary [4]. Therefore, it might be insightful 

to distinguish planning as second order problem solving from 

“ordinary” problem solving. If, in line with Newell & Simon, 

one considers the planning task in organizations and 

institutions to be a problem solving process, the question 

appears how planners construct an initial representation. Do 

they start with an overview or are they just trying? In the first 

situation there is an explicit state space to start with. In the 

second situation the state space is reformulated again and 

again. 

 Hierarchical and opportunistic planning: The discussion 

about the relation between planning and problem solving is 

closely connected to the way the planning (or problem 

solving) procedure is carried out in practice: hierarchical, 

opportunistic, or even chaotic. In the first place because the 

suggestion may be present that solving a problem with or 

without an overview is done straightforward. One just has to 

follow a couple of rules from top to bottom and one ends up 

with a solution. In the second place the issue of the overlap 

between planning and problem solving very much depends on 

the format of representations in the information processing 

system of the human planner. Do planners use production 

rules? How are these rules controlled? Or do planners use 

schemata and frames? Both issues come together in the 

discussion started by Hayes-Roth & Hayes-Roth [5] about 

hierarchical and opportunistic planning. 

 Hierarchical planning means that there is a nested number 

of goal and sub-goal structures or a hierarchy of 

representations of a plan. The highest level in the hierarchy 

may be a simplification or an abstraction, whereas the lowest 

level is a concrete sequence of actions to solve (a part of) the 

planning problem. One solves a planning problem by starting 

at the highest level and then one continues by realizing subgoals until one reaches the final solution. Hayes-Roth & 

Hayes-Roth relate this to a distinction in the overview and the 

action aspect of plans that they successively call planformation and plan-execution [5]. 

 Unjustly, but quite understandably, the hierarchical 

approach is attributed to Newell & Simon. They started to talk 

about problem solving in terms of problem spaces, goal 

hierarchies, and universal sub-goaling. We consider this 

attribution to be at least partly wrong - one only has to recall 

Simon’s bounded rationality concept - but we are not going to 

discuss the issue here [8]. 

 In a contradistinction to the hierarchical view on planning, 

Hayes-Roth & Hayes-Roth propose a so called opportunistic 

approach to planning. This non-hierarchical planning assumes 

that a plan is created with the help of some kind of mental 

blackboard where pieces of information, relevant cues, and 

possible sub-goals are stored. They claim and show that 

planning happens asynchronously and is determined by the 

momentary aspects of the problem. No fixed order of operations exists; plan creation and the steps to be taken grow out 
of the problem stage at hand. When planners solve a planning 

problem, they may start with the top-goal, but very soon they 

loose track of the goal structure and then they continue to 

fulfill the goals that are reachable within reasonable time. The 

hierarchy very soon vanishes and what remains is some sort of 

heterarchy. Therefore, this kind of planning behavior is called 

opportunistic. 

 Although the contrast with the hierarchical approach may 

be large, a strong similarity is also present. In the hierarchical 

as well as in the opportunistic approach the fundamental 

assumption is that planning is problem solving, that can best 

be described in terms of problem spaces, production rules, and 

goals. That is to say that the basic descriptive structure is the 

same for both, but that real behavior within the problem space 

is executed differently. 

 With regard to the problem space description, hierarchical 

as well as opportunistic planning differ from the perspective 

defended by Riesbeck & Schank [9]. The representation of 

planning problems is described in terms of scripts and frames 

consisting of objects, slots, and relations. The information in 

the cognitive system, necessary to make a plan, is semihierarchically structured. This means that some kind of 

representational skeleton or framework is retrieved from 

memory. Stored plans contain guidelines for resolution of sorts 

of problems. In this process two stages exist. First a skeleton 

plan is found, and second the abstract steps in a plan are filled 

with concrete operations. Although general cognitive 

processing is involved in making a plan the emphasis in this 

approach is on the memory system. Plans at different levels of 

abstraction and in different formats are stored in and retrieved 

from memory. There are strong similarities with the approach 

to planning that Hoc proposed [2]. 

 It is very difficult to reveal the different mental 

representations planners use in solving planning problems. 

Asking them whether they use production rules or scripts is 

not reliable and might also give them a cue. 

 From the above discussions, we derive the following 

conclusions. Together, the paradigms that were discussed 

provide various interpretations of a cognitive approach to 

human planning. In this approach, planning is about how to 

find the actions that solve a problem or, more general, reach a 

goal. The process of planning is not neatly hierarchical but 

switches in level of abstraction and in the time frame under 

consideration. The process itself is about formulating goals, 

finding similar solved goals, finding existing plans, adapting 

plans, and storing plans in such a way that they can easily be 

found for future reference. 

 There is another kind of planning that is performed by 

humans. Instead of planning ones own activities, humans can 

be involved in coordinating the activities of others. Typically, 

this takes place in organizations, and we make the shift from 

planning and executing your own activities to planning of 

activities that are executed by others. 

Humans that plan organizational processes: Planning is a 

phenomenon that occurs at multiple places in an organization. 

In its most abstract sense, all activities that involve the 

determination of the future of the organization are dealing with 

planning. This includes strategic considerations that determine 

“where the organization must stand” in 10 years, less abstract 

issues such as growth targets or product innovations, but also 

very concrete decisions such as who will work at what time 

next week, or the exact production times and machine 

allocations of the production for the following week [10,11]. It 

is the type of planning about concrete entities that we 

primarily focus upon. That kind of planning is about 

coordination of activities of organizational members and the 

allocation of resources [12]. The types of activities and 

resources vary widely over organizations. A rough 

categorization that is based on the things that are planned is 

the distinction between production planning (machines, orders, 

machine operators), staff planning (shifts, personnel), and 

transportation planning (vehicles, routes, chauffeurs, 

shipments) [13]. 

 Although the variety in organizational planning problems 

seems large, there are also many characteristics that are shared 

by organizations. A first generic characteristic for planning 

problems in organizations is that it basically concerns the 

coordination of supply and demand, whereby (a) the supply 

consists of scarce capacity and (b) the way in which this 

capacity is put to use can make a difference with respect to the 

goals in the organization [13,14,15]. Examples are producing 

at low costs at a production facility, having enough phone 

operators at a call center, or taking care that all employees 

work the same amount of night shifts. A second shared 

characteristic is that the planning process is distributed over 

multiple human planners. This means that plans get made in 

parallel, and that coordination between the plans is needed. A 

third shared characteristic for all organizational planning 

activities is that they are organized hierarchically. Planning 

problems in organizations are too complex to be solved by one 

person, so some kind of division in sub-problems is necessary. 

Therefore, there are approximate plans for the long term and 

detailed plans for the short term. This induces the need to 

coordinate; plans at higher hierarchical levels define the 

decision space for lower hierarchical levels. An example of 

such a hierarchy is strategic planning versus rough capacity 

planning versus production scheduling. 

 Not much literature or theory exists about the relations 

between the planning domain, the planning task, the 

organization of the planning, and the performance of plan 

execution. Most analyses are limited to task models, for 

example, McKay et al. [16], Mietus [17], Dorn [18], and 

Sundin [19]. Lack of a theory to explain the relation between 

planning complexity, planning organization, task performance, 

and planning support makes it is difficult to pinpoint the cause 

of the planners’ discontent, to attribute the causes of poor 

organizational performance to the planning, or to analyze and 

design planning practices. For example, the cause of poor 

factory performance can be the mere impossibility of matching 
the requirements (e.g., there is not enough capacity available 

to meet the demands), the clumsiness of the organization of the 

planning, the inadequacy of the human planner to solve 

complex problems, the absence of specialized planning 

support in practice, or a combination of these factors. 

 In order to make generic statements about the planning 

task, it is important to know what the task performance 

depends upon (notice that by performance we mean execution 

without a qualitative connotation). According to Hayes-Roth 

& Hayes-Roth, the determinants of the planning task are 

problem characteristics, individual differences, and expertise 

[5]. That the task performance depends on individual 

differences and expertise is no surprise. This applies to all 

tasks. But the fact that the task performance also depends on 

problem characteristics leads to the statement that it is possible 

to describe a planning problem, at least partly, independent 

from the planner. 

 Clearly, approaches of planning your own activities deal 

with other questions than approaches for organizational 

planning. In section 3, we will analyze in what way planning 

of organizations differs from planning of your own actions. 

First, however, we will look at planning by artificial agents. 

Artificial agents that plan their own activities: Artificial agents 

such as (simulated) robots that plan their own behavior need to 

be able to deal with uncertainty and incomplete information in 

their task environment. For such agents, planning is a means to 

reach the goal, just as it is with human problem solving. Due 

to the close resemblance of human and artificial agents, 

planning of artificial agents is very much related to the 

problem solving approaches as described earlier. Techniques 

from Artificial Intelligence are used to let such agents function 

more or less independently in their environment, and react on 

unforeseen events [20,21,22]. Much of the planning research 

in Artificial Intelligence stems from the wish to let 

autonomous actors or agents (such as robots) perform tasks 

without prescribing how the task should be carried out [23]. 

Most Artificial Intelligence methods, whether they are called 

algorithms, procedures, or heuristics, are based on state space 

descriptions. An agent or actor finds himself in a state, in 

which it can perform a limited number of actions. An action 

changes the state, after which it can again perform a number of 

actions [24]. The agent keeps on choosing and performing 

actions until the state it gets in somehow satisfies its goal. 

Planning is one way in which the agent can reach its goal. 

Other ways are, for example, trial and error or full search. To 

make a plan, an agent somehow anticipates the future by 

simulating the actions he will make. This requires the 

existence of (internal) representations. The original link to 

physical entities has been relinquished somewhat so planning 

agents are now often only computer programs that find a plan 

merely for the sake of research, and therefore not necessarily 

execute it. In this paradigm, planning is searching for a 

sequence of actions that will bring the agent from its current 

state in the goal state. Models of human problem solving, 

which were discussed in the previous subsection, have 

provided researchers in Artificial Intelligence with starting 

points for the planning functions of their artificial agents. 

Many examples are based on the initial General Problem 

Solver (GPS) of Newell & Simon [3], which constructs a 

proposed solution in general terms before working out the 

details, the opportunistic planning paradigm, and script-based 

planning. Here it becomes clear that models of human problem 

solving are closely related to the anticipation and planning of 

artificial agents. 

Machines (computers) that plan organizational processes: A 

lot of planning research deals with automatically finding (or 

generating) plans for future organizational processes. Usually, 

this is about making a quantitative model that can search 

efficiently for good solutions. At first glance, the same kind of 

reasoning is used as in cognitive sciences: a problem space is 

set up and the aim is to find a state that satisfies all constraints 

and scores well on goal functions. The states are (just like in 

the cognitive problem solving approaches) transformed by 

operators. The difference is that states and operators comprise 

something else than the ones in cognitive science, namely 

values on variables and mathematical operations [25]. 

 Models exist for all kinds of processes such as routing of 

trucks, staff scheduling, job shop scheduling [26,27], and flow 

shop scheduling. Some of the scientific fields that deal with 

this kind of research are Operations Research (e.g., linear 

programming, nonlinear programming, all kinds of heuristics), 

and Artificial Intelligence (constraint satisfaction programming, genetic algorithms). Although the approaches of course 

differ, they also possess common characteristics. They are 

based on an analysis of the entities that are scheduled. For 

example, to make an algorithm for a planning problem in a 

flow shop one must know the capacities of machines, setupand cleaning times, the number and sizes of orders, the 

processing characteristics, etc. All these characteristics can be 

used to determine the best way to navigate through the 

problem space of possible solutions. An example of how such 

knowledge can be used in an algorithm is to start to plan on 

the bottleneck first, because it is often the sensible thing to do 

in order to avoid problems in a later stage of the planning 

process. Most techniques are somehow limited in the kinds of 

characteristics that they can handle. For example, a linear 

programming model cannot deal with nonlinear constraints, 

and temporal reasoning is tacky to implement in many 

mathematical techniques. Therefore, the domain analysis must 

be translated in the quantitative model, and the solution must 

be translated back to the application domain [28]. 

 Computer programs that create schedules are rarely used 

on their own. The fact that information is lost during 

abstraction and translation of the domain into the model is 

widely recognized. For that reason, mathematical solution 

techniques are usually used in the context of decision support 

systems, where a planner can manipulate and change a plan 

manually so he is not bound to the solution that is presented by 
an algorithm. 

 As with the distinction between humans that plan for 

themselves and humans that plan for organizational processes, 

the approaches that deal with computer programs that plan for 

their own actions differ from approaches that deal with 

computer programs that plan for organizational processes. The 

differences have to do with the characteristics of the actors and 

will be analyzed in the next section. 

3. ANALYSIS OF THE PLANNING ACTORS

In section 2, we discussed four topics that are relevant for 

planning in general: (1) an entity must make the plan, (2) an 

entity must execute it, (3) the entity that makes the plan must 

have a model of the future, and (4) the plan exemplifies the 

ability of the planning entity to use the model of the future to 

lead the executing entity to the goal state. We now have nearly 

all the ingredients available to make a reasonable comparison 

between the different perspectives on planning. The goal of 

this comparison is to gain insight in the limitations that the 

approaches for the respective perspectives have and to see 

where those limitations come from. In the end, this should lead 

to a better understanding of the “planning” phenomenon, and 

perhaps the respective approaches can learn from each other. 

In section 2, we used topics 1 and 2 to describe four distinct 

planning areas. Topic 3 and 4 are used to assemble a number 

of characteristics with which the performance of planning 

actors can be analyzed and compared. The aspects that will be 

discussed in detail and that are used for comparisons are: a) 

the way in which the approaches deal with complexity; b) 

closed versus open world assumptions; c) the information 

processing mechanism and its architectural components such 

as memory and attention; d) the representations; e) 

communication, meaning, and interpretation; f) the 

characteristics of coordination; and g) aspects of execution of 

the plan. 

Complexity reduction: Planning problems are assignment 

problems for which a limited set of structurally similar 

solutions exist. Theoretically, all solutions of a planning 

problem can be calculated in order to choose the best solution. 

Unfortunately, even the most seemingly simple planning 

problems are transcomputational [29], which means that 

enumeration of solutions is not practically possible. To 

overcome this, planning actors must choose a way to somehow 

look at a limited number of viable solutions. Indeed, many of 

the differences between the kinds of planning actors (see 

section 2) can be explained by the way in which they reduce 

the complexity of their planning problems [7]. Some ways in 

which a planning actor can deal with the complexity are: 

1. Opportunistic planning. The planning actor takes 

decisions without any structure; only momentary aspects 

matter [5]. 

2. Plan partitioning. Plans are often multi-dimensional. The 

search space can be limited by first making plans for the 

individual dimensions, and then putting the plans 

together [30]. An example is a production plan that 

coordinates machine operators, machines, and 

production orders: separate plans can be made to assign 

machine operators to machines, and orders to the 

machines. 

3. Multiresolutional planning. A plan can be made at 

multiple levels of resolution. The plan at a low level of 

granulation will have a lower complexity. This plan will 

constrain the search space of the plan at a higher level of 

granulation [7], so the total number of to be assessed 

alternatives is lessened. 

4. Learning. Different plans can contain similar structures 

that can be reused in similar circumstances. (The abstract 

connotation is intended since learning can be based on a 

wide variety of aspects of the planning process.) 

In both plan partitioning and multiresolutional planning, 

multiple plans are created. This decomposition must be a 

closed-loop process [7, p 265], i.e., the plans must together 

provide a complete plan. This is of special interest for multiagent planning systems (such as an organization), where the 

plans can be made by different people and the coordination 

issue arises. Furthermore, each of these multiple plans is a 

(possibly complex) plan in itself, and can therefore be subject 

to each of these four strategies recursively. 

"Closed world" vs. “open world”: Looking from a generic 

perspective, the planning task itself can be called a synthetic or 

configuration task. From a task perspective realizing a suitable 

plan or solving a planning problem requires three nearly 

decomposable phases. In state space descriptions the first 

phase is the design of a (complex) initial state, of goal state(s) 

and of admissible operations to change states. The second 

phase is given the admissible operations to search for an 

(optimal) solution. In many cases search does not give an 

optimal solution. The most one may get is a satisfying solution 

and even that is often not possible. Then, the third phase starts 

in which one goes back to the initial state and the admissible 

operations and changes these in such a way that a solution is 

found. Formulated in other words, the phases of (1) initial 

state, (2) search, no solution, and (3a) start again with a new 

initial state follow the so-called "closed world" assumption. 

This is the necessary sequence if algorithms are applied. 

However, there is another way of dealing with the third phase 

which is more usual, especially if humans have to make a plan. 

If the second phase does not give an optimal or satisfactory 

outcome given the constraints and goal functions, the planner 

already is so much involved in the planning process, that 

because he has a glimpse of the solution given the constraints, 

he takes his ”idea” of a solution for compelling. He therefore 

changes the initial state and the admissible operations, that is 

the constraints, in such a way that they fit the preconceived 

solution. This order of phases can be named the "open world" 

approach. It consists of (1) initial state, (2) search including 
not finding a real or established fixed solution, and (3b) 

adjustment of initial state according to the “fixed” solution 

reality. This sequence of activities is what human planners 

whether in the industry or doing errands frequently and with 

great success do, but formalizing such knowledge for use in a 

computer program or robot seems to be very difficult. 

Information processing mechanism and architectural 

components: An information processing mechanism embodies 

the way information is selected, combined, created, and 

deleted. The mechanism itself needs a physical or 

physiological carrier. Various possibilities are already present, 

such as the brain as our neurological apparatus, the layered 

connection system of a chip in a computer, a human individual 

in an organization, or a group of interconnected individuals in 

an organization. The most relevant distinction is the one in 

internal and external mechanism. With internal we mean that 

there is no direct access to the system from outside. Internally 

controlled, but not directly visible processes take place in the 

system. The cognitive system and the chip are internal, but 

they differ in the sense that the latter is designed which means 

that its operations are verifiable. External are information 

processing mechanisms such as groups of individuals or 

organizations. In other words, the kind of predictive model 

that is needed to anticipate ones own actions differs from the 

kind of model that is needed to anticipate actions of others. 

With respect to planning, this distinction is of course relevant 

if one realizes that if the plan needs to be communicated, a 

translation is necessary between the physical carrier and the 

receiver, which must be reckoned with during planning. This 

is the case when a planner makes a plan that is executed by 

others. 

 An architecture is a set of components of which the 

arrangement is governed by principles of form or function 

[21]. A cognitive architecture consists of memory components, 

of attention processors, of sensory and motor components, and 

of various kinds of central processors. The division is by 

function and the components are all implemented in 

neurological structures in the brain. Two other material 

structures for architectural layout are the chip and the 

constellation of a group of individuals. The same kind of 

components can be discerned for the computer, consisting of 

memory, sensory and motor components, and central 

processors. For a group of individuals the architecture is 

different because the constituting elements are similar as for 

the individuals, but the roles and tasks are different. Again, the 

discussion about the character of the architecture boils down to 

a discussion about internally or externally defined. Internal is 

the cognitive architecture, whereas chips and groups of people 

can be dealt with externally. 

 (Internal) representations: In cognitive science the conceptual 

framework to deal with representations can be found in the 

approaches of classical symbol systems, connectionism, and 

situated action [31,32,33,34,8]. The basic idea is that humans 

as information processing systems have and use knowledge 

consisting of representations and that thinking, reasoning, and 

problem solving consist of manipulations of these 

representations at a functional level of description. A system 

that internally symbolizes the environment is said to have 

representations at its disposal. Representations consist of sets 

of symbol structures on which operations are defined. 

Examples of representations are words, pictures, semantic 

nets, propositions, and temporal strings. A representational 

system learns by means of chunking mechanisms and symbol 

transformations [32]. An entity that makes a plan for itself can 

of course misinterpret its position in the environment, for 

example because it cannot represent its environment or 

because it cannot manipulate its representation of the 

environment adequately. Furthermore, an entity that makes a 

plan for others can additionally have this problem with respect 

to the entities that must execute the plan. Representations are 

also immediately relevant for anticipation. A description of a 

future state in whatever symbol or sign system is the core of 

any discussion on anticipation. Rosen, for example, defines an 

anticipatory system as “a system containing a predictive model 

of itself and/or its environment, which allows it to change state 

at an instant in accord with the model’s predictions pertaining 

to a later instant” [35]. Someone who makes a plan for an 

organization does not need a model of itself, but a model of 

others and their environment. This can complicate 

communication and interpretation of the to be planned system. 

Communication, meaning, and interpretation: Communication 

means the exchange of information between different 

components. Depending on whether we are talking about 

internal or external information processing entities, 

communication means restrictions on the kinds of symbols or 

signs that are used for the exchange. If we relate this to the 

before mentioned discussion about representations, the various 

kinds of signs have different consequences. Clearly, sign 

notations are more powerful, but also more restricted than sign 

systems, which in turn are more powerful than just sign sets 

[36,8,37]. Unambiguous communication requires sign 

notations, but we know that all communication between 

humans is not in terms of notations. If computers require sign 

notations and humans work with sign systems, then if the two 

have to communicate, the one has to adjust to the other. Until 

recently, most adjustments consist of humans using notations. 

Now, interfaces are designed that allow computers to work 

with less powerful - in terms of semantic requirements -, but 

more flexible sign systems. This means that computers can 

deal with ambiguity. For mental activities no explicitness 

(channels, codes etc.) is necessary; for planning as an external 

task it is essential. 

Coordination: Coordination concerns attuning or aligning 

various entities that are not self-evident unities. Information 

processing in a cognitive system is a kind of coordination 

mechanism (with no direct access). It is internal or mental. The 
Natural actor Artificial actor

Self planning Organization 

planning Self planning Organization planning

Complexity reduction 

Plan partitioning; 

Opportunistic planning; 

Learning 

Plan partitioning; 

Multiresolutional 

planning 

Plan partitioning; 

Learning Plan partitioning 

Close vs. open world Fixing the reality to the solution that is found; 

reformulate the starting-point 

Searching for a solution that fits the (modeled) 

reality 

Information processing mechanism

Information processing 

needs not to reckon with 

the outside world 

Translation of internal 

internally coded 

information is necessary

Information processing 

needs not to reckon with 

the outside world 

Translation of 

internally coded information is necessary; 

designed 

Architectural 

components

Neurological: memory structures, attention 

processors 

Electronic: memory 

structures, attention 

processors 

Program components: 

procedures, variables

Representations Self-representation Representation of others Self-representation Representation of 

others 

Communication, 

meaning, and 

interpretation

Mostly communication 

with sign systems or sign 

sets 

Communication with 

sign notations 

Coordination Only with respect to 

anticipated actions 

Coordination of actions 

of others 

Only with respect to 

anticipated actions 

Coordination of actions 

of others 

Planning, execution, 

and control Intertwined Separated Intertwined Separated 

TABLE 1: Characteristics of kinds of actors related to what they are planning and for whom

coordinating processor is cognition itself. No explicit code is 

necessary. If the code is made explicit and obeys the 

requirements of a notation, then we can design an artificial 

intelligent agent that in its ultimate simplicity could be a chip. 

In case of a set of entities that not by themselves are a coherent 

unity, various coordination mechanism can be found, such as a 

hierarchy, a meta-plan, mutual adjustment, a market structure, 

and many others [38,39]. The important difference with the 

single agent is that these coordination mechanisms are external 

and of course with direct access. 

Planning, execution and control: Making a plan, executing it, 

and monitoring its outcomes in reality are valued differently in 

planning your own actions and in planning actions of others 

(i.e., organizational processes). The planning in organizations 

usually is decoupled from the execution of the plan. There are 

two main reasons why the planner is someone else than the 

one who executes the plan. First, planning is a difficult job that 

requires expertise and experience. This is the organizational 

concept of task division. Second, a planner must be able to 

weigh the interests of many parties. Therefore, he must have 

knowledge about things that go beyond the limits of the 

individual tasks that are planned. The consequence of this 

decoupling is almost always inflexibility with respect to 

adaptation. For errand tasks the possible division in terms of 

sub-tasks may be interesting, but can in reality be intertwined 

with flexible adaptation after unforeseen events. If the 

controlling entity is itself a unity, discussions about transfer, 

communication, sign systems to do the communication, and 

representations are almost trivial. This does not make the

planning task itself simpler; it only prevents the occurrence of 

ambiguity, interpretation, and meaning variance. 

 We now have discussed a number of planning approaches 

in section 2, and a number of generic planning characteristics 

in this section. Table 1 summarizes the findings. Evidently, 

measures to relate the performance of an actor to its planning 

activities are context dependent, because the differences in 

contexts can make performance measures incomparable. For 

example, the uncertainty in the task environment of an actor 

makes it hard to establish a clear cause-effect relation between 

plan and execution. Still, cross functional analyses add to our 

understanding of the mono-disciplines, and can help to get a 

better understanding of the relation between planning and 

performance. 

4. CONCLUSION

Planning is a much debated, highly controversial, and multifaceted issue. We stated that various kinds of actors can be 

discerned: natural, artificial, and collective actors. We also 

discussed that there is no easy exchange between the various 

planning approaches. Management and organization, cognitive 

science, mathematics, artificial intelligence, and economics, 

although all are discussing important issues in planning, do not 

start with the same problem formulation. We approached the 

issue of planning by looking especially at the entity that makes 

a plan. By looking at the kind of actors, their characteristics, 

and the level of description of the entities and components 

involved, we stated that discussions about the relation between 

planning and performance do not have to end in controversies 

and avowed misunderstandings. We have sketched the 
components and ingredients of planning actors and we showed 

that comparisons can be made and that positions can be 

clarified. 

 Are there good reasons to discuss planning issues in 

greater detail? We think there are two good reasons. The first 

is that any planning (or weaker: any anticipatory) system 

ultimately acts in an open world. There is nothing wrong with 

the closed world assumption, but in the end it is part of an 

open world. Switching between open and closed worlds is 

something human information processing can easily do, but it 

is difficult to get it realized for artificial (software) and 

collective systems (organizations). The second reason is that 

whether we like it or not, more and more of our fellow 

“intelligent” companions are software actors (agents) and we 

are interacting with them. Artificial and collective actors are 

also planning, but something seems to be different. This 

incompatibility cannot be solved by imposing one approach on 

all kinds of actors. It can only be realized if we know what 

precisely natural actors do when they make plans. 

REFERENCES 

[1] Miller, G.A., Galanter, E. & Pribram, K.J., Plans and the 

structure of behavior. Holt, Rinehart and Winston, 1960. 

[2] Hoc, J-M., Cognitive psychology of planning. Academic Press: 

San Diego, 1989. 

[3] Newell, A. & Simon, H.A., Human Problem Solving. Englewood 

Cliffs, New Jersey: Prentice-Hall, 1972. 

[4] Das, J.P., Karr, B.C. & Parrila, R.K.,Cognitive Planning. New 

Delhi: Sage, 1996. 

[5] Hayes-Roth, B. & Hayes-Roth, F., A cognitive model of 

planning. Cognitive Science, 3, pp. 275-310 (1979). 

[6] Newell, A., Shaw, J.C. & Simon, H.A., Elements of a theory of 

human problem solving, Psychological Review, 65, 151-166 

(1958). 

[7] Meystel, A.M. & J.S. Albus. Intelligent Systems: Architecture, 

Design, and Control. New York: John Wiley & Sons, 2002. 

 [8] Jorna, R. J., Knowledge representation and symbols in the mind.

Tübingen: Stauffenburg Verlag, 1990. 

[9] Riesbeck, C.K. & Schank, R.C. Inside case-based reasoning. 

Hillsdale, New Jersey: Lawrence Erlbaum Associates, 1989. 

[10] Smith, S.F. & O. Lassila, Toward the Development of Flexible 

Mixed-Initiative Scheduling Tools. In: Proceedings ARPA-Rome 

Laboratory planning initiative workshop, Tucson, AZ, 1994. 

[11] Wilkins, D.E., Can AI planners solve practical problems? 

Computational Intelligence, 6 (4), pp. 232-246 (1990). 

[12] Myers, K.L., Advisable Planning Systems. In: A. Tate (ed.), 

Advanced Planning Technology, California: AAAI Press., 1996 

[13] Jorna, R.J., Gazendam, H., Heesen H.C. & Wezel W. van., 

Plannen en Roosteren: Taakgericht analyseren, ontwerpen en 

ondersteunen. Lansa: Leidschendam, 1996. (Planning and 

Scheduling: task oriented analysis, design and support). 

[14] Veloso, M.M., Towards Mixed-Initiative Rationale-Supported 

Planning. In: Tate, A. (Ed.). Advanced Planning Technology. 

Menlo Park, CA: AAAI Press, 1996. 

[15] Verbraeck, A., Developing an Adaptive Scheduling Support 

Environment. Delft: Ph.D. Thesis, University of Delft, 1991. 

[16] McKay, K.N., F.R. Safayeni & J.A. Buzacott, ‘Common sense’ 

realities of planning and scheduling in printed circuit board 

production. International Journal of Production Research. Vol. 

33, Nr. 6, pp. 1587-1603 (1995). 

[17] Mietus, D.M. Understanding planning for effective decision 

support. Groningen: Ph.D. thesis, University of Groningen, 1994. 

[18] Dorn, J., Task-oriented Design of Scheduling Applications. In: 

J. Dorn & K.A. Froeschl (ed.). Scheduling of production 

processes. Chichester, England: Ellis Horwood. (1993). 

[19] Sundin, U., Assignment and Scheduling. In: Breuker, J. & W. 

van der Velde (Eds.) CommonKADS library for expertise 

modeling: reusable problem solving components. Amsterdam: 

IOS Press, 1994. 

[20] Sacerdoti, E.D., The Nonlinear Nature of Plans. Proceedings of 

the Fourth International Joint Conference on Artificial 

Intelligence, pp. 206-214 (1975). 

[21] Curry, K.W. & A. Tate., O-Plan: The Open Planning 

Architecture. Artificial Intelligence, 51, 1 (1991). 

[22] Beetz, M., Concurrent Reactive Plans. Lecture Notes in 

Artificial Intelligence. Berlin: Springer-Verlag, 2000. 

[23] Fikes, R.E., & N.J. Nillson., STRIPS: a New Approach to the 

Application of Theorem Proving to Problem Solving. Artificial 

Intelligence, 2, pp 189-208 (1971). 

[24] Meystel, A.M., Theoretical foundations of planning and 

navigation for autonomous robots. International Journal of 

Intelligent Systems, 2, pp. 73-128 (1987) 

[25] Sanderson, P.M., The Human Planning and Scheduling Role in 

Advanced Manufacturing Systems: An Emerging Human Factors 

Domain. Human Factors, 31 (6), pp. 635-666 (1989). 

[26] Fox, M.S., Constraint-Directed search: A Case Study of JobShop Scheduling. Ph.D. Thesis, Carnegie Mellon University, 

1983. 

[27] McKay, K.N., F.R. Safayeni & J.A. Buzacott,. Job-Shop 

Scheduling Theory: What Is Relevant? Interfaces, 18, 4, pp. 84-

90 (1988). 

[28] Prietula, M.J., W. Hsu & P.S. Ow, MacMerl: Mixed-Initiative 

Scheduling with Coincident Problem Spaces. In: Zweben, M. & 

Fox, M.S. Intelligent Scheduling. San Francisco: Morgan 

Kaufman, 1994. 

[29] Klir, G.J. Facets of systems science. New York: Plenum Press. 

1991. 

[30] Wezel, W. van, Tasks, hierarchies, and flexibility: Planning in 

Food processing industries. Groningen, SOM-series, 2001. 

[31] Posner, M.I. (Ed.), Foundations of cognitive science. Boston, 

Mass.: The MIT-Press, 1989). 

[32] Newell, A., Unified Theories of Cognition. Cambridge: Harvard 

University Press, 1990. 

[33] Dölling, E., Semiotik und Kognitionswissenschaft, Zeitschrift 

für Semiotik, 20, 1-2, pp. 133-159 (1998). 

[34] Smolensky, P., On the proper treatment of connectionism. 

Behavioral and Brain Sciences, 11, pp. 1-74 (1988). 

[35] Rosen, R., Anticipatory systems; philosophical, mathematical, 

and methodological foundations. IFSR International Series on 

Systems Science and Engineering – Volume 1. Oxford: Pergamon 

Press, 1985. 

[36] Goodman, N., Languages of Arts. Brighton, Sussex; The 

Harvester Press, 1968. 

[37] Jorna, R.J. & B. van Heusden, Semiotics and informationpsychology: a case for semio-psychology. Theory & Psychology, 

Vol 8.3, pp. 755-782 (1998). 

[38] Thompson, J. D., Organizations in Action. New York: 

McGraw-Hill, 1967. 

[39] Gazendam, W.H.M., Variety controls variety. Groningen: 

Wolters-Noordhoff, 1993. 
Global Optimization via SPSA

John L. Maryak and Daniel C. Chin

The Johns Hopkins University, Applied Physics Laboratory

11100 Johns Hopkins Road, Laurel, Maryland 20723-6099

john.maryak@jhuapl.edu and daniel.chin@jhuapl.edu

Phone (240) 228-4959 and (240) 228-4956

FAX (240) 228-1093

ABSTRACT

A desire with iterative optimization techniques is that the algorithm reaches the global optimum rather

than get stranded at a local optimum value. In this paper, we examine the theoretical and numerical

global convergence properties of a certain “gradient free” stochastic approximation algorithm called

“SPSA,” that has performed well in complex optimization problems. We establish two theorems on the

global convergence of SPSA. The first provides conditions under which SPSA will converge in

probability to a global optimum using the well-known method of injected noise. The injected noise

prevents the algorithm from converging prematurely to a local optimum point. In the second theorem,

we show that, under different conditions, “basic” SPSA without injected noise can achieve

convergence in probability to a global optimum. This occurs because of the noise effectively (and

automatically) introduced into the algorithm by the special form of the SPSA gradient approximation.

This global convergence without injected noise can have important benefits in the setup (tuning) and

performance (rate of convergence) of the algorithm. The discussion is supported by numerical studies

showing favorable comparisons of SPSA to simulated annealing and genetic algorithms.

KEYWORDS: Stochastic Optimization, Global Convergence, Stochastic Approximation,

Simultaneous Perturbation Stochastic Approximation (SPSA), Recursive Annealing

1. INTRODUCTION

A problem of great practical importance is the problem of stochastic optimization, which may be stated

as the problem of finding a minimum point, p ∈ R

*

q , of a real-valued function L(q ) , called the “loss

function,” that is observed in the presence of noise. Many approaches have been devised for numerous

applications over the long history of this problem. A common desire in many applications is that the

algorithm reaches the global minimum rather than get stranded at a local minimum value. In this paper,

we consider the popular stochastic optimization technique of stochastic approximation (SA), in

particular, the form that may be called “gradient-free” SA. This refers to the case where the gradient,

g(q ) = ¶L(q ) /¶q , of the loss function is not readily available or not directly measured (even with noise).

This is a common occurrence, for example, in complex systems where the exact functional relationship

between the loss function value and the parameters, q , is not known and the loss function is evaluated

by measurements on the system (or by other means, such as simulation). In such cases, one uses

instead an approximation to g(q ) (the well-known form of SA called the Kiefer-Wolfowitz type is an

example).
The usual form of this type of SA recursion is:

)

ˆ ˆ (ˆ ˆ qk +1 = qk − ak gk qk

, (1)

where ˆ (q ) gk is an approximation (at the k

th step of the recursion) of the gradient g(q ) , and { } ak

 is a

sequence of positive scalars that decreases to zero (in the standard implementation) and satisfies other

properties. This form of SA has been extensively studied, and is known to converge to a local minimum

of the loss function under various conditions.

Several authors (e.g., Chin (1994), Gelfand and Mitter (1991), Kushner (1987), and Styblinski

and Tang (1990)) have examined the problem of global optimization using various forms of gradientfree SA. The usual version of this algorithm is based on using the standard “finite difference” gradient

approximation for ˆ (q ) gk. It is known that carefully injecting noise into the recursion based on this

standard gradient can result in an algorithm that converges (in some sense) to the global minimum. For a

discussion of the conditions, results, and proofs, see, e.g., Fang et al. (1997), Gelfand and Mitter

(1991), and Kushner (1987). The amplitude of the injected noise is decreased over time (a process

called “annealing”), so that the algorithm can finally converge when it reaches the neighborhood of the

global minimum point.

A somewhat different version of SA is obtained by using a “simultaneous perturbation” gradient

approximation, as described in Spall (1992) for multivariable ( p >1 ) problems. The gradient

approximation in simultaneous-perturbation SA (SPSA) is much faster to compute than the finitedifference approximation in multivariable problems. More significantly, using SPSA often results in a

recursion that is much more economical, in terms of loss-function evaluations, than the standard version

of SA. The loss function evaluations can be the most expensive part of an optimization, especially if

computing the loss function requires making measurements on the physical system. Several studies

(e.g., Spall (1992), Chin (1997)) have shown SPSA to be very effective in complex optimization

problems. A considerable body of theory has been developed for SPSA (Spall (1992), Chin (1997),

Dippon and Renz (1997), Spall (2000), and the references therein), but, because of the special form of

its gradient approximation, existing theory on global convergence of standard SA algorithms is not

directly applicable to SPSA. In Section 2 of this paper, we present a theorem showing that SPSA can

achieve global convergence (in probability) by the technique of injecting noise. The “convergence in

probability” results of our Theorem 1 (Section 2) and Theorem 2 (Section 3) are standard types of

global convergence results. Several authors have shown or discussed global convergence in probability

or in distribution (Chiang et al. (1987), Gelfand and Mitter (1991), Gelfand and Mitter (1993), Geman

and Geman (1984), Fang et al. (1997), Hajek (1988), Kushner (1987), Yakowitz et al. (2000), and

Yin (1999)). Stronger “almost sure” global convergence results seem only to be available by using

generally infeasible exhaustive search (Dippon and Fabian (1994)) or random search methods

(Yakowitz (1993)), or for cases of optimization in a discrete (q -) space (Alrefaei and Andradottir

(1999)).

The method of injection of noise into the recursions has proven useful, but naturally results in a

relative slowing of the rate of convergence of the algorithm (e.g., Yin (1999)) due to the continued

injection of noise when the recursion is near a global solution. In addition, the implementation of the

extra noise terms adds to the complexity of setting up the algorithm. In Section 3, we present a theorem

showing that, under different (more demanding) conditions, the basic version of SPSA can perform as a
global optimizer without the need for injected noise. Section 4 contains numerical studies

demonstrating SPSA’s performance compared to two other popular strategies for global optimization,

namely, simulated annealing and genetic algorithms; and Section 5 is a summary. The Appendix

provides some technical details.

2. SPSA WITH INJECTED NOISE AS A GLOBAL OPTIMIZER

Our first theorem applies to the following algorithm, which is the basic SPSA recursion indicated in

equation (1), modified by the addition of extra noise terms:

q k+ =q k − ak gk q k + qkwk)

ˆ ˆ (ˆ ˆ

1

, (2)

where p wk ∈R is i.i.d. N (0,I ) injected noise, a a k k = / , q q k k

k

/ loglog 2= , a > 0 , q > 0 , and ˆ (•) gk is the

“simultaneous perturbation” gradient defined as follows: (3)

ˆ ( ) (2 ) [ ( ) ( ) ]

−1 (+) ( −)

≡ Δ + Δ − − Δ + − k k k k k k k k kg q c L q c L q c e e ,

where ( ),

±

k k

c e are scalars, p Δk ∈R , and the inverse of a vector is defined to be the vector of inverses.

This gradient definition follows that given in Spall (1992). The ke terms represent (unknown) additive

noise that may contaminate the loss function observation, kc and Δkl are parameters of the algorithm,

the kc sequence decreases to zero, and the Δkl components are chosen randomly according to the

conditions in Spall (1992), usually (but not necessarily) from the Bernoulli ( ±1 ) distribution. (Uniformly

or normally distributed perturbations are not allowed by the regularity conditions.)

In this Section, we will refer to Gelfand and Mitter (1991) as GM91. Our theorem on global

convergence of SPSA using injected noise is based on a result in GM91. In order to state the theorem,

we need to develop some notation, starting with the definition of a key probability measure, hp , used in

hypothesis H8 below. Define for any h > 0 : h hdp (q )/ dq exp( 2L(q )/h )/Z

2

= − , where

∫

= − p RZ L q h dq

h

exp( 2 ( ) / )

2

. Next, define an important constant, C0, for convergence theory as follows

(GM91). For t ∈R and p u1,u2 ∈ R , let

I t d s ds g s ds

t 2

2 0

1

1 2

( ,u ,u ) inf | f ( )/ (f ( )) |

f

= +

∫

,

where the inf is taken over all absolutely continuous functions pf : R → R such that 1 f(0) =u and 2 f(t) =u ,

and |•| is the Euclidean norm. Let ( , ) lim ( , , ) u1 u2 u1 u2 V I t

t→∞

= , and { | ( ) 0} S0 = q g q = . Then

sup ( ( , ) 2 ( )) 1 2 2

,

2

3

0

1 2 0

u u u

u u

C V L

S

≡ −

∈

.

We will also need the following definition of tightness. If K is a compact subset of p R and {Xk } is a

sequence of random p -dimensional vectors, then {Xk } is tight in K if X0 ∈K and for any e > 0 , there

exists a compact subset p Ke ⊂ R such that P(Xk ∈ Ke) >1−e , ∀k > 0 . Finally, let )

ˆ

) (

ˆ

ˆ (

*

k gk qk g qk

z ≡ − and let

superscript prime (′) denote transpose.

The following are the hypotheses used in Theorem 1.

H1.. Let p Δk ∈R be a vector of p mutually independent mean-zero random variables

{ , ,..., } 1 2 Δ Δ Δ ′

k k kp such that { } Δk

 is a mutually independent sequence that is also independent of
the sequences }

ˆ

,...,

ˆ {q1 qk −1

, { ,..., }

( )

1

( )

1

±

−

±

k

e e , and { ,..., } w1 wk−1, and such that Δki is symmetrically

distributed about zero, | Δki |≤ a1 < ∞ a.s. and Δ ≤ < ∞

−

2

2

| | a ki E , a.s. ∀ i,k .

H2.. Let ( +)

k

e and (−)

k

e represent random measurement noise terms that satisfy ( ) 0

( ) ( )

− =

+ −

k k k

E e e

a.s. ∀k , where Ek denotes the conditional expectation given Ik ≡ the sigma algebra induced by

, ,..., , ,..., }

ˆ {

*

1

*

0 1 k−1 1 k− q w w z z . The { }

(±)

k

e sequences are not assumed independent. Assume that

≤ < ∞

±

3

( ) 2

[(e ) ] a k kE a.s. ∀k .

H3. L(q ) is a thrice continuously differentiable map from p R into 1 R ; L(q ) attains the minimum value

of zero; as |q |→ ∞ , we have L(q ) → ∞ and | g(q ) |→ ∞ ; inf(| ( ) | − ( ( ))) > −∞

2

g q Lap L q (Lap here is

the Laplacian, i.e., the sum of the second derivatives of L(q ) with respect to each of its

components); (q ) ≡ ∂ (q ) / ∂q ′ ∂q ′∂q ′

(3) 3 L L exists continuously with individual elements satisfying

≤ 5 < ∞

(3)

| ( )|

1 2 3

q a i i iL .

H4. The algorithm parameters have the form ga a k c c k k k = / , = / , for k = 1,2,... , where a, c > 0 ,

0 q/ a > C , and g ∈[1/ 6, 1/ 2) .

H5. [(4 4) /(4 3)] lim inf( ( ) (| ( ) | | |))

| |

1/ 2

q q q q

q

p − p − < g ′ g

→∞

.

H6. ± Δ ≤ 4 < ∞

2

)) ˆ Ek(L(qkck k a a.s. ∀k .

H7.Let wk be an i.i.d. N (0,I ) sequence, independent of the sequences }

ˆ

,...,

ˆ {q1 qk −1

, { ,..., }

( )

1

( )

1

±

−

±

k

e e ,

and { ,..., } Δ1 Δk−1.

H8.For any h h h > 0, Z < ∞; p has a unique weak limit p as h → 0 .

H9.There exists a compact subset K of p R such that }

ˆ {qk

 is tight in K .

Comments:

 (a) Assumptions H3, H5, and H8 correspond to assumptions (A1) through (A3) of GM91;

assumptions H4 and H9 supply the hypotheses stated in GM91’s Theorem 2; and the definitions of

ak and qk given in equation (2) correspond to those used in GM91. Since we will show that

assumption (A4) of GM91 is satisfied by our algorithm, this allows us to use the conclusion of their

Theorem 2.

(b) The domain of g given in H4 is one commonly assumed for convergence results (e.g., Spall

(1992)).

We can now state our first theorem as follows:

Theorem 1: Under hypotheses H1 through H9, qk

ˆ

 converges in probability to the set of global minima

of L(q ) .

Proof: See Maryak and Chin (1999), and the remark on convergence in probability in GM91, p.

1003.

3. SPSA WITHOUT INJECTED NOISE AS A GLOBAL OPTIMIZER

As indicated in the introduction above, the injection of noise into an algorithm, while providing for global

optimization, introduces some difficulties such as the need for more “tuning” of the extra terms and
retarded convergence in the vicinity of the solution, due to the continued addition of noise. This effect on

the rate of convergence of an algorithm using injected noise is technically subtle, but may have an

important influence on the algorithm’s performance. In particular, Yin (1999) shows that an algorithm of

the form (2) converges at a rate proportional to log log (k + const) , while the nominal local convergence

rate for an algorithm without injected noise is 1/3k , i.e., )

ˆ

(

1/3 *

k q k −q converges in distribution (Spall

(1992)). These rates indicate a significant difference in performance between the two algorithms.

A certain characteristic of the SPSA gradient approximation led us to question whether SPSA

needed to use injected noise for global convergence. Although this gradient approximation tends to

work very well in an SA recursion, the SPSA gradient, evaluated at any single point in q -space, tends

to be less accurate than the standard finite-difference gradient approximation evaluated at q . So, one is

led to consider whether the effective noise introduced (automatically) into the recursion by this

inaccuracy is sufficient to provide for global convergence without a further injection of additive noise. It

turns out that basic SPSA (i.e., without injected noise) does indeed achieve the same type of global

convergence as in Theorem 1, but under a different, and more difficult to check, set of conditions.

In this Section, we designate Kushner (1987) as K87, and Kushner and Yin (1997) as KY97.

Here we are working with the basic SPSA algorithm having the same form as equation (1):

)

ˆ ˆ (ˆ ˆ q k+1 = qk − ak gk qk

, (4)

where ˆ (•) gk is the simultaneous-perturbation approximate gradient defined in Section 2, and now

(obviously) no extra noise is injected into the algorithm. For use in the subsequent discussion, it will be

convenient to define

)| )

ˆ

) (

ˆ

) (ˆ (

ˆ

( bk q k ≡ E gk qk − g qk אk, and ) | )

ˆ

) ( ˆ (

ˆ

) ˆ (

ˆ

( k k gk k E gk k ke q ≡ q − q א ,

where אk denotes the s -algebra generated by }

ˆ

,...,

ˆ

,

ˆ {q1 q 2 q k

, which allows us to write equation (4) as

)] ˆ) (

ˆ

) (

ˆ

[ (

ˆ ˆ

k 1 k k k k k bk k q + =q − a g q + e q + q . (5)

Another key element in the subsequent discussion is the ordinary differential equation (ODE):

q = g(q )

&

, (6)

which, in Lemma 1 of the Appendix is shown to be the “limit mean” ODE for algorithm (4).

Now we can state our assumptions for Theorem 2, as follows:

J.1 . Let p Δk ∈R be a vector of p mutually independent mean-zero random variables

{ , ,..., } 1 2 Δ Δ Δ ′

k k kp such that { } Δk

 is a mutually independent sequence and Δkis independent of

the sequence }

ˆ

,...,

ˆ {q1 qk −1

, and such that Δki is ∀ i,k symmetrically distributed about zero,

| Δki |≤ a1 < ∞ a.s. and Δ ≤ < ∞

−

2

2

| | a ki E .

J.2 Let (+)

k

e and (−)

k

e represent random measurement noise terms that satisfy (( ) | ) 0

( ) ( )

− א =

+ −

E k k ke e

a.s. ∀k . The { }

(±)

k

e sequences need not be assumed independent. Assume that

א ≤ < ∞

±

3

( ) 2

((e ) | k) a kE a.s. ∀k .

J.3 (a). L(q ) is thrice continuously differentiable and the individual elements of the third derivative

satisfy ≤ 5 < ∞

(3)

| ( )|

1 2 3

q a i i iL .

(b). L(q ) → ∞ as q → ∞ .
J.4 The algorithm parameters satisfy the following: the gains ak > 0 , ak →0 as k → ∞ , and

∑

∞

=

= ∞ k 1ak. The sequence { }kc is of form gc c k k = / , where c > 0 and g ∈[1/ 6, 1/ 2) , and

∑

∞

=

< ∞ 0

2

( / )

k

k k a c .

J.5 The gradient g(q ) is bounded and Lipschitz continuous.

J.6 The ODE (6) has a unique solution for each initial condition.

J.7 For the ODE (6), suppose that there exists a finite collection of disjoint compact stable invariant

sets (see K87) K1,K2,...,Km , such that UiKi contains all the limit sets for (6). These sets are

interpreted as closed sets containing all local (including global) minima of the loss function.

J.8 For any h h h > 0, Z < ∞; p has a unique weak limit p as h → 0 ( hZ and hp are defined in Section

2).

J.9 ∑ =< ∞ ∀

k

i

i i E e k

1

) |

ˆ

| (q .

J.10 For any asymptotically stable (in the sense of Liapunov) point, q , of the ODE (6), there exists

a neighborhood of the origin in p R such that the closure, Q2, of that neighborhood satisfies

q +Q2 ≡{q + y : y ∈Q2} ⊂ Θ , where p Θ ⊂ R denotes the allowable q -region. There is a

neighborhood, Q1, of the origin in p R and a real-valued function ( , ) H1 y1 y2, continuous in

Q1 ×Q2, whose y1-derivative is continuous on Q1 for each fixed y2 ∈Q2, and such that the

following limit holds. For any c,Δ > 0 , with c being an integral multiple of Δ , and any functions

( ), ( ) 1 2 y • y • taking values in Q1 ×Q2 and being constant on the intervals [iΔ,iΔ+ Δ), iΔ < c , we have

[∑ ∑ ] ∫

Δ −

=

+ −

Δ = + +

′

Δ

=

( / ) 1

0

1

1

,

2

0

1 1

)

ˆ

( ( ), ( )) lim sup log exp ( ) (

c c

y y y q i

im m

j im n j n j

m n

E i b

m

H s s ds . (7)

Also, there is a function ( ) H2 y 3 that is continuous and differentiable in a small neighborhood of

the origin, and such that

= ∫H ( (s))ds

0

2 1

c

y 







′ Δ

Δ ∑ ∑Δ −

=

+ −

=

+ +

( / ) 1

0

1

1

,

)

ˆ

lim sup log exp ( ) (

c

y q

i

im m

j im n j n j

m n

E i e

m

. (8)

A bit more notation is needed. Let T > 0 be interpreted such that [0,T] is the total time period

under consideration in ODE (6). Let

( , ) sup[ ( ( )) ( , )

( , ) 0.5[ (2 , ) (2 )

2 1 2 1 2

1 2 1 1 2 2 1

1

b y y b y y y

y y y y y

y

L g H

H H H

= ′ − −

= +

, (9)

and, for 1f(0) = x∈ R , define the function

∫

=

T

S T L s s ds

0

( ,f ) (f ( ),f ( )) &,

if f(•) is a real-valued absolutely-continuous function on [0,T] and to take the value ∞

otherwise. S(T,f ) is the usual action functional of the theory of large deviations (adapted to our

context). Define ∑

−

=

≡

1

0

n

i

n ai

t , and ∑

−

=

= +

1

0

k

i

n i

n

k

t a . Define }

ˆ {

n

k

q and (•)

n

q by

= x ∈Θ

n

0

ˆq , )ˆ

ˆ (

ˆ ˆ

1

n

n k n k k

n

k

n

k

q q a +g + q

+

= − , and n

k

n

q t q

ˆ

( ) = for [ , )

1

n

k

n

k

t t t

+

∈ .

Now we can state the last two assumptions for Theorem 2:
J.11 For each d > 0 and i =1,2,..., m , there is a r -neighborhood of Ki

, denoted ( ) Nr Ki, and

d r > 0, Tr < ∞ such that, for each , ( ) N Kix y ∈ r, there is a path, f(•) , with x T y f(0) = , f( y) = ,

where Ty ≤ Tr and f d S(Tr, ) ≤ .

J.12 There is a sphere, D1, such that D1 contains UiKi in its interior, and the trajectories of (•)

n

q

stay in D1. All paths of ODE (6) starting in D1 stay in D1.

Note 1. Assumptions J1, J2, and J3(a) are from Spall (1992), and are used here to characterize the

noise terms )

ˆ

( bk q k and )

ˆ

( k ke q . Assumption J3(b) is used on page 178 of K87. Assumption J4

expresses standard conditions on the algorithm parameters (see Spall (1992)), and implies

hypothesis (A10.2) in KY97, p. 174. Assumptions J5 and J6 correspond to hypothesis

(A10.1) in KY97, p. 174. Assumption J7 is from K87, p. 175. Assumption J8 concerns the

limiting distribution of qk

ˆ

. Assumption J9 is used to establish the “mean” criterion for the

martingale sequence in Lemma 2. Assumptions J11 and J12 are the “controllability” hypothesis

A4.1 and the hypothesis A4.2, respectively, of K87, p. 176.

Note 2. Assumption J10 corresponds to hypotheses (A10.5) and (A10.6) in KY97, pp. 179-181.

Although these hypotheses are standard forms for this type of large deviation analysis, it is

important to justify their reasonableness. The first part (equation (7), involving noise terms

)

ˆ

( bk q k) of J10 is justified by the discussion in KY97, p. 174, which notes that the results of their

subsection 6.10 are valid if the noise terms (that they denote xn) are bounded. This discussion

is applicable to our algorithm since the )

ˆ

( bk q k noise terms were shown by Spall (1992) to be

( ) ( 0) O c

k

2

ck → a.s. The second part (equation (8), involving noise terms )

ˆ

( k ke q ) is justified by

the discussion in KY97, p. 174, which notes that the results in their subsection 6.10 are valid if

the noise terms they denote dMn (corresponding to our noise terms )

ˆ

( k ke q ) satisfy the

martingale difference property that we have established in Lemma 2 of the Appendix.

Now we can state our main theorem:

Theorem 2. Under assumptions J1 through J12, qk

ˆ

 converges in probability to the set of global minima

of L(q ) .

The idea of the proof is as follows (see the Appendix for the details). This theorem follows from results

(in a different context) in K87 for an algorithm ) ]

ˆ

[ (

ˆ ˆ q k+1 = qk − ak g q k +z k

, where zk is i.i.d. Gaussian

(injected) noise. In order to prove our Theorem 2, we start by writing the SPSA recursion as

) ]

ˆ

[ (

ˆ ˆ *

k 1 k ak g k k q = q − q +z +

, where )

ˆ

) (

ˆ

ˆ (

*

k gk qk g qk

z ≡ − is the “effective noise” introduced by the inaccuracy

of the SPSA gradient approximation. So, our algorithm has the same form as that in K87. However,

since *

k

z is not i.i.d. Gaussian, we cannot use K87’s result directly. Instead, we use material in Kushner

and Yin (1997) to establish a key “large deviation” result related to our algorithm (4), which allows the

result in K87 to be used with *

k

z replacing the zk in his algorithm.

4. NUMERICAL STUDIES: SPSA WITHOUT INJECTED NOISE
4.1. Two-Dimensional Problem

A study was done to compare the performance of SPSA to a recently published application of the

popular genetic algorithm (GA). The loss function is the well-known Griewank function (see Haataja

(1999)) defined for a two-dimensional ( , ) 1 2 q = t t ′ , by:

( ) cos( 100)cos[( 100) / 2] [( 100) ( 100) ]/ 4000 1

2

2

2

L q = t

1 − t2 − − t1 − + t − − ,

which has thousands of local minima in the vicinity of a single global minimum at q = (100,100)′ at which

L(q ) = 0 . Haataja (1999) describes the application of a GA to this function (actually, to find the

maximum of −L(q ) ) based on noise-free evaluations of L(q ) (i.e., ek = 0 ). This study achieved a

success rate of 66% (see Haataja’s Table 1.3, p.16) in 50 independent trials of the GA, using 300

generations and 9000 L(q ) evaluations in each run of the GA. Haataja’s definition of a successful

solution is a reported solution where the norm of the solution minus the correct value, *q , is less than

0.2, and the value of the loss function at the reported solution is within 0.01 of the correct value of zero.

We examined the performance of basic SPSA (without adding injected noise) on this problem, using

a a a /(A k ) k = + , with A = 60, a =100 and a = .602 , a slowly decreasing gain sequence of a form that has

been used in many applications (see Spall (1998)). For the gradient approximation (equation (3)), we

chose each component of Δk to be an independent sample from a Bernoulli (±1) distribution, and

g

c c k k = / , with c = 10 and g = .101 . Since we used the exact loss function, the ke noise terms were zero.

We ran SPSA, allowing 3000 function evaluations in each of 50 runs, and starting the algorithm (each

time) at a point randomly chosen in the domain [−200, 400]×[−200, 400]. Haataja’s q -domain was also

constrained to lie in a box, but the dimensions of the box were not specified. Hence we chose a domain

that is a cube centered at the global minimum, in which there are many local minima of L(q ) (as seen in

Haataja’s (1999) Figure 1.1). SPSA successfully located the global minimum in all 50 runs (100%

success rate).

4.2. Ten-Dimensional Problem

For a more ambitious test of the global performance of SPSA, we applied SPSA to a loss function

given in Example 6 of Styblinski and Tang (1990), which we will designate for convenience as ST90.

The loss function is:

( ) (2 ) 4 cos( )

1 1

1 2 ∑ ∏

= =

−

= −

p

i

i

p

i

i L q p t p t ,

where p =10 and ( ,..., ) 1 = ′ p q t t . This function has the global minimum value of −40 at the origin, and a

large number of local minima. As in the two-dimensional study above, we used the exact loss function.

Our goal is to compare the performance of SPSA without injected noise with simulated annealing and

with a GA.

For the simulated annealing algorithm, we use the results reported in ST90. They used an

advanced form of simulated annealing called fast simulated annealing (FSA). According to ST90, FSA

has proven to be much more efficient than classical simulated annealing due to using Cauchy (rather than

Gaussian) sampling and using a fast (inversely linear in time) cooling scheme. For more details on FSA,
see ST90. The results of their application of FSA to the above L(q ) are given in Table 1 below (FSA

values taken from Table 10 of ST90). Table 1 shows the results of 10 independent runs of each

algorithm. In each case (each run of each algorithm), the best value of L(q ) found by the algorithm is

shown. In their study, although FSA was allowed to use 50,000 function evaluations for each of the

runs, the algorithm showed very limited success in locating the global minimum. It should be noted that

the main purpose of the ST90 paper was to examine a relatively new algorithm, stochastic

approximation combined with convolution smoothing. This algorithm, which they call SAS, was much

more effective than FSA, yielding results between those shown in Table 1 for GA and SPSA.

For the genetic algorithm (GA), we implemented a GA using the popular features of elitism (elite

members of the old population pass unchanged into the new population), tournament selection

(tournament size = 2), and real-number encoding (see Mitchell (1996), pp. 168, 170, and 157,

respectively). After considerable experimentation, we found the following settings for the GA algorithm

to provide the best performance on this problem. The population size was 100, the number of elite

members (those carried forward unchanged) in each generation was 10, the crossover rate was 0.8,

and mutation was accomplished by adding a Gaussian random variable with mean zero and standard

deviation 0.01 to each component of the offspring. The original population of 100 (10-dimensional) q -

vectors was created by uniformly randomly generating points in the 10-dimensional hypercube centered

at the origin, with edges of length 6 (so, all components had absolute value less than or equal to 3

radians). We constrained all component values in subsequent generations to be less than or equal to 4.5

in absolute value. This worked a bit better than constraining them to be less than 3, since, with the

tighter constraints, the GA got stuck at the constraint boundary and could not reach local minima that

were just over the boundary. All runs of the GA algorithm reported here used 50,000 evaluations of the

loss function. The results of the 10 independent runs of GA are shown in Table 1. Although the

algorithm did reasonably well in getting close to the minimum loss value of –40, it only found the global

minimum in one of the 10 runs (run #8). In the other nine cases, a few (typically two or four) of the

components were trapped in a local minimum (around ± pi radians), while the rest of the components

(approximately) achieved the correct value of zero. Note that the nature of the loss function is such that

the value of L(q ) is very close to an integer (e.g., –39.0 or –38.0) when an even number (e.g., 2 or 4)

of components of q are near ± pi radians.

We examined the performance of basic SPSA (without adding injected noise), using the

algorithm parameters defined in Subsection 4.1 with A = 60, a =1 , a = .602 , c = 2 , and g = .101 . We

started q at t

i = 3 radians, i =1,..., p , resulting in an initial loss function value of −31 . This choice of

starting point was at the outer boundary of the domain in which we chose initial values for the GA

algorithm, and we did not constrain the search space for SPSA as we did for GA (the initialization and

search space for FSA were not reported in ST90). We ran 10 Monte Carlo trials (i.e., randomly

varying the choices of Δk). The results are tabulated in Table 1. The results of these numerical studies

show a strong performance of the basic SPSA algorithm in difficult global optimization problems

.
Table 1. Best Loss Function Value in Each of 10 Independent Runs of Three Algorithms

Run SPSA GA FSA

1 −40.0 −38.0 −24.9

2 −40.0 −39.0 −15.5

3 −40.0 −39.0 −29.0

4 −40.0 −38.0 −32.1

5 −40.0 −37.0 −30.2

6 −40.0 −39.0 −30.1

7 −40.0 −38.0 −27.9

8 −40.0 −40.0 −20.9

9 −40.0 −38.0 −28.5

10 −40.0 −39.0 −34.6

Average Value −40.0 −38.5 −27.4

Number of Function Evaluations 2,500 50,000 50,000

5. SUMMARY

SPSA is an efficient gradient-free SA algorithm that has performed well on a variety of complex

optimization problems. We showed in Section 2 that, as with some standard SA algorithms, adding

injected noise to the basic SPSA algorithm can result in a global optimizer. More significantly, in

Section 3, we showed that, under certain conditions, the basic SPSA recursion can achieve global

convergence without the need for injected noise. The use of basic SPSA as a global optimizer can

ease the implementation of the global optimizer (no need to tune the injected noise) and result in a

significantly faster rate of convergence (no extra noise corrupting the algorithm in the vicinity of the

solution). In the numerical studies, we found significantly better performance of SPSA as a global

optimizer than for the popular simulated annealing and genetic algorithm methods, which are often

recommended for global optimization. In particular, in the case of a 10-dimensional optimization

parameter (q ), the fast simulated annealing and genetic algorithms generally failed to find the global

solution.

APPENDIX (LEMMAS RELATED TO THEOREM 2 AND PROOF OF THEOREM 2)

In this Appendix, we designate Kushner (1987) as K87, and Kushner and Yin (1997) as KY97. Here

we are working with the basic SPSA algorithm as defined in equation (4):

)

ˆ ˆ (ˆ ˆ q k+1 = qk − ak gk qk

.

We first establish an important preliminary result that is needed in order to apply the results from K87

and KY97 in the proof of Theorem 2.

Lemma 1. The ordinary differential equation (eq. (6) above),

q = g(q )

&

,

is the “limit mean ODE” for algorithm (4).

Proof: Examining the definition of limit mean ODE given in KY97, pp. 174 & 138, it is clear that we

need to prove that )] ( )

ˆ

) (

ˆ

[ ( ) (

1 1

g q e q b q g q

m n

k n

k k k k m ∑ + + →

+ −

=

 w.p. 1 as m,n → ∞ . Since Spall
(1992) has shown that ) 0

ˆ bk

(qk → w.p. 1, we can conclude using Cesaro summability that the

contribution of the )

ˆ

( bk q k terms to the limit is zero w.p. 1. For the )

ˆ

( k ke q terms, we have by

definition that )] 0

ˆ E[ek

(qk = ; hence, by the law of large numbers, the contribution of the )

ˆ

( k ke q

terms to the limit is also zero. Q.E.D.

Our next Lemma relates to Note 2 in Section 3.

Lemma 2. Under assumptions J1, J3(a), and J9, the sequence )} ˆ { ( k ke q is a אk-martingale difference.

Proof: It is sufficient to show that ∑ =≡

k

i

k k k M e

1

)

ˆ

(q is a אk-martingale. Assumption J9 satisfies the first

requirement (see KY97, p.68) of the martingale definition, that E M k < ∞ . For the main

requirement, we have for any k:

( ) [ ( ) ] 1 1 1 1 1| ,...,

ˆ E Mk+

| Mk,...,M = E ek+ qk+ + Mk Mk M

) | ,..., ]

ˆ

[ ( 1 1 M M1 M E e k k+ k+ k= + q

)]| }

ˆ

)|

ˆ

) ( ˆ (

ˆ {[ ˆ ( = M k + E gk+1 qk+1 − E gk +1 qk +1 qk+1 M k

}

ˆ

)]| ,

ˆ

)|

ˆ

) ( ˆ (

ˆ {[ ˆ ( ˆ 1 1 1 1 1 1

1

= + + + − + + + +

+

M k E E gk k E gk k k M k k

k

q q q q q

Mk E E gk k Mk k E E E gk k Mk k Mk

k k

= + + + + − + + + =

+ +

]

ˆ

) | ,

ˆ

) [ ( ˆ (

ˆ

) | ,

ˆ

( ˆ ( ˆ 1 1 1 ˆ 1 1 1

1 1

q q q q q q

,

where 

1

ˆ

k+

E

q

 denotes expectation conditional on 1

ˆq k+

, and all equalities concerning conditional

expectations are w.p. 1. Q.E.D.

A key step in the proof of our main result (Theorem 2 below) is establishing the following “large

deviation” result (Lemma 3). Let Bx be a set of continuous functions on [0,T] taking values in Θ and

with initial value x . Let 0 Bx denote the interior of Bx, and Bx denote the closure.

Lemma 3. Under assumptions J4, J5, J6, and J10, we have

inf ( , ) lim inf log { ( ) }

0

x

n n

x

B n

S T P B

x

− ≤ • ∈

∈

f q

f

lim suplog {q ( ) } inf ( ,f )

f

P B S T

Bx

x

n n

x

n ∈

≤ • ∈ ≤ − , (9)

where n Px denotes the probability under the condition that x

n

q (0) = .

Proof: This result is adapted from Theorem 10.4 in KY97, p. 181. Note that our assumption J10 is a

modified form of their assumptions (A10.5) and (A10.6), using “equals” signs rather than

inequalities. The two-sided inequality in (9) follows from J10 by an argument analogous to the

proof of KY87’s Theorem 10.1 (p. 178), which uses an “equality” assumption ((A10.4), p.

174) to arrive at a two-sided large deviation result analogous to (9) above. Q.E.D.

We restate our main theorem:

Theorem 2: Under hypotheses J1 through J12, qk

ˆ

 converges in probability to the set of global minima

of L(q ) .

Proof: This result follows from a discussion in K87. Theorem 2 of K87, (p.177) describes

probabilities involving expected times for the SA algorithm (system (1.1) of K87) to transition
from one Ki

 to another. The SA algorithm he uses can be written in our notation as

) ]

ˆ

[ (

ˆ ˆ q k+1 = qk − ak g q k +z k

, where zk is i.i.d. Gaussian (injected) noise. The K87 Theorem 2 uses

the i.i.d. Gaussian assumption only to arrive at a large deviation result exactly analogous to our

Lemma 3. The subsequent results in K87 are based on this large deviation result. Recall that

the SPSA algorithm without injected noise can be written in the form ) ]

ˆ

[ (

ˆ ˆ *

k 1 k ak g k k q = q − q +z +

.

Since we have established Lemma 3 for SPSA, the results of K87 hold for the SPSA algorithm

with its “effective” noise { }

*

k

z replacing the { } z k sequence used in K87. In particular, K87’s

discussion (pp. 178, 179) of his Theorem 2 is applicable to our Theorem 2 context (SPSA

without injected noise), which corresponds to K87’s “potential case.” Note that our

formulation corresponds to the K87 setup where b(x,x ) = b(x) in his notation, which, by the

comment in K87, p. 179, means that his discussion is applicable to his system (1.1) and hence

to our setup. In his discussion on p. 179, K87 indicates that the difference between the

measure of Xn (which corresponds to our qk

ˆ

) and the invariant measure (which we have

denoted hp ) converges asymptotically (n,k → ∞,h →0 ) to the zero measure weakly. This means

that, in the limit as k → ∞ , qk

ˆ

is equivalent to p in the same sense as in Theorem 2 of Gelfand

and Mitter (1991), and the desired convergence in probability follows as in Theorem 1 above.

Q.E.D.

ACKNOWLEDGEMENT

This work was partially supported by the JHU/APL Independent Research and Development Program

and U.S. Navy contract N00024-98-D-8124.

REFERENCES

[1] Alrefaei, M.H. and Andradottir, S. (1999), “A Simulated Annealing Algorithm with Constant

Temperature for Discrete Stochastic Optimization,” Management Science, 45, pp. 748-764.

[2] Chaing T-S., Hwang, C-R., and Sheu, S-J. (1987), “Diffusion for Global Optimization in n R ,”

SIAM J. Control Optim., 25, pp. 737-753.

[3] Chin, D.C. (1997), “Comparative Study of Stochastic Algorithms for System Optimization Based

on Gradient Approximations,” IEEE Trans. Systems, Man, and Cybernetics – Part B:

Cybernetics, 27, pp. 244-249.

[4] Chin, D.C. (1994), “A More Efficient Global Optimization Algorithm Based on Styblinski and

Tang,” Neural Networks, 7, pp. 573-574.

[5] Dippon, J. and Fabian, V. (1994), “Stochastic Approximation of Global Minimum Points,” J.

Statistical Planning and Inference, 41, pp. 327-347.

[6] Dippon, J. and Renz, J. (1997), “Weighted Means in Stochastic Approximation of Minima,” SIAM

J. Control Optim., 35, pp. 1811-1827.

[7] Fang, H., Gong, G., and Qian, M. (1997), “Annealing of Iterative Stochastic Schemes,” SIAM J.

Control Optim., 35, pp.1886-1907.

[8] Gelfand, S.B. and Mitter, S.K. (1991), “Recursive Stochastic Algorithms for Global Optimization in

d R ,” SIAM J. Control Optim., 29, pp. 999-1018.
[9] Gelfand, S.B. and Mitter, S.K. (1993), “Metropolis-Type Annealing Algorithms for Global

Optimization in d R ,” SIAM J. Control Optim., 31, pp. 110-131.

[10] Geman S. and Geman, D. (1984), “Stochastic Relaxation, Gibbs Distributions, and the Bayesian

Restoration of Images, IEEE Trans. Pattern Analysis and Machine Intelligence, PAMI-6, pp.

721-741.

[11] Haataja, J. (1999), “Using Genetic Algorithms for Optimization: Technology Transfer in Action,”

Chapter 1, pp. 3 – 22, in Evolutionary Algorithms in Engineering and Computer Science,

Edited by K. Miettinen, M.M. Makela, P. Neittaanmaki, and J. Periaux, Wiley, Chichester.

[12] Hajek, (1988), “Cooling Schedules for Optimal Annealing,” Mathematics of Operations

Research, 13, pp. 311-329.

[13] Kushner, H.J. and Yin, G.G. (1997), Stochastic Approximation Algorithms and Applications,

Springer, New York.

[14] Kushner, H.J. (1987), “Asymptotic Global Behavior for Stochastic Approximation and Diffusions

with Slowly Decreasing Noise Effects: Global Minimization Via Monte Carlo,” SIAM J. Appl.

Math., 47, pp. 169-185.

[15] Maryak, J.L. and Chin, D.C. (1999), “Efficient Global Optimization Using SPSA,” Proc. Amer.

Control Conf., San Diego, June 2-4, pp. 890-894.

[16] Mitchell, M. (1996), An Introduction to Genetic Algorithms, MIT Press, Cambridge, Mass.

[17] Spall, J.C. (2000), “Adaptive Stochastic Approximation by the Simultaneous Perturbation

Method,” IEEE Trans. Automat. Control, 45, pp. 1839-1853.

[18] Spall, J.C. (1998), “Implementation of the Simultaneous Perturbation Algorithm for Stochastic

Optimization,” IEEE Trans. Aerospace and Electronic Systems, 34, pp. 817-823.

[19] Spall, J.C. (1992), “Multivariate Stochastic Approximation Using a Simultaneous Perturbation

Gradient Approximation,” IEEE Trans. Automat. Control, 37, pp. 332-341.

[20] Styblinski, M.A. and Tang, T.-S. (1990), “Experiments in Nonconvex Optimization: Stochastic

Approximation with Function Smoothing and Simulated Annealing,” Neural Networks, 3, pp. 467-

483.

[21] Yakowitz, S. (1993), “A Globally Convergent Stochastic Approximation,” SIAM J. Control

Optim. 31 pp. 30-40.

[22] Yakowitz, S., L’Ecuyer, P., and Vazquez-Abad, F. (2000), “Global Stochastic Optimization with

Low-Dispersion Point Sets,” Operations Research, 48, pp. 939-950.

[23] Yin, G. (1999), “Rates of Convergence for a Class of Global Stochastic Optimization Algorithms,”

SIAM J. Optim., 10, pp. 99-120.
Ontology in Performance Evaluation

Edward Dawidowicz

Command and Control Directorate

US Army, CECOM, RDEC

The criteria for measurement of performance of

intelligent systems allow a qualitative

comparison of one system against another. The

performance measurement allows us not only to

judge the achieved level of success or failure, but

also to evaluate cost effectiveness of a particular

design. However, the performance criteria alone

is not sufficient to give an objective evaluation

of the system. A thorough approach requires the

consideration of methods and devices for

performance evaluation. The need for this

thorough approach requires us to take a look at

the ontology of performance of intelligent

systems.

.

Before we attempt to define methods of

performance measurements we need to

reexamine what are the distinctive elements

which make a system intelligent and how do they

relate to each other. These elements require

scrutiny individually and as whole [1] as well as

the mechanisms, by means of which they

interact.

The questions that are worthwhile to examine are

those that reflect processes at a particular level of

system functioning associated with

organizational echelons. An intelligent system is

goal driven. The goals are both internal and

external. The system has to be able to

decompose goals into subgoals giving an

emergence of hierarchy of goals. The system

representation, also hierarchical, plays an

important part in developing the ontology and

requires a distinct attention [3].

Another tempting aspect for discussion is the

topic of ontology of self-referentiality of

intelligent system [2]. Particularly considering

the new and different ways in which intelligent

systems conceptually forms and specifies

representation of objects as a particular

manifestation of self-referentiality. That indeed

requires special attention when formulating

ontology.

A few question arise as we ponder the meaning

of the role of ontology in performance

evaluation:

1. What is the purpose of ontology and how

does it helps us to find the answers we are

looking for?

2. What are the framework, development

methodologies and life-cycle maintenance?

3. Can it provide an objective representation of

performance and associative measurement

devices and methodologies?

4. Should the ontology reflect the system

functioning and collaboration with other

systems?

5. An intelligent system has a user who

interacts with it. A user maybe another

system or a human. Does that mean that we

need two ontologies, one for the user and the

other for a system under evaluation?

6. And finally when we are addressing

complex systems, and an intelligent system

is a complex system, should we not consider

ontologies of many hierarchical levels [3]?

References:

1. John H. Holland, "Emergence: From Chaos

to Order", Helix Books

2. Roger Penrose, "Shadows of the Mind",

Oxford university press, 1994

3. Alexander Meystel, James Albus, "

Intelligent Systems: Architecture, Design

and Control, Wiley, NY, 2002.
Using Ontologies to Evaluate Knowledge-based Systems

Michael Gruninger

NIST

Many systems have hidden assumptions about

their domain that must be rendered explicit if we

are to identify the principles on which they are

based. Verified ontologies provide one way of

making these assumptions explicit. A verified

ontology consists of a specification of a class of

mathematical structures together with a proof of

two fundamental properties:

• Satisfiability: every structure in the

class is a model of the ontology’s

axioms;

• Axiomatizability: every model of the

ontology’s axioms is isomorphic to

some structure in the class.

Strictly speaking, we only need to show that a

model exists in order to demonstrate that an

ontology is satisfiable. However, in the

axiomatization of ontologies, we need a

complete characterization of the possible models.

For example, if we are considering the domain of

activities, occurrences, and timepoints, to show

that a theory is satisfiable, we need only specify

an occurrence of an activity that together with

the axioms are satisfied by some structure. The

problem with this approach is that we run the

risk of having demonstrated satisfiability only

for some restricted class of activities. For

example, a theory of activities that supports

scheduling may be shown to be consistent by

constructing a satisfying interpretation, but the

interpretation may require that resources cannot

be shared by multiple activities or it may require

all activities to be deterministic. Although such a

model may be adequate for such activities, it

would in no way be general enough for our

purposes; we would want a comprehensive

theory of activities that explicitly characterize

the classes of activities, timepoints, objects, and

other assumptions that are guaranteed to be

satisfied by the specified structures.

When implementing knowledge-based systems,

we are faced with the additional challenge that

almost no existing software application has an

explicitly axiomatized ontology. However, we

can model a software application as if it were an

inference system with an axiomatized ontology,

and use this ontology to predict the set of

sentences that the inference system decides to be

satisfiable. This is the Ontological Stance, and is

analogous to Dennett’s intentional stance, which

is the strategy of interpreting the behavior of an

entity by treating it as if it were a rational agent

who performs activities in accordance with some

set of intentional constraints.

Using the ontological stance, we can define

capabilities of knowledge-based systems and

explain why certain techniques fail when

extended to new domains. In particular, we can

characterize the knowledge used in a given

domain, and how this knowledge influences a

particular reasoning task. Ontologies support a

semantic assumption-based approach to tractable

reasoning -- rather than identify syntactic classes

of theories, we can reason about the assumptions

that the ontology entails.

An example of this is the CardWorld Ontology

that axiomatizes shape-based object recognition

in scenes consisting of 2D surfaces with

occlusion. The ontology allows characterization

of the complexity of finding a model of an image

together with the axioms of the ontology. In

particular, tractable subclasses can be defined by

• Assumptions on images (e.g. accidental

alignments)

• Assumptions on scenes (e.g. layered

surfaces)

• Assumptions on depiction (e.g. errors in

edge detection)
Towards an Ontology of Performance

Line Pouchard

Computer Science and Mathematics Division

Oak Ridge National Laboratory

Specifying the concepts for an ontology of

performance advances PERMIS’s project to

define metrics of intelligence. The multiple

factors briefly mentioned below highlight a few

aspects of the complexity and depth of concepts

and issues to be considered. It is therefore useful

to keep in mind questions that arise when

developing an ontology:

• What will be the purpose, use and scope of

this ontology? Defining a consensus on

terminology, defining machine-readable

schemas, a practical and/or theoretical

purpose, interoperability between systems,

interface between experts in related fields

are good examples.

• Are domain specific ontologies

advantageous?

• Who will be the users and stakeholders of

this ontology? (human communication,

computerized interaction, human-machine

interaction?)

• How and by whom will it be developed?

Which language is chosen for representing

the ontologies?

The performance of a distributed system results

from trade-offs between properly weighed

factors that include computational costs and

communication overhead on the one hand, and

computational and communication benefits on

the other. Computational benefits include the

number of transactions per seconds or

milliseconds, the throughput of Input/Output of

the system as a whole, and its response time.i

Computational costs depend on the load of each

component, e.g. number and complexity of

processes utilized in performing the task at hand,

and the consumption of computational resources.

Communication costs may depend on the

number of components, their model of

interaction and the general architecture of a

system.

Computational costs and benefits depend on a

compromise between amount and type of

resources consumed by the system. For instance,

in large distributed systems (order of dozen of

nodes distributed across the country) analyzing

data-intensive scientific data (order of

petabytes) such as the type of applications for

which Grid computingii is designed, the

computational costs also vary depending on the

type of architecture. The type of architecture

here means distributed data and centralized

processing, versus distributed data and

distributed processing. If data needs to be

shipped to a central very powerful computer

(super-computer of the type pioneered by Cray

research)iii, as current implementation of Grid

applications require, the load on computational

resources will be large and so will be

computational costs. The load in term will affect

response time that depends on hardware

performance as well as the algorithms driving the

hardware.

In the case of distributed data and distributed

processing, data is no longer shipped to a central

location. Instead, a software code or a software

component is moved to a remote computational

resource close to data storage.

Communication costs may depend on the

number of components interacting with each

other, the availability and cost of network

bandwidth, and the architecture of

communication. Architecture of communication

here may mean the protocol of communication iv

(for example contract-net protocol in multi-agent

systems, asynchronous communication such as

message passing in parallel systems).

Communication overhead may also include

factors related to the structure of a system,

whether a hierarchical structure or a web-based

structure are used. In the hierarchical structure,

children interact with its parents and children; in

a web-based structure, any component may a

priori interact with any other.

Affecting performance, one also finds factors

related to the individual performance of a

component independently of the performance of

the system as a whole. The knowledge base of a

component and the complexity of its rules for

reasoning upon input are such factors. Thus

several levels of measuring performance in a

system may be envisioned.
What Role Can Ontologies Play to Improve the Performance of Intelligent Systems?

Craig Schlenoff

Intelligent Systems Division

National Institute of Standards and Technology

There are at least three overarching questions

that one may ask regarding the role of ontologies

in performance evaluation. They are:

1. How can an ontology play a role in

evaluating intelligent systems?

2. How can an ontology within an intelligent

system be evaluated?

3. How can ontologies play a role in helping an

intelligent system perform at a higher level

of performance?

This write-up focuses on this third question.

When most people think about the role of

ontologies in performance evaluation, they often

ask question such as:

• What will be the purpose of the ontology?

• How formal of an ontology does one need?

• How does one create such ontologies?

To attempt to answer these questions, one can

imagine introducing an ontology (or set of

ontologies) into an autonomous vehicular

system’s knowledge base. Let’s assume that the

ontology consisted of various objects that the

vehicle expected to encounter in its environment,

as well as the important characteristics of each

object. This ontology would help to answer

question such as:

• Based upon the data we get from our

sensors, what are the objects we perceive in

the environment at the given time, with

appropriate levels of confidence?

• What characteristics of those objects do I

need to be most concerned about?

With the introduction of ontologies representing

factors that effect the motion of objects (such as

an ontology representing the “rules of the road”,

motion limitation of certain vehicles, the

network of roads in the environment), we could

answer questions such as:

• Where do we expect an object to be at a time

in the future?

• What is our risk of colliding with the object

assume the motion patterns do not change?

Assuming the ontology has clearly defined

semantics, we can use the ontology to

unambiguously exchange information among

different autonomous vehicles (or between a

vehicle and a human) that are working together

to jointly accomplish a goal.

With the introduction of an ontology of actions

that an autonomous vehicle is able to perform,

we could answer questions such as:

• How can the overall goal of the vehicle be

decomposed into actionable items?

• What are the appropriate actions in a given

situation?

Based on these ontologies, if the entire

environment around the vehicle at any given

time could be completed modeled, we could

evaluate the planner by answering questions such

as:

• Has the planner identified all possible plans?

• Does the chosen plan truly accomplish the

stated goal?

Therefore, I contend that ontologies can play a

role in improving the performance of almost

every part of autonomous vehicular systems, and

most likely in intelligent systems as a whole.

However, that is not to say that there aren’t

various technical challenges that have to be

addressed before ontologies can be used in this

context. Some of the questions that need to be

address include:

• How can ontologies be linked to other types

of representations, including sensor data?

• Can ontologies respond quickly enough to

be useful in a real-time environment?

• Are ontologies reusable in intelligent

systems, and if so, what is the best

mechanism to share them with each other?

• How can one evaluate the performance of

the ontology?
Role of Ontologies in Performance Evaluation

Lawrence A. Welsch, Ph.D. – LWelsch@nist.gov

There are many possible roles that an ontology

may have evaluating the performance of

intelligent systems. I only list some of the more

obvious roles that I see

Correctness of Results

Solutions to problems and answers to questions

from the system being evaluated may not always

be what is expected. For example, an expected

answer might be 4-wheel vehicle. The system

being evaluated might respond go-cart, car,

truck, vehicle, tank, ice cream bar, or any one of

a number of answers. Car and go-cart are

probably correct answers. Truck may or may not

be correct, but is certainly a vehicle. Tank is a

vehicle, but not wheeled. Ice cream bar would

not even be close. An ontology would help make

those decisions rapidly.

Was the Explanation Correct

When performing a test of intelligence, the tester

may want to see an explanation of how the

answer was derived. An ontology can be used to

1. determine whether or not the knowledge

used in an explanation is consistent (may be

indeterminate within the time allotted);

2. determine whether or not the steps are

logical; and

3. whether or not a better explanation exists.

Why did the System being

Evaluated do X?

Sometimes judgements of performance are made

based upon the behavior of the system. If we

understand why the system is behaving as it is,

then there is not need to ask for an explanation.

However if we do not understand then we need

to ask why. An ontology can be used to

understand the behavior of a system being

evaluated and if it doesn’t then to ask why. If the

system being evaluated has made an incorrect

assumption in it’s explanation then an ontology

could be used to probe the rationale for the poor

assumption.

How Optimal was the Answer?

Different systems have different quantities of

knowledge, represented in different ways. Given

absolute or total knowledge of the problem

domain, then how good was the answer, solution,

explanation given by the system compared with

the best possible answer, solution, explanation

for the problem/question.
Ontologies OF and FOR Intelligent Systems

A. Meystel

Drexel University, NIST

1. Constructing an ontology of Intelligent System (IS) is a nontrivial issue since its (IS)

existence has not been demonstrated, and the term is used as a metaphor. It raises a

number of additional questions. Not the last among them is whether existential problems

can be legitimately formulated for the domain of imaginary worlds. The answer is

probably an affirmative one, since imaginary worlds do exist (at least in the subsystem of

planning) and thus generate corresponding ontologies. Another question is linked with

processes of reflexia that emerge within intelligent systems. This really opens a can of

worms since individual and group processes of reflection are based upon the property of

self-reference – not a trivial subject for the theory of IS ontology. This set of problems is

difficult but is pleasurable because it requires surveying a multicultural (at least,

multidisciplinary) domain including psychological and linguistic references as well as

engineering ones.

2. Constructing an ontology for Intelligent System (IS) is inescapable because its (IS)

functioning dwells upon input, output, and intermediate (e. g. interface, etc.) ontologies.

The need in multi-resolutional ontologies is looming, although the ontology community

temporarily (eternally?) resists making this one of the regular issues of research. The

difficulty of the latter subject is in the fact, that despite of the unity of the multiresolutional ontology of the IS, it needs consistency checks to be conducted at each level.

Nevertheless, the community seems to be well prepared for solving this issue, too.
タイトル 

as a test bed for Measuring 

Performance

Minoru Asada

Adaptive Machine Systems

Osaka University, JAPAN

August 13th, 2002

PerMIS-02@NIST

nations around world, and about 1000 

media people.

 About 120,000 visitors during one press d

and four open public days

 The first humanoid robot league

 13 teams from 6 nations

 ROBOTREX (Robot Trade & 

Exhibitions)

 50 companies, universities, and institutes

Outline of my talk

 RoboCup

 Purpose, Current State, and Issues.

 Technological issues towards 

finial goal: Humanoid league

 Levels of autonomy

 One leg standing, walk, PK, 

and free style

 Future issues

What’s RoboCup?

An attempt to foster intelligent robotics 

research by providing a standard problem

Video Clips What’s RoboCup?
タイトル 

0

20

40

60

80

100

120

140

1997 1998 1999 2000-

Euro

2000-Mel 2001-

Seattle

2002-

Fukuoka

Humanoid

4-legged

Sim

F2000

F180

Why RoboCup? (I) Why RoboCup? (I)

 Landmark Project:

 to set a goal high enough so that a series of 

technical breakthroughs is necessary to 

accomplish the task

 this set of technologies should form the 

foundation of a next generation of industries

 widely appealing and exciting

Ultimate Goal Æ

To beat the human world cup champion 

team by a team of 11 humanoids

Why RoboCup? (II) Why RoboCup? (II)

 Standard Problem:

 a systematic approach to promote research 

using common domain, soccer!

 Comparison with “Chess Problem”

Chess RoboCup

Environment Static Dynamic

State Change Turn taking Real time

Info. accessibility Complete Incomplete

Sensor Readings Symbolic Non-symbolic

Control Central Distributed 

Research Issues in RoboCup (I) Research Issues in RoboCup (I)

 Mechanical design for individual 

robots

 Robust Sensing, especially, vision 

(object discrimination and tracking)

 Self-localization and map building

 Control Architecture

 Communication

Research Issues in RoboCup (II) Research Issues in RoboCup (II)

 Multi-agent systems in general

 Behavior learning for complex tasks

 Combining reactive and modeling 

approaches

 Real-time recognition, reasoning, plann

and action execution in a dynamic 

environment

 Cross modal association (Sensor fusion) 

 Strategy acquisition

 Cognitive modeling in general
タイトル 

 Simulation: Coach, Visualization

 Real robot: Small, Middle, Legged, 

and Humanoid

 RoboCupRescue

 Simulation and real robot

 RoboCupJunior

 Soccer, Dance, and Rescue

ow cos, Saa ode, v.s. , 

perception, broadcasting

 Secondary Domain ÆRoboCup-Res

Soccer 

Simulation 

League

 Teamwork

 On-line learning

 Coach 

competition

 Visualization

Real Robot Leagues

 Small Size league: A table tennis table, an 

orange golf ball, and global vision..

 Middle Size league: 3X3 table tennis tables

official soccer ball, and local vision..

Real Robot Leagues (cntd.)

 Legged league: Sony AIBO Type robots, 4 on 4.

 Humanoid league: Four classed according to 

the size. One leg standing, walk, PK, and free 

style

Small-size league

 1997~ Global vision:

 Perception: Sharing 

global information Æ

reliable and real-time 

detection of multi 

mobile robots and ball.
タイトル 

g

the filed to encourage 

the team plays.

 Evolution from 

individual behavior to 

team plays.

Middle-size league (cntd.)

 Global reconstruction by LRFs (C. S. Freiburg, 

Germany)  1997~ 5 on 5 Footsul-4 ball

 2000~ 4 on 4 Footsul-5 (official ball)

 Omni-directional vision, Reactive behavior, 

and social ones.

Middle-size league (cntd.)

Middle-size league (cntd.)

 Holonomic Vehicles: 

Omni-directional 

movement

Middle-size league (cntd.)

 2002~ No walls

 Quick motions

 Team plays
タイトル 

 1998:exhibition (Osaka U.,CMU,ParisVI )

 1999~:Official league (2m×3m,3 on 3)

 computer

 actuator

 mechanism

Legged league (cntd.)

 Various kinds of behaviors: Ball handling

 Teamwork: social behavior based on 

vocal communication

Legged league (cntd.)

 2001~: New platform

 2002~:3m×4m,4 on 4,wireless 

communication

Humanoid league

 2002: the first humanoid robot soccer.

 4 kinds of size:40cm,80cm,120cm,180cm

 Perfomance factor towards fully autonomous 

humanoid robot: Platform, power supply from 

outside, remote brain, human control

 Japan (5), Sweden (3), Singapore (2), New 

Zealand, Australia, Denmark

Why humanoid robot?

 One extreme application: test-bed 

for brain science.

 Current application: 

entertainment, pet robots.

 Another extreme application: 

practical use in our daily life??? Æ

HRP by Japanese government.
タイトル 

running, turning, jumping,.....

 Cognitive performance: object 

recognition/tracking

 Combined behaviors: object following, 

object avoidance, kicking a 

stationary/moving ball,...

 Auditory system, planning Æ Future

Benchmark for the humanoid league 

[Kitano & Asada 98] Standard Components 

and Software Resources

 OpenR: Open Architecture 

for High DOFs and Sensors.

OpenR SDK (June, 2002)

 Humanoid Robot Project 

supported by Japanese 

government

HRP robot (Honda P3-base)

HRP 2 robot (original) Æ

Perfomance Factor

 We would like to trigger developments towards fully 

autonomous self-build humanoid robots.

 Therefore, we took so-called performance factors for 

the different dimensions with regard to autonomy.

1. external power cord

2. computer outside robot

3. remote control

4. Platform

 1.2 is assigned for each item and if more then one is 

applicable then they are multiplied (1.2, 1.44, 1.728, 

2.0736). 

Perfomance Factor (cntd.)

 These factors were either used

1. as penalty factor in the walking the time t

was multiplied by them or 

2. as handicap (in penalty kiccking the score 

divided by them). 

 They are working quite well (with regard 

the above stated intention) and will certai

prefer the more autonomous robots but w

also allow for semi-autonomous ones if th

performance is much better then that of t

autonomous ones. No changes needed.
タイトル 

ts. t was doe uuoa by asg eveybody 

the audience to perform this challenge together with 

the robots.

penalty which is linearly increasing: 20 sec/1st 

touch, 40 sec/2nd touch, 60 sec/3rd touch etc. 

 Champion:

 Nagara (Japan) 

 81,64, and 61 seconds 

 3.29 (p/f: 1.0)

 Second:

 Robo-Erectus (SG)

 209, 109, and 183 secs.

 4.932 (p/f: 1.2)

Challenges: Penalty kick  Total behavior coordination with walking, one leg 

standing, kicking, and balancing.

 The physical height of the striking robot was used to 

determine the distance between ball and striker 

while the measurements of the goals were only 

available for the two categories (40 cm and 80 cm 

height). 

Challenges: Penalty kick (cnt

 First, to give the striker a realistic chance we 

introduced a 5 sec latency after the starting whist

before the goalie may start to walk towards the ba

reduce the angle which could be used to score a go

 Second, the line of the goal area was used a strict 

demarcation line to avoid the collision. 

 The was so light that it often went astray due to sm

uneven parts in the field.

Challenges: Penalty kick (cntd.)  First, to give the striker a realistic chance we 

introduced a 5 sec latency after the starting whistle 

before the goalie may start to walk towards the ball to 

reduce the angle which could be used to score a goal. 

 Second, the line of the goal area was used a strict 

demarcation line to avoid the collision. 

 The was so light that it often went astray due to small 

uneven parts in the field.

Challenge: free style

 Honda Asimo’s example performan

and digest from humanoid league.
タイトル 

Sad ooe eg: dcuo decde ea

sensor feedback or open loop. Introduction 

disturbance to check it.

 PK: from PK to 2 on 2!

 Free style: A test bed for humanoid researc

general

Materials and Basic 

Components

 Surface Materials

Soft, Embedded-sensory systems

 Frame Materials

Light Weight

 Power Supply

45 min x 2 + PK, etc.

Weights and Safety

 Energy Saving Architecture

Materials and Basic 

Components

 Actuation Systems

Current Motor-Gear system is too 

fragile

Robust Parallel System is necessary

Artificial Muscle

 Mechanical Design

Joint systems, etc.

Totally new design?

Basic Control Issues

 High Performance Mobility

Basic Walking is accomplished

Run and Jump

 Behavioral Robustness

Can it safely fell down and stand 

up again?

 Behavioral Complexity

Sensory Systems

 Vision and Touch

 Auditory System

 Other Sensing Systems

 Sensor-Fusion

 Sensory-Motor Integration
タイトル 

 Strategy Planner

 Learning

 Brain and Cognitive Science

got wins from human teams since 19

 Small size league: Human won until 

1998, but no more since 1999. 

 Sony AIBO league: robot team won!

 But, human teams have not got used 

operate robots. What happened if th

learned more?

RoboCupRescue Simulation

500 x 500 m region in 

Nagata Ward, Kobe City 

Multi-layered human interface

with information filtering

RoboCupRescue Simulation (cntd

RoboCupRescue Real robot

 Evaluation of 

cooperation 

deployed in three 

stage rescue 

situations by NIST

RoboCupJunior
タイトル 

 Humanoid league: regulations

Padua, Italy

 July, 2004: The eighth RoboCup at 

Lisbon, Portugal 

Acknowledgement

 RoboCup Federation,

 NPO RoboCup Japanese Committee

 Humanoid Chair: Prof. Dr. Thomas Christaller

http://www.robocup.org/
Evolving Solutions that are Competitive with Humans

David B. Fogel

Natural Selection, Inc.

3333 N. Torrey Pines Ct., Suite 200

La Jolla, CA 92037

dfogel@natural-selection.com

Extended Abstract

Ever since the advent of the modern digital computer, we've tried to generate machines that are

as intelligent as humans. Indeed, this was the primary goal of early artificial intelligence (AI)

researchers. The "general problem solver" served as one example of efforts along this line, and

it's lack of success was disappointing to many in the AI community. One of the primary

deficiencies of early, and even modern, efforts in AI has been a lack of a definition of the

intelligence that is sought. There is no accepted definition of intelligence, let alone "artificial

intelligence." In place of careful definitions, the Turing Test evolved as a surrogate criterion for

judging intelligence.

The famous Turing Test involves an interrogator asking questions of a woman and man via a

teletype machine, with the goal of correctly identifying the woman. The woman presumably

gives truthful answers to questions, while the man may lie. Turing proposed replacing the man

with a computer and suggested that if a computer can fool the interrogator into believing that it is

the woman as often as a man can fool the interrogator then the machine will be said to have

passed the test. Interestingly, Turing never claimed that passing the test meant that such a

machine would be "intelligent," an issue that he described as being "too meaningless to deserve

discussion."

In retrospect, the Turing Test is no more a test for intelligence than it is a test for femininity. If a

man can fool an interrogator into believing that he is the woman, that does not make the man a

woman. Similarly, just because a computer might fool an interrogator into believing that it is

intelligent does not make the computer intelligent. Nevertheless, the Turing Test has had a

profound impact on efforts to simulate behaviors that we associate with intelligence, mainly as

we observe them in ourselves. Unfortunately, over time, the impact was mainly to narrow the

focus of AI to simply generate programs that could compete with humans in specialized areas,

such as chess. The mechanism for generating the required behavior became irrelevant, as all that

was of importance was the end result. The culmination of this process is Deep Blue, a very fast

machine that can beat Garry Kasparov in chess, but is no more "intelligent" than a calculator, or

a hammer.
Rather than begin from the perspective of the Turing Test, an alternative perspective begins with

the concept of decision making. For an organism to be intelligent, it must make decisions. It is

pointless to speak of the intelligence of something that does not make decisions. A decision can

be defined to arise when available resources are allocated. Note that a range of possible

decisions, possible allocations, must be available otherwise there really is no decision at all.

Logically, decision making requires a goal, for decision making in the absence of a goal is

pointless. Thus we must inquire as to where goals come from.

In natural systems, the primary goal instilled in all living organisms is survival. Those organisms

that do not possess this goal may be "successful," but are uninteresting from an evolutionary

perspective. Thus behaviors can be judged in how well they support this ultimate goal, and

subgoals that underlie it. More generally then, intelligence can be defined as "the ability for a

system to adapt its behavior to meet its goals in a range of environments." This capability of

intelligent decision making can be observed strikingly in the evolving phyletic lines of organisms

(the reader is free to choose which ones), such as frogs and insects in which cryptic coloration,

poison, reliable signaling, and mimicking have all been invented to meet the primary goal of

"avoiding being someone else's lunch." No single individual invented any of these "tricks," rather

the intelligent organism in these cases is the evolving line of individuals.

Taking this cue from nature, it is reasonable to assess the intelligence capability of a machine that

evolves solutions to problems in a manner similar to that of evolving phyletic lines in the natural

environment. From the perspective of performance comparison, many efforts in evolutionary

computation have been measured in light of human capabilities. For example, L. Fogel (Artificial

Intelligence Through Simulated Evolution, Wiley, 1966) compared the ability of graduate

students and an evolutionary program operating on finite state machines to predict sequences of

symbols. The results showed that the evolutionary program was competitive or slightly more

capable than its human competitors. In Germany, in the mid-1960s, H.-P. Schwefel used an

evolutionary algorithm to create a new design for a flashing nozzle, which exceeded the

capabilities of the previous human design. There are many other relevant results in the literature.

More recently, the author and a colleague (Kumar Chellapilla) investigated the ability for an

evolutionary algorithm to learn to play checkers at a level that is commensurate with human

experts, without relying on human expertise about checkers. Instead, neural networks were

used to evaluate candidate board positions based only on the inputs found in the number,

location, and types of pieces on the board. Furthermore, the neural networks were not told

which games were won, lost, or drawn. Only an overall point value was assessed to each neural

network, which signified the total value earned over a series of games.

Starting from randomly weighted connections, a population of neural networks evolved, using

random variation of the weights of each neural network and a selective mechanism to eliminate

poor-scoring networks, over 100 generations to be competitive with "Class B" human players

on the Internet. With some modifications of the input design to the neural networks, which
allowed a recognition that the game is played on a two-dimensional board, and 840 generations,

the best-evolved neural network (called Blondie24) was able to compete with human experts

and finished in the top 500 of over 120,000 people on the Internet site, www.zone.com. More

details on this effort can be found in D. Fogel's book, Blondie24: Playing at the Edge of AI,

Morgan Kaufmann, 2002.

The results indicate that evolution is a suitable mechanism for creating intelligent behavior in

machines, and that it can learn to generate behavior that is competitive with human experts even

without relying on human expertise. As computer hardware increases in speed according to

Moore's Law, it is important to recall that this acceleration in speed is not sufficient to generate

intelligent machines. The software that this hardware will execute is critically important. The

results described here and presented in the plenary lecture indicate one step toward creating

intelligent machines that may someday possess an ability to adapt their behavior, to meet their

goals, in a range of environments that is commensurate with our own abilities.
Evaluation Methods for Human-System Performance of Intelligent Systems

Dr. Jean Scholtz

Visualization and Usability Group

Information Access Division

Information Technology Laboratory

National Institute of Standards and Technology

Abstract

Intelligent systems are becoming more and more of a reality but

with the exception of very special purpose systems, completely

autonomous systems are not yet the norm. In reality, we need to

have humans who monitor the systems, intervening when

necessary. As systems increase in intelligence, the goal for

human-in-the-loop activities should not be to eliminate the

human, but rather to create a human-system partnership with

greater capabilities than the individual components. We

currently view intelligent systems and the operators or

supervisors of these systems as separate components and

conduct evaluations in the same vein. For intelligent systems to

become more useful and acceptable, we need to consider the

“system” as a synergistic composition of software behaviors,

possibly embedded in a physical component such as a robot, and

the human interacting with this virtual or physical component.

Our objective is to design this team interaction in such as way

that the intelligence of the team is greater than the intelligence

of any one of the parts.

Keywords: Human-robot interaction, situational

awareness, human-computer interaction, evaluation

methodologies, intelligent systems.. 

1. Introduction

In our work we are concerned with intelligent systems

embodied in hardware (robots). Human-robot interaction

is fundamentally different from typical human-computer

interaction (HCI) in several dimensions. [8] notes that

HRI differs from HCI and Human-machine Interaction

(HMI) because it concerns systems which have complex,

dynamic control systems, exhibit autonomy and

cognition, and which operate in changing, real-world

environments. In addition, differences occur in the types

of interactions (interaction roles), the physical nature of

robots, the number of systems a user may be called to

interaction with simultaneously, and the environment in

which the interactions occur.

The interaction roles of supervisor, operator, and peer are

defined in [17]. Upon further consideration we have

subdivided two of these roles resulting in five different

interaction roles: supervisor, operator, mechanic,

teammate, and peer. The supervisory role involves

monitoring the intelligent system and seeing that any

interventions that are needed are handed off to the proper

individual. We have subdivided the original operator role

into an operator role and a mechanic role. An operator is

needed to work “inside” the robot; adjusting various

parameters in the robot’s control mechanism to modify

abnormal behavior; to change a given behavior to a more

appropriate one; or to take over and tele-operate the robot.

The mechanic interaction is undertaken when a human

needs to adjust physical components of the robot, such as

the camera or various mechanical mechanisms. The peer

role has been divided into a teammate role and a

bystander role. The teammate role implies the same

relationship between humans and robots as it does in

human-human interactions. Teammates of intelligent

systems can interact at an “implementation level.” The

commands a teammate can give to a robot should not

change the nature of the plan or mission but allows

adjustments due to the dynamics of a particular situation.

A bystander does not explicitly interact with a robot but

needs some model of robot behavior as the bystander will

be in the same physical space as the robot and needs to

co-exist.

The second dimension is the physical nature of mobile

robots. Robots need some awareness of the physical

world in which they move. As robots move about in the

real world, they build up a “world model” [2]. The

robot’s model needs to be conveyed to the human in order

to understand decisions made by the robot as the model

may not correspond exactly to reality due to the

limitations of the robot’s sensors and processing

algorithms.

 A third dimension is the dynamic nature of the robot

platform and its effect on performance and capabilities.

In typical human-computer interactions the assumption is

that the computer is working and that behavior does not

change over time. In assessing user interactions with the

internet, we are starting to question this assumption as the

workload and the time delays at any particular time can

affect what the user does and how satisfied the user is

with the experience. The fact that robot capabilities can

change implies that functionality at time 1 may not be
available at time 2 and this has to be factored into the

human-robot interaction.

In typical human-computer interactions the cognitive state

of the human has been largely ignored. The notion of

affective computing [15] is just starting to appear in

commercial products. Human computer interaction

specialists in such domains as air traffic control [12],

safety critical systems, and military systems have been

concerned with these issues for some time and have

attempted to designed user interfaces that are usable in

these conditions. The current trend is to design interfaces

that detect the status of the user and adjust accordingly.

Consideration of environmental conditions and the effect

on the users is a necessity in HRI.

In human-robot systems several of the interaction roles

(supervisor, teammate, bystander) may need to interact

with a number of systems simultaneously. These systems

may be operating completely independently or they may

be functioning as a team. Moreover, several types of

interactions could be occurring at the same time; for

example, a supervisor might be overseeing a robot which

is also interacting with a teammate. Typical HCI

considers 1:1 situations; one user to one computer. In

HRI, we have the possibility of a 1:N situation where one

person is interacting with N robots and N:1 where a

number of people are interacting with the same robot.

The autonomous nature of intelligent systems has been a

subject of discussion for some time in the human factors

world [13]. This changes the user’s role from being in

control to monitoring and intervening when necessary.

This introduces the concept of “being out-of-the-loop”

and raises the issue of how to alert the user to an

exception and how to bring the user up to speed to

quickly and effectively intervene.

2. Approach

Traditional HCI takes a user-centered approach [14] while

others in the automation field have taken a system-centric

approach [3]. We are taking an information-centric view.

It is necessary to determine what information the user

needs to understand what the intelligent system is doing

and when intervention is necessary, and what information

is needed to make any intervention as effective as

possible. This understanding is basic to the design of a

user interface that is able to present the appropriate

information to the user. Intervention requires one more

component: a language for the user and the intelligent

system to use in resolving the problem. The final part of

the problem is actually carrying out the intervention,

assessing the situation correctly and giving advice or

performing a necessary action for the intelligent system.

We propose six different issues in evaluation that must

be considered to evaluate the overall human-intelligent

system interaction:

1. Is the necessary information for the human to be

able to determine that an intervention is needed

present?

2. Is the information presented in an appropriate

form?

3. Is the interaction language efficient for both the

human and the intelligent system?

4. Are interactions handled efficiently and

effectively - both from the user and system

perspective?

5. Does the interaction architecture scale to

multiple platforms and interactions?

6. Does the interaction architecture support

evolution of platforms?

The first four issues are relevant to all intelligent systems.

If we are concerned with supporting 1:N and N:1

interactions, we must evaluate the scalability of the

interaction architecture. If we are interested in using the

architecture over a period of time, we must consider how

the evolving behaviors of new intelligent systems will be

supported.

Usability evaluations of desktop software products use

three metrics: effectiveness, efficiency, and user

satisfaction [10]. Due to the dynamic nature of intelligent

systems separating the evaluation into two pieces, getting

the proper information to the user and the actual

performance of the user/system in the interaction,

produces a finer granularity of understanding. Users may

have all the information they need but the interaction can

fail for other reasons. Likewise, the interaction may be

successful without users having the proper information.

By separating the evaluation into these pieces, we reduce

the risk of counting these cases in the results.

These evaluation questions cannot presently be answered

in a general sense. Our approach is to narrow both the

domain and the role of the human interaction and

systematically explore the space. After we have explored

a number of roles and domains of interaction, we will

examine the results to determine if there are

commonalties that can be expressed as guidelines for

interaction guidelines.

3. Evaluation Methodologies

The six issues listed above are evaluated using different

types of evaluations. In this section we discuss the types

of evaluations appropriate for each issue.

3.1. Information Presence and Presentation

To determine if the necessary information is presented –

and in the correct form – we are customizing a situational
awareness assessment methodology. Situational

awareness [6] is the knowledge of what is going on

around you. The implication in this definition is that you

understand what information is important to attend to in

order to acquire situational awareness. Consider your

drive home in the evening. As your drive down the

freeway and urban streets there is much information you

could attend to. You most likely do not notice if someone

has painted their house a new color but you definitely

notice if a car parked in front of that house starts to pull

out in your path.

Level One of situational awareness (SA) is the basic

perception of information in your surroundings. For

example, in driving did you notice the cars to the left,

right, front and rear of your vehicle? Failures to perceive

information can result as short comings of a system or

they can be due to a user’s cognitive failures. In studies

of situational awareness in pilots, 76% of SA [11] errors

were traced to problems in perception of needed

information. Level Two of situation awareness is the

ability to comprehend or to integrate multiple pieces of

information and determine the relevance to the goals the

user wants to achieve. A person achieves the third level

of situational awareness if she is able to forecast future

situation events and dynamics based on her perception

and comprehension of the present situation.

The most common way to measure situational awareness

is by direct experimentation using queries [5]. The task is

frozen, questions are asked to determine the user’s

situational assessment at the time, then the task is

resumed. The Situation Awareness Global Assessment

Technique (SAGAT) tool was developed as a

measurement instrument for this methodology 7]. The

SAGAT tool uses a goal-directed task analysis to

construct a list of the situational awareness requirements

for an entire domain or for particular goals and sub-goals.

Then it is necessary to construct the query in such a way

that the operator’s response is minimized. For example, if

a user were being queried about the status of a particular

robot, the query might present the robot by location rather

than replying on the user to recall a name or to understand

a description. The various options for status could be

presented as choices rather than relying on the user to

formulate a response that might not include all the

variables desired. SAGAT queries are constructed to

include measures of all three levels of situation

awareness. Queries related to placement of nearby

vehicles in a driving domain would measure level one

situation awareness. Queries that ask about vehicle

activity, such as cars that have just switched lanes or

increased or decreased speed would address level two.

Level three, prediction, would be measured by queries

such as the likelihood that the car in front of you will

move into the far left lane. This could be determined by

the observation of a turn signal or the previous pattern of

behaviors of lane switching by that automobile.

3.2. Interaction Performance

Information presence and presentation evaluations are

user-centric. Interaction performance evaluations need to

take into account the performance of both the human and

the intelligent system. We are concerned with measuring

the ability of the user to formulate the correct interaction

and the system to understand and carry this out. This type

of evaluation can be conducted as a typical HCI

evaluation [4]. To elaborate, a set of tasks are

constructed and explained to the user. The user is then

directed to use the interface to accomplish each task. The

training and the expertise of the user can confound this

evaluation. In general, the users should be chosen from

the representative population of users and given the same

amount of training as those users would be given. If there

are a number of diverse users, then different classes of

users should be identified and between five and eight

users from each class should be used in the evaluation.

From the user perspective the metrics should reflect the

number of tasks that the user is able to successfully

complete, the time for each task, and a user satisfaction

measure that can be obtained using a standard

questionnaire [16].

The system performance is also factored into this metric.

The effectiveness measure has two components – the user

executing the correct interaction and the system

responding correctly to this interaction. Likewise the

efficiency metric would be the sum of the user time for

the interaction to be specified and the system time for it to

be carried out.

Currently we have not considered the notion of mixed

initiative interaction; that is, the intelligent system notices

that an interaction by the user is needed and notifies her

of this. This would require ensuring that these

notifications are seen and understood by the user (the

situation awareness measurement) and that the correct

interaction is selected and carried out (the interaction

performance portion).

3.3. Support for Scalability and Evolution

Support for 1:N and N:1 interactions should be evaluated

using the information presence and presentation, and

performance methods. The information to be displayed

in each case and the presentation of that information

needs to be determined and evaluated using a situational

awareness assessment. The performance measures need

to ensure that the user can identify the appropriate

interaction for the appropriate platform within the

appropriate time. This will be critical if a number of

heterogeneous platforms are being used. In the case of
multiple people interacting with the system

simultaneously, it will be interesting to determine how to

display that information and what effect this will have on

the interaction of any given role. For example, how will

the operator behavior change if a number of bystanders

are present when she needs to teleoperate a robot to get it

into a building?

Evaluating the interaction support for evolution is more

difficult. Intelligent systems will evolve and be capable

of undertaking more tasks successfully and

communicating with users at higher levels of abstraction.

This will definitely necessitate re-examining the

interaction language, but the information that is needed

has to be reconsidered as well. A more robust level of

autonomy might be supported as effectively using more

abstractions in the first level of information presented.

3.4 Evaluation Methodologies for Different Roles

The five roles defined here, supervisor, operator,

mechanic, teammate, and bystander, clearly have different

information requirements and different interactions. Is it

feasible to use the same type of evaluation to measure the

performance of all roles?

We expect that the supervisor, operator, and mechanic

will have access to a specialized display of information

from the intelligent system. This display may be on a

workstation, laptop or a small handheld device. However,

this display will give the appropriate situational

awareness to the users. Teammates may not have access

to such a device and bystanders certainly will not. A

different methodology is needed in these situations. This

will be discussed in section 5.

All of the interaction roles will have access to some

subset of the interaction vocabulary. A particular

interaction may be selected using a typical command

language, keyboard input, voice, or pen-based types of

interactions. The action might even be initiated by using

“physical” manipulation. For example, moving in front

of a robot and touching a sensor on the robot to cause an

action to occur would be an example of “physical”

manipulation. The same performance evaluations will be

used in all roles. However, in cases where no specialized

display is available, the challenge will be to make the

interaction choices known to the user.

4. Case Study One: Developing the

Situational Awareness Assessment Tool

In our current work, we have narrowed the domain to a

mobile, robotic platform that is given the task of driving

from one place to another in a complex urban

environment. The human-robot interaction role is that of

the supervisor, similar to a driving coach with a student

behind the wheel. As the capabilities of the student driver

increase, the driving coach should be able to pay less

attention and only give guidance when she detects the

possibility of a problem. However, our driving coach

will be remote. For the first part of the work we are

investigating the first two questions: what information is

necessary for the human to decide that intervention is

needed and what is the appropriate presentation of this

information?

The first step is to determine what information is needed

by the user. In the driving domain this task is easier than

in most because of the amount of information available.

There are numerous studies on driving [15] and our own

experience that we will turn to for the initial design of the

user interface.

We currently characterize the information using four

categories: static environment; dynamic environment;

platform information, and task information. The static

environment consists of information in the environment

that does not change or at least changes very infrequently.

In the driving domain, this would be the location of cross

streets and intersections; the type of roadway; whether

there are stop signs, stop lights, or other traffic controls.

Dynamic environment information examples are the

amount of traffic present, the pedestrians traffic if the

driving environment is urban at the time, and the status of

the traffic light. Examples of vehicle information are the

speed of the vehicle, the amount of fuel left, and the

condition of the vehicle such as non-working turn

indicators. Task information is the knowledge of the

destination; the current distance to the destination; how

far to the next decision point.

For given situations, the information needs change. For

example, approaching a green light, drivers should look

for different hazards than when approaching a red light.

Once the information for selected situations is determined

and the user interface is designed, the awareness

assessment tool is constructed. As explained in the

earlier section, this is accomplished by using a simulation

and freezing the simulation at a certain point. The

simulation screen is blanked out and the user is directed

to answer a series of questions to determine what her

situational awareness is at this time. The queries that are

constructed are the most important aspect of the

assessment methodology. Again, expert elicitation in

some form is used to obtain this information. This can be

done by observations of performance, verbal protocols,

interviews, or questionnaires. The results can be

combined and later verified by a number of subject

experts. In the driving domain, we are utilizing a tutorial

used to teach driving [16]. The tutorial presents a number

of situations and the student is asked to identify potential
hazards or take action to prevent accidents. The

information given is the front view, the rear and side view

mirrors and the instrument panel. The accompanying

instructor manual identifies the information that students

needed to have identified. We will use this information to

construct our queries.

The presentation of the queries should be done so that the

user can quickly answer. The users should not be asked

to recall information that is not relevant to the situation.

For example, asking a user to designate if there are cars to

the left, right, front (within 2 car lengths) and back

(within 2 car lengths) of his car on a multilane highway is

fine. Requesting the number of cars for most other

situations is not fine.

The analysis will be done for each situation that we

present and for the different information classifications

that we have identified. We will use situations from

highway driving, urban driving, and illustrating normal

conditions as well as hazardous conditions.

We intend to use our user interface and the situational

awareness assessment results as a baseline. These will be

made public. Others interested in this particular domain

could either construct a new user interface to display the

same information we have identified and compare their

results. Alternatively, different information could be

displayed in the user interface and the results compared

with a baseline.

Once we have completed our information presence and

presentation evaluation we will proceed to the

performance evaluation. We intend to also look at

scalability of the user interface from one to multiple

robotic platforms.

5. Case Study Two: Examining User

Mental Models in the Bystander Role

We are also working at the opposite end of the spectrum

and looking at evaluation methodologies where no

specialized visual user interface is present. We have

designed an experiment to examine the effects of

consistency and expectedness of behavior on bystanders’

abilities to construct a mental model of the robot’s

capabilities. We are using a Sony Aibo  1

 for this

experiment and as the robot has a dog-like appearance we

have designed behaviors that one would normally expect

of a dog (playing with a ball, sitting) and others (singing,

dancing) that would not be expected.

 

1 ∗

 The identification of any commercial product or trade name

does not imply endorsement or recommendation by the National

Institute of Standards and Technology.

Observations of the users as they interact with the robot

will be recorded and users will be asked after the

experiment to identify what robot behaviors will result

from various interactions on their part. Results from the

first round of this study will be available this fall.

6. Conclusions

We have defined five different interaction roles for

humans and intelligent robotic systems. We have also

defined six issues that constitute “performance of

intelligent systems.” We have outlined the evaluation

methodologies that can assess these measures of

performance. We are currently conducting evaluation

experiments for two types of interaction roles in two

domains: the supervisory role in a driving domain and a

bystander role in a social interaction domain. Our future

plans are to use the framework suggested by roles and

domains to systematically explore evaluation

methodologies for human-robot performance. Our tools

and results will be made publicly available as our research

progresses.

7. Acknowledgements

This work is funded by DARPA, contract number E710.

The author would like to thank Dr. James Albus and

Elena Messina for their support and encouragement in

including the human element in their research programs.

Brian Antonishek, NIST and Siavosh Bahrami, University

of California, Irvine are working on the experiments

discussed in this paper.

References

1. AAA Foundation for Traffic Safety. 2002.

driver-ZED.

2. Albus, J. 1997. 4-D/RCS: A Reference model

Architecture for Demo III, NISTIR 5994,

Gaithersburg, MD. March.

3. Brown, M. and Leveson, N. 1998. Modeling

Controller Tasks for Safety Analysis. Presented at the

Workshop on Human Error and System Development,

Seattle, April.

4. Dumas, J. S. & Redish, J. C. 1993. A practical

guide to usability testing. Norwood, NJ: Ablex

Publishing.

5. Endsley, M. R., 2000. Direct Measurement of

Situation Awareness: Validity and Use of SAGAT in

(Eds) Mica R. Endsley and Daniel J. Garland .

Situation Awareness Analysis and Measurement,

Lawrence Erlbaum Associates: Mahwah, New Jersey.

6. Endsley, M. R., 2000. Theoretical Underpinnings of

Situation Awareness: A Critical Review, in (Eds)

Mica R. Endsley and Daniel J. Garland . Situation

Awareness Analysis and Measurement. Lawrence

Erlbaum Associates, Publishers: Mahwah, NJ

7. Endsley, M. R. 1988. Design and evaluation for

situation awareness enhancement. In Proceedings of

the Human Factors Society 32nd Annual meeting (Vol.
1, pp 97-1010 Santa Monica, CA: Human Factors

Society.

8. Fong, T., Thorpe, C. and Bauer, C. 2001.

Collaboration, Dialogue, and Human-robot

Interaction, 10th International Symposium of Robotics

Research, November, Lorne, Victoria, Australia.

9. Gugerty, L. 1997. Situation Awareness during

Driving: Explicit and Implicit Knowledge in

Dynamic Spatial Memory. Journal of

Experimental Psychology: Applied. Vol. 3 (1),

42-66.

10. ISO 9241-11. 1998. Ergonomic requirements

for office work with visual display terminals

(VDT)s part 11 Guidance on usability.

11. Jones, D.G. and Endsley, M. R. 1996. Sources of

situation awareness errors in aviation. Aviation,

Space and Environmental Medicine 67(6), pp. 507-

512.

12. Leveson, N. de Villepin, M., Daouk, M. , Bellingham,

J., Srinivasan, J., Neogi, N., Bachelder, E., Pilon, N.,

and Flynn, G. 2001. A Safety and Human-Centered

Approach to Developing New Air Traffic

Management Tools. ATM 2001, Albuquerque NM,

December

13. Leveson, N. and Palmer, E. 1997. Designing

Automation to Reduce Operator Errors In the

Proceedings of Systems, Man, and Cybernetics

Conference, Oct.

14. Norman, D. 1986. Cognitive Engineering in Donald

Norman and Stephen Draper (Eds.) User-centered

design:new perspectives on human-computer

interaction, Erlbaum Associates: Hillsdale, N.J, 31-

62.

15. Picard, R. (1997), Affective Computing, MIT Press,

Cambridge.

16. The Software Usability Measurement Inventory.

(SUMI) Accessed July 12, 2002.

http://www.ucc.ie/hfrg/questionnaires/sumi/

17. Scholtz, J. (2002) Creating Synergistic CyberForces

in Alan C. Schultz and Lynne E. Parker (Eds.), MultiRobot Systems: From Swarms to Intelligent

Automata. Kluwer.
1

Lifelike Robotic Collaboration requires Lifelike Information Integration

Ron Cottam, Willy Ranson and Roger Vounckx

The Evolutionary Processing Group, IMEC Brussels

Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels

ron@cottam.be

Introduction

This paper is directed towards the definition

of a systemic characteristic suitable for the

“intelligent” core of a cooperative robot. A

major issue in developing cooperative

systems is the nature and degree of their

autonomy. It is rare (rather, impossible!) that

a communicated set of instructions for

carrying out a task will be exhaustively

complete, and with the passage of time the

instruction set and the task’s requirements

often diverge. Neither complete dependence

on control by a human “master”, nor

complete autonomy from control is suitable

or desirable within such a context, but it is

as yet unclear how a satisfactory contextdependent intermediate modus can be

developed which is conducive to

cooperation while not stifling any preexistent or nascent capability for creative

problem-solving. We believe that detailed

examination of a number of more general

aspects of system operation should predate

attempts at defining performance measures

for “intelligent” systems per se.

We wish to address four specific aspects of

the performance and visualization of large

information-processing networks. Firstly

(1), the character of information transport

through the networked connections of large

systems; secondly (2), the way in which

establishment of a hierarchical structure can

alleviate some of the resulting problems;

thirdly (3), the relationship between

rationality and emotion within such a

scheme; and fourthly (4), the manner in

which information is integrated and

visualized in human “thought” – which

brings us right back to our first chosen

systemic aspect (1).

Our starting point is the recognition that our

environment cannot be completely described

in any detailed manner by using a closed

formal system of rationality. The

representations we use for parts of our

surroundings are all to some extent

approximate in ways which relate to the

varying nature of their interactions at

different scales. Deviations from exact

correspondence between descriptions of an

entity at its different scales reside in the

inter-scalar interfaces, where interactions are

naturally complex and predictability is

limited. As a simple example of this

difficulty we can take a Boolean AND gate

with 2 inputs and 1 output. Conversion

between the 4 possible input states and 2

possible output states is controlled by the

logical rules which correspond to the predefined gate function, but even so the gate’s

operation is irreversible because information

is lost in the course of its operation.

Reversible state compression demands the

retention of all independent information, but

the only way an AND gate can be made

reversible is by recourse to non-local

memory... more of this later. In the

meantime we should simply note that

wherever there is cross-scale information

transport we can expect problems in the

application of closed formal rationality.

1. Large Systems

Looking into the heart of a system, we often

describe its pathways and their meeting

points by the simple picture of a network of
2

interlinked lines and nodes. An example is

the ball-and-stick models which are used to

describe molecules in chemistry. The balls

represent not only entities, but also

communicational nodes; the sticks represent

major communicational pathways. If all of

these pathways are similarly simply

specified, whether as globally existing or

globally absent, then the system is relatively

easy to describe, and it can be referred to as

being minimally complex. If, however, all of

the pathways are individually specified, then

the description is of necessity far more

complex. We must also decide very clearly

where we are looking at things from in

painting our picture, as in reality we only

have one point of view at one point in time.

A system can be described from an external

platform as a set of “order parameters”,

where accessible characteristics are purely

global ones, or from an internal point of

view, where accessible characteristics are

limited to local ones. It can also be

described from a quasi-external platform as

an externally viewed set of internal

relations. This latter picture corresponds to

just about every system analysis which we

carry out, but unfortunately in a system

which exhibits real scale effects internal

detail is inaccessible through the application

of formal rationality, or at the very least

only approximately (although conventional

science commonly presupposes this not to

be the case - and no, we have not forgotten

quantum mechanics here!).

So, our most usual “quasi-external” view is

self-contradictory in non-formally-rational

systems! We cannot equate the properties of

different scales of an even marginally nonformally-rational system, or even arbitrarily

change viewpoint within one and the same

scale level without addressing the associated

information transformations. Working from

the simplifying presupposition that nearby

viewing platforms will most resemble the

one we are currently standing on, we will try

to approach this problem by distinguishing

between directly and indirectly accessible

inter-elemental system connections 1. Direct

relationships are established by interelemental negotiation of both rationality and

context, directly and intimately between the

elements concerned, as shown in Figure 1.

Indirect relationships between elements are

those which of necessity pass through other

intermediate elements, which may then be

free to impose their own modifications on

forwarded information: choice of the

viewing platform imposes an asymmetry on

the resulting view. This problem makes an

appearance even in very simple systems: it

is the basis of the difficulty most usually

referred to as the Newtonian three-body

problem.

Direct relationships

established by

inter-elemental

negociation of

both rationality

and context

Indirect relationships

only accessible at the

viewing platform in a

form moderated by

the intermediate subsystemic elements

Viewing

platform

Figure 1. Direct and indirect relationships for a

3-body system with a chosen platform.

We can extend this distinction of direct and

indirect linkages to larger ball-and-stick

system models. Given 2 elements, we will

have 1 direct link and no indirect ones; with

3 elements there will be 2 direct links and 1

indirect; with 4 elements, 3 direct, 3

indirect; with 5 elements, 4 direct, 6 indirect,

and so on. As we move to larger randomlyconnected systems the relationship between

 

1 Clearly, the criticism we make here of “quasi-external”

viewpoints can equally be applied to the argument we are

ourselves presenting; but not, so far as we are aware, in a

manner which leads ultimately to its destruction: we are

trying to present a conceptual argument, and not a set of

formally related parameters.
3

direct and indirect links takes on a clear

form: the number of direct links goes up as

the number of elements N; the number of

indirect links goes up as the square of the

number of elements N

2

/2, as shown in

Figure 2 (note that this effect is to some

extent alleviated in scale-free networks [1],

but that it never entirely disappears).

1

1 10 100 1e3 1e4 1e5 1e6

10

100

1e3

1e4

1e5

1e6

1e7

1e8

1e9

1e10

1e11

1e12

Number of system elements

Number of direct & indirect links

Figure 2. The growth of direct and indirect links in a

large multi-element system.

The populations of direct and indirect

character co-evolve at very different rates.

For a system with one million direct links,

there are a possible million-million indirect

ones: for large systems indirect links are

likely to dominate massively, depending on

the complexity of the relationship between

local and global structures. The character we

can attribute to a complete system is

ultimately controlled by this direct/indirect

balance, as is the robustness of a network

with respect to reductions in interconnection

viability. The co-evolution of direct and

indirect relations in large systems leads

ultimately to two different independent

systemic characters. One corresponds to the

"normally scientific" view, which depends

on formally-rational cross-scale information

transport, the other corresponds to parts of

the holistic system which are inaccessible to

a "normally scientific" viewpoint, and which

are associated with the distributed nature of

indirect relations. Complete representation

of systemic interactions with an

environment requires the evaluation of both

of these characters. If we simply describe a

quasi-externally viewed system in terms of

the reductively specified interactions we risk

missing out the majority of the systemic

character! (except if we are dealing with

time-independent (clocked) artificial formal

"machines" such as idealized digital

computer systems). We believe that it is this

bifurcation of systemic character into dual

reductive and holistic parts, and the

difference in rational accessibility between

the two systemic characters, which has led

to the conventional split between body and

mind, where the body is automatically

associated with direct "scientific" biosystemic relations and the "mind" is

naturally "difficult" to understand within the

context of a "normally scientific" rationality

which presupposes that all essential

systemic aspects can be related to a single

localized platform.

2. Hierarchical Stepping Stones

Large systems exist between two extremes:

their unification as a single entity and the

assembly of their smallest components. In

any system, natural or artificial, where the

spread in scale between these extremes is

very large, intermediate self-supporting

descriptive levels emerge (or are created) to

facilitate transit across the entire scalespread of the system (e.g. stairs,

semiconductor inter-band traps, VLSI

design, stars in the universe, ...). This aspect

of large systems is so pervasive that we can

formulate a universal model for the resulting

hierarchical systems [2] (Figure 3), whose

properties are very closely tied in with the

arguments of Section 1 above. Here each

level of the coupled "model" hierarchy

represents one and the same entity, for

example a tree. Successive hierarchical

levels describe the entity with a

progressively changing degree of detail,
4

from the most elaborate to the most simple.

For example: ..., a tree as atoms, a tree as

molecules, a tree as cells, a tree as

branches, a tree "as itself", ... These

successively simpler representations of the

entity "contain" progressively more and

more sub-scalar detail which is hidden from

locally-scaled ecosystemic interaction.

N

onlocality

Localization

Newtonian

potential

wells

Dimensionally reducing

overall complexity

Reducing digital complexity

Increasing digital complexity

CONTAINING 

CONTAINED 

analog

complexity

digital

complexity

N

onlocality

Localization

Newtonian

potential

wells

Dimensionally reducing

overall complexity

Reducing digital complexity

Increasing digital complexity

CONTAINING 

CONTAINED 

analog

complexity

digital

complexity

Figure 3. A generalized complementary hierarchical

evolutionary system.

At a given level of the model hierarchy, the

relevant representation provides a partial

"en-closure" of sub-scalar detail, leading to

simplification of the relationships between

the entity and its locally-scaled environment

as a trade-off against representational

precision. Possibly the most important

aspect of natural hierarchies is just this, that

through the establishment of a series of

related progressively abstracted models of

low-level detail, high level "forms" are not

constrained to operate within the complex

temporal limitations of their low-level

interactions. The apparently most simple

model is the one which contains the most

hidden sub-scalar detail. Within a

computational paradigm this "hiding" of

sub-scalar detail makes it possible for

biological organisms to develop mechanisms

for multi-temporally-scaled reactions to

external stimuli, promoting survival in a

complex hostile environment. Our own

brains use a mechanism of this kind in the

context of "fear-learning" [3].

The model levels appear as Newtonian

potential wells in an otherwise nonNewtonian multi-dimensional and multiscaled phase space between nonlocality (on

the left) and localization (on the right) [4].

Moving between adjacent model levels, we

encounter both kinds of complexity (digital

and analog). Towards the left hand side of

the assembly models are related to a global

conservatism, and towards the right hand

side to a local causality: the assembly forms

a coupling structure between these two

aspects of nature. Movement through

different model levels towards the right

corresponds to a reduction in the containing

digital complexity of models and an increase

in the contained digital complexity. It is

worth adding that the Newtonian potential

wells which correspond to the different

model levels are regions of the universal

phase space where global and local effects

are self-consistent. This is a fundamental

aspect of the stability and computability of

nature. A major consequence is that, within

these Newtonian regions, local causal

interactions can proceed within limited

temporal scales without fear of contravening

a more global conservatism. However, the

viability of such a structure as a general

model of hierarchical systems depends on a

fine balance between the isolation or "enclosure" of adjacent levels with respect to

each other and the degree of intercorrelation which is necessary to support

their stability and that of the hierarchy: a

degree of inter-level correlatory information

transport is vital: too much is fatal!

3. Ecosystemic Rationality and

Emotion

The significance of the general hierarchical

assembly we propose is that it represents the

intermediate structure which we can
5

observe, not only between the

complementary pair of “extra-real”

nonlocality and “extra-real” perfect

localization, but between and internal to any

and all high-level "extra-real" complements.

We can find complementarities of this kind

everywhere as soon as we start looking for

them: quantum and classical descriptions;

organisms and their ecosystems; ... There is,

however, a further complementary twist to

the story. The hierarchical assembly we

propose dissociates into two distinct and

complementary systems of rationality.

Closure of rationality-pairs

Combined result

Figure 4. Interleaved “normal” and “complementary”

rationalities in a generalized birational entityecosystem representation associated in local

birational pairings.

One, of “normal” rationality, is associated

with the Newtonian potential well model

levels, and is reductive towards perfect

localization. The other, of “complementary”

rationality, is associated with the

intermediate complex regions, and is

reductive towards perfect delocalization [5].

The two systems are interleaved to give the

complete structure which we showed earlier

in Figure 3. It does not appear accidental

that this binary complementary structure

matches that of quantum-holographic

vector-reconstruction information

processing [6]. The result is a set of lowlevel local complements where a "normal"

(local) level is always associated with a

(local) ecosystemic "complement" level, as

shown in Figure 4. The summation of both

levels at any scale results in complete

systemic description: the ecosystemic level

provides a local but “normally inaccessible”

store for all of the information which is

eliminated through the “formally rational”

compression to the current level through the

stepping stones from the lowest level

description (see the argument about a

Boolean AND gate in Section 1).

Interestingly, and probably unavoidably, this

situation corresponds closely to the two sets

of different information which are invoked

during quantum error correction techniques.

“Normal”

rationality

“Complementary”

rationality

“Complementary”

cross-scale

“Normal”

cross-scale

Figure 6. Interleaved “normal” and “complementary”

multi-meta-scaled cross-scale rationality systems,

based on their interleaved “normal” and

“complementary” model assemblies.

We should remember that the Newtonian

hierarchy must be globally stabilized by

interactions right across the system between

all scales. Noting that by “understanding”

we usually imply “seeing the relationships

between the level we are talking about and

both higher and lower adjacent ones”, we

believe that inter-scalar interactions generate

first a “hyper-scale” descriptive level, and

then progressively a hyper-scalar hierarchy

superimposed on the initial scalar one. A

similar interaction for the “complex”

hierarchy results finally in a pair of hyperscale hierarchies, as shown in Figure 5.

It should be noted that at the highest hyperscale levels, correlation between the

developments of the two systems becomes

increasingly less relevant, as the two

structures progressively separate from each
6

other in association with the degree of their

individual abstraction. At the highest levels,

each of the hyper-scale manifestations is

clearly and distinctly independent, and

identifiable with a different kind of

rationality [7].

This corresponds to the conventional picture

we hold of our own thought processes; on

one side there is a “rationality” which

corresponds to scientific logic, on the other a

very “difficult-to-categorize” but effective

“irrationality” we refer to in terms of

“emotion”. The two are complementary:

failure of (rational) logic in pragmatic

situations can be circumnavigated by

recourse (irrational) emotion; failure of an

emotional approach can be rectified by the

application of logic. Neither one can

successfully exist alone: reason needs

emotion, emotion needs reason. Our

civilization habitually focuses on only one

of the pair: reason is everything;

emotionally, even, we feel we “should” be

logical!

4. Information Integration and

Consciousness

As we all (sic) were taught in school, the

mammal eye works to create an inverted

image of the viewed scene at the retina

(whose orientation is rectified by the brain).

Not so. There is no integrative capability at

the retina to perform this function. Any

“image” is generated much later, in the

various layers and centers of the brain: it

only “exists” within the (abstract)

unification of high-level consciousness, and

never in any “real” sense describable by

science. If you are viewing this text via a

computer screen or through the printed

word, the same constraint holds: it does not

exist at all as a unified entity outside your

brain or imagination, merely as a collection

of informational elements devoid of any

implicit organization, which was transmitted

through the Internet by a means which has

been formally (scientifically) structured

through the application of our imagination to

achieve our aim of reproducing arbitrary

“patterns” across space and time. The same

argument holds for the entirety of our

environment: it is all beyond representation

by (current) science. Not only does this

description apply to “objects”, it applies

equally well to any and every subject of

discussion.

Most particularly, in the current context of

interest, we should not expect to find that a

robot is capable of responding as a “black

box” to external stimulus on the basis of an

internally integrated “motive”, except where

that “motive” is completely relatable to its

formally unified degenerate representation –

namely the binary “it exists” or “it doesn’t”!

Such a quasi-hierarchical relationship (along

with any “algorithmic” complexity it

exhibits) is both nominally and functionally

trivial when compared to the styles of real

complexly-hierarchical operation which

characterize living organisms. We should

consequently beware of attributing

anthropomorphic integrative unification to

the internal workings of a “black box” robot

unless it is entirely predictable (a character

which corresponds exactly to the quasihierarchical condition referred to above), in

which case any resemblance of its actions to

those of a human is far from likely, to say

the least! So, can we describe and develop

robots “in our own image” by the

application of scientific techniques, or not?

Or does the problem which must be

addressed reside elsewhere?

Descriptions of the natural world and the

placing of robots within it which derive

from Evolutionary Natural Semiotics (ENS)

by way of signs are untouched by this

dilemma. In the context of ENS, any

formalized representation is derived

pragmatically (but less-than-algorithmically)
7

from its own scale-local grounding, and the

various scale-localizations are coupled

through and within the context of a globalto-and-from-local correlation which

mediates between the scale-local groundings

of a global grounding which it also creates

(!). “Reality” (in a scientific reductionist

sense) then refers to nothing more that the

lowest level of description which we can be

bothered to deal with, whether that be the

atomic level, super-strings, membranes,

people, trees or psychological states.

The descriptions which we habitually

employ for “systems” which are internally

structured in a network-like manner are

suitable if, again, the network structure is

amenable to complete (formal) integration

reductio ad adsurdum, but for a “system”

which exhibits “useful” complexity, they are

worthlessly simple or simplified. Within

ENS such representations (where we view

the “system” as a whole and simultaneously

its network-like internal structure) have the

character of (dubious) quasi-external

representations, whose (cautious)

applicability depends primarily on their

degree of representational equilibrium.

Much effort is currently being expended in

developing “internalist” models of

operational situations, rather than the

“externalist” ones said to be characteristic of

scientific endeavor. It is difficult to imagine,

however, how a uniquely internalist

representation of a “conscious” or aware

state can or could be useful: its existence

would imply not only the usually-quoted

criterion of lack of knowledge of the causes

of received stimuli, but also the complete

absence of any attempt to investigate or

imagine the origins of those stimuli. To

investigate in such a way requires the

construction of an (imagined) externalist

model of the situation: to not do so seems to

imply lifelessness! Consequently, it makes

more sense to describe living interactions as

a negotiation between internalist and

externalist representation, through a process

which mirrors the internal-external

negotiations which lie at the roots of human

consciousness and moderated autonomy [8].

Human consciousness is “singular”, in that it

only exists as an individual unified “entity”.

It is within the “sufficient interpretation”

and correlation of a multiplicity of

informational details that this text becomes

(nothing more... just “becomes” itself)

within our consciousness. Its existence

emerges from the process of integrative

interpretation (or interpretive integration, if

you prefer). This process, of the emergence

of the informal from the formal

(simplistically describable as emergence of

the analog from the digital), is the very

nature of living entities. It appears most

obviously, but not uniquely, in the

generation of analog protein folding from

the digital code of DNA. Science does not

merely omit this emergence from its

confines; it expels it, as being too difficult to

deal with. A lifelike nature is by definition

external to a scientific development!

Cooperative Intelligent Systems

So, how are we to develop “lifelike”

cooperative intelligent systems? Ultimately,

not through uniquely digital computation,

although this can provide effective

interfacing between a central information

processor and the outside world. This is

itself the manner in which our own brains

operate: a central really parallel processing

style, whose operation is most closely

related to the superposition-and-selection

mechanisms of quantum mechanical

interaction [9, 10], and integration and

differentiation of the results of this

processing to serve localized output and

input nodes. Currently this style of

integration and differentiation is far beyond

our constructional capabilities, and while a

prime target must be to investigate and
8

develop lifelike information integration, we

can nevertheless achieve useful preliminary

results if we couple our targets to the means

which are available, so long as we do not

fool ourselves into thinking that this will be

sufficient.

Two routes (at least) present themselves for

simulation of a desirable computational

structure. One depends on the axonite mesh

proposition of Karl Pribram [10], in which

the outputs from a large number of senderneurones are distributed in parallel as a

quasi-wave to a large number of receptorneurones, simulating the nonlocal

distribution of solutions which characterizes

quantum superposition, and storing the

associated information in a distributed

manner as a “collapse” of the wave at the

receptor dendrites. This strong contender

matches well with experiments carried out to

define the neural location of consciousness

[11]. The other route depends on the

mathematical distribution of information

across a large parallel processing network by

the recursive integration of Dempster-Shafer

probability into diffuse rationality [12]. It

remains, however, difficult to see how either

of these routes can provide a sufficiently

“intelligent” information integration to

generate any “real” consciousness in an

artificial structure, and long-term hopes

most probably rest with currently advancing

projects which aim to introduce less-thanformal computation into the hardware

elements of computer processing, rather than

with the simulation of parallel processing

via digital software.

References

[1] Barabási, A-L. “Scale-free Networks.” 4

th

International Conference on Complex

Systems, Nashua, USA, June 10-14, 2002.

Forthcoming publication.

[2] Cottam, R., Ranson, W. and Vounckx, R. “A

biologically consistent hierarchical

framework for self-referencing survivalist

computation.” In: Computing Anticipatory

Systems: Casys'99 3

rd Internat. Conf.:

Liège, Belgium, Ed. Dubois D. M., AIP

Conference Proceedings, 517, pp. 252-262,

2000.

[3] LeDoux, J. E. “Brain mechanisms of emotion

and emotional learning.” Current Opinion

Neurobiol., 2, 191-197, 1992.

[4] Cottam, R., Ranson, W. and Vounckx, R.

“Localization and nonlocality in

computation.” In: Information Processing

in Cells and Tissues, Eds. Holcombe, M. &

Paton, R., New York: Plenum Press, pp.

197-202, 1997.

[5] Cottam, R., Ranson, W. and Vounckx, R. “Life

as its own tool for survival.” In: Proc. 43rd

Meeting of the International Society for the

System Sciences, Eds. Allen, J. K., Hall, L.

W. & Wilby, J., Pacific Grove, USA: ISSS,

#99-168, pp. 1-12, 1999.

[6] Schempp, W. “The DNA replication of

molecular genetics: a control paradigm of

the quantum holographic information

channel.” 4th International Conference on

Information Processing in Cells and

Tissues, Leuven, Belgium, August 13-17,

2001. Forthcoming publication.

[7] Cottam, R., Ranson, W. and Vounckx, R. “A

diffuse biosemiotic model for cell-to-tissue

computational closure.” BioSystems, 55,

159-171, 2000.

[8] Cottam, R., Ranson, W. & Vounckx, R.

“Consciousness: the precursor to life?” In:

The Third German Workshop on Artificial

Life: Abstracting and Synthesizing the

Principles of Living Systems, Thun: Verlag

Harri Deutsch 1998, pp. 239-248, 1998.

[9] Cottam, R., Ranson, W. and Vounckx, R.

“Emergence: half a quantum jump?” Acta

Polytechnica Scandinavica. Ma 91, 12-19,

1998.

[10] Pribram, K. H. “Proposal for a quantummechanical basis for selective learning.” 4th

International Conference on Emergence,

Odense, Denmark, July 31 – August 4,

2000. Forthcoming publication.

[11] Tononi, G. and Edelman, G. M. “Consciousness

and complexity.” Science, 282, 5395, 1846-

1851, 1998.

[12] Cottam, R., Ranson, W. and Vounckx, R.

“Diffuse rationality in complex systems.”

Interjournal of Complex Systems, Art. 235,

1998.
Integrating Effective Planning Horizons into an Intelligent Systems

Architecture

J. P. Gunderson and L. F. Gunderson

Gunderson and Gunderson, Inc.

ABSTRACT

One metric of the intelligence of a system is its ability to

perform tasks in the face of dynamic changes to the

environment. This requires that an autonomous system be

capable of responding appropriately to such changes. One such

response is to effectively adapt the allocation of resources from

planning to execution.

By adapting the resource allocation between cognition

and execution, an intelligent system can produce shorter plans

more frequently in environments with high levels of

uncertainty, while producing longer, more complex plans when

the environment offers the opportunity to successfully execute

complex plans.

The effective planning horizon is developed from an

analysis of mathematical models of classic autonomous system

and from current research in cognitive science. Experimental

results are presented showing the performance gain from an

effective planning horizon based system.

From this simplified feedback control model, the

Effective Planning Horizon concept is extended to a more

realistic intelligent system architecture, and the concepts of

bounded rationality, intelligent heuristics and the judgment

analysis "lens model" are shown to be analogs of the effective

planning horizon.

Keywords: Effective Planning Horizon, Interleaved

Plan/Execution, Probabilistic Planning, Intelligent System.

1 INTRODUCTION

The world is not a perfect place, and if intelligent

systems are to function effectively they must be capable

of handling both the uncertainties of the world outside

themselves and those created by their own limitations.

One working definition of intelligence is the ability to

adapt to unexpected failure. Using this definition,

intelligence is defined by the ability of the system to

achieve goals in the face of failure. In order to do this,

the system must have some mechanism to detect that its

current method for satisfying its goals is insufficient, and

to select and apply new methods.

In this paper we present the concept of an effective

planning horizon as a requirement for intelligence. We

start by discussing the reasons that an effective planning

horizon is necessary. We place the model of an effective

planning horizon into a model of an intelligent system.

Then we develop a mathematical definition of the

effective planning horizon. This definition is tested in

the domain of a simulated Autonomous Underwater

Vehicle. In the final sections of the paper we present

arguments to show that the effective planning horizon

concept can easily be transformed into its analogs in

sensing, acting, and goal selection.

2 WHY AN EFFECTIVE PLANNING HORIZON

Why can’t an intelligent system just make one plan?

The answer to this lies in the balance between the

complexity of all environments and the limited

computational resources that any intelligent system can

apply to the problem.

It is obvious that all natural environments and all

but the simplest of synthetic environments are

stochastic. Even in a simple deterministic environment,

the observations made by an entity have a probability

distribution [1]. As demonstrated below, if the

environment is not deterministic, then the longer the

plan needed to meet the goal, the less likely it is to

succeed. However, even in an interleaved planningexecution system, too short a plan length will often lead

to sub-optimal plans.

Alternatively a system could generate all

conceivable plans in advance and maintain those in a

plan library [2]. But, in any realistic domain, it is not

possible for an intelligent system to completely explore

all the possible ramifications of a plan of action in a

realistic time [3]. In addition, to time constraints, there

are memory constraints. In fact for one nominally

intelligent system, only 7±2 objects can be held in

working memory at any one time [4]. This seriously

limits the number of plans that can be considered. This

has led to the concepts of “bounded rationality” in both

biologic [5] and machine based intelligence [6].

Thus, it appears that all intelligent systems (whether

biologic or electronic) must make trade-offs between

cognition and action. There are many mechanisms for

placing bounds on cognition, and in this paper we focus

on the concept of a planning horizon.

3 A MODEL OF AN INTELLIGENT SYSTEM

The model we use is based on the elementary loop of

functioning (E.L.F.) model of Meystel and Albus [6].

This model assigns responsibility for four critical tasks

to independent processes in an iterative - sense, model,
plan, act loop. We have extended this model by adding a

new process, validate. This new process has the ability

to compare the observed result of the previous cycle

with the expected result. It then updates the other

processes with the observed success or failure of the

cycle. This is show graphically in Figure 1.

This model allows for the representation of

intelligence as a semi-lattice. In this representation, the

intelligence of a system is defined by the ability of the

system to perform each of these processes. We utilize

goal satisfaction rates as a primary metric of

intelligence. The intelligence of the system is a 5-tuple

of the intelligence of each of the processes. The

intelligence of each of the processes is incommensurate

with the intelligence of the other processes, thus

establishing the partially ordered set needed for the

lattice.

Figure 1 Extended model of Intelligent Systems

4 INTELLIGENT SYSTEMS

Since the earliest work in machine intelligence, a

concern has been the computational burden required for

‘intelligent’ behavior. With the representation of

planning as search [8] came the exponential growth of

the number of world states explored with the increase in

search depth. The number of world states explored is a

measure of the computational complexity of the

planning process. It has been long accepted that as

uncertainty and dynamism increase in an environment,

the need for more reactive systems also becomes greater

[9]. In recent work, quantitative relationships have been

suggested for the impact of three types of domain

uncertainty on the ability of an autonomous system to

achieve goals.

This work classifies domain uncertainty into three

categories:

Sensor uncertainty;

Effector uncertainty; and,

Uncertainty caused by exogenous events.

These three types of uncertainty have been used in a

simulation system that measures the goal satisfaction of

a simulated maintenance robot under widely varying

levels of each of these types of uncertainty [10].

These results suggest that autonomous systems are

very sensitive to even low levels of uncertainty in the

environment, with overall goal satisfaction dropping to

75% with the introduction of 10% sensor errors. In

addition, this research discovered that the ability to retry

on failure was very effective at allowing the system to

maintain goal satisfaction in the face of all types of

uncertainty. The simulated robot was more robust (i.e.,

able to satisfy goals as the environment changed) when

it used different deliberation and execution mixes in

response to different levels of uncertainty in the

environment.

5 DEFINING THE EFFECTIVE PLANNING

HORIZON

Beginning with early two-player game models, it has

been common to place a limit on the search depth used

to explore options. This was often implemented as an

arbitrary limit on the ply-depth of an alpha-beta search

tree.

In two player games of perfect information, it is

implicitly assumed that if the computational resources

were sufficient, the ply-depth could be increased until a

perfect game analysis was performed. However, when

an intelligent system is playing against the real world,

conditions change. Since there is an inherent variability

in the world, perfect information is impossible. Under

these conditions, there appears to be a hard limit on the

planning horizon – a line beyond which planning is

useless, since it is impossible to know the state of the

world in which the planning is taking place.

We approach the idea of an effective planning

horizon from the perspective of the expected value of

additional planning. What is the value associated with

extending my plan, and what is the likelihood that the

world will be in the state I planned for?

E( plan) = Pr( plan) *Value(Plan) (1)

While solving the exact value of equation 1 may be

impossible, it is possible to make some reasonable

assumptions about the terms, and from these

assumptions draw some conclusions about the

characteristics of the expected value of planning in

dynamic and uncertain domains.

We make three assumptions about the actions in a

plan:

1. The action achieves some state that is needed to

achieve a goal (or goals);
2. Each action has some finite probability of

failure; and,

3. All the actions in the plan must succeed for the

plan to succeed.

Under these assumptions it is clear that a first

approximation of the probability of the plan succeeding

is:

= ∏stepsPr( plan) Pr(Step)

(2)

This can be further simplified by assuming that all

actions have some common probability of success,

reducing Equation 2 to:

length

Pr( plan) = Pr(step)

(3)

Approximating the value associated with a plan of given

length is more problematic, however for any given

domain and set of possible goals to achieve some useful

assumptions can be made.

First, let us envision the complete range of problems

that our intelligent system might be required to solve.

This range could be defined as the cross product of all

possible initial conditions with all possible final states.

Then let us imagine the plans that might be utilized to

transform the world as we find it into the world as we

desire it. Clearly, very few of the (Initial, Final) pairs

can be transformed by plans utilizing a single action. A

few more, perhaps, can be achieved with two-action

plans, more yet by three action plans, and so on. If we

further take the view that we wish the simplest, or most

likely, plan to satisfy our needs, then there will be few

pairs that require 1000 action plans, and fewer still that

require 2000 action plans. If we model the value of a

plan of size N as the number of (Initial, Final) pairs that

can be satisfied by a plan of length N or less, we get a

sigmoidal, or logistic curve. This curve can be closely

approximated with an equation like (3) below:

length

a

Value plan

1 2

( ) 1

+

= − (4)

Finally, combining the two terms into the expected value

of a plan of length N, for a given domain, and task

assignment, we find that the resulting curve has the

characteristic shape shown in Figure 2.

This finding is consistent with the analysis that, there

exists some point beyond which the costs associated

with planning exceed the benefits, which causes the

expected value to decrease. Under the assumptions made

above, we can conclude that there is a range of plans,

which provide the optimal benefit to the deliberative

system. This range is defined as the effective planning

horizon (EPH). If the planning depth is less than the

EPH, the probability that effective solutions will be

produced is too low. If the planning depth exceeds the

EPH, the probability that significant amounts of

computational resources will be expended planning for

situations that never occur is high. If those resources

had been applied more effectively, the rate of goal

satisfaction could increase. However, while this analysis

suggests that it is beneficial to adjust the planning

horizon to the domain and goals of the intelligent

system, it does not provide any mechanism to achieve

this adjustment.

Prediction of Expected Plan Value as a

function of plan length

0

0.5

1

1.5

2

13579

111315

1719

Plan Length

E(Plan)

Figure 2 - Expected Value of Planning

5.1 METHODS OF ADJUSTING THE PLANNING HORIZON

In this paper we investigate a method of selecting an

effective planning horizon, adapted directly from the

notion of placing a limit on the search depth. The

planning system used is an interleaved, probabilityaware, forward chaining planning and execution

environment. It is a general-purpose planner in that all

domain specific information is encoded as part of an

input file. Included in this domain specific information

are naive probabilities of success for the available

actions. The planning system uses this information to

produce the plan with the highest observed probability

of success.

These experiments were run using an autonomous

underwater vehicle domain. In this domain the planner is

embedded in the autonomous underwater vehicle

(AUV). The types of tasks assigned to the AUV include

autonomous navigation, the deployment of monitoring

devices within enemy controlled territory, and avoiding

or escaping detection by enemy Anti-Submarine

Warfare (ASW) surface vessels.

A typical task assignment would be to:

1. Begin from Home base, at the surface, carrying

a deployable monitor;
2. End at Home base, at the surface, with the

monitor deployed at a location just outside the

mouth of the enemy harbor; and,

3. By the way, don’t get detected by any enemy

ships while you do it.

If the system has perfect knowledge, and nothing

changes, this task is a straight forward planning exercise.

However, in this domain, the enemy ship moves while

the AUV is executing its mission and the AUV has

limited sensor range. This forces the planner to develop

a plan, begin to execute it, and respond to failures.

These failures could include finding the ASW ship in its

path or being detected during a move. More complex

failures might involve having to switch goals to escape

detection and then re-target the goal of dropping the

monitor, once it has lost its opponent.

The planning and execution system is interleaved,

so during the execution of actions, it receives feedback

about the actual state of the world (at least those parts

which it can sense) and it compares the actual state with

its expected state. As long as the expected state of the

world and the actual state agree, the system continues to

execute the planned actions. If the states do not match,

several options are available:

1. Continue with the current plan,

2. Develop a new plan to meet the current goals,

3. Re-evaluate the goals, and develop a plan to

meet the new goals, or

4. Do nothing, and hope that the world changes.

After this evaluation, a new action is issued, and the

process begins again. Since the planner is deeply

embedded in the execution loop, strict limits on

computation must be imposed to assure responsive

behavior of the system.

5.2 SEARCH DEPTH CONTROL

Search depth control has been used to limit computation

for as long as planning as search has been used. In

general there are two forms of this control: limiting the

exploration depth directly – such as ply-depth limits, or

plan length limits- and world set limits. In the latter case,

the planning system monitors the total number of

individual worlds explored, and at some pre-determined

limit, stops exploration. This has benefits when coupled

with search control rules which allow the planner to

‘focus attention’ on areas of the plan which might be

more fruitful. The experimental planning system used

here uses this world count form of search depth control.

To explore the impact of limiting search depth on

plan success rates the senario is analyzed under a range

of allowable search depths.

6 EXPERIMENTAL RESULTS

The experiments were designed to answer the question

“For a given domain, and a given task mix, at what point

does it stop paying to plan?” Using the analysis in

Section 5, we measure the marginal value of planning by

measuring the success rate of the plans produced, and

the computational complexity of producing the plan. All

experiments are run on the same interleaved planning

and execution system, coupled with an external

simulator of the domain.

6.1 EXPERIMENT SETUP

The domain used is that of the AUV, described above.

The AUV can navigate in a world of 18 possible

locations, at up to three depths for each location. Any

motion has a risk of causing the AUV to be detected,

however the deeper the AUV the lower the risk, In

addition to movement actions, the AUV can change

depth, deploy a monitor, and cause a tracking ASW

vessel to lose track by going deep and drifting. The

AUV carries one monitor, and the task requires the AUV

to deploy that monitor at a specific location and return to

base undetected.

Figure 3 - Autonomous Underwater Vehicle

domain

The task assigned to the AUV in these experiments

is to travel from home base (Location A) to Location P,

release a monitor, and return to Location L for pickup

(sub-lp10.prob). It has the additional requirement of

remaining undetected during the mission, and should it

be detected it must cause the enemy vessel to lose its

lock before being picked up (Figure 3). The AUV has

six general types of operators to apply:

Table 1 Relative probability of success for the

AUV domain. Failure can mean either the AUV is

detected, the operator fails, or both.

Operator Type A priori Probability

of Success

A

U

R T

G I

B C D

E

S

P Q

O

N

M

L

H

Shallow or Surface

Deep Water
Travel Surface 0.45

Travel Shallow 0.90

Travel Deep 0.95

Change Depth 0.99

Release Monitor 0.99

Drift (can cause opponent to lose

detection)

0.25

For the assigned task, the naïve probability of

success is approximately 0.46, if the plan with highest

probability is selected, or 0.00015 if the plan with the

minimum number of operators is selected. One

additional complicating factor is the behavior of the

ASW vessel. This unit moves around the map in a

random walk, and if it occupies a location it blocks the

AUV from traveling to that location. The location of the

ASW is initially unknown by the AUV, however, the

AUV can sense the presence of the enemy vessel if it

enters or leaves an adjacent location.

A single simulation consists of assigning a task,

executing the mission, and recording the number of

individual world states explored by the planner during

the execution of the mission and the success or failure of

the mission.

Two baseline simulations are initially run. In the

first the planning horizon required for success by a noninterleaved planning system is determined. Since a noninterleaved system must be able to produce a single plan

to meet all the mission goals, the required search depth

is much greater than that required by an interleaved

planner. In this experiment the planning horizon is

increased until the planning system reaches the point

where it can produce a complete, successful plan. This

established the minimum planning horizon for a noninterleaved planner. Since the planner is only allowed to

execute a single cycle, the success rates of this plan

cannot be effectively compared with the success rates of

the interleaved simulations.

The second baseline addresses this problem by

using the planning horizon established by the first

baseline. However, the planning/execution system is run

in interleaved mode, allowing the planner to correct

failures. This second baseline establishes the plan

success rate and the minimum number of world states

required for an interleaved planner operating without a

search depth limit.

The experimental simulations use an interleaved

planning system, with a range of planning horizons. In

all experiments a minimum of 100 independent

simulations are run at each planning horizon. In

interleave mode, the system is allowed to compete up to

five planning/execution cycles. The average number of

world states examined during the each simulation and

the average success rate of the mission are recorded.

6.2 DATA ANALYSIS

The data analysis is straightforward. We calculate the

value of the planning horizon (PH) as:

MegaWorldsExplored

SuccessRate

Value(PH ) = (5)

The value is the success rate over the work expended

(million worlds explored to achieve the success rate).

Baseline 1 established that it is necessary to

examine approximately 13,000 world states, and the

success probability is 0.14 for the non-interleaved

planning system. Due to the interaction with the ASW

vessel, this is significantly below the naïve probability

(0.46). In effect, most of the computational resources

used by the intelligent system are expended planning for

situations that simply do not occur during the execution.

For Baseline 1 the Value(PH) = 10.76.

Using the planning horizon established by Baseline

1, Baseline 2 achieves a success ratio of 0.74. However

it explores 66,000 world states, for a Value(PH) = 11.21.

Interestingly, even though the success ratio increased,

the number of world states explored did so

proportionally. This data is presented in Table 2.

Table 2 Baseline performance of complete

planning system. In Baseline1 a single iteration of the

planning system was allowed, in Baseline2 five

iterations were completed.

Test Worlds Success Rate Value(PH)

Baseline1 13,000 0.14 10.76

Baseline2 66,000 0.74 11.21

The experimental simulation was run with the range

of planning horizons shown in Table 3. Note that the

peak value of value(PH), 68.7 is approximately 6 times

the value achieved by Baseline 2. This shows that at the

effective planning horizon, the planning system is

achieving equivalent success rates with significantly less

computational cost.

Table 3 Table of the computational resources

required for varying planning horizons, the achieved

success rates, and the Value of the invested

computational resources.

Planning

Horizon

Worlds Success Rate Value(PH)

100 9,605 0.02 2.08

150 14,200 0.04 2.81

200 18,854 0.06 3.18

250 12,813 0.88 68.7
300 14,586 0.88 60.3

350 16,717 0.91 54.4

400 19,358 0.89 46.0

450 20,026 0.94 46.9

500 22,315 0.90 40.3

550 22,726 0.93 40.9

600 27,615 0.86 31.1

Sub-lp10 Problem

0

10

20

30

40

50

60

70

80

100200300400500600

Planning Horizon

Value(P

H)

Limited Planning Horizon

Baseline2

Figure 4 - Plotted Value of Effective Planning

Horizons from the AUV problem

In Figure 4, the value of the planning horizon, as

defined in Equation 5 is plotted against the search limit

imposed on the planner. The first three points are

characteristic of planning horizons that are too short to

allow the planning system to find successful plans. The

range from 250 – 350 represents the range of the EPH

where successful plans are being found with minimum

computational investment. The range above 350 is

characteristic of wasted resources, planning for

situations that never occur.

The effective planning horizon is the global

maximum of this curve. It should be noted, that this

figure closely resembles the analytic solution shown in

Figure 2. The “Baseline2” line is the value achieved by

Baseline 2.

It is clear that limiting the planning horizon in

interleaved intelligent systems allows the system to

achieve success rates which are comparable to complete

planning systems, at a fraction of the invested resources.

While the current data results from adjusting the search

limit utilized by the planning system, several other

mechanisms exist to adjust the planning horizon,

including waypoint based planning [11], and the use of

assumptive systems [12], which was successfully

applied during the 1994 AAAI Robot Contest.

7 EXTENSION TO A COMPLETE

INTELLIGENT SYSTEM MODEL

The preceding discussion describes the benefits of an

effective horizon in planning. However, this argument

can be extended to all of the other processes.

Sensing

Validation

Re-Goaling

Acting

In the next section we sketch some ways in which

effective horizons might be achieved for these processes.

Much of this discussion will be derived from work done

on biologic systems.

7.1 EFFECTIVE SENSING HORIZONS

One model of the sensing process can be taken from the

work of Egon Brunswik [1]. In this lens model, shown

graphically in Figure 5, perception can be modeled as a

linear weighted sum:

=

=

n

i

s i i

y w x

1

(6)

Where ys = the judgment of the condition of target s

ye = the actual environmental condition of

the target

n = the total number of cues available to the judgment

maker

xi = value of cue i, where i goes from 1 to n

wi = the weighting of cue i

ye

Distal

(Environmental)

Variable

x1

x2

xn

.

.

.

Ecological

Validity

ys

Subject

Judgment

Cue

Utilization

Validity

Achievement

w1

w2

wn

Figure 5 - The Lens Model of Egon Brunswik

For this process the effective sensing horizon will

be a balance between using so few cues that the

perception in invalid and using so many cues that the

cognitive system is overloaded.

7.2 EFFECTIVE VALIDATING HORIZONS

Validation of the successful completion of a cycle is

critical if the intelligent system is to adapt to unexpected
outcomes. However, given limited sensing ranges, and

long distance effects, it can be resource intensive to do

complete validation. In some cases, the system may have

to develop and achieve complete new sub-goals to

accomplish this phase. Yet, the costs of proceeding

without validation can be extreme. Thus any intelligent

system must strike a balance between the costs and

benefits of validating the world state.

In addition, when things go wrong, an intelligent

system must assess the probable causes of the failure,

and make improvements or the same ineffective actions

will occur again and again. If the mission failed because

a sensor was incorrectly interpreted causing a specific

action to fail, it is not intelligent to expend resources

improving the action. However, correctly attributing the

source of a failure is extremely resource intensive, and

limits must be placed on its application.

7.3 EFFECTIVE GOAL RE-PRIORITIZATION HORIZONS

Intelligent systems do not pursue the same goals at all

times. Consider the mother alligator, who under normal

circumstances has a hair trigger bite reflex. Yet, this

same alligator will carefully carry its young about

cradled in those same jaws. Clearly, this intelligent

system is re-prioritizing its goals in response to changes

in its environment.

However, this ability to re-goal comes at a cost. Regoaling requires the system to use cognitive resources

that could be applied in other ways, and errors in goal

prioritization can reduce the success probabilities of the

system. This is the same dynamic tension that exists in

the selection of an EPH.

7.4 EFFECTIVE ACTION HORIZONS

Just as all biologic intelligent systems have limits on

cognition, there are limits on both the number and range

of actions the system can undertake to achieve a goal,

and the quality of those actions. In biologic systems

“Use it or lose it” applies, yet limited time is available

for practice. With mechanical systems one tends to

think of the range of actions that are available as fixed at

the time of construction, and the quality (probability of

success) as constant. However, as bearings wear out, and

rubber gripping fingers age, the ability of the system to

meet its goals degrades, until new resources are applied.

While self-repair is beyond the current capabilities of

most machine-based intelligent systems, they can

possess the ability to update the reliability of actions to

reflect changes in the system itself.

8 CONCLUSIONS

The focus of this paper is on utilizing limited

computational resources to improve the effectiveness of

intelligent systems. Drawing on research from existing

biological intelligent systems and current research into

machine-based intelligent systems, an analytic definition

of the Effective Planning Horizon was developed.

Success probability in stochastic domains was

presented as the key metric for evaluating intelligent

systems, and several secondary measures including the

expected value of planning, and plan value as a function

of computational load were introduced as supporting

concepts for the Effective Planning Horizon.

The EPH is a measure of the dynamic and variable

nature of the environment, and the goals and limitations

of the intelligent system. From these characteristics it is

possible to establish a horizon beyond which additional

planning is ineffective.

A simulated domain was presented which is

representative of the types of tasks we expect deployed

intelligent systems to undertake, and using a planning

and execution system designed for these demanding

domains, experimental data was collected. The data

collected demonstrates that it is possible to achieve the

same levels of success as the computationally expensive

complete exploration of a plan space, at significantly

lower cost. This lowered cost translates into lowered

demands on the system, or increased speed of execution

by the intelligent system, which can be crucial design

requirements of future embedded intelligent systems.

Finally, the same principles which led to the

formalization of the Effective Planning Horizon were

applied to the other processes in feedback control theory

based intelligent systems, suggesting other mechanisms

that can be used to improve goal satisfaction by

intelligent systems.

9 REFERENCES

[1] Brunswik, E. Perception and the Representative

Design of Psychological Experiments, University

of California Press, Berkeley, CA, 1956.

[2] Schopper, M. J. “Universal Plans for Robots in

Unpredictable Environments,” Proceedings of the

10

th

International Joint Conference on Artificial

Intelligence, 1987, pp. 1039 – 1046.

[3] Ginsberg, M. L. “Universal Planning: An (Almost)

Universally Bad Idea,” Artificial Intelligence, vol.

10, num. 4, pp. 40-44, 1989.

[4] Miller, G. “The Magical Number Seven, Plus or

Minus Two: Some Limits on Our Capacity for

Processing Information,” The Psychological

Review, vol. 63, pp. 81-97, 1956.

[5] Gigerenzer, G. and Todd, P. M. Simple Heuristics

That Make Us Smart,” Oxford: Oxford University

Press, 1999, ch. 1, pp. 3-34.

[6] Bratman, M. E. Israel, D. J. and Pollack, M. E.

“Plans and Resource-Bounded Practical
Reasoning,” Computational Intelligence, vol. 4,

num. 4, pp. 349-355, 1988

[7] Meystel, A.M. and Albus, J.S. Intelligent Systems

Architecture, Design, and Control. N.Y.: Wiley

Interscience, 2002, ch. 2, pp 90-92

[8] Newell, A. and Simon, H. A., Computers and

Thought, Fiegenbaum, E. A. and Feldman, J., eds.,

N.Y.:McGraw-Hill, 1963,

[9] Brooks, R. A. “A Robust Layered Control System

for a Mobile Robot,” IEEE Journal of Robotics and

Automation, vol. RA-2(1), pp. 14-23, 1986

[10] Gunderson, J. P. and Martin, W. N. “The effects of

Uncertainty on Plan Success in a Simulated

Maintenance Robot Domain,” Journal of Theoretic

and Experimental Artificial Intelligence, vol. 12,

pp153-164, 2000

[11] Gunderson, J. P. and Martin, W. N. “Applications

of Data-Mining to Sub-Plan Selection in

Autonomous Planning Systems,” in Proceedings of

the IEEE Systems, Man and Cybernetics

Conference, 2001, pp 1459-1464.

[12] Nourbakhsh, I. R., Interleaving Planning and

Execution for Autonomous Robots, Boston: Kluwer

Academic Publishers, 1997, ch. 7, pp 99-107.
1

MOBILE ROBOT POSE TRACKING FOR PERFORMANCE ANALYSIS

Alan M. Lytle*, Kamel S. Saidi, William C. Stone

Materials and Construction Research Division

Building and Fire Research Laboratory

Michael Shneier

Intelligent Systems Division

Manufacturing Engineering Laboratory

National Institute of Standards and Technology

*Building 226, Room B144, MS-8611, Gaithersburg, Maryland 20899

alan.lytle@nist.gov, ph. 301-975-6048

ABSTRACT: The NIST Construction Metrology and Automation Group, in cooperation with

the NIST Intelligent Systems Division, is researching robotic structural steel placement as part of

a project to develop an Automated Steel Construction Testbed. The initial phase of this project

centers on tracking a six degree-of-freedom robotic crane with a laser-based site measurement

system to provide position feedback for autonomous steel assembly. Follow-on efforts will use a

high-resolution LADAR scanner co-registered with the site measurement system to provide

world model data. The combination of these two advanced metrology systems provides an

opportunity for testing performance characteristics of mobile intelligent systems.

KEYWORDS: intelligent control, intelligent systems, performance metrics, 3-D coordinate

measurement systems

1.0 INTRODUCTION

The NIST Construction Metrology and

Automation Group (CMAG) is developing a

robotic structural steel placement system for the

testing and validation of advanced tools,

methodologies, and standards for automated steel

construction. This system, the first phase of the

NIST Automated Steel Construction Testbed

(ASCT), will demonstrate autonomous “pick and

place” assembly of structural steel components

using a six degree-of-freedom (DOF) robotic

crane and an external pose estimator [1].

The base platform is the NIST RoboCrane,

which is an inverted Stewart platform parallel

link manipulator. The pose (position and

orientation) estimator, a laser-based site

measurement system (SMS), provides absolute

cartesian position feedback to RoboCrane’s

Real-Time Control System (RCS) for trajectory

planning and dynamic control. A world map of

the robot work volume including target

components and obstacles is created prior to

operations using the SMS.

Future work on the ASCT will include

incorporating a high-resolution LADAR (laser

detection and ranging) system to create and

update the world map, thus eliminating the

requirement for a human operator to digitize the

scene with the SMS prior to operations. The

LADAR scans will be meshed and then

registered to the SMS.
2

The use of an independent, external

measurement system to track a mobile robot

within an environment mapped and registered to

the tracking system provides interesting

opportunities for conducting mobile robot

performance analysis. In a future experiment,

the NIST Intelligent Systems Division (ISD), in

cooperation with CMAG, will use this

combination of metrology instruments to test the

navigation and sensing systems on board the

NIST robotic HMMWV test vehicle as part of

the U.S. Army’s Experimental Unmanned

Ground Vehicle System (DEMO III) program.

This paper discusses the development of the

robotic structural steel placement system and a

proposed test methodology for the DEMO III

sensor package performance analysis.

2.0 ASCT

2.1 Operational Concept

Four laser transmitters are positioned on the site

perimeter to illuminate the work volume of

RoboCrane with reference beams (Figure 1). A

digital model of the construction plane including

any obstacles is created using the SMS digitizing

wand.

Figure 1: Illuminating the Work Site with the

SMS.

This world model is then updated with the

positions of the as-built structure and the target

beam through a process of automatic part

identification (barcode), part model database

access, and part fiducial point measurement

using the mobile digitizing wand (Figure 2).

Figure 2: Measuring component locations with

the SMS digitizing wand.

The current pose of RoboCrane is measured

from onboard SMS sensors, and the path planner

calculates the required transformations for beam

pickup and delivery. RoboCrane then executes

the movements (Figure 3).

Figure 3: Steel beam placement with

RoboCrane.

2.2 RoboCrane

RoboCrane is an innovative cable-driven

manipulator invented by the NIST Intelligent

Systems Division and further developed and

adapted for specialized applications over a period

of several years [2,3,4,5]. The basic RoboCrane
3

is an inverted Stewart platform parallel link

manipulator with cables and winches serving as

the links and actuators, respectively. The

moveable platform, or “lower triangle,” is

kinematically constrained by maintaining tension

in all six cables that terminate in pairs at the

vertices of the “upper triangle.” This

arrangement provides improved load stability

over traditional lift systems and enables 6 DOF

payload control.

The version of RoboCrane used in the ASCT

project is the Tetrahedral Robotic Apparatus

(TETRA) (Figure 4). In the TETRA

configuration, all winches, amplifiers, and motor

controllers are located on the moveable platform.

The upper triangle only provides the three tie

points for the TETRA cables, allowing the

device to be retrofitted to existing overhead lift

mechanisms.

Figure 4: RoboCrane – TETRA Configuration

2.3 Site Measurement System

The SMS uses commercially available positioning

technology (3D-I) produced by Arc Second, Inc.

in the Constellation and Vulcan product

families. (3D-I, Constellation and Vulcan are

registered trademarks of Arc Second, Inc.)

These systems use stationary, active-beacon

laser transmitters and mobile receivers to provide

millimeter-level position data.

2.3.1 SMS Description

Both Constellation and Vulcan systems use

eye-safe laser transmitters to triangulate the

position of a tuned optical detector. Each

transmitter emits two rotating, fanned laser

beams and a timing pulse. Elevation is

calculated from the time difference between fan

strikes. Azimuth is referenced from the timing

pulse. The field of view of each transmitter is

approximately 290° in azimuth and +/- 30° in

elevation/declination. The recommended

minimum and maximum operating ranges from

each transmitter are 5 m and 50 m, respectively.

Line-of-sight to at least two transmitters must be

maintained to calculate position. The

Constellation receivers each track up to four

transmitters and wirelessly transmit timing

information to a base computer for position

calculation. The Vulcan system is a selfcontained digitizing tool with two optical

receivers on a rigid pole. A vector projection

along the line formed by the two optical

detectors allows 3-D measurement of the tool

tip. Vulcan can track only two transmitters at

one time; however, the transmitter selection can

be manually switched between any of the four

available. Recovery of positional data following

momentary signal blockage takes approximately

one second.

2.3.2 Prior 3D-I / Mobile Robot Integration

Early efforts to use this laser technology for

mobile robot navigation showed that although the

system was capable of guiding a mobile robot

[6], its use was restricted due to loss of track at

relatively low vehicle speeds [7]. Upgrades to
4

the positioning technology continued and a

successful combination of indoor 2-D map

creation and autonomous navigation was

demonstrated in a research project at the

Rochester Institute of Technology [8] (Figure 5).

Subsequently, a single receiver Constellation

system was installed on an autonomous lawn

mower at the Carnegie Mellon University Field

Robotics Center and provided positional

reference in a large outdoor setting [9].

Figure 5: SMS Receivers on the RIT/SME

“RC” Robot.

2.3.3 The SMS on RoboCrane

Three SMS receivers are mounted on

RoboCrane at the vertices of the lower triangle

(Figure 6). The receiver locations are registered

to the manipulator during the initial setup process

in the local SMS coordinate frame. For

convenience, all measurements are calculated in

the local SMS coordinate frame, though if

required, mapping to an existing world coordinate

frame could be accomplished. Receiver timing

signals and diagnostic data are wirelessly

transmitted to a base station computer running

Arc Second’s proprietary position calculation

software. Position and SMS diagnostic

information is polled at approximately 7 Hz using

a NIST-developed data communications

application. Position data from the three

receivers are used to calculate RoboCrane’s

pose. Diagnostic data such as number of visible

transmitters, excess signal noise or multipath

reflections are also provided for each position

calculation and is used to assess the quality of

individual position fixes.

Figure 6: RoboCrane with SMS.

3.0 PERFORMANCE METRIC

ANALYSIS OF THE DEMO III SENSOR

SUITE

The NIST Intelligent Systems Division has

provided research services developing control

system architectures, advanced sensor systems,

and standards to achieve autonomous mobility

for various DOD unmanned ground vehicle

programs including the Army Experimental

Unmanned Ground Vehicle System (DEMO III)

[10]. In a future experiment, high-resolution

LADAR scanning and the SMS will be used to

quantify the performance characteristics of the

DEMO III sensor suite.

The test vehicle used by NIST is an Army

HMMWV instrumented with a number of

sensors (Figure 7). These include a LADAR

range sensor that returns a 90° by 20° range

image, a pair of color cameras for stereo

imaging, a color camera, a line scan LADAR,

and a wide-angle panoramic image mosaic

obtained by stitching images from three color

cameras. The vehicle also has an inertial

navigation system (INS) and a GPS receiver.

An artifact field consisting of variable-sized box

structures distributed within a nominal 30 m x 30

m area of clear terrain will be created on the
5

NIST grounds. CMAG will create a “groundtruth” digital map of the artifact field and then

track the HMMWV test vehicle within that field.

Figure 7: NIST Robotic HMMWV Test

Vehicle.

The artifact field will be scanned from several

viewing angles using the using a high-resolution

LADAR. The LADAR frame data will then be

post-processed and manually meshed to create a

multi-view map of the entire test area (Figure 8).

The artifact field will also be measured using the

SMS digitizing tool to create control points to

overlay the SMS tracking data on the LADAR

map.

Figure 8: Sample High-Resolution Multi-Scan

LADAR Image (Traffic Intersection).

Three SMS receivers will be mounted on the

HMMWV test vehicle. These receivers will be

mounted high on the vehicle to ensure each

receiver has line-of-sight to at least two of the

four transmitters located on the perimeter of the

test field during tracking. Fiducial points on the

vehicle will be measured to map the SMS

receiver locations to the vehicle coordinate

system. The HMMWV test vehicle will then

traverse the test field at slow speeds and the

track data will be written at  ̃ 7 Hz. Track data

from each receiver will then be post-processed

to provide time-stamped vehicle pose (Figure 9).

Figure 9: Test Vehicle on Artifact Field.

The 6 DOF vehicle track measured by the SMS

will then be overlaid on the LADAR map. This

will then be compared to the world map created

by the DEMO III sensor suite to evaluate its

performance. The performance of individual

sensors and the performance of the system as a

whole will be measured.

There are a number of measurement goals for

the sensor characterization experiment. One is to

see how accurately each of the sensors can

measure the real terrain and the sizes, shapes,

and positions of objects on it. Others are to see

how well the sensors are registered and how

well their positions in the vehicle's coordinate

system can be determined. While the sizes of

the objects within the test range can be

measured accurately, the terrain is not entirely

flat, and the high resolution LADAR will be used

to determine ground truth. The SMS will be used
6

to determine how well the INS system works.

Tables 1 through 5 provide manufacturer’s

performance data for the measurement systems

that will be used in the test.

4.0 CONCLUSION

The combination of high-resolution LADAR

imaging with a laser-based SMS provides an

opportunity to create a “ground-truth” digital

model of an autonomous vehicle’s environment

and then track the vehicle as it traverses, senses,

and models that environment. The “groundtruth” map, complete with time-stamped vehicle

pose, can then be compared to the platform’s

world model to quantify the performance of the

on board navigation and sensing suite. A future

experiment using the NIST HMMWV test

vehicle on an artifact site will evaluate this

method of mobile robot performance metric

analysis.

5.0 REFERENCES

1. Lytle, A. M., Saidi, K. S. and Stone, W. C.,

“Development of a Robotic Structural Steel

Placement System.” Proc., 19th

International Symposium on Automation

and Robotics in Construction, Washington,

D.C., September 23-25, 2002.

2. Stewart, D., “A Platform with Six Degrees

of Freedom,” Proc. of the Inst. of

Mechanical Engineering, Volume 180(15),

Part I:371-386, 1965-1966.

3. Albus, J. S., Bostelman, R. V., Dagalakis, N.

G., “The NIST ROBOCRANE, A Robot

Crane”, Journal of Robotic Systems, July

1992.

4. Bostelman, R., Albus, J., Dagalakis, N.,

Jacoff, A., “RoboCrane Project: An

Advanced Concept for Large Scale

Manufacturing,” Proc, Association for

Unmanned Vehicles Systems

International, Orlando, FL, July 1996.

5. Bostelman, R. , Albus, J., Stone, W.,

“Toward Next-Generation Construction

Machines.” Proc. Of American Nuclear

Society 9th International Topical Meeting

on Robotics and Remote Systems, Seattle,

WA, March 4-8, 2001.

6. Beliveau, Y. J., Fithian, J. E., and

Deisenroth, M. P., “Autonomous Vehicle

Navigation with Real-Time 3-D Laser Based

Positioning for Construction.” Automation in

Construction, (5) 1996.

7. Lytle, A. M., “Development of a Remotely

Piloted Vehicle (RPV) with Real-Time

Position Measurement (RtPM) for

Hazardous Waste Site Characterization.”

Master’s Thesis, Virginia Tech, Blacksburg,

VA, 1994.

8. Rochester Institute of Technology, Society

of Manufacturing Engineers (Student

Chapter 18), Robot Construction Project,

2001.

www.rit.edu/~smewww/project/rc/rc.html

9. Batavia P. H., Roth S. A., Singh S.,

“Autonomous Coverage Operations In SemiStructured Outdoor Environments.” Proc.,

IROS, Lausanne, Oct. 2002.

10. Shoemaker, C. M. and Bornstein, J. A.,

“The Demo3 UGV Program: A Testbed for

Autonomous Navigation Research.” Proc.,

IEEE International Symposium on

Intelligent Control. Gaithersburg, MD

1998.

Table 1: GDRS Area-Scan Ladar Specifications (HMMWV Test Vehicle).

Property Specification

8 laser beams, 1 rotating mirror With 8 facets

Scan resolution 32 lines x ?180 pixels

Scan coverage 20 x 90

Angular resolution 0.658 x 0.5

Maximum frame rate 60 scans/s but 30 scans/s
7

Range 5 m to 70m (vertical surface)

Range resolution/standard uncertainty ±7.6 cm / 15 cm

Data measurement rate Range: 345,600 measurements/s

Day/Night Operation Range Independent of ambient light

Table 2: Real-Time Performance of Applanix POS LV 420 Intertial Navigation Unit

(HMMWV Test Vehicle).

POS LV 420-RT GPS Outage Duration (minutes)

(using DGPS) 0 min 1 min 3 min 5 min 10 min 20 min

X, Y Position (m) 1.0 1.5 1.75 2.0 2.5 3.5

Z Vertical Position (m) 1.5 to 2.0 2.0 2.0 2.0 2.5 3.0

Roll & Pitch (deg) 0.02 0.02 0.02 0.02 0.02 0.02

True Heading (deg) 0.02 0.02 0.02 0.04 0.06 0.10 0.20

Table 3: Performance of Applanix LV 420 with post-processing (HMMWV Test Vehicle).

POS LV 420 GPS Outage Duration (minutes)

(post processed) 0 min 1 min 3 min 5 min 10 min 20 min

X, Y Position (m) 0.02 0.12 0.40 0.75 1.5 2.5

Z Vertical Position (m) 0.03 0.15 0.50 0.65 1.0 2.0

Roll & Pitch (deg) 0.005 0.005 0.005 0.007 0.007 0.09

True Heading (deg) 0.02 0.02 0.02 0.03 0.035 0.035

Table 4: Manufacturer’s Specifications - Riegl LMS Z210 Ladar.

Property Specification

Scan coverage 80° x 330°

Angular stepwidth 0.072° to 0.36°

Angular readout accuracy 0.036°

Frame scan rate 1 °/sec to 15 °/sec

Minimum Range 2 m

Maximum Range 350 m (25 mm resolution, natural target)

Range resolution 25 mm or 50 mm, selectable

Standard uncertainty Resolution + Distance error of = ±20 ppm

Table 5: Manufacturer’s Specifications - Constellation and Vulcan.

Property Specification

Vulcan Constellation

Transmitter coverage 60° x 290° 60° x 290°

Transmitters required for position calculation 2 2

Maximum number of observable transmitters 2 4

Nominal Laser rotation rate 40 Hz to 50 Hz 40 Hz to 50 Hz

Minimum Range 5 m 5 m

Maximum Range 50 m 35 m

Angular resolution =90 μrad =90 μrad

Data rate (position calculation) 10 Hz 7 Hz
8

Standard uncertainty (Instrument x,y,z – U66) ± (1 + D/10000) [mm]

D (mm) - distance between

transmitters

10*RSS (250, D*8) [μm]

RSS- Root Sum Squares

RSS(A,B) = {A2+B2}

1/2

D (m) –distance from farthest

transmitter

No point data averaging

DISCLAIMER: Certain commercial equipment, instruments, or materials are identified in this report in order to specify the

experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the

National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are

necessarily the best available for the purpose.
An Uncertainty Propagation Architecture for the Localization Problem

Arnaud CLERENTIN, Laurent DELAHOCHE, Eric BRASSART, Cyril CAUCHOIS

CREA - Centre de Robotique, d'Electrotechnique et d'Automatique

IUT, département Informatique, Avenue des Facultés, 80000 Amiens – France

{Arnaud.Clerentin, Laurent.Delahoche}@u-picardie.fr

ABSTRACT

In this article, a dynamic localization method based on multi target

tracking is presented. The originality of this method is its capability

to manage and propagate uncertainties during the localization

process. This multi-level uncertainty propagation stage is based on

the use of the Dempster-Shafer theory. The perception system we

use is composed of an omnidirectional vision system and a

panoramic range finder. It enables to treat complementary and

redundant data and thus to construct a robust sensorial model which

integrates an important number of significant primitives. Based on

this model, we treat the problem of maintaining a matching and

propagating uncertainties on each matched primitive in order to

obtain a global uncertainty about the robot configuration.

KEYWORDS : mobile robot localization, omnidirectional

vision, uncertainty management, multi target tracking

1 INTRODUCTION

Localization is a fundamental problem in mobile robotics.

Mobile robots have to be able to locate themselves in their

environment in order to accomplish their tasks. In order to act

in a robust way and to increase the reliability in operation, the

robot should consider data as uncertain and all decision

should be made using data of an appropriate level of

certainty. The localization method presented in this paper has

the particularity to integrate uncertainty quantification and to

propagate low-level data uncertainties along the localization

process. The goal is to obtain a global uncertainty about the

robot localization. In this purpose, we propose an architecture

which allows to manage and propagate uncertainty. The

Dempster-Shafer theory [8] is the key tool of this

architecture. Indeed, this formalism enables to easily treat

uncertainty since it permits to attribute mass not only on

single hypothesis, but also on union of hypothesis. We can

thus express ignorance. This is the main difference with

Bayesian theory.

Localization methods can be classified as being relative

(based on the use of proprioceptive data) or absolute (based

on the use of exteroceptive data). Absolute methods consist in

determining the robot’s position with the only use of

exteroceptive data: the robot’s configuration is calculated in

the environment reference without using previous

information [1][5]. But the problem of this kind of

localization is linked to the matching stage between the

sensorial model and the theoretical map of the environment:

this stage can be highly combinative and non robust in

connection with multiple solutions, for example with

symmetrical environments. In order to increase the reliability

and decrease the computation time of these methods, the use

of multi target tracking can be interesting. In the case of the

localization problem, multi target tracking can be seen as a

propagation of an initial matching. This paradigm is

abundantly treated is the literature, for example by Bar

Shalom [4]. The methods generally used are probabilistic

ones and the two main are JPDAF (Joint Probabilistic Data

Association Filter) [4] and MHT (Multiple Hypothesis

Tracker) [3]. But these two methods have some drawbacks.

They need to know the false alarm rate. The JPDAF takes

into account a fixed number of targets and doesn’t initialize

new tracks. The MHT has combinatorial problems.

Therefore, we propose in this paper a multi target tracking

method for the localization problem based on the DempsterShafer theory used in a framework called extended open

world [7]. Since this method uses DS theory, it naturally

integrates our uncertainty propagation architecture and

enables to manage an uncertainty for each target. It allows

also to treat the problem of target apparition and momentarily

disappearance.

This paper is organized as follow. In a first part, we present

our perception system. Then we deal with the target

classification stage based on the exploitation of the

complementary and redundancy of the data provided by our

perception system. Section 4 explains our target tracking

algorithm. The paper ends with experimental results

presentation.

2 THE OMNIDIRECTIONAL PERCEPTION

SYSTEM

Our original perception system uses two omnidirectional

sensors in cooperation: the omnidirectional vision system

SYCLOP and a panoramic range finder system [10] (Fig. 1).

These two sensors have been developed and used

independently within our laboratory. The range finder system

is an active vision sensor [10]. It allows to obtain a robust

omnidirectional range finding sensorial model. The interest of

this system is on the one hand its low cost and on the other

hand its robustness facing a high incidence angle. The

SYCLOP system [2], similar to the COPIS one [14], is

composed of a conic mirror and a CCD camera. It enables us

to get radial straight lines which characterize angles of every
vertical object such as, for example, doors, corners, edges

(Figure 2)...

Figure 1: The perception system and the prototype we built.

Figure 2: Principle of the omnidirectional sensor SYCLOP

This two omnidirectional sensors association is interesting

since it permits to manage some complementary and

redundant information within the same sensorial model :

 With SYCLOP, the radial straight lines give the angular

position of every vertical object, but the information of

depth cannot be achieved in one acquisition: it is not

possible to differentiate with this only sensor the notion

of opening (corridor, opening of door...) and the notion

of vertical object (closed door, radiator,...) (Figure 3).

 The range-finder system, following a segmentation stage

[10], permits to exploit sensorial primitives that are

segments. In this case we have the notion of depth, but it

is impossible to differentiate two vertical objects placed

in the same alignment: two closed doors placed on the

same wall. It misses the notion of angle that will be

provided by the SYCLOP system.

So this association enables to construct a highly descriptive

sensorial model, richer than the models obtained with each

sensor individually (Figure 3).

Figure 3: Principle of the omnidirectional sensorial cooperation.

3 SENSORIAL MODEL CONSTRUCTION

3.1- Segment primitives determination and associated

uncertainty computation

The final primitives of the sensorial model are segments.

They are determined with two types of approaches [6]:

 data complementarily approach. The first case concerns

the data detected by SYCLOP but not by the depth

sensor. In this case, the treatment consists in cutting up

segments gotten with the range finder in subsegments

according to the radial straight lines of the vision system

(case 2 of Figure 4). The second case concerns the data

detected by the depth sensor but not by SYCLOP. In this

case, the breakpoint gotten by the Duda Hart

segmentation method is directly considered (case 3 of

Figure 4).

 data redundancy approach. The redundant aspect is

characterized by the detection of a vertical landmark with

the two sensors (Figure 4). In this case, we use the radial

straight line to determine the segment endpoint. Indeed,

we consider that the SYCLOP sensor has a better angular

precision.

Figure 4: The different cases of the cooperation algorithm.

After the determination of the sensorial model, we compute

the reliability, i.e. the uncertainty of each segment. This stage

is preponderant for the multi-target tracking stage presented

in this article. In this purpose, we take into account five

criteria.

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 very

sure.

The third criteria is the segment density of points. As shown

in [10], a major drawback of this kind of triangulation depth

sensor is a decreasing resolution with an 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).

The fourth criteria analyzes if the segment is detected by

one or by the two sensors. The worst case occurs when the

two extremities of the segment are detected only by the laser

range finder (case 1 of Figure 5). The best case occurs when

the two extremities are detected by the two sensors (case 5 of

Figure 5). Between these extreme cases, we can distinguish

three others cases [6].
case 1

S1

Range finding

segments

Radial

line

Radial

line

case 5

Figure 5 : the two extreme cases of the fourth criteria.

The last criteria concerns a gray level curve 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 values from

this line. If the 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 6).

(gray level)

(degree)

Figure 6: an example of gray level curve concerning the fifth criteria.

The fusion of these five criteria is made thanks to the

Dempster-Shafer theory [8]. Our frame of discernment (FOD)

is composed of two elements: "YES" and "NO"

corresponding to the assertions “The segment exists” and “the

segment does not exist”. We show on Figure 7 one of the five

BPAs which integrates the ignorance quantification. The

Dempster rule of combination [8] gives mseg(YES), mseg(NO)

and mseg(Θ). The segment uncertainty is denoted by this set

mass mseg. We have studied on 50 experimental sensed map

the conflict between these five criteria. Indeed, these five

criteria are redundant and conflict can arise. Experimentally,

we have noticed that it is not important (mean conflict =

0.13). This shows that these criteria are pertinent and lead to a

consensual decision. But, in certain cases, the conflict is high.

So, we have decided to work in an open world context [12],

i.e. not to normalize. Indeed, in case of high conflict, as

Zadeh showed, a normalization can lead to an aberration. On

the other hand, a non normalization gives us a precious

indication about the conflict between the five criteria. So we

report the conflict (the mass on ∅) to the ignorance Θ.

m1: mean distance of the range findings points from the 

segment

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 2,5 6

(cm )

B.P.A

B.P.A .ofYES

B.P.A .of{YES,NO}

B.P.A .ofNO

Figure 7: B.P.A of the first classification criterion ({YES,NO}=Θ)

3.2- High level primitives determination

The next stage consists in determining high semantic level

primitives which are: "corner", "edges", "wall" and "other"

(Figure 8). The "other" class characterizes landmarks which

are not "corner", "edges", "wall".

Figure 8: High level semantic primitives.

We use the high semantic level entities "corner", "edge" and

"other" because the azimuth angle of the junction of the two

segments is a "strong" angle (important existence

probability): it is a discriminating angle in connection with

the occultation problem. The angles of a segment primitive

can be false angle due to occultation.

As in the previous step, we compute an uncertainty linked

to each primitive. This uncertainty is determined by

propagating the segment(s) uncertainty(ies) computed on the

previous step. We reach this aim in two stages. Firstly, we

determine the type of the primitive. Secondly, we compute its

uncertainty.

We determine the primitive type by fusing two criteria

(Figure 9). The first criteria m1 is the angle α between two

consecutive segments S1 and S2 of the sensorial model. The

second criteria m2 is the minimal distance d between the

"junction" extremities of the two segments S1 and S2. The

belief functions of these two criteria are discussed on [9].

Figure 9: angle criteria and minimal distance criteria.

The fusion is made thanks to the Dempster rule of

combination and enables to obtain the mass set mtype by

fusing m1 and m2. The two criteria taken into account are

complementary, so there is no conflict. The taken decision is

the one which has the maximal credibility.

The second stage consists in computing the high level

primitive uncertainty. In this purpose, we take into account

two uncertainties:

 the uncertainty of the segment(s) composing the

primitive

 the uncertainty on the primitive type computed on the

first stage.

gray level curve of

segment 8. The

approximation is good,

this segment is sure

gray level curve of segment

18. The approximation is

bad, a landmark has not

been detected by Syclop
The FOD is composed of two elements: YES and NO

corresponding to the assertions “YES, the primitive exists”

and “NO, the primitive does not exist”. The first criteria

m1prim is linked to the segment uncertainty coefficient mseg.

For a primitive wall: 











Θ = Θ

=

=

( ) ( )

( ) ( )

( ) ( )

1

1

1

prim seg

prim seg

prim seg

m m

m NO m NO

m YES m YES

For a primitive corner, edge or other composed of two

segments S1 et S2:















Θ = Θ ⊕ Θ

= ⊕

= ⊕

( ) ( ) ( )

( ) ( ) ( )

( ) ( ) ( )

1 2

1

1 2

1

1 2

1

S

seg

S

prim seg

S

seg

S

prim seg

S

seg

S

prim seg

m m m

m NO m NO m NO

m YES m YES m YES

The second criteria m2prim for a primitive of type T is

computed according to the following rules:



















Θ = = −

= =

=

∑

∑

⊆Θ ≠ ∩ ≠∅

⊆Θ ∩ =∅

( ) ( ) ( ) ( )

( ) ( ) ( )

( ) ( )

, ,

2

,

2

2

m m A Pl T Cr T

m NO m A Cr T

m YES Cr T

A A T A T

prim type

A A T

prim type

prim

The mass for the YES is equal to the belief we have on T,

i.e. the credibility of T Cr(T). The mass for the NO is equal to

the disbelief on T, i.e. the mass which is not on T. The mass

for Θ represents the uncertainty about T, i.e. the mass which

is on focal elements which include T. Doing this, we respect

the constraint that the mass sum must be equal to 1.

By fusing the two criteria m1prim et m2prim, we obtain the

uncertainty of the primitive through mprim(YES), mprim(NO)

and mprim(Θ). Doing this, we estimate the uncertainty of the

high level primitives by propagation of the segments

uncertainty.

So, at the end of this step, we have four lists of primitives (a

list of corners, of edges, etc.) with an associated uncertainty

for each primitive through the set mass mprim. This uncertainty

includes the uncertainty about the type of the primitive and

the uncertainty about the existence (the reliability) of the

segments which compose the primitive.

4 DYNAMIC LOCALIZATION METHOD

4.1 - Algorithm

Our localization method is based on a tracking of high

semantic level primitives: we propagate the matching made at

an acquisition n on an acquisition n+1. So, the problem to

solve is the following: propagation of an initial matching on

the acquisitions realized during the robot’s displacement. The

initial matching is done in manual way or with the absolute

localization method presented in [6]. Then we try to pursue

the matching. To confirm a matching propagation, we must

before generate a prediction which will be compared to the

observations. So we have developed a prediction system

based on a linear extrapolation of the azimuth angle curves of

the high level primitives (on experimental results, we can

note that the angles variation is locally linear): we generate a

predictive observation vector composed of angles got by

linear extrapolation (Figure 11). For example, if we examine

the evolution of the landmark angles Θ1, Θ2 and Θ3 (Figure

10), we remark that the curve can be extrapolated in order to

have a prediction Θ4p. If a matching is done between Θ4p

and an angle observation, the track is propagated.

Figure 10: evolution of landmark angles.

Figure 11: principle of angular measures extrapolation.

Our prediction heuristic is robust since it is based on angle

curves of high level primitives: the extrapolated measures

correspond to “strong” angles whose evolution curves can not

confuse themselves because they do not suffer of occultation

phenomena.

At this level, the problem is to match for each type of

primitive the p angular observations obtained at the

acquisition t with the q predictions. These q predictions are

computed from the Nmes last observations. To reach this aim,

we use the Dempster-Shafer theory in the framework of

extended open word [7] because of the introduction in the

FOD of an element noted * which represents all the

hypothesis which are not modeled.

For each prediction Qj (j∈[1,q]), we apply the following

algorithm.

 The frame of discernment Θ is composed of:

 the p observations (Pi means “the prediction Qj is

matched with the observation Pi”)

 and the element * which means “the prediction Qj

cannot be matched with one of the p observations”.

So:Θ = {P1,P2 ,...Pp ,*}

 The matching criterion is the angular difference between

observation Pi and prediction Qj (Figure 11).

 For each observation Pi, we compute :

 ( ) mi Pi the mass associated with the proposition “Pi is

matched with Qj”.

 ( ) mi Pi the mass associated with the proposition “Pi is

not matched with Qj”.

 ( ) mi Θi the mass represented the ignorance concerning

the observation Pi.

The BPAs are shown on Figure 12.
m1: difference in angle between prediction and 

observation

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 15 30 (degrees)

BPA

m(Pi)

m(Pi U Pi)

m(Pi)

Figure 12: BPA of the matching criterion.

After the treatment of all the Pi observations, we have p

triplets : ( ) m1 P1 ( ) m1 P1 ( ) m1 Θ1

( ) m2 P2 ( ) m2 P2 ( ) m2 Θ2

...

( ) mp Pp ( ) mp Pp ( ) mp Θp

We fuse these triplets and we get mmatch(P1), mmatch(P2),...,

mmatch(Pp), mmatch(*) and mmatch(Θ) by using the condensed

formulas obtained by Gruyer in [11].

 The final decision is the one which has the maximal BPA.

Experimentally we can note that ambiguities can appear

after this step, but only on the segment primitives: a segment

observation Pi can be matched with two segment predictions

Qi, this case is impossible in the reality. So we use, like

Gruyer, only for this class of primitives, a traditional

assignment Hungarian algorithm to match one observation

with one prediction [11].

Finally, this matching method enables us to easily manage

primitive appearances and disappearances:

 If an element Pi of the FOD cannot be matched, Pi is an

appeared primitive and a track can be initialized.

 If a prediction Qj is matched with *, the track is

temporarily or definitively lost.

4.2 - Management of an appearance

From the position computed with the matched primitives,

we try to match the appeared primitives with the primitives of

the theoretical map which is composed of four lists (a list of

wall, a list of corner, etc.). In other words, we try to initiate a

new track. We have to distinguish two cases: primitives wall

and the other primitives.

For each appeared primitive wall, we have considered three

correspondence tests applied on all the theoretical wall

primitives [13] :

 angular difference α between the two segments,

 difference in length (Ls-Lm) between the two segments,

 distance D between the centers of the two segments.

Figure 13: The three matching criteria.

The fusion of these three treatments is made thanks to the

Dempster-Shafer theory. Our FOD is composed of two

elements: YES and NO corresponding to those assertions :

"Yes, we can match the two walls" and "No, we can not

match the two walls". For each criterion, we have determined

the BPAs m1, m2 , m3 (see Figure 14 for an example of BPA).

m1: difference in angle between the two segments

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 7,5 15

(degrees)

B.P.A.

B.P.A. of YES

B.P.A. of {YES,NO}

B.P.A. of NO

Figure 14: :basic probability assignments of the first matching criteria

({YES,NO}=Θ)

We can then perform the combination calculation thanks to

the Dempster-Shafer rules without renormalization [12] in

order to get a mass set mm. The non-renormalization gives us

a precious indication about the conflict. Generally, we have

experimentally noticed that this conflict is null, but, in a few

cases, it can be high. This occurs for example when we

examine two parallel walls. So, if the conflict k is superior to

0.7, we think this value is too high and we take a prudent

decision: we don't match the two segments. If k<0.7, we can

take a decision and the segments are matched if BPA for the

YES mm(YES) is superior to the BPA for the NO mm(NO).

For each other primitive (corner, edge, other), we consider

two correspondence tests (Figure 15):

 The difference between the robot-sensorial primitive

distance dseg and the robot-map primitive distance dmap.

 The difference between the sensed primitives angle Θseg

and the theoretical primitive angle Θmap.

 

Figure 15: The two matching criteria

As the previous case, our FOD is composed of two

elements: YES and NO. The fusion is realized according to

the same strategy as the wall primitives.

4.3 - Management of a disappearance

As we will see in paragraph 4.4, if a matching is not

propagated, the track is not immediately cancelled but its

uncertainty increases. If this uncertainty becomes too high,

we definitively cancel this track.

4.4 - Track uncertainty management

For each track, we manage an associated uncertainty with

the help of the Dempster-Shafer theory. Our FOD for each

track is composed of two elements: “YES” and “NO” which

mean “Yes, the track exists” and “No, the track does not

exist”. Two stages are managed:
Uncertainty initialization stage. In the case of a primitive

appearance, the initial uncertainty mtrack 0 at time 0 takes into

account the uncertainty of the primitive mprim (paragraph 3.2)

and the uncertainty of the first matching mm (paragraph 4.2).

So, the two criteria are:













Θ = Θ

=

=

( ) ( )

( ) ( )

( ) ( )

1

1

1

prim

prim

prim

m m

m NO m NO

m YES m YES

m1 takes into account the uncertainty of the primitive.

 m2 which takes into account the uncertainty of the first

matching through mm(YES) [9].

We have noticed on experimental results that conflict can

appear, but it occurs in only one case: a good matching of an

unreliable primitive. Our strategy to manage this conflict is to

reduce the weight of the primitive uncertainty m1 by an

operation of discounting [8]:

if m1(YES)>0 :











Θ = − −

= × −

= × −

( ) 1 ( ) ( )

( ) ( ) (1 ( ))

( ) ( ) (1 ( ))

1 1 1

1 1 1

1 1 1

m m YES m NO

m NO m NO m NO

m YES m YES m NO

α α α

α

α

We obtain mtrack 0 (YES), mtrack 0 (NO) and mtrack 0 (Θ) by

merging mα

1 and m2 using the Dempster combination rule.

If mtrack 0 (NO) > mtrack 0 (YES), then we consider that the

uncertainty is too high and we don’t initialize the track. This

taking into account of the primitive uncertainty enables us not

to work with all the primitives, we privilege the “robust” and

reliable primitives.

Uncertainty propagation stage. Then, if the matching can

be propagated, the track uncertainty is updated by taking into

account:

 In relation with time t-1: the track uncertainty at time t-1

 In relation with time t: the primitive uncertainty and the

matching uncertainty at time t.

Let be mtrack t-1 the mass set of the track at time t-1. The

three set masses m1, m2 and m3 concerning the 3 criteria are:

 









Θ = Θ

=

=

( ) ( )

( ) ( )

( ) ( )

1

1

1

prim

prim

prim

m m

m NO m NO

m YES m YES

m1 takes into account the

primitive uncertainty at time t.

 m2 takes into account the uncertainty of the matching at

time t mmatch computed on paragraph 4.1 [9].

















Θ = Θ

=

=

−

−

−

( ) ( )

( ) ( )

( ) ( )

3 1

3 1

3 1

track t

track t

track t

m m

m NO m NO

m YES m YES

m3 takes into account the track

uncertainty at time t-1.

We adopt the strategy described in the uncertainty

initialization stage: a high conflict only appears when we

realize a good matching of an unreliable primitive and we

discount the mass of the primitive uncertainty.

mtrack t(YES), mtrack t(NO) and mtrack t(Θ) are obtained by

fusing m1, m2 and m3.

If the matching is not propagated, the uncertainty of the

track increases. In this case, we fix the BPA mmatch as follow:











Θ =

=

=

( ) 0.8

( ) 0.2

( ) 0

match

match

match

m

m NO

m YES

This mass set has been determined experimentally in order

to obtain a regular increase of the track uncertainty. So, if

mtrack t-1 is the BPAs of the track at time t-1, we update the

uncertainty mtrack t using the Dempster rule of combination

with mtrack t-1 and mmatch.

If mtrack t(NO)>mtrack t(YES), then we consider that the track

uncertainty is too high and the track is definitively lost. Here

again, the taking into account of the primitive uncertainty

enables us to privilege the tracks with reliable primitives.

Transition between a primitive wall and an other

primitive. We manage in our system the transition between

the primitives corner, edge, other and the primitives wall. An

example of such transition is shown on Figure 16. At time t,

the robot detects one of the two faces of the edge and this

face is classified as a wall primitive. At time t+1, the two

edge faces are visible from the robot and it detects an edge

primitive. The wall detected at time t and the edge detected at

time t+1 correspond to the same track. So we use the

uncertainty of the wall track at time t to initiate the

uncertainty of the edge track at time t+1.

Figure 16: an example of transition wall → edge

4.5 - Localization uncertainty

The last step of our uncertainty propagation architecture is

to compute the uncertainty of the robot localization. This aim

is reached with the help of the Demspter Shafer theory and

the FOD is composed of the two elements YES and NO

corresponding to the assertions “Yes, the localization is

correct” and “No, the localization is not correct”. We take

into account p+2 criteria.

The first criterion is the number of high level primitives

used to localize the robot. Indeed, if we use few primitives,

the localization is not reliable.

The second criterion is a ratio concerning the number of

detected primitives and the number of matched primitives.

Indeed, if we detect a lot of primitives but if we match only a

little few primitives, this can mean that a problem occurs in

the classification process or in the matching process. So the

localization may be unreliable.

number of detected primitives

number of matched primitives ratio =

The last p criteria are the uncertainty of the p tracks

managed by the robot, i.e. the p mass sets mtrack t computed in
the paragraph 4.4. If the tracks are uncertain, the localization

will be uncertain. Since we merge an important number of

mass sets and since the Dempster operator is not idempotent,

we apply an operation of discounting on the p mass sets mtrack

t. The discounting coefficient is different if the mass set mtrack

t concerns a wall primitive or an other primitive (corner, edge

and other): we privilege in the fusion process the “strong”

primitives corner, edge and other.

These p+2 criteria are fused according to the Dempster rule

and we obtain a mass set ml which quantifies the localization

uncertainty. This uncertainty is directly issued of the

uncertainties of the low-level data which have been

propagated, as shown on Figure 17.

Figure 17 : Uncertainties propagation during the localization process.

5 EXPERIMENTAL RESULTS

We have tested our algorithm on several acquisitions

made in an indoor environment (the end of a corridor shown

Figure 18 whose theoretical map in possession of the robot is

on Figure 19). The omnidirectional acquisitions and the

localization algorithm are computed in a Pentium PC located

on our mobile robot.

On Figure 18, we show an example of high level

primitives sensed map. We report on Table 1 the different

masses about the primitives uncertainty.

Figure 18: high level primitive map and the real environment.

On a two paths made in the corridor by our robot mobile

SARAH, we can note on 42 acquisitions made every 30 cm

that the robot’s position is determined correctly with a good

precision: the mean error is equal to 13 cm in position and 3

degrees in orientation (Figure 19).

On Figure 18, we represent only the tracked landmarks of

the second trajectory. We can remark that our tracking is

robust and efficient: among all the important number

sensorial primitives, the tracked primitives are correctly

identified and the tracks are generally never lost until the

landmarks become invisible from the robot. We show on

Figure 21 the uncertainty evolution of edge 6. The initial

matching is done manually and the mass set is set as follow:

mtrack 0(YES)= mtrack 0(Θ)=0.5, mtrack 0(NO)=0. The landmark

is tracked until acquisition 7, so the BPA for YES mtrack

t(YES) increases. Then, it becomes invisible from the robot.

So the BPA for YES decreases until acquisition 12 where the

BPA for NO is superior to the BPA for YES. So the track is

definitively lost.

Primitive number Type m(YES) m(NO) m(θ)

1 Edge 0.72 0.08 0.20

2 Corner 0.64 0.16 0.20

3 Wall 0.64 0 0.36

4 Wall 0.91 0 0.09

5 Wall 0.07 0 0.93

6 Wall 0.83 0 0.17

7 Edge 0.33 0.25 0.42

8 Edge 0.70 0.03 0.27

9 Wall 0.50 0 0.50

10 Wall 0.11 0 0.89

11 Wall 0.47 0 0.45

12 Corner 0.78 0.03 0.18

Table 1: uncertainties of the primitive model.

0

100

200

300

400

500

600

700

Edge 6

Edge 13

Segment 10 Segment 11

Segment 20

Edge 12

Segment 48

Figure 19: theoretical map and localization results ('+'=real position,

'x'=computed position).

20 22 24 26 28 30 32 34 36 38 40

0

50

100

150

200

250

300

350

Segment 20

Edge 13

Segment 10

Segment 48

Edge 12

Angle

(degree)

Acquisition numbe

Figure 20: the tracked landmarks (‘+’=corner,point=edge, segment=wall)

6 2

1

5 4 3

7

8

9

11

10

12
y g

0

0,2

0,4

0,6

0,8

1

1 2 3 4 5 6 7 8 9 10 11 12

acquisition number

B

P

A

m track t (YES) m track t(NO) m track t ({YES,NO})

Figure 21: uncertainty evolution of landmark edge 6.

Finally, we see on Figure 16 an example of a double

transition edge-segment-edge. Until acquisition 32, the two

faces of the edge are visible. On acquisition 33, one face is

visible, so a primitive segment is detected but we don’t

initiate a new track since this segment belong to the edge

previously tracked. On acquisition 37, the robot can detect a

new edge (edge 12 on Figure 19) that contains the segment.

As the previous case, we don’t initiate a new track but we

prolong the current track.

Uncertainty evolution of edge 13/segment 48/edge12

0

0,2

0,4

0,6

0,8

1

1,2

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

acquisition number

BPA

m track t(YES) m track t(NO) m track t({YES, NO})

Figure 22: uncertainty evolution of edge 13/segment 48/edge 12.

On Figure 23, we show the evolution of the localization

uncertainty. The uncertainties of the first acquisitions are

weak : the number of tracked primitives is high. Then this

number decreases, so the uncertainty increases, i.e. ml(YES)

decreases. After acquisition 46, several new tracks are

initialized and the uncertainty becomes weak.

0

0,2

0,4

0,6

0,8

1

2225283134374043464952

Acquisition number

BPA

m(YES)

m(NO)

m({YES,NO})

Figure 23 : localization uncertainty.

6 CONCLUSION

In this paper, we have studied and implemented a multi

level uncertainty propagation architecture. After a multi

criteria fusion stage based on the use of the Dempster-Shafer

theory, we obtain a multi-valued sensorial map which permits

to quantify the credibility of the high level primitives. These

primitives are then used in our dynamic localization method

based on a propagation of an initial matching This method

solves two problems linked to the multi target tracking: the

propagation of an uncertainty concerning the landmark tracks

and the treatment of the apparition and momentary

disappearance of a track. This multi-target tracking paradigm

has been tested on several robot’s path in a large structured

indoor environment and has provided good results concerning

the matching maintaining and the preciseness of the

localization. An extension of this work could concern the

linear angular prediction which is mono criteria. A prediction

based on a dynamic model or combining 'proprioceptive'

could be used and would allow the system to operate on fast

moving vehicles.

7 REFERENCES

[1] S. Thrun, "Finding Landmarks for Mobile Robot Navigation",

IEEE Int. Conf. on Robotics and Automation, May 1998, pp.

958-963.

[2] L. Delahoche, B. Marhic, C. Pégard, P. Vasseur - “A

navigation system based on an omnidirectional vision sensor”,

Proc. of Int. Conf. on Intelligent Robots and Systems, France,

Sept. 1997.

[3] D.B. Reid, “An algorithm for tracking multiple target”, IEEE

trans. on automatic control, vol. AC24, n. 6, pp. 843-844,

Dec. 89

[4] Y. Bar-Shalom, « Multitarget-Multisensor tracking :

Applications and Advances vol.II. », Artech House, 1992.

[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] A. Clérentin, L. Delahoche, E. Brassart, “A multi sensor

cooperative approach for the mobile robot localization

problem”, Proc. of the workshop Performance metrics for

Intelligent Systems. PERMIS2000, Washington, Aug 00

[7] C. Royère, D. Gruyer, V. Cherfaoui, « Data association with

believe theory », 3rd int. conf. on information fusion FUSION

2000, Paris, France, 2000

[8] G.A. Shafer, “A mathematical theory of evidence”, Princeton :

university press, 1976.

[9] A. Clérentin, L. Delahoche, E. Brassart “Omnidirectional

sensors cooperation for multi-target tracking”, IEEE Int. Conf.

on Multisensor Fusion and Integration MFI 2001, Aug. 01,

Germany, pp. 335-340

[10] A. Clérentin, C. Pégard, C. Drocourt “Environment

exploration using an active vision sensor”, Proc. IEEE Int.

Conf. on Intelligent Robots and Systems (IROS’99), Korea,

Oct. 1999.

[11] D. Gruyer, V. Berge-Cherfaoui, “Matching and decision for

vehicle tracking in road situation”, Proc. IEEE Int. Conf. on

Intelligent Robots and Systems (IROS’99), Korea ,Oct. 1999.

[12] Smets Ph., « The nature of the unnormalized beliefs

encountered in the transferable belief model », Proceedings of

the 8th Conference on Uncertainty in Artificial Intelligence,

San Mateo, Californie, (1992) 292-297

[13] Crowley J., « Navigation for an intelligent mobile robot » -

IEEE Journal on Robotics and Automation, Vol. RA-1, n°1,

pp. 31-41, March 1985

[14] Y. Yagi, Y. Nishizawa, M. Yachida, “Map-based navigation

for a mobile robot with omnidirectional image sensor COPIS”,

IEEE Trans. on Rob. and Aut. Vol 11, pp. 634-648, Oct. 1995.
Derived Performance Metrics and Measurements

Compared to Field Experience for the PackBot

Tom Frost, Christopher Norman, Scott Pratt, Brian Yamauchi

iRobot Corporation, 22 McGrath Highway, Suite 6, Somerville, MA 02143

Bill McBride, Greg Peri

South West Research Institute, San Antonio, Texas

ABSTRACT

Preparing an Unmanned Ground Vehicle for missions

in abusive, dangerous environments requires suitable

tests to define the system capabilities. Well-designed

performance metrics can provide the government and

industry designers with an understanding of how the

system should be used in the field and how the system

can be improved. This paper describes the metrics and

measurements used for testing the PackBot system and

compares those metrics and measurements against

insights gained in field experience.

I. INTRODUCTION

The PackBot System, shown in Figure 1, is a

ruggedized, man-transportable Unmanned Ground

Vehicle system that provides a remote presence in

dangerous locations. Reconnaissance and manipulation

of a remote environment can be performed while the

operator remains safe. The PackBot was designed

primarily for Mobile Operations in Urban Terrain

(MOUT). Designs for situational awareness

capabilities and obstacles

negotiation capabilities are driven

by anticipated urban combat

scenarios. The MOUT

requirements have resulted in a

system that also has many

applications in other dangerous

combat operations and Urban

Search and Rescue (USAR)

operations.

To prepare the PackBot for the

hazardous duties that it will

encounter in the real world, the

PackBot has been tested at iRobot’s

facility and at the Small Robotic

Vehicle Test Bed at the South West

Research Institute, SwRI, at San

Antonio, Texas. The PackBot

system was also exercised at the Army simulated

MOUT city at Fort Drum, New York. The predecessor

to the PackBot system was tested at a testing ground in

Rockville, Maryland. These tests helped to understand

and measure the performance of the PackBot before the

system was used in the real world. The real world

scenario where the PackBot was employed was

operations at the World Trade Center (WTC) disaster

site. These experiences at the WTC disaster site held

many lessons for future Unmanned Ground Vehicle use

in the real world. This paper will look at the derived

performance metrics and compare these to field

experience.

II. PACKBOT HISTORY

The PackBot was developed by iRobot Corporation

under DARPA’s Tactical Mobile Robotics (TMR)

program. iRobot began developing mobile robots for

the TMR program in 1997 by creating a proof-ofconcept robotic platform designed for MOUT called the

Urban Robot. The Urban Robot was designed to be a

small man-portable surveillance robot that could

negotiate urban terrain. Under

subsequent DARPA contracts, the

Urban Robot platform became

continually more rugged and

sophisticated. The later versions

incorporated sonar and infrared

rangefinders with a more powerful

CPU for onboard sensor

processing. Under the PackBot

contract, iRobot was assigned to

develop a more robust and

complete robotic system capable

of surviving the abuses of real

operations. Developing the

PackBot became one of the

primary focuses of the TMR

program and iRobot Corporation

was selected as the system

integrator.

Figure 1. PackBot System.
III. MOBILITY

The PackBot is a tracked robot vehicle designed for use

in both urban and wilderness environments. PackBot is

equipped with two main treads, used for locomotion,

and two articulated flippers with treads that are used to

climb over obstacles The PackBot can be fitted with

extra treads for additional mobility, see Figure 2. Since

the robots developed under DARPA’s TMR program

were primarily designed for urban environments, this

commonality between USAR and MOUT is a large

contribution to ease of mission transferability.

Urban environments typically include open spaces such

as city streets and building interiors. Common obstacles

that robots encounter in urban environments include:

curbs, stairs, small rubble piles, pipes, railroad tracks,

furniture, and wires. The ability to surmount these

obstacles is essential to the success of these platforms.

The PackBot system was capable of traversing all of

terrain encountered at the MOUT city at Fort Drum.

With very little training, operators were able to drive

the PackBot up stairs and through doorways. However,

the MOUT city did not have piles of rubble that would

be encountered from buildings that have been damaged

in explosions or earthquakes.

The SwRI tests consisted of outdoor obstacles. Many

of the obstacles, such as pipes and rubble piles, are also

informative for predicting performance in urban

environments. The obstacle course at the SwRI site

consisted of various natural and man-made obstructions

selected as a representative subset of robot-scale

impediments to cross country movement. The

following list is a description of the obstacles used for

testing the PackBot and a brief description of the

PackBot’s performance with each obstacle:

• Railroad ties. No difficulty traversing

• Pipes of ten different diameters ranging from 1.25

inch to 9 inch. The robot had no difficulty

traversing these pipes. The robot traversed the

largest pipe by lifting itself onto the pipe using its

flippers and doing a “backflip”.

• Drainage Culvert. 24-inch wide culvert with two

45-degree bends. No difficulty traversing.

• Bamboo Forest. The bamboo forest consists of a

matrix of 2 inch PVC pipes on 6 inch centers. The

maze width is one pipe-width larger than the width

of the robot. Under tele-operation, the PackBot

failed to get through the maze on the first try in

under the 20 minutes time limit. On the second try

the robot was able to pass through the maze in 17

minutes using lessons learned from the first

attempt and using the pose capabilities of the

PackBot.

• Rock channel. The obstacle has rocks the size of a

typical man-packable robotic vehicle. The obstacle

was traversed without any problems. The

articulators, power, and low center of gravity of the

PackBot contributed to the successful negotiation.

• Large, medium, and small rock beds. These beds

are populated with rocks that are football sized,

softball size, and hockey-puck sized, respectively.

The robot was able to traverse all three beds

successfully. However, it went out of bounds (off

the bed) in one out of the six runs.

• Dirt furrows. The furrows were dry, loose dirt

formed into ridges in a 7 ft wide by 30 ft long

obstacle. The robot had no difficulty moving

through this obstacle.

• Vegetation obstacle. The course was divided into

four sections with crops ranging in size from lawn

grass to heavy crops, greater than 18 inches high.

The light and medium crops were traversed with no

difficulty. The heavy crops made the direction of

the PackBot difficult to determine and the PackBot

moved outside the course in several of the runs.

The articulated head/neck unit being developed for

Figure 2. PackBot with additional flippers.
the TMR program will make navigation in thick

brush easier.

• Flat Sand Pit. The pit was filled with dry sand and

the robot transitioned the pit without difficulty

• Sand Furrows. Dry sand was formed into sand

dunes. The robot had no difficulty with this

obstacle.

• Mud pit. A pit 4 in by 7 ft by 16 ft was filled with

a mud slurry. Figure 3 shows the PackBot after

traversing the mud pit. The PackBot could go

straight through the mudpit. If the PackBot turned

6-8 times in the center of the pit, the PackBot

would become mired. The SwRI report stated “the

robot has very good mobility in mud, despite its

failure to complete the entire test.”

• Inclined ramp. The ramp is adjustable from 0 to 60

degrees. With no payload, the PackBot climbed

the ramp, was able to hold position, and was able

to skid steer in both directions. With 22.5 pounds

of payload, the PackBot was able to ascend and

descend up to 55 degrees and able to traverse 45

degrees.

• Curb Height. The PackBot climbed 13 inch curbs.

• Cattle Grating. Metal pipes, 2 inches in diameter,

can be moved to different positions. The PackBot

traversed this obstacle at all possible gap settings

(other than unreasonable ones spaced farther apart

than would be expected in an actual animal guard.

• Stairs. The stairs were sets of: wooden 9 inch

risers and 11 inch runs, wooden 7.5 inch risers and

11 inch runs, metal 6.5 inch risers and 12 inch

runs. The PackBot was able to climb all of the

stairs.

• Speed Runs. The speed tests recorded an average

(cruising) speed of about 5 mph. With its power

booster, the PackBot can achieve burst speeds over

8 mph.

The SwRI tests provided the PackBot designers with

excellent information on the PackBot’s mobility

characteristics tested against precise metrics. Results of

the tests were very positive and there was no critical

design changes required because of mobility

shortcomings.

Since disaster areas and urban terrain are covered in all

types of debris, having as much mobility as possible

greatly increases the success of USAR missions.

Although tracks provide the core of essential mobility,

having additional modes of mobility is advantageous.

When negotiating rough terrain, robots often flip over.

The PackBot’s flippers enable it to perform selfrighting. The flippers on the PackBot also provide

extra mobility, since they are coupled to the main drive

tracks, creating a larger adjustable contact surface. The

articulated flippers help prevent the robot from being

immobilized due to high-centering, enable the robot to

climb taller objects, and can help propel the robot

forward through dense vegetation through continuous

rotation.

At the WTC disaster site, the terrain where USAR

operations took place could be divided into two types,

the rubble pile, see Figure 4, and buildings for

clearance. The rubble pile was not something that these

robots had previously encountered and the robots were

not specifically designed for the rubble pile terrain. The

extreme conditions presented the robots with problems

in various areas. The wreckage site had such an

incredible amount of debris that mobility was very

difficult. The huge pile of twisted steel was challenging

for humans to climb, and more difficult still for robots

Figure 3. PackBot after Traversing the Mud Pit.

Figure 4. World Trade Center Rubble Pile.
to negotiate. The mere size of the pieces of steel could

be insurmountable. It was very difficult for small

mobile robots to traverse such an environment. The

PackBot was not able to negotiate the rubble pile

except for specific places. For the most part, there were

no crevasses in the rubble pile big enough for the

PackBot to explore. Usefulness of the PackBot was

demonstrated in the surrounding area for building

clearance.

The sites requiring clearance and inspection around the

rubble pile were strewn with paper and debris. A layer

of dust several inches thick covered the area. The

PackBot’s debris rejection system on the treads was

successful, allowing the PackBot to drive through large

amounts of debris. This system was previously tested

at SwRI with the small rock bed, dirt tracks, and rock

channel. The PackBot did not detrack in the WTC

operations. The PackBot was used to demonstrate

building clearance in the buildings surrounding the

WTC. A building clearing operation was performed in

an area that had been previously cleared by rescue

workers and the PackBot system was shown to be

useful for clearing buildings. The PackBot moved

through the environment looking for people in the

buildings and inspecting structural integrity of

staircases. USAR personnel at the WTC observing the

demonstration deemed the building clearance capability

useful. The PackBot moved through the buildings

including many staircases without difficulty except for

one staircase. The problematic staircase was covered

with dust and the metal was slippery such that the

PackBot was not able to get traction on the steps.

The WTC disaster highlighted that specific terrain

encountered with USAR operations is difficult to

predict, but having a well designed general mobility

capability that will climb rubble, self-right, climb

staircases, and is maneuverable enough to go through

hallways is useful in many scenarios. The designers of

the PackBot feel that the terrain such as the rubble pile

could not be negotiated without a large increase in

complexity and expense in the mobility mechanisms.

IV. DURABILITY

The PackBot was designed from the ground up with

considerations for impact resistance, waterproofing,

vibration resistance, electromagnetic resistance, low

electromagnetic signature, and a wide operation

temperature range. The SwRI tests examined the

following metrics.

• System Shock. The system survives shocks of 400

G’s. 400 G’s translates to a 10 ft drop onto

concrete. The system was fully operation after

multiple drops from 10 ft in positions of rear,

forward, and belly down.

• Waterproof. The system is rated for water depths

of up to three meters. At SwRI the system was

tested in a water channel just covering the

PackBot, see Figure 5, and in a pond with depths of

up to 5 ft. The PackBot had no problem with the

water tests.

• Electromagnetic. The electromagnetic resistance

capabilities of the PackBot are restricted from

public release.

• Temperature Range. The known operation range

of the PackBot is 20 degrees Fahrenheit to 120

degrees Fahrenheit. While the temperature range

was not explicitly tested at the SwRI tests, the

ambient temperature reached 116 degrees

Fahrenheit.

The PackBot has an onboard health sensor suite that

sends messages back to the Operator Control Unit on

the status of the system. Examples of messages sent

back to the operator are the thermal sensor readings

throughout the system. The system automatically

scales back power to motors if the motor is in danger of

damage from overheating. The operator can override

the thermal protection in the case of a critical mission.

The waterproof capability makes the PackBot more

practical for use in the field. The system can be used in

rain and can be cleaned with a water hose. The system

is also sealed against dust. Many of the combat

situations where a robot would be used, such as in

Afghanistan, are very dusty environments.

Figure 5. PackBot Emerging From Water Test
At the WTC, the PackBot’s durability contributed to

the systems usefulness. The general ruggedness of the

PackBot translates to less maintenance and more

readiness. The results from the SwRI tests indicated

that the PackBot would be a system with excellent

durability and throughout the WTC deployment, the

PackBot proved to be a durable system.

V. COMMUNICATIONS

The PackBot requires a single 802.11b digital link from

the Operator Control Unit to the PackBot vehicle. This

link carries the real-time digital video stream as well as

the other status information from the robot. In testing,

the PackBot’s communications link has reached

distances greater than one kilometer line of sight. For

missions where the system will not be able to maintain

line of sight or in an electro-magnetically sensitive

environment such as an ordnance deactivation mission,

the PackBot system has a fiber-optic spooler that

releases fiber optic cable and draws the cable back in.

The PackBot also has a payload for RF amplifiers for

extended range RF missions. The payload system

allows other communication systems to be developed

and integrated in a payload slot with ease.

The WTC area, in particular the area directly on the

rubble pile, presented significant obstacles for RF

communication. The rubble was heavily strewn with

attenuating metallic debris and the airwaves were

awash with RF radiation from a multitude of radios and

equipment. For these reasons, and the fact that tether

operations guarantee full frame rate video feedback, the

tethered robot systems were used almost exclusively on

the WTC rubble pile and were inherently more reliable

during operations. Tests that demonstrate a robots

ability to pay out its tether and keep the tether from

becoming ensnared would be useful. Radio Frequency

interference was anticipated as a major and crippling

issue once the robots penetrated deep within the rubble.

Exercises were conducted with RF-controlled robots in

the area directly surrounding the rubble pile. These

areas consisted of blown out, unsafe buildings, wrecked

vehicles, and rubble-and-dust-strewn streets. Most of

the buildings in this area had not collapsed and, as a

result, the areas of robot operation were not as densely

congested with metallic debris. This permitted the

freedom of RF operation well inside many buildings

that were structurally unsound or not yet deemed safe.

Although the robots operated freely within many areas

of these structures, the operations were conducted with

the knowledge that any robots losing communications

within unsafe structures were to be considered

irretrievable. The dynamic and intermittent nature of

RF communications highlights the need for robot-based

autonomy to assist the operator during communication

blackouts. iRobot is working on autonomous behaviors

that detect these situations and react with algorithms

designed to navigate or backtrack the robot platform to

areas of better communications. Test ranges testing

this ability would be of value. In addition, iRobot is

working on the ability to use multiple robots relaying

communications one-to-another from deep within a RFimpenetrable building. Tests that evaluate this

capability would be useful.

VI. SITUATIONAL AWARENESS

Situational awareness, in the context of PackBot

missions, refers to the ability of the operator to

understand the environment that the PackBot is

exploring. This understanding depends on the

particular mission and may include:

• Presence of disaster victims, enemy fighters,

friendly fighters, civilians.

• Medical condition of detected people.

• Location of Booby traps and mines.

• Integrity of building structure.

• Layout of the area.

• Location of items of interest within the area.

Test courses specifically designed for the above aspects

of situational awareness, would be useful.

Selecting the most appropriate sensors for robot

situational awareness and understanding how to exploit,

merge and interpret the various data provided by the

sensors is an extensive area of research. The PackBot

can be equipped with various sensors including:

cameras, sonars, infrared sensors, and laser scanners.

The primary source of information for situational

awareness on the PackBot has typically been standard,

low-light, or infra-red video cameras.

The PackBot is equipped with a differential GPS

system. At the test course in Rockville, autonomous

waypoint navigation was demonstrated, see Figure 7.

Using only the GPS system, the Packbot demonstrated

back-tracking through maze-like courses resembling

minefields.

The PackBot is equipped with a compass, roll sensor,

tilt sensor, and 3-axis accelerometers. These sensors
increase situational awareness by providing the

operator with additional information on the PackBot’s

status and position

Many of the tests conducted at SwRI indirectly tested

some aspects of situational awareness. For example,

the vegetation test, examined the PackBot’s ability to

determine where it was so that it could move through

the vegetation course.

At the WTC disaster, the PackBot used cameras as the

main source of data for situational awareness. Both

color and b/w video was used. As expected, the color

was beneficial for the operator’s understanding of the

PackBot’s environment. The PackBot also used a

forward-looking infrared (FLIR) camera. The FLIR

was useful in both dark and light environments. Since

the environment at the WTC was covered in a thick

dust, trying to discern various objects and details of the

environment became difficult. The gray dust tended to

mute all colors, but having a thermal based view of the

world provided an alternate perspective on the

immediate environment. Certain aspects of the

environment were not readily evident using visible light

or low light cameras; however, thermal imaging made

these details apparent. The FLIR was particularly

useful in detecting the presence and location of people

in low-light environments. Tests examining the

operator’s ability to discern details through a camera

would be useful.

VII. DEPLOYMENT

The deployment of a robot system can be broken down

into:

• Delivering the robotic platform to the deployment

area

• Setting up the control system

• Operating the robot

All of these areas must be well thought out for an

Unmanned Ground Vehicle’s mission to be successful.

The PackBot was developed for ease of deployment.

The PackBot is shipped in a padded case that can be

lifted by a single person. The Operator Control Unit

(OCU) and battery chargers are shipped in a separate

case. The robot is deployed by pulling the base chassis

out of the case and pushing the flippers onto the

chassis. The deployment does not require any tools.

The base chassis without batteries weighs 28 pounds so

the pieces of the robot can be distributed among a team

for carrying to the operation site. Ease of deployment

of the PackBot was not tested in a formal setting.

Our experience at the WTC served as an education in

the deployment issues associated with real emergency

situations. A wide range of scenarios was present at the

disaster site, requiring different deployment strategies.

Automobiles were used to transport the robot systems

and operators from the Javitts Convention Center to the

WTC site. At other times automobiles were used to

transport the robot systems and operators to a standoff

point from which the operators then transported the

system to the operation area. Not all areas were easily

accessible and some required the PackBot to be carried

on foot for long distances (up to approximately one

mile). The robot systems needed to be carried over

debris and rubble that the robot themselves could not

traverse. This highlights the need for robot systems to

be lightweight, compact, and man-portable. In other

cases, operators carried equipment and robots in their

arms and strapped to their backs while riding on ATVs

that ferried them to the operation area. The robots did

not deliver themselves to the operation area to conserve

battery life and because some of the operating areas

were not directly accessible.

After arriving at the area of operation, the control

system had to be set up. Control stations for the

PackBot at the WTC consisted of a human-machine

interface of joysticks and buttons for sending

commands to the robot. At the time of the WTC effort,

the OCU hardware was not rugged or weather-resistant.

This resulted in instances where the robot was not used

for fear that the system’s components were inherently

unreliable. Since that time, the OCU software has been

ported to more rugged OCU hardware, including a

wearable OCU that has been used in military exercises.

Having a man-portable OCU was shown to increase the

type of missions where the PackBot could be used.

Figure 7. GPS Waypoint Autonomous Navigation
Experience at the WTC illustrated the need to transport

the PackBot, OCU, and any other equipment, by foot to

the area of operation and the necessity for it to be setup

in a short time. A well thought-out container with

intuitive locations for each piece of equipment is a

necessity. These containers should be capable of being

roughly transported in the back of a truck and carried

easily. Minimizing the number of cables and plugs is

also important. Every piece of equipment and tool

needed at the control site must be included in the

deployment plan.

During a deployment, the operation of the robot

requires an operator’s undivided attention. Additional

people are needed at the control station to alert the

robot operator if the operation area becomes too

dangerous. All of the robots were driven under direct

tele-operation without any additional autonomous

behaviors aiding the operator.

One of the lessons learned at the WTC was the need for

a rugged, waterproof robot system that is man-portable

and easily set up. As a result, the development of wellpackaged rugged OCUs has been mandated for all

Tactical Mobile Robots. In order to be effective during

these operations and flexible enough to adapt to the

varied and extreme conditions associated with USAR

missions, the robot systems should require a minimal

amount of well-packaged support gear and cannot be

limited by the logistics of their support or deployment.

VIII. MODULARITY

The PackBot platform is a versatile platform that can

deliver a wide range of payloads that sense and

manipulate the

environment.

Modularity is central to

the PackBot design, see

Figure 8. Each robot

has eight payload

interface connectors

providing a variety of

standard buses and

system power. Each

standard payload port

contains 10/100 full/half

duplex Ethernet,

FARnet (an iRobot

networking protocol),

two differential analog

video channels, two

general purpose digital

pins (serial, if needed), USB, and power sourcing or

sinking. Payloads that have been designed or are being

designed include cameras and lighting units,

manipulators, fiber optic spoolers, and hybrid-electric

generators. The on-board computer is a 700 MHz,

Mobile Pentium III Processor with a 100 MHz system

bus and 256 MB of SDRAM. The computing power of

the onboard computer is available for running software

required for controlling payloads.

The PackBot implements a methodology for “snap-on”

modular payloads that are quickly and easily

interchanged to suit the particular or unique mission at

hand. The flexibility and importance of this concept

was proven at the WTC site when robots were able to

change cameras, lights and tethers as the buildings and

areas that were searched presented various technical

and physical challenges.

Due to the modular design of iRobot’s PackBot, a

payload was developed that specifically addressed

challenges that were anticipated for the WTC and

USAR operations in general. Despite being designed

with MOUT operations in mind, the PackBot was

adapted to address USAR missions. The payload

developed had multiple cameras, infrared illumination,

2-way audio, and a lens cleaning system. In addition to

the payloads that were developed in preparation for

deployment to the WTC, the standard buses provided

on the PackBot payload connectors allowed for several

technologies to be incorporated on site. This allowed

the robot to make use of sensors and equipment specific

to USAR missions. The experiences at the WTC

demonstrated the flexibility of a modular architecture

and the necessity for robot configuration on a per

mission basis.

Payloads used at the WTC were limited to

stationary cameras (including a FLIR), light

sources (IR and Halogen), up to four battery

packs, and Cat 5 cable spoolers. But the

versatility, the advantages, and the

disadvantages of the overall modular

concept were apparent. Even though all

payloads were developed for harsh

environmental exposure, great care had to

be taken when attempting to replace, adjust,

or modify payload configurations. Fine

concrete dust and debris covered every

square inch of the robots’ surfaces after

every deployment.

Useful metrics for modularity include the

Figure 8. PackBot Modularity.
amount of time and cost required to add a simple

payload to the system.

IX. ENDURANCE

A critical metric for field operations is the maximum

length of a mission that can be performed. At the SwRI

tests, the PackBot demonstrated run times of 2 hours

with constant activity. The maximum mission times

can be extended to over 10 hours if the robot is

stationary for most of the mission. These mission times

represent the standard usage of two batteries. Four

batteries can be used to increase the mission time.

Endurance tests at SwRI demonstrated that the PackBot

can carry payloads more than doubling its 35-pound

base weight without significant impact to its mission

time. The PackBot achieves its long run times with the

selection of power saving components throughout the

vehicle.

At the WTC, the lengthier mission times translated to

less down time for the robot returning to swap batteries.

Although the mission length was not an issue on the

specific PackBot missions, there were scenarios at the

WTC where a long mission length would be

advantages. For example, in conducting extensive

building clearance, the operation may be slowed down

by the robot returning to the home base every two to

three hours for a battery exchange.

X. CONCLUSION

The PackBot performed well at the World Trade Center

disaster site in each area of mobility, durability,

situational awareness, communications, deployment,

modularity, and endurance. Many of the characteristics

of the PackBot were well understood from the testing

conducted at SwRI, iRobot, and other exercises such as

the Fort Drum MOUT city. The application of PackBot

technology at the WTC disaster demonstrated that

technology developed for MOUT is transferable to and

useful in USAR missions although these operations

presented unique challenges. The robust mobility and

the flexibility provided by the modular nature of the

PackBot enabled the robots to operate effectively and

adapt to new situations.

The widely varying conditions and environments

encountered at the WTC disaster site confirmed that no

single size or configuration of robot could address all of

the difficulties that an USAR mission can present,

however the PackBot provides a useful capability for

many situations encountered. The TMR program has

made great progress in advancing robot technology and

much of what it has done is applicable to areas outside

of MOUT missions. The experiences at the WTC show

that there is great benefit in using robotic technology in

search and rescue operations, yet there remains work to

be done in developing specific USAR robot technology.

Additional metrics specifically designed for USAR

applications would be helpful. For example, metrics

could measure mobility over rubble and building

debris, as well as communications range in indoor

environments (both intact and damaged). Metrics could

also test the ease of deployment by measuring weight

and time to setup. The performance metrics developed

by SwRI for testing mobile robots provided valuable

information about the PackBot’s capabilities. The

SwRI metrics indicated that the PackBot would

perform well in a wide variety of rugged environments,

and our experiences at the WTC confirmed the SwRI

tests.
Intelligent Robots for Use in Hazardous DOE Environments

David J. Bruemmer, Julie L. Marble, Donald D. Dudenhoeffer,

Matthew O. Anderson, Mark D. McKay

Idaho National Engineering and Environmental Laboratory

 Idaho Falls, ID 83415-3779, U.S.A.

ABSTRACT

In Robotics and Intelligent Machines in the U.S. Department

of Energy: A Critical Technology Roadmap, the DOE has

identified the potential for Robots and Intelligent Machines

(RIM) to greatly reduce cost, improve worker health and

safety, augment product quality and increase overall

productivity. In its long-term vision, the DOE has predicted

that such RIM capabilities will be as pervasive and

indispensable for the DOE and national interests as the PC is

today. However, for this vision to be realized, critical issues

pertaining to the interaction of humans and intelligent

machines must be further explored and new technologies

developed. In terms of time, cost and safety, ‘usability’ may

well prove to be the most crucial component of RIM systems

for remote handling of radioactive and hazardous materials

and a wide variety of other operations. In this paper we

examine the metrics used by the DOE to compare baseline

radiation survey techniques with a teleoperated robotic survey

recently conducted at the Idaho National Engineering and

Environmental Laboratory (INEEL). Further, the paper

discusses the difficulties and limitations of teleoperation

evident from this deployment. To meet the current and future

goals of the DOE, it is absolutely necessary to move beyond

teleloperation and develop robot intelligence that can be

interleaved with human intelligence to mitigate these

difficulties. In response to this need, the INEEL has developed

a mixed-initiative robotic system which can shift modes of

autonomy on the fly, relying on its own intrinsic intelligence

to protect itself and the environment as it works with human(s)

to accomplish critical tasks.

KEYWORDS: Automation, Cognitive science, Human

factors, Intelligent robots, Mobile robot dynamics, Robots.

1. INTRODUCTION

The DOE’s Environmental Restoration and Waste

Management Robotics Technology Development Program

explains that manual work within hazardous environments is

slow and expensive. Worker efficiency is low due to

protective clothing and, in some cases, exposure limits that

require work to be accomplished in several minute intervals.

Even when exposure limits are not an issue, fatigue is often

induced by confined spaces and by the highly repetitive nature

of certain tasks. The cost of a given project is increased

because of the special materials needed to protect workers and

the environment, and because of the additional wastes

generated in the form of contaminated clothing, rags, tools,

etc.. Moreover, time required to accomplish missions in

hazardous environment is adversely impacted not only by low

worker efficiency, but also by the need to prepare the workers

and instrument the site.

Consequently, the United States Department of Energy

(DOE) continually seeks safer and more cost-effective

technologies for use in decontaminating and decommissioning

nuclear facilities. As part of the FY 2000 and 2001

Large-Scale Demonstration and Deployment Projects

(LSDDP), the Idaho National Engineering and Environmental

Laboratory (INEEL) collaborated with the Russian Research

and Development Institute of Construction Technology

(NIKIMT). This collaboration resulted in the development of

the Robotic Gamma Locating and Isotopic Identification

Device (RGL&IID) which integrates DOE Robotics

Crosscutting (Rbx) technology with NIKIMT Russian gamma

locating and isotopic identification technology.

While the new robotic solution offered significant

improvements in terms of time, cost, worker exposure and the

quality of data acquired, the remote nature of this new

technology presented new human-robot interaction challenges.

Humans were required to enter the building to instrument the

environment with cameras and to assist the robot during the

execution of the task. Moreover, during the actual

deployment, the robot was only allowed to move at very slow

speeds due to the limitations of visual feedback to the

operator. In answer to these challenges, the INEEL has

developed a dynamic autonomy architecture for the same

system used in the RGL&IID deployment.

The new approach presented in this paper permits the

robot to take initiative to protect itself and the environment. In

fact, the human-robot dynamic has changed from a masterslave relationship to that of a mixed team which allows

interaction between peers. When compared with the recent

RGL&IID technology, the new mixed-initiative system will

remove much of the need for prior instrumentation, remove

the need for expert operators, reduce the total number of

operators, eliminate the need for human exposure and greatly

reduce the time needed for preparation and execution of the

task.
2. TELEOPERATED RADIATION SURVEY

Historically at the INEEL, a radiation control technician

(RCT) and industrial safety personnel first enter a facility in

order to establish accurate conditions for planning purposes.

When performing an initial radiation survey, the RCT uses a

standard Geiger-Mueller pancake probe to gather radiological

information. Once this initial entry has been completed, a

video technician may also be required to enter and collect

video coverage. Finally a team of sampling technicians is sent

into the facility to collect samples used to determine

contamination levels and identify which isotopes are present.

Typically, this data is then used to aide decontamination and

decommissioning (D&D) planning activities (see Figure 1).

Figure 1. Baseline Sample Collection for Laboratory

Analysis.

2.1 RGL&IID Deployment

To prove that remote, robotic systems could improve on this

baseline, the RGL&IID was deployed in July, 2001 at Test

Area North (TAN) 616 (see Figure 2). TAN is located at the

north end of the INEEL, about 27 miles northeast of the

Central Facilities Area. TAN was established in the 1950s by

the U.S. Air Force and Atomic Energy Commission Aircraft

Nuclear Propulsion Program to support nuclear-powered

aircraft research. Upon termination of this research, the area's

facilities were converted to support a variety of other DOE

research projects. TAN 616 was built in 1954 as a liquid

waste treatment facility. As a result of treating thousands of

gallons of liquid nuclear processing waste, there are various

levels of contamination present in the facility.

Figure 2. TAN 616.

Three rooms within TAN 616 were surveyed using the

RGL&IID: the Operating Pump Room, the Control Room, and

the Pump Room. All of these rooms are filled with process

piping and equipment at various levels, making make a

manual survey very difficult and time consuming to perform.

The intent of this demonstration was to gather empirical data

to assess the value of using a remote, robotic system. The

metrics considered included reduction in cost, accelerated

schedule, improvement in safety, and reliability of data.

2.2. Deployment Results

When compared to baseline assessment methods, the most

significant benefit of the RGL&IID deployment was the

quality of the results relative to the safety of the workers.

Although the RGL&IID deployment did not eliminate the

need for workers to enter the contaminated area, it did reduce

the need for human exposure. The RGL&IID was compared to

the following baseline activities: the initial RCT entry, an

entry to collect video, and a final entry to collect sample

information. The RGL&IID was able to collect dose

information, video coverage, and isotopes present in a single

unmanned entry.

Radiation exposure to workers supporting the RGL&IID

deployment was cut by more than a factor of 10 over baseline

activities. During baseline characterization, workers received

82mRem of radiation exposure. During the deployment of the

RGL&IID, workers received 7mRem of radiation exposure. In

addition, the RGL&IID provided radiation survey results

instantly and the complete facility survey was accomplished in

3 days. It took workers using baseline characterization

methods 3 months to accomplish the same results. The

baseline activities began in August of 2000 and were not

complete until November of 2000. Some of the results from

the laboratory analyses were not available until January 2001.

The laboratory radiological analysis confirmed the presence of

Cs-137, Co-60 and Am-241. This same data was available

within minutes after the RG&IILD performed the scan.

The deployment of the RGL&IID did require more

workers than the baseline characterization. However, during

the baseline sampling activities, six entries with as many as

six individuals per entry were made, totaling 60 work hours
spent in the contaminated area. During the RGL&IID

demonstration, only two technicians and one RCT were

required to enter the contaminated facility for a total of 10

work-hours spent in a contaminated area. All others

associated with the project were able to complete the

objectives from outside the contaminated areas. As a result of

workers spending less time in the radiation areas, individuals

involved in the RGL&IID deployment received 10 times less

radiation dose than workers involved in baseline activities.

In addition, the two technicians and one RCT who did

enter the facility during the demonstration did so only to assist

the movement of the RGL&IID up and down a flight of stairs

and to check air quality prior to entering the facility. These

individuals maintained as much distance between themselves

and the highest contaminated areas as possible. In contrast,

the baseline samplers were required to come in direct contact

with the contaminated material in order to collect

representative samples.

The financial cost of collecting the radiation

measurements using the RGL&IID was about half the cost of

the baseline technology. In addition to the benefit of

significant cost reductions, this technology also generates

significantly more data. For example, whereas the baseline

survey included 10 point samples, the RGL&IID collected

about 20 scans. Each scan covers as little as one square foot

or as much as several square feet and may have as many as 64

point measurements. Altogether the RGL&IID deployment

resulted in over 200 point measurements that covered over

100 square feet of wall and floor area. The RGL&IID has the

capability of providing 100% coverage if needed.

2.3 Limitations to the Teleoperation Approach

Although the 2001 robotic deployment offered a means to

reduce human exposure, it did not fully remove the human

from the hazardous environment or make it possible for a

single human to control the robot. In fact, the baseline survey

required only three people, whereas the RGL&IID required

six. If robotic systems are to be truly cost-effective and

efficient, this ratio of six humans to one robot must be

reduced.

Moreover, the data presented above says nothing about the

inherent limitations and risks of teleoperation. Teleoperation

requires high-fidelity video, reliable, continuous

communication, and costly, dangerous efforts to instrument

the environment a priori. As a mechanical ‘subordinate,’ the

robot was dependent on continuous, low-level input from a

human and was poorly equipped to cope with communication

failures or changes in operator workload. In fact, while

training within a mock-up facility, operators lost control of the

vehicle due to a communication failure. Since the last

command received by the robot before communications were

lost had been a forward acceleration command, the robot

continued to accelerate across the room and actually ran right

through the walls of an adjacent test bed environment. As a

result, the robot’s control system was immediately changed to

have a “watchdog” system that halted the robot once it

recognized that communications had failed.

Even so, communication proved to be the limiting factor

governing human-robot interaction during the teleoperated

deployment. Thick concrete shielding, typical to radiological

controls, made it extremely difficult for high-bandwidth

communication to support the strictly teleoperated system. As

a result, it was necessary for a human to physically place a

large antenna directly into the opening of the TAN 616

building. As the robot traveled further from this antenna, the

possibility of communication dropouts increased. In fact,

operators completely lost contact with the robot at one point

during the deployment when the robot traveled out of range.

The robot stopped after several seconds once it recognized

that communication had been lost. Since the robot was merely

a passive tool, it was unable to reorient itself or attempt to

reestablish communication. If humans had been unable to

enter the environment, the robot would have been lost forever

and unable to complete its task. Fortunately, a human was able

to move the antenna slightly further into the doorway of the

building and communication was reestablished.

The 2001 RGL&IID deployment required weeks of

preparation including training operators in mock-up

environments. Early on, these training exercises indicated that

cameras positioned on the robot would not be sufficient to

support teleoperation. The camera could not see the immediate

obstacles surrounding the wheels – the very obstacles that

posed the greatest threat. As a result, it was necessary to

instrument the environment a priori with elevated cameras.

These cameras were tethered to allow sufficient bandwidth for

high resolution video and were set up in the environment by

humans. Human placement of tethered cameras is a common

practice in nuclear remote inspections throughout the DOE

complex. This drawback to teleoperated approaches is further

pronounced by the fact that these cameras must be bagged and

produce additional contaminated waste once the operation is

complete.

Although the cameras were deemed sufficient for the task,

operators explained that such a strategy is inherently limiting.

The first limitation is that adequate lighting is required to

support vision-based teleoperation. Secondly, such cameras

are usually unable to provide complete visual coverage. In

fact, operators reported blind spots when using the same

robotic system and cameras within a different, larger building

at the site. In one instance, as the robot rounded a corner and

left the visual field of one camera, the last thing the operators

saw was the robot begin to tip over. Fortunately, the robot

righted itself and was able to complete the task successfully.

Nonetheless, the incident emphasizes the need for the robot to

provide better feedback and, ideally, to be able to take

initiative to protect itself in critical situations.
3.0 MUTUAL INITIATIVE CONTROL

Throughout the DOE complex, teleoperated systems have

often failed to address the limitations of telepresence inherent

to current communication technologies. On the other hand,

attempts to build and use autonomous systems have failed to

acknowledge the inevitable boundaries to what the robot can

perceive, understand, and decide apart from human input.

Both approaches have failed to build upon the strengths of the

robot and the human working as a cohesive unit. In response

to limitations of both approaches, research efforts at the

INEEL have developed a novel robotic system that can

leverage its own, intrinsic intelligence to support a spectrum

of control levels. We submit that rather than conceive of

machines as mere tools or, on the other hand, as totally

autonomous entities that act without human intervention, it is

more effective to consider the machine as part of a dynamic

human-machine team. Within this team, each member is

invested with agency – the ability to actively and

authoritatively take initiative to accomplish task objectives.

Within this schema, each member has equal responsibility for

performance of the task, but responsibility and authority for

particular task elements shifts to the most appropriate

member, be it human or machine. For instance, in a remote

situation, the robot is usually in a much better position than

the human to react to the local environment, and consequently,

the robot may take the leadership role regarding navigation.

As leader, the robot can then “veto” dangerous human

commands to avoid running into obstacles or tipping itself

over.

The resulting robotics system, pictured in Fig. 3.,

including hardware, software, and interface components, can

slide between roles of ‘subordinate,’ ‘equal’ and ‘leader.’ The

ability of the robot to change its level of autonomy on the fly

supports changing communication, cognitive, perceptual and

action capabilities of the user and robot. With the new system,

communications dropouts no longer result in the robot

stopping dead in its tracks or, worse, continuing rampant until

it has recognized that communications have failed. Instead, the

robot may simply shift into a fully autonomous mode.

 For this system to meet its goals, we must provide robust

mechanisms which allow the robot to protect itself and the

environment. To do so we fuse a variety of range sensor

information including inertial sensors, compass, wheel

encoders, laser range finders, computer vision, thermal

camera, infrared break beams, tilt sensors, bump sensors,

sonar, and others. The robot does not assume that these

sensors are working correctly, but rather continuously

evaluates its own perceptual capabilities and behavior. Novel

sensor-suites and fusion algorithms enhance capabilities for

sensing, interpreting, and "understanding" environmental

features. Also, a great deal of work has focused on providing

situation awareness to the user that can appropriately support

the current level of interaction. With the new system we are

not limited to visual feedback. Instead, the robot is able to

abstract information about the environment at many levels

including terse textual descriptions of the robot’s local

surroundings.

Given the desire to employ robots in hazardous, critical

environments, the ability to shift a robot in and out of the

leadership role presents a conundrum. The user comes to rely

on the self-protective capabilities of the robot and yet, at

times, must override them to accomplish a critical mission.

For instance, when faced with an unknown boxobstructing the

path, the user may shift the robot out of the leadership

responsibility for navigation, but grant the robot the “right” to

refuse human commands when the physical resistance to

motion is beyond a certain threshold. This allows the human

to attempt to push the box out of the way without exerting

dangerously high force on the robot. For other tasks, the user

may need to drive the robot to where it is touching an obstacle

in order to take a sample. The user can curtail the robot’s

collision avoidance initiative and yet customize a “last resort”

channel of initiative based on bump sensors and short-range

infrared break beams.

Ideally, we need control systems that allow the user to

configure the autonomy of the robot on the fly, activating

“channels of initiative” that crosscut broad categories. The

roles of each team member are bounded by a complex and

changing web of capabilities and limitations to which each

member must adapt and respond. The ability of the human to

develop accurate understanding of robot behavior is essential

if this adaptive role switching is to work effectively. One of

the most fascinating areas of future work is the need for the

robot to be imbued with an ability to understand and predict

human behavior.

3.1. Theory of Robot Behavior

The need for human and robot to predict and understand one

another’s actions presents a daunting challenge. For each level

of robot initiative, the user must develop a unique set of

expectations regarding how the robot behaves, that is, an

understanding or theory of the system’s behavior, here after

referred to as a theory of robot behavior (TORB). By TORB

Figure 3. Current robot configuration.
we mean that the human operator is able to quickly and

accurately predict:

1. Actions the robot will take in response to

stimuli from the environment and other team

members;

2. The outcome of the cumulative set of actions.

In our research we are not concerned with developing a

formal model of robot cognition, but rather require that the

human understand and predict the emergent actions of the

robot, with or without an accurate notion of how intelligent

processing gives rise to the resulting behavior. When a

human team member is faced with a robot that can orchestrate

task elements, the critical issue will not be how the robot or

machine “reasons,” but rather whether the human team

members can accurately predict robotic responses and

understand how cumulative actions and responses converge to

fulfill task objectives.

Many applications require the human to quickly develop

an adequate TORB. One way to make this possible is to

leverage the knowledge humans already possess about human

behavior and other animate objects, such as pets or even video

games, within our daily sphere of influence. For example,

projects with humanoids and robot dogs have explored the

ways in which modeling emotion in various ways can help (or

hinder) the ability of a human to effectively formulate a

TORB [1],[2].

Regardless of how it is formed, an effective TORB allows

humans to recognize and complement the initiative taken by

robots as they operate under different levels of autonomy. The

ability to predict and exploit the robot’s initiative will build

operator proficiency and trust. The development of a theory of

robot behavior will also allow the user to switch between and

configure the robot’s levels of initiative to suit the needs and

components of the task at hand.

3.2. Theory of Human Behavior

Just as the human develops a theory of the robot’s behavior,

the robot must be able to understand and predict the human

members of the team in order to adapt to their needs. This is

not to say that machines must possess complex mental models

or be able to discern our intentions. Rather, it is necessary to

raise the level of interaction between the human and robot

based upon readily available, non-intrusive workload cues

emanating from the operator. The robot’s theory of human

behavior may be a rule set at a very simple level, or it may be

a learned expectation developed through practiced evolutions

with its human counterpart. The robot must possess some

means to infer the need for intervention. Currently, accurate

and non-intrusive collection of these cues is difficult at best,

and those measures that have been used are unreliable at worst

[3].

The answer to this dilemma is to reduce the human signals

down to a prescribed set of channels, which are available as an

integral part of the interaction of the human with the machine,

and which the machine can use to configure its behavior and

level of initiative. Interaction between the robot and human

may be through direct communications (verbal, gesture, touch,

radio communications link) or indirect observation (physically

struggling, erratic behavior, unexpected procedural deviation).

Interaction may also be triggered by the observation of

environmental factors (rising radiation levels, the approach of

additional humans, etc.). The robot’s expectations must allow

it to recognize human limitations and anticipate human needs

without second-guessing the human’s every move. When

robots do intervene with their human counterparts, the

human’s TORB must be able to explain why the robot has

stepped in and what this shift in control means for the task at

hand.

3.3 Dynamic Role Changing

The benefits of allowing the team members to change roles

within the team significantly increases team flexibility and

reliability in task performance. However, if the interface and

human-robot system are not designed in accordance with

critical principles of human factors in mind, dynamic role

changing may result in mode confusion, loss of operator

situation awareness, loss of operator confidence in assuming

supervisory control, and degraded and potentially catastrophic

performance [4]. Systematic human-centered design is

necessary to insure that the robot autonomy conforms to the

ways in which humans assign and manage tasks.

Appropriate feedback is required when roles and levels of

initiative change. Failure to inform the operator when the

robot has overridden commands will lead to distrust of the

system, unless the behavior is beneath the level of operator

concern. This phenomenon has been studied within the airline

industry with pilots and the automatic pilot mode of operation.

[5]. Feedback from the robot should not only include the mode

change, but also an indication of the reason for the change.

For optimal performance of the team, the human must be able

to develop expectations regarding when and why the robot

will be motivated to initiate a new level of initiative. In order

for the human’s theory of system behavior to comprehend and

exploit robot initiative, the robot’s autonomy should be

structured hierarchically such that at any given time, the user

will know the bounds on what initiative the robot can take.

Consequently, the INEEL has developed a control system that

supports four clearly distinct levels of human intervention.

4. INTELLIGENT AUTONOMY

Within the last five years, researchers have begun in earnest to

examine the possibility for robots to support multiple levels of

user intervention. Much of this work has focused on providing

the robot with the ability to accept high level verbal, graphical,

and gesture-based commands [6], [7], [8]. Others have

implemented robots that understand the limitations of their

autonomous capabilities and can query the user for appropriate
assistance [9], [10]. Goodrich et al. [11] have performed

experiments which involve comparing the performance of

human-robot pairs using different modes of human

intervention. However, very little work has emphasized true

peer to peer interactions where the robot is actually able to

shift modes of autonomy as well as the user. Sholtz [12]

discusses the need for this kind of peer-peer interaction, and

provides categories of human intervention including

supervisory, peer to peer and mechanical interaction (e.g.

teleoperator). Our research to date has developed a control

architecture that spans these categories, supporting the

following modes of remote intervention:

1. Teleoperation;

2. Safe Mode;

3. Shared Control;

4. Full Autonomy.

For each of these levels of autonomy, perceptual data is

fused into a specialized interface (shown in figure 4) that

provides the user with abstracted auditory, graphical and

textual representations of the environment and task that are

appropriate for the current mode. Currently, this interface is

used on a touch screen tablet PC made by Fujitsu Corp..

Within this interface, blockages are shown as red ovals and

resistance to motion is shown as arcs emanating from the

wheels. The robot relays a great deal of synthesized, highlevel information (including suggestions and requests for help)

to the user in a textual form using the feedback textbox within

the image window. Also note that the robot provides textual

reports on environmental features at the bottom of the map

window and reports on communications status at the bottom

of the robot status window. The robot status window provides

a variety of information about the status of the robot including

pitch and roll, power, heading, speed and a fusion of this

information into a single measurement of “health.”

The user can move the robot by touching the arrows or

may use a joystick or other game controller. It is possible to

pan and tilt the camera automatically by touching regions of

the visual image. Currently, we are still working to integrate

Figure 4: Current interface used for mixed-initiative control of the robot
the on-the-fly mapping capabilities with the interface shown in

figure 4. As we continue this task, the interface will allow a

number of autonomous tasks (e.g. searching a specified region

or going to a goal location) to be issued by interacting with the

map itself.

4.1. Teleoperation

We have taken the interaction substrate used in previous

INEEL teleoperated robotic systems and revamped it through

feedback from people who have deployed such systems.

Within teleoperation mode, the user has full, continuous

control of the robot at a low level. The robot takes no initiative

except to stop once it recognizes that communications have

failed.

4.2. Safe Mode

Within safe mode, the user directs the movements of the robot,

but the robot takes initiative to protect itself. In doing so, this

mode allows the user to issue motion commands with

impunity, greatly accelerating the speed and confidence with

which the user can accomplish remote tasks. The robot

assesses its own status and surrounding environment to decide

whether commands are safe. For example, the robot has

excellent perception of the environment and will stop its

motion just before a collision, placing minimal limits on the

user to take the robot’s immediate surroundings into account.

The robot also continuously assesses the validity of its diverse

sensor readings and communication capabilities. The robot

will refuse to undertake a task if it does not have the ability

(i.e., sufficient power or perceptual resources) to safely

accomplish it.

4.3. Shared Control

The robot takes the initiative to choose its own path, responds

autonomously to the environment, and works to accomplish

local objectives. However, this initiative is primarily reactive

rather than deliberative. In terms of navigation, the robot

responds only to its local (~ 6-10 meter radius), sensed

environment. Although the robot handles the low level

navigation and obstacle avoidance, the user supplies

intermittent input, often at the robot’s request, to guide the

robot in general directions. The problem of deciding how and

when the robot should ask for help has been a major line of

HRI enquiry and will be a major issue in our upcoming human

subject experiments.

4.4 Full Autonomy

The robot performs global path planning to select its own

routes, requiring no user input except high-level tasking such

as "follow that target" or "search this area” specified by

drawing a circle around a given area on the map created by the

robot. This map is built on the fly and uses frontier-based

exploration and localization to perform searches over large

areas including multiple rooms and corridors. The user

interacts with the map to specify tasks and can guide the robot

and infuse knowledge at an abstract level by selecting areas of

interest and identifying sensed environmental features, which

then become included within the map.

 These levels of operator intervention can greatly improve

on the opportunities provided to the operators of a strictly

teleoperated system such as the one used in the RGL&IID

deployment. The human user can switch between these modes

to cope with different components of the task. For instance,

when a user wishes to move into a new room s/he simply

points the robot at a door and then allows the robot to guide

itself through the doorway – a task that reportedly took

teleoperators many minutes of trial and error.

The latest development, and perhaps the most innovative

aspect of our project to date, is that we have imparted a

"theory of human behavior" within the robot's intrinsic

intelligence, which allows the robot to assess human

performance. Before we implemented this theory of human

behavior, the robot was already able to use its knowledge of

the environment and its own proprioception to take initiative

and refuse to accept dangerous commands. However, the level

of robot initiative was always controlled by the human. The

“theory of human behavior” allows the robot to switch modes

when the robot recognizes that the human is performing very

poorly. This theory of human behavior is based primarily on

the frequency of human input and the number and kind of

dangerous commands issued by the user. For instance, if the

human has repeatedly placed the robot or the environment in

danger, or if the human has been unsuccessful in extricating a

robot from a cluttered area, the robot will step in and take over

from the operator. Although the human can ultimately

override this capability, it provides a means for true peer-peer

interaction.

5. PERFORMANCE METRICS & OPERATIONAL

IMPACT

The Department of Energy’s Robotic and Intelligent Machine

(RIM) Initiative has set forth a number of functional

objectives to be achieved using robotic and intelligent

systems. Some of these metrics include:

- Reduction in exposure to specific hazardous materials;

- Reduction in monitoring costs;

- Secondary waste reduction;

- Productivity increase;

- Production defect reduction. [13]

While these metrics represent increases in “performance”

they do not necessarily reflect the full impact of inserting an

“intelligent” system into an operation over existing humancentered tasks. Several additional areas that must be

considered which are evident within the testing conducted at

the INEEL include:

- Operator trust;
- Operator job satisfaction;

- Revised group organization;

- Task skill set adjustment;

- Training re-alignment

- Preparation;

- Consequence of asset loss and contingency / recovery

plans.

Consider the reduction of personnel exposure within the

INEEL experiment, decreasing from 82mRem to 7mRem and

the reduction of total survey time from 3 months to 3 days.

These represent dramatic performance gains. Now consider

the fact that although remote controlled, the operation of the

RGL&IID actually required more personnel, albeit in different

roles. The RGL&IID deployment did not eliminate the

necessity to utilize a human element for compartment entry,

but changed his task from conducting a survey to that of

helping the robot ascend and descend a flight of stairs. Also,

specific training was required in a mock environment to

support the operator’s new task of teleoperation.

In addition to the work that the INEEL is doing in remote

characterization, some of these same impacts can be seen in

the U.S. Air Force and its deployment of the Predator UAV.

While Bosnia and Afghanistan have proven the worth of the

Predator in remote sensing and ordnance delivery, the Air

Force is currently trying to adjust to its operational and

organizational impact. Currently being assessed by the Air

Force is the proper skill mix for pilots, the correct crew ratio,

and training. [14] Additionally, consider the aspect of job

satisfaction for a pilot used to flying a plane in the midst of the

action now confined to monitoring a Predator control panel

miles from the front.

The introduction of an intelligent system be it a mobile or

embedded system must be view not only in terms of specific

task performance, but also in relation to the overall impact that

the system imparts upon how “business” was done in the past.

While this change in not necessarily bad or good, it must be

examined.

6. REFERENCES

[1] Brooks, R., Brazeal, C., Marjanovic, M., Scassellati, B.,

& Williamson, M. “The Cog Project: Building a

Humanoid Robot.” In Computation for Metaphors,

Analogy, and Agents, Springer Lecture Notes in

Computer Science, 1562, C. Nehaniv, Ed., SpringerVerlag, Berlin, 1998.

[2] Brooks, R. A., Flesh and Machines, New York: Pantheon

Books, 2002, pp. 51-52.

[3] Wickens, C. D., Engineering Psychology and Human

Performance, (2nd ed.), NY, NY: Harpers Collins

Publishers, Inc. 1992.

[4] M. A. Goodrich and E. R. Boer. Designing HumanCentered Automation: Tradeoffs in Collision Avoidance

System Design. IEEE Transactions on Intelligent

Transporation Systems, v.1,no.1, March 2000.

[5] Abbott, K. A., S. M. Slotte, D. K. Stimson, “Federal

Aviation Administration Human Factors Team Report on:

The Interfaces Between Flightcrews and Modern Flight

Deck Systems,” Federal Aviation Administration,

Washington, DC, Tech. Rep. June, 1996. [Online].

Available: http://www.faa.gov/avr/afs/interfac.pdf, pp.

D1-D3.

[6] D. Perzanowski, A.C. Schultz, W. Adams, M. Skubic, M.

Abramson, M. Bugajska, E. Marsh, J. G. Trafton, and D.

Brock, Communicating with Teams of Cooperative

Robots, In Proceedings from the 2002 NRL Workshop on

Multi-Robot Systems, Washington, D. C., March, 2002.

[7] Kortenkamp, D., Huber , E., and Bonasso, R. P.,

Recognizing and interpreting gestures on a mobile robot.

In Proc. of AAAI 1996. Portland, OR, August, 1996.

[8] Voyles, R., and Khosla , P., Tactile gestures for humanrobot interaction. In Proc. of IEEE/RSJ Conf. On

Intelligent Robots and Systems, Volume 3, 7-13. 1995.

[9] Fong, T., Thorpe, C., Baur, C., Robot as Partner: Vehicle

Teleoperation with Collaborative Control, In Proceedings

from the 2002 NRL Workshop on Multi-Robot Systems,

Washington, D. C., March, 2002.

[10] Murphy, R. R., and Rogers, E. 1996. Cooperative

assistance for remote robot supervision. Presence 5(2):

224-240.

[11] Goodrich, M. A. and Olsen, D. R., Crandall, J. W. and

Palmer, T. J. Experiments in Adjustable Autonomy, In

IJCAI 2001 Workshop on Autonomy Delegation and

Control, Seattle WA, August, 2001.

[12] Sholtz, J., Human-Robot Interactions: Creating

Synergistic Cyber Forces, In Proceedings from the 2002

NRL Workshop on Multi-Robot Systems, Washington, D.

C., March, 2002.

[13] Department of Energy. “Robotics and Intelligent

Machines in the U.S. Department of Energy: A Critical

Technology Roadmap”,

http://www.sandia.gov/isrc/RIMfinal.pdf, October 1998,

p.15.

[14] Butler, Amy, Building Predator Fleet Compounds Need

for UAV Aircrew Planning, In Inside the Air Force, June

21, 2002.
On communicating with semantic machines

Walter J Freeman

Department of Molecular & Cell Biology

University of California at Berkeley CA 94720-3200

http://sulcus.berkeley.edu

Abstract

Semantics is the essence of human communication. It

concerns the manufacture and use of symbols as

representations to exchange meanings. Information

technology is faced with the problem of using

intelligent machines as intermediaries for interpersonal

communication. The problem of designing such

semantic machines has been intractable because brains

and machines work on very different principles. A

solution to the problem is to describe how brains create

meaning and then express it in information by making a

symbol as a representation to another brain in pairwise

communication. Understanding of the neurodynamics by

which brains create meaning may enable engineers to

build devices with which they can communicate pairwise,

as they do now with colleagues, though not with words,

but with shared actions. 

Key Words: EEG patterns, intentionality,

neurodynamics, phase transitions, semantics

1. Introduction

The most challenging of the three branches of

semiotics is called semantics [5]. It deals with the

relation between meanings and representations, a

relation often referred to in artificial intelligence and

philosophy as 'intention', 'aboutness' (a thought,

belief or memory is 'about' something), or 'symbol

grounding'. Computers are very good in the other

two branches (syntactics, which is the relation of

symbol to symbol as found in dictionaries) and

pragmatics (the relations between symbol and action

like those of traffic signals). They are inept in

semantics. The reason for this ineptitude stems from

deep differences between brains and computers. von

Neumann [19] surmised nearly half a century ago:

"We have now accumulated sufficient evidence

to see that whatever language the central

nervous system is using, it is characterized by

less logical and arithmetic depth than we are

normally used to. ... Thus the outward forms

of our mathematics are not absolutely relevant

from the point of view of evaluating what the

mathematical or logical language truly used by

the central nervous system is. [W]hatever the

system is, it cannot fail to differ considerably

from what we consciously and explicitly

consider as mathematics. p. 81"

Brains are exceedingly capable of grasping the

salient features of complex situations and social

relationships, which are captured in such words as

'value', 'significance', 'import', or 'bottom line', in a

word, 'meaning'. It is my conclusion in this essay

that meanings exist only in brains, where they take

the place of the internal representations that

computers use. My conclusion is based on research

into the spatiotemporal patterns of active states of

brains in animals, that accompany and support the

animals' performance of the cognitive tasks involved

in learning to respond appropriately to simple stimuli

that signify events and circumstances that are vital to

their welfare. I find that sensory cortices receive the

information that the sensory receptors provide from

stimuli, and that this information, once it has arrived

in cortex, triggers the construction of activity patterns

in brains that constitute the meaning of the stimuli.

These patterns over-ride the sensory-driven

information [4], which is then discarded, so that

everything that an animal learns about its

environment has been constructed within its brain

from its own experiences.

In order to translate these findings into terms that

engineers will require to emulate in hardware the

performance of brains in wetware, some further

consideration of the biological basis of meaning is

required. A meaning state is an activity pattern that

occupies the entire available brain [3]. The

construction begins with formation within the animal

of an activity pattern that embodies its immediate

goal, such as food, shelter or a mate, the achievement

of which requires acquisition of information from the

environment. That information is got by intentional

action into the environment, followed by sensory

stimulation and learning from consequences of the

action. A stimulus such as a light, an odor, or a tone

contains information that serves to represent to the

animal the state of its environment. It is a material

object or process that is equivalent to a book, face, or

gesture for humans. It is a part of the environment

that has no meaning in itself. The French poet Paul

Valéry [18] wrote:

"I have already explained what I think of literal

representation; but one cannot insist enough on

this: there is no true meaning of a text. No
author's authority. Whatever he may have

wanted to say, he wrote what he wrote. Once

published, a text is like an implement that

everyone can use as he chooses and according to

his means: it is not certain that the maker could

use it better than someone else. p. 1597."

My analysis of brain activity patterns shows that

sensory cortical activity patterns that are triggered by

stimuli are selected by the stimuli but are determined

by the history and context of the relations of the

individual to the stimuli [4,5]. These brain activity

patterns are states of meaning. They occur in the

dynamic state space of a brain as trajectories of

discrete steps marked by cortical phase transitions.

The patterned active states are called wave packets [2].

The way in which they are made by the selforganizing brain dynamics that controls behavior is a

pivotal topic in this essay.

The dynamics of brains that creates meaning can

be emulated in computer models of brain function

[4,12,15]. This step requires that a major problem be

addressed: the relation between representation and

meaning in brain function. The Shannon-Weaver

information theory is representational, because it

divorces meaning from information and therefore does

not apply directly to brains. Shannon [16] wrote:

"The fundamental problem of communication is

that of reproducing at one point either exactly or

approximately a message selected at another

point. Frequently the messages have meaning;

that is they refer to or are correlated according to

some system with certain physical or conceptual

entities. These semantic aspects of

communication are irrelevant to the engineering

problem. p. 380."

In Section 2, I sketch some of the principal

elements of communication, as a basis for discussing

a pathway toward solutions through a better

understanding of the biological basis of meanings,

which grows from behavioral actions. Meaning

stems not from the rule-driven operations between

symbols embedded within syntactical systems such as

computers, nor from conventional 'computing with

words'. it stems from shared actions. In Section 3, I

summarize the main observations on sensory cortical

wave packets. In Section 4, I enlarge the description

to include the limbic system and the origin of

intentional behavior. In Section 5, I take up the

critical difference between linear and circular causality

that underlies the distinction between deterministic

and self-organizing systems. In Section 6, I discuss

in more detail the relations between meaning and

representation. In Section 7, I summarize.

2. Communication by representations

Operational discreteness is essential for

communication in dialogue. A pair of brains can act,

sense, and construct in alternation with respect to

each other, just as dogs circle, and as two humans

plan, speak, listen, and hear. Consider brains A and

B interacting [Fig. 1], where A-B are parent-child,

wife-husband, rabbit-dog, philosopher-biologist,

neuroscientist -rabbit, etc. A has a thought that

constitutes some meaning M(a). In accordance with

this meaning A acts to shape a bit of matter in the

world (a trace of ink on paper, a vibration of air, a set

of keystrokes on e-mail, movements of the face, etc.)

to create a representation (a sign or symbol for

humans, merely a sign for animals, in both cases,

information) directed at B, R(ab). B is impacted by

this shaped matter and is induced by thought to create

a meaning M(b). So B acts to shape a bit of matter

in accordance with M(b) in a representation R(ba),

which impacts on A to induce M(a+1).

Fig. 1. The schematic shows the roles of

representation in the communication of

meaning between individuals by the exchange

of information through use of representations.

A method is proposed to replace one of the

communicants with a machine

And so on. Already by this description there is

implicit recognition of a discrete recurrent flow of

conversation like the tides, so that meanings M(i)'s

as constructions of thoughts become the internal

active states, and the R(ij)'s as attributes of matter

become the external representations. The interchange

requires a coordinated succession of phase transitions

in both communicants. By its relatively fixed nature

an :external" representation can be used over and over,

just as we use a letter, word, ideograph or equation.

It cannot be said to contain or carry meaning, since

the meanings are located uniquely inside A and B and

not between them. The same R induces different

meanings M(i) in any other subject C who may

intercept the transmission of a representation. The

objects that are used to communicate are shaped by

meanings that are constructed in A and B iteratively,

and they induce the constructions of meaning in B

and A alternately. If communication is successful,
then the internal meanings will come transiently into

harmony, as manifested by cooperative behavior such

as dancing, walking in step, shaking hands,

exchanging notes, ringing bells, etc. Symbols

persist in books and stone tablets, while minds

fluctuate and evolve until they die.

3. Observations on the electric fields, the

electroencephalogram (EEG)

A biological approach to the problem of meaning is

to study the evolution of minds and brains, on the

premise that animals have minds that are prototypic

of our own, and that their brains and behaviors tell us

what essential properties are common to animal and

human minds. Experimental measurements of brain

activity (EEG) that follows sensory stimulation of

animals show that sensory cortices engage in

construction of activity patterns in response to

stimuli [2]. The operations are not those that are

characteristic of computers, which include filtering,

storing, retrieval, template matching, pattern

completion, gradient descent, or correlation

mechanisms. Each construct is by a phase transition,

in which a sensory cortex switches abruptly from one

basin of attraction to another, thereby changing one

spatial pattern instantly to another like a succession

of frames in a cinema.

The transitions in the primary sensory cortices

(visual, auditory, somatic and olfactory [1]) are shaped

by interactions with the limbic system, which

establish multimodal unity, selective attention, and

the intentionality of percepts. The interactions of the

several sensory cortices and the limbic system lead to

goal-directed actions in time and space. Each cortical

phase transition involves synaptic changest

hroughout the forebrain that constitute learning, so

that a unified and global trajectory is formed

cumulatively by each brain over its lifetime. Each

spatial pattern reflects the content of past and present

experience [5], that is, a meaning.

The most important experimental finding is that

the neuroactivity patterns in sensory cortex, which

form during perception of conditioned stimuli by the

animals, are not invariant with respect to unchanging

physicochemical stimuli. The brain activity patterns

are found to change slightly and cumulatively with

any change in the significance of the stimuli, such as

by changing the reinforcement, or with the addition of

new stimuli [4]. From numerous tests of this kind

the conclusion is drawn that brain patterns reflect the

value and significance of the stimuli for the animals,

not fixed memory traces.

Each pattern forming in response to the

presentation of a stimulus is freshly constructed by

chaotic dynamics in the sensory cortex, in

cooperation with input from the limbic system that

implements the supporting processes of attention and

intention. It expresses the history, existing state, and

intent of the animal rather than the actual incident

stimulus. The patterns cannot be representations of

meanings of stimuli, either. They are observable

manifestations from the material substrate of the

meanings that are induced by stimuli, or that emerge

from self-induced instabilities in the sensory and

limbic systems. Their trajectory constitutes the

evolution of a brain in its growth of experience [13].

Similarly, a semantic device must be given

opportunity to practice, experience, and grow in

abilities to communicate.

The mechanism by which the formation of a

wave packet is triggered is of particular interest.

When an animal or human receives sensory

information, it is carried not by any small number of

axons from receptors but by a massive barrage of

action potentials. A glimpse of a face, for example,

includes all of the detectors for motions, contours,

colors, and binocular disparities of the face, and also

whatever background against which the face is

glimpsed, such as a crowd, a factory or a battlefield.

The process in mammals involves a dozen or more

specialized areas in each sensory cortex that process

the sensory information, with multiple feedback

pathways among them.

Despite this enormous complexity, recognition

occurs within half a second. The mechanism

suggested by EEG analysis is that an entire sensory

cortex is destabilized by input that is gated by a rapid

eye movement (a microsaccade), or its equivalent in

other sensory systems such as a sniff or a finger

motion. When it is destabilized, the cortex jumps

from one state to another. The transition is

completed within 3-7 msec of onset [4, 7]. It is

followed within 25-35 msec by the formation of a

spatial pattern of amplitude modulation [AM, Fig. 2]

of a chaotic carrier wave that persists for 80-100

msec. The AM pattern is accompanied by a spatial

pattern of phase modulation [PM, Fig. 2] that is

radially symmetric with a fixed phase velocity in all

directions. The PM pattern is measured by fitting to

it a cone in the 2 dimensions of the brain surface [7].

These two features, the AM and PM patterns,

serve to characterize the spatial and temporal location,

size, duration and content of the wave packet that is

triggered by sensory input. The AM pattern

manifests the meaning of the stimulus, not the

stimulus in itself, because the AM pattern changes

when the context or significance of the stimulus is

changed [4] [Fig. 2, right]. In contrast, the location

and sign of the apex (maximal lead or lag) of the PM
cone are random variables that do not reflect the

properties of the stimulus that evoked it or its

meaning for the subject [Fig. 3] [1,4,7].

Fig. 2. Left. EEG traces from an 8x8 electrode

array (4x4 mm) in 100 msec burst from olfactory

bulb after band pass filtering in the gamma range

(20-80 Hz). Middle. Spatial AM patterns from

RMS amplitudes on first trial set with control

and odor amyl acetate. Right. Two weeks later

in session 3 the stimuli were the same, but the

AM patterns had evolved to new forms, showing

that they are not representations. They are

context dependent and individualized for each

animal, and they change with variations in the

reinforcement.

The randomness of the sign implies that the apex

cannot signify a pacemaker for the oscillation, which

in any case is aperiodic. The proposed explanation is

that the phase gradient manifests the formation of a

wave packet by a 1st order phase transition [9], for

which the location of the apex reveals the site of

nucleation, and the velocity conforms to the finite

rate of spread of the state change in a distributed

medium, in the case of cortex by the conduction

velocities of axons running parallel to the pial surface

[7].

The phase gradient shows that the populations of

neurons in the wave packet are not oscillating in

phase at zero lag, but that they do so with leads or

lags that increase with distance from the apex. This

feature provides a soft boundary condition for the

wave packet, which can be specified by the half-power

diameter. The mode and 95% inclusion diameters are

shown by circles in Fig. 4, which are superimposed

on a diagram of the rabbit forebrain as seen from

above. Wave packets having these properties were

found in all of the sensory cortices examined. The

8x8 electrode arrays were placed on the primary

sensory receiving areas, as shown by the rectangles

indicating the size and locations of electrode arrays.

The 64 electrodes were used to record the EEGs from

the sensory cortices, in order to calculate the AM and

PM patterns in the cortical activity.

Fig. 3. Phase distributions were measured

with respect to the phase of the spatial ensemble

average at the surface of the olfactory bulb and

fitted with a cone in spherical coordinates. The

sketch is a projection of the outline of the bulb

as it would appear on looking through the left

bulb onto the array on the left lateral surface of

the bulb. A representative set of isophase

contours is at intervals of 0.25 radians/mm. The

locations of the apices of the cones on the surface

of the sphere (2.5 mm in radius) are plotted from

the center of the array to the antipode. The square

outlines the electrode array. The standard error of

location of points was twice the radius of the

dots. From Freeman and Baird [6].

Fig. 4 . The outline shows the left cerebral

hemisphere of the rabbit as seen from above.

The rectangles show the approximate locations of

the 8x8 arrays placed on the olfactory bulb (OB),

prepyriform cortex (PPC), somatomotor cortex

(SOM), auditory cortex (AUD), and visual cortex

(VIS), and a 2x8 array on the entorhinal cortex

(ENT). The inner circle shows the modal

diameter of phase cones. The outer circle shows

the diameter including 95% of cases. The

vertical line is the zero stereotaxic reference.

Diameter inner circle: 15 mm. Adapted from

Barrie et al. [1].
The massive size of the areas of cooperation

leading to formation of wave packets provides an

explanation for the speed with which pattern

recognition takes place. The immense cloud of action

potentials that is driven by sensory input from a

stimulus undergoes a 1st order phase transition that is

equivalent to formation of a rain drop from a cloud of

water molecules. In the process of the formation of a

condensation disk, a spatial pattern of output is

selected as the phase transition places the sensory

cortex into the basin of an attractor that has been

selected by the stimulus. The process repeats at

frame rates between 2 and 7 Hz, as shown by the

covariance of the successive phase cones with the low

frequency oscillations in the theta range of the EEG

[7].

4. Neural base of intentional action

The making of a representation is an intentional

action. All intentional actions begin with the

construction of patterns of neural activity in the

limbic system, which has been shown by use of

lesions and by comparative neuroanatomy and

behavior to be a product of the limbic system [11].

In mammals all sensory input is delivered to the

entorhinal cortex, which is the main source of input

to the hippocampus, and the main target of

hippocampal output [Fig. 5]. Goal-directed action

must take place in time and space, and the requisite

organ for the orientation is the hippocampus with its

'short term memory' and 'cognitive map'.

Fig. 5. A schematic diagram summarizes the

main elements in the dynamics of intentional

behavior to aid in the design of a KIV device

capable of intentional action, including semantic

communication. From Freeman [6].

For example, hunger is an emergent pattern of

neuroactivity that expresses the requirements of brains

and bodies for metabolic fuel and building material.

It induces a phase transition in the neural populations

of the forebrain under the influence of sensory stimuli

from the gut and the brain's own chemoreceptors for

its chemical state. It is also shaped by

neurohormones from nuclei in the brain stem. The

emergent pattern impacts the brain stem and spinal

cord, leading to stereotypic searching movements that

are adapted to the immediately surrounding world.

Feedback from the muscles and joints to the

somatosensory cortex provides confirmation whether

the intended actions are taking place. The impact of

the movements of the body on sensory input is

conveyed to the visual, auditory and olfactory

systems. All of these perceptual constructs, that are

triggered by sensory stimuli and are dependent on

prior learning, are transmitted to the limbic system,

specifically to the entorhinal cortex, where they are

combined. When, for example, an animal detects an

odor of food, it must hold the immediate memory of

the concentration, move, take another sniff, and

compare the two concentrations in order to decide

which way to move next. The difference in strength

has no meaning, unless the animal has a record of

where it was when it sensed the first concentration,

which way it moved, when the second sample was

taken, and to where. This information provides a

basis for determining distance and direction in its

environment from itself to its intended goal. These

basic operations of intentional behavior are properties

of the limbic system. The same requirements hold

for all distance receptors, so it is understandable that

evolution has led to multimodal sensory convergence

that performs space-time integration on the

multisensory percept, the Gestalt, not on its

components prior to their assembly. These

operations are already commonplace in robotics,

though less so their integration into goal states.

In the description thus far the flow of neural

activity is counterclockwise through loops from

sensory systems to motor systems, then through

proprioceptive and exteroceptive loops outside the

brain back to the sensory systems. Within the brain

there is a clockwise loop that sustains the flow of

activity constituting reafference (the leftward arrows

from "motor systems" through "entorhinal cortex" to

"sensory" systems, then rightward to close the loops).

When a motor act is initiated by the limbic system, it

issues a command as an activity pattern descending

into the brain stem and spinal cord. Copies of this

activity pattern are sent clockwise along these internal

pathways to all of the sensory systems by the

entorhinal cortex. These 'efference copies' [17]

prepare the sensory processors for the impact on the

sensory systems of the movements of the eyes, head,

ears, and body and, most importantly, the efference

copies sensitize the sensory cortices selectively by

shaping their attractor landscapes to respond only to

stimuli that are appropriate for the goal toward which

the action has been directed. The efference copy has

also been denoted as a 'sense of effort' [3]. They are
the essence of selective attention. These concepts are

familiar in feedback control; they need to be

generalized in the context of intentional behavior.

5. Linear versus circular causality in selforganizing systems

The conventional view of sensory cortical function

holds that stimuli activate receptors, which transmit

information to sensory cortex through a linear causal

chain, leading eventually to a motor response to the

initiating stimulus. Contrariwise, modeling with

nonlinear dynamics shows that the stimulus is

typically not the initiating event. Rather it is the

search for the stimulus that arises in conjunction with

an evolving goal in the limbic system, which

emerges in a recurrent manner from prior search and

its results. This is circular causality at the level of

intentional behavior [10].

Much lower in the hierarchy of brain

organization is another instance of circular causality.

This is the creation of the wave packet in the primary

sensory cortex, which consists of the destabilization

of a pre-existing mesoscopic state by the introduction

of intense barrages of microscopic sensory input. In

this case the transition from a prior basin of attraction

to a new one, which has been facilitated by limbic

modulation, is guided by the sensory input that

activates a learned nerve cell assembly comprising a

small subset of cortical neurons. The input from the

receptors includes both the expected stimulus and the

massive receptor discharge evoked by everything that

is in the background. The total receptor input forces

the instability and triggers the phase transition, and

the nerve cell assembly that is activated by the

expected stimulus selects the basin of attraction that

captures the cortical system. Then the entire domain

of the primary sensory cortex transits to the pattern

that emerges as the system converges to the attractor

in the basin, which in the words of Haken [10]

"enslaves" the whole set of cortical neurons by acting

as an "order parameter". This new active state has

been characterized by Ilya Prigogine [14] as a

"dissipative structure", that constitutes, in his words,

the "emergence of order out of chaos".

The similarity of the properties of neural activity

in the various parts of the limbic system to those in

the primary sensory cortices [1,4] indicates that

populations of neurons there also maintain global

attractors, which are accessed by nonlinear phase

transitions, and which are responsible for the genesis

of goal states, their motor patterns controlling goaldirected actions, and the reafference patterns that

prepare the sensory cortices for the consequences of

those actions.

The construction of a device that can simulate the

creative dynamics of the brain has been based

primarily on the dynamics and architecture of the

olfactory system, both in software [8,12] and in

hardware [4,15]. The basic unit of the construction is

a neuron population called a KO set, that is roughly

equivalent to an average neuron [2]. Its timedependent dynamics is governed by a linear 2nd order

ordinary differential equation that is evaluated by

fitting its solution as a sum of two exponentials to

derive the coefficients for the rate of rise of the

impulse response and the passive decay rate of the

membranes. Its input is provided by lines that

terminate in simulated synapses represented by gain

coefficients that are subject to change by learning, and

its output is bounded by a static nonlinear gain curve,

which is the derivative of the sigmoid curve [6]

relating dendritic current amplitude to pulse density

output of the population. An interactive population

of excitatory neurons is called a KIe set, and is made

by feedback connections between two excitatory KOe

sets in positive excitatory feedback. Similarly a KIi

set is made by feedback connections between two KOi

sets in positive feedback. KI sets have zero and nonzero point attractors and can generate sustained

excitatory and inhibitory biases. A KII set is made

by negative feedback connections between a KIe set

and a KIi set. It is has both point and limit cycle

attractors.

The interconnection and interaction of three KII

sets with distributed feedback delays forms a KIII set,

that is capable of aperiodic, nonconvergent, sustained

output governed by a chaotic attractor, in addition to

outputs governed by point and limit cycle attractors.

The nonlinear gain curve, which is the derivative of

the sigmoid function, governs pulse density in

relation to dendritic current density [2,4,8]. The

attractor landscape determines the spatial patterns of

EEG of sensory cortices, which are experimentally

observed from 8x8 arrays of recording electrodes with

a simulation using a KIII set embodying an 8x8 array

of coupled KII sets in the OB layer of a KIII set [8].

Each node in Fig. 5 is equivalent to a KIII set. The

cooperative synaptic interactions among them support

a KIV set, which is responsible for primitive forms

of intentional behavior.

6. A hypothesis on the relations of

meanings and representations

The idea is proposed that representations are formed

by the motor systems through the forward,

counterclockwise flow of neural activity. The motor

commands are formed at the mesoscopic level by the

interactions of neurons and neuronal populations,

under the 'enslaving' influence of the global attractor
landscape of the KIV set. The commands place the

motor systems of the brain stem and spinal cord into

appropriate basins of attraction. The representations

emerge as spatiotemporal patterns of activity in the

effectors of the body (the musculoskeletal apparatus,

the autonomic nervous system, and the

neuroendocrine nuclei), which respond to the volleys

of action potentials from motor neurons at the

microscopic level, i.e. single motor units activated in

concert. The movements of the body, supported by

the autonomic and neuroendocrine back-ups, express

the internal meaning states by gestures, vocalizations,

shaped objects, etc. These actions change the sensory

inflow of the actor in a goal-directed manner. The

actions elicit sensory feedback not only to the

individual in action. The representations, as intended,

activate patterns of receptor discharge in other beings,

that place their sensory cortices into the expected

basins of attraction. The receivers likewise create

patterns of meaning, that lead to up-dates in their

limbic systems, re-formation of motor commands,

and behaviors that re-transmit representations to the

original actor. Thereby, the circular causal chain is

maintained between two or more communicators.

The internal clockwise back flow of neural

activity serves as an order parameter to modulate and

shape the neural activity patterns of the sensory

cortices, which transmit the states of their neural

populations before and after the expected inputs have

occurred, and also if they do not occur as expected, or

at all. Modulation comprises not only the reafference

but also the exteroceptive and proprioceptive feedback

as well. I infer that the organisms constructing and

transmitting representations cannot know their

meanings until the sensory consequences have been

delivered to their own limbic systems. More

generally, a poet, painter, or scientist cannot know

the meaning of his or her creation until after the act

has been registered as an act of the self, nor until the

listeners and viewers have responded with reciprocal

representations of their own, each with meaning

unique to the recipients.

Why do brains work this way? Animals and

humans survive and flourish in an infinitely complex

world despite having finite brains. Their mode of

coping is to construct hypotheses in the form of

neural activity patterns and test them by movements

into the environment. All that they can know is the

hypotheses they have constructed, tested, and either

accepted or rejected [5,13]. The same limitation is

currently encountered in the failure of machines to

function in environments that are not circumscribed

and drastically reduced in complexity from the real

world. Truly flexible and adaptive intelligence

operating in realistic environments cannot flourish

without meaning.

This assembly of interacting wave packets may

be seen as a mechanism supporting consciousness,

which, in the neurodynamic view, is a spatiotemporal

pattern of activity that occupies the entire forebrain.

It is an internal state variable that has a trajectory

composed of a sequence of transitory states that

correspond to awareness. Its regulatory role

transcends that of the operator in a thermostat,

although they share the properties that instantiate the

difference between the state of the environment and

expectation, such as a sensed temperature and a set

point, and that initiates corrective action respectively

by intentional action or by turning a heater on or off.

The difference is that the simple machine state

variable has little history and no capacities for

learning or determining its own set point, but the

principle is the same: the internal state is a form of

energy, an operator, a predictor of the future, and a

carrier of information that is available to the system

as a whole. The feedback device is a prototype, an

evolutionary precursor, not to be confused with

awareness, any more than tropism in plants and

bacteria is to be confused with intentionality. In

animals and humans, the operations and informational

contents of this global state variable constitute the

experience of causation.

7. Summary

Semantics deals with the relation between meanings

and representations, widely known as intention,

'aboutness', or the symbol grounding problem.

Brains obtain information about their environments

through the consequences of the intentional actions

that they execute using their bodies.

Studies of the spatiotemporal patterns of

electroencephalographic (EEG) potentials that are

induced by conditioned stimuli in the primary sensory

and limbic cortices of trained animals have shown

that the information thus obtained is used to construct

meanings and is then discarded. Computers use

representations for information processing and

symbol manipulation, but brains have no internal

representations. They deploy dynamic neural

operators in the form of neural activity patterns that

construct and implement meaning but not

information. Observers can describe these patterns as

information, but that does not imply that the brains

do so, or need to. Brains construct external

representations of their meanings in the form of

shaped objects or movements as their mechanism for

expressing their internal states. Examples are facial

expressions and gestures in animals and words in

humans. Those material constructs are made with the

intent to elicit meaning in other brains, but they have

no meanings in themselves and do not carry meanings
as if they were buckets or placards. Meanings can

only exist in brains, because each meaning expresses

the entire history and experience of an individual. It

is an activity pattern that occupies the entire available

brain, constituting a location in the dynamic state

space of a brain. EEG data indicate that neural

patterns of meanings in each brain are based in

mesoscopic wave packets that follow trajectories in

discrete steps. Each step is demarcated by a 1st order

phase transition that enables formation of

spatiotemporal patterns of chaotic oscillations in the

gamma range. Amplitude modulation of the carrier

wave is the mode of expressing meanings. These

wave packets do not represent external objects; they

embody and implement the meanings for each

individual of his or her interactions with the

environment. 

Engineers who propose to make semantic

machines are faced with the task of defining meaning,

which at present exists only in brains, and then with

the task of learning how to design machines that can

make or cause meaning in themselves. The

requirements on network models to simulate the

chaotic dynamics of brains include global though

sparse connectivity, continuous time dynamics, and

distributed spatial functions in two-dimensional arrays

of nonlinear integrators. Digital hardware may suffice

to emulate the biological functions of sensory cortex

in brains by use of nonlinear difference equations as

in KIII sets [8], provided that the problems can be

solved of attractor crowding and numerical

instabilities that inhere in digital representations of

chaotic dynamics [12]. Digital simulation is a useful

step toward analog simulation in VLSI [15], by

means of which to attain the computational speed that

will be required for real-time operation of the device.

In this way, the next step toward machine meaning

can be to use a KIII model of a sensory cortex as an

interface between the unconstrained real world, which

is infinitely complex, and the finite state automaton

that will rely on a dedicated digital computing system

as the main support for its artificial intelligence.

That is, a model from brain dynamics can provide the

eyes and ears for a conventional computer, that can

enable the device to interface effectively with the

infinitely complex environment that it will share

with its designers, and about which it can

communicate its views.

Acknowledgment

This work was supported in part by a NASA grant

(NCC 2-1244). A preliminary version of this work

appeared in Freeman [5].

References

[1] Barrie JM, Freeman WJ, Lenhart M. Modulation

by discriminative training of spatial patterns of

gamma EEG amplitude and phase in neocortex of

rabbits. J. Neurophysiol. 76: 520-539. 1996.

[2] Freeman WJ. Mass Action in the Nervous

System. New York: Academic, 1975.

[3] Freeman WJ. How Brains Make Up Their Minds.

New York: Columbia University Press, 2001.

[4] Freeman WJ. Neurodynamics. An Exploration of

Mesoscopic Brain Dynamics. London UK:

Springer-Verlag, 2000.

[5] Freeman WJ. A neurobiological interpretation of

semiotics: meaning, representation, and

information. Inform. Sci. 124: 93-102, 2000.

[6] Freeman WJ, Baird B. Relation of olfactory EEG

to behavior: Spatial analysis. Behav. Neurosci.

101:393-408, 1987.

[7] Freeman WJ, Barrie JM. Analysis of spatial

patterns of phase in neocortical gamma EEGs in

rabbit. J. Neurophysiol. 84: 1266-1278, 2000.

[8] Freeman WJ, Chang H-J, Burke BC, Rose PA,

Badler J. Taming chaos: Stabilization of

aperiodic attractors by noise. IEEE Trans.

Circuits & Syst. 44: 989-996, 1997.

[9] Freeman WJ, Rogers LM. Fine temporal

resolution of analytic phase reveals episodic

synchronization by state transitions in gamma

EEGs. J. Neurophysiol., 87: 937-945, 2002

[10] Haken H. Synergetics: An Introduction. Berlin:

Springer-Verlag, 1983.

[11] Herrick CJ. The Brain of the Tiger Salamander.

Chicago IL: University of Chicago Press, 1948.

[12] Kozma R, Freeman WJ. Chaotic resonance:

Methods and applications for robust classification

of noisy and variable patterns. Intern. J.

Bifurcation & Chaos 10: 2307-2322, 2001.

[13] Piaget J. The Child's Conception of Physical

Causality. New York: Harcourt, Brace, 1930.

[14] Prigogine I. From Being to Becoming: Time

and Complexity in the Physical Sciences. San

Francisco: WH Freeman, 1980.

[15] Principe JC, Tavares VG, Harris JG, Freeman

WJ. Design and implementation of a biologically

realistic olfactory cortex in analog VLSI. Proc.

IEEE 89: 1030-1051, 2001.

[16] Shannon CE. A mathematical theory of

communication. Bell System Tech. J. 27:379-

623, 1948.

[17] Sperry RW. Neural basis of the spontaneous

optokinetic response. J Comp. Physiol. 43:

482-489, 1950.

[18] Valery P. Oeuvres. Etablie et annotee par Jean

Hytier. Paris, Gallimard, p1597, 1957.

[19] von Neuman J The Computer and the Brain.

New Haven CT: Yale University, 1958. 
Modeling Interactive Intelligences

Luis O. Arata

Department of Fine Arts, Languages, and Philosophy

Quinnipiac University

Hamden, CT 06518

ABSTRACT

This paper explores modeling the adaptive mechanisms of

autonomous agents. The focus is on reflexive interaction as a

looping action that creates a “self.”

 Imitation and play are adaptive components of reflexive

interactions that can provide gradual modifications of the

agent’s self. Little by little, the autonomous agent reinforces

what seems to work and phases out other options through an

internal editing process.

 A performance metrics for reflexive interaction would

have to depend on the task at hand within a context, rather

than try to be an absolute measure. Evaluation of

performance has to be flexible enough to account for multiple

intelligences especially when innovation is possible.

KEYWORDS: reflexive interaction, open loops,

modeling, play.

INTRODUCTION

This paper explores modeling the adaptive

mechanisms of autonomous agents. The focus is

on reflexive interaction, which is the making of

open loops that combine feedback and

feedforward capabilities to interact dynamically

with the environment.

 Feedback and feedforward can be seen as

reaction and proaction, as sensing and probing.

An agent that can loop effectively the work of its

sensors, probes, and tools would then have the

capacity to interact reflexively with its

environment. The agent’s mechanism of reflexive

interaction constitutes a "self."

 The agent’s looping action creates internal

maps to translate interactions and compare them

continuously to previous ones stored in its

memory in order to adapt to changes. Imitation

and play are components of reflexive interactions

that can provide gradual modifications to the

internal maps of the self in adaptive systems. The

mapping process itself can be compared to the

writing and editing of a text, where grammar,

data, and ideas interact to form an effective map.

Little by little, the autonomous agent reinforces

what seems to work and phases out other

options through an internal editing process.

 To explore reflexive interaction, I begin with

a presentation of how Rodolfo Llinás describes

mapping, and Gerald Edelman’s related concepts

of reentry and binding. Next, I rely on Jean

Piaget’s model of adaptation in order to examine

the function of imitation and play in an

autonomous agent. These concepts come

together in the model of the self that Rodolfo

Llinás developed as a device that situates the

agent in an environment and helps it navigate

safely. Finally, I look at mobility of a reflexive

agent fueled by two factors: an external changing

environment and internal changes that motivate

the agent to drift into what Stuart Kauffman calls

the adjacent possible.

 A performance metrics for reflexive

interaction would have to depend on the task at

hand within a context, rather than try to be an

absolute measure. Evaluation of performance has

to be flexible enough to account for multiple

intelligences. It should not limit the freedom to

develop diverse approaches in the making of

agents as well as in the way agents carry out

tasks especially when innovation is possible.
1. REFLEXIVE INTERACTIONS

When an autonomous agent performs tasks in an

environment, change can happen to both the

environment and the agent. This process of

mutual change is an interaction [1]. Interaction,

rather than action upon objects, complicates the

dynamics of a task but it does help model more

closely how events happen in the real world, so

to speak.

 Next, we note that interactions form a link

that can create a circulation or a loop between

the linked parts. This is reflexivity. The

performance of an autonomous agent is affected

by its ability to reflect as it interacts.

 Two questions then come up. How does

reflexivity work to enhance the agent’s ability to

perform a task? And how could this reflexivity be

gauged so that it may be fine tuned with respect

to tasks?

 In I of the Vortex, Rodolfo Llinás develops

a fascinating model of the self, based on

interactive feedback and feedforward loops. He

begins with a view of the brain as a system that

does isomorphic sensory-motor transformations

of the outside world. This creates representations

that help the body act in the outside world.

Llinás then follows the lessons of the sea

squirt. This tiny sea creature has mobile state

followed by a plant-like one. During the first

phase, it has a brain. But when it finally attaches

itself to a surface, the sea squirt digests its own

brain along with the tail that provided motility.

Llinás concludes that “the evolutionary

development of a nervous system is an exclusive

property of actively moving creatures” [2]. The

nervous system and particularly the brain are

predictive instruments that allow the organism to

move more safely in search of food, often in a

potentially hostile environment. The brain creates

working models of the environment to give the

body interactive navigational capabilities. Llinás

imagines that such models are very much dreams

of our brain, and in the waking state those

dreams are guided and shaped by the senses:

“the fact is that we are basically dreaming

machines that construct virtual models of the real

world.” In effect, what we perceive is a virtual

world.

 The sense of self emerges from interactions in

the brain as it coordinates actions. The self is an

avatar of sorts within the brain’s representation

of the world. Our actions follow the

displacements of the avatar in the brain’s maps of

the environment mediated by the senses. In

actual dreams, when the senses are dormant, the

self moves through a recreated world made of

collages of memories patched through internal

logics.

 Sensations, including the elusive selfawareness, are what reflexive loops feel like in

order to help us navigate. Pain and pleasure are

guiding sensations. Self-awareness is perhaps the

most complex manifestation of this cybernetic

system.

In A Universe of Consciousness, Edelman

describes a reflexive mechanism at work in our

own consciousness. It is a signaling process that

takes place along reciprocal connections. He

calls it “reentry.” Edelman sees reentry as the key

mechanism that binds all our cognitive

mechanisms into a cohesive self. He considers

this massively parallel function to be the uniquely

distinguishing feature of higher brains. But rather

than use reentry as a feature that differentiates

higher from lower brains, whatever that could be,

we can assume that reentry is to varying degrees

a feature of any brain. This feature then can help

in the more general modeling of reflexive

interaction.

 Reflexive neural interaction works within the

complex topologies of our brain to create the

sense of self out of weaving memories. Edelman

suggests that memory is creative rather than
replicative: “every act of memory is, to some

degree, an act of imagination” [3]. Memory for

Edelman is a pragmatic process that always

remembers in and from the present. It is simply

the ability of an agent to repeat or suppress an

action. This ability seems to be at the heart of the

sense of self.

 When we weave together the work of Llinás

and Edelman, we get a rich model of the form

and function of the self. According to their

combined views, then, it is the reflexivity of the

self that would allow autonomous agents to carry

out tasks.

 The question of gauging and fine-tuning

reflexivity with respect to tasks is more

complicated conceptually. Llinás speculates that

our sense of self and what could be called

“intelligence” may well be an emergent property

of how our brain wired itself as a navigational

tool. He concludes that there are many possible

architectures for cognition. Ours does not have

to be the only one. In this case, the evaluation of

intelligence may have to be done with respect to

each separate architecture. In other words, there

are multiple intelligences. In this case, Howard

Gardner has shown that we cannot have a single

measure for all of them. He believes that the

notion of assessment has to be reinvented and

suggests using simulations to gauge how

individuals perform in more realistic and

diversified situations rather than using

standardized metrics for all intelligences [4]. This

implies that for gauging the performance of

autonomous agents with respect to realistic tasks,

simulations rather than metrics should be used.

2. ADAPTATION, SELECTION,

IMITATION, AND PLAY

Piaget presented adaptive behavior as a

combination of accommodation and assimilation.

Pure accommodation is imitation [5]. Pure

assimilation is play. In accommodation, the

individual seeks to copy a situation that calls for

adaptation and changes following the rules of the

external situation. In assimilation, the individual

plays with the situation and tries changes it in

order to embrace it. In other words, in imitation

the individual tends to change the most in the

process of copying, representing, or following

external imperatives. In play, the person jiggles

the external situation and changes it in order to

absorb it. Piaget indicated that adaptation

involves varying combination of those two

extremes.

 A key virtue of Piaget’s model is that it

incorporates naturally the function of play.

Imitation has received plenty of attention, but

play tends to be underestimated or ignored

altogether. Yet it is a key element in agent

autonomy, interaction, and development.

 A question then comes up. How do imitation

and play help stimulate reflexivity in agents?

 Play involves a reconfiguration of elements

being assimilated. This means that there is a

certain metamorphosis at work with the play

elements. They are rearranged until something

happens. This interaction produces a new

meaningful weaving, a new order, a variation, or

what could even be seen from the vantage point

of a previous order as imperfection or error

within the new configuration. But this

imperfection becomes innovation when seen from

the reconfigured perspective. Such is the creative

work of play.

 It is important to recognize at this point that

adaptation or selection in nature do not yield an

exclusive match between the selected agent and

the environment. Adaptation does not produce a

fittest agent. Edelman has noted that selectional

or adaptive systems share a remarkable

property: they can use many structurally different

ways to achieve similar results. He gave this

property the unfortunate name of “degeneracy.”

We can call it diversification. In evolutionary

terms, nature seems to play out all possibilities
that yield viable results. Nature tends to favor

multiple adaptive solutions. Play is what helps us

try out all possibilities. Play is an engine of

diversification. Its presence in Piaget’s model of

adaptation favors the use of multiple points of

view or different approaches in the construction

of autonomous agents for a given task.

 Returning to the relation between play and

reflexive interaction, we see that play with its

tendency for overflowing boundaries, testing

constraints, and diversifying, tends to excite the

agent so that it has to constantly readapt. This

exercises reflexivity.

 But play can make or break an agent. Play

needs to be bounded somehow so that it allows

the agent to exercise its reflexivity without

pushing it past a breaking point.

 Imitation, on the other hand, is linked with

representation. This helps the agent map its

environment in connection with given tasks. We

need to distinguish, however, between copying

structures imitated, and transforming them into

maps based on the agent’s system of

representation. Imitation for autonomous agents

is then a transformation and a translation from

something perceived outside of the agent to

something inside the agent that allows it to

interact more effectively with what is perceived.

Imitation makes maps that are webs of

memories. The sense of memory is the one we

saw before based on Edelman’s view. It is nonrepresentational. Woven memories, although not

a copy of what is perceived, do evoke it in a

functional way. The agent uses the map of

memories to help with navigation, placing itself in

it.

 We can take a closer look at the form and

function of play in a created autonomous agent.

How would it work? Could play have a

purpose? First of all, it is important to recognize

that play is interactive. It does not rest entirely on

the side of the player. The player needs a

partner. That partner is outside the player. This is

perhaps the single most reason why play has

been mostly overlooked before in its cognitive

function. It has not been noticed that there is play

in the environment. Natural environments give us

room to play. They invite play. That may be why

children play. As adults we tend to play less

because we need to function in created structures

that are often set in their ways and restrict play.

These created structures lack the flexibility of

natural environments. Our constructed

environments do not allow for play, unless they

are playgrounds or have been designated as toys.

Our non-artistic creations come with built-in

purposes. Deviations from expected uses are

usually not welcomed.

 How could play enter into autonomous agent

design? What enhancement of the agent could it

bring about? To think about play in relation to

created autonomous agents we need to have an

uncertain environment to begin with. If everything

in the environment is determined, if rules of

operation are fixed, if goals are absolute, then

there is no room to play. But if rather than goals

we think of tasks, if the journey is at least as

important as the destination, and if the

environment has the potential for surprises, then

we can think about play.

 Surprise is the order of the day in

laboratories, for example. It is unfortunate that

theorist shun Murphy’s laws. Can a created

autonomous agent also play when given a task in

an uncertain environment? How can we design it

with that type of intelligence? How do we gauge

the ludic capabilities of an agent? The

introduction of flexibility into designs is a first

step. It is a passive response to play. The next

step is to design agents that can assimilate as they

play. I think we don’t even have preliminary

solutions modeled after these questions because

play has hardly been a factor in design. But we

can make some observations.

 First of all, the agent has to be able to alter

rules. Secondly, the agent has to have tools that
can be used in unintended ways. Let’s consider

now Piaget’s sense of play as assimilation of the

environment to the individual’s existing structures.

For an agent, those structures have to be open,

flexible, so that they can interact in unexpected

ways. Secondly, the agent’s tasks have to be

defined in fuzzy terms so that there is room to

play. Perhaps finally, operating rules can be

allowed to yield new tentative combinations that

could yield unexpected results. The original rules

should not be discarded as new ones emerge.

They all go into a widening repertory of

behaviors and models.

 We can say then that the agent learns through

play about its own system and about the

environment. Assessment of the ludic side of an

agent could then be linked to the quality of what

it learns with respect to very broadly specified

tasks. Contrary to what is often said, play does

have a non-trivial function. In a created

autonomous agent this function could be the

performance of self-motivated activities in an

environment that invites tinkering and exploration.

This generates discovery and learning new ways.

 The value of play is well understood in the

arts. An actor plays a role because there is room

for interpretation and self-expression that can

yield surprise and improve the performance as

gauged by audience response. Salvador Dalí

used to say that to innovate one must first master

previous techniques. When we play with

techniques and tools using pre-existing

knowledge, then something new can emerge

within that set of elements. Picasso liked to point

out that painting wins in the end—not the painter.

This underscores that the agent at play cannot

have full control of the actions. The agent opens

up and exposes itself to the environment to invite

the unknown in and play with it. The writer Annie

Dillard perhaps summed this best by observing

that the art object “is a cognitive instrument

which presents to us, in a stilled and enduring

context, a model of previously unarticulated or

unavailable relationships among ideas and

materials” [6].

 Perhaps we can use more effectively as a

model for created agents Llinás’ conclusion that

we are dreams guided by the senses. The

construction of the agent’s self has to incorporate

internal reflexivity. It has to allow somehow for

self-creation using Piaget’s sense of assimilation.

Play allows created agents to have autonomy in

uncertain environments. The internal structure of

the agent has to be able to learn from such play

and place in memory what it considers valuable.

An agent’s cognitive structure can be designed

so that it recognizes new objects by playing with

them to detect actions associated with the object

and turning such associations into usable

knowledge. Play, then, is a feedforward

interactive behavior: it tests and tags new objects

through tinkering. It has a quick trial-and-error

component that can probe the environment and

see what fits the agent’s tasks and behaviors.

Play can be seen as a form of communication

with the uncertain and the unknown.

3. DISPLACEMENTS

Finally, I would like to touch upon issues of agent

displacement as they relate to play. What selfmotivates autonomous agents move or change?

Conversely, what would prevent autonomous

agents from drifting away from preset tasks?

Since we are focusing on autonomy, we can

exclude direct external influences such as

instructions given periodically to the agent, or

built-in engines.

 We can imagine that two factors may affect

autonomous agents. One is passive and due to

the change of the external environment because

this would tend to affect the agent’s functioning.

The other is internal change that comes about as

the agent interacts in new ways with the

environment. This second factor may be active if
it becomes entangled with the agent’s sense of

self and the agent feels in control of changes.

 Stuart Kauffman proposed in Investigations

that biospheres are constantly reorganizing and

innovating [7]. He noted that within this uncertain

environment, autonomous agents have the

tendency to propagate their systems of

organization into adjacent possibilities, and create

new order. Of course, such displacements and

interactions would affect to some degree the

agent’s internal systems. The broad implication is

that nature may be constructing itself through the

interactions of autonomous agents. But Kauffman

is not suggesting that an evolutionary vector is at

work here. This process of creation is a natural

drift, as Francisco Varela once proposed [8].

We can see it as the result of play from the part

of agents.

 Nature seems to go for viability rather than

optimization. This may well be because in a

complex environment it is simply impossible to

optimize, particularly when there is interaction

between guests and host. The way to proceed is

to diversify viable options in a given environment

and let them evolve. This is where the function of

play becomes critical: it stimulates diversification.

From a design perspective, we can call this

multiple modeling. This agrees with what Llinás

indicated for cognitive systems: they can have

many possible architectures.

 The difference between evolution and drift

may well be mostly a matter of perspective. As

Varela noted, Darwinian evolution favors

optimization, whereas natural drift calls only for

viability. But viability becomes optimization when

selection criteria become so stringent that there is

only one option left in the end. For practical

purposes, it is better to require the less stringent

test of viability. This gives the agent more room

to play. Optimization needs a clear definition of a

landscape in the first place, which in a natural

environment is a daunting if not impossible task.

Viability does not. It is self-testing, so to speak.

 This suggests that performance evaluations of

autonomous agents in a natural environment or

any other environment subject to unpredictable

changes should be based on viability rather than

optimization. One possibility is to gauge the

quality of play by the number of viable solutions

that an agent can produce for a given task in an

environment. Control or enhancement of the

agent’s displacements may affect its

performance. Running models to tweak their

parameters may help gauge such displacements

and fine-tune them for specific tasks. Play control

would require building boundaries that focus and

restrict interactions, as well as insulate the agent

from external changes. Play enhancements would

come about by opening boundaries to give the

agent more freedom in certain chosen directions.

 Play then fuels reflexive interactions between

the agent and its environment, as well as between

the agent’s self and its maps. These mechanisms

can help the autonomous agent adapt to an

environment to carry out tasks that yield viable

behaviors and outcomes.

4. REFERENCES

[1] Arata, Luis O. "Reflections about

Interactivity," 1999. Internet location:

http://media-in-transition.mit.edu/articles/index

arata.html.

[2] Llinás, Rodolfo. I of the Vortex: From

Neurons to Self. Cambridge: MIT Press,

2001. P. 17.

[3] Edelman, Gerald M. and Giulio Tononi. A

Universe of Consciousness: How

Matter Becomes Imagination. New York:

Basic Books, 2000. P. 101.
[4] Gardner, Howard. Intelligences Reframed:

Multiple Intelligences for the 21st Century.

New York: Basic Books, 1999. P. 207-10.

[5] Piaget, Jean. Play, Dreams, and Imitation

in Childhood. New York: Norton, 1962.

[6] Dillard, Annie. Living by Fiction. New

York: Harper & Row, 1982. P. 184.

[7] Kauffman, Stuart. Investigations. New

York: Oxford, 2000.

[8] Varela, Francisco, Evan Thompson, and

Eleanor Rosch. The Embodied Mind.

Cambridge: MIT Press, 1991.
STRUCTURED APPROACH TO THE INTELLIGENT SYSTEM

DESIGN

Dr. Leonid M. Polyakov

Professor of the Department of Computer Systems and Math

Globe Institute of Technology

New York, NY

Leonid@globeinstitute.org

ABSTRACT

Artificial Intelligence (AI) is a science of intelligence system

design. Existing definitions of intelligence don’t answer some

important questions of engineering procedures. What kinds of

intellectual tasks do we have? Who is more intelligent or smarter: a

scientist or a wood-maker (human or machine), a metal-maker or a

wood-maker? How to design a system with reasoning as the most

powerful intellectual function? What is intuition? Can we design a

system with intuition?. All these topics are subjects of discussion in

this paper. The goal of this paper is to find active, productive may

be not the best way to determine the starting position and some

directions of intelligent system design.

Keywords: intelligence, intuition, associative thinking, fuzzy,

agent classes, intelligence classes, structure, reasoning,

preposition logic, predicate logic, knowledge base, rules of

reasoning, application rules, design.

INTELLIGENCE DEFINITION

There are many different definition of intelligence [1--19],

but none of them give the answer acceptable by a scientific

community.

First of all, intelligence is a fuzzy term. In some cases it is

very difficult to draw a line between intelligent and nonintelligent natural and artificial systems. For example,

biological adaptation or any kind of evolution can be

presented as learning intelligent ability or non-intelligent

process. It is difficult to determine when expert system

became an AI system. All intellectual activities are triggered

by the goal. “A system can be intelligent only in relation to a

defined goal...” [11]. All kinds of intellectual activities in the

specific area are based on knowledge, but intelligence is not

knowledge. Knowledge is a “tool” of intelligence. If you

don’t understand a goal, you are not capable to reach it. An

ability to learn is an important intellectual ability that can

improve knowledge. Knowledge reinforces intellectual

activities. There are two components of intelligence: general

intelligence that is inherited at birth, and knowledge-based

intelligence that can be improved by learning. Twin studies

support this approach but the twin result of intelligent level

measurement depends on intelligent definition and the

measurement method that still are problems. Professor Ulric

Neisser (Cornell University) notes [30] that in isolated areas

where the gene pool has been unaffected by migration, the

longer that children attend a school, the higher their I.Q.’s on

average. The knowledge base is a module, organized memory

of an intelligent system and knowledge is just a content of

this base. “Intelligence is an internal property of the system,

not a behavior” [20], but a behavior is the main criterion of

an intelligence level. This level can be determined by a test.

The natural system inherits strong information through

genetic code. They have very strong general intelligence. The

artificial system has relatively weak information power from

the hardware and the software.

Inherited “brain power” of natural intelligence is determined

by power of a neuron net (number of neurons and power of

connections: value of a weight function, a threshold and a

transfer function). A process of knowledge collection creates

an information flow through the neuron net and increases

power of connections (Hebb). As a result “brain power”

increases. In a simple brain model a neuron is a variable with

two values: ON and OFF. The simple rule of knowledge

(if...then) in KB can be presented as a variable with two

values. The more rules the more connections between the

variables the higher intellectual power of a system. In AI

systems that are not based on neuron net technology,

increasing a number of rules in KB increase a number of

virtual connection between the different parameters as well.

The knowledge base is the main source of information and

intellectual power of artificial systems. Inheritance is the

main source of natural intelligence power.

A definition is not a description of a system design. Good

definition presents a term from the user (customer,

supervisor, etc.) point of view and helps to recognize it

among the other terms. It should be as simple as possible.

Now we can try to design an intelligence definition.

influence mental and physical behavior in accordance

General intelligence (inherited or hardware intelligence)

is an organized combination of conscious and unconscious

potentials (cognitive and expressive potentials) in a

sentient system that able to direct and with a system goal.

General intelligence is a capability opposite to ability of the

system. It can be evaluated indirectly through electrical and
chemical brain activities are measured by instrumentation. A

level of fuzziness determines a level of confidence.

Knowledge-based intelligence can be defined as a

knowledge-based general intelligence (or ability) of a

domain-oriented system to act under existing constraints

(limitations) and reach external or internal goals or

decrease the distance between the starting and the goal’s

stages. A goal’s description can be presented in crisp,

fuzzy, or probability and statistics theory languages. This

definition covers not just cognitive power but a power of

sensing system and the actuators. In this case cognitive

power is limited by knowledge and extended by learning.

Knowledge-based intelligence can be evaluated by behavior

tests. General intelligence of AI systems can be evaluated by

reading of design documentation and program source code.

Unfortunately access to this information usually is not

available under secrecy conditions. Both of these definitions

of intelligence agree with existing two-factors, multipleintelligence, and information-processing theories of natural

intelligence [23]. This is the extreme definition. As a working

definition of AI system it is possible to accept: the system

with one or more intellectual abilities (Fig 1) or the

system that emulate one or more intellectual abilities.

Note: a condition statement “if-then” in a hard coded

program is not an element of a knowledge base. The

conventional closed-loop information system (control

system) is not knowledge-based. Only the intelligent system

is based on knowledge. This statement supports fuzzy nature

of intelligence definition.

The time it takes to execute the goal is one of many

important characteristics of a system performance such as

learning ability, duration of the object recognition, etc. and

should not be incorporated into the definition. The statement

“...a goal should be reached for a certain period of time...”

does not make any sense and does not make the definition

better.

In some discussions we can hear that sometimes a high

intelligent system performs some specific job worse then a

lower level of a intelligence system (in human society we

have the same). So what, don’t use the tractor instead of the

hammer. The right choice is a very important characteristic

of human and artificial intellect as well.

CLASSIFICATION OF THE

INTELLIGENCE TASKS AND

ABILITY OF THE AGENTS TO ACHIEVE

THEIR GOALS

The system design is based on set of desirable system tasks

(abilities) and relationships between them. A conventional

software design technology creates the programs for the

specific problem solution. From the programmer point of

view AI is a software design technology to create programs

with intellectual abilities. These programs can be used for

wide area of the problem solutions.

What kinds of intellectual tasks do we have? Who is more

intelligent or smarter: a scientist or a wood-maker (human or

machine), a metal-maker or a wood-maker? In [23] we can

read: “Who’s more intelligent: a Supreme Court Justice or

professional golfer?” Task classification can help to design

system.

Intelligence abilities can be presented as the multilevel

structure [2,32]. But this structure presents a system view

from one side. A multilevel structure of functions

(abilities) (see Fig. 1) with expressive and cognitive thinking

at the upper levels of the structure; learning, problem solving,

and etc at the middle level; and generalization, reasoning,

conceptualization, induction, information collection,

perception, etc. at the lower level of the structure presents the

system from another point of view. Perception can be

presented as a set of the different signal, emotion – as a set of

the different kinds of emotions. Conceptualization itself

consists of two levels: identification of important

characteristics and identification of how the characteristics

are logically linked. Certainly this structure is based on some

level of simplification of the relationship as well as the set

size of abilities. But any way, this structure can help to

determine the set of abilities related to the certain goal, their

relationships, and determine the metric structure to evaluate

the system intelligence levels. It takes longer to exercise the

upper level abilities than the lower level abilities. Different

tasks need different sets of abilities to fulfil these tasks.

“Animal behavior ought to be used as a model to define a

hierarchy of intelligence tasks”[28].

The structure of the intelligent functions was discussed early,

for example, in [20]. In accordance with the definition in this

paper “intelligence is an ability...” but what kind of abilities

are “the information and values the system has stored in its

memory”? The mixture of different levels like reasoning and

problem solving (reasoning is the lower level ability

relatively to problem solving), reward and punishment with

value judgement (reward and punishment is the lower level

ability relatively to value judgement) creates the wrong

structure. Computation power (speed, sophistication of the

algorithm of computation or something else?) and number of

processors, knowledge representation mechanisms and

symbols (symbols of what?), and many others are placed in

one row as dimensions of intelligence.

In [1] and [2] we have a very clear answer to the goal

importance problem. The goal is a result of the intelligent

system actions. “A system can be intelligent only in relation

to a defined goal or environment”[11]. Different tasks,

different areas of activities have different goals. Similar goals

can be combined into one class, which we can call the goal
class. The goal class (similarity) is determined by minimal

set of abilities to fulfil the goal of the task with the same

weight functions of each ability. All agents that exercises the

same minimal set of abilities to carry out the goal with the

same set of weight functions can be combined into one class

which we can call the agent class. The members of the same

agent class can fulfil the goals of the same goal class.

A scientist, a wood-maker, and a metal-maker are trained to

perform different classes of tasks (goal classes) and we

cannot make any comparisons between different agents of

different agent classes. So, it is impossible to compare a

scientist and a handyman, as long as they fulfill different

tasks under different goals. In some cases it is possible to

combine the systems with visible different intelligence levels

into one agent class. For example, agent from the “handyman

class” and agent from the “scientist class” can be combined

into one class if these systems act under similar goals as for

example, surviving, reproduction, repairing something that

does not need any special scientific knowledge, etc.

Performance of these systems and level of their intelligence

can be compared. Multiple-intelligence theory [23] supports

this point of view. Achievement of the same goal by the

different agents usually involves the same set of their abilities

with the same set of weight functions. It is impossible to

compare a car and bookstore even if you use the money scale

to evaluate them. But as soon as you look at them as

investment choices (taxi or shop), you will be able make a

comparison: the same goal (profit) and the same set of

characteristics. The stock market permits the use money scale

to compare almost everything because the same investment

goal and the same parameters of evaluation. Good gamblers

in reality use vector function, but non-sophisticated people

play by price difference.

It is reasonable to suppose that a scientist has better training

in abstract abilities than a handyman. It is reasonable to make

serious decision about differences of the intelligence level of

these systems. Different domain applications are determined

by different sets of abilities. But it is possible that a

handyman (human or machine) has grater level of

intelligence (special abilities) then a scientist (human or

machine). If these handyman’s special extra abilities are not

fit to the his/her/its kinds of activities then they can not be

utilized in the professional activities of a scientist and a

handyman as well. Performance of the different tasks utilizes

the certain limited sets of intelligent abilities. In this case a

very smart metal-maker will not be able to use full his/her/its

available intelligence power and will not be able to

demonstrate the full set of abilities that are not important to

fulfil standard metal-worker task.

In order to make an evaluation of a real “brain” power of the

system, we should assign a reasonable and comparable goal

level. It is important to avoid using the overqualified agent.

By the way, it is a big problem of the job market.

Human intelligence is not a subject-oriented set of abilities.

We are not talking about a genius; we are talking about

ordinary people. I myself don’t understand the nature of

genius. Machine intelligence (for the time being) is a subjectoriented ability. There are different levels (capacities) of

intelligence. Sometimes different levels of performance

(skills) can be presented as different levels of intelligence.

Different levels of performance are determined in many cases

by limitation of one or more elements of the system.

Advanced upper level abilities of the intelligent structure

(generalization, conceptualization, etc.) are not guarantying a

high level of the skills. For example, low capability of the

sonar sensors can prevent a person to be a musician even if

he/she/it has a suitable capability of the rest of the

subsystems. Beethoven was not a deaf man; he lost his ability

to hear. Composer as music designer can “hear” his music

with his inner “sensor”. The famous woman Helen Keller,

author and educator was deaf, blind and mute but she had a

sensitive tactile system and sense of smell. She learns to

“hear” and to speak and she was able to make her great

intellectual power work [14]. A scientist with a high level of

intelligence may have a problem doing a manual job if

he/she/it does not have suitable actuators. A “handyman” is

not a handyman without hands. There are two choices to

design the definition of intelligence: to extend definition and

include sensors and actuators or to add separate explanation

of sensors and actuator importance. As soon as we talk about

intelligence as ”...an ability of a system to act

appropriately...”[1], we include an actuator into this

definition. No sensors – no knowledge, no actuators – no

performance; and it is impossible to evaluate the level of

intelligence.

Globe Institute of Technology has strong positive

experiences to reeducate people of different backgrounds into

very good programmers. Our experience shows that a

medical doctor, a psychologist, an engineer, a teacher, and

people who worked in many other fields, can fulfill tasks of

the high level programmer and they like doing it. There are a

lot of people who are good scientists, medical doctors or have

other professions and at the same time are good writers or

musicians, biologists or good mathematicians, etc. These

examples support the assumption that human intelligence is

determined by a goal achievement activity level but not by

area of application. In other words, agents in many different

domains can be combined in one agent class if they exercise

the same minimal set of abilities at the same levels.

 AGENT WITH REASONING. THE STRUCTURE

DESIGN

Reasoning is the most powerful intellectual function but it is

not easy to emulate it. The main problem is determined by

the nature of reasoning that is based on computation with

words instead of computation with numbers. There are a lot

of different approaches to the knowledge representation in

the agents. The most important languages of knowledge
representation are preposition logic and predicate logic.

Agent models of reasoning based on preposition and

predicate logic are topics of this discussion.

Reasoning, as we know, is the process of drawing conclusion

from facts. There is a lot of research dedicated to the

problems of reasoning and the agent structure design

[7,9,18]. All of them are based on representation of

knowledge as rule-based, semantic net, or frame structure

knowledge base. These knowledge bases (KB) include just

application knowledge (AKB) (domain oriented KB). Rules

of reasoning are applied on AKB in different ways for

different agents. This approach decreases the level of

universality of the agent. Most existing systems with

reasoning are not universal theorem provers (http://wwwformal.stanford.edu/clt/ARS/Entries/acl2). These systems are

based on rules of reasoning and don’t work with application

knowledge. Some of them, like ACL2, are designed as multiKB with (Deductive machinery, Dynamics, Persistence).

However, all these systems are based just on preposition

logic. The most interesting result in the area of reasoning is

the Jess language ( Jess, the Java Expert System Shell

http://herzberg.ca.sandia.gov/jess/demo.html). This language

is based on just one KB-AKB. Information is presented by

predicate logic. Rules of reasoning are incorporated into a

source code.

A possible way to increase the level of universality of the

agent is by creating the double KB agent structure. The first

KB is application knowledge base (AKB); the second one is

rule of reasoning KB-RKB. RKB is universal KB. It can be

used with different AKB. The Double-KB structure of a

system (the programmer Mr. U.Rozenblad) is shown on

Fig.2. Complicated application rules should be decomposed

to simple rules by DeMorgan’s, associative, and other laws.

The idea of a multi-KB in search engines also was described

by Dr. Lotfi Zadeh in “The Prototype-Centered Approach to

Adding Deduction Capability to Search Engines- The

Concept of Protoform” (BISC letter, 21 Dec 2001)

http://www.cs.berkeley.edu/People/Faculty/Homepages/zade

h.html . In this letter: “The deduction database is assumed to

consist of logical database and a computational database,

with the rules of deduction...” Rules of deduction are

Implication Elimination, And–Elimination, And-Introduction,

etc. These rules transfer rules of application in canonical

form. Transformation can be done during of application role

presentation or during a program execution. First way is more

time efficient, second one does not change of application

roles presentation and make them easy recognizable.

Advantages of reasoning rules separation from a program:

1. Simple choice of the set of rules from the prepared list of

rules for each area of application.

 2. Standardization of a program by coding only reading

functions and functions of recognition. The standard program

can be easy designed and testing. The standard program has a

high level

of universality and can be easy adapted to the different areas

of application.

3. Separation of rules of reasoning from a program makes a

program easy readable, better understandable, and as a result

more reliable.

INTUITION

It is not the question: does machine have intuition or doesn’t

have it? If we are machines and we have intuition then a

machine has intuition. The problem is to define the word

intuition to make it worktable. There is a lot of different

definitions [ 3,8,10,12,15,16,18,21,24,25,31,32,34]. From the

practical point of view we need the positive, constructive

approach even if in the beginning we design system just with

the realization of the simple process of the intuition imitation.

The most famous definition of intuition is “the immediate

knowing, or learning of something without the conscious use

of reasoning; instantaneous apprehension” (Webster’s New

universal unabridged dictionary). The difference between

intuition and association (by Webster’s) is: the first is a nonconscious process, the second is a conscious process. This

definition is not productive.

It is impossible to extract knowledge from nothing. If you

never heard about the stock market or brain surgery, you will

never have intuitive decision in these areas. Knowledge

extraction is a conscious process. There are two conditions

under which one idea is able to recall another. “These

conditions may be classified under two general heads, the law

of contiguity (in reality is law of associations), and the law of

associations (in reality is law of reasoning). The first states

the fact that actions, sensations, emotions, and ideas, which

have occurred together, or in close succession, tend to

suggest each other when any one of them is afterward

presented to the mind. The second indicates, or ideas tend to

recall their like from among previous experiences. On their

physical side the principles of association correspond with

the physiological facts of reexcitation of the same nervous

centers” (Webster’s New universal unabridged dictionary).

These two definitions relate to two different processes. One is

associated thinking, second one is intuition.

The memory is a network hierarchy [23]. It is arrangement of

nodes or categories such that concrete ideas are at the bottom

of the hierarchy and are connected to more abstract ideas

above them. The most abstract ideas are at the top. Intuition

is the process of searching a problem solution and ideas

along the hierarchy of a memory. Association is the

process of searching a problem solution and ideas

through direct relationship between them. Analogy is

based on semantic similarity, similarity of memorization time

combination of the objects in the set based on different

criterions, etc. Intuition is a result of free “travel” through the

memory structure. The typical example of associative

learning is a baby learns to associate the smell of its mother
with food (classical conditioning). A student learns that

working hard usually produces good grades (operating

conditioning) [26]. This definition is worktable, reasonable

and non-contradictable. Such presentation of intuition may

not be the best but is very productive for artificial intelligence

system design. In [33] there is description of personality with

intuition as a personality with “focus on implication and

inferences.”

Associative thinking creates the net between different

objects, events, and images. Intuition is not just the search of

the similar solution of the problem but sometimes is

“design” of a solution as a sophisticated assembly of the

several elements. In this case we deal with more complicated

procedures. From the external point of view intuition looks

like associative thinking.

 In opposite, the research and decision searching are

motivated intendment organized processes of a solution of a

problem searching. Importance of intentionality is mentioned

by many philosophers. For Edmund Husserl (German

philosopher) intentionality is “one essential feature of any

consciousness”. For Jean-Paul Sartre (French

philosopher and writer) “intentionality is consciousness”.

Spontaneous brain activities can be triggered by a non-verbal

fuzzy defined problem that is dominated in the memory at

this particular time. In this case, accidental knowledge

activates the algorithm searching for patterns, history,

relationships and etc. to find solution of the problem. The

more data and information that is stored in the memory, the

better the result of the intuition process. The higher

information diversity the more efficient an intuition solution.

There are two kinds of information: genetic and non-genetic.

In artificial systems genetic information is stored in the

hardware and partly in software and contributes to the

artificial intuition.

Intuition is an “automated” high speed process. Cognitive

thinking in most cases is a low speed “manual” process

executed under control of human will.

All knowledge about objects and processes has to be

presented as models designed from the different points of

view (structural models, math models, logical models,

chemical models, electrical and information model, etc). For

example, a human body can be presented in the different

ways as a structured model, a chemical model, an

information model, a mechanical model, etc. Such ways of

knowledge presentation make it possible to easily identify

common features in different areas. The structured

organization of the knowledge in the memory is a very

important condition of effective functioning of the artificial

intuition. In our case, (artificial system). we don’t have

problem of the natural brain in attaching meaning to the

symbolic representation [21]. Existence of the memory

makes reasonable the materialist point of view and cognitivist

point of view as well [21]. Anyway this problem is not a

subject of our discussion.

In the reconstruction of new knowledge when any past event

or experience is recalled, the act of recollection tends to bring

again into use other events and experiences that have become

related to this event in one or more of certain specific ways

this association. Associative memory refers to the ability to

recall complete situations from partial information These

systems correlate input data with information stored in

memory Information can be recalled from even incomplete

input. Associative memory can detect similarities between

new input and stored patterns [10, 12, and 21]. So intuition

and association should work together. Realization of the

associative memory can be done as the Hopfield Neuron

Network [21].

Spontaneous brain activities can be triggered by spontaneous

interest of the system to the problem. For example the

problem of dangerous environment for the system existence.

It can be the cause of spontaneous problem formulation.

Spontaneous undependable problem formulation is possible

just in case of the availability of the powerful sensor system.

This system collects information about simple, separately

non-dangerous events, puts it together independently from a

human will, looking for patterns and creates a sense of

danger. The process and information are presented in fuzzy

description.

Let us look at the simple scenario. At nighttime you left a

party with your friends and were going home. You were

thinking about the good time you had. Suddenly you step-into

a dark street as a part of your way home (level of darkness

may be different-fuzzy descripted). Nothing is wrong around

but your body becomes alerted even if you try to calm

yourself through reasoning. Intuition vs. reasoning!

Intuition can win because reasoning is based on the same

knowledge! Reasoning can just add some new information

and knowledge. As a result, correction of the sense and

behavior can be obtained.

When we meet a stranger, we receive a complex of

information about his/her appearance, body language, and

way of talk, etc. Our brain compares this information with

the fuzzy or statistical models of a “good” or “bad” object

appearance, behavior, etc. and creates our “fuzzy” impression

model about this object. “Good” or “bad” object models are

based on our previous experience. This situation was

emulated on the computer (the programmer Ms. N.

Elisseeva). The system was able to generate intuitive

impression at the meeting with a stranger (Fig. 3).

Unintentional brain activity can include the testing

procedures. One day I sent an e-mail but forget to attach my

file I promised to my friend. I was sure that I did not make a

mistake. In the middle of the night, I suddenly woke up and
realized that I did not attach the file. My brain was testing my

activities stored in the short memory against the goal

procedure and sent me the error massage. It’s remind

automatic virus testing software when we rebut the computer

without special activation. Certainly, it is just analogy. This

ability the control a human activities is a very useful part of

Artificial Intelligence. The “Testing” module can do this test.

The described approach can be illustrated by another

example. Suppose we have the AI system, which has

extensive working experience in the different areas of

knowledge and powerful learning ability from the

experienced external teacher. The knowledge is represented

as the models: linguistic, math, logical, structured, etc, above.

Suppose the system has knowledge about a damper, which is

presented as a linguistic model (damper, controller of

acceleration, brakes, and etc) and as a math equation of a

damper:

y=(1+e –t/T )

or the transfer function

W(p) = k/(1+Tp),

as a physical object (hydrodamper, pneumatic damper,

capacitor, inductance, robber damper, spring, mechanical

brakes, electrical brakes, flywheels etc), and so on. All these

models create the hierarchical structure in the knowledge

base. The more abstract the description, the higher location

levels. The linguistic description belongs to the higher level.

The physical description belongs to the lower level. Suppose

we have a control system and would like to reduce the

acceleration of the moving parts. The AI system has

information (through the sensors) about the problem and

starts looking at a solution of the problem without our

interference. The search procedure is shown in Fig.4. Each

level represents a new level of goals. Each new goal

motivates a next search step.

Intuition can be activated in the slipping stage when the brain

is working without participation of the human will. In an

interview with The New York Times (Nov. 14, 2000) Dr.

Terrence J. Sejnowski (a neuroscientist at the Salk Institute in

San Diego) said: “There has always been a close connection

between sleep and creativity, which may be a byproduct of

the way that nature chose to consolidate memories”.

CONCLUSION:

 1. Intelligence consists of two parts: inherited and

developed.

 2. Intelligence abilities can be presented as the functional

multilevel structure. Similar goals of the agents can be

combined into the goal class. The goal class is

determined by minimal set of abilities to fulfil this goal

of the task and one set of weight function.

 3. All for each alternative – class member agents that

exercises the same minimal set of abilities and common set

of weight functions to carry out the goal can be

combined into the agent class. The members of the same

agent class can fulfil the goals of the same goal class.

4. The structure of intelligence system should be designed as a

two-knowledge base system. One is an application knowledge

base, another one is a reasoning knowledge base.

 5. Intuition is the process of searching a problem solution and

ideas along the hierarchy of a memory. Association is the

process of searching a problem solution and ideas through

direct relationship between them.

REFERENCES:

[1] Albus J., Outline for Theory of Intelligence. IEEE

Transactions on Systems, Man, and Cybernetic, ol. 21, No 3.

May/June, 1991.

[2] Albus James S., Meystel Alexander, Behavior Generation

in Intelligent Systems, NIST.

[3] Artificial Intelligence With Dr. John McCARTY

Conversation On The Leading Edge Of Knowledge and

Discovery With Dr. Jeffry Mishlove, 1998.

[4] Boden, Margaret A., Artificial Intelligence and Natural

Man, Basic Books, Inc., New York, NY, 1977.

[5] Bock, Peter, The Emergency of Artificial Intelligence:

Learning to Learn, The AI Magazine, Fall, 1985.

[6] Charnik, Eugene and McDermott, Drew, Introduction to

Artificial Intelligence, Addison-Wesley Pub. Co., Reading,

MA 1985. .

 [7] Cawsey A. The Essence of Artificial Intelligence.

Prentice Hall, 1995

 [8] Computers and The Mind with Howard Rheingold.

Conversation On The Leading Edge of Knowledge and

Discovery with Dr. Jeffry Mishlove, 1998.

[9] Dean T., Allen J., Aloimonos Y. Artificial Intelligence.

Theory and Practice. The Benjamin/ Cummings Publishing

Company, 1995.

[10]Decision Support and Expert Systems1997. .

Management Support Systems by Efraim Turban.

Prentice Hall. 1995.

 [11] Finkelstain Robert, A Method For Evaluating the “IQ”

of Intelligent System, Preliminar Proceedings “Performance

Metrics for Intelligent Systems Workshop, August 14-16,

2000, Gaithersburg, MD.

 [12] Foundations of Neural Networks by Khanna, AddisonWesley, 1990.

 [13] Huffman Karen, Vernoy Mark, Vernoy Judith,

Psychology in Action, John Wiley & Sons, Inc

 [14] Keller Helen. The Story of My Life, 1902.

 [15] Language And Consciousness. Part 4: Consciousness

and Cognition with Dr. Steven Pinker. Conversation On The

Leading Edge Of Knowledge and Discovery With Dr. Jeffry

Mishlove, 1998.

 [16] Meystel A. Evolution of Intelligent Systems

Architectures. What Should Be Measured?

Performance Metrics for Intelligent Systems. Workshop.

August 14-16, 2000, Gaithersburg, MD
[17] Mind Over Machine With Dr. Hubert Dreyfus.

Conversation On The Leading Edge of Knowledge and

Discovery with Dr. Jeffry Mishlove, 1998

[18] Mind As A Myth with U. G. Krishnamurti.

Conversation On The Leading Edge of Knowledge

and Discovery with Dr. Jeffry Mishlove, 1998.

[19] Myers David G. Psychology, Worth Publish, 1995.

[20] Negnevitsky M. Artificial Intelligence. A Guide to

Intelligence Systems, Addison-Wesley, 2001

[21] Neural Networks and Physical Systems with

Emergent Collective Computation Abilities by Hopfield,

J. Proceedings, Natural Academy of Sciences USA 79,

1985. Their Minds, PHOENIX, 1999.

 [22] Panos Antsaklis, Defining Intelligent Control. Report

of the Task Force on Intelligent Control, IEEE Control

Systems, June 1994.

 [23] Plotnik R. Introduction to Psychology, Brooks/Cole

Publishing Company, 1995

 [24] Philosophy, History & Problems by Samuel Enoch

Stumpf, McGraw- Hill, 1994.

 [25] Psychology by Peter Gray, Worth Publishers, 1999.

 [26] Psychology by Crider A. B., Goethals G. R.,

Kavanaugh R. D., Solomon P. R. Harper Collins College

Publishers, 1993.

[27] Russell Stuart, Norvig Peter, Artificial Intelligence. A

Modern Approach, Prentice Hall, 1995.

[28] Subhash Kak, Grading Intelligence in Machines: Lessons

from Animal Intelligence, Preliminary Proceedings

“Performance Metrics for Intelligent Systems Workshop,

August 14-16, 2000,

 [29] Winston, Patrick Henry, Artificial Intelligence, AddisonWesley Pub. Co., Reading, MA 1985

 [30] The Rising Curve, edited by Proof. Ulric Neisser,

American Psychological Association, 1995.

 [31] The Transcendence Of The Ego. An Existentialist

Theory Of Consciousness by Jean-Paul Sartre. Hill and

Wang-New York, 1997.

[32] The Oxford Companion to MIND, edited by Richard L.

Gregory, Oxford University Press, 1987

[33] Tieger P.D. and Barron- Tieger B. Do What You Are.

Little, Brown and Co. 1995.

[34] Unlocking your Subconscious Wisdom. Part 1: Using

Intuition with Dr. Marcia Emery. Conversation On The

Leading Edge Of Knowledge and Discovery With Dr. Jeffry

Mishlove, 1998.
Fig. 1.Structure of the most important intelligent abilities

Intellectual

abilities

Expressive Cognitive (Logical)

Imagination Emotions Learning Creativity (Problem solving)

Generalization Conceptualization Induction

Perception

Judgement

Reasoning

Conceiving

Recognition

Identification of

important

characteristics

Identification of

how the

characteristics are

logically linked

Sensation

Intuition

Associative

thinking

Discrimination
 RKB AKB

 System

 Fig. 2 The double-KB system structure

 Amygdala converts words into real feeling [23].

 Occure: 80% 20% 20% 80%

Level of good feeling from a circle: 0.8 – 0.2 = 0. 6

Level of good feeling from a circle: 0.8 – 0.2 = 0. 6

“Good” and “bad” can be described as fuzzy variables.

Fig. 3 The memory structure.

Reasoning

Knowledge base

(Rules of reasoning)

Database (Factscurrent situation)

Inference engine

User interface Developer interface

Translator

Application

Knowledge base

(Application rules)

Feeling

Good feeling Bad feeling

Good object Bad object

Circle Square Circle Square
 The goal description

 Math models

Fig. 4. Acceleration control system design (search through the goal hierarchy).

Fig. 5. The Artificial Intuition System Structure.

Acceleration

control

Damper

Electrical

equipment

Hydraulic

equipment

Physical

objects

(models)

Brake

Math

models

Physical

objects

(models)

Acceleration changing

Fast ® Slow

Information

structure

creation

Knowledge

base

Fuzzy

problem

description

Knowledge

extraction

Solution

synthesis

Problem

description

defuzzines

Trigger of

action

Sensor

system

Solution

Model

design

Testing
1

SEMIOTIC FUNDAMENTALS OF INFORMATION PROCESSING

IN HUMAN BRAIN

Leonid I. Perlovsky

Air Force Research Lab., 80 Scott Rd., Hanscom AFB, MA 01731

Tel. 781-377-1728; e-mail: Leonid.Perlovsky@hanscom.af.mil

ABSTRACT

The paper discusses a mathematical nature of

signs and symbols, and relates it to information

processing and understanding, structure of the

mind and brain, learning, and pattern

recognition. I discuss past limitations of

algorithms and neural networks, combina-torial

complexity, the roles of concepts and emotions

in mind’s mechanisms, and various types of

logic underlying mathematical techniques. A

mathematical theory of semiosis, adaptive

processes of sign interpretation, is described; it

includes a similarity measure between signals

and internal representations and fuzzy dynamic

logic, a mechanism of the similarity

maximization. Mathematical mechanisms of sign

and symbol processing are presented and

related to the functioning of mind.

KEYWORDS: semiotics, symbols, fuzzy

dynamic logic, neural networks, emotions,

concepts, intelligent systems.

1. SEMIOTICS, MIND, AND BRAIN

Semiotics studies signs and symbols, which

are generally understood as entities designating

some other entities in the world or in the mind.

Using words like mind, thought, imagination,

emotion, concept represents a specific

challenge: people use these words in many

ways colloquially, but their use in science and

especially in mathematics of intelligence has not

been uniquely defined and is a subject of active

research and ongoing debates [

1

]. Whereas

standardized definitions come at the end of the

development of a theory (like “force” was

defined by the 2

nd Newton’s law, following

centuries of less precise usage) this paper

adheres to a following guidance: we need to

make sure that our definitions: (1) are

mathematically exact, (2) correspond to the

usage in scientific and mathematical community,

(3) correspond to the general usage. According

to a dictionary [2], mind includes conscious and

unconscious processes, especially thought,

perception, emotion, will, memory, and

imagination, and it originates in brain. These

constituent notions will be discussed throughout

the paper. Specific neural mechanisms in brain

“implementing” various mind functions constitute

the relationship between the mind and brain; we

will discuss how the mathematical descriptions

of mind are implemented in brain.

In mathematics and in “Symbolic AI” there

is no difference between signs and symbols.

Both are considered as notations, arbitrary nonadaptive entities with axiomatically fixed

meaning. But in general culture, symbols are

understood also as psychological processes of

sign interpretation. Jung emphasized that

symbol-processes connect conscious and

unconscious [3], Pribram wrote of symbols as

adaptive, context-sensitive signals in the brain,
2

whereas signs he identified with less adaptive

and relatively context-insensitive neural signals

[

4

].

In this paper I use “symbol” as a symbolprocess, corresponding to general notions of

symbol in culture and psychology. The symbolprocesses are closely related to the processes

of thinking, and a mathematical theory suitable

for the description of sym-bols is closely related

to the mathematical description of the working

of the mind.

A broad range of opinions exists on the

mathematical methods suitable for the

description of the mind. Founders of artificial

intelligence thought that formal logic was

sufficient [

5

] and no specific mathematical

techniques would be needed to describe the

mind [6]. An opposite point of view is that there

are few specific mathematical constructs, “the

first principles” of mind. Among researchers

taking this view is Grossberg, who suggests that

the first principles include a resonant matching

between lower-level signals [7] and higher-level

representations and emotional evaluation of

conceptual contents [8]; Zadeh develops theory

of granularity [9], Meystel develops hierarchical

multiscale organization with specific intra-level

closed-loop structures [

10]; and the author,

suggests similarity measures between lowerlevel signals and higher-level representations [11]

and the fuzzy dynamic logic [

12] among first

principles of mind.

2. MIND, LOGIC, AND COMPLEXITY

Understanding the meaning of signals

coming from sensory organs involves

associating the subsets of signals corresponding

to an object with internal representations. This

recognition activates internal brain signals

leading to mental and behavioral responses

involved in understanding.

Developing mathematical descriptions of the

very first recognition step of this seemingly

simple association-recognition-understanding

process has not been easy, a number of

difficulties have been encoun-tered during the

past fifty years. These difficulties have been

summarized under the term combinatorial

complexity (CC) [

11]. The problem was first

identified in pattern recognition and

classification problems in the 1960s and was

named “the curse of dimensionality” [13]. The

following thirty years of developing adaptive

statistical pattern recognition and neural

network algorithms led to a conclusion that

these approaches often encountered CC of

learning requirements. Rule-based systems

were proposed to solve the problem of learning

complexity. An initial idea was that rules would

capture the required knowledge and eliminate a

need for learning. However, rule systems and

expert systems in the presence of variability,

encountered CC of rules. Model-based

systems were proposed to combine advantages

of adaptivity and rules by utilizing adaptive

models, but they encountered computational

CC (N and NP complete algorithms).

Combinatorial complexity has been related

to the type of logic, underlying various

algorithms and neural networks [

14]. Formal

logic is based on the “law of excluded third”,

according to which every statement is either

true or false and nothing in between. Therefore,

algorithms based on formal logic have to

evaluate every little variation in data or internal

representations as a separate logical statement

(hypothesis); a large number of combinations of

these variations causes combinatorial

complexity. In fact, combinatorial complexity of

algorithms based on logic has been related to

the Gödel theory: it is a manifestation of the

incompleteness of logic in finite systems [

15].

Multivalued logic and fuzzy logic were

proposed to overcome limitations related to the

law of excluded third [16]. Yet the mathematics

of multivalued logic is no different in principle

from formal logic. Fuzzy logic encountered a

difficulty related to the degree of fuzziness, if

too much fuzziness is specified, the solution
3

does not achieve a needed accuracy, if too

little, it might become similar to formal logic.

Another view on these difficulties can be

obtained by comparing mathematical techniques to human mind. An essential role of

emotions in the working of the mind was

analyzed from the psychological and neural

perspective by Grossberg [17], from the neurophysiological perspective by Damazio [18], and

from the learning and control perspective by the

author [

19]. One reason for engineering

community being slow in adopting these results

is the cultural bias against emotions as a part of

thinking processes. Plato and Aristotle thought

that emotions are “bad” for intelligence, this is a

part of our cultural heritage, and the founders of

Artificial Intelligence repeated it. Yet, as

discussed in the next section, combining

conceptual understanding with emotional

evaluations might be crucial for overcoming the

combinatorial complexity as well as the related

difficulties of logic.

3. MODELING FIELD THEORY (MFT)

Modeling field theory [

11], summarized

below, associates lower-level signals with

higher-level representations, resulting in

understanding of signals, while overcoming the

difficulties described in the previous section. It

is achieved by using flexible measures of

similarity between the representations and the

input signals combined with the fuzzy dynamic

logic. Modeling field theory is a multi-level,

hetero-hierarchical system. This section

describes a basic mechanism of interaction

between two adjacent hierarchical levels of

signals (fields of neural activation); sometimes, it

will be more convenient to talk about these two

signal-levels as an input to and output from a

(single) processing-level.

At each level, the output are concepts

recognized (or formed) in input signals. Input

signals X are associated with (or recognized, or

grouped into) concepts according to the

representations-models and similarity measures

at this level. In the process of associationrecognition, models are adapted for better

representation of the input signals; and similarity

measures are adapted so that their fuzziness is

matched to the model uncertainty. The initial

uncertainty of models is high and so is the

fuzziness of the similarity measure; in the

process of learning models become more

accurate and the similarity measure more crisp,

the value of the similarity increases. We call this

mechanism fuzzy dynamic logic.

3.1 Internal Models, Learning, and

Similarity

 During the learning process, new

associations of input signals are formed resulting

in evolution of new concepts. Input signals

{X(n), n ∈ N}, is a field of input neuronal

synapse activation levels, X = {Xd, d = 1,...

D}; a set of concepts {h ∈ H} is characterized

by internal parameters {Sh} and by models

(representations) of the signals {Mh(Sh,n)}

corresponding to concepts {h}. For each

model h, the set of parameters is denoted as Sh

= {S

a

h

, a = 1,... A}. Learning process increases

a similarity measure between the sets of models

and signals, L({X},{M}). The similarity

measure is a function of model parameters and

associations between the input synapses and

concepts-models. A similarity measure is

designed so that it treats each model as an

alternative for each subset of signals

L({X},{M}) = 

n∈N

∏

h∈H

∑ r(h) l(X(n) | h), (1)

here l(X(n)|h) (or simply l(n|h)) is a conditional

partial similarity between signal vector X(n) and

model Mh (when mapping this terminology onto

its implemetation in the brain, n and h are neural

indexes numbering individual neurons or small

groups of neurons). For example, l(n|h) can be
4

selected as a probability density function. Then

L is a total likelihood (this interpretation does

not require statistical independence among

signal vectors n and n': dependencies are

accounted for by model dependencies on {n}).

In the process of learning, concept-models

are constantly modified. From time to time a

system forms a new concept, while retaining an

old one as well; alternatively, old concepts are

sometimes merged. Formation of new

concepts and merging of old ones require a

modification of the similarity measure (1); the

reason is that more models always result in a

better fit between the models and data. This is a

well known problem, it can be addressed by

reducing (1) using a “penalty function”, p(N,M)

that grows with the number of models M, and

this growth is steeper for a smaller amount of

data N. For example, an asymptotically

unbiased maximum likelihood estimation leads

to multiplicative p(N,M) = exp(-Npar/2), where

Npar is a total number of adaptive parameters in

all models (this penalty function is known as

Akaike Information Criterion, see [

11] for

further discussion and references).

In case, when a set of observations, N,

corresponds to a continuous flow of signals, for

example, a flow of visual stimuli in time and

space, it is convenient instead of eq.(1) to

consider its continuous version,

L = exp ?

N

 ln (

h ∈H

∑ r(h) l(X(n) | h) ), (2)

where N is a continuum, such as time-space. In

this case, models describe continuous modeling

fields and maximization of similarity L can be

compared to minimization of action in a physical

field theory.

3.2 Fuzzy dynamic logic and MFT

The learning process consists in estimating

internal parameters S and associating subsets of

signals with concepts by maximizing the

similarity (1). When likelihood is used as a

similarity measure, this is a problem of the

maximum likelihood estimation. Note, that (1)

contains a total of HN items; this is a source of

the combinatorial complexity in many algorithms

(called maximum hypothesis testing) which

attempt to maximize similar expressions by first

maximizing each item over the parameters and

then finding the maximal item.

Modeling field theory solves this problem

by utilizing fuzzy dynamic logic [

11,20]. Let us

introduce association variables f(h|n)

f(h|n) = r(h) l(X(n)|h) /

h '∈H

∑ r(h') l(X(n)|h'). (3)

Eq.(3) looks like the Bayes formula for a

posteriori probabilities, if l(n|h) are conditional

likelihoods. An internal dynamics of the

Modeling Fields (MF) is defined as follows,

df(h | n)/dt = f(h | n) 

h '∈H

∑ {[δhh' - f(h'|n)] ·

[?lnl (n|h')/?Mh'] ∂M'

h'/∂Sh' · dSh'/dt, (4)

dSh/dt={?

N

f(h|n)[?lnl(n|h)/?Mh]∂M'

h

/∂Sh, (5)

here

δhh' is 1 if h=h', 0 otherwise. (6)

Parameter t is the time of the internal dynamics

of the MF system (like a number of internal

iterations). A more specific form of (5) can be

written when Gaussian-shape functions are used

for conditional partial similarities,

l(n|h) = G(X(n) | Mh(Sh, n), Ch). (7)

where G is a Gaussian function with mean Mh

and covariance matrix Ch (this is not a

necessary assumption, but it will simplify some

discussions later, also, it is not same as usual

Gaussian limitation, in fact, it is not much of a

limitation at all, because a weighted sum of

Gaussians in (1) can approximate any positive
5

function). And let us specify the dynamics of the

MFT as follows,

dS

a

h

/dt = [Yh

-1

]

ab

Zh

b

 , (8)

dCh/dt = -0.5Ch

-2

?

N

f(h|n)[Ch-Dnh Dnh

T

]; (9)

Dnh = ( X (n) – Mh' ) , (10)

Yh

ab

 = ?

 N

f(h|n)[M

;a

hCh

-1

M

;b

h

], (11)

Zh

b

 = ?

 N

f(h|n)[M

;b

hCh

-1

Dnh], (12)

here superscript T denotes a transposed rowvector; summation is assumed over repeated

indexes a, b; and (;) denotes partial derivatives

with respect to parameters S with

corresponding indexes:

M

;b

h

 = ∂Mh/ ∂S

b

h .

(13)

The following theorem was proven.

Theorem. Equations (3) through (6) (or (3)

and (8 through 12)) define a convergent

dynamic system MF with stationary states

defined by max{Sh}L.

It follows that the stationary states of an MF

system give the maximum similarity solution of

the model-based pattern recognition problem.

When likelihood is used as similarity, the

stationary values of parameters {Sh} are

asymptotically unbiased and efficient estimates

of these parameters [

21]. A computational

complexity of the MF method is linear in N.

3.3 MFT hierarchical organization

The previous sub-section described a single

processing layer in a hierarchical MFT system.

An input to each layer is a set of signals X(n),

or in neural terminology, an input field of

neuronal activations. An output are the

activated models Mh(Sh, n); it is a set of models

or concepts recognized in the input signals.

Equations (3-6) or (3) and (7-12) describe a

loop-process: at each iteration (or internal-time

t) the l.h.s. of the equations contain association

variables f(h|n) and other model parameters

computed at the previous iteration. In other

words, the output models “act” upon the input

to produce a “refined” output models (at the

next iteration). This process is directed at

increasing the similarity between the models and

signals. It can be described as an internal

behavior generated by the models.

The output models initiate other actions as

well. First, activated models (neuronal axons)

serve as input signals to the next processing

layer, where more general concept-models are

recognized or created (internal behavior within

the MFT system). Second, concept-models

along with the corresponding instinctual signals

and emotions may activate behavioral models

and generate behavior directed into the outside

world (a process not contained within the

above equations).

MFT describes an intelligent system

composed of multiple adaptive intelligent agents:

each concept-model is an agent, which is

"dormant" until activated by a high similarity

value. When activated, it is adapted to the

signals, so that the similarity increases. Every

piece of signal may activate several concepts,

which "compete" with each other, while

adapting to the new signals.

3.4 MFT theory of mind

MFT dynamics, (3) and (4-6) or (7-12),

describes an elementary process of perception

or cognition, in which a large number of modelconcepts compete for incoming signals, modelconcepts are modified and new ones are

formed, and eventually, connections are

established among signal subsets on the one
6

hand, and model-concepts on the other.

Perception refers to processes in which the

input signals come from sensory organs and

model-concepts correspond to objects in the

surrounding world. Cognition refers to higher

levels in the hierarchy where the input signals

are concepts activated at lower levels and

model-concepts are more complex and

correspond to situations and relationships

among lower-level concepts.

A salient mathematical property of this

processes ensuring a smooth convergence is a

correspondence between uncertainty in models

(that is, in the knowledge of model parameters)

and uncertainty in associations f(h|n). In

perception, as long as model parameters do not

correspond to actual objects, there is no match

between models and signals; many models

poorly match many objects, and associations

remain fuzzy; this can be described more

specifically, if Gaussian functions are used for

l(X|h): for poorly matched models, the

covariances, Ch, are large (that is, model

uncertainties are large), which in turn prevents

f(h|n) from attaining definite (0,1) values.

Eventually, one model (h') wins a competition

for a subset {n'} of input signals X(n), when

parameter values match object properties, Ch'

becomes smaller than other Ch, and f(h'|n)

values become close to 1 for n∈{n'} and 0 for

n∉{n'}. Upon the convergence, the entire set of

input signals {n} is divided into subsets, each

associated with one model-object, Ch become

small, and fuzzy a priori concepts become crisp

concepts. Cognition is different from perception

in that models are more general, more

abstracts, and input signals are the activation

signals from concepts identified (cognized) at a

lower hierarchical level; the general

mathematical laws of cognition and perception

are similar in MFT. Let us discuss relationships

between the MFT theory, theory of solitons in

non-linear systems and concepts of mind

originated in psychology, philosophy, linguistics,

aesthetics, neuro-physiology, neural networks,

artificial intelligence, pattern recognition, and

intelligent systems.

Solitons and MFT resonances. The

physical nature of concepts of mind in MFT is

similar to that of solitons. If the data X(n) are all

given from the very beginning, equations (3-6)

or (7-12) converge to a fixed point of MFT

system. This fixed point is comprised of a

number of resonances [22] between the field of

models and field of data, in other words, the

models come into a resonance with the data,

and the system stays in this resonant state.

Formation of a resonance takes different time

(number of iterations) for various models, and it

is more proper to talk about each model

coming into a resonance with a corresponding

data subset. If there is a continuous flow of

data, X(n,t), a resonance is a long-living state

(long comparative to a single iteration cycle).

The nature of this resonance between the

modeling fields and the data field is such that a

particular subset of data (corresponding to an

object h) "drives” the modeling-field to a

specific value (or pattern) Mh(Sh,n,t), and

these modeling-field values "drive” the

association-fields, f(h|n), to {0,1} values. It

follows that concepts of mind in MFT theory

are resonant states, or solitons of a highly

nonlinear MFT system. It is interesting to note

recent results [

23] establishing relationships

between solitons in certain nonlinear systems

and theorems of inversive geometry. More

research is needed to establish general

relationships between concepts of mind as longliving resonant states in a nonlinear system and a

body of results obtained in the theory of

integrable systems and solitons [24].

Elementary thought-process, consciousness, and unconscious. A thought-process or

thinking involves a number of sub-processes

and attributes, including internal representations

and their manipulation, attention, memory,

concept formation, knowledge, generalization,

recognition, understanding, meaning, prediction,

imagination, intuition, emotion, decisions,
7

reasonining, goals, behavior, conscious and

unconscious [7,10,11].

A “minimal” subset of these processes has

to involve mechanisms for afferent and efferent

signals [22], in other words, bottom-up and topdown signals coming from outside (external

sensor signals) and from inside (internal

representation signals). According to Carpenter

and Grossberg [

22] every recognition and

concept formation process involves a

“resonance” between these two types of

signals. In MFT, at every level in a hierarchy the

afferent signals are represented by the input

signal field X, and the efferent signals are

represented by the modeling field signals Mh;

resonances correspond to high similarity

measures l(n|h) for some subsets of {n} that are

“recognized” as concepts (or objects) h. The

mechanism leading to the resonances is given by

(3-6) or (7-12), and we call it an elementary

thought-process. The elementary thoughtprocess involves elements of conscious and

unconscious processes, imagination, memory,

internal representations, concepts, instincts,

emotions, understanding and behavior as further

described later.

A description of working of the mind as

given by the MFT dynamics was first provided

by Aristotle [

25], describing thinking as a

learning process in which an a priori form-aspotentiality (fuzzy model) meets matter (sensory

signals) and becomes a form-as-actuality (a

concept). Jung suggested that conscious

concepts are developed by mind based on

genetically inherited structures of mind,

archetypes, which are inaccessible to

consciousness [3]; and Grossberg [7] suggested

that only signals and models attaining a resonant

state (that is signals matching models) reach

consciousness.

Understanding. In the elementary thought

process, subsets in the incoming signals are

associated with recognized model-objects,

creating phenomena (of the MFT-mind) which

are understood as objects, in other words

signal subsets acquire meaning (e.g., a subset

of retinal signals acquires a meaning of a chair).

There are several aspects to understanding and

meaning. First, object-models are connected

(by emotional signals [8,11,19]) to instincts that

they might satisfy, and also to behavioral

models that can make use of them for instinct

satisfaction. Second, an object is understood in

the context of a more general situation in the

next layer consisting of more general conceptmodels, which accepts as input-signals the

results of object recognition. That is, each

recognized object-model (phenomenon) sends

(in neural terminology, activates) an output

signal; and a set of these signals comprises input

signals for the next layer models, which

‘cognize’ more general concept-models. And

this process continues up and up the hierarchy

of models and mind toward the most general

models a system could come up with, such as

models of universe (scientific theories), models

of self (psychological concepts), models of

meaning of existence (philosophical concepts),

models of a priori transcendent intelligent

subject (theological concepts).

Imagination. Imagination involves

excitation of a neural pattern in a visual cortex in

absense of an actual sensory stimulation (say,

with closed eyes) [

7

]. Imagination was often

considered to be a part of thinking processes;

Kant [26] emphasized the role of imagination in

the thought process, he called thinking “a play

of cognitive functions of imagination and

understanding”. Whereas pattern recognition

and artificial intelligence algorithms of recent

past would not know how to relate to this [5,

6

],

Carpenter and Grossberg resonance model [22]

and the MFT dynamics both describe

imagination as an inseparable part of thinking:

imagined patterns are top-down signals that

prime the percepting cortex areas (priming is a

neural terminology for making neural cells to be

more readily excited). In MFT, the imagined

neural patterns are given by models Mh. MFT

(in agreement with neural data) just adds details
8

to Kantian description: thinking is a play of

higher-hierarchical-level imagination and

lower-level understanding. Kant identified this

“play” [described by (3-6) or (7-12)] as a

source of aesthetic emotion; modeling aesthetic

emotion in MFT is described later.

Mind vs. Brain. Historically, the mind is

described in psychological and philosophical

terms, whereas the brain is described in terms

of neurobiology and medicine. Withing scientific

exploration the mind and brain are different

description levels of the same system.

Establishing relationships between these

description is of great scientific interest. Today

we approach solutions to this challenge [

27],

which eluded Newton in his attempt to establish

physics of “spiritual substance” [

28]. General

neural mechanisms of the elementary thought

process (which are similar in MFT and ART

[

22]) have been confirmed by neural and

psychological experiments, this includes neural

mechanisms for bottom-up (sensory) signals,

top-down “imagination” model-signals, and the

resonant matching between the two [

29].

Adaptive modeling abilities are well studied with

adaptive parameters identified with synaptic

connections [30]; instinctual learning mechanisms

have been studied in psychology and linguistics

[

31].

Instincts and emotions. Functioning of the

mind and brain cannot be understood in

isolation from the system’s “bodily needs”. For

example, a biological system (and any

autonomous system) needs to replenish its

energy resources (eat); this and other

fundamental unconditional needs are indicated

to the system by instincts, which could be

described as internal sensors. Emotional signals,

generated by this instinct are perceived by

consciousness as “hunger”, and they activate

behavioral models related to food searching and

eating. In this paper we are concerned primarily

with the behavior of recognition: instinctual

influence on recognition modify the objectperception process (3) - (6) in such a way, that

desired objects “get” enhanced recognition; it

can be accomplished by modifying priors, r(h).

Aesthetic emotions and instinct for

knowledge. Recognizing objects in the

environment and understanding their meaning is

so important for human evolutionary success

that there has evolved an instinct for learning

and improving concept-models. This instinct is

described in MFT by maximization of similarity

between the models and the world, (1).

Emotions related to satisfaction-dissatisfaction

of this instinct are perceived by us as harmonydisharmony (between our understanding of how

things ought to be and how they actually are in

the surrounding world). According to Kant [32]

these are aesthetic emotions.

Intuition includes an intuitive perception

(imagination) of object-models and their

relationships with objects in the world, as well

as higher-level models of relationships among

simpler models. Intuition involves fuzzy

unconscious concept-models, which are in a

state of being learned and being adapted

toward crisp and conscious models (a theory);

such models may satisfy or dissatisfy the

knowledge instinct in varying degrees before

they are accessible to consciousness, hence the

complex emotional feel of an intuition. The

beauty of a physical theory discussed often by

physicists is related to satisfying our feeling of

purpose in the world, that is, satisfying our need

to improve the models of the meaning in our

understanding of the universe.

Beauty. Harmony is an elementary aesthetic

emotion related to improvement of objectmodels. Higher aesthetic emotions are related

to the development of more complex “higher”

models: we perceive an object or situation as

aesthetically pleasing if it satisfies our learning

instinct, that is the need for improving the

models and increasing similarity (1). The highest

forms of aesthetic emotion are related to the

most general and most important models.

According to Kantian analysis [32], among the

highest models are models of the meaning of
9

our existence, of our purposiveness or

intentionality, and beauty is related to improving

these models: we perceive an object or a

situation as beautiful, when it stimulates

improvement of these highest models of

meaning. Beautiful is what “reminds” us of our

purposiveness.

Theory testing and future directions. The

general neural mechanisms of the elementary

thought process, which includes neural

mechanisms for bottom-up (sensory) signals,

top-down “imagination” model-signals, and the

resonant matching between the two [33], have

been confirmed by neural and psychological

experiments (these mechanisms are similar in

MFT and ART [

22]). Adaptive modeling

abilities are well studied and adaptive

parameters have been identified with synaptic

connections [34]; instinctual learning mechanisms

have been studied in psychology and linguistics

[

35]. Ongoing and future research will confirm,

disprove, or suggest modifications to specific

mechanisms of model parameterization and

parameter adaptation (5) or (8), reduction of

fuzziness during learning (9), similarity measure

(1) as a foundation of aesthetic instinct for

knowledge, relationships between psychological

and neural mechanisms of learning on the one

hand and, on the other, aesthetic feelings of

harmony and emotion of beautiful.

Differentiated forms of (1) need to be

developed for various forms of the knowledge

instinct (child development, language learning,

etc.) Future experimental research needs to

study in details the nature of hierarchical

interactions: to what extent the hierarchy is

“hardwired” vs. adaptively emerging; what is a

hierarchy of learning instinct? theory of

emerging hierarchical models will have to be

developed (that is, adaptive, dynamic, fuzzy

hierarchy- heterarchy).

4. THINKING PROCESS AND

SEMIOTICS

Semiotics studies symbol-content of culture

[

36]. For example, consider a written word

"chair". It can be interpreted by a mind to refer

to something else: an entity in the world, a

specific chair, or the concept "chair" in the

mind. In this process, the mind, or an intelligent

system is called an interpreter, the written

word is called a sign, the real-world chair is

called a designatum, and the concept in the

interpreter's mind, the internal representation of

the results of interpretation is called an

interpretant of the sign. The essence of a sign

is that it can be interpreted by an interpreter to

refer to something else, a designatum. This

process of sign interpretation is an element of a

more general process called semiosis which

consists of multiple processes of sign

interpretation at multiple levels of the mind

hierarchy.

In classical semiotics [37] words sign and

symbol were not used consistently; in this

paper, a sign means something that can be

interpreted to mean something else (like a

mathematical notation, or a word), and the

process of interpretation is called a symbolprocess, or symbol. Intepretation, or

understanding of a sign by the mind according

to MFT is due to the fact that a sign (e.g., a

word) is a part of an object-model (or a

situation-model at higher levels of the mind

hierarchy). The mechanism of a sign

interpretation therefore involves first an

activation of an object-model, which is

connected to instincts that the object might

satisfy, and also to behavioral models that can

make use of this object for instinct satisfaction.

Second, a sign is understood in the context of a

more general situation in the next layer

consisting of more general concept-models,

which accepts as input-signals the results of

lower-level sign recognition. That is, recognized

signs comprise input signals for the next layer

models, which ‘cognize’ more general conceptmodels.

A symbol-process of a sign interpretation
10

coincides with an elementary thought-process.

Each sign-interpretation or elementary thought

process, a symbol, involves conscious and

unconscious, emotions and concepts; this

definition connecting symbols to archetypes

(fuzzy unconscious model-concepts)

corresponds to a usage in general culture and

psychology [3,4]. As described previously, this

process continues up and up the hierarchy of

models and mind toward the most general

models. In semiotics this process is called

semiosis, a continuous process of creating and

interpreting the world outside (and inside our

mind) as an infinite hierarchical stream of signs

and symbol-processes.

REFERENCES

 

1

 For the discussions and further references see:

Grossberg, S. (1988). Neural Networks and Natural

Intelligence. MIT Press, Cambridge, MA. Albus, J.S.,

Meystel, A.M. (2001). Engineering of Mind: An

Introduction to the Science of Intelligent Systems.

Wiley, New York, NY. Meystel, A.M., Albus, J.S.

(2001). Intelligent Systems: Architecture, Design, and

Control. Wiley, New York, NY. Perlovsky, L.I. (2001).

Neural networks and intellect: using model based

concepts. Oxford University Press, NY.

2

 The American Heritage College Dictionary,

Houghton Mifflin, 3rd ed., 2000, Boston, MA.

3

 Jung, C.G. (1934). Archetypes of the Collective

Unconscious. In the Collected Works, v.9,II,

Bollingen Series XX, 1969, Princeton University Press,

Princeton, NJ.

4

Pribram,K. (1971). Languages of the Brain. Prentice

Hall.

5

 Newell, A. (1983). Intellectual issues in the history of

artificial intelligence. In The study of information, ed.

F.Machlup & U.Mansfield, J.Wiley, New York, NY.

6

 Minsky, M. (1988). The Society of Mind. MIT Press,

Cambridge, MA.

7

 Grossberg, S. (1988). Neural Networks and Natural

Intelligence. MIT Press, Cambridge, MA.

8

 Grossberg, S. & Levine, D.S. (1987). Neural dynamics

of attentionally modulated Pavlovian conditioning:

blocking, inter-stimulus interval, and secondary

reinforcement. Psychobiology, 15(3), pp.195-240;

9

 Zadeh, L.A. (1997). Information granulation and its

centrality in human and machine intelligence.

Proceedings of the Conf. on Intelligent Systems and

Semiotics '97. Gaithersburg, MD, pp. 26-30.

 

10 Meystel, A. (1995). Semiotic Modeling and

Situational Analysis. AdRem, Bala Cynwyd, PA.

11 Perlovsky, L.I. (2001). Neural networks and

intellect: using model based concepts. Oxford

University Press, NY.

12 Fuzzy dynamic logic is discussed mathematically

below, it is an extension of fuzzy logic of Zadeh, and it

is not directly related to other techniques of

“dynamic logic”.

13 Bellman, R.E. (1961). Adaptive Control Processes.

Princeton University Press, Princeton, NJ.

14 Perlovsky, L.I. (1998). Conundrum of Combinatorial Complexity. IEEE Trans. PAMI, 20(6) p.666-70.

15 Perlovsky, L.I. (1996). Gödel Theorem and

Semiotics. Proceedings of the Conference on

Intelligent Systems and Semiotics '96. Gaithersburg,

MD, v.2, pp. 14-18.

16 Jang,J.-S.R., Sun,C.-T., and Mizutani, E. (1996).

Neuro-Fuzzy and Soft Computing: A Computational

Approach to Learning and Machine Intelligence.

Prentice Hall, Upper Saddle River, NJ.

17Grossberg, S.&Schmajuk, N.A. (1987). Neural

dynamics of attentionally modulated Pavlovian conditioning: Conditioned reinforcement, inhibition, and

opponent processing.Psychobiology,15(3),p.195-240.

18 Damasio, A.R. (1995). Descartes' Error: Emotion,

Reason, and the Human Brain. Avon, NY, NY.

19 Perlovsky, L.I. (1998). Cyberaesthetics: aesthetics,

learning, and control. STIS'98, Gaithersberg, MD;

Perlovsky, L.I. (1999). Emotions, Learning, and

Control. Proc. International Symp. Intelligent Control,

Intelligent Systems & Semiotics, Cambridge MA,

pp.131-137

20Perlovsky, L.I. (1996). Mathematical Concepts of

Intellect. Proc. World Congress on Neural Networks,

San Diego, CA; Lawrence Erlbaum Associates, NJ,

pp.1013-16; Perlovsky, L.I.(1997).Physical Concepts

of Intellect. Proc. Russian Academy of Sciences,

354(3), pp. 320-323.

21 Cramer, H. (1946). Mathematical Methods of

Statistics, Princeton University Press, Princeton NJ.

22 G.A.Carpenter & S.Grossberg, 1987, A massively

parallel architecture for a self-organizing neural

pattern recognition machine, Computer Vision,

Graphics and Image Processing, 37, 54-115.

23 B.G. Konopelchenko & W.K. Schief, 2001,

Reciprocal figures, graphical statics and inverse

geometry of the Schwarzian BKP hierarchy, will be

published in Studies in Applied Mathematics (2002);

B.G. Konopelchenko & W.K. Schief, 2001, Menelau’s

theorem, Clifford configurations and inverse geometry

of the Schwarzian KP hierarchy, to be published.

24 V.E. Zakharov, 1991, What Is Integrability?

Springer-Verlag Wien NewYork.
11

 

25 Aristotle, IV BC, Metaphysics, tr. W.D.Ross,

Complete Works of Aristotle, Ed.J.Barnes, Princeton,

NJ, 1995.

26 Kant, I. (1790). Critique of Judgment, tr.

J.H.Bernard, Macmillan & Co., London, 1914.

27 Grossberg, S. (2000). Linking mind to brain: the

mathematics of biological intelligence. Notices of the

American Mathematical Society, 471361-1372.

28 Westfall, R.S. (1983). Never at Rest: A Biography of

Isaac Newton. Cambridge Univ Pr., Cambridge.

29 S.Grossberg, Neural Networks and Natural

Intelligence. MIT Press, Cambridge, MA, 1988,

S.Zeki, A Vision of the Brain Blackwell, Oxford,

England, 1993; W.J. Freeman, Mass action in the

nervous system. Academic Press, New York, NY,

1975.

30 C.Koch and I.Segev, Edts., Methods in Neuronal

Modeling: From Ions to Networks MIT Press,

Cambridge, MA 1998; D.Hebb, Organization of

Behavior J.Wiley & Sons, New York, NY, 1949.

31 J.Piaget, The Psychology of the Child Tr.

H.Weaver, Basic Books, 2000); N.Chomsky, in

Explanation in Linguistics, ed. N.Hornstein and

D.Lightfoot Longman, London, 1981. Jackendoff, R.

(2002). Foundations of Language : Brain, Meaning,

Grammar, Evolution, Oxford Univ Pr.; Deacon, T.W.

(1998). The Symbolic Species : The Co-Evolution of

Language and the Brain. W.W. Norton & Company.

32 Kant, I. (1790). Critique of Judgment, tr.

J.H.Bernard, Macmillan & Co., London, 1914.

33 S.Grossberg, Neural Networks and Natural

Intelligence. MIT Press, Cambridge, MA, 1988,

S.Zeki, A Vision of the Brain Blackwell, Oxford,

England, 1993; W.J. Freeman, Mass action in the

nervous system. Academic Press, New York, NY,

1975.

34 C.Koch and I.Segev, Edts., Methods in Neuronal

Modeling: From Ions to Networks MIT Press,

Cambridge, MA 1998; D.Hebb, Organization of

Behavior J.Wiley & Sons, New York, NY, 1949.

35 J.Piaget, The Psychology of the Child Tr.

H.Weaver, Basic Books, 2000); N.Chomsky, in

Explanation in Linguistics, ed. N.Hornstein and

D.Lightfoot Longman, London, 1981.

36 Sebeok, T.A. 1995, Sign: An Introduction to

Semiotics. Univ. Toronto Press, Toronto, Canada.

37 Peirce, C.S. (1935-66). Collected Papers of Charles

Sanders Peirce. Harvard Univ. Press., Cambridge,

MA; Morris C. (1971). Writings on the General

Theory of Signs, ed. Th. A. Sebeok. The Hague:

Mouton.
		
	

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Beyond the Turing Test: Performance Metrics for Evaluating

a Computer Simulation of the Human Mind

Nancy Alvarado, Sam S. Adams, Steve Burbeck, Craig Latta

IBM, Thomas J. Watson Research Center

{nanalv, ssadams, sburbeck, crl}@us.ibm.com

Abstract

Performance metrics for machine intelligence (e.g., the

Turing test) have traditionally consisted of pass/fail tests.

Because the tests devised by psychologists have been aimed

at revealing unobservable processes of human cognition, they

are similarly capable of revealing how a computer

accomplishes a task, not simply its success or failure. Here

we propose the adaptation of a set of tests of abilities

previously measured in humans to be used as a benchmark

for simulation of human cognition. Our premise is that if a

machine cannot pass these tests, it is unlikely to be able to

engage in the more complex cognition routinely exhibited by

animals and humans. If it cannot pass these sorts of tests, it

will lack fundamental capabilities underlying such

performance.

1. Introduction

What constitutes success for designers using computational

autonomous development to create intelligent systems? The

observation of change over time with experience in an

environment is neither learning nor development unless that

change results in closer approximation to some performance

goal. Further, neither development nor learning necessarily

indicates that a machine has behaved intelligently. While it

may be interesting to observe how systems respond to

different environmental challenges and how parameters affect

a system’s behavior, eventually some attempt to guide

learning must be made if these systems are to shed much light

on human cognitive processes or prove useful in applications. 

This was the challenge we faced in developing a set of

performance goals for the Joshua Blue Project. Joshua Blue

applies ideas from complexity theory and evolutionary

computational design to the simulation of a human mind on a

computer. The goal is to enhance artificial intelligence by

enabling the emergence of such capacities as common sense

reasoning, natural language understanding, and emotional

intelligence, acquired in the same manner as humans acquire

them, through situated learning in a rich environment.

Because our goal is to simulate human cognition, our

performance goals arise from observing how humans behave

in similar circumstances. Thus our project has been guided by

findings in developmental and cognitive psychology.

If a system is conceived as a “black box,” engineers work

from inside the box to implement the capabilities that will

achieve a desired outcome within a given set of constraints.

In contrast, psychologists work from outside the box, reverseengineering what already exists in the human mind to develop

plausible explanations for observed behavior. These different

interests converge in the need to specify performance goals

for autonomous learning systems because, as in psychology,

such testing must be performed from outside the box. Thus,

the paradigms used by psychologists to test hypotheses about

how the human mind functions provide a ready metric for

assessing how closely computer behavior approximates

human performance in well-defined contexts. In these tests,

the observed behavior of an autonomous system becomes

proof of what must be occurring inside a computer black box

designed by an engineer. This is important for those systems

that cannot be inspected directly – where the changes taking

place are hidden from the system’s designers or difficult to

interpret, as is typically the case with architectures based on

artificial neural networks. 

For many purposes, it may not matter that a computer

accomplishes a task in a manner similar to humans. In our

case, because the goal of the Joshua Blue Project was to

simulate human understanding, thinking in the same manner

as humans has been an important design goal. Our reasoning

has been that every human capacity exists within the

interdependent context of the human mind for a specific

reason. Thus, approximating human functioning as closely as

possible in all respects seems necessary to success at

modeling a mind with rich, robust human-like cognitive

abilities. This goal is not as restrictive as it seems, given the

variation that exists among humans. Experience may teach us

which abilities are essential, which are not, and how much

deviation is possible without sacrificing the cognitive

qualities of the resulting system. 

When a comparison between machine and human

functioning is proposed, it seems natural to ask “which

human shall we compare our machine with?” This question

arises because humans do not all perform alike. Psychologists

have addressed this by describing behavior in terms of

distributions. The “normal” or bell curve for intelligence is

one such description of the range of human performance on a

specific measurement instrument (such as an IQ test). Such

curves permit one to place a particular score or measured
behavior within the context of behavior for a large number of

others. Thus it provides not simply pass/fail information but a

basis for comparison with the range of human variation.

Norms exist for most published tests. When machines

become more autonomous in their learning and have greater

flexibility in what they can do, when their learning

experiences are richer and more varied, governed by the

machine’s own choices, the behavior of intelligent systems

will also be less predictable and more variable. Statistical

techniques for describing variance and comparing behavior,

like those used by psychologists, will become more important

in assessing machine performance. Such techniques will be

needed to determine whether different observed behavior

reflects learning or simply change. 

One longstanding test of human-like computer intelligence

has been the Turing Test [1], now conducted each year as the

Loebner Competition [2]. A drawback of that test is that

human functioning can be mimicked by systems that have

little if any human-like cognitive capabilities. The

performance goal can be met by simulating human behavior

convincingly (to the satisfaction of human judges) for a

relatively brief period of time in a restricted domain of

discourse. Surface behavior is partially simulated but the

underlying cognition producing that behavior is not. We

believe that meaningful performance tests must incorporate

the idea that it matters how behavior is accomplished, not

simply that it occurs [3]. 

When a developmental process is considered important to

acquiring human-like understanding, as we believe it to be,

then a dimension of change in stages with milestones

approximating human development is introduced. The time

frame for a computer need not be the same as for a human

child because its training experiences may differ, but the

order of acquisition of abilities, the interrelationship of

change in one domain with change in another, and so on,

should be considered. Taking a modular approach, as Fodor

suggests, temporarily simplifies evaluation of developmental

sequence and inter-relationships among processes, but at

some point separate processes become unified, domainspecific knowledge converges, sensory data is integrated, and

more general or encompassing cognition emerges. 

Ultimately, as a measure of success, we envision Joshua

Blue acquiring the capacity to pass not an adult Turing Test,

but a “Toddler Turing Test.” Success would mean that our

system would answer unrestricted questions posed by adults

in the same manner as a three year old toddler would, with all

of the common sense, naïve physics, burgeoning language

use, and social understanding of a preschool child. The true

test would be whether Joshua Blue is distinguishable from

human three year olds, not whether Joshua Blue fools an

adult into believing it is not a computer, through faked typing

errors, colloquialisms, illogic or scripted emotional responses.

2.Overview of the Test Suite

The proposed test suite is intended to assess whether

certain essential, prerequisite cognitive abilities exist in a

system. This required identification of foundational abilities

without which a system cannot be considered a simulation of

human cognition. The approach to testing for these abilities is

twofold. First, changes in internal system values and other

parameters indicating changes in mental state can be

monitored and measured, and related to observed behavior.

Second, well-understood paradigms from psychology can be

adapted for use as performance benchmarks. In many cases,

such paradigms have demonstrated existence of unobservable

cognitive processes through observable and quantifiable

behavior. By comparing system results to results obtained

with animals and humans, designers can make stronger

claims about the capabilities of that system. 

The test suite is divided into three parts, as shown in Table

1. The goal for systems simulating the general properties of

human cognition must be to pass all of the tests using a

single, generalized system. Ultimately, passing an

unrestricted toddler Turing test would require success in all

three parts of this initial test suite, plus a great deal more. 

3. Tests of Associative Learning

We believe that a system capable of learning from its

environment must be endowed with motivation that gives it

the impetus to explore its world, recognize reward and

punishment, form goals and seek to satisfy them. While

certain drive states may be innate to the system, it should also

form acquired goals and use past experience to determine a

course of action. This test suite is designed to assess a

system’s ability to direct its own behavior by recognizing

environmental cues that signal reward or punishment. The

tests are based upon animal testing in the field of behavior

modification and learning. An excellent overview of this

work is presented by Klein [4]. 

3.1 Tests Showing Formation of Associations

In classical conditioning, associations are formed between

environmental cues (called conditioned stimuli) and elicitors

of affective or motivational states (called unconditioned

stimuli). Training occurs by presenting these two kinds of

stimuli together until one becomes a predictor of the other

and is responded to as if it were the other stimulus. Passing

this test requires the ability to recognize salient features of the

environment and associate them with the internal responses

evoked by a co-occurring unconditioned stimulus. 
Table 1. Overview of Performance Metrics for Evaluating a Simulation of Mind

Associative

Learning

Classical

Conditioning

Forms associations between a predictive environmental cue and an

accompanying stimulus

Presence of a Mind Forms mental representation of two cues as a single predictor,

demonstrates memory for previously encountered predictive cues

Instrumental

Conditioning

Demonstrates cause-effect learning and responds to changes in

amount or rate of reward or punishment

Purposeful

Behavior

Acquires goals and expectations, shows “learned helplessness” with a

lack of ability to achieve goals, shows escape/avoidance behaviors

Social

Cognition Social Encoding Categorizes self and others by salient features and behavior, forms

stereotypes, forms a self schema

Social Inference Forms expectations (heuristics) based on observed correlations and

covariation among properties and behaviors of others, exhibits biases

resulting from use of such heuristics

Causal Attribution Attributes motives to others, exhibits biases resulting from such

attributions, generalizes its self schema to others

Representation of

Self

Forms a representation of self observable in self-preserving or

enhancing behavioral choices in contexts that threaten self-image

Empathy and

Attachment

Generalizes the sense of self to others, demonstrates changes in affect

depending upon affiliation with others, shows affect-guided helping

behavior

Language

Acquisition

Prelinguistic

Structural

Competences

Performs combinative operations on sets, acquires phoneme

combinations appropriate to a specific language, progresses through

cooing to babbling and prosody mimicking speech, acquires

pragmatics of communication, understands communicative intentions

of others

Intentional

Communication Acquires and uses words to accomplish goals both through

associative learning and imitation of observed others

Word Acquisition

Sorts words by grammatical types (e.g., noun, verb), learns

grammatical rules in same sequence as children, overgeneralizes

grammatical rules 

Cross-Language

Comparisons

Demonstrates language-specific competence in a language other than

the first language taught, repeating the three tests above.
Given the capacity to form associations through experience

in an environment, classical conditioning can be tested in a

virtual environment using an embodied agent endowed with

minimal sensors and effectors, motives, affect, and

proprioception of both actions and affective states. In order to

pass the classical conditioning tests, the system must form a

classically conditioned association between an unconditioned

stimulus (e.g., satisfaction of “hunger”) and a conditioned

stimulus (cues signaling availability of “food”). This

association should result in observed changes in behavior. If a

pleasant stimulus is replaced by an aversive (unpleasant)

stimulus, then fear-based conditioning should result in

avoidance and escape behaviors. 

A rich literature described in most learning theory

textbooks [4, 5] specifies quantitative relationships between

factors affecting learning and observed behavior. These

experiments can be used as a highly specific model against

which the behavior of a system can be compared.

3. 2 Tests Demonstrating Mental Representation

A series of studies in the learning literature were used to

demonstrate that certain classical conditioning effects are the

result of expectations, which cannot exist unless experience is

being represented mentally. Several phenomena can be used

to show the existence of mental representations in a system.

This use of behavioral paradigms to demonstrate existence of

mental representations illustrates that the goal of a study that

measures behavior can be far more than simply to

demonstrate competence in performing that behavior. It can

also demonstrate indirectly what must exist in the less

observable parts of the system. The reasoning is that the

observed behavior, although often uninteresting in its own

right, could not be performed if certain important processing

were not occurring. Two examples are presented here, but

there are many more in the learning literature.

Normally, when two cues to an event are present, the more

salient cue (the better predictor) will block conditioning of

the lesser cue. However, if the two cues are perceived as part

of a unified percept, instead of as two separate events, then

the opposite effect occurs and presence of the highly salient

cue potentiates the response to the less noticeable cue. This

potentiation cannot occur without formation of a mentally

unified percept incorporating both cues. Similarly, in

backward blocking, a new aversive event becomes associated

with a previous environmental cue not actually present during

the unpleasant experience. If no memory of the cue were

formed, it could not become associated with the aversive

stimulus that came later. This provides an indirect way of

confirming that a system is forming mental representations of

its world and associating percepts with each other

3.3 Cause-Effect Learning Tests

Operant and instrumental conditioning (cause-effect

learning) paradigms test whether associations are formed

between environmental stimuli and voluntary behavior,

governed by reward or punishment. Virtual or physical

environments similar to the mazes and runways presented in

classic psychology experiments can be created to test such

learning. These include runways along which an agent or

robot can move, T-mazes and radial-arm mazes permitting

choice of paths, two-compartment shuttle boxes to test escape

and avoidance learning, more complex mazes like those used

by Tolman [6] to demonstrate existence of cognitive maps,

virtual or physical “Skinner boxes” (single and two-choice

lever-pressing boxes). Equivalent behaviors can be devised,

such as moving to a specific region of the environment to

obtain reward.

To pass these tests, a system must show a reliable

relationship between reward, punishment, and subsequent

behavior. The system should increase its behavior in response

to a reinforcer, decrease it in response to a punishment or in

the absence of a reinforcer. It should also conform to effects

of changes in size, rates and timing of reinforcement, as

described in the literature on schedules of reinforcement [4,

5]. Further tests of complex contingencies can show that the

system has the capacity to monitor longer chains of behavior

and form more complex goals.

Learning theory includes descriptions of several more

sophisticated learning phenomena involving expectations,

multiple reinforcers and behavioral allocation (dividing

attention between two sources of reward). We expect that a

system should show similar behaviors, including Crespi’s

depression effect, punishment effects, a partial reinforcement

effect, adherence to Premack’s principle and Herrnstein’s

matching law, all classic learning phenomena described in

textbooks [4, 5]. Because stimulus control phenomena are

closely related to ability to discriminate, identify relevant

features, and form concepts and categories, they are not

discussed here, but are described in Section 5.

3.4 Tests of Purposeful Behavior

Simple cause-effect learning does not necessarily imply

that an organism has formed a mental representation of its

goal states. Tolman’s [6] paradigms can be adapted to

demonstrate existence of purpose, expectations, and mental

maps in a system. A system must be capable of representing

aspects of its environment for future use, even when those

representations are not directly related to a current operation

or goal state. Latent learning studies can be used to show that

mental representations are being formed without reward. 

Learned helplessness and phobias are both examples of

phenomena demonstrating the existence of expectations.

Learned helplessness occurs when an organism is subjected

to punishment but can do nothing to escape it, and it results in

a change in expectations about effectiveness of future

activity. Phobias represent acquisition of a classically

conditioned association between some environmental cue and

an aversive event. Observation of phobias indicates formation

of expectations about negative events, plus beliefs about what

behaviors will prevent those events. Suitable paradigms for

testing each of these can be adapted.

4. Tests of Social Cognition
Tests of two or more agents interacting in a shared

environment permit application of a system’s basic cognitive

capacities to social interactions. In addition to the capacities

needed to function alone in such an environment, the system

must communicate, compete and form alliances with other

agents, understand the behavior and intentions of others, and

find ways to coexist with them while also attaining important

goals. Fiske and Taylor [7] provide a thorough overview of

paradigms for studying human social cognition.

4.1 Tests of Social Encoding Processes

Social encoding can be tested in a simplified, virtual

multiple-agent environment by creating two types of

embodied agents: (1) agents with similar bodies and

behavior; (2) agents with dissimilar bodies and behavior.

Scenarios can be manipulated so that these agents compete

with, punish (show aggression), or cooperate with and help

the system being tested. From this experience, the system

should form an appropriate mental representation of both like

and unlike agents and behave according to its previous

experience with those agents. Stereotypes should be formed

as mental representations of the characteristics of these

groups of agents. Stereotypes can be demonstrated by

observing an experienced system’s behavior toward an

ambiguous new agent that includes some but not all of the

salient characteristics of previously learned social categories.

Once social categories have been learned, increased

ambiguity in classification of new agents should result in

interruptions of behavior and affective distress in the system.

4.2 Tests of Social Inference

Social inferences are based on observed correlations and

covariation among properties and behaviors of other agents

and environmental events and features. Once learned, these

become heuristics used in human reasoning. Existence of

such heuristics is observed in specially devised contexts

where they produce inference “errors.” If a system has

acquired heuristics, then it should demonstrate the same

errors as humans, instead of strictly mathematical/statistical

decision processes. These should result in the biases

enumerated in most textbooks [7], including: (1) framing

effects, use of extreme cases, over-reliance on small samples

or biased samples; (2) salience of immediate experience; (3)

inability to combine joint probabilities; (4) inability to

identify diagnostic information; (5) inability to correct for

regression artifact; (6) irregular or improper weighting of

cues. 

4.3 Tests of Causal Attribution

Humans recognize that living beings have agency – the

ability to control their own actions in goal-directed ways.

Thus they attribute motives to others and understand the

actions of others in terms of intentions. While people

understand that circumstances can dictate behavior, they

prefer to attribute the actions of others to inherent motives,

preserving a belief in free will. This is reflected in certain

attribution biases [7], including the fundamental attribution

error, the actor-observer effect, and self-serving biases.

Environments can be created for testing fundamental

attribution error, the false consensus effect (where we

consider our behavior to be more representative of the

behavior of others than it really is), and self-serving bias. 

4.4 Tests of a Representation of the Self

People tend to form representations of the self (self

schemas) along dimensions that are important to them in their

lives. Complexity of the self-schema is also determined by

life experiences. Thus a system’s self-schema should depend

upon its experiences. In humans, tasks involving perception,

memory and inference are used to demonstrate the

characteristics of the self schema. These can be adapted to

show that a system has formed a schema whose

characteristics vary with experience [7]. For example, selfschemas change to more closely resemble those of others

surrounding a person. Thus the existence of a self-schema can

be demonstrated through changes in behavior that occur when

the system is placed in a new environment, among different

social agents than were present when the self-schema was

formed. Maintenance or preservation of a self-schema

motivates much human behavior. A cybernetic theory of selfattention and self-regulation has been proposed by Carver &

Scheier [8]. This theory of regulation, with predicted

consequences for failure, provides a detailed model for

testing a system’s self-schema in different contexts.

4.5 Tests of Empathy and Attachment

In humans, empathy and attachment are the two main

mechanisms for regulating social interactions. Both depend

upon the cognitive evaluations and interpretations made of

social situations. When a system is endowed with affective

responses, these should mediate social interactions in ways

consistent with what is observed in humans. Tests of

emotion-motivated helping behavior can be adapted to assess

empathy [7]. Berscheid [9] proposes a theory that predicts

how emotion changes in long-term relationships, readily

testable in the simple social contexts described earlier. 

5.Tests of Language Acquisition

The criteria for assessing machine language acquisition are

that a computer’s language must be used with the flexibility

of a real language, but must not be so constrained that one

language can be learned but not another (e.g., English but not

Chinese). Two aspects of language acquisition are

intertwined: (1) understanding of the structural properties of

the representations comprising language; (2) understanding of

the content of concepts represented by language.

Understanding of concepts appears to precede language

acquisition, although a child’s preexisting concepts are in turn

modified by later language learning. The complexities of the

structure of language are so great that many theorists

hypothesize innate mechanisms for acquiring structural
understanding, and even for acquiring conceptual

understanding. 

An important prerequisite to language learning is the

ability to perform combinative operations on sets. Langer

[10] provides convincing evidence that this ability is needed

in order to form the class inclusion hierarchies essential to

categorization. He also argues that these operations (recursive

mapping of cognitions, correspondence mapping,

substitutions and equivalence relations) are the foundation of

abstraction and form the basis for linguistic rewrite rules.

Some theorists, such as Pinker, hypothesize a

straightforward mapping of words to “mentalese” nonverbal

representations, guided by innate mechanisms. Other theorists

assert that general associationist learning can account for

language acquisition, especially if social imitative learning

directs a child’s attention to what is relevant during learning

[11, 12]. Thus, a system must acquire language socially and

must have the mechanisms for imitative social learning in

place before acquiring language. 

5.1 Tests of Prelinguistic Structural Competences

The first year of life is spent acquiring prelinguistic

competences supportive of ultimate language development.

These include a progression from cooing and laughing to

vocal play, babbling, and utterances that mimic the prosody

of speech, acquisition of the nonverbal pragmatics of

communicative interactions, such as smiling, gaze orienting,

turn-taking, pointing and showing, and mimicking behaviors.

The child also acquires an understanding of the

communicative intentions of adults [12]. A first task is to

learn to segment a flow of speech sounds into meaningful

combinations of phonemes (morphemes). The system’s

developing language should show increasing inclusion of

language-specific permissible combinations of phonemes. A

set of tests requiring the system to classify items into sets and

to perform the combinative operations on those sets described

by Langer [10] can assess whether the conceptual cognitive

abilities prerequisite to language learning exist.

5.2 Tests of Intentional Communication 

Intentional communication requires use of language

instrumentally to accomplish goals and an understanding of

the ways in which others do so. The social paradigms

described earlier can be adapted to create situations in which

a system must learn to use words to accomplish specific

goals. Placed into a new environment, the system must be

able to learn to use words through observation and imitation

of the behavior of other social agents inhabiting the same

environment. 

5.3 Tests of Word Acquisition

An understanding of grammar is necessary for word

acquisition because grammar constrains the possible

meanings of new words. While this grammatical knowledge

may be innate, some theorists argue that the statistical

frequencies in language call attention to the structural

properties of language sufficiently to permit grammatical

knowledge to emerge [11]. Either way, tests of word

acquisition begin with tests of grammatical knowledge. A

system must be able to tell the difference between nouns,

verbs, and adjectives, in a simple sorting task. Further, the

system must use that knowledge to classify objects. This can

be tested by adapting paradigms used with young children,

such as those demonstrating shape bias with nouns [11]. Tests

of more sophisticated grammatical knowledge demonstrate

rule acquisition by observing occurrence of errors resulting

from the overgeneralization of rules. These errors should

gradually disappear with greater experience.

5.4 Cross-Language Comparisons

Once a system has demonstrated competence in one

language, it must pass the same set of tests to demonstrate

competence in a different language. 

6. Conclusion

There are many other aspects of cognitive functioning that

might be assessed. We propose this battery as a place to start.

If a system cannot pass these tests, it is unlikely to be able to

engage in the more complex cognition routinely exhibited by

animals and humans. Because all of these tests focus upon

abilities quantitatively measured in humans, they provide a

ready-made benchmark for simulation of human cognition.

The greater challenge is to create learning environments

sufficient to enable machines to acquire these abilities if they

are truly capable of doing so.

Notes

This paper was originally presented at the Second

International Conference on Development and Learning,

ICDL 2002, MIT, Cambridge, MA. Portions have been

revised in response to comments by conference attendees.

Nancy Alvarado may be reached at the University of

California, San Diego, Center for Brain and Cognition

(alvarado@psy.ucsd.edu).

References

[1] A. M. Turing, “Computing Machinery and Intelligence,”

In Readings in Cognitive Science: A Perspective from

Psychology and Artificial Intelligence, A. Collins & E. Smith

(Eds.), pp. 6-19, Morgan Kaufmann, San Francisco, CA,

1950/1991.

[2] H. Loebner, “Loebner Prize,” described at:

http://www.loebner.net/Prizef/loebner-prize.html.

Breazeal, C. 2001. Designing Sociable Machines. The MIT

Press. Forthcoming.

[3] J. R. Searle, 1980. Minds, brains, and programs,

Behavioral and Brain Sciences, 3, 417-424.

[4] S. B. Klein, 1996. Learning: Principles and Applications.

McGraw-Hill Inc., New York, NY.

[5] G. Bower & E. Hilgard, 1981. Theories of Learning.

Prentice-Hall, Inc., Englewood Cliffs, NJ.
[6] E. C. Tolman and C. H. Honzik, 1930. “Insight” in Rats.

University of California Publications in Psychology, 4, 215-

232.

[7] S. T. Fiske and S. E. Taylor, 1991. Social Cognition.

McGraw-Hill Inc., New York, NY.

[8] C. S. Carver & M. F. Scheier, 1981. Attention and Selfregulation: A Control Theory Approach to Human Behavior,

Springer-Verlag, NY.

[9] E. Berscheid, M. Snyder, & A. Omoto, 1989. Issues in

studying close relationships: Conceptualizing and measuring

closeness. In C. Hendrick (Ed.), Review of Personality and

Social Psychology: Vol 10. Close Relationships (pp. 63-91).

Sage, Newbury Park, CA.

[10] J. Langer, 2001. The mosaic evolution of cognitive and

linguistic ontogeny. In M. Bowerman and S. Levinson (Eds.),

Language Acquisition and Conceptual Development.

Cambridge University Press, New York, NY.

[11] L. Smith, 2001. How domain-general processes may

create domain-specific biases. In M. Bowerman and S.

Levinson (Eds.), Language Acquisition and Conceptual

Development. Cambridge University Press, New York, NY.

[12] M. Tomasello, 2001. Perceiving intentions and learning

words in the second year of life. In M. Bowerman and S.

Levinson (Eds.), Language Acquisition and Conceptual

Development. Cambridge University Press, New York, NY.
Experimental Evaluation of Subject Matter Expert-oriented

Knowledge Base Authoring Tools

Robert Schrag1, Mike Pool1, Vinay Chaudhri2, Robert C. Kahlert3, Joshua Powers1, Paul

Cohen4, Julie Fitzgerald1, and Sunil Mishra4

1) Information Extraction & Transport (IET), Inc.

1911 North Fort Myer Drive #600

Arlington, Virginia 22209

last-name@iet.com

2) SRI International

333 Ravenswood Avenue

Menlo Park, CA 94025

{vinay, smishra}@ai.sri.com

3) Cycorp, Inc.

3721 Executive Center Drive #100

Austin, Texas 78731

rck@cyc.com

4) Department of Computer Science

University of Massachusetts

Amherst, MA 01003

cohen@cs.umass.edu

ABSTRACT

We describe a large-scale experiment in which non-artificial

intelligence subject matter experts (SMEs)—with neither artificial

intelligence background nor extensive training in the task—author

knowledge bases (KBs) following a challenge problem

specification with a strong question-answering component. As a

reference for comparison, professional knowledge engineers (KEs)

author KBs following the same specification. This paper

concentrates on the design of the experiment and its results—the

evaluation of SME- and KE-authored KBs and SME-oriented

authoring tools.

Evaluation is in terms of quantitative subjective (functional

performance) metrics and objective (knowledge reuse) metrics that

we define and apply, as well as in terms of subjective qualitative

assessment using several sources. While all evaluation styles are

useful individually and exhibit collective power, we find that

subjective qualitative evaluation affords us insights of greatest

leverage for future system/process design. One practical

conclusion is that large-scale KB development may best be

supported by “mixed-skills” teams of SMEs and KEs

collaborating synergistically, rather than by SMEs forced to work

alone.

KEYWORDS: knowledge acquisition, evaluation

1 INTRODUCTION

The authors are engaged in a joint research program—

Rapid Knowledge Formation (RKF)—to develop and

evaluate technology to enable SMEs to build very large

KBs. Two teams respond to challenge problems posed by

an independent evaluator. We report on a large-scale

evaluation conducted during the summer of 2001.

The RKF teams are led by Cycorp and SRI

International. The independent evaluator is IET. More

comprehensive information about the evaluation—

including a full challenge problem specification—is

available at http://www.iet.com/Projects/RKF/. For more

general program information, see

http://reliant.teknowledge.com/RKF/.

In the remainder of this paper, we first outline our

approach to evaluating KBs. Then we describe the

“textbook knowledge” challenge problem (TKCP) presented

to SMEs and KEs for KB authoring, teams’ tools,

experimental procedures, and results from each style of

evaluation. We close with discussion/conclusions.

2 KB EVALUATION APPROACH

We consider KB evaluation along three dimensions:

functional performance (with subjective metrics),

economics (with objective metrics), and intrinsic quality

(subjective and non-metric). Here we elaborate on these

dimensions and our methodology. In a later section we

describe results.

To evaluate functional performance, we follow Cohen

et al. [3] in posing test questions (TQs) to authored KBs

and scoring their answers against defined criteria. Our

criteria fall into three major categories: Representation (with

criteria Query Formulation, Term Quality, and

Compositionality), Answer (with criterion Correctness,

only), and Explanation (with criteria Content Adequacy,

Content Relevance, Intelligibility, and Organization). While

the Answer category obviously addresses a KB’s

functional performance, we argue that high-quality

question representations and explanations also confer

valuable (input- and output-oriented) functionality to KBs.

To evaluate economics, we follow Cohen et al. [2] in

addressing reuse—the extent to which knowledge created

earlier is exploited in the creation of subsequent knowledge.

We require that authored knowledge (including constants

and axioms) bear labels of authorship and creation time.

Other things being equal, greater reuse is considered more

economical.

Others—[4], [5]—have suggested (without employing,

to our knowledge, in large-scale comparative evaluation)

qualitative criteria for assessing intrinsic properties of KBs
and ontologies. Inspired by these, we formed a KB Quality

Review Panel from among technology providers and

evaluators to assess the following properties: Clarity or

Style, Maintainability or Reusability, Correctness or

Accuracy, Appropriate Generality, Appropriate

Organization, and Logical Propriety. While we discussed

making this evaluation quantitative (by adapting our

Functional Performance scoring methodology described

below), the Panel ultimately agreed that free-form

commenting along these dimensions would be the most

fruitful initial step.

We drew on two other sources, besides the Panel, in

our subjective qualitative evaluation: post-evaluation SME

survey responses and evaluator observations. Findings

regarding RKF tools’ strengths and weaknesses were

consistent across all three sources. RKF tool developers

have taken these results seriously and have begun

appropriate modifications to their tools.

2.1 Additional Related Work

The series of Knowledge Acquisition Workshops (KAWs)1

has emphasized the evaluation of generic problem-solving

methods (PSMs) and performance on the associated

problem-solving tasks more than that of knowledge for its

own sake. This appears to reflect a difference in emphasis

or philosophy: whereas the KAW community has focused

on the PSM as the primary reusable artifact, the RKF

community has focused on KBs themselves as reusable

artifacts that should, in principle, be applicable to any

problem-solving task.

3 TEXTBOOK KNOWLEDGE CHALLENGE

PROBLEM

The TKCP’s KB authoring task is to:

1. Capture knowledge about DNA transcription and

translation from about ten pages of an

introductory undergraduate molecular biology

textbook for non-majors [1];

2. Ensure that the authored KBs are capable of

correctly answering test questions about the

subject material, (extending or revising KBs as

necessary).

We chose a textbook source because it serves as a

circumscribed reference that offers an intuitively justified

basis for required KB content scope. We chose molecular

biology because it is a largely descriptive science and

because it is of interest to the sponsor. We chose [1]

because it largely eschews description of laboratory

procedures or scientific history in favor of material

phenomena.

The TQs were consistent in difficulty with TQs

typically found on Web-available quizzes on molecular

biology. Questions appearing in the textbook itself

typically required representation of (e.g.,

hypothetical/counter-factual) situations that were entirely

 

1

 See http://ksi.cpsc.ucalgary.ca/KAW/.

novel compared to the basic material presented in the text.

These were judged by the RKF community to be unsuitable

(too difficult) for use in evaluation of current SME-oriented

KB authoring technology. The TQs were similar in style

and difficulty to IET-created sample questions (SQs)

covering material in earlier chapters of the textbook. SQs

were provided to teams before the evaluation. TQs were

not so disclosed.

Besides the primary KB authoring tools described in

the following section, RKF teams were required to include

facilities for SMEs to pose TQs and to package their

answers for evaluation. They also were required to prepare

various instrumentation capabilities in support of metrics

computations.

Teams’ tools included substantial TKCP-relevant

knowledge before they were handed off to SMEs. Given

the premise that a large, general/reusable KB facilitates the

construction of more specific KBs, teams were allowed to

“prime the pump” of knowledge development by seeding

KBs with prerequisite (e.g., pertaining to earlier—largely

review—textbook chapters) and background (including

high-level/abstract) knowledge or reasoning abilities

deemed appropriate (according to defined ground rules) to

support the authoring of the textbook’s target knowledge.

4 TOOLS UNDER EVALUATION2

Cycorp’s “KRAKEN” tools are supported by a substantial

KB based on a higher-order formal predicate logic. The key

strategies of SME-oriented KB interaction are natural

language (NL) presentation and a knowledge-driven

acquisition dialog with limited NL understanding. The KB

includes thousands of predicates and understands

thousands of English verbs. Cycorp’s approach might be

described as maximalistic, domain-pluralistic, and

conceptually precise. The KRAKEN tools aim to exploit (as

leverage) a substantial KB to bring SMEs past an

otherwise-steep learning curve by productive collaboration

in this sophisticated knowledge representation milieu.

SRI’s “SHAKEN” tools are supported by a relatively

sparse KB based on the frame formalism. The key strategy

of SME-oriented interaction is graphical assembly of

components. The KB includes a few hundred predicates

serving as conceptual primitives (the components). SRI’s

approach might be described as minimalistic, domainuniversal, and conceptually coarse. The SHAKEN tools

may be seen as skirting traditional knowledge

representation complexity by presenting an entirely new

metaphor with great intuitive appeal.

5 EXPERIMENTAL PROCEDURES

IET collaborated with George Mason University (GMU) to

establish a SME KB authoring laboratory at GMU’s Prince

William County, Virginia campus. Eight (mostly graduate)

biology students participated in the TKCP evaluation, four

 

2 More detailed tool descriptions appear in appendices. Here we

include the briefest salient sketches.
working with Cycorp’s KRAKEN tools, four with SRI’s

SHAKEN tools. All worked full-time from mid-May until

mid-July, 2001. The first week of this period was devoted to

classroom-style training of SMEs by teams. The next two

weeks were taken up with an evaluation dry run that

included shake-down of tools in the installed context and

limited additional, informal training. The evaluation-proper

was held during the TKCP’s final four weeks. It covered

about seven pages of the textbook and included 70 TQs

(about 3 pages and 10 TQs having been covered in the dry

run). The actual test material covered five subsections of

the textbook’s target material. SMEs were allowed to

author this material in any order they liked, but IET would

not release one subsection’s TQs to a SME until s/he had

completed work on TQs for earlier subsections.

Subsequent to training, SMEs had no direct contact

with the teams’ KEs. Instead, to deal with tool

understanding issues that might arise, IET staffed the SME

lab full-time with a “gatekeeper” KE who mediated contacts

with the teams (including bug reports and fixes). The

gatekeeper KE also provided a subjective window on SME

activity. Teams were allowed to augment KBs during the

evaluation in accordance with the TKCP’s pump priming

ground rules.

Besides these SMEs, two KEs from each team also

participated (off-site from the SME lab) by addressing the

same KB authoring tasks using tools of their choice. SRI

KEs used the same SHAKEN tools available to the SRIassigned SMEs. Cycorp KEs usually authored knowledge

in CycL (a KE-oriented knowledge representation language)

using a text editor, rather than with the SME-oriented tools

in KRAKEN. Cycorp KEs did not author all target textbook

knowledge during the evaluation. Instead, they relied on a

base of target knowledge that Cycorp had first developed

in support of its internal pump priming requirements

identification, then excised before tool delivery to SMEs.

(This was due to unavoidable personnel overlap between

Cycorp’s pump-priming and TKCP-participating KEs.) SRI

KEs were given the same option but elected to author the

textbook knowledge during the evaluation. All KEs

authored TQ representations and developed answers

independently.

SMEs and KEs participants were required to answer at

least 75% of the TQs presented for each subsection. In the

results below, we include for each subsection the 75% of

each participant’s answered questions with the highest

overall scores, padding with 0s as necessary. One of these

subsections (“Signals in DNA Tell RNA Polymerase Where

to Start and Finish”) was particularly troublesome for the

Cycorp SMEs. After they had spent well over a week

working on it and were all well less than halfway to

reaching their answered-TQ quota, IET asked them to

proceed to the next subsection to ensure that they had the

chance to address most of the target material. (The SRI

SMEs had completed their work and performed reasonably

well on this 25-TQ subsection.) Because of this gatekeeper

KE intervention, the authors have by consensus excluded

this subsection from results analyses below.

Our functional performance scoring is both manual and

subjective. We employ multiple scorers with expertise both

in knowledge representation and in biology. We have

historically achieved highly consistent results by

articulating specific, value-by-value scoring guidelines for

all criteria against the following, relatively coarse, generic

framework: 0—no serious effort evident/completely offbase; 1—mostly unsatisfactory; 2—mostly satisfactory;

3—(for practical purposes) perfectly adequate. To arrive at

an overall score for functional performance on a given TQ,

we: threshold scores for the last two ancillary criteria so

that they do not exceed the highest score for (one of) the

earlier, primary criteria; average scores for each criterion

within a category; then average the category scores.

6 EXPERIMENTAL RESULTS

6.1 Functional Performance Results

The major functional performance results are reflected in

Table 1.

Table 1: Means of teams’ KEs’/SMEs’ means of TQ scores

KEs’ performance (the “gold standard” from RKF’s

perspective) was better than SMEs’ with high statistical

significance, but SMEs performed within 90% of the level

achieved by their teams’ KEs. We take the latter to reflect

the relative effectiveness of teams’ SME lab-fielded

technology. There was no statistically significant

difference across teams between the averaged scores of

respective SMEs or KEs—either overall or at the criterion

category level.

In a more detailed (unpublished/available upon

request) treatment, we note statistically significant

interactions among scores along the dimensions of

individual SMEs, subsections, and question types in a

categorization. All of these interactions washed out in the

overall scores. We also note a “ceiling” effect, in that

answer scoring with respect to several individual criteria

exhibits large proportions of (highest-score) 3s. Elements

likely contributing to this ceiling include our consistently

accessible (i.e., low) quiz-level TQ difficulties and SMEs’

consistent efforts to develop (supporting knowledge and)

high-quality answers before moving on to additional TQs.

6.2 Economic / Reuse Results

Cohen et al. [2] profiled HPKB knowledge reuse as the

fraction of knowledge items previously existing in a given

context. We again have two main reuse contexts to explore:

that of constants in axioms and that of axioms in the

Team User type Representation Answer Explanation Overall

Cycorp SME 1.66 2.46 2.30 2.14

Cycorp KE 2.54 2.58 2.56 2.56

SRI SME 1.84 2.12 2.08 2.01

SRI KE 2.09 2.48 2.40 2.32
explanations/proofs of answers to TQs. (To economize, we

include only the latter analysis.)

Axiom reuse results appear in Table 2. Results are

given for each participant (designated by monikers). Each

participants’ (KB’s) mean overall score and mean number of

axiom occurrences used to answer a TQ are included here

for reference. “UA” stands for “User-authored Axioms”

and “PDA” for “Pre-Defined Axioms.” “Used” indicates

that the noted number of axioms actually appears in the

explanation to one of the participant’s answered TQs.

“Unused” pertains to user-authored axioms that are not so

used in a TQ (e.g., because the participant used them to

author a subsection’s target material before receiving its

TQs). “Reused” pertains to user-authored axioms used to

answer more than one TQ.

Table 2 includes only one each KE and SME entry for

Cycorp because of difficulties at evaluation time with KB

instrumentation and later with information extraction.

These reuse results are still incomplete, as may be noted by

comparing the numbers of TQs answered/scored for

Functional Performance and numbers of TQs scored for

reuse. A further issue of note is that the Cycorp KE,

cycMW, (legitimately) authored much general knowledge

directly into Cyc, as pump priming, where it is counted as

pre-defined rather than user-authored.

We present (in Table 2‘s last columns) three varieties

of reuse percentages: of user-authored axioms that appear

in more than one TQ; of user-authored axioms that appear

(at all) in TQs; and of appearing pre-defined out of all

appearing axioms. From an economic standpoint, we

comment merely that the latter reuse rate seems (uniformly)

sufficiently high to justify the claim that relevant prior

content has significant benefit for KB development.

We had an additional motivation (beyond economics)

to examine reuse of user-authored axioms across TQs.

RKF’s functional performance evaluation criteria, being TQbased, could not address the generality of knowledge

across different TQs. Evaluators were interested in

quantitative metrics of cross-TQ axiom reuse as a hedge

against unprincipled, one-shot axiom “hacks” without

lasting value.

Table 3 reports numbers of TQ occurrences for each

reused user-authored axiom.

Table 2: Reuse data by SME/KE

Team Type Moniker

TQs 

> 32

TQs in 

[17 32]

TQs in 

[9 16]

TQs in 

[5 8]

TQs in 

[3 4]

TQs 

= 2

Cycorp SME Tweety 0 0 0 0 2 20

Cycorp KE cycMW 0 0 0 0 5 8

SRI SME Amoeba 0 0 2 2 5 86

SRI SME Celula 0 0 1 69 28 59

SRI SME Iflu 0 0 0 7 53 51

SRI KE sriPN 0 0 3 186 67 57

SRI KE sriAS 0 1 6 97 57 81

SRI SME Vaccinia 0 1 1 20 99 106

Table 3: Incidences of axiom occurrence counts across TQs

Superficially, high axiom TQ-incidences occurred much

more frequently for users of SRI’s SHAKEN tools than for

Cycorp’s KRAKEN tools. (The axiom TQ-incidence

patterns for pre-defined axioms are qualitatively similar.)

However, these data do not appear to indicate cross-team

differences in knowledge generality. Cycorp SME

Tweety’s axiom TQ-incidence profile is quite similar to that

of Cycorp KE cycMW whose work—with highly respected

representations—received the highest mean overall

functional performance score. Axiom TQ-incidence profiles

are also similar across SRI’s KEs and SMEs. We

tentatively attribute the cross-team profile differences to:

compactness of (arbitrary-arity) CycL relations compared to

binary relations resulting from translating SHAKEN’s

frames for axiom-counting purposes (suggesting a scaling

factor for axioms counted in a given TQ); and conceptual

coarseness, compared to pre-defined predicates in Cyc, of

SHAKEN’s built-in relations (leading to greater

applicability across TQs). Thus, we find no overall

quantitative pattern indicating deficiency of appropriate

knowledge generality for either team.

6.3 Subjective Qualitative Results

Deficiencies Identification: The KB Quality Review Panel

concluded that SMEs, working alone, performed quite well

at selected KB authoring tasks, but were less effective at

others. SMEs with both teams were generally adept at

placing and choosing concepts from the pre-existing

ontology (i.e., they created and used knowledge at correct

levels of specificity) and at general process description (i.e.,

they implemented Event-Actor vocabulary with accuracy

and ease). The Panel highlighted as shortcomings in SME

KBs the following major types: incompleteness,

Team Type Moniker

Mean 

Overall 

Score

Functional 

Performance 

TQ count

Reuse 

TQ 

count

UA 

used

UA 

reused

UA 

unused

PDA 

used

UA: 

reused / 

used

UA: used / 

(used+unused)

Used: 

PDA / 

(PDA+UA)

A SME Tweety 2.17 39 31 100 34 1881 103 27.16% 10.14% 47.65%

A KE MW 2.85 43 35 111 13 102 150 11.71% 52.11% 42.53%

B SME Amoeba 2.29 30 30 538 32 1355 304 15.81% 31.75% 62.41%

B SME Celula 1.68 25 25 143 58 490 465 73.71% 33.65% 27.99%

B SME Iflu 2.26 34 34 100 53 2211 323 41.89% 11.47% 36.20%

B KE PN 2.58 35 35 166 132 462 313 90.72% 54.94% 53.32%

B KE AS 2.50 34 34 296 106 1086 340 52.27% 33.50% 57.80%

B SME Vaccinia 2.59 35 35 254 163 1526 383 71.84% 17.75% 47.16%
redundancy, and non-reusability. After describing these

deficiency types below, we take up the question of their

sources in the tools and in the KB authoring task.

Both teams’ SMEs’ KBs exhibit incompleteness, of

three different kinds: content incompleteness (failure to

describe a process fully, even where the textbook had);

hierarchical incompleteness (failure to include natural

siblings of a created concept); and interconnectedness

incompleteness (failure to articulate obvious relationships

between concepts).

SHAKEN SMEs’ KBs exhibit significant redundancy

attributable to limitations of the evaluated tools’ inability to

reason about authored concepts from the several distinct

perspectives called for by different TQs. KRAKEN KBs

also exhibit some redundancy. This usually is not of SMEauthored knowledge, owing rather to re-creation by SMEs

of pre-defined concepts.

Both teams’ SMEs’ KBs included concepts of suspect

reusability. Mainly these were predicates, attributes, or

concepts that combined concepts unnaturally—in a

fashion that seemed difficult to reuse.

Suspected Deficiencies Sources: The Panel’s and SMEs’

combined attributions of the above-noted deficiencies to

major tool and TKCP task sources including the following

(in order of increasing challenge such sources seem likely

to pose RKF tool providers): TQ- and textbook-focused

SME orientation, absence or inaccessibilityof pre-defined

knowledge, limited logical expressibility, and inherently

difficult representation problems. We consider these in

turn below.

Some KB incompleteness (especially of the content

variety) are attributable to SME’s attempts to tailor

authoring in anticipation of unreleased TQs or in

consideration of released TQs. (I.e., sometimes authoring

favored TQ effectiveness over general applicability or

reuse.)

That SMEs were explicitly directed to focus on

authoring textbook content may explain some hierarchical

and interconnectedness incompleteness.

Some of the above-noted deficiencies resulted from

incompleteness in the pump-primed KBs that they received.

SHAKEN did not allow SMEs to facet general collections

into collections of different kinds of collection subtypes.

A SME noted that this would have facilitated clearer

hierarchical placement.

While KRAKEN SMEs had access to a substantial

background KB and sophisticated representation language,

this potential came at a price: access tended to be at times

insufficient, during other times overwhelming, thereby

limiting and even hampering SME productivity and

expressive possibilities. Gatekeeper KE reports and SME

surveys mentioned the labor-intensiveness of what turned

out to serve as Cycorp SMEs’ major axiom entry mode—

browsing through existing axioms to discover one (with an

appropriate predicate) to use as a template for editing and

assertion.

Both teams’ SMEs were—by design—somewhat

limited in the logical forms they could use to express

knowledge. SHAKEN SMEs were unable to make many

assertions that deviated from the form (∀x (Ax ⊃ (∃y) (B x

y)). KRAKEN users had access to more logical forms via a

richer vocabulary of rule macro predicates, though interface

issues again caused more general rule construction to be

prohibitively difficult here.

A major indication frequently occurring in both team’s

KBs of inherently difficult representation problems is

predicates lacking specificity, argument types, or

supporting axiomatization. Another indication is

impoverished versions of assertions whose formal

representation would require complex logical expressions.

Feasibility Assessment: While it is clear that plausible

near-term improvements to these tools (and their captured

background knowledge) could address some of the abovenoted shortcomings, it also seems (to the present authors)

that KB authoring generally does include inherently

difficult representation problems whose solution demands

well developed logical skills and balancing different

engineering principles. The ambition reflected in the

present experiment to create tools that can empower a SME

to full KB authoring independence—in arbitrary contexts—

appears yet too grand.

While we have clear evidence that SMEs can author

some high-quality knowledge in a sophisticated domain, we

lack evidence that they can author high-quality predicates,

analyze and refine background knowledge, develop rule

paths to make sophisticated inferences work, or develop

complex logical expressions required for some assertions.

Also, it is not obvious how the existing tools could be

refined to address such requirements.

Recommendation: We suggest that the KB development

community’s focus ought not be on tools that support KB

authoring by “lone” SMEs (except where authoring tasks

are relatively precisely defined and tools are fielded to

support SMEs in a relatively mature authoring process).

On the contrary, it should be on empowering SMEs to

perform those KB authoring tasks they can be empowered

to perform well. We believe the nascent RKF tools

demonstrate a significant advance in such SME

empowerment, and we recommend that in future

experimental and developmental settings the relative

strengths that SMEs and KEs bring to KB authoring should

be exploited in a true “mixed-skills” team—a synergistic

partnership.

We have some evidence that lightly trained SMEs are

capable of significantly enhancing KE efforts to provide

background knowledge that will be relevant to a KB

authoring task. As a sequel to the TKCP evaluation, IET

conducted a separate three-week evaluation intended to

allow SMEs to explore teams’ tools in a less structured

setting. Eight (now tool-savvy) SMEs participated in an

“expert knowledge” challenge problem (EKCP), pursuing

KB authoring topics related to the life cycle of the Vaccinia
virus—for which teams had authored no pump priming

knowledge. An IET KE who had prepared some EKCPsupporting background knowledge development (in CycL)

found that a Cycorp SME (who had not effectively

authored Cyc predicates working alone) was readily able to

contribute an informal specification that greatly facilitated

the KE’s work in extending the background knowledge to

support the SME’s needs.

We envision such interactions occurring throughout

the KB authoring process, with SMEs and KEs contributing

dynamically. The KE’s role is always to perform

sophisticated KB authoring tasks currently beyond SMEs’

reach. We believe that the SME-feasible task set should

expand naturally (in a “bootstrapping” fashion) over time,

as the talents of SMEs are mined and new tools are

developed to meet opportunities presented by existing

tools and authoring processes.

7 DISCUSSION / CONCLUSION

All styles of evaluation are useful in different contexts.

Quantitative metrics are genuinely valuable for some

purposes—e.g., inspiring a friendly competition among

groups working in a common research initiative or

demonstrating progress to an uninitiated, numbers-oriented

supervisor. By far the long pole in the evaluation tent,

however—from a system/process engineering, diagnostic

point of view—remains subjective qualitative assessment.

This is borne out by the comparative substance of our

offered conclusions based on this activity and by the

incorporation of insights and adoption of suggestions by

technology providers working to develop the next

generation of SME-empowering KB authoring tools.

We have seen that all three evaluation styles used here

complement one another. The different quantitative metrics

assist in each other’s mutual interpretation (as, for example,

when we appeal to Functional Performance in

understanding Reuse), acting together as a synergistic set

of reinforcements and consistency checks. We expect our

effectiveness in the overall KB authoring enterprise to grow

as the collective body of such techniques for

understanding quality issues in KB artifacts, tools, and

process continues to mature in a science of knowledge

development.

8 REFERENCES

[1] Alberts, B.; Bray, D.; Johnson, A.; Lewis, J.; Raff, M.;

Roberts, K.; and Walter, P. Essential Cell Biology: An

Introduction to the Molecular Biology of the Cell, ch. 7,

pp. 211–222, Garland Publishing, Inc., 1998.

[2] Cohen, P.; Chaudhri. V.; Pease, A. and Schrag, R. “Does

Prior Knowledge Facilitate the Development of Knowledgebased Systems?” In Proceedings of the Sixteenth National

Conference on Artificial Intelligence, pp. 221–2266, 1999.

[3] Cohen, P.; Schrag, R.; Jones, E.; Pease, A.; Lin, A.; Starr,

B.; Gunning, D., and Burke, M. “The DARPA HighPerformance Knowledge Bases Project.” AI Magazine

19(4):25, 1998.

[4] Gruber, T. “Toward Principles for the Design of

Ontologies Used for Knowledge Sharing,” Technical

Report, KSL-93-04, Department of Computer Science,

Stanford University, 1993.

[5] Gruninger, M.; and Fox, M.S. “Methodology for the

Design and Evaluation of Ontologies,” in Proceedings of

the IJCAI-95 Workshop on Basic Ontological Issues in

Knowledge Sharing, 1995.

9 ACKNOWLEDGEMENT

This research is sponsored by the Defense Advanced

Research Projects Agency (DARPA) of the United States.

This document has been approved for public release,

distribution unlimited.
APPENDIX A: CYCORP’S KRAKEN TOOLS

After over fifteen years of common sense knowledge base

building, the Cyc project is well-equipped for Cyc to

actively assist in its own extension. With its large set of

common classes and instances, relationships and rules and

knowledge contexts, Cyc has commenced its supervised

learning process, pushing the envelope of what it knows.

And, as learning occurs on the fringe of existing

knowledge, leveraging and reusing this knowledge is key.

This approach is realized in the KRAKEN system. 1

The KRAKEN team metaphorically framed the task of

extending a large knowledge base by viewing Cyc as akin

to a child with limited proficiency in English. This (very

young) “child” speaks CycL, a first-order predicate

calculus-like language, as its “mother tongue” and has

some knowledge of the common world. It has rudimentary

notions of English, enough to verbalize most of its beliefs

clearly, and can read simple English sentences with

occasional help. In this view, the SME becomes a “teacher”

who engages in a dialog with KRAKEN and exploits

analogy, disambiguation dialog, and knowledge

expectations to extend the system. Concurrently, the SME

teaches the system how to express new information in

English.

The KRAKEN team identified several key KB

authoring tasks that KRAKEN could assist with: locating

existing knowledge; adding knowledge through cut-andpaste techniques; fulfilling explicit knowledge expectations;

reading simple sentences; deducing relations from

examples; and assembling structured knowledge

components (e.g., non-trivial queries and rules) from short

described scenarios. In addition, KRAKEN helps with

correctness verification and strengthening of new

knowledge.

For a KB of the size of Cyc, locating pieces of existing

knowledge is a task in itself. At this writing, Cyc

encompasses about 1.1 million assertions constructed from

over 120,000 concepts and 5000 relations. Such dimensions

make any “list-them-all” approach to searching impossible.

However, Cyc also knows over 16,000 English verbs and

nouns and over 2000 proper names and can therefore offer

a natural language index into its knowledge. Once “within

the vicinity” of particular concepts and relations, browsing

is feasible. Additional organization is provided by Cyc’s

knowledge contexts (“microtheories”).

Once a SME knows upon which pieces of knowledge

to build, the KRAKEN system provides multiple ways for

the learning process to proceed.

 

1

In order to achieve this goal, Cycorp teamed with Hans Chalupsky

at the University of Southern Clifornia’s Information Sciences

Institute, Ken Forbus' Qualitative Research Group at Northwestern

University, and the Artificial Intelligence Applications Institute at

the University of Edinburgh to construct the KRAKEN system

around Cyc.

One principled approach is the explicit representation

of knowledge expectation—e.g. knowing that when told of

a new artist to ask for famous works by that artist. As new

terms are introduced, KRAKEN will ask the SME concrete,

salient questions. This approach is especially interesting,

as the KRAKEN team is adding support for a SME to teach

the system such knowledge expectations as well.

One major goal of the KRAKEN team has been to

support KB authoring using simple English sentences.

KRAKEN parses the sentences into an underspecified

representation, which is then reformulated, based on the

analysis of applicable argument constraints, into CycL.

During reformulation, KRAKEN attempts to solidify the

quantification, an aspect vital to knowledge engineering

and highly ambiguous in natural languages. (Compare the

class-level statement, “A dog is a mammal,” to the instance

level statement, “A dog is in the yard.”) Like anyone

learning English, KRAKEN asks for help when it gets stuck.

KRAKEN ensures—within bounds of reason—that the

new information is semantically valid and neither in

contradiction nor redundant with existing information. Even

more important, KRAKEN attempts to fine-tune the

strength of statements by suggesting ways to change their

specificity or generality. Since stating knowledge at the

correct level of generality requires mastering the available

alternatives, KRAKEN guides the SME to subsumed or

covering statements. This approach also exploits the

human ability of recognition, instead of relying on recall.

No predicate set is ever complete, and KRAKEN

provides the ability for the SME to define new

relationships. The acquisition paradigm is structured

around use cases: the SME provides KRAKEN with

examples of how the predicate will be employed. This not

only allows KRAKEN to compute the new relationship’s

argument constraints automatically but also jump-starts the

population of the relationship and provides KRAKEN with

believed suitable exemplars for communicating these

relationships to other users.

For the assembly of more complex knowledge

constructs, such as non-trivial queries and implications, the

KRAKEN team has chosen an almost story-like approach:

the SME lays out a scenario for KRAKEN, consisting of the

involved terms and the relationships between these. Once

the scenario has been “narrated” in this fashion, KRAKEN

assembles the relationships and terms into a query or an

implication.
APPENDIX B: SRI’S SHAKEN TOOLS

The claim of the SHAKEN effort is that SMEs, unassisted

by AI technologists, can assemble models of mechanisms

and processes from components. These models are both

declarative and executable, so questions about the

mechanisms and processes can be answered by

conventional inference methods (for example, theorem

proving and taxonomic inference) and by various taskspecific methods (for example, simulation, analogical

reasoning, and problem-solving methods). A related claim

is that relatively few components, perhaps a few thousand,

are sufficient for SMEs to assemble models of virtually any

mechanism or process. We claim that these components are

independent of domain, and that assembly from

components instantiated to a domain is a natural way for

SMEs to create KB content.

The research in this project exploits and extends

previous work in KBs, process description languages,

qualitative physics, systems dynamics, and simulation.

One scientific innovation is the idea of declarative and

executable models (DEMs) assembled from components.

The declarative aspect of DEMs supports conventional

inference, whereas the executable aspect supports

reasoning by simulation. For example, the declarative part

of a model of aerosols is sufficient to answer questions like,

“Will a 5-micron filter afford protection against this

aerosol?” while the executable part is necessary to model

the dispersal pattern of the aerosol.

The development of libraries of components made

available to SMEs via restricted natural language based,

graphical, or templatized interfaces is the principal means

by which logic-oriented knowledge representation

formalisms become accessible to ordinary users. Every

modeling technology shows this progression:

spreadsheets, finite-element packages, statistical packages,

chemical synthesis software, Macsyma and Mathematica,

architectural and CAD packages, graphics and HCI

systems, etc. are accessible to ordinary users because they

offer libraries of components. As a practical matter, then, it

makes sense to provide SMEs with libraries of modeling

components. As a scientific matter, we believe we can

develop components that represent how humans think

about mechanisms and processes.

The SHAKEN system has the following major

functional components: a knowledge base, an interface for

entering knowledge and asking questions, and a knowledge

server.

The KB, also called the component library, contains a

collection of components representing (1) general

knowledge about common physical objects and events,

states of existence, and core theories, including time, space,

and causality, and (2) more specialized knowledge about

microbiology and biological warfare agents. By a

“component,” we mean a coherent set of axioms that

describe some abstract phenomenon (e.g., the concept

“invade”) and that are packaged into a single

representational unit.

The SHAKEN KB evaluated here contained roughly

250 components representing domain-independent events.

These components would make copious use of core

theories of time, space, and partonomy [7].

A graphical interface for knowledge entry enables a

SME to assemble KB components. By “assembly,” we

mean the connection of components from the component

library. The system evaluated here supports four basic

operations: “connect,” “specialize,” “unify,” and “add” [7].

Axioms are derived from the graphical representation, and

the SME does not have to be trained in formal logic. The

graphical representation is created by a combination of

manual and automatic means.

The question-asking interface plays a central role in

knowledge entry. A SME must be able to understand what

is already encoded in the system, to locate components for

assembly, and to ask arbitrary questions. SHAKEN returns

answers in an easily understood format, and a SME is able

to control the level of detail in an answer. SHAKEN as

evaluated here supported parameterized questions—

derived from a viewpoint grammar [6]—and similarity

search. Presentation of answers to a SME is controlled

using explanation design plans.

The knowledge server provides facilities for efficient

storage and access, supports inference for answering

questions and for assembly of components, and includes

both general-purpose inference and special-purpose

inference. For SHAKEN as evaluated here, reasoning

support was provided by the Knowledge Machine (KM)

representation system.

REFERENCES FOR APPENDIX B

[6] Acker, L. and Porter, B. “Extracting Viewpoints from

Knowledge Bases,” in Proceedings of the Twelfth National

Conference on Artificial Intelligence Annual Conference

(AAAI-94), pp 547–552, 1994.

[7] Barker, K., Porter, B., and Clark, P. “A Library of Generic

Components for Composing Knowledge Bases,” in

proceedings of the International Conference of Knowledge

Capture, 2002.
A Task Domain for Combining and Evaluating Robotics and 

Cognitive Modeling Techniques

Nicholas L. Cassimatis, J. Gregory Trafton, 

Alan C. Schultz, Magdalena D. Bugajska, and William Adams 

Naval Research Laboratory, Code 5515 and 5513 

4555 Overlook Ave. SW 

Washington, DC 20375-5337 

ABSTRACT 

Building systems that integrate different artificial intelligence 

techniques to achieve a higher level of total intelligence is very 

difficult. In order to build integrated systems, simplifying 

assumptions or abstractions are usually made when working in a 

specific domain. As a result of these assumptions and abstractions, 

the proper evaluation of integrated artificial intelligence techniques 

can be quite challenging. We suggest that the domain of hide and 

seek is a particularly well-suited task for integrating robotics and 

higher-level reasoning mechanisms such as computational cognitive 

modeling. Three different instantiations of integrated systems in the 

“hide and seek” domain, which combine cognitive-level algorithms 

with lower level algorithms for perception and navigation are 

discussed. 

KEYWORDS: Robotics, Cognitive Architectures, Cognitive 

Modeling, Performance Metrics 

1. INTRODUCTION 

How do we build intelligent systems, and evaluate the 

underlying algorithms? There are, generally, two main ways 

of building intelligent systems. The first possibility is to focus 

on a relatively small sub domain and build a system or set of 

algorithms that solve problems in that sub domain very well. 

Working in this manner generally leads to very efficient 

methods of solving a relatively narrow set of tasks. There are 

many researchers (including some of the current authors [1]) 

who build these kinds of highly specific systems. The second 

possibility is to build complex systems that solve a larger class 

of problems but that may be less efficient at each task and 

perhaps at the whole task. There are some researchers 

working on these kinds of problems (e.g. [10]), but building 

integrated systems is very difficult for a number of reasons. 

First, the individual techniques are developed using 

different assumptions about their use (e.g., the input/output 

relationship with the environment they are in). Second, 

because most systems are designed for different domains, 

combining two techniques often involves modifying and 

adding new domain specific elements to the design. Finally, 

because each research group’s assumptions and domain are so 

unique, each new group or project must reinvent machinery 

that is relatively incidental to their main interest. For 

example, a researcher trying to develop probabilistic reasoning 

techniques to aid in robot navigation must spend considerable 

effort acquiring and configuring a robotics platform with the 

appropriate sensors and actuators to test possible new 

techniques. 

Each of the above integration issues makes it difficult to 

evaluate the effectiveness of any new technique or system. It 

is almost impossible to compare two techniques when one 

assumes video input from the environment and the other 

assumes sonar; or when one is designed for an office 

navigation task and the other for air traffic control. 

Finally, the structural difficulties of integrating various 

techniques may encourage researchers to ignore or abstract 

away from difficult issues that hold back the field’s progress. 

For example, there is concern [5] that when people working 

on “high-level” artificial intelligence techniques abstract away 

from perception and mobility issues, or when people working 

on perceptual and mobility techniques ignore high-level 

inference, they are actually ignoring the true substance of 

intelligence which lies at the interface between the two. 

In Section 2, we present the task of hide and seek as a 

particularly well-suited task for integrating and evaluating 

artificial intelligence techniques. Section 3 describes three 

different instantiations of integrated systems, which combine 

cognitive-level algorithms with lower level algorithms for 

perception and navigation, and which use the hide and seek 

domain. 

2. THE HIDE AND SEEK DOMAIN 

In order to address these evaluation and integration issues, we 

are organizing a substantial amount of our research around the 

“hide-and-seek” task domain. This domain is forcing us to 

face the difficult integration problems between “high-level” 

cognitive architectures (for example, ACT-R [2] and 

Polyscheme [7]) and systems (such as SAMUEL [9]) for 

sensing and moving in a physical environment. In this 

section, we describe how using a robotic platform in this 
domain allows us to study a surprisingly wide range of issues 

in intelligence, perception and mobility. 

2.1. Perception and Mobility 

Agents that engage in hide-and-seek obviously cannot avoid 

the need to address a wide range of problems in perception 

and mobility. To find their targets, for example, agents must 

be able to identify (object recognition) and move towards their 

targets (path planning) without damaging the environment 

(obstacle avoidance). More generally, any information agents 

can gather perceptually will help them seek and navigate to 

their targets and the more efficiently they navigate to the 

target, the better they will be at hide-and-seek. 

Figure 1. The robot that emerges from behind the screen can be 

the same robot that went behind the screen from the left because 

there was nothing behind the screen to block its motion. The 

barrier did not go behind the screen until after the ball did. 

2.2. Temporal reasoning 

In order to succeed at hide-and-seek, agents must perceive and 

reason about events that occur during various temporal 

intervals. The relations of those intervals among each other 

are important for predicting the outcomes of events and 

therefore the locations of objects that an agent might be 

seeking. Figure 1 shows a simple example of this. In the 

figure, a robot rolls behind an occluding screen and then a 

robot that looks the same rolls out. Next, someone places a 

large barrier behind the screen. Because the barrier was 

placed there after the robot-rolling event, you can assume that 

the space behind the screen was empty during the robotrolling event and that the robot that emerged from the screen 

is the same as the robot that moved behind the screen. 

Figure 2 presents the same scenario, except that the robot 

rolls behind the screen immediately after someone put the 

barrier behind the screen. In this case the robot that emerged 

from the screen cannot be the same as the robot the rolled 

behind the screen because this it did not have time to go 

around the barrier and it could not go through the barrier. 

Figure 2. One knows that the robot that rolls out to the right is 

different from the robot that rolls in from the left because the 

barrier behind the screen would keep the left robot from rolling 

out. 

In these very simple, illustrative cases and in more 

complex situations such as hide-and-seek, the task requires 

agents to make many temporal inferences in order to keep 

track of seeker or target agents and objects. 

2.3. Logical deduction, falsification, default 

reasoning and explanation 

Researchers using logical approaches to artificial intelligence 

have encountered many difficult issues regarding deduction, 

falsification, default reasoning and explanation, and they have 

constructed many sophisticated logical theories to deal with 
them. The following example shows that even the simplest 

physical interactions involve these issues. 

In Figure 3a, a ball rolls towards a screen. In Figure 3b, 

it rolls behind the screen, but in 3c it fails to emerge from 

behind the screen and an object that blocked it is posited 

behind the screen. One can crudely formalize the inference 

that the ball should come out of the screen thus: 

At(ball, left-of-screen, t1) ^ 

Moving(ball, right, t1) ^ 

Empty(behind-screen) 

Æ 

At(ball, behind-screen, t2) ^ 

Moving(ball, right, t2). 

At(ball, behind-screen, t2) ^ 

Moving(ball, right, t2) ^ 

Empty(behind-screen) 

Æ 

At(ball, right-of-screen, t3) ^ 

Moving( ball, right, t3 ).

The inference that the ball emerges from the screen depends 

on the assumption that: 

Empty(behind-screen).

 

When the ball fails to come out from the screen, you infer that 

the proposition, 

Empty(behind-screen), 

is not true and that there must be something behind the screen 

blocking the ball: 

At(something, behind-screen, t2) ^ 

something != ball.

Many traditional issues from the formal logical study of 

intelligence arise here: what can you assume and why; what 

does it take to falsify an assumption; when there is more than 

one explanation for an event; which do you chose; etc. These 

are the usual issues surrounding explanation and default 

reasoning and they also occur whenever you try to build an 

effective hide-and-seek system. 

2.4. Belief revision and reason maintenance 

Any system that reasons in almost any nontrivial domain must 

often infer or assume facts that it must later revise. Because 

the system could have inferred more facts based on the 

originally assumed fact, revising its belief about the original 

fact is much more complicated than simply retracting it [8]. 

The system must retract all beliefs it inferred using the 

original fact that are not otherwise justified. Building systems 

that can revise their beliefs correctly has been a challenge for 

artificial intelligence researchers, even for those trying to 

build good models of common sense physical interactions. 

Figure 3. The ball rolls behind the screen (A), but does not roll 

out (B). There must be an object behind the screen that blocked 

it (C). 

 

Consider an example. Figure 4a shows a scene where a 

screen occludes a table. A block is dropped above the table, it 

falls behind the screen and you infer that it comes to rest on 

the table. Then, when you are told that there is not just one 

table, but that there are two separated tables, as in 4b, you 

must revise your belief about where the ball went when it fell 

behind the screen. In this case, you assume it fell on the floor. 

In general, in order to ascertain the location of any object, 

an intelligent system must make inferences about the object’s 

location, which often depend on provisional information. For 

a system playing hide and seek, if the system spends time 

waiting for or attempting to acquire more definite information, 

the target would have more time to get away. Thus, any 

system that engages in the hide-and-seek task must be able to 

revise provisional beliefs and inferences that followed from it. 

2.5. Planning, searching, problem solving 

Events often have more than one possible outcome and 

systems can usually execute more than one action at any given 

time. The sequence of possible actions and/or inferences 

about event outcomes creates a huge “problem space” of 
possible world states and an intelligent system must choose a 

sequence of actions and/or inferences to achieve an adequate 

state. 

Figure 4. If, as appears in A, there is only one block behind 

the table, then you infer that it came to rest on the table. When 

you learn instead that there are two tables with a gap between 

them, as in B, you infer that the block fell through the gap and 

rests on the floor. 

The need to search through problem spaces is most 

obvious in the hide-and-seek task when it involves robot 

mobility issues. Many algorithms for planning complex paths 

involve creating a visibility or region graph of the space and 

then searching the graph using traditional artificial intelligence 

search techniques. 

Robots designed for the hide-and-seek task, however, 

need planning and search for much more than mobility alone. 

In the following example, we demonstrate that tracking the 

path of a simple ball can require searching through problem 

spaces that involve more than just the location of the ball. 

Figure 5 illustrates a simple physical interaction that requires 

backtracking search. Behind the screen in Figure 4 are two 

buckets. On the left, bucket A is filled with water and on the 

right, bucket B is full of hot coals. Figure 4 also shows a ball 

falling behind the screen. The ball is white and shaped 

roughly like a ping-pong ball, though it may be made of 

plastic or rubber. You see the ball fall behind the screen, 

though you neither see nor hear any further sights or sounds 

because the ball is too light to have dislodged anything and the 

screen masks soft noises. If your task is to figure out if the 

ball fell into bucket A or B, you might imagine that the bucket 

fell into bucket B and infer the consequences. To infer the 

consequence of landing in bucket B, you need to know if the 

ball is rubber or if it is plastic. You can imagine that it is 

rubber, infer that you would smell burning rubber, sense that 

you do not smell anything burning and therefore conclude that 

the ball is not rubber if it fell in B. Likewise, you can infer 

that the ball is not plastic if it fell into B because when you 

imagine a rubber ball lying in burning coals, you imagine a 

certain smell that you do not perceive. So if the ball fell 

behind B, it is neither rubber nor plastic. But you know it was 

one of these, so you know that the ball did not fall into B, but 

instead fell into A. 

Similarly, the hide-and-seek task requires a broad array of 

search abilities. 

Figure 5. Bucket A is filled with water and bucket B is filled 

with hot coals. The ball falls into one of the two buckets. 

2.6. Probabilistic inference 

In many instances of hide-and-seek where events have more 

than one possible outcome, some are more likely than others. 

Seeking a target efficiently is often difficult when a scenario 

involves several possible series of outcomes, because the 

seeker must decide which of the many outcomes is most 
likely. Attempts to make such decisions are often called 

“uncertain reasoning” or “probabilistic inference”. 

Imagine an example like the last one, with the only 

difference being that you know more about the probabilities of 

each uncertainty. Bucket A takes twice the area of bucket B 

and the odds that the ball is plastic are 5:1. You are certain 

that you did not hear a splash, but are uncertain whether you 

smell any new smells. What are the odds that the ball is in A 

and what are the odds that it is in B? 

This example shows that in order to keep track of the 

most likely positions of targets, agents must engage in 

probabilistic inferences. Similarly, hide and seek requires 

similar reasoning. 

2.7. Social reasoning, communication and humanmachine interaction 

When a seeker agent is attempting to find an autonomous 

intelligent agent, it must be able to reason about that agent's 

mental state. Depending on the kind of target agent, this 

implies that competent behavior in the hide-and-seek task 

requires thought about emotions, beliefs, desires, personality 

traits, etc. 

When a team of seeker agents is searching for a target, 

then each team member must be able to communicate with 

other team members in order to coordinate their behavior and 

execute a coherent search strategy. Depending on the 

communications abilities of the seeker agents, this could 

involve language use at all levels: speech recognition to 

decompose the acoustic signal from other agents into words, 

syntax and semantics to determine the meaning of the words 

and pragmatics to understand how the other agent(s) intend an 

utterance to fit into the larger joint seeking joint project. 

When the seeking team contains both humans and robots, 

then the hide-and-seek task becomes a medium for studying 

robot and machine interaction with humans. 

We believe that the full array of research issues in social 

reasoning, communications and human-machine interaction 

can be studied in the hide-and-seek domain. 

2.8. Putting it all together 

Our claim is that hide and seek is an excellent domain with 

which to study intelligence using integrated systems. We have 

presented several needed behaviors that seeker and target 

agents must have. We now go through a simple example of 

playing hide and seek to outline where each of these behaviors 

is needed. Bolded phrases below correspond to the behaviors 

we have discussed earlier. 

Let us assume that we will play hide and seek with a 

robot. The robot will be the seeker agent ("It") first and 

search for the target in a room full of boxes, tables, and desks. 

The target initiates the game with the robot by talking to it 

(communicating with it, using human robot interactions, 

probably language generation and language 

comprehension). Next the robot counts to 10 and starts 

searching for the target agent. The robot must move around 

the environment in the room while avoiding obstacles 

(perception and mobility). The robot may also draw on its 

past experience playing hide and seek to determine that some 

places are better to hide behind and search those places first 

(probabilistic inference). 

If the robot searches behind a box first, it may then 

assume that the target agent will not be there later (unless it 

moved) (temporal reasoning). If the robot then searches the 

entire room and does not find the target agent, it may reason 

that the target must have moved to a different (previously 

searched) hiding place while the robot was searching for it 

(logical deduction, falsification, default reasoning, and 

explanation). The robot must then decide to search the room 

again and re-check positions that it had already searched 

(belief revision and reason maintenance). The robot may 

decide that if the target agent can move around, it should 

search the room in such a way that allows it to see the 

maximum (or most likely) places the target agent would move 

to (planning, searching, problem solving). Once the robot 

finds the target agent, it must tell the target that it was found 

and perhaps even give it some feedback on how good the 

hiding behavior was (social reasoning, communication, and 

HRI). 

2.9. Hide and seek in the real world 

Many real world tasks are instantiations of the basic hide and 

seek domain. In the military, there are missions that directly 

require these skills, including ISR (Intelligence, Surveillance 

and Reconnaissance), RSTA (Reconnaissance, Surveillance, 

and Target Acquisition), and special operations including 

concealment. In non-military domains, hide and seek can be 

found in areas as diverse as Urban Search and Rescue, and 

inspection of facilities (e.g., searching nuclear containment 

facility and superfund sites). 

3. INTEGRATED SYSTEMS 

We now describe the three intelligent systems where highlevel algorithms in the form of computational cognitive 

models are integrated with low-level perception and mobility 

algorithms, and which use various hide and seek domains. 

Available results will be briefly described (and the reader 

directed to appropriate papers for complete results). 

3.1. A hybrid reactive/cognitive architecture for 

micro-air vehicles 

We have developed a hybrid cognitive-reactive system that 

combines more traditional reactive, stimulus-response (S-R) 

behaviors with cognitive models [4][12]. In this work, we 

merge a cognitive model and a reactive system into a control 

system for autonomous vehicles. For this study, the system 
integrates SAMUEL, an evolutionary algorithm-based rule 

learning system [9] with ACT-R, a computational cognitive 

architecture [2]. In our hybrid system, the learning algorithm 

handles reactive aspects of the task and provides an adaptation 

mechanism, and the cognitive model handles the higher-level 

cognitive aspects such as planning and reasoning. The 

cognitive model also provides cognitive realism of the 

behavior. 

Our hybrid controller was implemented for a 

simulated distributed micro air vehicle task. In the 

MAV task, group of vehicles cooperate to perform 

reconnaissance and surveillance, a version of the 

“seeking” task. We assumed each vehicle could detect 

certain ground features below the vehicle and obstacles, 

including other MAVs, within a defined range to the 

dies of the MAV. As a group, the MAVs needed to 

maximize the information gain about the ground 

features, concentrating on areas of more importance, 

and minimizing duplication of effort. In previous work, 

we successfully used genetic algorithms to evolve MAV 

control rule sets that could accomplish the above 

surveillance task [3][13]. 

The cognitive model implemented in ACT-R was 

based on the data collected during human-subject 

experiments performed at NRL and described in greater 

detail in [12]. In those experiments, the human 

operators would control the MAVs by directing them to 

goal locations using a point-and-click interface to the 

simulator. In this study, ACT-R, just like a human 

operator, was responsible for providing 2D goals to 

individual MAVs based on the current perception of the 

world. ACT-R’s perception of the environment was 

closely matched to the perception of the human 

operator. ACT-R could “see” the position and state of 

all MAVs, and the position and value of discovered 

regions of interest. 

SAMUEL was used to evolve stimulus-response 

rules to perform the collision-free reactive navigation 

behavior for the simulated MAVs. Each MAV used the 

same behavior evolved by SAMUEL in conjunction with 

the goals provided by ACT-R to safely navigate to a 

specified location. The current MAV sensor information 

is mapped to the conditions of the stimulus-response 

rules. The action of the rule that is activated specifies 

the action of the vehicle. 

We found the performance of the hybrid controller to be 

comparable to the performance of the human controller, while 

allowing more vehicles to be controlled with fewer collisions. 

The model seems to capture some of the human's behavior and 

performance, while it also allows for higher levels of 

reactivity, which the humans were not able to handle. This 

suggests that our hybrid system is adequately modeling the 

humans' high-level cognitive functions, and also the difficult 

low-level reactive aspects. 

3.2. Polyscheme 

In order to study how to integrate multiple, seemingly 

incompatible, inferential and representational techniques, we 

used the Polyscheme cognitive architecture to develop the S6 

system [7] that reasons about simple physical events that it 

perceives. This was especially helpful in understanding how 

high-level inference and planning techniques might combine 

with and help perceptual algorithms. 

S6 views interactions in a simple physical world through 

a 2-dimensional projection of that world. S6 keeps track of 

the identity of objects, infers the character and existence of 

events it cannot see, predicts the outcome of events, explains 

events and nonevents and revises its inferences when it 

receives new information. S6 successfully reasons about 

many scenarios researchers present to infants and young 

children in order to study their knowledge of the physical 

world. 

S6 combines specialized representation and inference 

techniques for identity, time, events, causality, space and paths 

to successfully deal with a wide range of situations. The 

knowledge representation schemes S6 uses include scripts, 

frames, logical propositions, neural networks and constraint 

graphs. The inference schemes S6 implements include script 

matching, rule matching, backtracking search, neural network 

propagation and counterfactual reasoning. 

3.3. A learning cognitive model for playing the 

game of hide and seek 

In our efforts to add cognitive models for higher level 

reasoning to traditional mobile robotics control, and to 

demonstrate the idea that more effective human-robot 

interactions are possible by using these computational 

cognitive models, we are modeling hide and seek behaviors in 

people, and using these to control a robot. 

We have built a simple computational cognitive model of 

hide and seek. The model is based on a case study of a 3.5 

year old learning to play hide and seek, specifically the 

learning that occurs as the child learns good and poor places to 

hide. The computational cognitive model is built within the 

ACT-R framework [2] and models the reasoning the child 

goes through as she plays and tries different hiding places. 

This is a very difficult task because there is very little 

feedback, very few trials, and very few suggestions. 

The child went from hiding in a room with her eyes shut 

("if I can't see you, you can't see me" strategy) to hiding under 

an upholstered chair in a different room. The model currently 

captures aspects of the child's learning by building a schematic 

representation of hiding and learning that some places are 

good to use as hiding places (e.g., under is good if the object is 

opaque; hiding under a piano is bad). The model also uses a 
simple ontology to reason about hiding given few suggestions 

("Do not hide out in the open") and limited feedback ("You 

hid in a good place"). We are currently in the process of 

putting this computational cognitive model on a robot to play 

a simple game of hide and seek. 

On the robot, perception is handled with a simplified 

perception model. Each object in the room is “labeled” with a 

color target whose color indicates the class of the object (e.g. 

desk or chair), and whose size indicates the approximate size 

of the object (so that the model can determine if it is big 

enough to hide behind. A color camera and a color blob 

detection algorithm [6] are used to find suitable objects to hide 

behind. 

Low-level mobility of the robot is handled by a system 

that combines reactive navigation and collision avoidance, 

explicit path planning, map learning, and localization. This 

system is described in detail in [11]. 

4. GENERAL COMMENTS, FUTURE WORK 

The proper evaluation of integrated artificial intelligence 

techniques can be quite challenging. In this paper, we 

presented the domain of hide and seek as a particularly wellsuited task domain for evaluating the integration of low-level, 

reactive algorithms with higher-level reasoning mechanisms. 

Three different instantiations of integrated systems that 

combine cognitive-level algorithms with lower level 

algorithms for perception and navigation, were described. 

We continue to push the integration of computational 

cognitive models into our systems. We believe that 

incorporating cognitively plausible behavior will permit more 

natural interactions between humans and robots. Using 

computational cognitive architectures and cognitive models 

can ease the ways in which robots communicate with their 

human team members, and vice versa. We have been 

exploring the addition of cognitive models for two goals. 

First, allowing the robot to use the same representations and 

qualitative reasoning as the human will allow for more 

effective and efficient communication. Second, endowing the 

robot with behaviors based on cognitive models of human 

performance allows the robot to exhibit behaviors that are 

similar to how a human might perform a task, thereby 

enhancing social human-robot interaction. Not only does this 

improve interactions with the robot’s human team members, 

but is also critical for robots that need to interact with 

bystanders. We will test these arguments in future research. 

5. REFERENCES 

[1] Altmann, E. M. and J.G. Trafton (2002). Memory for 

goals: An activation-based model. Cognitive Science, 26, 39-

83. 

[2] Anderson, J. R. and C. Lebiere (1998). The Atomic 

Components of Thought. Mahwah, NJ: Lawrence Erlbaum. 

[3] Bugajska, M. D, A. C. Schultz, J. G. Gregory, S. Gittens, 

and F. Mintz (2001). "Building Adaptive Computer 

Generated Forces: The Effect of Increasing Task Reactivity on 

Human and Machine Control Abilities." In Late-Breaking 

Papers Proceedings of GECCO-2001, San Francisco, CA. 

[4] Bugajska, M. D., A. C. Schultz, J. G. Trafton, M. Taylor, 

and F. Mintz (2002). "A Hybrid Cognitive-Reactive MultiAgent Controller." In Proceedings of 2002 IEEE/RSJ 

International Conference on Intelligent Robots and Systems 

(IROS), EPFL, Switzerland; September 30 – October 4, 2002. 

[5] Brooks, R. “Intelligence without representation”. 

Artificial Intelligence, 47:139--159, 1991. 

[6] Bruce, J., T. Balch, and M. Veloso (2000). “Fast and 

Inexpensive Color Image Segmentation for Interactive 

Robots.” In Proceedings of 2000 IEEE/RSJ International 

Conference on Intelligent Robots and Systems (IROS), Japan; 

October 2000. 

[7] Cassimatis, N. L. (2002), Polyscheme: A Cognitive 

Architecture for Integrating Multiple Representation and 

Inference Schemes. Ph.D. Dissertation, MIT Media Arts and 

Sciences Section. 

[8] Doyle, J. 1979. A truth maintenance system. Artificial 

Intelligence, 12:231-272. 

[9] Grefenstette, J., C. L. Ramsey and A. C. Schultz (1990). 

"Learning sequential decision rules using simulation models 

and competition," Machine Learning, 5(4), 355-381, October 

1990, Kluwer. 

[10] Lenser, S., J. Bruce, and M. Veloso. In A. Birk, S. 

Coradeschi, and S. Tadokoro, editors, RoboCup-2001: The 

Fifth RoboCup Competitions and Conferences. Springer 

Verlag, Berlin, 2002. 

[11] Schultz, A., W. Adams, and B. Yamauchi (1999). 

"Integrating Exploration, Localization, Navigation and 

Planning With a Common Representation,'' Autonomous 

Robots, v6 n3, May 1999; IEEE Press, pp. 293-308. 

[12] Trafton, J. Gregory, Alan C. Schultz, Magdalena D. 

Bugajska, Shaun Gittens, and Farilee Mintz (2001). "An 

Investigation of How Humans and Machines Deal with 

Increases in Reactivity." Proceedings of Tenth Conference on 

Computer Generated Forces and Behavioral Representation 

(CGF&BR), Norfolk, VA; May 15-17, 2001. 

[13] Wu, A. S. and A. Agah (1999). “Evolving Control for 

Distributed Micro Air Vehicles,” proc. Of the IEEE 

Conference on Computational Intelligence in Robotics and 

Automation, IEEE, 174-179 
Modelling, Self and Consciousness:

Further Perspectives of AI Research

Ricardo Sanz

Universidad Politecnica de Madrid, Spain  ́

Alexander Meystel

Drexel University, USA

Abstract:

Sound measurement of intelligence cannot be reduced to

just measurement of performance. It is necessary to measure

the real capabilities of the behavior generation engine of machines to be able to determine with precision their suitability

for any particular task. We will see that it is necessary to focus

on the architecture of the systems. The paper will present a

summarial description of inner machinery of intelligence and

how this architecture can serve as the basement for higher

mental functionality. This will lead us to the formulation of a

theory of conscious behavior and a the proposal of a research

program focused into the nature and mechanisms of machine

consciousness.

Keywords:

Intelligence, performance, mental architecture, self, consciousness.

1 Introduction: Measuring What ?

As Lord Kelvin said, ”to measure is to know ́ ́ and hence the

importance of measuring intelligence to know better about it.

Obviously, we know that the problem is not easy just from the

very beginning: Is ”Measuring of Intelligence ́ ́possible ?

In our search for machine replacements of humans in boring, dangerous or economically unviable activities, we use

to check performance of machines [M indM] against performance of humans [M indH] or cognitive models of humans

[M indC ]. However, do we sufficiently understand ourselves

[M indH]? or do we sufficiently understand the systems we

design, manufacture, and use [M indM]?

Generally speaking, we can consider two ways of measuring

intelligence: with regard to a particular task or independently

of any particular task (Pease [1] refers to this last form as a

priori intelligence).

Intelligence manifests itself in the autonomous successful

performance of tasks. Or, to be more precise, in the autonomous successful performance of a task by a specific agent

in a concrete context [2].

Autonomy[AGENT,TASK,CONTEXT]

Extending the base idea of measuring a priori intelligence

we need to measure this faculty independently of the concrete

task, the concrete context and the concrete agent; otherwise

what we will have is a concrete, particular measure, not very

helpful to compare systems with a wide application domain

(this being the case of conventional IQ tests, that just measure

the capability of performing these tests and where extrapolation of results to other activities is highly risky).

To be able to obtain a measure of pure (a priori) intelligence

we need to eliminate from the equation such factors as concrete bodies, concrete tasks and concrete contexts. This will

leave the pure essence of intelligence. This vision of intelligence matches our intuitive, abstract notion of intelligence as

a central faculty independent of particular factors that surround

the activity. How can this possibly be achieved ?

In this paper we will try to identify the core essence of intelligence to be able to directly measure its capabilities instead

of measuring the result of these capabilities in a concrete task.

The conclusion of this identification will lead us to a research

program that re-gains that old dream of artificial intelligence:

building conscious machines.

2 Architecture and Performance

Presuming a functional equivalence of basic building materials, all our theories of mind [M indH], [M indM] and

[M indC ] lead us to the concussion that only mental architecture can account for intelligent systems performance.

The perceived intelligence is strongly correlated with success. Intelligent systems architecture is a critical factor for

success in the performance of any task [3]. Architecture is

hence the point to focus our search for an a priori measure of

1
intelligence [4]. Bad architectures lead to non performing systems.

Ignoring sterile differences between reactive and deliberative intelligence, all these theories do constitute interpretations

that depend critically on representation of goals, states, contexts and bodies [5].

That representation is a central factor of intelligent performance has been know for decades. Execution engines do exploit representations to derive agent’s actions. These execution engines receive varying names depending on the concrete

task at hand: planners, behavior generators, predictors, etc. All

them exploit the information about the world stored in a model

(a world model) to derive actions. Minds are control systems

based on models.

This leads to a typical architectural pattern for

representation-based control system: the elementary loop of

functioning perceive-represent-plan-act that is used as an

elementary building block for more complex architectures

(see Figure 1).

Physical System

Sensor Actuator

Filter World Executor

Model

Filter World Executor

Model

Figure 1: A basic, two layered, model-based control architecture. Each layer constitutes an elementary loop of functioning.

The effectivity of a concrete pair of components

[model,engine] depends on the particular factors for autonomy mentioned before: task, context and agent. This

means that a concrete pair, for example [ordinary differential

equations, Runge-Kutta simulator] can be better than other

pair [first order logic predicates, resolution engine] for a

concrete task, for example tank temperature prediction, in

a particular context, for example a well-engineered refinery,

for a specific agent, for example a model-based predictive

controller.

In many cases, this specificity lead us to sacrifice generality when dealing with constraints to attain specific execution

properties (speed, robustness, cost). For example, mutiresolutional representation and control hierarchies offer cost effective solutions with bounded resources; for speed enhancement, compiled representations and engines adapted for them

are employed.

Generality, however, is an extremely desirable property for

a pair [representation, engine]. Generality is the mark of pure

intelligence. Tradeoffs do obviously exist and have been used -

mistakenly- as arguments against the suitability of general representations for the construction of intelligent agents [6].

Generality is out of questioning, however, because if we

want to give to our systems control mechanisms with a high

degree of a priori intelligence we need generality to overcome

the barriers of the three factors: task, context, agent.

The broader the set of solvable tasks the greater the intelligence of the machine. This was that old dream of the Ultimate

Problem Solver. For example: a washing machine is more intelligent if it is also able to minimize water consumption.

The greater the context-independence of the controller the

higher the intelligence of the machine. This means that the

controller can reach its objectives in a variety of execution contexts, i.e. is robust against variations in its execution environment, being able to handle uncertainty in a proper way. For

example, a transelevator in an automated warehouse is more

intelligent if it can avoid people eventually obstructing its way.

And last, machines that tolerate alterations in their own bodies and still fulfil their objective are more intelligent. For example, a car controller that can maintain car stability with a

broken tire is more intelligent.

3 Systems with Self

Evolutionary pressure has forced a race-of-brains in the biosphere. One of the highest advances is when systems become

able to extend its own capabilities. This means that the system can go beyond current engines and representations, current contexts and tasks, and even its current body. The highest levels of intelligence are those that not only do learning

(enhancing engines) but also modelling (enhancing representations).

Intelligent natural systems do learn autonomously, or are

provided externally the units of knowledge that are required

for their successful functioning. This last approach is simpler

in the case of artificial systems with limited scope. Anticipatory systems [7] can do all this process autonomously. The results of knowledge acquisition are models that capture reality

(with the precision and level of resolution needed for the task).

These models can be —easily— organized into automata models and hence the usefulness of computers to implement intelligent systems.

Truly intelligent systems do have models of their world and

the tasks they perform and are able to enhance them. In fact,

this is what all the business of science is about. Better models

of reality to overcome, with our technologies, all the barriers
Physical System

Sensor Actuator

Filter World Executor

Model

Query Engine Meta

Level

Figure 2: A not so basic, two layered, model-based control

architecture. The metalevel provides introspection over an elementary loop of functioning.

from the past. Based on our better models of the external world

(the context) we can do things (tasks) that go far away of what

our grandfathers were able to do (with a similar body)1.

The last step is easy to see: Truly intelligent systems do

also model themselves. True intelligent systems maintain continuously updated, continuously enhancing representations of

themselves. True intelligent systems are self-conscious.

Using this internal representations of themselves, intelligent machines can use their reasoning engines to reason about

themselves and act accordingly. This representation and reasoning do also include more basic representation and reasoning processes (see Figure 2); intelligent systems do have metalevel representations and reasoning systems that, coupled with

a query engine and a language interface, are used to interchange mental states with others: the states of the representations and reasoning processes (see Figure 3).

This simple analysis capture a commonsense thought: To be

truly effective, intelligent systems need to be aware of themselves, i.e. need to be self-conscious. The nature of the self is

this continuous perception and control of the body of the agent

(see Figure 4). This is the ghost in the machine.

4 Autonomous Performance of Systems with Self

Automata models used by intelligent systems consist of explanations of the environment, states of the system and the appropriate rules of action. The basic process implemented in the

loop of functioning is reproduced in Figure 5.

This model of reality need not be unitary but composed by a

collection of elementary models. The set of all these models is

aggregated together by the representation system of the agent.

1Consider, for example, the increase in life expectancy in the last fifty years

based on better models of human inner workings.

Signal 

processing

Actuators Knowledge Representation Repository

Decision Maker and Behavior 

Generation

SP KRR

S SE A

DM SP KRR

S SE A

DM

SP KRR

S SE A

DM

World Model 

Exchange for 

Horizontal 

Synchronization

Orders/Plans to 

lower echelons

Orders/Plans to lower 

echelons

Orders/Plans from 

World Model Higher echelon

Exchange for 

Vertical 

Synchronization

Sensory 

Reporting

SP KRR

S SE A

DM

Figure 3: Another not basic, layered, model-based control architecture. The multiresolutional heterarchy goes beyond the

single elementary loop of functioning into a collection of interacting loops.

Figure 4: The nature of the self: the model of the body inside

the model of the world and a loop of model-based control over

it.

Umwelt

Model

Situated

Behavior

Set of

Rules

Figure 5: The process of modelling the world to behave inside

it generates a semiotic loop.
Representation as a system of models appears in both natural

and constructed systems.

This representation system also includes intelligent system

goals that are distributed across the heterarchy of models of

reality. In a similar way, we can think that autonomy is distributed over the architecture with the same level of granularity. Each level has a component of autonomous behavior determined by one or many goals of this level and the corresponding

performance measures, and as the resolution becomes lower,

both behaviors and goals are generalized. Autonomy is embodied in the hierarchy of goals and performance measures

supported by internal models. In some sense, we can say that

automata models have a rudimentary form of free will.

The intelligent system has a certain degree of autonomy.

However, its activity is oriented toward the goal of the larger

system (lower resolution unity that the intelligent system belongs to).

Consider a bee. While everybody would agree about its

autonomy, no one will doubt that it is —the autonomous

activity— oriented toward the goal of the swarm. At least, it

must not contradict it in the long term. We also can talk about

the autonomy of a can-picking robot. But one should agree

that its autonomy is oriented toward the goal implanted into

this robot by the designer —a sort of lower resolution level.

In the case of artificial systems, this dependency between

levels of autonomy is so strict, that we strive only for bounded

autonomy in the design of these systems.

Actually, the mechanisms of autonomy in the overall system

as well as functioning of autonomic subsystems determine the

viability of the system.

At each level of resolution, one can talk only about the degree of autonomy enclosed within the goal-oriented activity.

This degree is determined by the self of this particular level.

The degree of autonomy is required to cope with the highresolution eventualities that at the lower resolution level are

parts of the uncertainty taken into account.

The question Why do we need systems with ”self ́ ́ ? has

a clear answer now: Having a self (a continuously updated

model of agent’s body) do increase intelligent systems performance.

To be more concrete, this increased performance can be

shown by the collection of tasks that depend on this schema

of representation and control:

• Introspection/reportability

– Optimization (reorganizing own inner processes)

– Socialization (collaborating with other agents)

• Fault handling

– Fault detection and isolation (finding problems)

– Diagnosis (identifying causes)

– Fault management (devising walkarounds)

• Autonomous behavior

– Regoaling (changing tasks)

– Reconfiguration (changing body)

– Tooling (changing context)

5 Intelligence, Self, Consciousness

In this process of intelligent systems analysis there are many

objectives. In some sense we are stepping toward [M indH] by

means of implementations of [M indM] that provide progressively accurate behavior. To our understanding, the architectural model presented here is a good unified theory of natural

and artificial intelligence systems.

This theory do explain some of the more difficult observed

aspects of human minds, while avoiding entering into the nonimplementable field of metaphysics.

One of the more puzzling aspects for human mind is the

uniqueness of “self”. As we have seen, the model of the

extended-plant (the body+the controller) can be single or multiple, not necessarily unitary. The question is: Is there any

reason for the uniqueness of the “self”?.

Our hypothesis is that unitary selves provide evolutionary

advantages (better autonomous performance). Having a single

self enables resolution of autonomous control problems with

scarce resources in the presence of higher degrees of uncertainty. The presence of the single self guarantees the coherence

of the set of multiresolutional goals of the intelligent system.

In relation with [M indH], there are no widely accepted explanations of conscious/unconscious behavior. Some authors

distinguish between unitary and dual explanations (having one

or two mechanisms for the conscious and the unconscious) and

multiple theories are available on both sides (See [8] for a good

survey).

One of the major problems is that while unitary explanations

are more aestetically pleasant, they fail to provide the necessary qualitative distinction between the conscious and the unconscious that many authors want to see. The origin of the

problem can be traced to the perceived distance between conscious and unconscious that from our point of view does not

exist. Consciousness is not a binary property, it only looks like

that because the interaction with external agents (with others)

is performed only an a concrete high level of the control hierarchy.

The main obstacles for a unified theory of mind

([M indH]+[M indM] are the chauvinism of human species

and the manicheism of most theories, that appears everywhere:

Representation/representationless, deliberative/reactive conscious/unconscious, symbolic/subsymbolic, biologic/beer can.
There are no intelligent systems without representation; every system that has a sensor has representation. Reactive control systems are just degenerate cases of deliberative control

systems (where deliberation is reduced to a simple I/O mapping).

There is even disagreement about what is consciousness.

Some authors distinguish three types: Access consciousness, reflective consciousness and phenomenal consciousness.

There are authors that distinguish upon seven types !!!.

The key for the emergence of self-consciousness is integration of information about the body with information about the

world (see Figure 6)[9].

Visible Light

Acceleration

GPS

IR/UV G-waves

Ladar

Temperature

Hunger

Attitude

X-Ray Vision

Superhumans

Mental

Model

Humans

Figure 6: The process of integrating information from incoming sensors lead to progressive world-awareness. Systems with

better sensors can be more aware if properly designed.

Consciousness increases as more information (from the outside, from the inside, from the mental processes, etc.) is integrated in a dynamical mental model that includes the self.

The -wicked- problem is not achieving consciousness but

achieving human-like consciousness (i.e. being recognized as

humans by other human minds) and this can be done only by

means of human-like reportability and a human-like mental architecture. But building humans is nonsense from a practical

perspective and recognizing consciousness in very alien systems is something that not everybody is prepared to do. As

Thomas Nagel would ask: What is it like to be a Tomahawk

missile ? [10]

6 Conclusions

Semiotic principles of computation provide a sufficient background for developing computer systems that demonstrate elements of self, consciousness, and free will.

The prerequisite for achieving this milestone in the intelligent computer systems development is focusing on interpretability of representations as the major factor of performance. This can be approached by developing multiresolutional (multigranular, multiscale) systems of knowledge representation equipped with a sufficient set of procedures to exploit

the representations.

Eventually, the integration of these hierarchical modelbased control loops will lead to ascertain the emergence of

SELF.

We can conclude that consciousness is just an operational

mode of a -not necessarily- complex controller. Being conscious is just having a running controller (being ON). Selfconsciousness appears by the very same method when the

sensed plant is the intelligent system proper.

As the multigranular system of semiotic closures emerges,

it arrives at the phenomenon of representing and monitoring

itself: self-consciousness. SELF [consciousness] is the multigranular system of semiotic closures constructed in representation for interpreting intentionalities of a system within its

blended global multiscale coordinates. SELF is also supported

by a multigranular system of goals that can be considered a

provisional reminder of the results earlier produced by its intentionality system.

References

[1] A. Pease, “Evaluation of intelligent systems: The high

performance knowledge bases and ieee standard upper

ontology projects,” in Proceedings of the Workshop Measuring the Performance and Intelligence of Systems, PerMIS’2001, (Mexico D.F.), September 4 2001.

[2] R. Sanz, F. Mat ́ıa, and S. Galan, “Fridges, elephants  ́

and the meaning of autonomy and intelligence,” in

IEEE International Symposium on Intelligent Control,

ISIC’2000, (Patras, Greece), 2000.

[3] J. Albus and A. Meystel, Engineering of Mind: An Introduction to the Science of Intelligent Systems. Wiley

Series on Intelligent Systems, New York: Wiley, 2001.

[4] H. H. Hexmoor, “Smarts are in the architecture!,” in

AAAI Spring Symposium, 1995.

[5] A. Meystel and J. Albus, Intelligent Systems: Architecture, Design, Control. Wiley Series on Inteligent Systems, New York: Wiley, 2001.

[6] R. A. Brooks, “Elephants don’t play chess,” Robotics and

Autonomous Systems, vol. 6, pp. 3–15, 1990.

[7] R. Rosen, Anticipatory Systems. Pergamon Press, 1985.

[8] J. G. Taylor, The Race for Consciousness. Cambridge,

MA: MIT Press, 1999.

[9] R. Sanz, “An integrated control model of consciousness,”

in Proceedings of the conference Toward a Science of

Consciousness, (Tucson (AZ), USA), April 8-12 2002.
[10] T. Nagel, “What is it like to be a bat?,” The Philosophical

Review, October 1974.

[11] M. Asada, E. Uchibe, and K. Hosoda, “Cooperative behavior acquisition for mobile robots in dynamically changing real worlds via vision-based reinforcement learning and development,” Artificial Intelligence,

vol. 110, no. 2, pp. 275–292, 1999.

[12] A. Sloman, “Virtual machines and consciousness,”

technical report, University of Birmingham, 2002.

http://www.cs.bham.ac.uk/research/cogaff/.

[13] C. Joslyn, “Levels of control and closure in complex

semiotic systems,” in Workshop on Closure: Emergent

Organizations and their Dynamics, (University of Ghent,

Belgium), May 3-5 1999.

[14] N. R. Jennings, “On agent-based software engineering,”

Artificial Intelligence, vol. 117, pp. 277–296, 2000.

[15] N. Shadbolt, “The shape of things to come,” IEEE Intelligent Systems, pp. 2–3, September/October 2001.

[16] A. Meystel and R. Sanz, “Self-identity of complex control systems,” in Proceedings of the Workshop on Theoretical Fundamentals of Intelligent Systems, (Durham

(NC), USA), March 11 2002.

[17] W. Dress, “A Bayesian approach to extracting meaning

from system behavior,” in Proceedings of IEEE Systems,

Man and Cybernetics Conference, 1998.

[18] P. Horn, “Autonomic computing: Ibm perspective on the

state of information technology.” IBM Research, 2001.
Information Access Technology: Does it Require Getting Involved in Mechanisms of

Mind and Intelligence?

Martin Herman

Information Access Division

NIST

The world produces between 1 and 2 exabytes (1018 bytes) of information each year --

about 250 megabytes for every man, women, and child on earth. [Lyman, Peter and Hal

R. Varian, "How Much Information," 2000, http://www.sims.berkeley.edu/how-muchinfo] Therefore better tools and technologies for information access and information

management are needed to take full advantage of the ever-increasing amounts of digital

information.

The discussion here will focus on technologies for accessing unstructured, digital

multimedia and other complex information, including text, web pages, images, video,

voice, audio, and graphics (both 2-D and 3-D). Examples of such technologies include

search and retrieval techniques; information filtering techniques; methods for

transforming speech, text, images, and video to representations that can be searched and

filtered; user interaction techniques, including multi-modal approaches, that provide

access to information; visualization methods that provide access to information; and

sensor data acquisition and management. Note that these areas overlap what are

considered to be traditional areas of AI, including speech processing and understanding,

image and video processing and understanding, natural language processing and

understanding, search techniques, data mining, text mining, speech mining, and

video/image mining.

Generally, I agree with the notion that information access technologies can be improved

through the use of AI. Although many AI technologies are currently not mature enough

to be applied effectively, a program that advances these technologies could lead to better

information access technologies. The following paragraphs discuss this further.

Traditional statistics-based information retrieval approaches seem to have reached a

performance ceiling. New approaches that combine the linguistic analysis used in natural

language processing with the statistics-based approach are showing great promise in

improving performance. The AI approach could become particularly important for areas

such as question-answering systems and summarization systems. In fact, from a

functional point of view, any system that can look up information in databases and on the

web and answer questions and generate summaries in a manner similar to humans is, by

definition, an intelligent system.

The transformation of speech, text, images, and video to representations that can be

searched and filtered requires a certain degree of “intelligence.” In particular, speech-totext and image/video understanding have traditionally been considered AI tasks. The

extraction of sophisticated metadata from video and images is a particularly difficult task,
and many would agree that extracting information such as objects, relationships among

them, events, people, and their goals from video is a task that requires intelligence.

There are many different kinds of user interaction techniques. The traditional graphical

user interface (GUI) used in much of today’s software is not considered “intelligent,” but

neither are they easy to use by “non-technical” people. One approach to the promise of

intuitive user interfaces is through perceptual user interfaces, those in which the machine

perceives and understands what the user is doing, and the user can interact with the

machine as if she is interacting with another human, through speech, natural language,

and gestures. This would require the machine being able to perform tasks such as

identifying and perceiving users and their actions and goals, and understanding and

anticipating user needs.

In summary, I believe that the field of AI is still quite immature. However, the potential

rewards to fields such as information access can be enormous if AI technologies can be

significantly advanced. An evaluation-driven, metrics-based program in AI could

significantly contribute to such a program.
Information Access: Do You Mind?

John Cugini, ITL

What's the Problem?

Successful execution of many information-based tasks depends crucially on contextual knowledge. Language

processing is particularly sensitive to context and I will concentrate on it in this abstract as the example par

excellence of knowledge-dependent information access.

The only type of system currently capable of deep semantic knowledge is the human mind. This system has two

notable and perhaps related aspects: 1) knowledge does not appear to be symbolically represented in any

straightforward way - no one expects to find logical propositions directly encoded in neurons. 2) human-based

knowledge appears to be intimately connected to consciousness - a phenomenon about whose nature there is simply

no consensus (e.g. see Daniel Dennett vs. David Chalmers). Several development paths (not mutually exclusive) for

intelligent access suggest themselves:

Muddle Through

Does successful information access really require a rich world model to provide context, or can we get by with brute

force (think Deep Blue) and/or statistical algorithms? Or perhaps we can get by with many "little" scope-limited

applications (e.g. airplane reservations - in which the "world model" is essentially a database system)?

It seems doubtful to me that a truly robust speech recognition system (one with performance comparable to a human

transcriber) or even a text-based question-answering system could be crafted in the absence of a more general

knowledge base. It's one thing to play chess using special chips and brute-force; quite another to interpret English.

Formal Representation

If extensive knowledge is required, can it be represented in a symbolic, formal (and hence manipulable) way, a la

CYC (see www.opencyc.org)? Is there a "critical mass" of such common sense knowledge? Must this system be

updated manually or might it achieve NLP capability sufficient to let it learn new information (including the

formation of new concepts) directly from text? Just because humans apparently don't represent knowledge this way

doesn't mean that it can't be done (airplanes don't need ot flap their wings to fly).

It's difficult to predict how fruitful this approach will be. It at least takes the problem of common sense knowledge

seriously. But it's a big world with a lot of facts - suppose you ask a friend about her round of golf and she e-mails

back: "Oh, it was pouring for a while - the clubs were practically flying out of my hands." What would it take to

build a system capable of concluding that her score was probably higher than usual? Or explaining why the clubs

were "flying"?

Non-symbolic approaches

Symbolic systems also have a problem of anchoring: even if the knowledge base correctly encodes the relationships

among concepts, there must be a set of base concepts that are not further defined linguistically. Although the

symbolic approach enables reasoning at the propositional level, at best it can encode conceptual knowledge only by

mapping words to other words.

But humans have, for instance, a irreducibly non-linguistic understanding of what "yellow" means; human

conceptual knowledge is intimately tied to sensory and emotional states (consciousness rears its ugly head), which

we can label ("yellow", "pain", "sorrow") but not further explicate. And surely there are many (most?) questions the

answering of which requires such non-verbal understanding.
What are the prospects for building a system that associates sensory inputs with concepts? Computer-based "neural"

networks can be trained to recognize textual characters - is this simply a good trick or does it scale up to recognition

of a wide variety of objects?

Conclusion

Enough smart people have been trying for a long enough time that one may safely conclude that the current

limitations of intelligent access are not accidental; it will take more than yet another clever algorithm to transcend

them. A significant world modeling capability is necessary to achieve major improvements over the current state of

the art. What the payoff curve looks like (system performance as a function of richness of representation) is one of

the major open questions.
Using Speech Technologies for Information Access: Does it Require Getting Involved in

Mechanisms of Mind and Intelligence?

John Garofolo

Information Access Division

NIST

Speech is arguably man's oldest and most natural form of communication. Speech and language

are also inextricably linked to human thought and intelligence. Therefore, the recognition and

understanding of spoken and written language was from the beginning an important component

of artificial intelligence research. Initial efforts at speech recognition using classic AI techniques

were thought to have failed because of the computational limitations of the time. Yet, even with

the major advances that have occurred in computing power over the last decade, successful

communication with machines using human language remains elusive.

While it's true that speech-based information systems (primarily telephone call centers) are being

deployed on almost a daily basis, these systems are typically highly constrained to a particular

limited vocabulary and/or discourse domain. If one tries to ask one of these systems a question

that is outside of its narrow scope or if one uses a word that is outside of its vocabulary, one is

quickly switched over to a human operator, or worse, sent into telephone oblivion. It's somewhat

ironic that the current wealth of speech recognition software/services as well as their limitations

are largely due to a mass decision by the research community to abandon traditional AI

approaches in the mid-to-late 70s and instead focus on probabilistic methods.

When traditional AI approaches became intractable when applied to language, statistical

approaches using variations of Hidden Markov models and neural nets were employed. For a

time, during the late 80s and early 90s, there were huge improvements in accuracy. To work

effectively, these approaches require large amounts of exemplary training data. So, large

amounts of money were devoted in the research community to developing transcribed recordings

of hundreds of hours of speech. While larger amounts of training data improved accuracy for

particular domains, significant generalizable improvements in the technology were really not

occurring. For a time, as progress began to stall, larger and larger training corpora – eventually

in the hundreds of hours -- were employed. However, it was soon learned that the problem

could not be solved with the amount of data that could be reasonably collected and transcribed

and it has been suggested that many thousands of hours of speech would be necessary for

significantly improved recognition using these techniques. Unfortunately, some researchers now

believe that statistical approaches are inherently limited and that further progress down that path

is unlikely. There are clearly at least three important components of recognition of speech by

humans that are not addressed by existing approaches: robust acoustic modeling, explicit

linguistic knowledge, and world/contextual knowledge. Because of these limitations, recognizers

were (and continue to be) limited to the types of vocabulary they could recognize and the

acoustic conditions within which they could work with any accuracy. Unfortunately, to date, the

speech research community by itself has been unable to address these issues.
Yet progress could not have been gauged at all if the community had not had a way to evaluate

recognition performance. Therefore, an important factor that cannot be overlooked in the

development of speech recognition technologies was the development of evaluation

methodologies and practices. In 1987, with DoD sponsorship, NIST began a series of speech

recognition evaluations that continue today. Through the years, the evaluation domains have

changed and become more realistic and challenging, but the approach and metrics have remained

relatively stable. These evaluations use "canned" recordings of speech and are, therefore,

repeatable. In the early years of speech technologies, only demo-based anecdotal so-called

evaluations were performed and great claims were made which could not be substantiated. This

brought about the "dark days" of speech recognition in the 70s when it was realized that speech

recognition (as well as other AI technologies) had been "oversold". The NIST evaluations helped

to reverse perceptions about speech recognition and demonstrated measurable progress over time

as well as provided direct comparisons between systems. As such, these evaluations have also

helped to propel the research into productive directions.

Recently, with NIST's assistance, the Defense Advanced Research Projects Agency (DARPA)

has begun a new program to explore new approaches in speech recognition technology. The

program has set out to improve performance in two ways: 1) By exploring novel approaches for

word recognition; and 2) By creating integrated technologies generating enriched transcripts.

This new area, deemed "Rich Transcription", is an effort at generating recognition output that

contains a variety of metadata in addition to the words that were spoken. The resulting rich

transcripts with speakers indicated, sentences marked, etc. can be rendered into human-readable

form and are also more useful for other downstream processing technologies such as retrieval,

information extraction, summarization, and translation. Further, the metadata can be "fed back"

into the recognizer to improve the word recognition itself. This new program provides a "back

door" back into the world of AI by integrating linguistic knowledge into the recognition process

and by rethinking that acoustic and language modeling process. Since the recognition process

will be tightly integrated with deeper linguistic processing to provide an understanding of the

structure and meaning of the words that were spoken, this is an initial step back to speech

recognition's AI roots. Other efforts at building speech understanding and discourse capabilities

are taking a similar approach.

NIST is widening the scope of its speech recognition evaluations to measure the accuracy of the

production of the Rich Transcription metadata as well as the words produced by the recognition

systems. It is hoped that this new, broader approach will help to propel the recognition and

understanding of human speech by machines to a new level which will fulfill one of the promises

of AI.
1

In Quest of Performance Metrics for Intelligent Systems—A Challenge

that Cannot be Met with Existing Methods

Lotfi A. Zadeh∗

As we move further into the realm of intelligent systems, the problem of devising

performance metrics for assessing machine intelligence looms larger and larger in

importance. The problem is there, but does it have a solution? A somewhat unorthodox

view which is articulated in the following is that (a) complete solution is beyond the

reach of existing methods; and (b) that a prerequisite to solving the problem is a better

understanding of a broader problem, namely, the basic problem of concept definability.

To this end, what is presented in the following is a sketch of what may be called a theory

of hierarchical definability, or THD for short.

In science, and especially in natural sciences and mathematics, there is a longstanding tradition of expecting that concepts be defined clearly and precisely. But as we

move from the natural sciences to the sciences of the artificial, two basic problems came

into view.

The first problem relates to the need to formulate our definitions in ways that can

be understood by a machine. For example, if I command a household robot to take the

dishes off the table, I must define what I mean by “take the dishes off the table.” Or, if I

instruct a machine to summarize a document, I must define what I mean by a summary.

And, how can I assess the MIQ (Machine IQ) of a machine that executes my commands?

The second problem is that we encounter, much more frequently than in the past,

concepts which do not lend themselves to precise definition. Among familiar examples of

such concepts are intelligence, creativity, autonomy, adaptivity, relevance, robustness and

causality.

We have been largely unsuccessful in formulating operational definitions of

concepts of this nature. Why?

A view that is advanced in the following is that the primary reason for the lack of

success is that the concepts in question, and many like them, are intrinsically fuzzy, that

is, are a matter of degree. Thus, when we try to define such concepts within the

conceptual framework of classical, bivalent logic, we encounter a fundamental

incompatibility—an incompatibility between crispness of definitions and fuzziness of the

concepts we try to define.

 

∗

Professor in the Graduate School and Director, Berkeley Initiative 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. Research supported in part by ONR Contract N00014-00-1-0621, ONR Contract

N00014-99-C-0298, NASA Contract NCC2-1006, NASA Grant NAC2-117, ONR Grant N00014-96-1-

0556, ONR Grant FDN0014991035, ARO Grant DAAH 04-961-0341 and the BISC Program of UC

Berkeley
2

Viewed in a slightly different perspective, the problem relates to the inadequate

expressive power of the definition languages which are at our disposal, namely, the

natural language and the language of bivalent logic. What this implies is that, to solve the

problem, we have to add languages with higher expressive power to our repertoire of

definition languages. This is the basic idea that underlies the theory of hierarchical

definability, THD.

In THD, the languages that we add are based on fuzzy logic since they must be

capable of serving as definition languages for fuzzy concepts. More specifically, the

definition languages in THD form a hierarchy represented as (NL, C, F, F.G, PNL),

where NL is the lowest member in terms of expressive power, and PNL (Precisiated

Natural Language) is the highest. It is understood that every member of the hierarchy

subsumes those below it.

The C definition language is the language of mathematical analysis, probability

theory and bivalent logic. This is the language that we learn when we take courses in

mathematics, probability theory and logic. The F language is the language of fuzzy logic

without granulation, and the F.G language is the language of fuzzy logic with

granulation. PNL (Precisiated Natural Language) is fuzzy-logic-based language with

maximal expressive power.

A simple analogy may be of help. In my progression of learning, I start with my

knowledge of a natural language. After entering a university and taking courses in

mathematics, I add to NL my knowledge of C. At this stage, I can use the union of NL

and C as a definition language. Then, I take a course in fuzzy logic. In this course, first I

learn F, then F.G and finally PNL. At the end, I can use PNL as a definition language,

with the understanding that PNL subsumes all languages below it in the hierarchy.

What is PNL? The basic idea in PNL is that a proposition, p, in a natural

language, NL, may be precisiated through translation into a precisiation language. In the

case of PNL, the precisiation language is the Generalized Constraint Language (GCL). A

generic generalized constraint is represented as Z isr R, where Z is the constrained

variable, R is the constraining relation and r is a discrete-valued indexing variable whose

values define the ways in which R constrains Z. The principal types of constraints are:

possibilistic (r=blank); veristic (r=v); probabilistic (r=p); random set (r=rs); usuality

(r=u); fuzzy graph (r=fg); and Pawlak set (r=ps). The rationale for constructing a large

variety of constraints is that conventional crisp constraints are incapable of representing

the meaning of propositions expressed in a natural language—most of which are

intrinsically imprecise—in a form that lends itself to computation.

The elements of GCL are composite generalized constraints that are formed from

generic generalized constraints by combination, modification, and qualification. An

example of a generalized constraint in GCL is ((Z isp R) and (Z, Y) is S) is unlikely.

By construction, the Generalized Constraint Language is maximally expressive.

What this implies is that PNL is the largest subset of a natural language that admits

precisiation. Informally, this implication serves as a basis for the conclusion that if a

concept, X, cannot be defined in terms of PNL, then, in effect, it is undefinable or,

synonymously, amorphic.

In this perspective, the highest level of definability hierarchy, which is the level

above PNL-definability, is that of undefinability or amorphicity. A canonical example of

amorphic concepts is that of causality. More specifically, it is not possible to construct a
3

general definition of causality such that given any two events A and B and the question,

“Did A cause B?” the question could be answered based on the definition. Equivalently,

given any definition of causality, it will always be possible to construct examples to

which the definition would not apply or yield counterintuitive results.

In dealing with an amorphic concept, X, what is possible—and what we generally

do—is to restrict the domain of applicability of X to instances for which X is definable.

For example, in the case of the concept of a summary, which is an amorphic concept, we

could restrict the length, type, and other attributes of what we want to summarize. In this

sense, an amorphic concept may be partially definable or, p-definable, for short. The

concept of p-definability applies to all levels of the definability hierarchy.

In essence, PNL may be viewed as a collection of ordered pairs of the form (p,

p

x

), where p is a precisiable proposition in NL and p x is a precisiation of p, that is, its

translation in GCL. In this sense, PNL may be viewed as a dictionary in which p is an

entry and p x is its meaning.

In scientific theories, a concept, X, is almost always defined as a crisp (bivalent)

concept, meaning that the denotation of X is a crisp set in its universe of discourse. In

THD, a concept, X, is associated with a quintuple (X, U, QCS, DF(L), D(DF)) in which

X is the concept; U is the space of objects to which X is applicable; QCS is the

qualitative complexity scale associated with X; DF(L) is a definition of X in a language

L; and D(DF) is the domain of DF, that is, the set of objects to which DF is applicable.

The concept of a qualitative complexity scale plays a key role in THD. Basically,

the qualitative complexity scale, QCS, is a linear clustering, QCC1, QCC 2, ..., QCCm,

of qualitative complexity classes of objects in U such that: (a) objects in QCCi are

roughly equally complex in relation to the definition, DF, of X; and (b) objects in QCCi+1

have higher complexity than those in Qi. For example, if X is the concept of volume,

then QCC2 may be class of objects like trees; and QCC 5 may be the class of objects like

clothing. Each language in the definability hierarchy is associated with a critical

threshold on the qualitative complexity scale such that the language cannot be applied to

classes above the critical threshold.

As the lowest member of the definability hierarchy, the C language has a low

expressive power, with the consequence that the associated critical threshold is near the

low end of the of the qualitative complexity scale. In particular, the C language cannot be

used to define fuzzy concepts. Thus, its use to define concepts which, in reality, are fuzzy

concepts, leads to counterintuitive conclusions. An example is the conventional Clanguage-based definition of stability. Since stability, in general, is a matter of degree, its

definition as a crisp concept leads to paradoxes similar to the ancient Greek sorites

paradox. To define stability as a fuzzy concept—which in reality it is—what is needed is

PNL. The same applies to the concept of causality. Thus, causality can be defined as a

crisp concept only for complexity classes which lie close to the low end of the qualitative

complexity scale.

Another important point is that almost every concept has some degree of

amorphicity, with a concept such as causality being amorphic to a high degree. But even

such basic concepts as volume, density, edge, derivative and optimality have domains of

amorphicity which are apparent in many real-world settings. What this implies is that

many basic concepts may require redefinition in terms of PNL.
4

Does PNL have a significant role to play in devising metrics of performance for

intelligent systems? This is an issue that is not addressed in the brief sketch of the theory

of hierarchical definability. But I have no doubt that it will, since the concept of

intelligence is much too complex to lend itself to analysis through the use of existing

bivalent-logic-based methods.
Robot Intelligence for Tunneling and Confined

Space Search and Rescue

John G. Blitch

Director

Center for Robot Assisted Search and Rescue (CRASAR)

P.O. Box 270346, Littleton CO 80127

303.697.7607, {crasarhq@aol.com, blitchj@aol.com }

ABSTRACT

The horrific nature of structural collapse and natural disaster has unquestionably risen to

the forefront of American perspective in the aftermath of recent terrorist attacks of 09/11/01. In

responding to these events, heroic rescue personnel are routinely faced with a tremendously

complex, hazardous, and often frustrating task environment that all too often leaves them without

success in the presence of seemingly endless streams of carnage and body bags. The use of

robots to assist in mitigating this challenge has been validated with a moderate degree of success

by recent response efforts at the World Trade Center, but the tele-operated nature of the

systems employed there did not fully exploit robot employment potential.

This paper presents the case for machine reasoning in the context of robot assisted

search and rescue. A description of the USAR challenge is presented first in an effort to

develop the reader’s appreciation for complexities and challenges of the problem at hand. This

is followed by a brief evolutionary review of mobile robot development and the motivation

behind a shift toward portable platforms. A more detailed review of CRASAR’s evolution and

its pioneering actions in response to the World Trade Center attack is provided next with

emphasis on platform shortcomings and the need for semi-autonomous control schemes. A

concept for USAR (Urban Search and Rescue) oriented micro-tunneling is presented next as a

new challenge in volumetric reasoning. The paper concludes with sequence of grand research

challenges for roboticists interested in the USAR task domain.

Keywords: robot, robotics, confined space rescue, search and rescue, tunneling, microtunneling, artificial intelligence, intelligent systems
Metrics, Schmetrics! How The Heck Do You Determine A UAV’s 

Autonomy Anyway? 

Bruce T. Clough, Technical Area Leader, Air Force Research Laboratory 

Wright-Patterson AFB, OH, Bruce.Clough@wpafb.af.mil 

ABSTRACT So, they had to be simple, apply to a broad range of

systems, and yet exhibit good resolution. Obviously a 

simple task, but first let’s look at what it means to be

autonomous. 

The recently released DoD Unmanned Aerial Vehicles

Roadmap [9] discusses advancements in UAV autonomy in

terms of autonomous control levels (ACL). The ACL concept 

was pioneered by researchers in the Air Force Research

Laboratory’s Air Vehicles Directorate who are charged with

developing autonomous air vehicles. In the process of

developing intelligent autonomous agents for UAV control

systems we were constantly challenged to “tell us how 

autonomous a UAV is, and how do you think it can be

measured...” Usually we hand-waved away the argument and 

hoped the questioner will go away since this is a very subjective, 

and complicated, subject, but within the last year we’ve been

directed to develop national intelligent autonomous UAV control 

metrics - an IQ test for the flyborgs, if you will. The ACL chart 

is the result. We’ve done this via intense discussions with other

government labs and industry, and this paper covers the agreed

metrics (an extension of the OODA - observe, orient, decide, and

act - loop) as well as the precursors, “dead-ends”, and out-andout flops investigated to get there. 

2. Quick Difference Between Autonomous 

and Automatic (our definition) 

Many people don’t realize that there is a significant

difference between the words autonomous and automatic.

Many news and trade articles use these words 

interchangeably. Automatic means that a system will do

exactly as programmed, it has no choice. Autonomous

means that a system has a choice to make free of outside

influence, i.e., an autonomous system has free will. For 

instance, let’s compare functions of an automatic system

(autopilot) and an autonomous guidance system: 

• Autopilot: Stay on course chosen. 

• Autonomous Guidance: Decide which course to take,

then stay on it. Keywords: autonomy metrics, machine intelligence 

metrics, UAV, autonomous control

Example: a cruise missile is not autonomous, but

automatic since all choices have been made prior to 

launch. 1. Background 

At top levels of the US Department of Defense an effort

has been initiated to coordinate researchers across the 

Services and industry in meeting national goals in fixedwing vehicle development. The Fixed-Wing Vehicle

Initiative (FWV) has broad goals across numerous vehicle 

technologies. One of those areas is mission management

of UAVs. Our broad goal is to develop the technology

allowing UAVs to replace human piloted aircraft for any 

conceivable mission. This implies that we have to give

UAVs some level of autonomy to accomplish the

missions. One of the cornerstones of the FWV process is

the establishment of metrics so one know that a goal is 

reached, but what metrics were available for measuring 

UAV autonomy? Our research, in conjunction with

industry, determined that there was not any sort of metric

as we desired. Thus we set out to define our own [Note

1]. 

3. We Need To Measure Autonomy, Not 

Intelligence

For some reason people tend to equate autonomy to

intelligence. Looking through the proceedings of the last 

NIST Intelligent Systems Workshop there are several

papers which do this, and in fact, the entire conference

sets the tone that “intelligence is autonomy” [3]. They are

not the same. Many stupid things are quite autonomous 

(bacteria) and many very smart things are not (my 3 year

old daughter seemingly most of the time). Intelligence 

(one of a myriad of definitions) is the capability of

discovering knowledge and using it to do something.

Autonomy is: 

• the ability to generate one’s own purposes without

any instruction from outside (L. Fogel)

But what characteristics should these metrics have? We

decided that they needed to be: • having free will (B. Clough) 

What we want to know is how well a UAV will do a task, 

or better yet, develop tasks to reach goals, when we’re not 

around to do it for the UAV. We really don’t care how 

intelligent it is, just that it does the job assigned. 

Therefore, intelligence measures tell us little. So, although 

we could talk about the Turing Test [1] and other 

intelligence metrics, that is not what we wanted. 

• Easily visualized such that upper management could 

grasp the concepts in a couple of briefing slides. 

• Broad enough to measure past, present and future

autonomous system development.

• Have enough resolution to easily track impact of

technological program investments. 
4. Well, It Should Be Easy To Find Metrics, 

One Has The Web And Other Info Sources, 

Right? 

Well, one would think so, but after an exhaustive onemonth search involving the author, Air Force Research

Laboratory Library Staff, and several other search

organizations we found two. Two. Now if the goal was

“find machine intelligence metrics” we would have been

inundated with piles of paper. In addition to the 

aforementioned workshop, we would be looking through

hundreds of publications and papers. Maybe it was a good

thing we were looking for autonomy metrics! 

We had thought that maybe, just maybe, the folks working 

distributed autonomous robotic systems had looked at this 

problem, but our questions to experts in that field revealed

that they are just starting to ask those questions themselves 

[4]. The problem the researchers have in this area is that

the metrics they are coming up with are task specific - 

they don’t have general metrics quantitatively measuring 

higher-level characteristics of autonomous robot control 

architectures. 

Figure 1: MAP Survival Space in which autonomous 

systems can be measured 

These level metrics are fully described in Table 1 below. 

The space has three metrics has the three metrics outlines 

above, and six levels. 

M e tric

M o b ility A c q u is itio n P ro tectio n

L e v e l

" - "

M o tio n On ly Oc c u rs

U nder A pplic a tion 

O f An E x te rn a l

F o rc e

O pera te s fro m a nonreplentis hab le energ y

sourc e (batte ry, pow e r

line, etc .)

N egativ e 

D e fensiv e 

cap abilities 

(phys ically m o re

fra g ile th an the 

env ironm ent)

0

N o M o tion A b ilitie s Z e ro energ y

c onsum p tion or

deliv e ry

Z e ro defensiv e

abilitie s (s tructu ra l

s trength equa l to

env ironm ent

1

M o v e s D e lib e ra te ly

In O ne D im e n s io n

C an dire c tly

extract/app ly ex te rnal

energ y w h en av a ilable

F light or hide 

beha v io r against

hos tile s tim u lu s

2

M o v e s D e libera te ly

in T w o D im ens ions

C an effc iently

e x tra c t/s to re /u tiliz e

exte rnal energ y

F ight or flight

beha v io r against

hos tile s tim u lu s

3

M o v e s D e lib e ra te ly

In T h ree 

D im ens ions

U s e s fo cus ed ta c tic s to

e ffic iently ex tra c t, s to re 

and u tiliz e ex te rnal

energ y

T a c tic a l fig h t/flight

beha v io r against

hos tile s tim u lu s

4

C apab le O f dua lm ode m o tion w ith

tools , v ehic les, or

applic a tion of

s pecific d e s ign 

e le m ents

U s es planned ta c tic s 

to effic iently ex tract,

s to re and utilize 

exte rnal energ y

T oo, v ehic le , o r

m a te ria l use in

fight/flig h t ta c tic s

5 H u m a n H u m a n H u m a n Table 1: Level Descriptors For MAP Survival Space 

So what were the two that we found? Los Alamos 

National Laboratory’s “Mobility, Acquisition, and 

Protection” space [6], and Draper Laboratory’s “Three

Dimensional Intelligence Space”[7]. The following is a 

short discussion of each [Note 5].

1. Los Alamos National Lab: Mobility, Acquisition,

and Protection (MAP) 

MAP comes from the lab of Mark Tilden, who develops 

simple robots based on analog circuits [10]. He needed a

way to quantify the autonomous nature of his systems, and 

teamed up with LANL Physicist Brosl Hasslacher to

develop the “Mobility, Acquisition, and Protection” space, 

or MAP for short. Figure 1 is a diagram of MAP from [6] 

As one might expect from the name, this method uses

mobility, acquisition, and protection to measure the ability 

of an autonomous system to survive in the world. 

• Mobility relates to the capability of utilizing 

movement in the environment. M0 implies no motion 

abilities where as M3 can move in three dimensions, 

and M- means that external force must be used to 

move object. 

MAP is actually quite a versatile visual tool, allowing 

disparate items to be plotted on the same page. Since there

are three metrics, one can use a “radar chart” to display 

the measurements of a particular autonomous system, and 

this is excellent, since upper management likes radar 

charts! The Los Alamos researchers also realized this and 

included a MAP radar chart in their report. Showing this

versatility, one can plot an ant, human, and a toaster on 

the same chart as is done in Figure 2! Tilden and 

Hasslacher successfully use MAP to illustrate the survival 

capabilities of the robots they design. 

• Acquisition relates to the ability to gather, store, and 

utilize energy. A0 implies zero energy consumption 

or delivery, A4 means planned tactics used to

efficiently extract, store, and utilize external energy, 

while A- indicates object uses a non-replenishable

energy store 

• Protection indicates the capability to defend oneself. 

P- indicates one is physically more fragile than the 

environment while P1 means one executes flight/hide

behaviors against hostile stimuli, and P3 demonstrates 

tactical fight/flight behaviors. 
MAP

Space

Multi-UAV Autono AV Autonomous Neural Net Neural Net SW

M e tric

L e ve l M o b ility C ontro l T a s k Pla nnin g S itu a tio n a l A w a re n e ss

1 N o n e, RPA O n ly N on e, R P A O n ly N o n e, R P A on ly, or

s en s or as c onduit

2 O p erator A s s is ted W ayp oin t or

fea tu re orien ted

L o w -level s en s or

p roc es s in g , e.g . vis u al

s ervoin g (tem p late 

trac k ing)

3

G et to w a yp oin t, d o

on e featu re-b as ed

c omman d

In terp ret g oals

in to a c tion

S ingle-S en s or m od el

m a tc h in g

4 In teg rate m u ltip le

action s

M u lti-A g en t

C olla b oration an d

C 2

In teg rated , m u lti-s en s or

fu s io n

Figure 2: Various Objects plotted in MAP survival

Space . Figure 3: Autonomous Control System Plotted On 

MAP 

Can this be used to measure the autonomy of UAVs? 

Possibly. Figure 3 shows a plot of a multi-UAV neural

net-based autonomous control system. This plot illustrates 

the limitations of MAP for our use. All UAVs would 

score M3 and A- - they move in three dimensions but

require stored fuel. Protection ranges between P0

(structural strength to absorb damage) to P4 (groups of

UAVs deliberately take out SAM sites). So, is this really

useful for FWV autonomy measurement? No. 

These metrics were described in a paper [7] written by the 

Draper Lab researchers last year. This paper contained 

several different options to measure both intelligence and

autonomy. Here we focus on the 3D Intelligence Space

outlined in Table 2.

One can see this metric space has a couple desireable 

attributes: 

• It has three metrics,so we can still use three-axis radar 

charts to represent the results, which will keep 

management happy(Figure CSD1). 

• Only one axis shows any variability, the others are 

fixed. 

• It has metrics which can be directly related to 

operational issues. 

• The metrics just do not address operational 

characteristics of UAVs. They do not relate the 

autonomy present in the vehicle to the capability to

perform useful missions. 

• The metrics do not address interaction between UAVs 

(teams, swarms, etc.) 

• The metrics do not allow us to adequately

discriminate between different levels of autonomy.

For instance, an RPV and an UAV with autonomy 

doing the same mission would score the same. 

So although using MAP seems to make sense for simple

robots, as a UAV autonomy measurement it isn’t

particularly useful. 

Table 2: 3D Intelligence Space 

Note that we made the distance between levels in Figure 4 

increasing exponentially to represent the difficulties 

So, the first metric space we examined could not fulfill our technicallly in going between steps. 

autonomous control system metric search, so we went on

to investigate the other candidate we found – the “3D 

Intelligence Space” of Charles Stark Draper Laboratory. 

We went ahead an plotted the same multi-UAV

autonomous control systems used earlier in evaluation 

MAP space on the Draper radar chart. The results of this

are in Figure 5. Note that this simple multi-UAV 

autonomous control system managed to “max-out” the 

metric space on all three axes, and highlight the fact that 

the resolution needs to be better. Other drawbacks 

include: 

2. Draper 3D Intelligence Space 

Charles Stark Draper Laboratory (Cambridge, MA) has

been developing robotic systems for military and other

Federal customers for a number of years. They saw the

same need to measure how well their systems could 

perform various tasks, and developed metrics under the

sponsorship of the Office of Naval Research.

• Task Planning axis needs to be renamed. Many

successful autonomous systems are based on pure

reactive behaviors (such as insects). Task planning

isn’t a prerequisite to autonomy, it just allows better 

reactions to complicated situations. 
Autonomy Radar my Radar Chart

Draper Metrics Mobility

Control Situation

Awareness

Task Planning

• Situational awareness is based on the number of 

sensors and how they are fused, not on whether or not

the autonomous system understands what’s going on

around it. In other words, this should be a 

measurement on how well the “big picture” is

comprehended and understood. 

Autonomy Radar Chart

Draper Metrics Mobility

Control Situation

Awareness

Task Planning

Multi-UAV Au -UAV Autonomous Neural Net SW

Figure 5: Autonomous Control System Plotted Using 

Draper Metrics 

5. Initial Autonomous Control Level (ACL) 

Chart 

We decided that since no existing metric space existed that

could be directly used, we would integrate the best

features of the ones we found with what we already used

internally to represent where our technology was going. 

Table 3 is that first cut at an Autonomous Control Level

(ACL) chart [Note 2]. 

Figure 4: Radar Chart Of Draper Metrics

The drawbacks not withstanding, the Draper metrics 

provided us another good way of looking at the world. 

Level Level Descriptor Perception/Situational Awareness Analysis/Decision Making Communication/Cooperation

10 Human-Like

9 Multi-Vehicle Detection & tracking of other air vehicles within Full decision making capability on-board Distributed cooperation with other air vehicles

Tactical airspace Dynamically optimize multi-ship group for tactical On-board deconfliction and collision avoidance

Performance situation Fully independent of supervision/control if

Optimization desired; No centralized control within multi-UAV group

8 Multi-Vehicle Detection & tracking of other air vehicles within Continuous mission/trajectory evaluation & replan - External supervision - abort/recall or new overall goal

Mission local airspace optimize for current mission situation On-board deconfliction & collision avoidance

Performance OK to operate in controlled airspace w/o external Avoid collisions and replan/optimize trajectory to meet Distributed cooperation with other A/V's

Optimization control goals, etc

7 Real-Time Detection of other A/V's in local airspace Continuous flight path evaluation & replan On-board collision avoidance

Multi-Vehicle Multi-threat detection/analysis on-board Compensate for anticipated system malfunctions, Uses off-board data sources for deconfliction & tracking

Cooperation weather, etc - optimize trajectory to meet goals, Hierarchical cooperation with other A/V's

manage resources, avoid threats, etc

6 Real-Time Detection of other A/V's in local airspace Event-driven on-board, RT flight path replan - goal On-board collision avoidance

Multi-Vehicle Single threat detection/analysis on-board driven & avoid threats Uses off-board data sources for deconfliction & tracking

Coordination RT Health Diagnosis; Ability to compensate for most Assumed acceptance of replan; External supervision -

failures and flight conditions - inner loop changes rejection of plan is exception

reflected in outer loop performance Possible close air space separation (1-100 yds)

5 Fault/Event Automated Aerial Refueling & Formation sensing Event-driven on-board, RT traj replan to new destination On-board derived vehicle trajectory "corridors"

Adaptive Situational awareness supplemented by off-board data RT Health Diagnosis; Ability to compensate for most Uses off-board data sources for deconfliction & tracking

Vehicle (threats, other A/Vs, etc) failures and flight conditions; Ability to predict onset of External supervision - accept/reject of replan

failures (e.g. Prognostic Health Mgmt) Possible close air space separation (1-100 yds) for

On-board assessment of status vs trajectory AAR, formation in non-threat conditions

4 Robust Response Threat sensing on-board RT Health Diagnosis (Can I continue with these Secure, within LOS electronic tether to nearby friendlies

to problems?); Ability to compensate for most Offboard derived vehicle "corridors"; Medium vehicle

Anticipated failures and flight conditions (e.g. Adaptive inner loop airspace separation (100's of yds)

Faults/Events control); Automatic trajectory execution; On-board Threat analysis off-board

assessment of status vs mission completion

3 Limited Response RT Health Diag (What is the extent of the problems?) Health Status monitored by external supervision

to Real Time Ability to compensate for limited failures (e.g. Off-board replan; Waypoint plan upload

Faults/Events Reconfigurable Control) Wide airspace separation requirements (miles)

Automatic trajectory execution

2 Pre-loaded RT Health diagnosis (Do I have problems?) External commands - alternative plans, approvals,

Alternative Plans Automatic trajectory execution (via waypoints) aborts

Preloaded alternative plans (e.g. abort) Reports status on request or on schedule

Wide airspace separation requirements (miles)

1 Execute Situational awareness via Remote Operator Robotic/Preprogrammed External control via low level commands

Preplanned Flight Control and Navigation Sensing Pre/Post Flight BIT Reports status on request

Mission Wide airspace separation requirements (miles)

No on-board knowledge of other air vehicles - all

actions are preplanned

0 Remotely Flight Control (attitude, rates) sensing N/A Remotely Piloted

Piloted Nose camera Vehicle status data via telemetry

Vehicle Situational awareness via Remote Pilot

Table 3: Initial ACL Metrics Chart 
We kept three metrics since we liked the idea of 

representing systems as areas on a radar chart when 

briefing management. We added ten levels for better

resolution between remotely piloted aircraft and fully

autonomous UAVs. The metrics related to operational 

issues while still being attached somewhat to 

technological systems. Populating the levels was a group

endeavor, with a team of researchers, program managers, 

tech area leads, and contractor experts determining the

meaning of each level: “1” was simple – the traditional

remotely piloted aircraft (RC-type) while “10” was 

“human”. The trick was populating the eight levels 

between. As with the Draper metric space in Figure 4 we 

represented the radar chart as having levels which are

exponentially more difficult. We then plotted the same

multi-UAV autonomous control system as before, and the 

result is in Figure 6. We recovered some of the resolution

lost in the Draper metrics; however, we still had some

issues with the metrics: 

• The metrics weren’t broad enough to cover UAVs

acting on strategic knowledge to achieve strategic

results. Our chart limited them to tactical thinking

only.

• One metric mixed cooperation with communication – 

mixing a “what” with a “how”. 

In general, we tried to stuff as much into three metrics as

possible to retain simplicity in briefing presentation and in 

the process lost the capability to split out issues of multiUAV control and human-UAV interaction, to name two.

We were going to press ahead and use this metric space 

when one of the autonomous control system development

engineers came up with a good idea [Note 3]. 

6. If You’re Replacing A Human, Why Not 

Measure Like One? 

The great insight was this: we are designing algorithms, 

agents if you will, to replace pilot decision functions. 

Machines replace human – so why not look to the human 

effectiveness community for metrics? Modify the OODA 

(observe, orient, decide & act) loop - originally developed

to show how to get inside your enemy’s decision loops - 

[8, & Note 4] for our use, and populate the levels with 

modifications of the qualifiers of the initial ACL chart. 

Table 4 is what we developed using this insight. The

same team of experts that developed the initial ACL chart 

also worked on the new ACL chart to ensure consistency

with earlier thoughts. We lost the three axes 

representation, which means we lost the ability to generate 

the “simple” radar charts which makes management

happy; however, we gained better resolution between

metrics which, at least in our thoughts, more than made up

for that. 

In itia l A CL C h a r t - Ex a m p l e

Comm/Coop

Perception

Analyze/Decide

Figure 6: Autonomous Control System Plotted On Initial 

ACL Radar Chart

Since we have developed the ACL chart, we’ve used it to

both assess the current UAV efforts, and to extract from 

that where our own technical efforts must go. Nationally,

we’ve developed time-phased autonomous system goals to

put our autonomous systems roadmap together. The ACL

has been published as part of the DoD UAV Roadmap [9],

and other DoD Labs use it to measure their autonomy

development. Locally we’ve developed technical area

roadmaps putting programs together to meet the timephased ACL goals. The ACL chart also acts as a program 

advocacy tool, allowing us to show management how each 

program fits into increasing ACL capability for each

metric, and also how each program investment integrates 

into the national strategy. Our experience from using it

for one budget planning cycle has been very positive: 

Multi-UAV Autonomous Neural Net SW

• Once management was briefed on the chart and it’s 

development (and some in management had

ownership in it’s development) it was accepted as the

tool to measure program goals. 

• It provided clear indications of where the technology 

was targeted and what national goal it met, allowing

better informed budget planning decisions. 

• We have common ground for talking amongst other

Federal technology development organizations, 

universities, and industry. Each of us has a much

clearer picture as to where technological programs fit. 

7. Summary 

Our work with autonomous UAVs indicated to us that we

needed metrics to measure the progress of our programs

building that autonomy. The same issues existed on a

national level, so we decided to develop metrics for the 

national-level effort, then apply those to our local program

planning process. 

Our literature search for autonomous system metrics only 

returned two references for metrics. Both we examined

and used on an example problem. Although each wasn’t 

directly applicable, concepts of each, integrated with our 

own existing ideas, formed an initial ACL chart. This 

chart was modified based on concepts human dynamists 

had developed - specifically the OODA loop. The 
resulting set of metrics captured our original intent. [Note 

4] 

Since development, the ACL metrics have been used 

successfully at the Air Force Research Laboratory in 

developing plans and programs in autonomous UAV 

controls research. The ACL chart is in current review at 

DOD levels to be applied across the services as part of the 

FVW initiative. With this development we are pressing 

ahead in the assessment of possible sub-metrics to better 

hone our program planning. 

8. References 

1. Turing, A. M. “Computing Machinery And 

Intelligence”, Journal of the Mind Association, Vol. 

LIX, No. 236. Oxford Press. UK. October 1950. 

2. Clough, B. Autonomous UAV Control System Safety – 

What Should It Be, How Do We Reach It, And What 

Should We Call It? NAECON 2000 paper. October 

2000 

3. A. Meystal et al., “Measuring Performance of 

Systems with Autonomy: Metrics for Intelligence of 

Constructed Systems,” NIST Workshop, 

“Performance Metrics for Intelligent Systems,” 

August 14-16, 2000 

4. Parker, L.E. “Evaluating Success in Autonomous 

Multi-Robot Teams: Experiences from ALLIANCE 

Architecture Implementations”. Unpublished (as of 

the time of this writing) paper. Center for 

Engineering Science Advanced Research, Oak Ridge 

National Laboratory. Fall 2000. 

5. “Measuring the Performance of Intelligent Systems” 

Web publication available from 

http://www.isd.cme.nist.gov/conferences/performance

_metrics/Measuring.html. Intelligent Systems 

Division, National Institute for Science and 

Technology, Spring 2000 

6. Hasslacher, B., Tilden, M. W. “Living Machines” Los 

Alamos National Laboratory. 1995. 

7. Cleary M., Abramson M., Adams M.B., Kolitz S. 

“Metrics For Embedded Collaborative Intelligent 

Systems” Charles Stark Draper Laboratory, Inc. 2000 

8. Boyd, J.R. Discourse on Winning and Losing. Found 

on the web at http://www.defense-andsociety.org/FCS_Folder/boyd.htm 

9. Unmanned Aerial Vehicles Roadmap 2000-2025. 

Office Of The Secretary Of Defense, Washington DC. 

April 2001 

10. Trachtman, P. “Redefining Robots”. Smithsonian 

Magazine. Smithsonian, Washington D.C. February 

2000. 

11. Sheridan, T., Parasuramam, R. “A Model for Types 

and Levels of Human Interaction with Automation”, 

IEEE Transactions on Systems, Man, and Cybernetics 

– Part A: Systems and Humans, VOL. 30, No. 3. 

IEEE May 2000.

9. Notes 

1. Not that we didn’t have any. We had already split 

autonomy into four levels depending on the amount of 

human interaction and where it occurred. These are: 

• Remotely Piloted: The UAV is simply a remotely 

piloted aircraft with the human operator making all 

decisions. 

• Remotely Operated: The human allows the UAV to 

do the piloting, but outer loop decisions are made by 

the human (like where to go and what to do once 

there). The UAV is a “mother-may-I” system, asking 

the human permission to do tasks. 

• Remotely Supervised: The human allows the UAV to 

execute its own tasks, only taking command if the 

UAV fails to properly execute them. 

• Fully Autonomous: The UAV receives goals from the 

humans and translates that into tasks which it does 

without human intervention. The UAV has authority 

to make all decisions. 

2. Most of the grunt work in putting the chart together 

was done by Dan Thompson, AFRL/VACC, and Dr. 

Alan Burkhard, AFRL/VAC - the rest of us got to 

snipe at it. 

3. The researcher’s name is Bob Smith, and besides 

coming up with decent ideas he also has developed a 

formation flight agent for UAV formations which 

uses a blend of deliberate and emergent behavior. 

4. Boyd’s OODA loop, originally developed to illustrate 

how to take advantage of an enemy, has been grasped 

wholeheartedly by business management folks. The 

observe, assess, design, and act (OADA) loop 

organizational dynamists use to explain how decisions 

are made is a direct descendant. 

5. I know, you’re wondering about Sheridan’s 

Autonomy Levels. Truth of the matter is that if you 

search for “autonomy”, “metrics”, “measuring 

autonomy”, etc., you don’t run into Sheridan. Had I 

searched using “teleoperation” I would have found 

the Sheridan Autonomy Levels [11]. 
ATR

7 Short track awareness - History and predictive battlespaTactical group goals assigned

Battlespace data in limited range, timeframe, and numbers Enemy trajectory estimated

Knowledge Limited inference supplemented by off-board data

6 Real Time Tactical group goals assigned

Multi-Vehicle Ranged awareness - on-board sensing for long range, Enemy location sensed/estimated

Cooperation supplemented by off-board data

5 Real Time Sensed awareness - Local sensors to detect others, Tactical group plan assigned

Multi-Vehicle Fused with off-board data RT Health Diagnosis; Ability to compensate for most

Coordination failures and flight conditions; Ability to predict onset of

failures (e.g. Prognostic Health Mgmt)

Group diagnosis and resource management

4 Deliberate awareness - allies communicate data Tactical plan assigned

Fault/Event Assigned Rules of Engagement

Adaptive RT Health Diagnosis; Ability to compensate for most

Vehicle failures and flight conditions - inner loop changes

reflected in outer loop performance

3 Robust Response Health/status history & models Tactical plan assigned

to Real Time RT Health Diag (What is the extent of the problems?)

Faults/Events Ability to compensate for most control failures and

flight conditions (i.e. adaptive inner-loop control)

2 Health/status sensors RT Health diagnosis (Do I have problems?)

Changeable Off-board replan (as required)

Mission

1 Execute Preloaded mission data

Preplanned Flight Control and Navigation Sensing Pre/Post Flight BIT

Mission Report status

0 Remotely Flight Control (attitude, rates) sensing Telemetered data

Piloted Nose camera Remote pilot commands

Vehicle

Individual task planning/execution to meet goals Group accomplishment of tactical goal with

minimal supervisory assistance

Coordinated trajectory planning and execution to meet Group accomplishment of tactical goal with

goals - group optimization minimal supervisory assistance

Possible close air space separation (1-100 yds)

On-board trajectory replanning - optimizes for Group accomplishment of tactical plan as external

current and predictive conditions assigned

Collision avoidance Air collision avoidance

Possible close air space separation (1-100 yds) fo

AAR, formation in non-threat conditions

On-board trajectory replanning - event driven Self accomplishment of tactical plan as externally

Self resource management assigned

Deconfliction

Medium vehicle airspace separation (100's of yds)

Evaluate status vs required mission capabilities Self accomplishment of tactical plan as externally

Abort/RTB if insufficient assigned

Execute preprogrammed or uploaded plans Self accomplishment of tactical plan as externally

in response to mission and health conditions assigned

Preprogrammed mission and abort plans Wide airspace separation requirements (miles)

N/A Control by remote pilot

Table 4: Final ACL Chart 

Level Level Descriptor Observe Orient

Perception/Situational Awareness Analysis/Coordination

10 Fully Autonomous Cognizant of all within Battlespace Coordinates as necessary

Battlespace inference - Intent of self and others Strategic group goals assigned

9 Battlespace (allies and foes).

Swarm Complex/Intense environment - on-board tracking Enemy strategy inferred

Cognizance

8 Proximity inference - Intent of self and others Strategic group goals assigned

Battlespace (allies and foes)

Cognizance Reduced dependance upon off-board data Enemy tactics inferred

Decide Act

Decision Making Capability

Capable of total indepenance Requires little guidance to do job

Distributed tactical group planning Group accomplishment of strategic goal with

Individual determination of tactical goal no supervisory assistance

Individual task planning/execution

Choose tactical targets

Coordinated tactical group planning Group accomplishment of strategic goal with

Individual task planning/execution minimal supervisory assistance

Choose targets of opportunity (example: go SCUD hunting)

ly

r
Towards Measuring the Performance of Architectural Components of

Autonomous Vehicular Systems

Craig Schlenoff, Lawrence A. Welsch, Raj Madhavan, Noah Zimmerman

National Institute of Standards and Technology

craig.schlenoff, lwelsch, raj.madhavan, noah.zimmerman @nist.gov

Abstract

For a vehicular system to act “intelligent”, the system

must be able to 1) sense in a dynamic domain; 2) model

the domain internally; 3) determine possible courses of

action to accomplish a goal in the domain; and 4) be able

to assess the various courses of actions to determine

which is best. The actions that the system ultimately

performs are a function of all of these components.

Solely assigning performance metrics to the resultant

action of the intelligent system does not evaluate any one

of these components individually, and therefore leaves

some doubt as to how to measure what each component

contributes to the overall behavior of the system. Thus

we are not looking at a single number, but a matrix of

numbers that characterize the performance of the system.

In this paper, we are exploring a mechanism to assign

performance metrics to the part of the system that models

the domain internally, the internal knowledge

representation of intelligent vehicular systems. We do not

consider that part of a system that translates the raw

sensory input from a vehicle’s sensors to other

representations. Rather we simulate a predefined set of

sensory inputs, and evaluate the resulting knowledge

representation based.

1 Introduction

Darwin was the first to propose the importance of

natural intelligence for biological entities. He

suggests that intelligence is the result of billions of

years of natural selection, emerging from a

competitive struggle for survival [1]. Measuring the

intelligence of intelligent systems presents several

challenges. A universal scalar value of intelligence

is difficult to ascertain in a machine due to the

restrictive nature of most domains.

Additionally, it is more difficult to make judgments

based on the relative success of particular

behaviors. However, in machines we have the

advantage of being able to monitor the internal

states. This enables us to make more accurate

deductions about 1) the methods employed by the

system to complete the task, and 2) the intermediate

states that it traversed. The system can then be

evaluated based on a relationship between the

complexity and efficiency of the method and the

precision of the final state.

There have been attempts to provide qualitative and

quantitative measure to knowledge representations

[10], though not, until recently, have they been

applied to measuring the internal knowledge

representations within autonomous vehicular

systems. Gruninger and Fox have applied the

concept of competency questions to formal

ontologies to test their ability to answer the

questions they were designed for [8]. McGuinness

et al. have also explored approaches to testing the

content of ontologies after multiple ontologies are

merged by using a tool called Chimaera [9]. More

recently work has been done to develop tests for

text retrieval systems [11] and autonomous vehicle

systems [12]. Research has also been done

considering the performance of rule chaining in

generic expert systems [13]. In this paper we are

considering how to best take advantage of RealTime Control System[1] architecture (described

below) to measure the performance of the

architectural components that contribute to the

vehicle’s behavior.

For a vehicular system to act in an intelligent

manner, the system must be able to 1) sense in a

dynamic domain; 2) model the domain internally;

3) determine possible courses of actions to

accomplish a goal in the domain; and 4) be able to

assess the various courses of actions to determine

which is best. The actions that the system

ultimately performs are a function of all of these

components. Solely assigning performance metrics

to the resultant action of the intelligent system does

not evaluate any one of these components

individually, and therefore leaves some doubt as to

how to measure what each component

contributes to the overall behavior of the system.

We have selected the Real-Time Control System

(RCS)[1] as the architecture for evaluating

intelligent systems. RCS is a hierarchical

distributed real-time control system architecture

that allows for modular and device independent

algorithms to be developed for intelligent systems.

A node in the RCS reference model architecture is

shown in Figure 1.
The functional elements of an intelligent system

can be broadly considered to include: behavior

generation (task decomposition and control),

sensory processing (filtering, detection, recognition,

grouping), world modeling (store and retrieve

knowledge and predict future states), and value

judgment (compute cost, benefit, importance, and

uncertainty). These are supported by a knowledge

database (KD), and a communication system that

interconnects the functional models and the

knowledge database. This collection of modules

and their interconnections make up a generic node

in the RCS reference model architecture. Each

module in the node may have an operator interface.

Though several contemporary architectures exist in

the literature for designing intelligent systems, our

motivation for selecting RCS is many fold:

• In the last fifteen years, behaviorist

architectures [2] [3] have gained popularity for

their ease of implementation. However, within

such architectures, long-term planning is not

possible since only a single behavior can be

selected for execution. Other disadvantages

include the inability to fuse sensor data to

arrive at a single best estimate of the state of

the world (in some probabilistic sense) and the

lack of internal representation of the world.

• RCS is a proven architecture with more than

200 person-years of research and development

in intelligent control theory. It has been

implemented and tested thoroughly both in the

industry and academia in different operating

domains under varying operating conditions.

For example, RCS has been implemented as

the reference model architecture for the design,

engineering, integration, and testing of

eXperimental Unmanned Vehicles for the DoD

Demo III program [1] [4].

• RCS is supported in terms of software and

updates and thus it constantly evolves through

a number of versions at National Institute of

Standards and Technology (NIST) and

elsewhere [5]. For additional advantages, see

pp. 128 of [6].

For the purpose of this paper, we are exploring a

mechanism to assign performance metrics to the

part of the system that models the domain

internally, the internal knowledge representation of

intelligent vehicular systems. We hold the sensory

component constant and do not consider the

behavior and value judgment components. In other

words, we simulate a pre-defined set of sensory

inputs, and evaluate the knowledge representation

based on those sensory inputs. There would be no

actions physically performed, nor would there be

any value judgment implemented. In this paper, we

explore developing a test harness for autonomous

systems, focusing on each combination of

knowledge representation components and

functions. Thus, the test harness can be seen as a

matrix, with the components along one axis and the

functions along the other, and each cell composed

of a series of questions testing the knowledge

representation's ability to provide the stated

function using the pertinent component, if

appropriate. For example, a question such as

"Where do you expect a given moving object to be

at time=10?" may be appropriate to test the

intelligent system's "prediction" function using its

"inferencing" and "knowledge being represented"

components.

In Section 2, we discuss a test harness, including

the data flow through the harness and the places in

the RCS hierarchy that would be appropriate to test.

Section 3 discusses the typical purposes/functions

of a world model. Section 4 describes the

components of any knowledge representation, and

discusses pertinent questions that could be asked to

test those components of the knowledge

representation. Section 5 brings the previous two

sections together into a matrix, and discusses future

work that should be done to address the

development of the proposed test harness.

2 The Test Harness

2.1 Data Flow

The goal of this work is to test the world modeling

capabilities of an autonomous vehicular system

without requiring the system to be physically

relocated to a test site, nor to require that the system

have to perform any physical behaviors. The

system’s world modeling capabilities would be

tested by a series of questions and answers, where

the answers to the questions would be assigned a

score based upon a series of performance
evaluation metrics. Figure 2, along with the

supporting text, shows the data flow pertaining to

the interaction a system would have with the test

harness, and is described in detail.

Figure 2 contains three main components: the

system being evaluated, the test harness, and the

knowledge base / performance evaluation

components. The test starts when the ‘system being

evaluated’ first registers by entering in its ID and

password (number 1 in Figure 2). At this point, the

user can choose between a series of sample sensory

data to use for the test, sorted and rated by its level

of difficulty (to be discussed in a future paper) (2).

The system then has a predetermined amount of

time to receive and process the data. After the data

is processed, a series of questions that correspond

to that data set are posed to the user (discussed in

Sections 3 and 4) (3). These questions may also be

rated by their level of difficulty. The user responds

to these questions by providing an answer, as well

as a description of how that answer was determined

(4). This information, along with the amount of

time that was taken to determine the answer, is

noted in the user’s profile. This information is the

passed to the test harness knowledge base where it

is compared with system’s knowledge base’s

response to the same questions (5).

The answers from the systems and the knowledge

base are then passed to the evaluation component,

where predetermined metrics are used to assign a

score to the system’s answer (6). The score would

be a function based upon the “correctness” of the

answer (e.g., the answer was two, but the system

thought the answer was four), the procedure used to

come up with the answer (e.g., what were the

equations used and the assumptions made when the

answer was being determined), the amount of time

it took to produce the answer, and the amount of

detail provided in the answer (e.g., the answer was

two, but the system responded with an answer of

“between one and five”). This score is then fed

back to the system’s profile to be logged (7), and

reported to the system (8).

There are many interesting and challenging

research areas within the scope of this framework,

including the types of sensor data to be presented to

the user, the types of questions that should be asked

to the user in response to the sensor data, the

information to store in the knowledge base to

evaluate the answers the system provides, the

appropriate evaluation metrics to use in evaluating

the answers (including the weights to put on each of

the factors described in the previous paragraph), the

details of the communication specifications

between the system and the test harness, the

interfaces and the representation of the information

to be passed between the various internal

components of the framework, as well as the

mechanism to allow a system to supply an

explanation of how an answer was produced. This

paper focuses solely on the questions that are asked

on the system being evaluated. Future papers will

focus on the other challenges mentioned above.

Figure 2: Data Flow in the Test Harness

Questions

presented to

user

System Being

Evaluated

Test Harness

Data to Be

Used for Test

Questions

User and Session Profile

User Login Information

User Background Information

Session Log

 Data Used

 Questions Asked

 Logged Answers

 Logged Score

Performance

Evaluation

Metrics

8 and Evaluation

7

System Logs In

System’s Answer and

knowledge base

answer fed to

performance

evaluation

Dataset presented

to user

2

4

Evaluation presented

to system

1

Evaluation fed

to profile

3

6

Answers fed to

the knowledge

base

5

System provides

answers

Test Harness

Knowledge

Database
2.2 Applying the Test Harness to

Various Components in RCS

The test harness described above is generic and

may be used to test an entire node in the RCS

hierarchy (as shown in Figure 1) or just a

component of a node. If the entire node is being

tested, then raw sensory data would be fed to the

“systems being evaluated” as input (as indicated by

the bottom left arrow entering the box) and the

output plan of the RCS node (as indicated by the

bottom right arrow exiting the box) would be

evaluated.

Instead of looking at the entire RCS node, one

could only test one or more components of the

node, thus focusing the attention on only a small

subset of the node. In this paper, we are interested

in the contribution of the World Model /

Knowledge Database component as shown in

Figure 3 below. In this case, we would be feeding

processed sensory data to the world model (thus the

sensory processing is not considered), and can

query the world model about what it perceives,

where is expects objects to be in the future, etc.

(thus the planning is not considered since the world

model is never asked to generate a plan, it is just

asked to answer questions about what it is

presented).

Figure 3: World Model and Knowledge Database

Although this paper solely focuses on applying the

proposed test harness to systems based upon the

RCS architecture, there is nothing in the design of

the test harness that precludes it from being applied

to other systems. The only assumption that this test

harness design makes is that there is a clear place in

the “system being evaluated” to which information

can be fed, that there is a clear place in the “system

being evaluated” from which information can be

read, and there is an appropriate set of questions

and evaluation metrics which can be applied to

evaluate the system.

In the next section of the paper, the functionality of

the test harness is exposed and test interactions

proposed.

3 Functions of a World Model

The world model can be thought of as a component

of the brain of the intelligent system. Just as the

brain contains a representation of the environment,

the world model contains a representation of its

surroundings, and as such, must be able to use that

representation to the benefit of the system that is

immersed in that environment. The world model

must inform the intelligent system on the potential

results of action, similar to the way the brain

informs the human body of the possible

consequences of actions.

The world model can be thought to be comprised of

four functions: maintenance and updating of the

knowledge base, prediction of sensory input,

response to queries for information required by

other processes, and simulation. This is described in

detail in [6]. In this section of the paper, we will

provide examples of the types of queries that the

world model would be expected to answer to

perform these functions.

3.1 Maintenance and Updating of the

Knowledge Database

The world model in its entirety is the intelligent

system’s best estimate of the world at the given

time. The world model can be thought of as

comprising a number of knowledge databases,

where each knowledge base is a store of

information about the world.

To ensure that the representation of the world is upto-date, the world model must constantly be

updated as new information is available. Examples

of ways that the world model could be updated

include:

1. As new processed sensor data is available and

entities are identified, the world model must

compare the actual location of the sensed

images to the location in which the world

model predicted that it would be. (What is the

difference between the actual location of entity

A and the predicted location of entity A?, How

can the current prediction parameters be

changed to provide more accurate predictions?)

2. As time elapses, information will move from

immediate experience to short term memory, to

long term memory. The world model must

seamlessly allow for the migration of

information into these parts of the world

model, as well as transform the representation
of this information between different

representation approaches. (What information

should be moved to short-term memory? To

long-term memory?)

3. As time elapses, new entities will appear in the

intelligent system’s environment, and some

entities will no longer exist. The world model

must be able to introduce these new entities

into the knowledge database, determine which

ones are most important to track, and delete

those entities that no longer exist or are no

longer of interest. (What new entities exist that

were not previously modeled in the knowledge

base? Which of these entities are important to

track? What are the pertinent characteristics of

those entities? What are the criteria for deleting

entities from the knowledge base?)

4. In the real world, relationships exist between

entities, events, and situations. It is important

to maintain these relationships within the world

model. (What are the important relationships in

a given environment?, How should those

relationships be represented?, For what time

extent do those relationships hold?)

3.2 Prediction of Sensory Input

In addition to capturing the data that is passed to it

by the sensors, the world model must also predict

where it believes the next set of sensed data will be.

Being able to accurately predict where an object is

expected to be at a time in the future is essential for

areas such as image processing, path planning, and

collision avoidance. Accurate prediction algorithms

allow the world model to better predict where an

object is expected to be at some time in the future,

along with a stated degree of uncertainty, and

therefore make plans that account for that predicted

future location.

Questions that may be asked within this function of

the world model include “What is the predicted

location of entity A given data pertaining to its

previous location?”, “What are the appropriate

algorithms to provide the prediction?”, “What are

the criteria for updating the prediction

parameters?”)

3.3 Response to Queries for

Information by Other Processes

The world model is the primary source for

information within the intelligent system. It is

designed to be an information repository, and as

such, must interface with other components of the

hierarchy that have a need to retrieve information

from it, whether explicitly or implicitly represented.

More specifically, the world model provides the

following functions:

1. The world model responds to requests from the

sensory processing, behavior generation, and

operator interface components of the hierarchy.

The sensory processing component may ask for

the predicted attributes and states of an entity.

The behavior generation component may

request the predicted identity of entities in the

environment, as well as characteristics of those

entities (e.g., if the entity was a car, how fast is

the car going? In what direction? What is the

fastest the car can go?, etc.). The operator input

may ask for the state of the intelligent system

at the current time. (What is the predicted

location, speed, orientation of entity A at time

= t+1?, What is the object perceived by the

sensors, and what are the pertinent

characteristics of it?)

2. The world model performs coordinate

transformations, when necessary, and accounts

for the motion of the sensor platforms that

affect sensor input.

3. The world model deduces additional

information from the knowledge database that

is not explicitly represented, but can be

deduced from the information that is

represented. (Given the information known

about an object, what additional information

can I infer about the entity that is not explicitly

represented?)

3.4 Simulation

In almost any application, it is useful to simulate

the results of an action before the action is

physically performed. More specifically, the

simulation aspect of the world model provides the

following functions:

1. The world model uses the knowledge in the

knowledge databases to simulate the results of

possible plans generated by the behavior

generation module.

2. The world model can compute all of the sets of

actions which can be performed to produce a

desired output.

3. The world model interfaces with the value

judgment component to evaluate the

cost/benefit of the proposed action based on the

simulation (What are the appropriate cost

algorithms?, Given a cost algorithm, what is
which plan provides the most benefit at the

least cost?).

4 Knowledge Representation

Measurements

The previous section described functions that the

world model within an intelligent system is

expected to perform. Based on those functions we

posed queries that the world model are needed to

support the functions. This section proposes

measures for the knowledge database within the

world model. By considering each measure against

each query, we derive the matrix described in the

conclusions.

The knowledge database can be thought of as

having three attributes: 1) the formalisms for

representing knowledge (i.e., how the knowledge is

captured), 2) the actual knowledge the system has

represented at any given time (e.g., the data that is

captured within the knowledge database), and 3)

the mechanism(s) available for accessing, querying,

and inferencing over the represented knowledge.

Each of these attributes provides a different set of

measures, for the value that each brings to the

overall world model.

4.1 Measuring the formalisms for

representing knowledge

A KD may contain a variety of different types of

formalisms for representing knowledge in its

database. For example the KD may contain

formalisms to represent:

• Raw sensory data collected directly from

sensors;

• Map and/or geometric data where map data

might provide coordinates for landmarks,

roads, and topological features.

• Symbolic and/or rule data that might contain

rules such as drive on the right side of the

road, or enter buildings through an opening;

and

• Links or associations between the different

types of data.

When determining the metrics for measuring the

formalisms for representing knowledge, one may

consider the following criteria:

1. The number of different types of

representations that the KD supports;

2. The complexity level the formalism can

support. For example, in the case of symbolic

representation, is the representation capable of

representing Boolean algebra, first order

predicate calculus, etc.;

3. The detail or granularity in which the

fundamental physical units may be represented;

4. The size of the largest set that be represented –

finite, countable, etc.; and

5. The number of mechanisms in which one can

group knowledge.

Each measure can be considered for each question

described in section 3. For example, for a particular

query, the measure would be the number different

types of representations of data that were involved

in generating a response to the query.

4.2 Measuring the actual

representation of the knowledge

At any given instant in time the world model has a

set of information that is captured within its

knowledge databases. One can measure the

captured knowledge using the following types of

metrics:

1. The quantity of different contexts/concepts1 that

are represented;

2. The quantity of contradicting knowledge,

possibly organized by contexts;

3. The scale of complexity [7] of the most/least

complex concept represented (not the complexity of

the formalism, but rather the concept itself);

4. The numbers of links among concepts; and

6. The depth of the hierarchy tree (e.g., how many

“levels” are in the representation?).

Again each measure would be considered against

the each query described in section 3.

4.3 Measuring the mechanisms for

accessing in and inferencing over

the knowledge database

Finally we need to evaluate the performance of the

mechanisms that respond to requests of the KD (the

inference or query mechanism). The measures

considered are:

 

1

 By concept/context is meant a collection of

knowledge that is not self-contradictory. Frequently

a context/concept is a way of organizing knowledge

so as to make the knowledge easier to find.
1. The length of time2

 the system takes to find a

particular fact, rule, assertion already in the

KD.

2. The minimal time to combine a fact with an

assertion;

3. The speed to switch representation formalisms

(with and without links);

4. The minimum time to combine knowledge in

one representation formalism with another; and

5. The quantity of different inferencing

mechanisms that exist.

Again, each measure would be applied to each

query. For example for the query What is the

difference between the actual location of entity A

and the predicted location of entity A?, the first

measure would be, the minimal time to retrieve a

fact necessary to addressing the query.

5 Conclusions / Future Work

In this paper, a test harness was introduced with an

emphasis on the types of questions that would be

needed to test the world modeling capabilities of an

intelligent system. One can imagine a series of

questions that would test certain expected functions

of the autonomous system’s world model, with

respect to specific characteristics of the knowledge

representation such as the way the knowledge is

represented, the exact knowledge that is

represented, and the mechanisms for querying that

knowledge. These questions would logically fall

into the matrix, as shown in Table 1, with specific

questions tailored for each cell in the matrix.

Work has recently been started on implementing

the framework of the test harness, using an agentbased infrastructure, in a web-based environment,

such that the interaction with the test harness would

be web-based calls with a web server located at

NIST. However, much work remains to be

completed.

As mentioned in Section 2, there are many

interesting and challenging research areas within

the scope of this test harness that have yet to be

addressed, including:

 

2

 Ideally, one would represent processing speed in

independent unit, where the actual time could be

based on multiplying the units by the appropriate

processing speed factor.

Knowledge Representation Characteristics

formalisms

for

representing

knowledge

knowledge

being

represented

mechanisms

for

information

access and

inferencing

maintenance

and updating

prediction of

sensory input

response to

queries for

information

required by

other

processes

W

o

r

l

d

M

o

d

e

l

F

u

n

c

t

i

o

n

s

simulation

Table 1: Test Matrix

• the types of sensor data to present to the user,

• the types of questions that should be asked to

the user in response to the sensor data,

• the information to store in the knowledge base

to help provide the information to evaluate the

answers the system provides,

• the appropriate metrics to use in evaluating the

answers (including the weights to put on each

of the factors described in the previous

paragraph),

• the details of the communication specifications

between the system and the test harness, and

• the interfaces and the representation of the

information to be passed between the various

internal components of the framework.

However, for any of these components to be

developed and tested, the overall framework must

exist. Therefore, the development and

implementation of the overall framework of the test

harness, with initial black boxes for each of the

individual components, is the first priority and thus

is currently being developed.

Additional future work will focus on applying the

test harness to other aspects of the autonomous

system architecture (as discussed in Section 2). To

be more specific, in this paper we only focused on

testing the system’s world model capabilities.

However, we could expand the parts of the

hierarchy being tested such that we allow the

system to generate plans, and compare those plans

to “optimal” plans as determined by the system’s

knowledge base which contains “perfect” world

knowledge. We could also test the autonomous

system’s sensory processing components, by

feeding in raw sensory data, and ask the
autonomous system questions based on the

processing of that data.

It would also be interesting to apply this test

harness to other architectures besides RCS.

Although, in theory, there is nothing RCS-specific

about this architecture, it would be interesting to

see how well the design holds up to other

architectures for autonomous systems.

References

[1] Albus J., “4D/RCS: A Reference Model Architecture

for Demo III, Version 0.1,” NISTIR 5994, National

Institute of Standards and Technology, Gaithersburg MD,

1998.

[2] Arkin R., Behavior-based Robotics, Cambridge, MA:

The MIT Press, 1998.

[3] Brooks, R. “A Robust Layered Control System for a

Mobile Robot,” IEEE Journal of Robotics and

Automation, vol. 2, no. 1, pp. 14-23, 1988.

[4] Shoemaker C., and Bornstein J., “The Demo III UGV

Program: A Testbed for Autonomous Navigation

Research,” in the Proceedings of the IEEE

ISIC/CIRA/ISAS Joint Conference, 1998, pp. 644-651.

[5] Gazi V., Moore M., Passino K., Shackleford W.,

Proctor F., and Albus J., The RCS Handbook: Tools for

Real Time Control Systems Software Development, New

York: John Wiley and Sons, 2001.

[6] Albus J., and Meystel A., Engineering of Mind: An

Introduction to the Science of Intelligent Systems, New

York: John Wiley and Sons, 2001.

[7] Ganter B., and Wille R., Formal Concepts Analysis:

Mathematical Foundations, Germany: Springer, 1999.

[8] Gruninger, M., and Fox, M.S., “The Role of

Competency Questions in Enterprise Engineering” in the

Proceedings of the IFIP WG5. 7 Workshop on

Benchmarking - Theory and Practice, Trondheim,

Norway. June 1994.

[9] Deborah L. McGuinness, Richard Fikes, James Rice,

and Steve Wilder. “An Environment for Merging and

Testing Large Ontologies.” in the Proceedings of the

Seventh International Conference on Principles of

Knowledge Representation and Reasoning (KR2000),

Breckenridge, Colorado, USA 12-15 April 2000.

[10] A. Turing. Computing Machinery and Intelligence.

Mind, 59, pp. 433-460. Reprinted in E. Feigenbaum and

J. Feldman. (Eds.) Computers and Thought, McGrawHill, New York, 1963.

[11] D. K. Harmon (Ed.). The First Text Retrival

Conference (TREC-1). NIST Spec. Pub. 500-207,

National Institute of Standards and Technology,

Gaithersburg MD, 1993.

[12] A. Jacoff, E. Messina, and J. Evans. A Standard Test

Course for Urban Search and Rescue Robots. in

Measuring the Performance of Intelligence of Systems:

Proceedings of the 2000 PerMIS Workshop August 14-16

200. NIST Spec. Pub. 970, National Institute of

Standards and Technology, Gaithersburg MD, 2001.

[13] William Mettrey. A Comparative Evaluation of

Expert System Tools. Computer pp. 19-31. Feb. 1991.
A Native Intelligence Metric for 

Artificial Systems 

John Albert Horst 

100 Bureau Drive, Mail Stop 8230 

The National Institute of Standards and Technology (NIST) 

Gaithersburg, Maryland, USA 20899-8230 

john.horst@nist.gov 

voice: (301)975-3430 

ABSTRACT 

We define native intelligence as the specified complexity 

inherent in the information content of an artificial system. The 

artificial system is defined as a system that can be encoded in 

some general purpose language, expressed minimally as some 

finite length bit string, and decoded by a finite set of rules 

defined a priori. Using this definition of native intelligence, we 

employ a chance elimination argument in the literature to form a 

simple, but promising native intelligence metric. Several 

anticipated objections to this native intelligence metric are 

discussed. 

1 INTRODUCTION

We define two perspectives on artificial system 

intelligence: (1) native intelligence, expressed in the 

specified complexity inherent in the information content of 

the system, and (2) performance intelligence, expressed in 

the successful (i.e., goal-achieving) performance of the 

system in a complicated environment. In this context, 

complexity is simply Shannon information [5]. Specified 

complexity is Shannon information matched with 

meaningful patterns (e.g., orthography, syntax, and 

semantics). Native intelligence aggregates things like 

potential intelligence, learning ability, system integration, 

richness and potential effectiveness of individual 

behaviors, and intellectual reasoning capabilities. These 

are fundamental, theoretical, and innate aspects of 

intelligence. The performance perspective on intelligence 

aggregates things like the efficiency of design and 

maintenance, real-time performance, and the ability to 

effect desired physical changes on the environment. These 

are external behavior characteristics that can be measured 

without knowing where the intelligence came from, how it 

is represented, or what algorithms are used internally to 

process the data and produce effective output. 

These two perspectives have a parallel with the 

distinction between theoretical and experimental sciences. 

In this regard, the native and performance perspectives are 

complimentary and not competitive. They also ought to 

give the same results when applied to the same quantities 

in identical systems. Each one ought to inform and 

contribute insight to the other. 

The essence of intelligence is completely nonmaterial. This must be true, of course, if a native 

intelligence perspective is to have any validity. 

Intelligence can be embodied, but it is completely 

independent of embodiment. For example, a finite state 

machine (FSM) for a particular behavior can be stored on a 

compact disk, located in the mind of the designer, or 

spoken out loud to an audience. The FSM is completely 

independent of the medium of storage, type of 

representation, or mode of transmission. It exists as 

information. The quantitative measurement of intelligent 

systems is becoming and will become an increasingly 

important thing to be able to do. The discovery of DNA in 

living things as the living blueprint of life forms an 

existence proof that the essence of living things is nonmaterial information. DNA is merely the medium; the 

information is the real intelligence1

. 

1.1 The value of a native intelligence metric 

A metric for quantifying the intelligence of a system 

independent of its performance, i.e., a native intelligence 

metric (NIM), is needed. A NIM would bring substantial 

gains beyond that possible from performance intelligence 

metrics alone. A measurement of native intelligence can be 

made prior to the simulation or execution of the actual 

system, since all that is necessary to apply the metric is the 

representation of the system (as a string of bits), the rules 

for interpreting the string, and the meaning matching the 

system (the semantics). To measure success earlier in the 

 

1 Actually, the concept that all hereditary characteristics come 

from the DNA and only the DNA (called Crick's Central Dogma) 

has been shown to be false [1]. 
design phase is known to be critical to successful 

engineering design, particularly for large scale, complex 

systems. This is true even if the NIM is suboptimal, 

namely, even if some intelligence actually in the system is 

missed by the NIM. The ability to define and measure 

intelligence merely from its native intelligence offers 

promise to improve important quantities such as system 

time-to-market and quality. A NIM will also allow a more 

straightforward debug of the system, since the problem and 

solution will be more localized. For example, if we know 

that a few bits in the string cause the problem, we may be 

able to easily fix the problem by correcting just those few 

bits. 

1.2 The relationship to living systems 

Our definition of native intelligence of artificial systems is 

substantially unlike what is known as "IQ intelligence." IQ 

intelligence is a type of performance intelligence 

(intellectual mostly) in which the result is used to measure 

the potential of the human subject. Furthermore, a 

fundamental difference between living organisms and 

artificial systems is that the latter are completely malleable. 

This is not just a difference but a distinct advantage. 

Therefore, IQ intelligence cannot and should not constitute 

a model for the native intelligence of artificial systems. 

In human children we often want to distinguish 

between the potential of a child to learn and how much 

they have actually learned. Being unable to do this is a 

source of incredible frustration. The problem is that we do 

not yet understand how to measure native intelligence of 

humans. I suspect that some day, perhaps soon, we will be 

able to make some level of a determination simply be 

interpreting the genetic information in the genome of each 

individual. This subject is the topic of a film entitled, 

Gattaca [2]2

, in which the protagonist overcame his genetic 

"destiny" in spite of an oppressive determinism in the 

surrounding culture. With humans this is a loaded topic, 

but with machines the situation is much more amenable, 

since we can design the system for analysis! In fact, we 

want to design the system for analysis. We want the 

"intelligence meter" to detect the maximum amount of 

intelligence, and since we have full control on how we 

encode the description of the design of the system (just like 

we can always encode a computer program into a finite 

sequence of bits), we will choose to encode it in a standard 

format so that the intelligence is measurable. 

 

2 Certain commercial companies and their equipment, 

instruments, or materials are identified in this paper in order to 

specify the experimental procedure adequately. Such 

identification is not intended to imply any judgement by the 

National Institute of Standards and Technology concerning the 

companies or their products, nor is it intended to imply that the 

materials or equipment identified are necessarily the best 

available for the purpose

With the NIM are we claiming that a system's 

performance is fully constrained by its initial determining 

information? Isn't this crass determinism? No, it is not. A 

system's performance is not fully constrained by the 

disembodied information describing the system, since 

performance depends on the nature of the environment, 

which will affect future performance. For example, if an 

adaptive filter adjusts parameters for a certain 

environment, subsequent exposure to another environment 

may cause instabilities in the filter. High native intelligence 

does not mean that the intelligence is realized. The NIM is 

not meant to exclude the system’s potential response to 

some future contact with its environment. There is a 

multitude of ways that the same intelligent system can by 

realized. Finally, living systems are still a great mystery 

and are arguably not fully defined by algorithms (in the 

way that we are defining artificial systems) [3]. 

1.3 Specified complexity and computational 

theory 

Measuring native intelligence is much like the problem 

scientists and mathematicians asked around the turn of the 

century. What are the natural classes of computing 

machines and can we measure the power of a machine? 

Are there limits of computation and if so what are they? 

Similarly our question becomes the following. How can we 

measure the intelligence of a system without ever seeing it 

perform or even simulating its operation? 

In a manner consistent with definitions of information 

in standard computational theory texts, we propose that the 

minimal information describing a system entails its 

intelligence, i.e., the intelligence is fully contained within 

the information describing the system [4]. Of course, by 

information we do not mean Shannon information, namely, 

that the information is merely complex [5]. Random noise 

is complex, but is not specified (i.e., it has no "meaning") 

and therefore conveys no real (useful) information. Rather, 

what we mean by specified complexity is that the 

information is both complex and has discernible meaning, 

which is to say that it has recognizable patterns that 

indicate the power of the system to perform complex and 

useful tasks. This specified complexity can be represented 

and analyzed without actually realizing the system and 

seeing it perform. We argue that intelligence can be 

distilled down into pure information, even a finite amount 

of information. The description of a computational 

machine can always be reduced to a finite string of 0's and 

1's. To represent a truly intelligent system, the system's 

information must be both complex (in the Shannon sense) 

and specified. In computation theory, information content 

of a machine is defined as the minimum representation of 

that machine. On the other hand, certain machines may 

look complex, but they may not do anything useful. 

Therefore, the system's description needs to be specified. It 
needs to have meaning that, in some way, is grounded in 

reality or truth. And furthermore, we have some way of 

measuring the amount of this connection, i.e., the greater 

the system connects with real meaning, the greater the 

intelligence. Here then is a method for measuring the 

native intelligence of systems: Measure the information 

content, using information theory and computation theory 

and measure the magnitude with which it can be matched 

to patterns representing truth-grounded semantics 

(something like a dictionary would be employed). For 

example, the DNA string is certainly complex, i.e., there is 

no known simple representation for the entire string (no 

simple equation generates the string of nucleotides) and 

DNA is also certainly associated with known truthgrounded semantics, i.e., it can be used to form proteins 

that are the machinery and computing engines of the most 

complex factory yet discovered, the living cell. Clearly, 

detecting and measuring the connection of a system's 

information with truth-grounded semantics is a substantial 

challenge. 

1.4 The analogy to linear systems and the 

mixed success of intelligent systems 

To generate an effective NIM, some mathematically sound 

concept of what fundamentally constitutes intelligence is 

needed. Why not use some of the existing IS models, such 

as neural nets, evolutionary programming, hierarchical 

models, behavior-based control, and fuzzy control? 

Because all these models share in common that they are 

primarily models for facilitating IS design and execution 

and do not in themselves constitute formal models of 

intelligence that will readily yield a NIM. 

Why not use linear systems theory as a model for a 

NIM? The successes of traditional linear system theory can 

in many ways be considered the target we would aim for in 

the development of a useful NIM. In traditional linear 

system theory we have a most agreeable situation. The 

dynamic behavior of many systems can be described as 

differential equations of motion and dynamics and, 

coupled with state space structures and linear control 

theory, we can determine absolutely whether the system is 

controllable, stable, or observable without doing any 

simulation or building and testing of an actual system. 

However, it is well known that when we add non-linearity 

and time variance, for example, we can measure the 

stability of only a small subset of the total space of all nonlinear systems. The mathematics also gets substantially 

more complicated and sophisticated. A large class of realworld systems (including discrete event systems) do not 

seem to submit easily to concise and simple mathematical 

descriptions. So this path (employing differential equation 

models) for determining the intelligence of systems has hit 

a fundamental barrier, it seems. 

What about using formal methods as a basis for a 

NIM? The NIM offered in this paper may have much in 

common with formal methods and further study of this 

connection should be pursued. Formal methods have had a 

very limited impact, but this may not be due to any 

limitations in the theory. The limited impact seems to be 

due to the fact that many systems do not submit easily to 

the formal syntax of predicate calculus and that the 

learning curve is too high for most system designers to 

comprehend formal methods in order to successfully use 

them. Tools for formal methods also seem to be lacking or 

need substantial improvements [6]. 

The field of intelligent systems research has rightly 

been criticized by the traditional control systems 

community as being open to the proliferation of ad hoc

design techniques, such as neural control, fuzzy control, 

hierarchical control, etc. These techniques are not 

grounded in physical and mathematical theories in the way 

that traditional control systems are grounded in the theory 

of dynamics and differential equations. There is certainly 

no commonly accepted equivalent to differential (or 

difference) equations for large-scale discrete-event systems 

(large-scale discrete-event systems may be considered to 

be the domain of IS). Traditional control systems can be 

measured in terms of their stability, controllability, and 

observability without realizing (or embodying) the system. 

These are metrics that allow us to measure the native 

intelligence of traditional control systems. For example, a 

system that goes unstable in a certain critical region would 

intuitively be less intelligent than one that has no such 

instability but in every other way performs equivalently. 

We have no such general metrics for measuring the native 

intelligence of large-scale discrete-event systems nor do we 

have the capability to compare the native intelligence of 

systems designed according to different methods. It is 

important to have quantitative measures for the relative 

performance of systems designed using neural control 

versus those designed by fuzzy control, etc. 

Certainly ad hoc systems are helpful and even 

necessary at a time when there is no accepted theory with 

which to build non-ad hoc systems. However, without a 

theory, we often witness a proliferation of inflated claims 

of system performance that can not be met. This has 

several time produced a backlash from funding 

organizations who made financial commitments based on 

these groundless claims. This is all the more reason why a 

well grounded NIM would be helpful. 

Furthermore, intelligent systems will have to be 

designed for analysis. But this is exactly what we do when 

we design linear, time-invariant control systems in a state 

space form. It's easier to analyze that way. Furthermore, 

vendors of systems will not want to hide the intelligence of 

their systems, but will want others to know for sure that 

their system is truly intelligent. The advantage of having a 
NIM should be obvious. That way we can measure 

intelligence prior to any commitment to simulation in 

software or testing in hardware. Computation theory can 

also help with measuring the degree to which the 

information (describing the system) matches with truthgrounded semantics. For example, a system that can 

recognize or distinguish between a broader set of input 

"languages" is more powerful than one that cannot. For 

example, a finite state automata (FSA) can be designed to 

recognize strings of the form 0*1*, but no FSA can be built 

that recognizes strings of the form n n 0 , whereas a push- 1

down automata (PDA) can be built to recognize strings of 

either type. Therefore, the PDA is more powerful 

(intelligent). 

Measuring intelligence is similar to what a 

cryptanalyst does normally except that when measuring 

intelligence we don't anticipate the element of planned 

deceit. The cryptanalyst is looking for signs that the bits 

have identifiable patterns underlying the randomness. If 

there is no randomness, there are no patterns, and no 

intelligent message underneath. With randomness, the bits 

could actually be truly random and therefore be nonsense. 

However, it may just appear to be random and the patterns 

are not obvious to the cryptanalyst. Ostensibly, an IS 

would be designed not to fool the IS metrics analyst to 

think the system is nonsense, but rather the IS would be 

designed so that its intelligence would be easily perceived 

by all. 

1.5 The nature of a NIM 

Perhaps the solution is not in an analogy to linear systems 

theory, as has been the hope, but rather in information 

theory, probability, and complexity theory. This is the 

research question we have asked ourselves and hope to 

provide some direction towards an answer. We argue that 

the essence of intelligence in living things is information 

(versus physics or chemistry). If this is so, we need to 

discover appropriate tools to correctly analyze that 

information to glean from it the level of intelligence in the 

system. 

We claim that chance, regularity, and intelligence are 

mutually exclusive. Regularity is indicated by signals of 

low complexity. Therefore simple Shannon information 

filters can eliminate regular systems from consideration as 

intelligent systems (recall our definition of native 

intelligence as complex and specified information, in 

which the level of complexity and specification is 

proportional to the level of intelligence). After first 

eliminating regularity, if we can measure the probability 

that a system arose from chance processes alone, then the 

intelligence in that system must in some simple way be 

related to that probability. In a manner similar to Shannon 

information theory, we propose that, if there is a measure 

of the probability that doesn't just measure the mere 

complexity of the information (Shannon), but measures the 

specified complexity of the information, then the 

expression p 2 − log , where p is the probability that the 

supposed specified complexity (of the system) arose purely 

through a logical chance hypothesis, is a theoretical 

measure of the system's native intelligence. Such a measure 

exists [7]. We will describe and examine this measure and 

suggest how it applies as a NIM. 

2 THE CHANCE ELIMINATION ARGUMENT

Our goal is to find a quantitative intelligence metric for an 

artificial system that can be applied to the minimal 

informational description of the system. Another way of 

looking at this problem is to ask ourselves, what is the 

probability that this system either arose merely by chance 

(or merely by regular processes)? Regularity is relatively 

easy to eliminate from consideration since the information 

can in this case be generated from a relatively small 

expression (or a small number of "lines of code"). It may 

contain many bits, but not have much information. An 

example of regularity is an infinitely alternating string of 

four 0's and four 1's. The lines of code are simple: write 

down four 0's, write down four 1's, repeat steps one and 

two. Clearly there is little intelligence in this system. So, 

having eliminated regularity, if the probability that chance 

did the work is high, the intelligence in the system will be 

low; if the chance probability is low, the intelligence will 

be high. Why is this so? There are only three options for 

the source of artificial system information: chance, 

regularity, or intelligence. It has to be one of the three. So 

if chance and regularity have sufficiently low probability, 

then the source must be intelligence. Say that the system 

needing analysis is encoded in what appears to be a 

random sequence. If we happen to know that it is the 

description of a complex space shuttle control system, we 

can readily eliminate chance and declare it to be 

originating from intelligence. Note that false negatives are 

possible (thinking it is a chance process when it is an 

intelligent process), but false positives are avoidable. 

A theory for chance elimination has been developed 

by Dembski in [7] and is called the Generic Chance 

Elimination Argument (GCEA). This theory forms the 

basis for the NIM development in this paper. Therefore, 

we will begin with a summary of the aspects of GCEA 

relevant to IS metrics. In the next section, we will 

investigate how the theory might be (simply) applied to IS 

metrics. 

We start with a subject, S, that observes an event, E. 

By analyzing the circumstances surrounding E, S defines 

the chance hypothesis, H, gotten from a reasonable chance 

process that might have been responsible for E. S discovers 

(doesn't matter how) a pattern, D, that matches the event, 
E3

. With D* as the event associated with the pattern, D, S 

calculates the conditional probability, P(D*|H), of the 

event, D*, assuming the chance hypothesis, H, is true. S 

tests whether P(D*|H) is less than a universal probability 

bound, δ . Dembski determined that δ = 150 10− from 

fundamental physical limits (space, particles, and time) 

within the known universe4

. Through knowledge of some 

side information, I, S computes the conditioned complexity 

φ( ) D | I of formulating the pattern, D, given the side 

information, I. “Complexity” in this context is defined 

within the domain of “complexity theory,” the most 

common example of which is computational complexity. 

Complexity theory is a dual with probability theory in the 

sense that with probabilities we are dealing with "events" 

conditioned by "background information" and with 

complexity theory we are dealing with "problems" 

conditioned by "resources." So formulating D is the 

"problem" and I are the "resources" for solving the 

problem. A problem conditioned by resources places 

φ( ) D | I in the domain of complexity theory. S computes a 

tractability bound, λ , which is used to calibrate the 

complexity values, since complexity, unlike probability, is 

essentially uncalibrated. λ is the upper bound of 

complexity, below which the side information, I, is 

sufficient to form the pattern, D. Finally, if P(D*|H) < δ

and φ( ) D | I < λ , S can infer that E did not occur 

according to H. This is a substantially abbreviated form of 

Dembski's argument, but is sufficient for our needs. We 

will now give an example to help clarify the GCEA. 

Say we are S and we stumble upon Stonehenge. We 

don't wonder whether humans carried the stones (some 

weighing over 5 × 104

 kg) to the site. However, we do 

wonder whether the particular arrangement of the stones is 

meant to align with certain seasonal, celestial events such 

as the positions of sun and moon at summer solstice and 

during eclipses. S declares that E is the particular 

arrangement of stones S encounters that may be exhibiting 

alignment with celestial bodies at certain seasonal times. S 

determines that the designer of Stonehenge had full view of 

the sky enough days in the year to be able to note seasonal 

occurrences. S further determines that at summer solstice, 

for example, the rising sun precisely aligns over the Heel 

 

3 Actually if D* is the event associated with the pattern, 

D, and the occurrence of E implies that D* also occurred, we say 

that D delimits E rather than D matches E. 

4 There are less than 1080 elementary particles in the 

known physical universe, no more than 1045 physical state 

transitions per second, and (assuming "big bang" cosmology) no 

more than 1025 seconds of time will ever be available in the 

known physical universe. So 1080 × 1045 × 1045 = 10150 is a 

liberal measure for the maximum number of possible state 

transitions of all the particles in the known universe throughout 

the total lifetime of the universe. 

Stone shining rays directly into the center of the monument 

in the inner horseshoe arrangement of stones. S notes that 

there are several of these curious alignments of lunar and 

solar events (including eclipses) with various stones. This 

alignment of events with stones constitutes D, our pattern. 

D* then constitutes the particular pattern of stones 

consistent with the alignments. E would then, at the least, 

be delimited by D, but for simplicity’s sake, say D* = E. 

Now we calculate the probability, P(D*|H), that D* could 

happen by chance alone, even though D* was gotten by 

assuming the matching of the stones with particular 

celestial events. The various celestial events and our 

prehistoric Stonehenge designer's awareness of these 

events constitute the side information, I. We calculate the 

tractability bound, λ , below which the particularly 

alignment of stones is possible given I. We then calculate 

φ( ) D | I , the actual complexity of forming the pattern 

given I. If P(D*|H) < δ and φ( ) D | I < λ , S can infer 

that the particular arrangement of aligned stones, E, did not 

occur according to the chance hypothesis, H. 

All these definitions and formulations are completely 

consistent with probability theory, complexity theory, and 

statistics. Note that the measurement is heavily dependent 

on the correctness of I (i.e., that I is statistically 

"sufficient"). For example, we must have sufficient 

knowledge whether the prehistoric Englishman could 

perceive those celestial objects and that the alignments 

have changed but slightly over these thousands of years. 

On the other hand, say we now know that the atmosphere 

of England was even more persistently overcast thousands 

of years ago than it is today. We might then conclude that 

φ( ) D | I > λ , i.e., our prehistoric Englishman caught sight 

of the celestial bodies so infrequently that the complexity 

of forming the match with the alignment pattern would be 

virtually impossible. Now a possible criticism here is that 

our knowledge of the prehistoric Englishman's weather 

conditions may be imprecise. In this case we may only be 

able to determine a range for φ( ) D | I . All that would be 

necessary then is to ensure that the upper end of the range 

is less than λ , the complexity tractability upper bound. 

Furthermore, even if the complexity happens to be 

tractable, i.e., φ( ) D | I < λ , let us say that only one stone 

is involved in the alignment pattern. This would mean that 

P(D*|H) would be quite high, since the random spatial 

arrangements of only one stone are relatively few 

compared to the random spatial arrangements of N stones 

with N >> 1. Therefore, it is conceivable that P(D*|H) > 

δ , particularly since δ is such a conservatively small 

number, even though φ( ) D | I < λ . 

3 CHANCE ELIMINATION ARGUMENT 

TRANSFORMED INTO A NIM 
Our goal is to apply the GCEA to the problem of 

developing a NIM. The GCEA is intended simply to 

eliminate the chance hypothesis from consideration, 

whereas we wish to discover a metric. However, in the 

process of eliminating chance, the GCEA has to compute a 

measurable quantity, namely, the probability of the pattern 

(transformed into event space) conditioned by the chance 

hypothesis, P(D*|H). The GCEA is also intended merely 

to compare P(D*|H) against a threshold δ , in order to 

(possibly) eliminate the chance hypothesis and infer 

intelligent agency. However, the artificial systems we 

normally wish to measure are those that we know 

intelligent agents have designed a priori, so no threshold 

comparison is needed. Probabilities are already calibrated, 

having a target range in the interval, [0,1]. The GCEA 

computes a complexity measure, φ( ) D | I , comparing it 

with λ , in order to ensure that the pattern is tractably 

constructible from the side information. Even though we 

know that our system is designed, it is certainly possible 

that it cannot be executed. Therefore, the role of the 

complexity measure should remain the same for the IS 

measure. Finally, the GCEA computes a probability and 

we are interested in a complexity measure. Since regularity 

can be easily eliminated from consideration, the only other 

options are chance or intelligent agency. Therefore, to the 

degree our candidate IS is not attributable to chance, the IS 

displays intelligence. Furthermore, the information 

theoretic method for converting a probability into an 

information measure is to take the negative of the 

logarithm base 2 of the probability. Since we've defined 

information as the specified complexity of the minimal 

representation of the IS, H) log P(D*| − 2 is the NIM we 

seek. So a larger NIM means higher system intelligence 

and vice versa. 

Therefore, the NIM consists the following steps: 1) 

identify E as the design of the IS of interest, 2) gather all 

the relevant side information, I, in order to identify the 

pattern, D, 3) identify the event D* that delimits E, 4) 

define the chance hypothesis, H, for the event D*, 5) 

compute the probability, P(D*|H), that D* might occur 

according to H, 6) compute the tractability bound, λ , by 

quantifying the complexity of forming the pattern with the 

minimal amount of resources, 7) compute the complexity 

of generating the pattern, D, from I, 8) test if φ( ) D | I < λ , 

if true, then continue to next step, or if false, stop (it has no 

intelligence if it is impossible to construct and/or execute), 

9) compute H) log P(D*| − 2 . 

How do we identify E? This is simply the particular 

system representation we wish to analyze. However, the 

system design needs to be in an analyzable format. This 

begins with a need for some unambiguous syntax that can 

be represented in something like the extended BackusNaur form (EBNF) [8]. How do we identify I and D? The 

relevant semantics will be captured in the side information, 

I, and the gathering and identification of side information 

is expected to be a challenging task. The challenge should 

lie mostly in the difficulty in assembling all the applicable 

semantic information. In fact, we can easily underestimate 

the intelligence in the system if we miss or overlook key 

information. Going back to the Stonehenge example, if we 

mistakenly conclude that the prehistoric man is inobservant 

and therefore is completely ignorant of eclipse events, but 

the eclipse alignments actually match with several 

alignment of stones, we will conclude that the stones 

display less intelligence in their particular arrangement 

than they actually do. This is a false negative, which will 

be very common. However, because complex specified 

information is typically highly differentiated to other 

complex specified information (e.g., Schubert's music is 

very different from J.S. Bach's), false positives will be 

highly unlikely. The use of formal and standard system 

specifications [9] should also be used in order to define 

what is meant by the syntax. Therefore, intelligence in the 

system will sometimes go unnoticed. Different than the 

Stonehenge example, system designers will want to ensure 

that the systems they design achieve the maximum 

(intelligence) value under the NIM. This is called design 

for analysis and is done all the time now, though more for 

performance metrics, since they are most common. 

However, the current practice of formal testing can be 

generally considered in the same category as the NIM [10]. 

4 OBJECTIONS TO THE NIM 

A possible objection to the NIM is that a system such as a 

neural net or an evolutionary algorithm starts out without a 

lot of intelligence, but as it learns, it grows in intelligence. 

Recall that we specifically do not define intelligence as the 

ability to realize certain tasks, but rather as the specified 

complexity inherent in the system at design time, prior to 

execution. The mere ability to learn should be considered 

intelligent, independent of, or prior to, having learned 

anything. The simple acquisition of knowledge, even 

behavioristic knowledge, should not be counted as an 

increase in intelligence. For example, a person with 

advanced Multiple Sclerosis may be intelligent even 

though he or she may neither see, speak, nor move. 

Another objection to the NIM is that certain intelligent 

systems, particularly learning and optimization approaches 

such as neural nets or evolutionary algorithms actually 

effect an increase in specified complexity as the system 

executes, because it solves complex optimization problems 

without front-loaded intelligence. However, it can be 

shown that through the designer's choice of the particular 

fitness functions, system structure, and initial populations, 

specified complexity is entered surreptitiously [11]. The 

No Free Lunch theorems are proof of this, since, for any 

particular optimization algorithm, if the fitness functions 
are not constrained within a certain domain, but are 

allowed to freely range throughout the entire domain of 

possible fitness functions, the optimization algorithm will 

not perform on the average any better than blind search 

[12]. So specified complexity can only be added by 

intelligent agents [13]. 

Another response to this objection is largely 

experiential. The author has now some fifteen years 

experience in the practical design of large-scale control 

systems for mining vehicles, inspection machines, and onroad autonomous vehicles. The consistently strong 

impression is how much sophisticated, up-front design 

intelligence (from the intelligent agent) is needed to 

generate a successful system. Furthermore, the 

maintenance and improvement of the system also requires 

enormous input of human intelligence, very far from the 

easily and powerfully evolving systems that are promised. 

Perhaps though, we have been using the wrong design 

paradigm (we have been using a hierarchical paradigm 

[14]). Because of the No Free Lunch Theorems, we 

suspect that switching to another design paradigm will 

make little difference in this. Furthermore, as a working 

intelligent system designer, we have, of course, had 

substantial interaction with other designers using other 

paradigms. Based on this informal testimony, we have no 

reason to believe that the other paradigms require 

substantially less input of external intelligent agency, both 

at design time and after. 

Yet another objection is that performance intelligence 

metrics are easier to obtain and furthermore are all that is 

needed. Besides the NIM is too hard to compute and 

obtain, i.e., too much work is required to make it 

worthwhile. This could be argued against linear system 

theory, which certainly requires a substantial mathematical 

and physical sophistication, and very few would argue that 

it has not been of substantial value to control system 

design. How is linear system theory like the NIM we 

propose? Linear system theory supplies an analytical 

theory and allows for metrics for measuring native 

capability, such as stability, controllability, and 

observability. Similarly, NIM is based on probability, 

complexity, and statistical theory and provides its own 

system capability (or intelligence) metric. As to the 

objection that the NIM is too hard to compute and obtain, 

just in the same way that the effort of converting 

homogeneous differential equations into state space form 

allows us to apply metrics, converting our IS into an 

appropriate format, should also allow ease of analysis. 

One might argue that elegant and simple designs 

performing some function should be considered more 

intelligent than more complex designs that perform exactly 

the same function. In this case we can consider as side 

information some metric like "design efficiency" that will 

increase H) log P(D*| − 2 for the more elegant design. 

Will the NIM perform well given a complex but useful 

system as well as an equally complex system but one that 

simply thrashes, performing no useful task? The key here 

is that P(D*|H) will be higher in the latter system, since the 

thrashing system is not specified even though it is 

complex. The transformed pattern, D*, will not be 

supported by side information, I, and so the chance 

hypothesis, H, will have increased support. Additionally, 

how will the NIM perform given two other systems, one 

buggy and one bug-free? Again the system with the bugs 

will fail to support the side information (that is, the side 

information affected by the bugs) that indicates increased 

intelligent agency, giving the chance hypothesis increased 

support. 

Finally, it will be argued that chance, regularity, and 

intelligence do not cover the entire space of possibilities 

and particularly that intelligence is not the only conclusion 

when there is specified complexity. Both Kaufmann [15] 

and Davies [16] argue that there must be an (as yet) 

undiscovered regularity that can generate specified 

complexity. Since this is not yet discovered and since there 

is little concrete evidence for its existence, it certainly is 

not unreasonable to maintain our claim. 

5 CONCLUSIONS AND FUTURE DIRECTION

We make no claim that the determination of the actual 

value of the NIM will be easily gotten, particularly as the 

systems under analysis become more complex. The precise 

determination of side information, the matching pattern, 

and the event probability under the chance hypothesis will 

be challenging to quantify with reasonable hope of 

accuracy. Clearly, many examples, starting with known 

and relatively simple systems (such as linear systems), 

should be attempted in order to exercise this metric. 

We may also discover that such a metric (or any 

intelligence metric, for that matter) cannot measure certain 

broad classes of systems. This would be disappointing, but 

should not stop us from pursuing a metric or even surprise 

us. The twentieth century has been known for the 

discovery of a wide variety of limitations. Special relativity 

posited a limiting speed for matter. Heisenberg discovered 

a fundamental uncertainty in measurement capability 

(infinite measurement accuracy in position and momentum 

simultaneously of a particle is impossible). Chaos theory 

realized that the trajectories of certain deterministic 

systems cannot be accurately predicted without infinite 

precision in the initial conditions. A broad class of 

problems is either not computable (like the general tiling 

problem) or unsolvable given our computing resources 

(like NP-hard problems). All these are extremely helpful 

discoveries, even if somewhat disappointing. At a 

minimum, they lessen inflated claims of performance by 

quantifying performance limitations. Perhaps some similar 

fundamental limitation for any intelligence metric is also 
inherent in some broad class of artificial systems. To find 

such a limitation and to parameterize it would be a 

worthwhile discovery. 

REFERENCES

[1] Barry Commoner, “Unraveling the DNA myth: The 

spurious foundation of genetic engineering,” Harpers, 

February, 2002. 

[2] Columbia Tristar Pictures 

[3] Roger Penrose, “The Emperor’s New Mind: Concerning 

Computers, Minds, and the Laws of Physics,” Oxford 

University Press, 1989. 

[4] Michael Sipser, Introduction to the Theory of Computation, 

PWS Publishing, 1997. 

[5] Claude E. Shannon, “A mathematical theory of 

communication,” Bell System Technical Journal, Vol. 27, 

July and October, 1948. 

[6] John C. Knight, Colleen L. DeJong, Matthew S. Gibble, and 

Luis G. Nakano, “Why are formal methods not used more 

widely?” Fourth NASA Formal Methods Workshop, vol. 

27, September, 1997. 

[7] William A. Dembski, The Design Inference: Eliminating 

Chance Through Small Probabilities, Cambridge University 

Press, 1998. 

[8] ISO/IEC 14977:1996(E), Extended BNF, ISO, Geneva, 

Switzerland, 1996. 

[9] John A. Horst, Elena Messina, Thomas Kramer, and HuiMin Huang, “Precise definition of software component 

specifications,” Ghent, Belgium, 1997. 

[10] Paul E. Black, Kelly M. Hall, Michael D. Jones, Trent N. 

Larson, and Phillip J. Windley, “A brief introduction to 

formal methods,” IEEE 1996 Custom Integrated Circuits 

Conference, 1997. 

[11] William A. Dembski, No Free Lunch: Why Specified 

Complexity Cannot Be Purchased without Intelligence, 

Rowman & Littlefield, 2002. 

[12] David H. Wolpert and William G. MacReady, “No free 

lunch theorems for optimization,” IEEE Transactions on 

Evolutionary Computation, vol. 1, no. 1, April, 1997. 

[13] Douglas Robertson, “Algorithmic information theory, free 

will, and the Turing test,” Complexity, vol. 4, no. 3, 1999. 

[14] J. S. Albus and A. M. Meystel, Engineering of Mind: An 

Introduction to the Science of Intelligent Systems, John 

Wiley and Sons, 2001. 

[15] Stuart Kaufmann, At Home In The Universe: The Search for 

Laws of Self-Organization and Complexity, New York: 

Oxford University Press, 1995. 

[16] Paul Davies, The Fifth Miracle, New York: Simon and 

Schuster, 1999. 
Dimensions of Intelligent Systems

Dr. Gary Berg-Cross

Knowledge Strategies Division, SLAG, Inc.

Potomac, Maryland 20854, USA

1. Abstract

As intelligent systems have become more fully functional and

commonly available, questions about their capabilities and

relationship with humans have increased. This paper builds on

the IS requirements ideas of Messina et al [2001] to explore

middle ground between anthropomorphic approaches like the

Turing test that rely on similarity to human behavior in an

"imitation games" and the narrowness of tests of chess mastery.

I contrast a system like Deep Blue which has a very fixed

environment in which it performs to more complex types such as

Associate technology. Deep Blue, I argue, is an example of

system whose performance is expert, but whose competence is

fragile and it may not satisfy extended definitions of competence

and performance intelligence that we measure in dynamic

environments. A clinical protocol system is used to explore the

basic functional capabilities and knowledge. Beyond symbol

processing and the knowledge level are grounded reactive

intelligent within more of an environmental/systems perspective.

I build on grounded systems to discuss the use of goals using,

learning-based systems and & multi-modal logics that

characterize “realistic” intelligent systems. It is argued that such

characterizations will evolve as IS design matures into grounded

intelligence and situated, rational agent systems. In the future

belief models and measures of rational coherence might be used,

as basic approaches to facilitate intelligent system performance

in dynamic environments.

Keywords: IS, Intelligent Systems, Turing Test, Cognitive

Model, situated cognition, BDI, Deep Blue, constructionism

1: Introduction

Investigation of artificial intelligence system capabilities

now has a long history with notable discussion stemming

from the original Turing test with many modern

elaborations. Motivating contests exist for passing a test

such as Turing's (Loebner Competition) as well as prizes

for tasks in chess and various robot competitions

(RoboCup, Office Navigation, Trash Pickup etc.).

However, something like a Turing Test, imitation of

human conversation, seems too difficult if we take it to

logical conclusions, while success in something like chess

as demonstrated by the triumph of Deep Blue seems too

narrow an achievement to feel that we have made general

progress on truly intelligent systems. Since the

intersecting concepts of performance, intelligence and

systems are complex, success with Intelligent Systems

may be aided by focus on tasks of intermediate

challenges. I will discuss several examples from the

medical realm to illustrate the challenge and the state of

affairs. From the example of patient safety performance I

build a framework of intelligence perspectives or

dimensions to organize the discussion. It has some clear

idea of successful performance. For diagnosis we can ask

if it is correct given ratings by an expert panel. Several IS

diagnosis systems have been implemented and indeed, by

expert ratings, they perform better than a typical

physician, yet they have little penetration in the healthcare

industry. Why? One reason is that the measure of

correctness is isolated and doesn't look at the total picture

including cost and maintenance issues. The system's

knowledge can be difficult to maintain and further

systems have difficulty fitting into the working

environment, an issue I discuss more under the topic of

Associate Systems.

Still another factor concerns the issue of patient safety

and system error. There are 4 categories by which human

performance is judged in relation to patient safety [Marx,

2001]. These are:

1. Human/system error

2. Negligent conduct

3. Reckless conduct

4. Knowing violations

We can see that judging human skill quickly gets beyond

mere performance when assigning these categories1.

Human/system error is a judgement that the system’s

performance was “inadvertent” and other than intended.

We make such errors every day with minimal

consequences and so might systems. The 2nd category,

negligence, is a more culpable behavior and in healthcare

is generally assigned when an individual has been harmed

by a failure to exercise skill, care and learning expected of

a provider [Marx, 2001]. Thus, we quickly leave a purely

behavioral domain and enter one with concepts like

“learning’ skill and intentions. There are several distinct

architectural levels that can be distinguished meaningful

beyond the “what” of behavior that include why (a

knowledge and cognitive level), how (functional /symbol)

level and what descriptions. This leads to higher

dimensions for judging behavior outlined in the four

dimensions or views as shown in Table 1. At the

 

1

 For current implementations we could agree that ISes

aren’t going to make category 4 safety errors until we

have systems whose intentions are explicit!
foundational level of behavior we can discuss the most

obvious aspects of performance. When we talk about a

system at this level the intent is not to go beyond the

dimension of its behavior. But already there are many

issues here, such as emerged from earlier attempts at

“Behavioral Psychology”. Our patient safety example

illustrates the problems in principle. A second dimension,

shown in Table 1 is a functional approach that is typically

couched in a stimulus, information processing, and

response model.

Dimension/Perspective Characterization

1. Behavioral/What Performance assumes not other

system/dimensional knowledge.

Such behavioral descriptions list the

 observable behavior exhibited by a

system when it is being applied or

executed. This model of system

 behavior (i.e., a series of episodes

of the system's activities) relies on

 observation.

2. Symbolic/Functional

Architecture (How)

Traditional information processing of

symbols. The functional model

describes an (implemented)

representational and computational

 commitments/architectural primitives.

3. Environmentally

Reactive (External why)

Intelligent behavior is a coherent

response to environmental challenge.

 To do this it may functionally

involve goals and world models. The

knowledge level description

describes a system in terms of the

 knowledge of the world and some

principles that are applied when

using that knowledge.

4. Goal-oriented and

Intentional (Internal why)

Agent-orientation to rationalize

 intelligence at a belief/goal/ intention

 level. At this level we have refined

 principles of rationality.

Table 1 Perspectives of Intelligent Systems (What,

how and why)

Prior PERMIS conferences in 2000 and 2001 provide a

broad discussion on the testing of Intelligent System (IS)

based both on behavioral performance, including

efficiency and effectiveness measures drawing on the

expectations of designers, as well as functional

capabilities including robustness and learning capabilities

etc. One way of pursing the question has been to take

performance measures of non-intelligent systems and to

attempt to add measures for intelligence [Messina et al

2001]. The simplest way to frame this has been to discuss

the main elements found in IS. Messina et al [2001]

propose several elements that make up a functionally

intelligent architecture including:

• behavior generation to deal with incomplete

commands (e.g. interpret commands, supplement

instructions),

• synthesize alternative behaviors and adjust plans;

• adjust sensory processing to deal with the unexpected

and unknown; and

• represent the world using an updatable, long-term

stores of knowledge, including commonsense notions.

 Such a listing fits the now classical Input, Process, Store

and Output information processing type of model of

intelligence such as shown in Figure 1. In such robotic

models, perception and behavior are treated as separate,

front end functions and "cognition" which, goes on in the

processor and memory functions controls the perceptual

and effector functions. This is a popular concept of

intelligence as basically cognition [Newell, 1982]- the

capacity to construct and manipulate symbolic

representations, i.e. "approximate models" that are

mapped to the environment and determine "appropriate"

action. This is also called a knowledge-oriented view,

since a system's knowledge is a way to describe behavior

(e.g. synthesizing, adjust plans etc.). Chong and BergCross [1990] provide an example of such work to

understand the types of errors that ISes might make.

Although models differ widely in terms of how

sophisticated their concepts of knowledge, cognitive

process and learning are Messina et al [2001] provide a

useful base list several of functional requirements for

testing cognitive systems e.g.. tests to measure the ability

to fuse data from multiple sensors, including the

resolution of conflicts. One of the goals of this paper is to

follow up this approach by applying this criteria and

additional characteristics raised in higher levels (3 and 4)

discussed later or one advanced systems to illustrate

assessment. A medical protocol planning system is used

later in the paper to illustrate this.

There are many alternative ways of distinguishing the

third distinction of intelligence. I call this Interactionist

following a view of intelligence that see it, like

knowledge, as open to interpretation and always relative

to others things that provide an environment [Clancy

1989]. Steele [1995], for example, follows this view and

sees self sufficiency in such interactions, which is called

agenthood, as the basis for intellect. In this view we

judge behavior as intelligent to the extent that it sustains

an agent in an environment. Mail delivery robots are

intelligent if they can successfully interact with and

navigate a mail environment, especially if it is complex,

uncertain and changes often. The key measurement is not

a specific behavior but is described in terms of the quality

of a result relative to this environment and what it

presupposes. That result can be categorized as "success"

in terms of a context. This is the same level of analysis as

provided in the patient safety example previous discussed.

Hence calling a system intelligent or its behavior

intelligent, or in error, is based on an external,

human/observe judgement as shown in Figure 1 [Van de
Velde 1995] rather than being a structural or even

functional part of a "system". This shift in view includes

the notion that knowledge is situational and cannot be

viewed as self contained. Instead, it is inherently

coordinated with an environment as situated context. If

we buy this interactionist view of intelligence we quickly

see the connection to a Turing Test (TT) which embeds

system intelligence in a human context [(Saygin et al.

2000]. The Turing test is behavioral and interactionist but

has a naive anthropomorphism implicit in a human

"imitation” games. “If one were offered a machine

purported to be intelligent, what would be an appropriate

method of evaluating this claim? The most obvious

approach might be to give the machine an IQ test .

However, good performance on tasks seen in IQ tests

would not be completely satisfactory because the machine

would have to be specially prepared for any specific task

that it was asked to perform. The task could not be

described to the machine in a normal conversation (verbal

or written) if the specific nature of the task was not

already programmed into the machine. Such

considerations led many people to believe that the ability

to communicate freely using some form of natural

language is both an essential attribute of an intelligent

entity and a confirming test of underlying competence.

Figure 1 Model of Information/Symbol Processing

But philosophers like Searle with his Chinese Room

argument challenge the Turing Test and its natural

language exchange as a basis for assigning intelligence.

This group maintains that the judgement of cognitive

phenomena cannot be solely on the basis of observed

input-output behavior. It is worth pointing out in passing

that many strong AI critics like the Interactionist view,

especially when it touches on the relative merits of

symbolic learning and connectionist learning for

implementing intelligence. Many find that Stevan

Harnad’s [1990] hybrid model to grounding symbols in

the analog world with neural nets is a useful approach.

The issue of the behavioral problems of the naive Turing

test are taken up later in the paper in the context of Deep

Blue’s intelligent behavior.

Following Dennett's [1987] philosophical formulation,

the fourth level explicitly considers intentions and belief,

such as our patient safety . In philosophy the so called

intentional stance position serves as a convenient, abstract

way of talking about intelligent systems, allowing us to

predict and explain their behavior without having to

understand or describe how the cognitive mechanism

actually works. Cognitive theorists and modelers have

elaborated this in terms of cognitive structures that are

capable of performing cognitive processes which in turn

use those structures. Developers have to make many

architectural decisions to actually implement such a

philosophy. In the main this is the view a useful or

“realistic” agent whether it is to be realized as

"intelligent" software or as an autonomous robot. Such

practical agents range from information gathering and

trading agents to autonomous vehicles and may include

real time physical capabilities, good for dangerous

situations beyond the human central nervous system

capacity.

The remainder of the paper is as follows. Section 2 walks

through an example of an intelligent system applied to

clinical guidelines to illustrate both the behavioral and

functional criterion of Messina et al [2001] as well as the

use of plans and goal reasoning in a system. Section 3

discusses the intelligence of Deep Blue using an

interactionist robotics and unified cognitive architecture

perspective. Section 4 returns to the Turing test and

expands the concept to bridge to more useful concepts of

judgement of intelligence and in particular the models that

employ beliefs and intentions. Section 5 summarizes

major findings, proposed initiatives arising from this

view, dusts off the old concept of Associate Systems and

proposes motivational competitions to enliven the field.

2. Gauging the Performance of

Intelligent Clinical Protocol System

One of the most prolific areas of AI research has been in

the medical realm which provides more representative

measures of intelligence than chess performance and

where there already exist regular monitoring efforts to

judge success. A survey of the entire field is well beyond

the scope of this paper, and even sub-areas, such as

Receptors

Effectors

Processor

Memory

E

N

V

I

R

O

N

M

E

N

T Model of

Environment
diagnosis are too diverse to cover easily. Instead I look at

one well researched area - the support provided by

intelligent system for clinical protocols/guidelines and a

system that has been designed to aid people in developing

and using clinical protocols also call guidelines. Clinical

protocols have been developed over the last 12 years

provide a quality standard of care for such things as

diagnostic and therapeutic procedures, typically based on

the consensus of experts. Protocols are increasingly in

widespread use and The American medical Association's

Directory of Practice Parameters listed over 1,500 several

years ago. However, there is little sound data to judge

effectiveness across medical practice and intelligent

processing has been researched to automate, support and

improve guideline-oriented medical care [Musen et al

1996]. One reason for selecting a clinical protocol

system is that task analysis of the field has been

conducted [Sharhar et al 1998]. along with system

development such as by the Stanford Medical Informatics

work with EON [Musen et al 1996]. That work includes

support of tasks for:

• determining the applicability of a guideline for a

given patient,

• generating recommendations for therapeutic 

interventions and lab tests via a protocol

• tailoring the recommendation to the context of the 

current patient situation and stage of protocol 

execution,

• monitoring the application of the protocol guideline,

• assessing the effectiveness of the guideline.

I use the Messina et al [2001] list of requirements for

testing Intelligent systems along with the performance

evaluation properties (I would call functional capabilities)

for ISes in non-numerical domains list and Musen et al

[1996] to illustrate several of EON's interesting features.

EON but is built from general, purpose software

components and has been applied to protocol-based care

in domains as diverse as oncology, hypertension, AIDS

and diabetes.

Requirement 1 & 13: "interpret high level, abstract, and

vague commands and convert them into a series of

actionable plans" and " to understand generic concepts

about the world that are relevant to its functioning and

ability to apply them to specific situations".

EON's main task is a general one, generate an acceptable

plan given the current clinical situation and relevant

guidelines. It must determine a patient's eligibility and

refines abstract plans to fit the situation. A relevant

property is its ability to deal with general and abstract

Information. EON's designers recognized that it needs to

be a general problems solver like a physician . Thus it

deals with both detailed, patient data in the medical record

and database and abstract protocol specifications. It

infers higher-level, interval-based concepts using timestamped, patient data. Conceptual abstractions are a

major feature of the EON approach and the PROTÉGÉ II

system is used to build the EON KBs emphasizing the use

of conceptual abstractions to define problem-solving

behaviors independently from the programming logic.

Also relevant is the ability to "deduce particular cases

from general ones". EON contains time abstraction,

general medical ontology (e.g. concepts and relations

between prescription, drug regime, medication, clinical

trial etc.) as well as disease and patient specific

knowledge/information. It uses an episodic skeletal plan

refinement method on very general time concepts to

instantiate a patient guideline plan. An illustration of this

is shown in Figure 2. This deduces particular plans from

abstract information in combination with very specific

information.

Figure 2 EON Skeletal Plan and Refinement Process

Requirement 2, 3 , 15 & 14: "to autonomously make

decisions as it is carrying out its plans" and to "re-plan

while executing its plans and adapt to changes in the

situation" and " work with incomplete and imperfect

knowledge by extrapolating, interpolating, or other means

" and " deal with and model symbolic and situational

concepts as well as geometry and attributes".

While EON is not an independent agent it's processing

includes setting sub-goals as part of its plan refinement.

An observer would see it going far beyond the original

specification in several steps:

1. Identify and propose a starting standard, 

abstract hierarchical plan
2. Instantiate the plan based on situation and 

decomposition and to allow execution (time 

constraints etc.)

3. Identify problems that might make application 

of this plan (practical drug admin challenges, 

side effects, etc.)

A property of the system is the ability to reschedule and

replan and adjust the plan to updated situations [Messina

et al 2001]. It also might be said to "recognize the

unexpected" in that the guidelines project out a path and it

will replan if deviations occur such as reduce AZT if

anemia develops or side effects develop. Similarly it

deals with incomplete information routinely. It typically

does not have the entire attribute value set to begin with

and generates queries of relational DBs that are processed

into patient history. EON deals with situations of time,

but distance and geometry, such as robotic path concerns

are not part of its knowledge base (KB).

Requirement 4-9: to "register sensed information with

its location in the world and with a priori data " and " fuse

data from multiple sensors, including resolution of

conflicts " and " to handle imperfect data from sensors,

sensor failure or sensor inadequacy for certain

circumstances" and " to direct its sensors and processing

algorithms at finding and identifying specific items or

items within a particular class" and to "focus resources

where appropriate" and " to handle a wide variation in

surroundings or objects with which it interacts" .

EON does none of this. It is not robotic. An IS is often

robotic in having sensor and/or effectors but many are

decision supports. Adding a robotic element to an IS is

discussed later in the context of interactions and a Total

Turing Test.

Requirement 10-12: to "deal with a dynamic

environment" and "map the environment so that it can

perform its job" and " update its models of the world, both

for short-term and potentially long-term".

As noted EON deals with changes in the patient situation

as well as changes to guidelines and phase of care.

However, it's function does not result in model-mapping

of the situation as might be implied here. Machine

learning approaches which do this routinely such as

embodied in the SOAR architecture are discussed later in

the paper.

Requirement 15: " to predict events in the future or

estimate future status".

EON does provide projections to allow comparison such

as for the T-Helper implementation of Eon for AIDS -

what is the situation 172 hours after symptomatic

treatment.

Requirement 16: "ability to evaluate its own performance

and improve".

EON does not have such ability.

3. Deep Blue's Brand of Intelligence and

Grounding in the Interactionist

Perspective

Chess was long seen as an extreme test of human

intelligence and an excellent domain for IS [Levinson,

1991]. Chess performance is easy to monitor because

success and skill categories are well defined. Studies of

experts have been conducted to construct cognitive

models which have been more broadly applied

(uncertainty management and problem space pruning for

example). However, as long as a dozen years ago

computer success at chess was largely based on brute

force computation using alpha-beta minimax search with

selective extensions IS [Levinson, 1991], rather than

elegant knowledge structures or complex processing

strategies - the intelligent parts of a cognitive model. This

was necessary to achieve effective time performance -

conventional AI techniques were too slow for real-time

response and chess is very much a time bound game. In

the late 90s Deep Blue achieved its victory and it's

processing capabilities are well known but the victory

raises some interesting issues. Foremost is, do we

consider Deep Blue intelligent? Behaviorally the answer

has to be yes. If we take strictly behavioral views of

intelligence in chess we may list the behavioral pattern

without making any claim of an agent's cognitive level.

Also by a judgement of interacting with its environment it

is successful. By performance measures Deep Blue is

intelligent, but this seems unsatisfactory on several other

levels. It is grounded in the main chess objects and how

they behave, but this is trivial, a game of simple rules.

Deep Blue doesn’t match up against the Messina et al

[2001] criteria. It has minimal sensing capabilities, no

commonsense knowledge to speak of, no ability to fill in

knowledge. An interesting sidelight is that the IBM team

found that while chess suggestions from experts were

useful, they could not always be relied upon to aid Deep

Blue's evaluation function - the essential process. What

the team wound up doing is to create a "knowledge-free"

machine using just available on-line chess databases to

give the system a statistical experience base. That is, the

final knowledge base had learned via working through a

lifetime of chess games - essentially an grounding in

chess reality, leading to its expertise, but the resulting

knowledge base lacks the more abstract knowledge such

as is often assumed underlies intelligence.
Now it is true that we typically are not talking about ISes

with the full range of human ability, but in many cases we

are talking about sensori-motor capacity and the ability to

distinguish things in the world. Broader world knowledge

is more typically learned by robots as summarized by

Harnad [1993]in his discussion of a revised test of

intelligence he calls the Total Turing Test (TTT):

Well, in the case of the Turing Test (TT), there

was more we could ask for empirically, for

human behavioral capacity includes a lot more

than just pen-pal (symbolic) interactions. There

is all of our sensori-motor capacity to

discriminate, recognize, identify, manipulate

and describe the objects, events and states of

affairs in the world we live in (the same objects,

events and states of affairs, by the way, that our

thoughts happen to be about). Let us call this

further behavioral capacity our robotic capacity.

Passing the TTT would then require

indistinguishability in both symbolic and

robotic capacity.

We can see this direction as also having been taken by

real-time robots to handle problems such as:

• Symbol grounding to the real world (easy in chess,

but not elsewhere)

• RT signal interpretation and planning under time

constraints

• Situatedness issues - how is behavior adjusted to

dynamic situations?

Subsumption architectures have been one attempt around

these. [Brooks 1986] The main assumptions behind these

attempts include:

• No attempt to construct an full, central symbolic

model of the environment

• Behaviors are not controlled by a central executive

looking at master plan, but may have a network of

behaviors that may excite or inhibit each other.

• Sensor interpretation, planning and execution are

not separated. Rather they are organized around

modular competencies.

• Complex behavior is not programmed in but

emerges from dynamic interactions between the

environment and the component behaviors.

As Harnad [1993] says,

Real transduction is in fact essential to TTT

capacity. A computational simulation of

transduction cannot get from real objects to

either robotic performance or symbolic

performance (not to mention that motor

interaction with real objects also requires the

output counterparts of transducers: effectors).

This is the requisite nonarbitrary argument for

the special status of transduction that we did not

have in the case of parallelism (or silicon). In

addition, there are other things to recommend

transduction as an essential component in

implementing cognition. First, most of the real

brain is either doing sensory transduction or

analog extensions of it: As one moves in from

the sensory surfaces to their multiple analogs

deeper and deeper in the brain, one eventually

reaches the motor analogs, until finally one finds

oneself out at the motor periphery. If one

removed all this sensorimotor equipment, very

little of the brain would be left, and certainly not

some homuncular computational core-in-a-vat

that all this transduction was input to. No, to a

great extent we are our sensorimotor transducers

and their activities, rather than being their

ghostly computational executives.

There are now examples of unified robotic systems that

include a commitment to symbolic manipulation as well

as sensory transduction and organized motor responses

which might satisfy the TTT, should we want to engage in

it. One classic one is ICARUS [Langley et all 1991],

which is made up of the standard 3 major components.

However, architecturally the sensory buffer proves input

to a mapper to the conceptual level, there is a reactive

action planner to identify appropriate actions for world

situations and there is a mapper of the action to an action

"scheme" that drives the effectors that connect to the

motor buffer.

The innovation here is that all 3 components use a similar

representation (hierarchical probabilistic concepts) and

reasoning is driven by a set of heuristics on the

classifications in the hierarchy. Thus we have an index of

world objects from perception, plans and more schemes

each of which is defined by attribute-value pairs with a

conditional probability of an attribute having a particular

value given membership in a particular class. As noted

by van de Velde [1995] by having consistent

representation we get natural integration between

perception, planning and action. Learning in inherent in

the architecture since an instance is sorted down a

hierarchy by class selection2. When a class is selected the

probability distribution of attributes is updated using

Classit's incremental function [Gennari et al 1989] or a

 

2

 One might use other approaches like Grossberg's

adaptive resonance theory (ART) to organize clusters as

a variety of levels and use the vigilance parameter to

adjust the degree of clustering.
new class is created. This integration provides a natural

symbol/concept grounding, which is not built in by a

knowledge engineer. There need not be world view,

rather, like reactive robots success is built through

interacting with the world. It goes two steps better than

reactive robots in that it creates:

1. manipuable concepts for planning as it

goes along.

2. builds coordinated/coupled processes

that are closed as well as defined by

their organization and the action

dynamics these processes imply.

This is also fundamentally different from the traditional

I-P-O architecture and the separation between perception

and action is largely gone. Both are reactions to

environmental changes and employ a planning cognition

to preserve or reach some state. Thus, with such an

architecture I could speak of a motor performance with

plans and percepts underlying it as an integrated thing.

More importantly, I might expect the IS to be able to

justify its actions by means of a trace of the activated

concepts and attribute values. It is a step, but not

necessarily as large a step, as we have with Deep Blue to

imagine a robust performance over a range of domains.

4. Goal- Oriented Agent Intelligence

with Beliefs, Desires and Intentions

In our final dimension I take a large step towards an

intelligent performance that might meet some of the

judgmental characteristics involved in patient safety. As

previously noted categories of patient safety involve a

judgement of the intentionality to do wrong. Like the

Turing test it implicitly rests on human ability to predict

and understand the behavior of “others” in complex

interactions. This may lead to misjudging the intelligence

of a system. As Hayes and Ford [1995] argue, the Turing

Test is fundamentally flawed for two reasons: it is a

basically poor experimental design, and it tests for the

wrong thing. It is the wrong design because while it

seems "unashamedly behavioristic and operationalistic",

yet it is based on hidden assumptions rising from on our

naïve psychology. Similarity to human behavior is just

not a sensible criterion for intelligence. Our social

experience provides an implicit, observer bias to assign

mentality and intentions to the system in a test and many

would argue that typical human use reasoning techniques

haven't found their way into typical intelligent systems.

E.g., humans use extremely complicated, temporally

extended mental images and associated planned intentions

to reason. It is the goal of the final dimension to build in

such capabilities so that such judgements of IS could be

justified.

In the prior section we briefly discussed goals within the

ICARUS architecture, but the dynamics of these was not

detailed. There is a large body of work to actively

incorporate such planning about goals and belief abilities

into ISes/intelligent agent architecture as more than

reactive systems. ICARUS fits into this class of

"cognitive architectures" as does SOAR [Newell, 1990].

SOAR, like ICARUS is interactionist in the sense that the

task environment determines the possible structure of

problem spaces. It is goal oriented in that problem

solving is built around control knowledge that selects

goals and sub-goals as it searches a problem state. To

understand its behavior we have to look at its functional

architecture and ontological commitments to knowledge

and goals. Globally problem solving involves search,

which is controlled by a context tree which might consist

of a 4-tuple of: goal, problem space, state and operator.

At any instant we may loosely say that this 4-tuple object

is what such an IS actively "knows". The cognitive

decision cycle (shown in Figure 3) consists of two phases

to manages the context tree by determining what slot

attributes should be changes. In the elaboration phase

long-term knowledge is represented by production rules

fire and those that fit the situational pattern fire in parallel

until now more changes in the 4-tuple object occur. In

this process new "preferences"3 for a part of the 4-tuple

context object may arise. In the second phase preferences

are evaluated via a decision procedure. The result is a

new contextual object better than any others. If such an

object fails based on preferences one of fours types

impasse is reached - tie. No-change, reject impasse or

conflict impasse. All impasses are solved by the same

goal- search process used in the cycle. Thus the system

has a unified approach to problem solving around goal --

based learning that uses environmental results expressed

in the problem statement and state as a factor.

SOAR is a pioneering effort which continues, but beside

being goal driven we may also introspect about intents.

Having a system aware of its performance was listed as an

IS feature by Messina et al [2001]. SOAR has a step in

that direction, but we wouldn't be comfortable speaking

of its intention to "monitor itself”. Such intentions built

into a system might satisfy professional guidelines for

patient safety.

Intentions have been added to agent architectures based

on Bratman's [1987] theory of human, rational behavior,

 

3

 Preference types are fixed simply at elements such as

feasibility, exclusivity, desirability, necessity ( require,

prohibit ), termination for desirability of alternative

objects occupying slots in the context tree.
which formalized the ideas of Belief, Desire and Intention

(BDI). BDI logics, such as developed by Rao and

Georgeff [1998] , are multi-model symbolic logic4 used

in implementations to make agents more “realistic.

Figure 3. SOAR decision cycle

Realistic reasoning separates deciding what to do for, how

to do it (planning), something like was the case in

classical information processing systems but with a higher

type of reasoning added. Both deciding and planning can

be computationally expensive and an agent needs a

strategy on when and how to drop an intentional

commitment (its not possible or not feasible etc.).

Because of this, implementations, while realistic are not

typically practical. I take up a proposal to make these

both practical and realistic in my final section. Modal

style logics have been most widely explored for styles of

intentional inference such as reasoning about time and

belief. Agent propositional “event” knowledge is

qualified as a temporal truth AKA belief, so deduction

can both exploit features implicit in such qualification

along with the context of the proposition. To say in the

past, something true in the past is still today true in the

past, asserts a global property implicit in the interpretation

of the modality past. An analogous property of belief is

positive introspection: if we believe something, we

believe we believe it. So each modality implicitly carries

a particular sort of inference and the use of different

modalities allows the applied logician to make

distinctions on the sort of inference. The benefit is

 

4

 A standard of classical truth functional propositional

and first order predicate logic.

economical notation, akin to natural language in the way

detail is encapsulated in context, but at the cost of the

functional transparency of extensional mathematics.

Before closing it is worth noting that BDI

implementations are typically more interested in

autonomous agents, rather than developing AI systems for

specialized purposes such as a particular medical

application. Can these be brought together in a tractable

way?

5. Summary and Directions towards

Practical & Realistic Intelligence

Having reached the BDI level with deliberative and

planning competence, let's discuss some of the possible

directions for making ISes successful and measuring their

performance. As we saw with the definitions of patient

safety and in the naïve Turing Test our judgments involve

concepts like intent which are not typically designed in a

functional architecture. However as ISes become

increasingly advanced, we can imagine drivers to systems

with all the Messina requirements s well as an additional

set to improve human –IS cooperation. Such cooperation

and working relationships were envisioned by DARPA's

AssociateTechnology program of the late 80s and early 90

[Berg-Cross 1991] in order to help:

• Handle increased amount of detail

(bookkeeping)

• Remove bias

• Broaden the experience base by combining

knowledge

• Provide easier visualization and

• Unify joint action

 In an associative relationship human and IS share goals

and tasks and communications. Interactions are structured

/designed to work in mutually cooperating ways. The

quality of IS decision and control depends greatly on the

quality of information generation on its interfaces. In such

interfaces we have the problems of situational awareness

and situated cognition. A healthcare provider working

with a traditional medical application is only modestly

supported in adapting to changing demands and contexts.

Working with an Associate IS makes it simpler in a way,

but like any introduction of a new agent we have the

problem of coordination - perception about patient status

and treatment effect, clinical path, relevant medical

procedures. Successful associate coordination would

involve at least 3 things besides the raw information

1. Abstract knowledge

2. Situated dialog

3. BDI based dialog
As we saw in SOAR and EON an approach to the

coordination problem is to combine abstraction with state

space representation of tasks. Abstraction, rather than

exhaustive reduction, deals with complexity by searching

for global relational properties that exist somewhat

independently of the knowledge elements whose behavior

they govern. The models of abstraction need to be

coordinated. Strong knowledge engineering and

ontology tools, for situational knowledge specification are

needed for this along with suitable browsers/query tools

and displays.

Flexible and intelligent dialog is also needed. It must be

situated to support coordination. Also as noted by McRoy

et al [1999] part of the problem is that right now humanhuman communication is different than human IS.

Systems provide large amounts of information and have

difficulty handling feedback about it. They are not

focused to human style communication, which makes

coordination difficult and limits the trust of ISes. In the

same way that some anthropomorphic judgements are

projected on systems, so biases against their capabilities

also may exist. Human interaction tends to be more

incremental and feedback is interpreted in terms of beliefs

and goals. For most interaction it is very important to

know that beliefs, goals and intentions are shared and that

beliefs about the dialog can be made explicit.5

I have used alternative views of the Turing test and Deep

Blue's performance with four dimensions of intelligence

to illustrate the difficulty of gauging success. My

speculation is that we are far enough from success on any

one dimension from achieving the type of success needed

to master patient safety problems such as inherent in

medical protocols. Obtaining a systematic view of system

intelligence in complex, dynamic environments has rarely

been a high-priority objective because obtaining realistic

reasoning is computatuionally expensive. I think however

it is time to consider some challenges (grand and

otherwise) for ISes. Such challenges that fall between TT

and chess. They could be small but interesting such as the

annual robot building competition sponsored by AAAI or

the self-replication contest held during the ICES98

conference. The object of this was to demonstrate a selfreplicating machine, implemented in some physical

medium, e.g., mechanical, chemical, electronic, etc.

 Larger efforts might be organized around work to

integrate levels such as shown in Figure 4. Essentially

there is a bottom up area taking the idea that intelligence

is a coherent, grounded response to environmental

 

5

 Vicente and Jens Rasmussen [1990] pursue a related

idea of ecological interface which includes a degree of

abstract and BDI.

challenge. What has been missing here is adequate

understanding of the tasks implied in the environmental

complexity going from the more predictable in chess to

the more dynamic. Setting up a suitable lab and test

environment would be a good area for NIST leadership.

Figure 4. Environmental task modeling and

Rationalizing Research: Areas of Integration

At the top level we have rationalizing research to have

challenges in certain areas. The goal of this level of work

is to be able to support the level of professional and legal

responsibility such as we saw implied in patient safety.

Henry Hexmoor and Gordon Beavers [2002] consider

such needs from an agent perspective and propose to

extended the intentional notions of Belief, Desire, and

Intention (BDI ) to include social “properties” of Value6,

 

6

 Values are understood as principles that govern the

agent’s behavior and which the agent will attempt to

uphold as end-goals

LT Memory

Knowledge of: Law, Conversation, Techniques, etc.

BDI level Goals, Attitudes

, Responsibilities, etc

about Law, Conversation, Techniques,

Missions, Coordination,

Properties of cups, people, robots, ...

Match

[

]

[

]

[

]

[

]

[

]

[

]

Changes

Working Memory

Situational assessment, intermediate results,

actions, goals, ...

Perception / Motor Interface

Environment

Rationalizing

Research

Env. task

Modeling

Research
Obligation, and Norm (VON)7

. These 3 provide a level),

of predictability, accountability, and responsibility to an

IS’s high-level goal behavior (Figure 5) which allow us

to talk of an associate system.

Figure 5 Intra-agent concepts

[Hexmore and Beavers, 2002]

“Our legal system holds the owners of software

agents responsible for the actions of those

agents, therefore, agents capable of considering

their responsibilities could offer some protection

to the owner of the agent. Such software agents

might be agents involved in electronic

commerce, automated teller machines, proxy

email agents, or robot assistants. Likewise in a

command and control situation, a commander is

responsible for the actions of the agents under

his/her control and therefore would have greater

confidence in responsible agents capable of

considering the repercussions of their actions.”

Henry Hexmoor and Gordon Beavers [2002]

Additional efforts would be needed to standardize the

abstract knowledge in ontologies necessary to achieve

generality. The standard upper ontology (SUO) is one

effort that might provide a start, but it should be tested by

application to a several domains such as healthcare. My

speculation is that situated BDI dialog will be needed to

achieve higher reliability, verifiability, and precision of

expression to support reasoning about other agents and

 

7

 Norms yield default behaviors that the agent is

expected to observe whenever the agent finds itself in a

situation to which the norm applies [Henry Hexmoor and

Gordon Beavers , 2002]

extensive human-agent interaction. Given a few rounds

of such work, it might be time to consider a true Turing

test.

References

Berg-Cross, G. Issues for Multiagent-Based Associative

Planning Systems, DARPA conference on Associate

Technology, George Mason University, 1991

Bratman, ME. Intention, Plans, and Practical Reason,

Harvard University Press, 1987

Rodney A. Brooks. A Robust Layered Control System for

a Mobile Robot. IEEE Journal of Robotics and

Automation, 2(1):14--23, March 1986.

Clancy, W. J. "The frame of reference problem in the

design of intelligent machines." In van Lehn, K. (ed.)

Architectures for Intelligence: The Twenty-Second

Carnegie Symposium on Cognition, pp. 357-423.

Hillsdale, NJ: Lawrence Erlbaum. 1989.

Gennari, J., Langley, P. and Fisher, D., "Models of

Incremental concept formation." Artificial Intelligence

(40) 11-61, 1989

Harnad, S. "The Symbol Grounding Problem". Physica D

42: 335-346. 1990

Harnad, S. Grounding Symbols in the Analog World with

Neural Nets. Think 2: 12-78 , Special Issue on

"Connectionism versus Symbolism" D.M.W. Powers &

P.A. Flach, eds. , 1993.

Hayes, P.J. & Ford, K.M. (1995) Turing Test Considered

Harmful, Proceedings of International Joint Conference

on Artificial Intelligence (IJCAI-95), pp. 972-977,

Montreal.

Langley P., McKusick K.B., Allen J.A., Iba W.F.,

Thompson K. (1991): "A Design for the ICARUS

Architecture" Procs. of the AAAI Spring Symposium on

Integrated Intelligent Architectures.

Hexmoor, Henry and Beavers , Gordon, In search of

simple and responsible agents, NASA Workshop on

Radical Agents, Jan. 2002

R. Levinson, F. Hsu, T. A. Marsland, J. Schaeffer, and D.

E. Wilkins. Panel: The Role of Chess in Artificial

Intelligence Research. In Proc. of the 12th IJCAI, pp. 547-

552, Sidney, Australia, 1991.

Marx, D. Patient Safety and the ‘Just Culture’: A Primer

for Health Care Executives. Columbia University Medical

Event Reporting System for Transfusion Medicine, April

norms ...values

roles

Obligations

percept

communicatio

n

plan

Intentions

beliefs

Desires

Obligati

on

Determina

tion

of

Belief

Revision

Intenti

on

agent

awareness

Conversa

tion

Role

Revisio

Attentio

n

beliefs

and desires

Plann

ing

act

data
2001. Available online at http://www.merstm.net/support/Marx_Primer.pdf

McRoy, S., Ali, S. Restificar, A. & Channarukeul, S.

:Building Intelligent Dialog Systems" , Intelligence,

Spring 1999

Messina,E., Meystel, A. and Reeker, L. Measuring

Performance and Intelligence of Intelligent Systems,

paper at PERMIS 2001.

Mark A. Musen, Samson W. Tu, Amar K. Das, Yuval

Shahar: A Component-Based Architecture for

Automation of Protocol-Directed Therapy. AIME 1995: 3-

13

Allen Newell: The Knowledge Level. Artificial

Intelligence 18(1): 87-127 (1982)

Newell, A. Unified Theories of Cognition, Harvard U

Press, 1990

A. S. Rao and M. P. Georgeff. Decision procedures for

BDI logics. Journal of Logic and Computation, 8, 293--

342, 1998.

Shahar, Y.; Miksch, S.; Johnson, P.: The Asgaard Project:

A Task-Specific Framework for the Application and

Critiquing of Time-Oriented Clinical Guidelines,

Artificial Intelligence in Medicine, 14, pp. 29-51, 1998.

Ayse Pinar Saygin, Ilyas Cicekli, and Varol Akman,

Turing Test: 50 Years Later, Minds and Machines, Vol.

10, No. 4., (2000), pp: 463-518

Steels L. (1995) The Biology and Technology of

Intelligent Autonomous Agents. NATO ASI Series F, vol

144. Springer-Verlag, Berlin.

Van de Velde, Walter, Cognitive Architectures - From

Knowledge Level To Structural. In Steels The Biology

and Technology of Intelligent Autonomous Agents.

1995.

Vicente, K. and Rasmussen, J. , The ecology of

human–machine systems II: Mediating "direct perception"

in complex work domain. Ecological Psychology 2, 3, pp.

207–249. 1990
A New Classification of Information: A Step on the Road to Interpretability

Larry H. Reeker

Information Technology Laboratory

National Institute of Standards and Technology

Gaithersburg, Maryland

larry.reeker@nist.gov

Albert T. Jones

Manufacturing Engineering Laboratory

National Institute of Standards and Technology

Gaithersburg, Maryland

albert.jones@nist.gov

Abstract

Complex systems, such as manufacturing

supply chains, are often modeled as a collection of

interacting components with information flows

between them. These components are frequently

responsible for making a wide range of decisions

that are implemented using an optimization,

heuristic, or control technique. The traditional

approach to system performance focuses on the

performance of these components. The view has

been that to improve the system performance one

had only to develop better techniques. In this paper,

we argue that inadequate attention has been paid to

the relationship between information and system

performance.

Information has played an important role in the

manufacturing systems of the past. It will play a

dominant role in the Internet-based manufacturing

systems of the future. To better design, engineer,

implement, and control these systems, we need a

fundamental understanding of information and its

effects on system dynamics. This paper contends

that we need a new characterization of information,

a delineation of its salient properties, quantitative

metrics for those properties, methods for computing

these metrics, and linkages between these metrics

and system performance. We focus principally on

the first of these, a new characterization of

information, and discuss the implications of

suggested characterizations for metrics and their

measurement, suggesting some approaches for

further research.

Keywords: chaos; complex system; entropy;

information; metrics; system performance;

ontologies; satisficing

1. BACKGROUND

The Internet has made the globalization of

manufacturing systems, commonly called supply

chains, a reality. This globalization has caused two

fundamental transformations in the behavior of

these systems. First, the rigid organizational

hierarchies, typified by the keiretsu in Japan, have

been replaced by more flexible, network-like

organizational structures. The tightly integrated,

closed relationships of the kieretsu had many

advantages for both the original equipment

manufacturers (OEMs) and the suppliers. The

OEMs had a ready set of local, qualified suppliers

who were ready, willing, and able to serve their

needs. The suppliers, on the other hand, had a

guaranteed customer who provided predictable

production and delivery dates. This captive

relationship shielded both the OEM and its suppliers

from the global marketplace. As history has shown,

the impact can be positive for a while; but, over

time, this shield will weaken the market position of

the OEM and the capabilities of the suppliers. After

years of observation and emulation, many

manufacturers are attempting to build a business

structure that will yield the benefits of the keiretsu,

but avoid its weaknesses. In these structures, which

are self-organizing and Internet-centric, the

suppliers and OEMs form a virtual supply chain.

This allows OEMs the freedom to choose the best

suppliers and suppliers the opportunity to find other

customers.

The second fundamental transformation caused

by the Internet involves the roles that OEMs and

suppliers play in the supply chain. Both have

evolved from systems that are principally producers

and consumers of physical objects into systems that

are also producers and consumers of informational

objects. This evolution has taken place in two

distinct phases. During the first phase, the OEMs

gradually shifted production of all components and

sub-assemblies to independent suppliers. Many of

these suppliers were located in other countries,

which reduced the direct labor cost but increased the

logistics and transportation costs. The suppliers

became the builders of components and the OEMs

became the final assemblers. During this phase, the

business aspects of these relationships, and the

information associated with them, were still handled

by telephone and paper.

During the second phase, which is still ongoing,

the Internet has made it possible to exchange

electronically not only design and production

information, but also business information. The

potential exists to conduct all business transactions

over the Web. Demand information, logistics
information, purchase-order information, warehouse

information, and so on, can be sent anywhere in the

world. The Internet can assure that these

informational objects are delivered on time and

error free. It cannot assure that supply chain partners

will interpret these objects in the same way.

Furthermore, as described below, decisions made on

the wrong interpretation can have dramatic impacts.

Lee, Padmanabhan, and Wang discussed

financial impacts that befell some major companies

that made purchasing and production decisions

based on a misunderstanding of a variety of

information. Hewlett-Packard stockpiled laser

printers, worth millions of dollars, in response to --

what turned out to be-- phantom orders from

resellers. Procter & Gamble saw wild fluctuations

in orders from their distributors, although its market

research showed that the demand for diapers had

remained constant. These are two among several

examples described in [Jones et al 02]. In each

case, the decision maker – software or human –

made the decision based on its understanding of all

available information and a belief that the markets

would, in fact, evolve as predicted. Unfortunately,

the understanding was incorrect and the resulting

predictions were grossly inaccurate. The authors

summarized the generic problem as follows,

"Distorted information from one end of the supply

chain to the other can lead to excessive inventory

investment, poor customer service, lost revenues,

ineffective transportation, and missed production

schedules."

In supply chains, this phenomenon is called the

bullwhip effect because small deviations in

customer demand can amplify quickly (whip)

through the entire supply chain. As indicated

above, these small deviations can lead to dramatic

changes in performance. This type of dependence

on initial conditions is typical of a special class of

non-linear, dynamic systems that are called chaotic.

Chaotic systems are a subset of the more general

class of complex systems.

In this background section, the references to

“information” have assumed that the reader has an

intuitive idea of what it is. After all, we talk about

“sending information” or “looking for information”

regularly, and the public recognizes a whole area of

technology as “information technology”. The word

“information” in all of these areas refers to at least

two fundamentally different things. One is a

physical aspect of information that allows it to be

communicated. That physical dimension is

absolutely essential. But there is something that is

often more important to the human user, and that is

the knowledge that is conveyed by the physical

manifestation, and we call that “information” too.

We are going to have to differentiate the two types

later in the paper; but for now, we will continue to

look for any effect that what we commonly call

“information” on systems, and on the extent to

which the information can be used to predict system

behavior. We will start with the physical

manifestation, which is at the heart of traditional

approaches.

2. TRADITIONAL APPROACHES

Complex systems, such as manufacturing

supply chains, are often modeled as a collection of

interacting components with information flows

between them. These components are frequently

responsible for making a wide range of decisions

that impact the behavior and performance of the

entire system. Early in his landmark book, The

Sciences of the Artificial, Herbert Simon [69] said

that in such systems, “it is the organization of the

components, not their physical properties, that

determine behavior”. We interpret this to mean that

information and the ability of components to deal

with information have a major impact on system

performance. Near the end of his book, Simon

argues that complex systems exhibit emergent

properties -- “given the properties of the parts, and

the laws of their interactions, it is not a trivial matter

to infer the properties of the whole”.

Simon goes on to say that the evolution of these

systems is typically non-linear, often chaotic, and

sometimes catastrophic. This means that the actual

performance of the system can deviate substantially

from the predicted performance. Furthermore,

small changes in initial conditions can lead to

dramatic changes in the evolution of the system.

Before discussing our approach, let us review

briefly the traditional approaches to these problems

2.1 Input Characteristics

An input, X, is characterized as either

deterministic or non-deterministic, depending on

whether its true value is known, or assumed to be

known, with certainty or not. A great deal of effort

has been spent trying to model non-determinism

with probability distributions. In some cases, such

as queuing systems, assumptions that lead to

specific forms for the distribution -- such as Poisson

arrivals and Exponential service -- are often made.

In most cases, however, distributions are estimated

statistically from sample data. Two approaches have

been used: a frequency approach and a Bayesian

approach.

2.1.1 Frequency Approach. The frequency

approach treats the true value X as an unknown

constant. The output of a frequentist statistical

analysis is an estimate of the expected value and
standard deviation X. Consider the simple case

where X is estimated from a sample of n

measurements that are assumed to be independent

and identically normally distributed random

variables with mean c and variance σ

2

. Let x and s2

denote the sample mean and the sample variance of

the n measurements. Then x, s2, and s are the

estimates of μ, σ

2

, and σ respectively. The

probability distribution of x, called a sampling

distribution, is also normal but with expected value

μ and variance σ

2

/n. The ratio s/√n is an estimate of

σ/√n. The standard deviation σ/√n, called

population standard deviation of the mean,

characterizes the tightness of the sampling

distribution of x about E(x) = μ. So s/√n is called

sample standard deviation of the mean, is a measure

of the uncertainty about x as an estimate of μ. Once

we have these estimates, we use them, the original

data, and a goodness-of-fit test to find the

distribution. This approach is very sensitive to the

sample size and the underlying normality

assumption.

2.1.2 Bayesian Approach. The Bayesian approach

starts with a prior probability distribution p(X),

which can be found using the principle of maximum

entropy [Jaynes 68]. This distribution represents the

state of knowledge about X before the data is taken.

The expected value, the variance, and the standard

deviation of the prior distribution p(X) are denoted

by E(X), V(X), and SD(X) respectively. The

relationship between the measurement data and X is

expressed by a function φ(data | X) that is obtained

by the rules of probability theory from the

probability distributions of individual measurements

that depend on X. After measurement data are

known, the function φ(data | X) may be regarded as

a function not of “data” but of X. When so regarded

this function is called the “likelihood function” of X

for given data and written as L(X | data). Bayes’

theorem states that the probability distribution of X

after measurement, called the posterior distribution

and denoted by p(X | data), is proportional to the

product of the L(X | data) and p(X). That is, p(X |

data) ∝ L(X | data) × p(X). This new distribution

represents the state of knowledge about X after

measurement. The expected value, the variance,

and the standard deviation of the posterior

distribution are denoted by E(X | data), V(X | data),

and SD(X | data) respectively. The posterior

expected value E(X | data) may be taken as the

estimated value of X. And the posterior standard

deviation SD(X | data) may be taken as the Bayesian

evaluation of uncertainty concerning X after

measurement. Unlike the frequency approach, the

validity of this approach does not depend on a

normality assumption or a large sample size.

2.2 System Evolution

System evolution is described in terms such as

continuous/discrete, linear/non-linear, static/

dynamic, and deterministic/stochastic. A particular

system will be characterized by some combination

of these terms. Complex systems such as

manufacturing supply chains are composed of many

manufacturing enterprises, each of which is a

system according to our earlier definition. Each of

these enterprises, in turn, is composed of many

components, which are also complex systems in

their own right. Given this, how can we predict the

behavior of the entire supply chain?

There is a growing consensus that many

manufacturing systems exhibit chaotic behavior

[Herrin 01]. This means that they are (1) non-linear

and dynamic, (2) discrete or continuous, and, more

importantly, (3) deterministic but subject to

stochastic influences. So, although they are

deterministic, their performance cannot be predicted

with certainty in advance. In fact, small changes to

the initial state may cause significant changes in the

evolution and the performance of the system. 1

Sometimes these changes are gradual and build up

over time (Figure 1a), sometimes they are sudden

and lead to instabilities in the system and a dramatic

degradation in performance (Figure 1b).

As noted above, the traditional approach to

dealing with these problems has been through some

type of optimization techniques for individual

systems or sub-systems. The characteristics of both

the inputs and the system dynamics determine the

types of technique that is used. If everything is

deterministic and linear, well-known operations

research, artificial intelligence, or control theory

techniques can be used. If determinism cannot be

assumed, then techniques that are more complicated

must be used. These techniques include utility

theory, stochastic optimization, discrete event

simulation, and stochastic control theory. Except in

the simplest cases, non-linearity is usually dealt

with by using a linear approximation.

Based on Simon's arguments, we believe that

focusing only on the techniques used to make

decisions will not improve necessarily the

performance of the system. We believe that more

attention should be focused on the inputs to those

decisions, information, which can tell us the nature

of the inputs and how they might mesh between

components of the system. There are, however,

fundamental scientific limitations to our

 

1

This is in stark contrast to linear systems, where

small changes in the inputs lead only to small changes in

the outputs.
understanding of information and its impact on the

behavior of complex, systems. In particular, the lack

of computer interpretable measures of the meaning

of, and associated uncertainties for, information can

lead to decisions that create chaotic and unstable

behavior in these systems.

 3. INFORMATION CHARACTERIZATION

If all the information that is important in

characterizing a system’s behavior could simply be

expressed in bits, it would provide a numerical

value that could be used to measure and control

system performance and make improvements, which

we need for the reasons expressed in the previous

sections. In a purely physical system, numerical

measures of energy output characteristics alone may

Figure 1. Actual vs Predicted Performance

tell us whether it is sufficient for a particular task

and also determine its sufficiency as a part of a

larger system. We can also determine energy

efficiency. But we still lack adequate measures of

information outputs (or internal information for

control purposes) from information or hybrid

physical- informational systems of any complexity.

The state of a system is what we are trying to

deal with, whether the system is physical or

informational or both. That state is an information

object, and the succession of states specifies all the

behaviors and the causes of those behaviors. When

the state information represents energy or forces, it

is expressible simply in numbers or vectors. It tends

also to be fairly local in its influence. Even if a

system is not strictly a Markov process, it is

frequently expressible in such terms. The physical

aspects of human speech and many other physical

phenomena have been fairly well characterized (as

indicated by predictive ability) using Markov

models.

In informational systems, the models seem

inherently to be significantly more difficult. This is

true even for natural language syntax, as Chomsky

showed: Markov models are not adequate. If one

factors in the needed semantics for one person to

understand another, it becomes obvious that in

human language understanding there is an enormous

amount of information that is stored for long periods

by a listener and used to understand a speaker at

unpredictable times in the future [Miller and

Chomsky 63]. One would like to think that

industrial supply chains would have more

constraints and that the information needed would

therefore be more localized; but that is not a

foregone conclusion. Standards are one way to

apply the needed limits, but standards require better

characterization and measurement of parameters.

Below we look at some problems with the use of

ordinary information theory as we examine the use

of system states and their components and

communication of knowledge, then look at some

other possibilities for characterizing the information

in systems.

3.1 Information Within Systems

The definition of a system state is often only an

abbreviation of the essential information needed to

characterize the system, as indicated in the

dictionary definitions:

State: Any of various conditions

characterized by definite quantities (as (i.e.

of energy, angular momentum, or magnetic

moment) in which an atomic system may

exist [Merriam-Webster, 2002]

State: The condition of a physical system

with regard to phase, form, composition, or

structure [American Heritage, Fourth

Edition, 2000]
State: The way something is with respect

to its main attributes [WordNet ® 1.6, ©

1997]

State: How something is; its configuration,

attributes, condition, or information

content. The state of a system is usually

temporary and volatile. [Free On-line

Dictionary of Computing, 2001]

The last of these definitions is closest to what we

need to characterizing the information in a system,

in that it mentions the information content; of

course, the other components it mentions are just

more information, but some of them may have

physical parameters. In the long run, it is probably

best to be inclusive for cases where information

plays an essential role and broaden the concept of

state (of a system) to the following:

State: All the information at a given instant

that is relevant to the behavior of the

system at any later time.

A trace of the system states under all of the

conditions in which it will operate is a full

informational description of the system. As an

example, consider a simple algorithm being

executed on a machine – say, a sorting algorithm. It

has a series of states that lead from inputs of

information (a list to be sorted and an ordering

relation) to outputs of the ordered list. There are

many well-known sorting algorithms, and each of

these, given the same input, will produce a series of

states, of which the last state will include knowledge

of the initial list and the ordered list. If we compare

two such series for a given input and different

algorithms, they tell us something about the

comparative properties of the algorithm, including

efficiency on the particular input (and, by

generalization, on whole classes of inputs). These

traces may also point out some subtle differences

between two algorithms, but if the states contain the

same information at a given time, we can assume

that they are doing the same thing. That is actually

a very strong requirement, of course, since if two

had some different information at the same time and

did not interact with another system or provide an

output at that time, the results would be of only

theoretical interest.

In certain cases, it is possible to give a

numerical measure to the amount of information (in

the Shannon sense) in a state, which can tell us little

about what the system is doing. In the sorting case,

for instance, if we compare algorithms for the

algorithms “quicksort” and “merge sort” by

computing amount of uncertainty about the final

ordering at each state, we find even though the

states have different information at given times,

aspects of their behavior are explained by the

information measures. For one thing, we can make

information-theoretic arguments that show why and

when they are most efficient and why they have the

same expected time in certain cases. But the

particulars of what they are doing and how they

differ are not in those figures; so if – for instance –

they had to stop after a given time, one might

provide a better output than the other.

The simple example, though not very important

in these two similar sorting algorithms, illustrates a

general problem that we have with the

characterization of information as negative entropy

(Shannon Information) in predicting system

behavior. There are other types of information

measures more closely related to computation, such

as Chaitin Information and Kolmogoroff

Information, but these measures, all based on

entropy suffer from similar problems to Shannon

Information. In general, the information is

incomplete because it does not convey knowledge,

but merely a measure of potential information in a

system. Potential information (amount) is not

adequate for understanding of information systems.

3.2 Potential and Mediate Information

Shannon’s information has been a highly

satisfactory measure of the physical transmission of

symbols over a communication channel. Since

communicated information always has a physical

dimension, the model is relevant, but the physical

part is only a carrier for what is actually meaningful,

and meaningfulness lacks a satisfactory theoretical

basis. The physical information that is sent out is

not meaningful until it is interpreted when it reaches

its recipient. Before that time, it is only data, or

“potential information”. Shannon himself did not

advocate using the term “information”, since he

pointed out that it did not concern meaning, but the

term has stuck.

A diagram may help to illustrate the

relationship between potential information and

meaningful information (often called “semantic

content” or “knowledge”). Modeling it requires a

theoretical construct, which will be called herein

mediate information. That mediate information

directs the process by which the potential

information becomes meaningful. To provide a

graphic example of these constructs and how they

interact, Figure 2 includes a version of the model

used by Shannon for a generic communication

channel. It is labeled for a particular example of

potential information (a spoken utterance in a

human language going – by sound waves – from

one person to another), with some ideas on the

relevant mediate information to make that type of

potential information meaningful. The successful
transmission of meaningful information in the case

of a simple linguistic utterance requires that a

certain amount of potential information be

transmitted, but it also requires a “hidden channel”

of mediate information that is not transmitted with

the potential information, but is known previously

by the sender and the receiver. It is as if the speaker

had encrypted something and sent a message whose

key was the mediate information sent by another

channel.

The mediate information needed to convert the

potential information to meaningful information in

Figure 2

2

 (and generally) is partly a set of

conventions that had been used by the speaker in the

belief that the listener would interpret the

information using the same conventions. They are

based on sensory capabilities or are learned from

experience with the world and the language or by

adopted standards, informal or formal. Though

most of the mediate information will be stored in the

minds of the speaker and listener, some of it may

arrive contemporaneously with the utterance, such

as situational information of a non-linguistic variety

in the example.

Shannon’s theory of communication – as

Warren Weaver pointed out – “at first seems

disappointing and bizarre [because it] “has nothing

to do with meaning” and the measure it provides

counter-intuitively links information with

uncertainty. But for what we are calling potential

information, Shannon showed the limitations of the

physical channel and also how to use that channel to

communicate within those limitations. He also dealt

with disruption and corruption (“noise”) of the

potential information and how to cope with those

problems (at a cost in efficiency, by adding

redundancy). The mathematical theory of

communication is recognized as a very important

scientific contribution and communication engineers

use techniques based on it routinely.

We need a similarly useful theory that deals

with the delivery of “meaningful information”, and

since we already have Shannon’s theory for

potential information, we need to approach the

mediate information. The new theory of mediate

information must also deal with how to cope with

noise, which may be more complex in the case of

meanings that it is for Shannon information. It is

clear that redundancy still plays a role, as humans

typically have multiply connected concepts for any

given word that they hear, and often multiple

possible interpretations of the structure of a given

string of potential information.

 

2

 Figure 2 is at the end of the paper.

3.3 Ontologies as Mediate Information

Figure 2 gives some idea about how the

utterance, which is physically received and contains

potential information, must be interpreted by

mediate information shared by the source person

and the destination person, but it really only

scratches the surface. What is this “human

knowledge” that is referred to and has its analogue

in other organisms and in artificial information

systems? If we wish to be very general about a

system, we can work with its ontology, which

should be, in our view, everything that the person

has that will interpret inputs from language, from

sensors, etc. There has been a lot written in recent

years on the topic of an individual system’s

ontology and what it may contain, and we will not

get into that directly in this paper.

In another paper, soon to be published [Reeker

02] it is argued that the needed extended ontology

(or worldview) for complex systems is in general

much more extensive than the ones that we see in

the literature. It is argued that ontologies are

inherently different within different individual

organisms and yet the organisms (like the two

humans in Figure 2) work together by making

assumptions that are approximately correct in most

instances. They also seek new knowledge if they

have a feeling that they lack essential knowledge or

feel that they do not understand or are not being

understood. Traditional hierarchies or lattices of

object classes, often called ontologies [Sowa 99],

must be strengthened or extended for purposes of a

scientific theory of knowledge and intelligence and

the practical engineering consequences of such a

theory. The notion of linking classification to

sensory processes (“grounding”) or to linguistic

terms that are so grounded is essential, but not

enough. Models that include explicit processes must

be integrated with the ontology, not swept under the

carpet as programs or parts of a knowledge base

separate from the ontology. Each process in which

an object is a participant can partially define the

object. This means that the task of discovering (in

organisms) or developing (in artifacts) an adequate

worldview for utilitarian purposes must be more

exacting than is sometimes implied. The additional

burden will not go unrewarded, however, as it can

improve the ability to engineer and evaluate

intelligent systems, to automatically integrate

systems, and to understand and control system

behavior.

What this all says is that the “fabric of

knowledge” is held together by a rich system of

links, and communicating people can usually find

some common links from their knowledge to

whatever they hear from the people they are

communicating with. The notion that the power of a
system for expressing mediate information is in all

of the links between concepts and not just in the

hierarchical nature of the system is not a new one

(see Woods [75]) There is evidence in human

cognitive processes that each action in which an

entity performs may modify its meaning. Perhaps

the strongest argument for this extended ontology

need is the nature of science, where there has

evolved a “fabric” of linked concepts that is shared

by millions of people with a good deal of consistent

understanding. The multiple connections and

extensibility of the linked concepts of that fabric is

widely considered to be a major strength of

scientific theory.

3.4 Techniques for Practical Integration and

Control

The use of information techniques to actually

improve the integration of complex information

systems for understanding and control is still a

research topic. There are some approaches that

appear hopeful, however, and these will now be

discussed. We have not mentioned human

intervention directly (which is the sole satisfactory

method today), but human interaction may be

involved in any of these techniques or combinations

thereof.

3.4.1 The State Comparison Approach. The ideas

described in section 3.1 above provides a clue to a

method of looking at two subsystems and checking

them with respect to their performance on given sets

of data. If the information comprising the initial

states of the two is the same and the information

given as inputs is the same, then one can sometimes

prove that the information given as outputs and the

final states will also contain the same information.

There may even be differences in the middle states

of the algorithms or the number of intermediate

states, but that does not matter in terms of the

information ultimately produced. Making sure that

this is true is clearly a strong requirement, and it

may not always be provable either true or false. But

the technique may be helpful in determining how

the information is utilized and transformed

(discussed in [Reeker, 1980]). It may be especially

interesting in conjunction with some of the next

three suggestions.

3.4.2 The “Work Analogy”. The information

measures that we have claimed to be inadequate for

meaningful information do still have properties that

we would suppose any information measure would

have. The most important of these is the insight that

information requires organization. If there were no

organization in the world, then we have, for sure,

what William James called a "booming, buzzing

confusion". In fact, we would not even know that it

was booming and buzzing because we would have

no ordered way of retrieving meaning, let alone

learning the words or their meaning in the first

place. Which leads us to the idea that we do learn

things and that learning is a form of organizing (of

which, more in the next section). Thermodynamics

tells us that organization takes energy, as the

entropy principle is always spreading

disorganization. Energy can be stored as “potential

energy” that is just waiting for a force field to let it

turn into kinetic energy.

The interesting thing here is that a force field

has direction, so the kinetic energy released will be

causing work to be done in that direction. Only

along the direction of the force, which has a vector

quantity, does that particular work get done. The

rest of the energy is dissipated in some other ways,

without necessarily doing any useful work. Is it

possible, one might ask, to express the measure of

meaning in an ontology through a set of vectors?

The reason that we are calling this the “work

analogy” is that potential information can be made

into knowledge (meaningful information) by its

transformation through mediate information, which

can be compared to a force field (where the

dimensions are computed merely by the three

geometric directions, as a cosine function of the

force). Unfortunately, if we take that view, we

come right back to the fact that we have too many

dimensions in any vector that might possibly be

broad enough to handle all of information. So does

the work analogy work? It might, if supported by

standard definitions of some dimensions.

There is some work ongoing already on putting

together a common upper ontology, that could be

extended to lower levels for specialized areas

[Standard Upper Ontology (SUO) Working Group

02] Suppose that standard were to give us N

orthogonal (or at least forming a vector space)

information parameters. Then we might derive

some analogy of the modern definition of work that

treats it as taking place in the direction of each of

these parameters, as a measure of meaningful

content. It is hard to see how that would help us,

since we are left with a space of arbitrary

dimension. Under the circumstances, that makes the

work analogy a problem, rather than a solution.

The psychologist and communication scholar

Charles E. Osgood developed a measurement of a

type of meaning (meaning being information

content in much the same way that work is energy

directed by a force) called connotative meaning.

Connotative meaning is related to an individual’s

personal ontology [Osgood, 57] because it includes

“shades of meaning” that may not be shared through

a strict definition. The connotation is intended only
to be partial meaning, to be coupled with the more

explicit denotation for a full definition in a

particular context. In trying to measure it, Osgood

postulated three dimension types or factors, within

which pairs of adjectives would indicate

denotations:

• Evaluative factor (example: good - bad)

• Potency factor (example: strong – weak)

• Activity factor (example: active - passive)

Osgood then measured each pair, for each factor, on

a seven point Likert scale. He then constructed an

n-dimensional space, n being the number of

adjective pairs, for his “semantic differential”.

Clearly, much more than the semantic

differential is needed to do the evaluation that can

lead to integration of several manufacturing systems

or bioinformatics systems. However, Osgood’s

ideas fit into the idea of fuzzy frameworks, and it

was an important step in trying to formalize the idea

of how the vocabulary of humans may vary.

Vocabulary, while not the same as ontology, is

closely linked, and provides a way to get at human

ontologies. So in a sense, it reflects the ontology in

an approximate way. The possibility presented by

this type of approach is most likely to be

determination of closeness of various concepts by

comparing dimensions based on a standard ontology

with certain standardized dimensions as a major. It

is not clear what value a unified measure based on

some sort of standard dimensions for all ontologies

would have, or how such standard dimensions

would be defined.

3.4.3 Machine learning. Machine learning is

becoming an important area in data and knowledge

management, because it can potentially allow the

development of enormous knowledge bases from

enormous amounts of data that would not be

economical or feasible for manual human

development and because it is the basis of the field

of data mining (along with data visualization, which

allows humans to participate in the mining). As a

short summary, there are three basic categories of

machine learning generally recognized:

unsupervised, supervised, and reinforcement

learning. The one that requires the least detailed

input information -- merely a similarity space in

which the data are shown and the dimensions are

attributes of the data – is unsupervised learning. If

one had a list of words classified by Osgood’s

semantic differential, then one could use

unsupervised learning to cluster them in ways that

reflected their denotational similarity. Clearly, the

same could be done with concepts in an ontology

based on attributes.

Supervised learning can actually learn to

recognize things that exhibit a certain set of

attributes, which are related in particular ways. It

can do this even in cases where people have a hard

time coming up with a computer program to

recognize those things. An example is the

astronomical phenomena that a particular

astronomer may want to study. The program is

given examples of things that exhibit a given

phenomenon and examples of things that do not

(preferably, some things that could be confused with

things that exhibit the phenomenon but do not). It

then looks at sky surveys, with their trillions (or

more) of objects and finds a set of those which

appear to exhibit the phenomenon. If taught well,

such a program can be quite helpful to the

astronomer, though it might make some mistakes

(both false positives and false negatives), so it needs

to be checked.

Reinforcement learning does not have to have

all the examples, but it needs to have conditions that

are rated “right” or “good” (which it will reward

with positive numerical amounts) or are “wrong” or

“bad” (which it will punish with negative numerical

amounts). It is based on one model of animal

conditioning. An example is a game-playing

program that has been rewarded for good moves and

punished for bad ones. The computer program TDGammon, which is probably the best backgammon

program in the world, learned by reinforcement

learning.

Statistical regression is another type of learning

that can be programmed into a machine, and neural

net models can also be used. Whatever type(s) of

machine learning are chosen, the point is that a

subsystem integrated into a complex system for

something like supply chain management may be

able to learn aspects of the behavior of other

subsystems. These might include ontologies and

state patterns, developing mediate information of

value in informational interactions, “self-adapting”

to the other subsystems in an integration process.

Alternatively, a learning program could be used to

find problems in the operation of full complex

system – and maybe (through reinforcement) to

alleviate the problems.

There is one more technique in human learning

that has received a lot of attention in machine

learning but has proven hard to implement in

practice. That is analogy (or case-based reasoning).

Humans use it regularly in what is called “transfer

of learning”. As mentioned earlier in this paper, an

informational objects in an ontology will have many

activities and certain other informational objects

linked to it. These will have certain attributes.

Having that informational object and all of those

links in his or her knowledge base, the person is
often capable of using an “approximately

structurally identical” informational object in a new

but somehow similar situation. This sort of transfer

is something that might help in integrating similar

systems and predicting their behavior.

In control, learning algorithms can be

considered as optimization algorithms. But we may

not need to optimize for systems to do what we want

them to do. Herbert Simon, whom we have

mentioned, and his observations on humans and

human organizations provide the clue to another

method that needs to be explored and the last one

that we will mention here.

3.4.4 Satisficing. As a final suggestion for research

directions, we turn again to the manner in which

people settle perceived differences in their

ontologies, using dialog to come to an approximate

compromise, which is often a “near-enough” joint

understanding. This is a sort of “satisficing”, one of

the important ideas stressed by Simon [57]. It is

embodied in his statement,

It appears probable that, however adaptive

the behavior of organisms in learning and

choice situations, this adaptiveness falls far

short of the ideal “maximizing” postulated

in economic theory. Evidently, organisms

adapt well enough to “satisfice”; they do

not, in general, “optimize”.

Herb Simon was awarded the Nobel Memorial Prize

for Economics in 1978 largely on this observation of

“bounded rationality”, backed up by empirical data

and theories that have supplied models for many

areas of science and are being explored carefully

today as a solution for intractable computational

problems [Zilberstein 97].

Although we are still trying to determine just

how Simon’s idea of “satisficing” would be used for

practical integration, it fits well with the use of

fallible learning, as described in §3.4.3, and as

suggested by Simon’s quotation above.

An encouraging thing about the work going on

in satisficing presently is that it can provide some

types of estimates of the bounds of rationality,

where it has to be bounded to solve the problem in a

reasonable time. We note that satisficing is being

implemented as approximate reasoning,

approximate modeling, optimal meta-reasoning,

bounded optimality, and combinations of all these

traits. In a situation where we are trying to compare

ontologies which are governed by the activities in

which they partake or attributes that need to be

calculated and that may use different programs

describing the activities or calculating the attributes,

we are on the edge of undecidability (the general

equivalence of two programs in undecidable). But it

may be possible to decide if they are close enough

to being the same to make them interoperable.

4. Summary

We do not yet know how to practically characterize

complex systems in ways that allow the prediction

of their behavior for purposes of optimal control.

The traditional methods that have been used, even

clever statistical methods that can handle limited

indeterminacy, break down under the complexities

that arises in supply chain management and other

big problems for which the development of systems

requires the integration of several complex

subsystems and which evolve inevitably with time.

These systems are informational in nature, or are

hybrid physical-informational systems, in which the

many informational dimensions add complexity not

readily handled by the traditional approaches. We

are looking at four approaches that might work

together with one another, and also with the

traditional approaches. One is replacing

optimization with satisficing in some of the

techniques; another is further exploring observations

of states that do the same things. A third is pursuing

programs that can define numbers of dimensions

that allow mediate information to be described in

vectors that can be controlled. The fourth uses

machine learning by subsystems of the performance

of other subsystems. Together, we hope these will

give us better tools for handling complex

informational systems like supply chain

management.

5. References

Herrin, G. [01], Chaos Theory Applied To

Manufacturing, MMS Online, Gardner Publications,

Inc. (http://www.mmsonline.com/articles/0499cim.html).

Jaynes, E. T. [68] Prior Probabilities, IEEE

Transactions on Systems Science and Cybernetics,

SSC-4, 221-241, 1968.

Jones, A., L. Reeker and A. Deshmukh [02], On

information and performance of complex

manufacturing systems, Proceedings of 2002

Conference of Manufacturing Complexity Network,

Cambridge, UK, ed. G. Frizelle and H. Richards,

173-182, 2002.

Information: A Key to Supply Chain Performance

A. Jones, L. Reeker and A. Deshmukh, World

Automation Congress 2002, Orlando, FL, ISOMA025.

Miller, G. A. and N. Chomsky [63], Finitary Models

of Language Users, Chapter 13 of Handbook of

Mathematical Psychology, vol. 2, Wiley, 1963.
Osgood, C. E., G. Suci and P. H. Tannenbaum [57],

The Measurement of Meaning. Urbana: The

University of Illinois Press, 1957. Reeker, L. [79],

Mathematical foundations of computer science: a

sampler, Australian Computer Science

Communications, 1, 4, 251-279, 1979.

Reeker, L. [00], Theoretical Constructs and

Measurement of Performance and Intelligence in

Intelligent Systems, in Meystel and Messina ed,

Measuring the Performance and Intelligence of

Systems: Proceedings of the 2000 PerMIS

Workshop, August 14-16, 2000, NIST Special

Publication 970.

Reeker, L. [02], The ontology: more than meets the

sensors, forthcoming, (talk delivered at Theoretical

Fundamentals of Intelligent Systems: Computational

Semiotics, part of the Joint Conference on

Intelligent Systems (JCIS); available from author).

Reeker, L. and Jones A. [01], Measuring the

Impact of Information on Complex Systems, in

Meystel and Messina ed, Measuring the

Performance and Intelligence of Systems:

Proceedings of the 2001 PerMIS Workshop,

September 4, 2001. NIST Special Publication 982.

Shannon, C. and Weaver W. [71], The

Mathematical Theory of Communication,

University of Illinois Press, Urbana, IL, 1971.

Simon, H. A.. [57], Models of Man: Social and

Rational, John Wiley, New York, NY, 1957.

Simon, H. A. [69], The Sciences of the Artificial.

Third edition, Cambridge, MA, MIT Press, 1996.

[First edition published 1969].

Sowa, J. F. [99], Knowledge Representation:

Logical, Philosophical, and Computational

Foundations, Brooks Cole Publishing Co., 1999.

Standard Upper Ontology (SUO) Working Group

[02], see http://suo.ieee.org.

Woods, William A. [75], "What's in a Link:

Foundations for Semantic Networks". In D. Bobrow

and A. Collins (eds.), Representation and

Understanding: Studies in Cognitive Science, New

York: Academic Press, 1975.

Zilberstein, S. [97], Formalizing the Notion of

"Satisficing", AAAI Spring Symposium on

Qualitative Preferences in Deliberation and Practical

Reasoning, Stanford, California, 1997.

MESSAGE

(Sound Waves)

Human Language

 Utterance

- Word meanings in the language - Human knowledge and sensory

processes - How to interpret structure of utterance - Semantic

mappings in the language from word meanings and structure to

meaning - Interpretation of stress, tone, duration , etc. of utterance

- Relevant non-linguistic and cultural contexts - Probably more.

MEANINGFUL INFORMATION

Meaning intended by Person 1 Meaning understood by Person 2

The two meanings will be approximately the same if the transmitted potential

information is received approximately as transmitted and the relevant mediate

information shared by Person 1 and Person 2 is similar enough.

Brain,

Person

1

Brain,

Person

2

Speech

Apparatus

Hearing

Apparatus

 MEDIATE INFORMATION

Fig. 2. Potential and Mediate Information Together are Needed to Convey Knowledge

(Meaningful Information).

 POTENTIAL INFORMATION

INFORMATIOINFORMATION
Refactored Characteristics of Intelligent Computing Systems

Christopher Landauer, Kirstie L. Bellman

Aerospace Integration Science Center, The Aerospace Corporation

cal@aero.org, bellman@aero.org

Abstract

We have discussed the following measurable characteristics of intelligent behavior in computing systems: (1) speed and scope of adaptibility to unforeseen situations; (2) rate of effective learning of observations; (3) accurate modeling and prediction of the relevant external

environment; (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) creation and use of symbolic language.

In this paper, we isolate some common underlying capabilities

for these characteristics, and show how they can all be produced using

those capabilities. We describe the architecture of a system that has

all of these underlying capabilities, using our Wrapping integration

infrastructure to coordinate and organize a large collection of models

and other computational resources. In particular, these models include

complete models of the system’s resources and processing strategies,

and therefore a model of its own behavior, which it can use to affect

that behavior.

Key Phrases: Computationally Reflective Infrastructure,

Constructed Complex Systems, Intelligent Autonomous Systems,

Knowledge-Based Polymorphism, Layers of Symbol Systems, Problem Posing Interpretation

1 Introduction

In an earlier paper [28], we described some important characteristics of intelligent computing systems, and discussed how

to make measurements of those characteristics for performance

assessment. In this paper, we find some common themes underlying our earlier list of capabilities, and show how the new list

of more fundamental characteristics can be used to implement

the more operational capabilities. This “refactoring” is an example of our principle of layers of symbol systems [8] [27] [29],

which reflects our observation that biological systems overlay

functionality in complex ways, at least partly due to their opportunistic exploitation of side effects. These overlays do not

remove the lower level functionalities; they merely supersede

them in appropriate contexts.

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 [13].

In our opinion, intelligence metrics can only be based

on observed system behavior (though the observations can, of

course, measure internal processes from an internal perspective, since we often have some kinds of internal access). Success in a particular task is not by itself the right criterion. Measuring performance to infer competence, even of externally observable behavior, is problematic, since we cannot make the

measurements over a sufficiently wide range of situations.

Similarly, the intelligence or even genius that invents and

develops good models for physical phenomena is not always

adequately (or even correctly) assessed by fitting the theory to

the data: Copernicus’ model of the solar system was much less

accurate than Ptolemy’s for many years after it was first formulated; the latter fit the observations far better. It wasn’t until

Kepler decided to use ellipses instead of circles, and Brahe provided much more and much more accurate data, that the suncentered model became dominant. This very long-term, partially social, comparison and evaluation process is not how we

want to assess the intelligence of systems that we build.

2 Autonomy, Intelligence, Creativity

Intelligence is much more mundane than programs playing

hard symbolic games or proving theorems. It is also much

more remarkable. The ability for an organism to exist in the

world, to move around, interact with its environment, and make

experience-dependent decisions, is one of the deepest mysteries of life. These things that most living things can do (at least

for a while) are much more interesting and difficult than the

things only humans can do, and the things most humans can

do “without thinking” are much more interesting and difficult

than the things a few humans can only do after much study or

training.

We view intelligence as sitting in a collection of related

qualities that include autonomy and creativity. We take autonomy to be a kind of lower bound for intelligence and creativity

to be a kind of upper bound, without actually assuming that

any of them implies the other. We frequently use postulated or

speculative explanations of autonomous, intelligent, and cre-
ative human behavior as a guide to what can be done, and as a

source for our study of what computer programs can be made

to do.

There are really only two classes of requirementsfor effective autonomy (both are difficult): robustness 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 never any optimization here,

since it takes too much time and produces non-robustsolutions.

In the previous paper [28], we concentrated on the measurement problem instead of the construction problem, though

we mentioned some definite ideas about how to build these interesting programs, based on our Wrapping infrastructure for

Constructed Complex Systems [18] [22] [24]. We discuss some

of them in more detail in Section 7.

3 Computing System Behaviors

We make the assumption that a computing system is designed

to help its users do something [14]. 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..

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 using a joystick instead of typed commands, and some immersive

Virtual Environments are commanded by user movement and

gesture (and eventually speech).

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

languages 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 [21]). 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 [12] [43] [7], 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 independent 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.

4 Characteristics of Intelligence

In this Section, we list the measurable characteristics of intelligent systems from [28], and discuss what they require and how

they might be implemented. We expand the characteristics into

their parts and organize them, so we can find a more basic list

of aspects that can produce these ones. We may or may not be

able to measure these underlying aspects, but we intend to use

them to get some notion of how independent the characteristics

are. It is clear to us that each of the characteristics above can be

implemented using many of the other characteristics at a lower

level of detail. These details will lead us to an architecture proposal in Section 7. In the next Section, we show how they can

be refactored:

1. speed and scope of adaptibility to unforeseen situations,
2. rate of effective learning,

3. accurate modeling and prediction of the relevant external

environment,

4. speed and clarity of problem identification and formulation,

5. effective association and evaluation of disparate information,

6. identification of assumptions and prerequisites, and

7. creation and use of symbolic language.

We make no claim that these are all the important characteristics; discovering others is the point of our research program in

this area.

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.

To adapt, a system needs many flexibilities, ways of deciding that change is needed, and ways to effect that change.

In particular, a very large number of computational resources,

many ways to use them (i.e., many different ways to map problems into applications of them), and a strong ability to decide

how to use them.

Another common attribute of intelligence is learning,

which for us is the rate of effective learning of observations,

behavior patterns, facts, tools, methods, etc. [37].

To learn, a system needs to be able to abstract a situation

into a representation of it, to identify the important parts, and

to change its processes to improve the performance. This improvement process is more than adjusting performance towards

an evaluation criterion, it is also at least partly about inventing

the appropriate criteria.

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.

To build models, a system needs to be able to identify the

important features and processes of a phenomenon in an environment, and to represent them in an explicit model in a conceptual space. It also needs to be able to compute abstractions

and evaluate their appropriateness.

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 a problem is specified as

a constrained search, humans seem to construct search spaces

and algorithms that are very problem-specific, often extremely

intricate, constructed using the constraints directly (i.e., not by

searching a large encompassing space, and masking or otherwise ignoring the parts outside the constraints).

To identify problems (and situations), a system needs to be

able to abstract the situation into some explicit representation,

to compare it to previously identified or encountered problems

and situations, and determine which aspects of those earlier situations are important for or relevant to the current one.

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

frequently associated with creativity.

To compute associations, a system needs to have appropriate representations of phenomena in many conceptual spaces,

and processes that examine and compare elements in those

spaces. These comparison processes underlie all grouping and

clustering of elements.

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).

To identify assumptions, a system needs to be able to examine its own reasoning processes, infer missing assumptions

from coverage failures in the implicit context, and discover

gaps in what can be represented and how it can be processed.

This process of discovering what is missing is one of the hardest

ones, since it requires the system to have some kind of notion of

entities and processes outside its own representational systems.

Perhaps the most important property of all, in our opinion, is the use of symbolic language for explicit representations, 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

for internal representation. This property is not altogether unchallenged, but despite the “behavior-based” intelligence work

[38], we believe representation to be essential at all levels of

autonomy and intelligence [8], especially for computing systems.

To use symbol systems and symbolic methods, a system

needs to base its entire processing on explicit representations,

and at the same time be able to change those representations

in part or entirely, based on internal assessments of success or

failure.

It is for this reason that we want these systems to have

complete models of their own processing [35], which we have

shown is not only not very hard, but is actually fairly straightforward when we allow multiple component resources that can
apply to the same problems in appropriate contexts. These selfmodels are directly connected to the behavior of the system,

either through compilation or interpretation, so that changing

the models changes the behavior. The models may be written

using wrex [21] [23], together with any other notations that the

system can interpret.

This use of the Problem Posing interpretation of programming languages leads to what we call a Problem Posing Interpretation of behavior: all behavior is applying computational

resourcesto posed problems, and while biologicalsystems generally do not have explicit access to those problems, we expect

our Constructed Complex Systems to be more informed. They

will have an active knowledge representation using wrex, and

they will use partial evaluation for situation-dependent local

code improvement [23]. They will use Computational Reflection to monitor and adjust their own behavior, because they will

be able to examine and alter their own representations.

5 New Abstractions - Refactoring

In this Section, we describe a few propertiesthat are common in

all of these characteristics, which we will identify as enabling

capabilities.

First and foremost is identification of important aspects of

a situation, which depends on the representational scheme in

use at the time. This process is a kind of focussing of attention

that is essential for any kind of intelligent behavior [2] [40].

The next important process is the determination of whether

or not a situation is a problem (and if so, what problem it is).

This process involves comparison to earlier situations, simulation of consequences of internal models of the currentsituation,

and assessment of their relative likelihood.

Next comes representing the situation in some collection

of conceptual spaces, using modeling spaces and model construction methods that allow models with variables that can be

specified later. This requires computational access to a very

large class of conceptual spaces, as well as to a variety of analyses within and among them.

An important part of limiting the consideration is deciding

what to ignore, that is, assessing the importance or relevance of

aspects of the representation, as well as that of the outstanding

problems at hand.

All of these process imply the ability to retain large numbers of these models, with multiplicitous indexing that allows

access in several different ways.

Finally, the system will need continual contemplation of

the models, their efficacy, the symbol systems in which they are

defined, and their efficacy, in order to assess its own adequacy

and the occasional need for re-expression.

To summarize, there are a number of different kinds of

computational resources that are needed to provide the properties we have listed: 

methods of abstraction and simplification



methods of evaluation in context 

methods of representation (conceptual spaces) 

methods of retention and retrieval 

methods of comparison and association

In addition, we require mappings from problems to resources

in context, as well as explicit representations of everything (the

contexts and problems, all of the resources, and the processes,

including the mappings and the reasoning methods). Then the

entire system is grounded in its symbol system definitions and

assumptions, which are themselves explicit, so that they can be

changed.

6 Intelligent Systems

These issues affect the design of Constructed Complex Systems [17], which are artificially constructed systems that are

managed or mediated by computing systems, since it is clear

that they will not have the same kind of experience in the real

world that makes biological systems so remarkable. The success or otherwise of the interaction between them and their human users will determine the usefulness of these systems, and

therefore of these measurements.

A human using a computing system of any kind is presented with a Virtual World, that is, an environment in which

the user is allowed to perform some limited set of control actions, and is presented with some limited set of information

displays. The number and variety of available control actions is

almost always very small, and only occasionally determinable.

The number and utility of information displaysis almost always

not enough. These appallingly limited worlds are so restrictive

in their scope and so poor in their quality of interaction that

using them proficiently becomes an exercise in excessive focus

on certain details, and can only be performed successfully by a

few people [36]. We would like to build systems that are much

more helpful.

This problem with inflexibility is why 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 and evaluating its own goals [18] [3] [15] [27].

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 [22] [24], one that also helps avoid

the most common difficulties found in complex computing systems: rigidity and brittleness.

7 System Architecture

Biological systems have much more flexible and powerful

adaptation properties than most constructed systems [11], and
a careful consideration of their properties provides stringent requirements 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 [39] [18] [34].

Our approach is to define a new kind of architecture [24]

[31] [32], based on our studies of “Integration Science” [9]

[10]. It includes our Wrapping integration infrastructure [23],

which uses Knowledge-based Polymorphism to provide a great

deal of flexibility of component interaction, our Problem Posing interpretation [21], which is a declarative interpretation of

all programming languages, so that posed problems can be separated from applicable resources, and our conceptual categories

[16] [26] [30] to provide a flexible representation mechanism

that separates model structures from the roles they play. These

design choices allow our systems to make their own abstractions [33], and construct their own models.

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) [41] [17]. 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 [12] [43] [7]

[10].

In addition, we take the hypothesized common origin of

language and movement processes [8] as a hint, since the implied layers of symbol systems can be implemented easily in

Constructed Complex Systems using a meta-level architecture

with Wrappings [18] [22] [24] [27].

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 somehow 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 reorganized, 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. In particular, the “get stuck”

theorems show that any Constructed Complex System, faced

with a complex external environment, will have to analyze and

invent its own symbols and representational mechanisms [20]

[25].

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 we want to build.

The essence of computation is interpretation of symbol

systems. The only operations that a digital computer can perform are copying and comparison. All arithmetic in digital

computers is via limited-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 implicit or 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. It

is far too easy to become constrained by the expressive mechanisms used [41], and part of our approach to intelligent computing systems reduces that dependence by making the symbol

system and representational mechanisms variable.

The theorems of Turing, Godel,  ̈ 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 study is one of our current directions of

research in Computational Semiotics [20] [24] [25].

There are several fundamental mathematical questions involved in this study: (1) how self-reference can be made not

only possible but sensibly computable (using the new selfreferential set theory [1] [5] [6]), (2) how formal mathematical structures can be extended to incorporate more information

about context (using situation theory [4] to capture the context of behaviors [42]), (3) how to move these formal structures into new contexts and assess the resulting validity (using

our conceptual categories [16]), (4) how to define mathematical structures before the basic elements are defined (also using

conceptual categories), (5) how to capture more of the modeling process in mathematical structures (using both situation

theory and conceptual categories), (6) how to decide when a

notational system is inadequate (using internally computable

evaluation criteria [20]), and (7) how to fix it.

These questions are part of our New Math Initiative [19],

since we believe that some of these questions require new kinds

of mathematics, or at least new applications of mathematics, instead of the “heroic engineering” and other systematic methods

that we have relied on thus far for building Constructed Complex Systems.

8 Conclusions

We care about measuring intelligence because we want to engineer devices that have some of those characteristics, and without some better measurement processes, we will have no re-
peatable way to evaluate and compare different 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 identified some fundamental enabling capabilities

that seem to underlie these properties, and described an architecture that includes all of these enablers, as a way to test our

assertions about the connection between them and intelligent

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.

References

[1] Peter Aczel, Non-well-founded Sets, CSLI Lecture Notes

#14, Center for the Study of Language and Information,

Stanford U., U. Chicago Press (1988)

[2] James S. Albus, Alexander M. Meystel, Engineering of

Mind: An Introduction to the Science of Intelligent Systems, Wiley (2001)

[3] K. S. Barber, C. E. Martin, “Agent Autonomy:

Specification, Measurement, and Dynamic Adjustment”, in Henry Hexmoor (ed.), Proceedings of

Agents’99/ACS’99: Workshop on Autonomy Control

Software, 1 May 1999, Seattle, Washington (1999)

[4] Jon Barwise, The Situation in Logic, CSLI Lecture Notes

No. 17, Center for the Study of Language and Information, Stanford U. (1989)

[5] Jon Barwise, John Etchemendy, The Liar: An Essay on

Truth and Circularity, Oxford U. (1987)

[6] Jon Barwise, Lawrence Moss, Vicious Circles, CSLI

Lecture Notes No. 60, Center for the Study of Language

and Information, Stanford U. (1996)

[7] Kirstie L. Bellman, “An Approach to Integrating and

Creating Flexible Software Environments Supporting

the Design of Complex Systems”, pp. 1101-1105 in Proceedings of WSC’91: The 1991 Winter Simulation Conference, 8-11 December 1991, Phoenix, Arizona (1991);

revised version in Kirstie L. Bellman, Christopher Landauer, “Flexible Software Environments Supporting the

Design of Complex Systems”, Proceedings of the Artificial Intelligence in Logistics Meeting, 8-10 March

1993, Williamsburg, Virginia, American Defense Preparedness Association (1993)

[8] Kirstie L. Bellman and Lou Goldberg, “Common Origin

of Linguistic and Movement Abilities”, American Journal of Physiology, Volume 246, pp. R915-R921 (1984)

[9] Kirstie L. Bellman, Christopher Landauer, “Integration

Science is More Than Putting Pieces Together”, in Proceedings of the 2000 IEEE Aerospace Conference (CD),

18-25 March 2000, Big Sky, Montana (2000)

[10] Kirstie L. Bellman, Christopher Landauer, “Towards an

Integration Science: The Influence of Richard Bellman

on our Research”, Journal of Mathematical Analysis and

Applications, Volume 249, Number 1, pp. 3-31 (2000)

[11] Kirstie L. Bellman and Donald O. Walter, “Biological

Processing”, American Journal of Physiology, Volume

246, pp. R860-R867 (1984)

[12] Richard Bellman, P. Brock, “On the concepts of a

problem and problem-solving”, American Mathematical

Monthly, Volume 67, pp. 119-134 (1960)

[13] K. Anders Ericsson, Reid Hastie, “Contemporary Approaches to the Study of Thinking and Problem Solving”, Chapter 2, pp. 37-79 in Robert J. Sternberg (ed.),

Thinking and Problem Solving, Academic Press (1994)

[14] Kenneth D. Forbus, Johann de Kleer, Building Problem

Solvers, A Bradford Book, MIT Press (1993)

[15] Catriona M. Kennedy, “Distributed Reflective Architectures for Adjustable Autonomy”, in David Kortenkamp,

Gregory Dorais, Karen L. Myers (eds.), Proceedings of

IJCAI-99 Workshop on Adjustable Autonomy Systems, 1

August 1999, Stockholm, Sweden (1999)

[16] Christopher Landauer, “Process Modeling with Conceptual Categories”, Paper dtkmi01 in Proceedings of

HICSS’00: The 33rd Hawaii International Conference

on System Sciences (CD), Track II: Decision Technologies for Management, Modeling Knowledge-Intensive

Processes Mini-Track, 4-7 January 2000, Maui, Hawaii

(2000)

[17] Christopher Landauer, Kirstie L. Bellman, “Constructed

Complex Systems: Issues, Architectures and Wrappings”, pp. 233-238 in Proceedings of EMCSR’96: Thirteenth European Meeting on Cybernetics and Systems

Research, Symposium on Complex Systems Analysis and

Design, 9-12 April 1996, Vienna, Austria (April 1996)

[18] Christopher Landauer, Kirstie L. Bellman, “Computational Embodiment: Constructing Autonomous Software Systems”, Cybernetics and Systems: An International Journal, Volume 30, Number 2, pp. 131-168

(1999)
[19] Christopher Landauer, Kirstie L. Bellman, “Where’s

the Math? The Need for New Mathematical Foundations for Constructed Complex Systems”, in Proceedings ICC’98: the 15th International Congress on Cybernetics, 23-27 August 1998, Namur, Belgium (1998)

[20] Christopher Landauer, Kirstie L. Bellman, “Situation

Assessment via Computational Semiotics”, pp. 712-717

in Proceedings of ISAS’98: the 1998 International MultiDisciplinary Conference on Intelligent Systems and

Semiotics, 14-17 September 1998, NIST, Gaithersburg,

Maryland (1998)

[21] Christopher Landauer, Kirstie L. Bellman, “Problem

Posing Interpretation of Programming Languages”, Paper etecc07 in Proceedings of HICSS’99: The 32nd

Hawaii Conference on System Sciences, Track III:

Emerging Technologies, Engineering Complex Computing Systems Mini-Track, 5-8 January 1999, Maui, Hawaii

(1999)

[22] Christopher Landauer, Kirstie L. Bellman, “Computational Embodiment: Agents as Constructed Complex

Systems”, Chapter 11, pp. 301-322 in Kerstin Dautenhahn (ed.), Human Cognition and Social Agent Technology, Benjamins (2000)

[23] Christopher Landauer, Kirstie L. Bellman, “Generic Programming, Partial Evaluation, and a New Programming

Paradigm”, Chapter 8, pp. 108-154 in Gene McGuire

(ed.), Software Process Improvement, Idea Group Publishing (1999)

[24] Christopher Landauer, Kirstie L. Bellman, “Architectures for Embodied Intelligence”, pp. 215-220 in Proceedings of ANNIE’99: 1999 Artificial Neural Nets and

Industrial Engineering, Special Track on Bizarre Systems, 7-10 November 1999, St. Louis, Mo. (1999)

[25] Christopher Landauer, Kirstie L. Bellman, “Symbol Systems in Constructed Complex Systems”, pp. 191-197 in

Proceedings of ISIC/ISAS’99: International Symposium

on Intelligent Control, 15-17 September 1999, Cambridge, Massachusetts (1999)

[26] Christopher Landauer, Kirstie L. Bellman, “Relationships and Actions in Conceptual Categories”, pp. 59-72

in G. Stumme (Ed.), Working with Conceptual Structures - Contributions to ICCS 2000, Auxiliary Proceedings of ICCS’2000: The 8th International Conference on

Conceptual Structures, 14-18 August 2000, Darmstadt,

Shaker Verlag, Aachen (August 2000)

[27] Christopher Landauer, Kirstie L. Bellman, “Reflective

Infrastructure for Autonomous Systems”, pp. 671-676,

Volume 2 in Proceedings of EMCSR’2000: The 15th European Meeting on Cybernetics and Systems Research,

Symposium on Autonomy Control: Lessons from the

Emotional, 25-28 April 2000, Vienna (April 2000)

[28] Christopher Landauer, Kirstie L. Bellman, “Some Measurable Characteristics of Intelligence”, Paper WP 1.7.5,

Proceedings of SMC’2000: The 2000 IEEE International Conference on Systems, Man, and Cybernetics

(CD), 8-11 October 2000, Nashville Tennessee (2000)

[29] Christopher Landauer, Kirstie L. Bellman, “Symbol Systems and Meanings in Virtual Worlds”, Proceedings of

VWsim’01: The 2001 Virtual Worlds and Simulation

Conference, WMC’2001: The 2001 SCS Western MultiConference, 7-11 January 2001, Phoenix, SCS (2001)

[30] Christopher Landauer, Kirstie L. Bellman, “Conceptual Modeling Systems: Active Knowledge Processes in

Conceptual Categories”, pp. 131-144 in Guy W. Mineau

(Ed.), Conceptual Structures: Extracting and Representing Semantics, Contributions to ICCS’2001: The

9th International Conference on Conceptual Structures,

30 July-03 August 2001, Stanford University (August

2001)

[31] Christopher Landauer, Kirstie L. Bellman, “Intelligent

System Architectures with Wrappings”, in Proceedings

of CASYS’2001: The Fifth International Conference on

Computing Anticipatory Systems, 13-18 August 2001,

Liege, Belgium (2001)

[32] Christopher Landauer, Kirstie L. Bellman, “Architectures for Autonomous Computing Systems”, Proceedings of ANNIE’2001: The 2001 Artificial Neural Nets

and Industrial Engineering Conference, Special Track

on Bizarre Systems, 4-7 November 2001, St. Louis, Missouri (2001)

[33] Christopher Landauer, Kirstie L. Bellman, “Abstraction

Based Software System Design”, Proceedings of ANNIE’2001: The 2001 Artificial Neural Nets and Industrial Engineering Conference, 4-7 November 2001, St.

Louis, Missouri (2001)

[34] Christopher Landauer, Kirstie L. Bellman, “Computational Infrastructure for Experimentsin Cognitive Leverage”, in Proceedings of CT’2001: The Fourth International Conference on Cognitive Technology: Instruments

of Mind, 6-9 August 2001, Warwick, U.K. (2001)

[35] Christopher Landauer, Kirstie L. Bellman, “SelfModeling Systems”, (to appear) in R. Laddaga, H.

Shrobe (eds.), “Self-Adaptive Software”, Springer Lecture Notes in Computer Science (2002)

[36] Thomas K. Landauer1, The Trouble with Computers,

MIT (1995)

[37] Pat Langley, Elements of Machine Learning, MorganKaufmann (1996)

[38] Maja J. Mataric, “Behavior-Based Control: Main Properties and Implications”, pp. 46-54 in Proceedings IEEE

1no relation
International Conference on Robotics and Automation,

Workshop on Architectures for Intelligent Control Systems, May 1992, Nice, France (1992)

[39] Maja J. Mataric, “Studying the Role of Embodiment in

Cognition”, pp. 457-470 in Cybernetics and Systems,

special issue on Epistemological Aspects of Embodied

Artificial Intelligence, Volume 28, Number 6 (July 1997)

[40] Alexander M. Meystel, James S. Albus, Intelligent Systems: Architecture, Design, and Control, Wiley (2002)

[41] Mary Shaw, William A. Wulf, “Tyrannical Languages

still Preempt System Design”, pp. 200-211 in Proceedings of ICCL’92: The 1992 International Conference on

Computer Languages, 20-23 April 1992, Oakland, California (1992); includes and comments on Mary Shaw,

William A. Wulf, “Toward Relaxing Assumptions in

Languages and their Implementations”, ACM SIGPLAN

Notices, Volume 15, No. 3, pp. 45-51 (March 1980)

[42] Lucy A. Suchman, Plans and Situated Actions: The

problem of human-machine communication, Cambridge

U. Press (1987)

[43] Donald O. Walter, Kirstie L. Bellman, “Some Issues

in Model Integration”, pp. 249-254 in Proceedings of

the SCS Eastern MultiConference, 23-26 April 1990,

Nashville, Tennessee, Simulation Series, Volume 22(3),

SCS (1990)
Software Design and Testing Using Petri Nets: A Case Study Using a 

Distributed Simulation Software System * 

S. Ramaswamy**, R. Neelakantan 

Software Automation and Intelligence Laboratory, Department of Computer Science 

Tennessee Technological University, Cookeville, TN 38505

Phone: (931)-372-3691. Email: srini@acm.org / srini@ieee.org

ABSTRACT: - This paper presents the application of 

Petri Nets in software design and testing using a distributed 

simulation application for naval radar interference 

resolution as a case study. Good software systems design 

promotes the “minimal” exertion of control over the 

different subsystem components; nevertheless, the 

knowledge of “critical” states of operation within one 

subsystem can orient other subsystems to react 

intelligently. Hence, a methodology for determining all the 

important “logical areas” of a subsystem is most crucial. 

The approach used here is based on a technique called 

Minimal Transition Cover Sets, or MTCS, which is a 

refinement of the minimal transition invariants of the 

corresponding Petri Net model of the system [1]. It allows 

for selective information sharing; one of the most 

important attributes of good object-oriented design. This 

paper explores this approach to aid the system designer 

build some high level knowledge about the different 

subsystem components. Using this high-level knowledge, 

the designer can then choose an appropriate design from a 

group of designs and thus can be in a position to 

qualitatively compare and contrast two or more design in 

terms of information sharing capabilities [2]. A further 

enhancement of this MTCS approach involves its use in 

building software test cases. It allows for the development 

of the necessary test cases in the verification of software 

system modules.

I. INTRODUCTION

Software systems designed to be distributed, 

interactive and intelligent (in a domain-specific sense), 

with ubiquitous human interfaces and the ability to exhibit 

intelligent cooperative behaviors will pave the way for 

achieving agile and flexible automation. With the advent of 

complex systems in modern day industry that operate in 

dynamically changing environments, software 

development for such systems calls for addressing the 

issues of increasing complexities in managing processes 

and communication between processes. It is becoming all 

the more imperative that the software systems be robust 

and error free in such real-world applications. Thus, 

 

 

* The research is supported, in part, by the Center for Manufacturing 

Research at Tennessee Technological University. 

** Please address all correspondences to Dr. S. Ramaswamy. 

demonstrating intelligent decision making capabilities 

while working with other subsystem entities is critical. 

This research is part of an effort towards the development 

of such flexible software systems. Thus, developing 

methods, tools and methodologies that aid in the design of 

software systems using such scalable, basic underlying 

structures is the principle objective of this research. 

Such software modules (or objects, procedures, subsystems as referred to in different programming and 

development paradigms) that make up a flexible software 

system are termed Intelligent Coordinating Entities, or 

ICE. The ICE concept is based on the observation that if 

software modules can be designed with a simple, welldefined underlying symmetrical coordination framework, 

(similar to the formation of snow crystals that come in 

infinitely different shapes and combine in various ways to 

form snowflakes) then they can be combined in various 

ways to implement different kinds of software applications 

in a multitude of application domains. 

This paper1 is organized as follows: Section II 

provides an overview of the Intelligent Coordinating 

Entities (ICE) concept, the use of Petri Nets for 

implementing ICE elements and their applicability to 

designing better software systems. Section III provides a 

brief introduction to the example distributed simulation 

system, the overall Petri Nets model of the system and its 

various subnets. Section IV provides the design analysis as 

well as test case design for “necessary” software 

verification. Section V concludes the paper. 

II. INTELLIGENT COORDINATING ENTITIES

The ICE design concept goes a step further than 

current agent based, and other related, paradigms to 

designing intelligent systems. ICE-based design seeks to 

extend current agent-oriented design approaches to 

implicitly include a structured coordination framework, 

which can be implemented through different enabling 

technologies (such as DCOM/CORBA/JINI) - necessary to 

bring such intelligent coordinated behaviors among 

constituent modules (ICE elements, or ICICLES) in 

modern-day systems to fruition. In a system, an ICICLE, 

therefore, should not only consist of a representation, 

1 Due to restrictions in length, throughout this paper, it is assumed that 

the reader is familiar with at least the basic concepts of Petri Nets. 

The references at the end provide abundant additional introductory 

material to Petri Nets [3, 4]. 
decision and reaction structure, but also include the 

mechanisms to orchestrate such reactions in a coordinated 

fashion using a well-defined communication structure as 

the underlying basis. Thus, a communication-centric

structure forms the core of every ICE element (similar to 

the hexagonal structure in the snow crystal analogy). 

Depending on the enabling technology used, it may or may

not be necessary to include the code for the communication 

structure within each ICICLE. However, depending upon 

the other players in the coordination, an ICICLE can 

selectively choose the appropriate information it is willing 

to share with the group by means of a dynamic 

"information-sharing" model. There are two fundamental

issues to designing such ICE elements - its communication 

and coordination structure. 

The basis for 

every ICE element is 

a well-defined,

flexible 

communication 

structure. Therefore, 

a software system

designed with ICE 

elements, or

ICICLES, assumes a 

networked structure 

of dynamically

interacting ICICLES. 

The interaction

capability of an

E may be 

either time-invariant 

or time-dependent. 

Hence, it may be different at different time instances 

depending upon the composition and needs of the system

(may be based on system goals and/or objectives) and the 

other elements involved in the interaction. Each ICICLE 

may or may not have a corresponding hardware resource 

associated with it - the implicit assumption being that all 

hardware devices have an inherent ability to communicate 

with other active elements within the group. This

communication capability may be platform and languageindependent and hence can be brought forth by using any

standard specifications such as DCOM, CORBA, JINI, 

etc., that provide immense distributed coordination 

capabilities. 

ICICL

Available printer

queue length

Incoming job request

Process job (one

active, rest queued)

Accept print job

Deny Print job

NACK / ACK

Completion

Petri Net Place

Petri Net Transition

Control token

Flow token

Figure 1. Petri Net Model of a

Simple Print Process 

Functionality

The communication framework, while providing the 

flexibility of interaction, does not by itself make an 

ICICLE intelligent. Thus, on top of the above mentioned 

communication framework, a well-defined coordination 

capability has to be built. This is to be implemented by

maintaining a dynamic "information-sharing" model

accessible by each ICE element. This model is to be built 

and/or updated periodically and possibly maintained 

consistently across a network. Any ICICLE will use this 

information to verify the availability of a service or other 

such information. Servers may provide the necessary

capabilities for model maintenance, while clients may not

have this capability. The model may be dynamically 

generated / updated at the server by exploding / imploding 

details on specific and available services depending on the 

interaction request from the client. With the server having

the ability to either provide, or refer to a provider of, such a 

service, the client will be able to assimilate information

about the server and hence act either pro-actively or 

reactively. This ability to interact and coordinate allows the 

embedding of domain-specific intelligence within client

applications. 

For example, assume that this technique is been used 

to build an automated factory floor. For simplicity, assume

that each factory floor resource, would therefore, in 

essence be designed as an ICICLE. Further assume that a 

resource, say R1, at the beginning of a job flow is stuck on 

some job. With its communication-centric structure, R1 is 

able to communicate this information to other members of

the group, if it chooses to do so. Correspondingly, other 

members of the group can reevaluate their goals / strategies 

on spending their resources more productively by reacting 

intelligently to this information. However, in designing 

such a system, the first order of priority is to devise a 

mechanism to discern the information to be shared! To 

accomplish this, as part of the development process, we 

propose to use a Petri-net based analysis technique to

analyze each ICICLE modeled by a Petri Net model and 

correspondingly extract the “information” to be shared 

using PN invariants. 

An ICE element must be able to readily perceive, 

appreciate, understand and demonstrate cognizant

behavior. For this reason, a design method must first

capture the important attributes of the functionality of the 

ICE element in its representation. In this regard, 

identification and handling of failures is of critical 

importance. A software modeling mechanism must

therefore be flexible and sufficiently rich, to allow a 

designer represent and understand local failure behavior in

detail. In our work, we use Hierarchical Time-Extended 

Petri Nets, or H-EPNs, [3] a hierarchical Petri-net 

modeling mechanism with the capability for selective 

expansion / contraction of subnets to model selective 

information sharing [1]. The base model for each ICICLE 

is derived by a bottom-up synthesis of smaller Petri net

models, each of which pertains to an atomic (i.e., a welldefined functionality such as the request and processing of

a connection, a robot move operation, etc.) functionality of 

the ICICLE. Composing these models into one higher-level

ICICLE model is accomplished by traditional Petri-net 

synthesis techniques. Models can be synthesized many

times over to create a nesting of sub-models, called 

subnets. Depending upon the level of information-sharing 

detail these subnets can in real-time be either collapsed in
to one subnet place or 

expanded by the ICICLE 

to derive the appropriate

information to be

shared. In detail, this 

occurs as follows: When 

a request for a service 

reaches a server from a 

client, the server first 

classifies the client to 

determine the level of

information sharing 

required. Depending on 

this classification, the 

server may choose to

collapse / expand some or all of the model details

contained in its subnet models to create a dynamic model.

Using this dynamic model, the appropriate information to

be shared is determined and communicated to the client. 

Often, in addition to requesting the server to perform prestated services, the client may require information about

the server to "act" intelligently. For example, a print 

handling functionality on a machine on such a network

may want to know if a particular printer is active 

and, if active, the length of its queue. With this 

information, the machine may decide to either 

submit its job to this printer or reroute it to another 

"less-taxed" printer. The printer ICICLE, therefore 

needs to have the ability to determine if it is busy 

and also the no of jobs it has on its queue (the 

number of tokens at the process job place in Figure 

1 using a dynamic model. It also communicates this

information to the machine that is authorized to print 

to it. A simple Petri net model for such printer 

functionality is illustrated in Figure 1. It is to be

noted that the "process job" place in Figure 1 can be 

further refined as a subnet to represent greater 

details of the print process. Also, a separate counting 

procedure can be integrated into the model to

determine the length of the queue. 

In case of a print request from a machine that 

either does not have the authority, or has very

minimal coordination privileges, to print to this 

printer, the printer may send back a "no 

acknowledgement" - NACK, or just relay back the 

information that it is busy. The flow tokens cause 

this subnet to become active in the coordination 

process. If the print process model is represented in

a higher-level net as a subnet without being 

expanded, the control token that represents the 

available queue length and the number of tokens in

the process job place will not be "visible". 

Therefore, the information on the queue length will 

not be available to this machine. 

III. EXAMPLE 

APPLICATION 

Interference detection and 

resolution among mobile naval radar 

units is critical to naval operations. 

Previous work in this area has 

involved the exploration of this

problem in a distributed simulation 

environment using three 

communication approaches viz. 

Master-Slave (MS), Locally

autonomous (LA), and Negotiation 

(NG) for coordinated interference 

resolution. In such applications, 

groups of ships work in close consensuses with one 

another for some collective and constructive goals. Hence, 

a study of their interaction and coordination mechanism is

of extreme importance at the design stage for writing 

reliable and good software for such real-time systems. 

Since every ship in a naval group is associated with

appropriate radar units for detecting and tracking purposes, 

hence, it is important that these radar units do not have 

Ship Class

Operations

Slave Watcher

Class

Operatioins

Switching

Algorithm

Detection

Algorithm

Slave Listener

Access

Database

Method

Mode Select

Algorithm

Access

Database

Method

Minimum Value

Calculation

A Diagram Showing how each class

is called in the orginal source code.

Figure 2. Overview of the Simulation Software 

System for Automated Interference Resolution

 Subnet for

SlaveWatcher Class Implements the

required algorithms

Switching

Algorithm

Slave

Listener

Detection

Algorithm

SlaveListener

Class Subnet

MasterClass

Thread

MasterThread

Subnet

Master Class

Subnet

MasterSender

Thread

MasterThread Class (acts as the master in MS

mode)

Compute Packet

for Master

Send information

Master

Highest Level

Structure Ship Class

Operations

SlaveWatcher

Class

Operations Sleep

Process

MasterSender

Subnet

3

t1

t2

t3

t4

t5

t6

t7 t8 t9

t10 t11

C

t12 alls

Calls

Calls

Calls

Figure 3. PN Model of the Distributed Simulation System 
frequency interference during operation. For normal and 

proper operation of these radar units, it is imperative that

interference-free frequency is available for each radar unit. 

Whenever interference occurs between radar units, a 

suitable mechanism needs to be adopted to resolve this

interference. This detection and resolution process that is 

associated with these affected radars is of extreme 

importance in time-critical situations. 

Since, these radar units work in a dynamically 

changing environment, such as frequent change in

direction, frequent change in coordinates of the ship and 

other such parameters that affect the frequency of 

operation, a need for automated negotiation for 

interference deduction and resolution between the affected 

radar units and a choice of an appropriate coordination 

framework is most desirable. 

Two important steps that have major impact in this 

automated negotiation for interference detection and 

resolution are (i) The process of determining interference, 

and, (ii) Resolution process of this interference. 

These two steps play very critical role in the proper 

and intelligent working of the naval radar units. Hence, due

to time-restrictions only these two steps have been studied 

and modeled. The first step, basically deals with the actual 

process of interference deduction. Two ships are said to

have interfering frequencies when their probability-ofinterference is greater than 0.001. This probability of 

interference Pint is a quantitative approach based on sound 

communication theory principles that tells us if 

interference can occur between two radars based on a set of 

parameters. So, the detection algorithm module determines 

(by reading the database for each pair of ship in the group) 

if the Pint value is greater than 0.001 and if so, switches to

a particular mode of operation that resolves this

interference. This mode of operation is a pre-computed 

mode for each ship based on the communication 

parameters such as position, direction etc. For more details, 

readers can refer to [5]. The second step involves the 

process of determining the mode of operation for each 

ship. The modules, switching algorithm and mode selection 

does exactly this. It reads the parameters for each pair of 

ship in the group from the database, and determines the 

mode of operation. This mode of operation is then written

in to the database for that particular pair of ship. Since, this 

kind of an application envisaged a real-time application 

development process; hence, this problem was taken as a 

specific case study to develop a Petri Net model and show 

that analysis of Petri Nets does produce useful and 

important results for software design and testing. Begin a 

real time application simulation; hence it became 

imperative to build a good design (that is robust and 

scalable) and to build “necessary” test cases for testing. 

Subnet for

Switching

Algorithm

Select the

Mode for i,j

ship

Read

database(i)

i=1

j=1

Write into

database

"the mode"

for ship i,j

Read

database(j)

j=0,

i++

j++

Subnet for

Detection

Algorithm

Read

database(i)

j=1

Read

database(j)

j=1,

i++

Read

database for

required

approach

j++

is j <=n-1

is i <=n-1

Write into

database the

new frequency

for ship i,j

i=1

YES

YES

NO

NO

Calculate

Pint

Is Pint >= .001

YES

NO

i,j represents a

counter for the no of

ships in the database

Select the required

mode of operation

Is j <= n

YES

NO

EXIT

Is i <= n NO

YES

EXIT

LA

MS

NG

t13

t14

t16

t15

t17

t18 t19

t20

t21

t22

t23

t24

t25

t26

t27

t28

t31

t30

t31

t32

t33

t34

t36

t35

t37

t38

t39 t40

t41

t42

t43

t44

t45

t46

Figure 4: Switching and

Detection Structure

Figure 4. PN Subnet Models for Interference 

Detection and Protocol Switching

Currently we are working on two issues, namely: (i)

building a Dynamic Switching Protocol (DSP) for 

switching between the three existing approaches based on 

Figure 5. Mode Section and Database Access 

Modules 
various conditions and simulated information. (ii)

Developing advanced negotiation protocols that

incorporate the advantages provided by the different modes 

in different situations. These protocols also allow several

ships to negotiate at any given instant. Given this 

background, it is but appropriate to understand (i) the 

design of the system with respect to information shared 

between the various subsystems; and (ii) the “necessary” 

testing to be performed on the simulation system before a 

fieldable prototype is built. The block diagram of the 

simulation software system is shown in Figure 2 and the 

Petri Net model is presented in Figure 3. 

The subnets for the interference detection and protocol

switching algorithm are presented in Figure 4. The first

subnet in Figure 4 is used for switching purposes. It reads 

the database for every pair of ship and for each pair, a 

mode is computed depending upon various ship and radar 

parameters. The other subnet in Figure 4 (on the right) is

used for interference detection. In this subnet, each pair of 

ship is selected from the group. Based on its parameters 

such as frequency, position and other factors, the 

probability of interference is computed. If this value, Pint, 

is greater than a predetermined value (0.001), frequency

interference is said to take place. So, in order to resolve 

this interference, a mode of resolution is selected from the 

database and dynamically switched to operate the radar 

units in one of the 3 possible modes namely, LA, MS and 

NG. This is process is then repeated for all pairs of ships in

the group. 

The first subnet in Figure 5 illustrates the actual mode 

computation process between two interfering agents. For 

each interfering agent, Pla, Pms and Png (P is an 

evaluation Index for various 

modes) are determined which are 

based on sound principles from

communication. Whichever value 

is less among Pla, Pms and Png 

that particular mode of operation is

selected for that particular ship. 

The smallest value is 

determined by calling a simple 

minimum value determination 

subnet, which is illustrated in 

Figure 6. 

IV. DESIGN ANALYSIS 

AND TEST CASE 

DESIGN

PN invariant analysis [2] were 

performed to identify the various

minimal T-invariants [4] on all the 

models generated. These are 

illustrated in Table 1.Transitions

with a ‘l’ followed by the 

corresponding subnet name

represent loop back transition which have not been shown 

in any of the Petri Net models. They have been included in

the design in order to build test cases for the individual

modules and for the invariant analysis. 

When a PN is used to represent a software design 

structure, the T-Invariants obtained from the PN model

gives all the independent sub-flows in the design. Based on 

these sub-flows, software testing can be done on the 

equivalent design. In order to generate the software test

cases, MTCS algorithm [1] is used on the T-invariants 

generated. The algorithm for the MTCS transition is 

already presented in [1] and hence not presented here. 

Depending upon the design requirements of the software 

designer, the transitions from the invariants can either be

refined to obtain the design choice transitions or testing 

Table 1. Minimal Invariants for the Various Subnets 

Subnet Name T-Invariants 

Switching Algorithm (sa) X1: t13 t14 t15 tlsa 

X2: t16 t19 t18 t17 

X3: t20 t21 t22 t23 t24 

Detection Algorithm (da) X1: t25 t26 tlda 

X2: t27 t29 t30 t31 t28 

X3: t32 t33 t34 t36 t37 t38 t41 t44 t45 t35 

X4: t32 t33 t34 t36 t37 t39 t42 t44 t45 t35 

X5: t32 t33 t34 t36 t37 t40 t43 t44 t45 t35 

X6: t32 t33 t34 t46 t35 

Mode Selection (ms) X1: t47 t49 tlms 

X2: t48 t50 t52 t54 t55 t58 t59 t60 t61 t62 t63 

X3: t48 t50 t51 t53 t55 t58 t59 t60 t61 t62 t63 

X4: t48 t50 t51 t53 t56 t57 t59 t60 t61 t62 t63 

X5: t48 t50 t52 t54 t56 t57 t59 t60 t61 t62 t63 

Access Database (adb) X1: t64 t66 t70 t72 tladb 

X2: t65 t67 t71 t73 tladb 

Minimum Value (mv) X1: t72 t74 t79 t81 tlmv 

X2: t72 t73 t76 t78 tlmv 

X3: t72 t73 t77 t80 tlmv 

X4: t72 t74 t75 t76 t78 tlmv 

Is Pla < Pms

Yes

No

Is Pms < Png

Is Pla < Png

Yes

No

app = " Pla"

app = "Pms" Yes

No

app = "Png"

t72

t73

t74 t75

t76

t77 t78

t79 t80

t81

Figure 6. Minimum Value Detection Subnet 
choice transitions. The methodology used to obtain this

refinement is discussed in detail in [1]. The decision 

associated with these transitions aid us in the design and 

testing of software. The transitions obtained from the 

design choice strategy aid in the decision of information 

hiding and encapsulation. Whereas the decision 

corresponding to the transitions obtained from the testing 

choice strategy can be used for building software test

cases. These transitions correspond to the inner most

conditions being checked in the actual software design. 

Building test cases on these decisions ensure that all 

independent sub-flows in the design have been checked or 

exercised at least once. Thus, building test cases on these 

MTCS transitions ensures that the equivalent software is 

being tested for their necessary and sufficient conditions. 

Applying the MTCS algorithm in [1], the various MTCS

transitions were obtained. These are shown in Table 2. 

The following presents the design and testing analysis of 

the results obtained. 

A. Design Analysis 

Switching Algorithm Subnet 

The design choice transitions for this subnet are t15, 

t16, and t20 or t15, t18, and t20. These transitions in the 

original PN model correspond to different decisions being 

made. In the first set, transitions t15 and t16 corresponds to

check that is made on the value of the variable ‘i’. These 

transitions are fired on the event that i becomes greater 

than the number of ships in the group (t15) or i is less than 

or equal to the number of ships in the group. This means 

that statements after this decision are executed repeatedly 

for every pair of ship in the group. These statements

actually correspond to a particular mode being assigned for 

every ship pair. So, in the actual design, how this mode 

assignment is done need not be revealed to other 

subsystems. The only information that needs to be known 

is whether all the ships have been assigned a particular 

mode of operation. From the other set of transitions we can 

get information as at which ship pair is being currently 

operated on. Even this decision might be quite crucial or 

relevant to other subsystems. So, extending this idea to the 

OOD, the statements that are executed after this decision 

(is evaluated to be true) can be declared as private methods 

and the variables ‘i’ and ‘j’ could be declared as a public

variable. 

Detection Algorithm subnet: 

For this subnet, the design choice transitions are t26, 

t27, and t36. Again t26 and t27 transitions correspond to

the value of ‘i’ being checked. That is, inside this subnet

the value of ‘i’ is checked with the number of ships in the 

group. This decision actually correspond to deciding 

whether all ship pairs have been formed and if there is an 

interference among any pair, a suitable mode is switched 

and interference is resolved. So, this decision might be 

crucial for other subsystems. How this interference is 

resolved can actually be abstracted and hence in terms of 

OOD, those processes that deal with this resolving 

technique can be declared as private and the variables ‘i’ 

and ‘j’ could be declared as public. Transition t36 is again 

is a further entry point decision in this subnet that can be 

clubbed with other higher decisions inside this subnet.

Mode Selection subnet: 

The design choice transitions are t48 and t49. The 

decision that correspond to these transitions is whether the 

number of interfering agents are less than or equal to two. 

For each of the interfering agent, a particular mode of 

operation is determined depending on a set of parameters. 

So, the decision of finding out whether a mode is been 

determined for each of the interfering ship might be crucial 

for other subsystems. So, the variable a1 can be declared as 

public and the methods and processes that follow this

decision can be declared as private. 

Access Database subnet: 

Here the transitions t64 and t65 correspond to the 

decision of whether a write is to be made in to the database 

or whether a read is to be done on a database. This decision 

of whether the database is in the write state or a read state 

might be useful for other subsystems. Hence, access data 

base method might be declared as a public method. 

Minimum Value subnet: 

 Here the transitions

are t73 and t74 which 

correspond to the 

cision of checking 

the first value with

the second value. 

Only from this 

decision can one go 

on further to find out

the smallest value. 

Since, this subnet

might be useful at

other places that 

might need to

de

Table 2. Various MTCS Transitions 

Subnet Name Greedy Choice 

Transitions 

Design Choice

Transitions 

Testing Choice 

Transitions 

Switching Algorithm t13 t16 t20 t15 t16 t20 or

t15 t18 t20 

t15 t18 t20 

Detection Algorithm t25 t27 t32 t26 t27 t36 or 

t26 t31 t38 t39 t40 t46 

t26 t31 t38 t39 t40 

t46 

Mode Selection t47 t48 t48 t49 or 

t52 t55 t56 t51 t49 

t52 t55 t56 t51 t49 

Access Database t64 t65 t64 t65 t64 t65 

Minimum Value t72 t73 t74 or

t75 t76 t77 t79 

t75 t76 t77 t79 
compute the minimum value, hence this method can be 

declared as a public method. 

B. Test Case Design 

The test cases derived from the MTCS transitions give 

all the necessary test cases that need to be performed for 

that particular module or subnet to be deemed error free. 

On the performance of these tests, the designer can be 

reasonably confident about the proper functioning of the 

modules as “independent and self contained”. The 

summary of the test cases for all the modules is shown in 

Table 3. Various modules were integrated in succession to 

form larger net models and individual test cases were 

generated for such modules. Due to the length restrictions 

of this paper, we are not presenting these results. Interested 

readers may contact the author for these results. 

V. CONCLUSIONS

The ICE-based design approach is (i) a clear and 

simple approach to design a software system that is 

distributed, interactive and has the ability to coordinate and 

communicate with other software, hardware and/or human 

elements. ICICLES will allow standard implementation 

patterns as well as facilitate the adoption of reusable 

software components that increases reliability and product 

quality. It will help reduce project management costs by 

keeping project teams focused on delivering improved 

functionality on well-defined ICICLES. 

Unique market requirements force organizations to quickly 

adapt to the rapid changes. Generic solutions will not 

always solve particular problems and meet the demands of 

certain industries. This issue raises the need for solutions 

that are scaleable, flexible, and can be tailored to the 

special project needs. Since each ICICLE will contain a 

mechanism for interaction with other members in the group 

and a dynamic modeling mechanism to determine 

"information-sharing" needs, it will provide a flexible 

framework for designing evolutionary and business 

software systems. 

ICICLES are based on a flexible communication 

structure with incrementally built coordination structures. 

Reasoning and other application-specific modules may be 

built based on domain-specific needs. Thus, software 

systems can be built as a combination of components that 

vary in their capabilities ranging from ones that are highly 

intelligent to those that are worker modules. Moreover, use 

of a dynamic cooperation model allows for components 

that can have a time-dependent interaction behavior. In a 

system with many ICICLES, the presence or absence of a 

particular component would thus be of little significance as 

long as the system is build with redundant structures - if a 

component is present and is willing to cooperate for 

providing some required service it will be utilized. This 

allows the required flexibility to leverage industry and 

vendor partnerships. 

There are a wide variety of issues to be addressed 

before the viable realization of this technique. Some of the 

basic issues to be addressed by our research include the 

following. (i) Should a dynamic PN model be generated 

and used exclusively at run time or can it be a pseudodynamic entity? (ii) Petri-net models are not immune to the 

state-space explosion problem. If so, what are the 

performance limitations of the dynamic generation of 

"huge" Petri net models. Can a tradeoff be achieved at any 

level either for the maximum number of allowable subnet 

expansions or for the number of places / transitions in the 

net? (iii) Most current algorithms to determine Tinvariants use a matrix based representation scheme. Such 

a scheme results in the manipulation of huge "sparse" 

matrices. Are there better algorithms to determine the 

minimal T-invariants? (iv) Can we build a 

certification/reward mechanism for ICICLE coordination? 

If so, how is this to be done - by static rules or by dynamic 

derivation of reward units? (v) What is a good measure of 

ICICLE and system stability? How does coordination 

affect stability? (vi) Can ICICLES coordinate over 

geographically distant networks - possibly using the worldwide-web as a backbone? How is performance affected in 

such a case? We hope that future research into these issues 

will enable the development of distributed, interactive (in a 

domain-specific sense) systems with ubiquitous human 

interfaces and the ability to exhibit intelligent cooperative 

behaviors. When research into the above issues is 

completed, it will allow the development of a repeatable, 

multi-phase process (of designing ICICLES) to yield 

production-caliber, deployable solutions in the shortest 

possible time. 

References 

[1] S. Ramaswamy, “A Petri net based approach for establishing 

necessary software design and testing requirements”, Special Session 

on Petri Nets in Systems Design, IEEE Conference on Systems, Man 

and Cybernetics, Nashville, Oct-2000. 

[2] S. Ramaswamy, A. Suraj, K. S. Barber, "An Approach for 

Monitoring and Control of Agent Based Systems", Proceedings of 

the 1997 IEEE International Conf. on Robotics and Automation, 

Albuquerque, New Mexico, April 1997. 

[3] S. Ramaswamy, K. P. Valavanis, K. S. Barber, "Petri Net Extensions 

for the Development of MIMO Net Models of Automated 

Manufacturing Systems", Journal of Manufacturing Systems, Vol. 

16, No. 3, May/June 1997, pp. 175-191. 

[4] T. Murata, “Petri Nets: Properties, Analysis and Applications”, 

Proceedings of the IEEE, Vol. 77, No. 4, April 1989. pp. 541-580. 

[5] S. Ramaswamy , K. Srinivasan , P. K. Rajan , R. MacFadzean , S. 

Krishnamurthy, "A Distributed Agent-based Simulation 

Environment for Interference Detection and Resolution", Special 

Issue on Software Agents and Simulation, SIMULATION, June 2001
Table 3. Recommended Test Cases for the Various Modules 

Subnet name Transition involved Test Cases Statement being executed Importance of this test 

Switching Algorithm t15 01. i > n 

02. i = n 

03. i < n 

Exit the subnet Loop Check 

t18 04. i < n 

05. i = n 

06. j < n 

j=1 and i++ Whether Incrementing of i takes place at the correct values of i and j 

to account for all ship pairs 

t20 07. i < n 

08. i = n 

09. j = n 

10. j < n 

11. j = n 

Actual mode selection operation Whether a mode is determine for all ships in the group 

Detection Algorithm t26 01. i > n 

02. i = n 

03. i < n 

Exit the subnet Loop Check 

t31 04. i < n 

05. i = n 

06. j > n 

j=1 and j++ Whether Incrementing of i takes place at the correct values of i and j 

to account for all ship pairs 

t38 07. i < n 

08. i = n 

09. j < n 

10. j = n 

11. Pint >.001 

12. Pint = .001 

13. Sel Mode = LA 

Operate in LA mode Forcing execution to check whether ship pair can operate in the LA 

mode 

t39 14. i < n 

15. i = n 

16. j < n 

17. j = n 

18. Pint >.001 

19. Pint = .001 

20. Sel Mode = NG 

Operate in NG mode Forcing execution to check whether ship pair can operate in the NG 

mode 

t40 21. i < n 

22. i = n 

23. j < n 

24. j = n 

25. Pint >.001 

26. Pint = .001 

27. Sel Mode = MS 

Operate in MS mode Forcing execution to check whether ship pair can operate in the MS 

mode 

t46 28. i < n 

29. i = n 

30. j < n 

31. j = n 

32. Pint < 0.001 

j++ To check whether after each ship pair operation, the next pair is 

being fetched 

Mode Selection t49 01. a1 > 2 

02. a1=2 

03. a1 < 2 

Exit the subnet Loop Check 

t52 04. a1 < 2 

05. a1 =2 

06. distance1 < distance2 

new1 = distance1 Computing new1 for the first pair of interfering ship when distance1 

< distance 2 

t51 07. a1 < 2 

08. a1 = 2 

09. distance1 > = 

 distance 2 

new1 = distance2 Computing new1 for the first pair of interfering ship 

t55 10. a1 < 2 

11. a1 = 2 

12. distance1 < 

 distance 2 

13. a1 = 1 

Compute vt, vt1, vt2 with first formula Computing the values for the first pair when distance1 < distance2 

t55 14. a1 < 2 

15. a1 = 2 

16. distance1 >= 

 distance 2 

17. a1 = 1 

Compute vt, vt1, vt2 with second formula Computing the values for the first pair when distance1 >= distance2 

t56 18. a1 = 2 

19. distance1 < 

 distance 2 

20. a1 <> 1 

Compute with formula2 Computing the values for the second ship when distance1 < 

distance2 

t56 21. a1 < 2 

22. a1 = 2 

23. distance1 >= 

 distance 2 

24. a1 <> 1 

Compute values with formula2 Computing the values for the second ship when distance1 < 

distance2 

Access Database Not implemented because actual design used vectors 

Minimum Value t75 01. pla < pms 

02. pla < png 

app=Pla When Pla is lowest 

t76 03. pla > pms 

04. pla = pms 

05. pms < png 

app=pms When Pms is lowest or equal to Pla mode and Png is greater than 

both these values 

t77 06. pla < pms 

07. pla > png 

08. pla = png 

condition pms<png being checked When Pla greater than Png but less than or equal to Pms 

t79 09. pla > pms 

10. pla = pms 

11. pms > png 

12. pms = png 

app=png When Png is lowest or equal to Pms and Pla greater than both these 

values 
Comparison Methodology for Robotic Operator Control Units

Robert Bialczak Ph.D., Jonathan Nida, Brian Pettitt

Technology Service Associates, Inc.

Michelle Kalphat

US Army Simulation, Training, and Instrumentation Command

ABSTRACT

Operator controls and interfaces for military unmanned systems

are being developed and used in both laboratory and field research.

These controls are considered to be an efficient method of

controlling unmanned systems in tactical settings and scenarios.

Performance comparisons of different controls and interfaces in

differing tactical environments need further study. This paper

proposes a methodology of evaluating two selected controls in a

laboratory experiment.

The Advanced Robotics Simulations STO research being

conducted at the Technology Development Center (TDC) at

STRICOM and The Mounted Maneuver Battlespace Laboratory

(MMBL) in Fort Knox, Kentucky is using two different control

systems. Both are based upon the OneSAF Testbed Baseline

(OTB). The first is a version of the OTB that has been extensively

modified. Currently it is not instrumented and cannot log the

user’s actions to the button press level. Instrumenting the SAF

would be a relatively straightforward exercise. The second, known

as the Operator Control Unit (OCU), has been developed for

Future Combat system Experimentation, is built on top of the

OTB. The OCU has been instrumented to facilitate analysis. The

OCU has the ability to send DIS (Distributed Interactive

Simulation) PDUs (Package Data Units) to a logger.

Once fully instrumented, the two controllers would then be

run in an exercise. The TDC and the MMBL both have

capabilities where 20-30 OCUs and a comparable number of

OTBSAF control stations could be utilized. Using the information

from varying FCS scenarios an exercise will allow us the ability to

analyze the user’s actions and the corresponding results. Some of

the questions to be examined include:

1) Compare survivability of robots controlled from

different stations

2) Compare time to execute commands

3) Compare steps necessary to execute commands

4) When an interesting event happened (e.g.

firefights, robot destroyed, etc.), and what actions

preceded the event

This research would allow the user community to evaluate not

only the effectiveness of the control stations, but in the future

evaluate the effectiveness of the man-robot units and interactions.

KEYWORDS: Robot, Control, Compare, Evaluate

1. MEASURING ROBOTIC CONTROLS

There are many ways to measure human to robot controls in

tactical scenarios. First you must decide the degree of

fidelity for the robotic vehicles and their environment. This

ranges from complete immersion in a real world scenario

(the robot is in the field in an actual scenario e.g. war) to

total simulation (the robot and its environment are modeled

by computers.) The most accurate method would be to

always test in real situations. Unfortunately that is

prohibitive across numerous domains. The complexity of

this is also intensified because many robotic systems are

still in the design phase. For the purposes of this paper and

this experiment we choose to simulate both the robotic

vehicle and its environment with a modified version of

OTBSAF and a under development experimental FCS

OCU. This simulation tool is accurate enough in most

battlefield scenarios. We will strive to make the

measurements as objective as possible. Only certain aspects

of the robotic controls will be measured. One problem that

will not be addressed is the level of operator training.

Obviously, a poorly trained operator will limit the use of the

control unit, so we will assume that all the operators have

been sufficiently trained on their control unit.

2. TEST FACILITY

The STRICOM TDC and the Mounted Maneuver

Battlespace Laboratory in Fort Knox, Kentucky is the

proposed facilities to conduct these experiments. Currently,

both facilities have two ways of controlling simulated

robotic vehicles at their respective sites – the OTBSAF

controller and the FCS Operator Control Unit. Data will be

collected with a logger which will record all Distributed

Interactive Simulation messages including experimental

Package Data Units for button presses from either controller

and any communications between the Operator Control Unit

and robotic platforms.
2.1 OTBSAF Controller

The first is with an extensively modified version of

OTBSAF 1.0. The mechanism for controlling vehicles with

this tool has not been changed from the OTBSAF 1.0

baseline. Feedback from controlled vehicles is through a

two dimensional tactical map which displays friendly and

other known vehicles’ status. This version of OTBSAF has

not been fully instrumented for button presses, but doing so

will be necessary before it can be used for this exercise.

2.2 FCS Operator Control Unit

The second way for controlling robots is with the Operator

Control Unit developed for the Mounted Maneuver Battle

Laboratory. The Operator Control Unit is a further

modified version of OTBSAF 1.0. This tool was designed

to simplify many of the user’s choices during combat, but

still allow flexibility in the system. The Operator Control

Unit was also designed to control all robotic entities in the

Future Combat System. Feedback from controlled vehicles

is through a tactical map as before and with realistic images

from sensors on the robotic platform. There have been

exercises with more than twenty Operator Control Units in

use at the same time. It has been completely instrumented

down to the button press level.

3. EXERCISE QUESTIONS

The experiment to be conducted at the TDC and Mounted

Maneuver Battlespace Laboratory facilities is proposed to

be on up to thirty OCUs and a comparable number of

OTBSAF control stations. The same scenarios will be

performed with only the robotic control station being

different at the start. After the selected scenarios have been

completed the information collected by the data logger can

be analyzed.

3.1 Mission Success

The most basic question to ask is which robotic controller

had the highest success rate for its robots completing their

assigned missions.

This may be a difficult problem to solve because of the

varying mission types and characteristics of the robots.

Scout vehicles may be evaluated on the percentage of

enemy vehicles identified or the stealthy ness of how they

conducted their mission. Direct fire robots may be evaluated

on the amount of enemy vehicles disabled and or destroyed.

The mission success for other types of robots (re-supply,

engineer) can be assessed in comparable manner.

3.2 Platform Survivability

The next evaluation criteria are which robotic controller

allow for the highest survival rate for its robots. The

meaning of “survival” must be discussed. Does survival

mean the robot is 100% functional or that it still has some

use to the controller? A control station that consistently has

more platforms completely functional at the end of the

exercise must be given more credit than one that has more

robots partially disabled (movement impaired, sensors

destroyed, etc.)

3.3 Executing Commands

Several ways will be used to evaluate the tasking of vehicles

from the control stations. Since the control stations will be

completely instrumented, either the time it takes or the

number of steps required to complete the command will be

captured. Another interesting question currently being

posed is what will the number of platforms destroyed be

while the user was trying to do other tasks or had their

attention diverted to another task and was it significantly

different between types of control stations.

4. KNOWLEDGE GAINED

If the appropriate scenarios are conducted and the correct

questions are asked and evaluated, the effectiveness of the

robotic control requirements can be assessed and evaluated.

This method is not limited to the OTBSAF control station

and the Operator Control Unit. Other control stations can

be integrated into this environment. The Demo III Operator

Control Unit was previously used in the Mounted Maneuver

Battlespace Laboratory simulated environment to train

soldiers before controlling actual robots in the field. An

effort to make the messages of the current Operator Control

Unit both JAUGS and 4D-RCS compliant is being

discussed. This would enable integration of another JAUGS

or 4D-RCS compliant controllers with minimal effort. In

the future with a consistent method to evaluate measure and

asses robotic control stations, the encompassing problem of

evaluating the man-robot unit and their interactions can be

addressed.
An Integrated Modeling and Simulation Methodology 

for Intelligent Systems Design and Testing 

Xiaolin Hu and Bernard P. Zeigler 

Arizona Center for Integrative Modeling and Simulation 

The University of Arizona 

Tucson, AZ, USA 85721 

www.acims.arizona.edu 

ABSTRACT 

Model continuity refers to the ability to use the same model of a 

system throughout its design phases. For intelligent systems, we 

can restrict such continuity to the intelligent control 

components, and more specifically, the models that implement 

the system’s decision making. behavior. In this paper, we 

show how a modeling and simulation environment, based on the 

DEVS formalism,, can support model continuity in the design 

of intelligent systems. For robotic systems, such continuity 

allows design and testing of the same control logic model 

through the phases including logical simulation, real-time 

simulation and actual execution. 

KEYWORDS: Model Continuity, Modeling, Simulation, 

Experimental Frame, Real Time Systems, Intelligent Systems, 

DEVS

1. INTRODUCTION 

One criterion for intelligence is the ability to make 

decisions in a timely manner. Certainly for systems 

expected to interact with the real world, such as robotic 

systems, real time constraints play a major role, although 

they may vary in stringency for different behaviors. With 

the rapid advance in processor speed, memory capacity, 

sensors and actuators, and dramatic increases in network 

technology, intelligence has a natural association with 

distributed systems, as exemplified by multi-agent 

systems. Unfortunately, the lack of good design methods 

and support tools has made software development for 

intelligent systems a bottleneck. To address the 

importance and complexity of real time software 

development, academic and commercial tool developers 

have proposed various real time software models and 

methods that represent different emphases on this 

problem. However, so far none of them fits very well to 

support real time software from a systematic way. A 

formal methodology is needed for real-time software 

development [1, 2]. The method should support software 

development for intelligent systems including designing, 

testing and execution in a systematic way, with a 

framework to integrate a system’s behavior, structure and 

timeliness together. 

In this paper, we describe an approach to develop 

real time software for intelligent systems. This approach 

is based on DEVS modeling and simulation framework 

[3]. Corresponding to the general “Design—Test—

Execute” development procedure, our approach provides 

a “Modeling—Simulation—Execution” methodology 

which includes several stages to develop real time 

software. In the modeling stage, Atomic and Coupled 

models are built to capture a system’s behavioral and 

structural properties. In the simulation stage, a series of 

simulators is chosen to simulate and test model’s 

behavior in an incremental fashion step. In the execution 

stage, the verified model is executed by real-time 

execution engine. It is important to point out that during 

the whole process, we maintain model continuity because 

the same model that has been designed will be simulated 

and then executed. For distributed systems, this 

continuity also means the coupling among the models is 

maintained even though the models are executed in a 

distributed environment. We believe keeping model’s 

continuity is an efficient way to manage software’s 

complexity and consistency. With model’s continuity, we 

are confident that the system in operation is the system 

we wanted to design and will carry out the functions as 

tested by simulation. 

This paper will start with the description of the 

methodology for a stand-alone real time system. Then it 

will scale up to distributed real time systems. For both 

systems, step-wise simulation methods are provided to 

simulate and exercise the model under test. Finally, we 

describe how experimental frame, a more general testing 

environment, can be integrated to test the model of 

interest while still preserving model continuity. 
2. MODELING, SIMULATION AND 

MODEL CONTINUITY 

Intelligent real time systems monitor, respond to, or 

control, an external environment. This environment is 

connected to the digital logic through sensors, actuators, 

and other input-output interfaces [4]. A real time system 

from this point of view consists of sensors, actuators and 

the real time control and information-processing unit. For 

simplicity, we will call this last one the control model. 

The sensors get inputs from the real environment and 

feed them to the control model. The actuators get 

commands from the control model and perform 

corresponding actions to affect the real environment. The 

control unit processes the input from sensors and makes 

decisions based on its control logic. Depending on the 

complexity of the system, the control model could have 

only one central component or it could have multiple 

parallel-processing subcomponents, which in turn may 

have their own sub-control units. 

Once we establish this view of a real time system as 

shown on the left side of Figure 1, we can model it easily. 

In our approach, sensors and actuators are modeled as 

DEVS Activities, which is a concept introduced by RTDEVS for real time system specification [5]. A DEVS 

Activity can be any kind of computer task. However, in 

the context of this paper, we only consider the 

sensor/actuator Activities. The control unit is modeled as 

a control model which might has a set of subcomponents. 

These subcomponents are coupled together so they can 

communicate and cooperate. With this approach, the 

control model acts as the brain to process data and make 

decisions. It could be a simple Atomic model or a 

complex hierarchical coupled model. Sensor/actuator 

Activities act as hardware interfaces providing a set of 

APIs for the control model to use. They are essentially 

hardware drivers for sensors and actuators. How to define 

an Activity and its APIs is dependent on how the designer 

delineates the “Control Model—Activity” boundary. For 

example, we can model a sensor module that may have 

its own control logic as a sensor Activity. Or we can also 

include that part of logic into our control model and only 

model the sensor hardware as an Activity. The clear 

separation between control model and activity’s functions 

makes it possible for the designer to focus on his design 

interest. In the context of intelligent real time systems, 

the control logic is typically very complex, as the system 

usually operates in a dynamic, uncertain or even hostile 

environment. As such, the control model is the main 

interest of design and testing. In our approach, simulation 

methods are applied to test the correctness and efficiency 

of this model. Tne continuity of this model is also 

emphasized during the whole process of the 

methodology. 

Before simulating the control model, we need to 

model the real environment as an environment model. 

This environment model is a reflection of how the real 

environment affects or is affected by the system under 

design. Meanwhile, a “simulated” sensor/actuator 

hardware interface is also needed for the control model to 

talk to the environment model. This is why we introduce 

the SimActivity concept. In contrast to an Activity, which 

drives real hardware and is actually being executed, a 

SimActivity imitates an Activity’s interface/behavior and 

Figure 1: Modeling, Simulation and Execution 

of Non-distributed Real Time System

is only used during simulation. A sensor SimActivity gets 

input from the environment model just as a sensor 

Activity gets input from the real environment. An 

actuator SimActivity does similar things as an actuator 

Activity too. Note that it is important for an Activity and 

its SimActivity to have the same interfaces, which are 

used by the control model in both simulation and real 

execution. By imposing this restriction, the control model 

can be kept unchanged in the transition from simulation 

to execution (it interacts with the environment model and 

real environment using the same interfaces). Thus, model 

continuity is achieved. 

As shown in the center of Figure 1, in the modeling 

stage, a simulated system is developed based on the real 

system. With this system, different simulation strategies 

can be applied to validate the control model. In DEVS, 

there is a clear separation between a model and its 

simulators, which gives us the flexibility to choose 

different simulators to simulate the same model. These 

simulators include fast-mode simulator, real-time 

simulator and distributed simulators. With these 

simulators, a model can be simulated and tested 

incrementally before its real execution. During the 

simulation stage, employing fast-mode (or logical time) 

simulators, if we find the simulated result is not what we 

Real time Control 

and Information 

Processing

Sensors Actuators

Real Environment Environment Model Real Environment

Sensor 

SimActivities

Sensor 

Activities

Actuator 

SimActivities

Actuator 

Activities

Real System Simulation & 

Verification Modeling Mapping Execution

Real Time Models

M2 M3

M1

Real Time Models

M2 M3

M1
expected, the model can be revised and then re-simulated. 

This “modeling-simulation-revising” cycle repeates until 

we are satisfied with simulation result or nothing more 

can be learned in the simulation stage. A more detailed 

description of how to choose and use different simulators 

is given in the next section. 

After the model is validated through simulations, it 

will be mapped to the real hardware for execution. For a 

non-distributed application, this mapping is the “Activity 

Mapping” to associate the sensor/actuator Activities to 

the corresponding sensor/actuators hardware. For a 

distributed application, an extra “Model Mapping” is 

needed to map a set of cooperative models to a set of 

networked nodes. By associating the models and 

Activities to their corresponding hardware, the system 

can be executed in a real environment. In execution, the 

control logic is governed by the control model, which has 

been validated in the step-wise simulation. If true model 

continuity from simulation to execution has been 

achieved, this control model will carry out the control 

logic just the same as it did when simulated. In practice, 

one may not be able to completely replicate the real 

environment in the environment model, and there will be 

potential for design problems to surface in real execution. 

When this happens, re-iteration through the stages can be 

more easily achieved with the model continuity approach. 

3. STEP-WISE SIMULATION AND 

TESTING 

Simulation technology has been widely applied to help to 

design and test real time systems. This technology 

provides a valuable tool for engineers to test and 

understand the system under design. When the 

complexity of a problem is too large to allow an 

analytical solution, simulation is the only option to 

investigate system configurations or operational modes 

prior to the implementation in the field. In this section, 

we will show how different simulation methods can be 

applied to incrementally simulate and test a stand-alone 

system. Simulations for distributed systems will be 

shown in section 5. 

As shown in step 1 of Figure 2, for a stand-alone 

system, three different simulation steps can be applied to 

test the model. They are fast-mode simulation, real-time 

simulation and hardware-in-the-loop simulation. These 

simulation methods apply different simulation 

configurations to test different aspects of the model under 

test. 

Figure 2: Step-wise Simulations of Nondistributed Real Time System

In fast-mode simulation, the control model is 

configured to talk to the environment model through 

sensor/actuator SimActivities. These models stay in one 

computer and a DEVS fast-mode simulator is chosen to 

simulate them. In fast-mode simulation, the flow of time 

is logical (not connected to a wall-clock). So, a fastmode simulator generates simulation results as fast as it 

can. Based on these results, we can analyze the data to 

see if the system under test fulfills the logical behavior as 

desired. 

Just as fast-mode simulation verifies a model’s 

logical behavior, real-time simulation verifies model’s 

temporal behavior. In real time simulation, the model’s 

setting is the same as in fast-mode simulation. However, 

the fast-mode simulator is replaced by a real-time 

simulator, which executes the model at the same speed as 

a real world clock. Since the simulation runs in real time, 

we can test a model’s temporal behavior such as checking 

if critical deadlines can be met. 

In fast-mode and real-time simulations, the model 

under test and the simulators reside in one computer. This 

computer is not the same computer as the one in which 

the model will actually be executed. Instead, a simulated 

environment is provided. However, not all components in 

a complex system can be modeled in adequate detail in 

computer simulation. Sometimes, the executing hardware 

can have significant impact on haw well a model’s 

functions can be carried out. For example, processor 

speed and memory capacity are two typical factors that 

can affect the performance of an execution. Thus, to 

make sure that the control model, having been validated 

in fast-mode and in real-time simulation, also can 

execute correctly in the real hardware, we adopt the 

hardware-in-the-loop (HIL) simulation [6,7]. As shown in 

step 3 of Figure 2, in HIL simulation, the environment 

model is simulated by a DEVS real time simulator on one 

Control 

Models SimActivityEnvironment Model

DEVS Fast-Mode Simulator

Step 1: Fast-mode Simulation: 

Verify Model Logical Behavior

Control 

Models SimActivityEnvironment Model

DEVS Fast-Mode Simulator

Step 1: Fast-mode Simulation: 

Verify Model Logical Behavior

Control 

Models SimActivityEnvironment Model

DEVS Real-Time Simulator

Step 2: Real-time Simulation: 

Verify Model Temporal Behavior

Control 

Models SimActivityEnvironment Model

DEVS Real-Time Simulator

Step 2: Real-time Simulation: 

Verify Model Temporal Behavior

Control 

Models Real 

Environment

DEVS Real-Time 

Execution Engine Step 4: Real Execution:

Interact with Real World

Activity Control 

Models Real 

Environment

DEVS Real-Time 

Execution Engine Step 4: Real Execution:

Interact with Real World

Activity

Control 

Models SimActivityEnvironment Model

DEVS RealTime SimulatorDEVS Real-Time 

Execution Engine

Step 3: Hardware-in-the-Loop Simulation: 

Test Model in Real Hardware

Real 

Environment

Activity Control 

Models SimActivityEnvironment Model

DEVS RealTime SimulatorDEVS Real-Time 

Execution Engine

Step 3: Hardware-in-the-Loop Simulation: 

Test Model in Real Hardware

Real 

Environment

Activity
computer. The control model under test is executed by 

DEVS real-time execution engine on the real hardware. 

This DEVS real-time execution engine is a stripped-down 

version of DEVS real-time simulator. It provides a 

compact and high-performance runtime environment to 

execute DEVS models. In HIL simulation, the model 

under test interacts with the environment model through 

SimActivities. These SimActivities act as simulated 

sensors or actuators. Real sensors or actuators can also be 

included into HIL simulation by using sensor/actuator 

Activities. The decision of which sensor/actuator will be 

real hardware and which sensor/actuator will be 

simulated SimActivities is dependent on the test 

engineer’s testing objectives. With different testing 

objectives, different combinations of real sensor/actuators 

and simulated sensor/actuators can be chosen to conduct 

an exhaustive test of the control model. Notice that in 

HIL simulation, as the control model and environment 

model stay on different computers, a bi-directional 

connection must be established between the two 

computers. We use LAN connection based on TCP/IP 

protocol because it is widely used in industry, can sustain 

high-speed data transfer, and very portable. This 

connection is taken care of by DEVS real-time simulator 

and execution engine so it is transparent to the model. 

Once we passed hardware-in-the-loop simulation, we 

are ready to leave the simulation stage for execution 

stage. As shown in step 4 of Figure 2, in real execution, 

DEVS real-time execution engine executes the control 

model. There is no environment model because the 

control model will interact with the real environment 

through the sensor/actuator Activities. 

4. DISTRIBUTED REAL TIME SYSTEMS 

With the advance of network technology, distributed real 

time systems are playing more and more important roles. 

Figure 3 shows an example distributed system with three 

nodes. Generally speaking, a distributed real time system 

consists of a set of subsystems. Like a stand-along 

system, each subsystem has its own control and 

information processing unit and it interacts with the real 

environment through Sensor/Actuators. However, these 

subsystems are not “along”. They are physically 

connected by network and logically they talk to each 

other and cooperate to finish a common task. Distributed 

real time systems are much harder to designed and tested 

because one subsystem’s behaviors may affect one or all 

of other subsystems. These subsystems influence each 

other not only by explicit communications, but also by 

implicit environment change as they all share the same 

environment. For example, in Figure 3, if Node 1 changes 

the environment through its actuators, this change will be 

seen by the sensors of Node 2, thus affects Node 2’s 

decision making. With this kind of influence property, 

it’s not practical to design and test each subsystem 

separately and then put them together. Instead, the system 

as a whole needs to be designed and tested. 

Figure 3: Modeling, Simulation and Execution 

of Distributed Real Time System

In our approach, a distributed real time system is 

modeled as a coupled model. This coupled model 

consists several subcomponents. Each subcomponent is 

corresponding to a subsystem of the distributed real time 

system. As described in section 2, these subsystems are 

also modeled as DEVS models, which consist of control 

model and sensor/actuator Activities. The control model 

of each subsystem interacts with the real world through 

sensor/actuator Activities. These subsystem models are 

coupled together (by connect one model’s output port to 

another model’s input port) so they can communicate. 

The coupling among the models is corresponding to the 

connection among the subsystems in the real world. 

To test the models of distributed real time systems, 

simulation methods are applied in our approach. For the 

purpose of simulation, environment model and 

sensor/actuator SimActivities are developed to simulate 

the real environment and sensor/actuator Activities. An 

Activity and its corresponding SimActivity share the 

same interfaces so the model using them can keep 

unchanged from simulation stage to execution stage. 

Different simulation methods can be applied to simulate 

and test the models incrementally. These simulation 

methods include centralized fast-mode and real-time 

simulation, distributed real-time simulation and 

hardware-in-the-loop (HIL) simulation. A more detailed 

description will be given in the next section. Note that 

each subcomponent can also be tested/simulated 

independently because DEVS has a well-defined concept 

of system modularity. 

After the models are validated by simulations, they 

are mapped to the real hardware for execution. Similar to 

a stand-alone system, each subsystem needs to conduct 

an “Activity Mapping” to associate the sensor/actuator 

Activities to the corresponding sensor/actuator hardware. 

In addition, as the models are actually executed on 

different network computers, a “Model Mapping” is 

Node1

Real Environment

Real System Simulation & 

Verification Modeling Mapping Execution

Sensor/ 

Actuator

Node2

Sensor/ 

Actuator

Node3

Sensor/ 

Actuator

Network Connection

Model1

Environment Model

Sim

Activity

Sim 

Activity

Sim 

Activity

Model Coupling

Node1 

(Model1)

Real Environment

Activity Activity Activity

Model Coupling over Network

Model2 Model3 Node2 

(Model2)

Node3 

(Model3)
needed to map the models to their corresponding host 

computers. These computers are physically connected by 

the network and they execute the models that are 

logically coupled together by DEVS coupling. To govern 

this mapping, a prototype Model Mapping Specification 

has been developed, which will map the models to their 

network nodes, while maintaining the coupling among 

them. As such, model continuity for distributed real time 

systems means not only the control model of each 

subsystem remain unchanged but also the coupling 

among the component models is maintained from the 

simulation to distributed execution. 

In real execution, the control model of each 

subsystem makes decisions based on its control logic. It 

interacts with the real environment through 

sensor/actuator Activities. If a model sends out a 

message, based on the coupling, this message will be 

sent across the network and put to another model’s input 

port. Again, with model continuity, all the subsystems 

will work and cooperate as were simulated. 

5. SIMULATION AND TESTING OF 

DISTRIBUTED REAL TIME SYSTEMS 

Distributed real time system is inheritly complex because 

the functions of the system are carried out by distributed 

computers over network. With our approach to model the 

whole system as a large coupled model, this model can be 

simulated and tested in our simulation framework. To 

enable simulation, environment model and 

sensor/actuator SimActivities are developed to simulate 

the real environment and sensor/actuator hardware. In 

this section, three different simulation methods are shown 

to give a step-wise simulation and testing of the models. 

These methods are central simulation, distributed 

simulation and hardware-in-the-loop simulation. To help 

to understand these methods, an example distributed real 

time system with two network nodes (two component 

models) is shown in Figure 4. 

The first step is central simulation. In central simulation, 

the two models and environment model are all in one 

computer. Fast-mode simulator and real-time simulator 

are chosen to simulate and test the model respectively. As 

fast model simulation verifies system’s logic behavior, 

real time simulation verifies system’s temporal behavior. 

As central simulation test models’ logic and temporal 

behavior in one computer, it doesn’t consider the network 

effect such as network delay. There are two ways to take 

account of this network factor. One way is to model the 

network and add the network model into central 

simulation. Another way is to run simulation over the real 

network. We adopt the second way to conduct distributed 

simulation of the system. In order to conduct a 

meaningful testing, the network the simulation is running 

should be the same or at least similar to the network the 

model will be really executed. As shown in figure 4, in 

distributed simulation, two models stay on two different 

computers. The environment model may stay on another 

computer or on the same computer as one of the models. 

The coupling between these computers remains the same, 

but it happens across the network. All of these models are 

simulated by real time simulators. These real time 

simulators take care of the underline network 

Figure 4: Simulation of Distributed Real Time 

System 

communication so it is transparent to the model. As such, 

there is not need to change the model for network 

communication. 

In distributed simulation, the real network is 

included so the system is simulated and tested over the 

real network. To further this test, real hardware the model 

will be executed can also be included into our simulation. 

This is the hardware-in-the-loop (HIL) simulation. In HIL 

simulation for distributed real time systems, one or more 

models can be distributed to their hardware to be 

simulated and tested. In the example of Figure 4, Model 1 

along with its real-time execution engine stays on the real 

hardware. Model 2, environment model and their real 

time simulators stay on other computers. These models 

still keep the same coupling. However, the model on real 

hardware may use some or all of its sensor/actuator 

hardware to interact with the real world. Similar to the 

description in section 3, different configuration can be 

applied to test different aspects of the model. 

After all these simulations, we have confidence that 

the distributed system will operate as we simulated. Then 

the models are mapped to the real hardware for 

execution. In real execution, DEVS real-time execution 

engine executes the model and take care of the underline 

Environment 

Model

DEVS Fast-Mode/Real-Time Simulator

Step 1: Central Simulation: 

Verify Model Logical and 

Temporal Behavior

Model1 Model2

Model Coupling

Environment 

Model

DEVS Fast-Mode/Real-Time Simulator

Step 1: Central Simulation: 

Verify Model Logical and 

Temporal Behavior

Model1 Model2

Model Coupling

Environment 

Model1 Model2 Model

Model Coupling

Real-Time 

Simulator

Real-Time 

Simulator

Real-Time 

Simulator

Network Connection

Step 2: Distributed Simulation: 

Verify Model in Distributed 

Environment

Environment 

Model1 Model2 Model

Model Coupling

Real-Time 

Simulator

Real-Time 

Simulator

Real-Time 

Simulator

Network Connection

Step 2: Distributed Simulation: 

Verify Model in Distributed 

Environment

Environment 

Model1 Model2 Model

Model Coupling

DEVS Real-Time 

Execution Engine

Real-Time 

Simulator

Real-Time 

Simulator

Network Connection

Step 3: Hardware-in-the-Loop 

Simulation: Test Model in Real 

Hardware

Environment 

Model1 Model2 Model

Model Coupling

DEVS Real-Time 

Execution Engine

Real-Time 

Simulator

Real-Time 

Simulator

Network Connection

Step 3: Hardware-in-the-Loop 

Simulation: Test Model in Real 

Hardware

Step 4: Real Execution:

Interact with Real World

Model1 Model2

Model Coupling

DEVS Real-Time 

Execution Engine

Network Connection

DEVS Real-Time 

Execution Engine

Step 4: Real Execution:

Interact with Real World

Model1 Model2

Model Coupling

DEVS Real-Time 

Execution Engine

Network Connection

DEVS Real-Time 

Execution Engine
network communication. The environment model is gone, 

as all models interact with the real environment. 

6. EMPLOYING EXPERIMENTAL 

FRAMES FOR TESTING 

In previous sections, we have shown that simulation 

methods can be applied to test distributed or nondistributed real time systems in an incremental fashion. . 

We have discussed a testing methodology that consists of 

an environment model and SimActivities in which 

control logic can be tested. Since such testing mainly 

focus on the interaction between the environment model 

and control model, a more general testing environment 

can be developed using the concept of experiment frame. 

An experimental frame is a specification of the conditions 

under which a system is observed or experimented with 

[3]. A typical experimental frame has three types of 

components: generator, which stimulates the system 

under investigation in a known, desired fashion; 

acceptor, which monitors an experiment to see that 

desired conditions are met; and transducer, which 

observes and analyzes the system outputs. In the context 

of real time software design and test, an experimental 

frame acts as a test module to serve the functions of a test 

event generator, test monitor and performance analyzer. 

The real environment in which the application is 

embedded is usually modeled and included in the 

experimental frame. A related example can be found in 

[8]. 

With the experimental frame concept, the example 

shown in Figure 4 to test a distributed system can be 

generalized as shown in Figure 5. Here, experimental 

frame replaces the environment model to provide a more 

general and powerful testing environment. The 

environment model is still needed inside the experimental 

frame to interact with control model. However, more 

special generators can also be added into the 

experimental frame to provide special case test. Notice 

that with experimental frames, not only can the control 

logic be tested and validated, but also the performance of 

the model, such as average response time, can also be 

measured by using a transducer. Moreover attributes of 

intelligent behavior can be captured through specialized 

experimental frames and tested in the various phases of 

development. 

Figure 5: Testing of Distributed Real Time 

System Using Experimental Frame 

In order to maintain model continuity, special 

attention has to be paid when introducing a test module to 

conduct testing. For example, in section 2 and 4, 

SimActivity has been introduced and we require that a 

SimActivity should have the same interfaces as an 

Activity. Similar restrictions are also needed when 

experimental frames are integrated into the system for 

model testing so that model continuity can be maintained. 

7. CONCLUSION 

Separation of models and simulators as distinct, 

though interacting, elements, supports model continuity. 

This means that the same model may be handled by 

different simulators appropriate to the design, testing, and 

execution phases of intelligent system design. Continuity 

of the control logic model is particularly important for 

design of real time, distributed intelligent systems, 

whose complexity would otherwise overwhelm the 

designers. Modeling and simulation environments, based 

on the DEVS formalism, can support such model 

continuity. An example in robotic system design has been 

developed and will be discussed in future papers. 

Experiment 

Frame

DEVS Fast-Mode/Real-Time Simulator

Step 1: Central Simulation: 

Verify Model Logical and 

Temporal Behavior

Model1 Model2

Model Coupling

Experiment 

Frame

DEVS Fast-Mode/Real-Time Simulator

Step 1: Central Simulation: 

Verify Model Logical and 

Temporal Behavior

Model1 Model2

Model Coupling

Experiment 

Model1 Model2 Frame

Model Coupling

Real-Time 

Simulator

Real-Time 

Simulator

Real-Time 

Simulator

Network Connection

Step 2: Distributed Simulation: 

Verify Model in Distributed 

Environment

Experiment 

Model1 Model2 Frame

Model Coupling

Real-Time 

Simulator

Real-Time 

Simulator

Real-Time 

Simulator

Network Connection

Step 2: Distributed Simulation: 

Verify Model in Distributed 

Environment

Experiment 

Model1 Model2 Frame

Model Coupling

DEVS Real-Time 

Execution Engine

Real-Time 

Simulator

Real-Time 

Simulator

Network Connection

Step 3: Hardware-in-the-Loop 

Simulation: Test Model in Real 

Hardware

Experiment 

Model1 Model2 Frame

Model Coupling

DEVS Real-Time 

Execution Engine

Real-Time 

Simulator

Real-Time 

Simulator

Network Connection

Step 3: Hardware-in-the-Loop 

Simulation: Test Model in Real 

Hardware

Step 4: Real Execution:

Interact with Real World

Model1 Model2

Model Coupling

DEVS Real-Time 

Execution Engine

Network Connection

DEVS Real-Time 

Execution Engine

Step 4: Real Execution:

Interact with Real World

Model1 Model2

Model Coupling

DEVS Real-Time 

Execution Engine

Network Connection

DEVS Real-Time 

Execution Engine
8. REFERENCES 

[1] Lee, E.A., “What’s Ahead for Embedded Software” 

IEEE Computer, Volume: 33 Issue: 7, Sep 2000. 

[2] Pimentel, A.D.; Hertzbetger, L.O.; Lieverse, P.; van 

der Wolf, P.; Deprettere, E.E. “Exploring embeddedsystems architectures with Artemis” IEEE Computer 

Volume: 34 Issue: 11, Nov. 2001 

[3] Zeigler, B.P., T.G. Kim, and H. Praehofer, Theory of 

Modeling and Simulation. 2 ed. 2000, New York, NY: 

Academic Press 

[4] Slan C. Shaw, Real-time Systems and Software, 2001, 

John Wiley & Sons 

[5] J.S. Hong, and T.G. Kim, “Real-time Discrete Event 

System Specification Formalism for Seamless Real-time 

Software Development,” Discrete Event Dynamic 

Systems: Theory and Applications, vol. 7, pp.355-375, 

1997. 

[6] Feijun Song; Folleco, A.; An, E., “High fidelity 

hardware-in-the-loop simulation development for an 

autonomous underwater vehicle” OCEANS, 2001. 

MTS/IEEE Conference and Exhibition, Volume: 1 , 

2001 

[7] Wells, R.B.; Fisher, J.; Ying Zhou; Johnson, B.K.; 

Kyte, M., “Hardware and software considerations for 

implementing hardware-in-the-loop traffic simulation “ 

Industrial Electronics Society, 2001. IECON '01. The 

27th Annual Conference of the IEEE , Volume: 3 , 2001 

[8] Schulz, S.; Buchenrieder, K.J.; Rozenblit, J.W.

“Multilevel testing for design verification of embedded 

systems” IEEE Design & Test of Computers 

Volume: 19 Issue: 2 , March-April 2002 
1

Multiple Neural Network Model Interpolation

Daniel C. Chin and Albert C. Biondo

Johns Hopkins University Applied Physics Laboratory

11100 Johns Hopkins Road

Laurel Maryland 20723-6099

daniel.chin@jhuapl.edu and albert.biondo@jhuapl.edu

Phone : (240) 228-4956

Abstract

This paper presents an efficient method for extracting a multi-model interpolation function from a

nonlinear system. The multi-model interpolation function consists of couple simplified time-varying

models in neural-network structure to dynamically approximate the nature of the physical phenomena to

be interpolated and extrapolated. The purpose of using the multi-model interpolation function is to

perform a real-time approximation. This paper demonstrates the interpolation in a simulated

environment, the underwater acoustic transmission loss generated from the NAVY-standard acoustic

propagation-loss model ASTRAL, which is not suited to real-time operation. The interpolation

includes initial learning period that is on the order of 20 minutes (more or less time depends on the size

of the parameter intervals and the complexity of the ocean environment), and the subsequent

interpolation speed will be measured in fractions of a second, a several orders-of-magnitude

improvement over conventional calculations. In addition, for the example presented here, the

interpolation error is within 1% of the actual transmission-loss value in a root-mean-square (RMS)

sense.

Keywords: Multi-model Interpolation; Multi-objective SPSA; Nonlinear Interpolator; Neural

Network; Nonlinear acoustic wave function.

1 Introduction

This paper develops a model-fitting technique to perform the interpolation and extrapolation of a

nonlinear time-varying system. The development is demonstrated on the problem of transmission loss of

underwater sound. The technique involves simplified time-varying multiple models, neural networks

(NN), and multi-objective simultaneous perturbation stochastic approximation (MSPSA). The

simplified models represent the local phenomena that change in time; NN projects the model variations;

MSPSA trains the NN-weights. The MSPSA was first introduced in Chin [1] and is based on the

simultaneous perturbation stochastic approximation (SPSA) developed by Spall [2]. A collection of

applications of NN in adaptive control of nonlinear systems can be found in Ng [3]. The localized

multi-model technique has shown accuracy and efficiency in the transmission loss interpolation.

The transmission loss function is highly oscillatory and quite variable. There is no simple

representation available to describe the sound wave propagation accurately. The various local medium

interactions and reflections give the function its erratic structure. An interpolation method suggested for

time-variant systems in Gohberg (Ed.) [4, pp. 153-259] was too complicated and worked only on a

single model. In a previous study, a linearized interpolation approach, a simple linear fit between

observed data points, was suggested and tried in FY98 Progress Repot to DARPA [5]. Although the

linearized interpolator would save computation time over the actual simulation calculation, the

preparation of the base transmission loss curves and the massive amount of data handling ultimately
2

make the linearized interpolation intractable. Also, the resulting interpolation errors were not uniform

throughout the parameter space interval desired for the interpolation.

This particular model-fitting design uses two independent neural networks as the base of

interpolation. The models are designed to fit the local physical phenomena and the NN’s store the

model variation information. The interpolator is expected to approximate the sound wave transmission

loss accurately within the training area along the transmission pass, therefore the training process should

provides NN the intermittent information. There are two ways to provide the intermittent information:

1) from an accurate model representation for the inverse estimation like the one introduced in Chin [1];

or 2) the intermittent observations derived from a base model like the ones discussed here for

interpolation. The intermittent observations are accessible in most simulation packages; the utilization

allows the model-fitting technique to use less number of simulated transmission passes and to gain more

information in preparation of the interpolator.

In comparison with a simple linearized interpolator, the model-fitting technique described here

requires longer time per interpolation, but takes orders of magnitude less preparation time. The

interpolation time for the model-fitting technique in comparison with the detailed simulation time is

negligible. The example presented in this paper shows that using 10 propagation loss curves is enough

to train the interpolator for a large portion of the parameter space where interpolation is desired. The

base-propagation loss curves for linearized interpolation would use order of magnitude amount more

transmission curves to achieve a comparable level of accuracy. The ability to use a few propagationloss curves to train the NN-weights for accurate interpolation makes the model-fitting technique

desirable in planing a real-time simulation-training mission that was questionable for the linearized

interpolator.

The NN-weight training procedure also is a very important task for the interpolator; it should

consider the matches for intermittent points and the divergent of two different objectives in the two

models. Given the variability and oscillatory behavior of the function, the training process also should

have some checks and balances. One way to deal with the multiple-objective problem is summing

these objectives and forcing them into a single objective algorithm. However, it is then very hard to find

the balance among the objectives and the convergence speed, see Chin [1]. The MSPSA algorithm

introduced in Chin [1] optimizes the independent model parameter sets (the parameters in one set have

no relationship with the parameters in other set) from relevant objectives and is suitable for training the

NN-weights here. Also, the parameter dependencies are different for the two models, the algorithm

could tailor the minimization procedure to accommodate the differences. In the end, MSPSA used

small number of detailed simulation curves to train the interpolation functions, achieved acceptable rootmean-square errors from the original simulation results, and made real-time operation feasible.

2 Underwater Sound Transmission Loss

Follow the principle of underwater sound in Urick [6], the sound propagating through the ocean was

described in three physical phenomena:
3

• Sound spreads while it propagates through the medium in three different ways: spherically,

cylindrically, and linearly.

• The medium absorbs sound energy, with the rate of absorption varying with the water temperature

and the acoustic frequency.

• Sound signals are also influenced by the reflections from the top and bottom of the ocean water

column. This influence is a function of the local bottom bathymetry and composition, as well as the

sea surface conditions (wave height).

These three effects also vary with the frequency of the sound signal.

Using two models could describe these three phenomena. One model approximates the energy

spreading and absorption because their equations are similar; the other model approximates the

reflections. The energy dissipation models dependents on water temperature at the referenced local

areas; the reflection model depends on both site structures and range from the sound source.

The transmission losses are represented as a ratio of the sound intensity at a given range, say p

nmi, to the intensity of a reference range. If TL represents the transmission loss and T represents the

signal intensity, the sub-indexes represent the values evaluated at the reference points, respectively.

Then, the relation between the transmission loss and signal intensity are as following:

















=

0

10log

T

T

TL p

p

. (1)

The intensity T changes along the transmission pass in a

non-linear pattern. For easier to approximate the

intensity reduction, the sound propagation (the thick line

inside the box on Figure 1) is divided into a fine fixedinterval grid, the grid points are located at unit marks for

convenience of data handling, in our study here uses

nautical mile marks. The marks 0, p on Fig. 1 are the

reference points near source and at receiver. The marks

1,2,L, k are the reference points on the grids.

The total sound transmission loss between source and receiver could be expressed as the

accumulation of transmission losses along the pass, as shown in (2); an expansion of Equation (1):





























+ +

















+

















=

− 0

1

1

10log 10 log 10log

T

T

T

T

T

T

TL

k

k

k

p

p L . (2)

The examples in Section 5 use the nautical mile as grids and 3-ft as the initial 0 reference points. The

receiver mark p is located between grids k and k+1 include k+1.

0

1

depth

Range

p

k

2

Figure 1: The reference points and sound propagation

pass
4

At each reference points, i ∈{0,1,L,k, p}, the transmission loss is also the total effect from all three

individual physical effects that are represented by two models. Let E

Ti

and R

i

T be the signal intensity

reduction value due to energy dissipation and reflection between the reference pointsi −1and i . The

total sound wave intensity at the reference point i would be

( )

R

i

E

Ti = d Ti − T . (3)

where d is the distance between interpolating position and reference point i −1, in (3) the interpolating

position is the position of reference point i . Substitute T i

i ∀ into (2), and then computes the total

transmission loss from source to receiver.

To simulate i

T from detailed non-linear models are both computational and computer I/O intensive

operations. Simplification of the data structure and retrieval system are the first couple steps in reducing

the computational burden and data handling problems. Because mass amount of data for the water

temperature profiles and detailed ocean basin information along the transmission pass are required in

computing the transmission losses from the non-linear accurate models. This paper utilizes a pair of NN

for so purpose.

The equation for energy dissipation intensity reduction formula E

i

T is a simple constant varying

mostly with the water temperature (assuming uniform value within two reference points) and could

theoretically have value range between 0 and 3 (not including 0). For generality, the equation is

expressed in two degree of freedom among E

i

T ’s as following: 













= −

2

1

r

T s

E

i

, (4)

where s and r are two constants with values between 0 and 1 that will be the output from NN (the

outcome could be in expected range for the interpolation area of interests, instead of the theoretical

range). There is no simple expression for R

i

T . This paper uses the first order of trigonometric function

to represent the energy gain from reflection as following:













+











=

720

sin

720

cos

p

b

p

T a

R

i

pf pj , (5)

where a, b, f, and j are four coefficients and would be the output from NN, π is the radian constant,

3.14159L. This equation may be changed due to environmental differences, e.g., with a higher order

representation for a more complicated environment.

The neural-networks are designed for tracking the variations of the coefficients used in (4) and (5)

among the intensity reduction functions due to environmental change along the transmission pass. The

neural-network for the energy dissipation model is a two-hidden layer network with four inputs and two

outputs. The number of weights for each of the two-hidden layers is five. The four inputs are

frequency, source depth, depth at the initial reference point, and delta range from the interpolation point
5

to the initial reference point. The two outputs denoted by s and r are the spreading factor and

absorption rate as defined before. The neural-network for the reflection model is a one-hidden layer

network with four inputs and four outputs. The number of weights for the hidden layer should be

changed according to the size of the geographical area, the bottom type and the sound frequency; a

larger area, more complex bottom types and higher frequencies will use a larger number of the NNweights. The inputs for this network are frequency, source depth, depth at the initial reference point,

and range of the range from source. The outputs for this network are the coefficients of a trigonometric

equation.

3 Interpolation Setting

The underwater sound transmission loss can be expressed in a more general term, such as a system y

consists of two models denoted by f (•) and g (•), and

y = F(f (•), g(•)), (6)

where the function F is nonlinear and model f and g are varying with time. The value of y can be

accumulated from a sequence of intermediate function values iy and

∑

=

=

p

i

i

y y

0

(7)

where iy is evaluated at the reference point i and i ∈{0,1,L,k, p}. Reference points 0 and p are located

at the boundary points; reference points {1,L,k} are located at the internal grid points along the

transmission pass. The individual iy is also a function of (•) if and (•) gi and

= ( (•) (•)) i i giy F f , (8)

Assuming the functions f ( ) i i • ∀ can be approximated by the same function with different coefficients

such as the one in (4), likewise for g ( ) i

i

• ∀ as the one in (5).

Let NN denote a neural network, with NNf and NNg the neural networks for models f and g,

respectively. Assume xf,i and xg,i are the input terms of NNf and NNg at the interval between reference

points i-1 and i. Then,

g i g i

f i f i

x NN g

x NN f

→ →

→ →

,

,

, (9)

where i

f and ig are the neural network output parameters associated with the two models at the same

interval and will be used as the coefficients of (•) i

f and (•) ig . Then these two functions could be

defined as ( ) i f i wf

f x |

,

 and ( ) i g i wg

g x |

,

, where wf wg

, are the weights of NNf, NNg. Function (•) i

f is the
6

function of xf,i based on the weight values of NNf; similarly function (•) i

g is the function of xg,i based on

the weight values of NNg. Letqf qg

ˆ

,

ˆ be the estimated variables forwf wg

, and yˆ be the approximation

value for y and

∑ [ ( ) ( )]

=

=

p

i

i f i f i g i g

y F f x g x

0

, ,

ˆ

, |

ˆ ˆ |q q . (10)

We are trying to minimize ( )

2

y − yˆ and combinations of ( )

2

ˆ

i i

y − y for all transmission loss curves to find

the best fit q f qg

ˆ

,

ˆ ofwf wg

, . Then we could use yˆ as an interpolation value from the given sets of input

{x f ,i} and {xg,i}, i.e. the input parameters defined at the reference points along sound transmission pass

as they are define in Section 2. This setting may easily be expanded into a system that involves more

than two models and may also be used in a control environment.

4 The Training Algorithm

The multiple-objective simultaneous perturbation stochastic approximation (MSPSA) algorithm

presented in [1] is used to train the neural-network weights. The algorithm attempts to minimize the

sum of the square difference between interpolation values and the computed values over the local

intervals, the reference points assumed in the derivation of the equations, as well as the entire data

range. The differences are calculated at each computed value, according to the resolution of the data.

The minimizations are completed over iterations. Any single iteration consists of many small steps from

the individual minimizations that are sequenced one-by-one; let us call the small step a minimization step.

The estimates of one minimization-step will be passed to the next step in the sequence as the previous

estimates of that step. The estimates from the last step in the sequence will be the estimates of the

iteration. The estimates of the iteration will be passed to the first minimization step in the next iteration

as the previous estimate of that step. This optimization algorithm assumes the data was generated with a

consistency setting, similar environment or limited interference. Simulation data has less of a

consistency problem then the real data. Even the inconsistency does exist among the data, the order of

the step sequence will not affect the outcome of the estimates, and it just effects the convergence speed.

The minimization-step procedure uses the equation stated in [2] as an iteration of the SPSA algorithm,

using two statistical perturbation estimates to approximate a gradient that updates the previous

estimates.

For convenience, let f represent the energy dissipation model and g represent the reflection model for

the underwater sound transmission loss system. The subscript-index i indicate the local ranges along

the transmission loss curve. Figure 2 shows the detailed training procedure of “one iteration” as follows:

1) Starting from the initial estimates or the previous iteration estimates, the first step (box 1) is to

minimize the local differences between yi and ( ( ) ( )) i i f i f g i g

y f x q g x qˆ | |

ˆ ˆ |

, ,

 on the weights of NNf while

holding the weights of NNg unchanged.

2) Using the 1st step estimates as the initials, the 2nd step (box 2) minimizes the full range difference

between y and y(g (x ) f (x ) i) ˆ

i g ,i

|qˆ

g

|

f ,i

|qˆ

f

,∀ on the weights of NNg while holding the weights of NNf

unchanged.
7

1

2

3

4

5

no yes

( )

2 min ˆ

i i

i

y − y for qf

ˆ with fixed g

q

ˆ

( )

2 min y − yˆ for qf

ˆ with fixed g

q

ˆ

∑( )

=

−

p

i

yi yi

0

2 min ˆ

for both qf qg

ˆ & ˆ

20 min e ≤1. e

Next Iter.

Repeat 1 – 3 for every pair of SR

Figure 2 One Iteration of Training Algorithm

3) Initializing from the 2nd step estimates, the 3rd step (box 3) minimizes the summation of the square

differences between yi and yi(f (x f i q f ) g(xg i qg )) ˆ

, |

ˆ

ˆ |

, ,

 on the weights of NNf and NNg for a global fit.

4) Box 4 – indicates repeating steps 1, 2, and 3 for all source-receiver (SR) pairs.

5) Box 5 evaluations the estimates of weights on both NNf and NNg. If the estimation error, e , is

greater than 20 min 1. e , the last known smallest error, then reject the estimates and repeat the

previous 4 steps. If the estimation error is within min 1.20e , then update the weights on both NNf

and NNg and proceed to the next iteration.

5. Example

ASTRAL (the Automated Signal Excess Prediction System (ASEPS) TRAnsmission Loss), is a Navy

standard model, included in the Navy’s Ocean and Atmospheric Master Library (OAML). OAML is

a collection of configuration-controlled models and databases, maintained by the Naval Oceanographic

Office (NAVOCEANO). ASTRAL was specifically designed to run rapidly, and is commonly used in

real-time simulations because it runs 10 to 1000 times faster than the traditionally more accurate

parabolic equation (PE) models, as well as other research models.

ASTRAL is primarily a range-dependent, adiabatic, range-smoothed mode theory model, with

additional separate algorithms to model important acoustic features that are not appropriately handled in

the primary algorithm. In particular, ASTRAL uses separate algorithms for convergence zone and

surface duct propagation [6]. ASTRAL can predict the range averaged transmission loss and vertical

angular arrival structure, but only the former quantity is considered here.

The selected model is expected to be run for all propagation calculations required during the Navy

simulations in which is used. Therefore, ASTRAL was run for a variety of environmental conditions and

operational parameters deemed reasonable for the simulation.
8

Assuming a simulation for purposes of real-time operator training, the oceanographic environment

for this example was located in the Sea of Japan. For simplification, a single set of propagation paths

with varying bathymetric details was used in the example. The differences between each of the paths

were in the receiver depth, source depth, sound frequency, and transmission range. We assumed that a

receiver was placed at certain discrete depths in a 200-ft interval from the surface, with the source

placed at various depths between the surface and 1500-ft. Each source and receiver pair was

generated using different frequencies, from 20 Hz to 10000 Hz. The total transmission range was 102

nmi; the resolution to which ASTRAL generated was 0.25 nmi.

The receiver depth, source depth, frequency, and transmission range define the parameter space

interval desired for the interpolation, or the interpolation area, which also defines the real-time operator

training area. In order to have the interpolator work properly; it has to learn the characteristics of the

transmission loss from the data computed

within the interpolation area. The main

criterion for data selection is that the

selected data has to contain all the important

features in the area. For the time being, we

were using trial and error to select 10

source-receiver pairs, each of them at a

different frequency. Figure 3 shows the

transmission loss surface formed by the 10

selected source-receiver pairs. The Y-axis

in the Figure indicates the 10 selected pairs

from 1–10; the X-axis indicates the total

transmission range from 1 – 102 (102 nmi in

0.25 nmi resolution); and the Z-axis shows

the scales of the transmission losses from 50

dB to 100 dB.

Figure 4 shows the transmission loss surface formed by the interpolation values at the same grid

points as in Figure 2. The interpolator learned the features from the first half of the range data, within

50 nmi. Figures 3 and 4 show the matching surface on the left half of the surfaces (shorter ranges < 50

nmi), while missing some characteristics on the right half of the surfaces (beyond 50 nmi). When we

included all of the range data to train the interpolator, the spike on the top-left end of the surface in

Figure 4 was clearly shown on the surface formed by the interpolation values resulting from that

interpolator. The RMS error for the transmission loss surface in Figure 4 is 0.5 dB, about 1 to 2% of

the actual loss values. The interpolator surface showed in Figure 4 takes 360 iterations, for real-time

operation 200 iterations would be sufficient for a 25-nmi range operation area; the RMS error for the

smaller range operation area was about 1 dB in the case mentioned in the abstract. Using the more

detailed simplification models, f

ˆ and gˆ , would have less RMS errors, but they will take a longer time

to train.

Figure 3 ASTRAL Generated Transmission Loss Surface
9

6. Summary

This paper presents a model-fitting technique for

use as an interpolator for underwater acoustic

transmission loss. This interpolator could be

useful in creating function approximation for one

local domain in the adaptive interpolator

discussed in Spall [8]. The error of this

interpolator was tolerable and the computation

speed was adequate for real-time training

operations. There is a tradeoff between

accuracy and desired speed  the more

accuracy required the more time required for the

NN to train. There is also a tradeoff between the

accuracy and the complexity of the operational

area. There is room for improvement in the

optimum source-receiver pair selection and in the

estimation model formulations. This technique

could be used for extrapolation and inversion processes.

Acknowledgements

This work was supported by DARPA under contract MDA972-96-D-0002 in support of the

Advanced Simulation Technology Thrust Area, Multiresolution Interaction Validity Project.

References

[1] Chin, D. C., “Simultaneous Perturbation Method for Processing Magnetospheric Images,” 1999,

Optical Engineering, v. 4, pp. 606-611.

[2] Spall, J. C., “Multivariate Stochastic Approximation Using a Simultaneous Perturbation Gradient

Approximation,” 1992, IEEE Trans. Autom. Control, v. 37, pp. 332-341.

[3] Ng, G. W., Editor, Application of Neural Networks to Adaptive Control of Nonlinear

Systems, 1997, Research Studies Press. LTD. & John Wiley & Son Inc., ISBN 0471 97263 0.

[4] Gohberg, I., Editor, Time-Variant Systems and Interpolation, 1992, Birkchasuer Verleg Basel,

ISBN 0-8176-2738-3.

[5] “FY98 Progress Report; Multiresolution Interaction Validity Project,” 1998, Advanced

Simulation Technology Trust, STD-R-2774.

[6] Software Test Description for the ASTRAL Model, Naval Oceanographic Office, NRL OAMLSTD-23E, 1994.

[7] Urick, R. J., Principles of Underwater Sound, 1976, McGraw-Hill, ISBN 0-932146-62-7.

[8] Spall, J. C., “Adaptive Model Fitting with Time-Varying Input Variables,” 1999, The Proceedings

of 1999 American Control Conference, San Diego, CA, pp. 1435 - 1440.

Figure 4 Interpolated and Extrapolated Transmission Loss

Surface.
A Three-Tier Communication and Control Structure for the Distributed 

Simulation of an Automated Highway System * 

R. Maarfi, E. L. Brown and S. Ramaswamy 

Software Automation and Intelligence Laboratory, Department of Computer Science 

Tennessee Technological University, Cookeville, TN 38505 

Phone: (931)-372-3691. Email: srini@acm.org / srini@ieee.org 

ABSTRACT: - This paper presents an Automated Highway 

Simulation using a hierarchy of communication to resolve 

advanced traffic situations. The simulation is being done to study 

communication paradigms, traffic concepts, and road design. The 

simulation is three dimensional; projected in a two-dimensional 

space, and implemented as a client/server and peer-to-peer system 

using Java. Vehicles communicate with each other using a linked 

graph called a 6 way DSM, or dynamic socket mesh. Cars also use 

a higher communication layer - called a segment controller, and 

segment controllers coordinate with each other using the 

simulation controller. 

I. INTRODUCTION

This paper presents the simulation of an automated 

highway system with intelligent vehicles using higher-level, 

off-road intelligence to aid in their guidance. This 

simulation is being designed to study communication 

paradigms, road design, and self-correcting systems. The 

concept of an automated highway system, or AHS, is 

nothing new. An AHS is a system that allows vehicles to 

drive themselves without human intervention. General 

Motors Corporation first introduced AHS in the 1939 World 

Fair. This system, and others that have followed, have made 

necessary the complete absence of human drivers. This 

simulation does not have that limitation. The main 

objectives of an automated highway system are not unlike 

any other system. The system should do its job as efficiently 

and as safely as possible while trying to take all errors that 

can occur into account. The system is designed to work 

around errors that may otherwise prevent it from 

functioning. 

It is important to mention that a few key concepts have 

emanated from a four-layer protocol used in Berkley’s 

PATH laboratories, funded by the Federal Highway 

Authority. The three-tier communication system discussed 

in this paper is designed to anticipate errors that may occur 

inside any part of the communication system itself, and will 

eventually be aware of mechanical failures that can occur 

and give the vehicle or the driver instructions. If any part of 

the communication system should fail, the remaining parts 

will change to a different operating mode to compensate for 

the failure. Each layer of the three-tier system is necessary 

for full functionality, but any layer can be removed during 

the simulation without catastrophic effects. 

Platooning is the most obvious method available to 

increase road traffic density. A platoon is a group of 

vehicles with just enough space between them to react to 

each other, possibly even moving at predefined speeds. 

Previous systems, however, have relied on slightly slower 

speeds and closer vehicle proximity for a highway 

utilization increase of four times what human drivers use 

today. This increases the capacity of the highway by 

properly utilizing available space. Platoons are isolated 

from each other, and from human drivers, by large gaps. 

Earlier studies have shown that platoons can be created 

without having an adverse effect on safety. In this 

simulation, since we allow human-controlled cars, platoons 

are restricted. This control is a parameter of the simulation 

controlled by the second tier. Cars will then stay at a twosecond following distance and switch lanes to maneuver 

around vehicles when necessary. With other humans 

around, the cars can be made to follow the normal rules of 

the road. 

An AHS is one of the most appropriate applications to 

test a design for a self-correcting system because of the 

unpredictability factors, specifically humans and mechanical 

failure. Since human-controlled cars are allowed in the 

system, there is even greater unpredictability. Weather can 

seriously alter the performance of a vehicle also, but this is 

not currently added to the simulation. Communication layer 

failure is already implemented. 

The simulation is implemented as a client/server 

networking application, written in Java. Vehicle to vehicle 

communication is allowed via peer-to-peer network 

connections, and communication between the segment 

controllers, the simulation controller and the vehicles are 

handled by client-server methods. This networking 

environment allows for a suitable way to test 

communication protocol and efficiency. The system was 

designed for efficiency in terms of processor utilization and 

network utilization. Each layer is handled at the lowest 

level possible, thus minimizing the need for passing 

information around the network. This is illustrated in Figure 

1. The communication architecture is divided into three 

layers: 

i. Vehicle communication – Possibly representing sensors 

or radio communication, this is a way for cars to get 

information about the immediate surrounding cars such 
as speed and position. In the simulation this is 

implemented by means of a linked list structure, the 

dynamic socket mesh or DSM. 

ii. Segment Controller – Segment controllers are local to 

specific sections of the highway. These sections are 

predefined internally, or are given to each segment 

controller by the simulation controller. Each vehicle 

only needs to communicate with the segment controller 

unless it is unavailable, then reaching the simulation 

controller is necessary. The segment controller informs 

a car of a malfunction in the six-way DSM. 

iii. Simulation Controller – Responsible for assigning 

sections of the highway to segment controllers. The 

simulation controller may also act as a segment 

controller if a segment controller is unavailable. Future 

plans involve routing of traffic during construction or 

accidents. 

The paper is organized into four sections. In section II, 

the vehicle communication, segment controller and 

simulation controller are described along with their 

respective issues. In section III, the details of 

implementation are discussed. Section IV concludes the 

paper. 

II. DESIGN ISSUES

In our research, we have noticed that some limited 

intelligence need to be available to the vehicles themselves. 

Intelligent vehicles are a solution to the overhead normally 

associated with previous roadside-control design 

bottlenecks. In order for vehicles to behave intelligently, 

they must first be able to communicate with each other and 

share some required information. This section describes the 

design architecture for that communication and the 

application developed as a test-bed for evaluating 

performance. We assume three kinds of cars to be available 

in the simulation; viz. (i) Computer-Controlled Cars: These 

cars are the “smart” cars that can be controlled by the 

simulation and demonstrate the highest degree of 

intelligence. (ii) Human-Controlled Cars: These cars are 

controlled by human operators from a terminal and are 

completely unpredictable – similar to any highway scenario. 

(iii) Traffic Cars: These are “dumb” cars on the highway 

that just cruise along to create traffic situations – they are 

not controlled by any intelligent decision making process. 

For our work the following three communication 

paradigms are readily applicable. The first, autonomous 

resolution; relies on receiving immediate data from the 

surrounding vehicles. This is a very limiting paradigm 

because in real-world situations vehicles may be unable to 

successfully request surrounding vehicles to even change 

lanes. The second approach, using a master/slave 

resolution, is not used unless a car is in a platoon. The 

platoon leader is then assumed to be the master, and directs 

computer-controlled cars in its platoon similar to the way a 

traffic cop directs traffic. 

The third paradigm, the mutual resolution paradigm, 

allows vehicles in close proximity to switch lanes without 

running into each other and to allow cars to switch into the 

desired lane in heavy traffic. This paradigm is used in our 

simulation for resolving the majority of highway maneuvers, 

however, application of a particular paradigm depends on 

the environment in the simulation. For example, if a human 

controlled car is in front of a computer-controlled car, the 

human controlled car cannot be asked to move out of the 

way or change speeds. A computer-controlled car is also 

unable to switch lanes, unless it is able to get clearance, 

which again may not be possible with a human-controlled 

car in the way. Mutual resolution, however, would allow a 

computer-controlled car to switch lanes first if, for example, 

it needed to reach the exit that would otherwise be blocked. 

A central controller is not necessary for this situation if each 

car is able to communicate its current goals to the other car, 

and each car is aware of what to do depending on the 

adjacent cars’ (shared information, if available) goals, its 

goals, and its surroundings. This is similar to a finite state 

machine. Each car in a normal situation makes its decisions 

based on a predetermined coordinating logic. 

There are advanced traffic situations involving 

nonstandard vehicles, such as ambulances, roadblocks and 

closed lanes. Using sensor data (simulated data this 

implementation), a car may be able to see a blocked lane or 

a stopped car, but the delay in seeing the block and being 

able to switch lanes may cause significant traffic blockage. 

Segment 

Controller 

Simulation 

Controller 

Cars on 

Highway 

Traffic cars 

Computer-controlled cars 

Human-controlled cars 

Client-Server message passing 

Peer-to-peer message passing 

Figure 1. Control and Communication Overview
The same case applies with an ambulance, as shown in 

Figure 9 and Figure 10. Sensor data will allow a car to 

identify a vehicle and its speed, but it would be unable to 

switch lanes if another vehicle were to be in the way. While 

the implementation is currently not present, a car might also 

be advised to take a detour if an accident has occurred or 

road construction prevents passage. These and similar tasks 

are handled by the segment controller. Each segment 

controller is responsible for an arbitrarily large section of the 

highway. The segment controller also informs cars of the 

existence of human drivers for resolving the decision to 

platoon and the speed at which to travel. 

Since a segment controller only oversees a relatively 

small, portion of the AHS, a higher communication level is 

necessary for the controllers to be aware of unfavorable 

occurrences down the road, such as weather, traffic, or road 

construction, between segments and to assign segments of 

the highway to their respective controllers. This task is 

given to the simulation controller. Without the simulation 

controller, vehicles are unaware of intelligent decision 

making to decide upon the most efficient route to their 

destination. Besides, the simulation controller also is incharge of initially setting up the necessary parameters of the 

simulation. 

Each level of communication, however, is designed to 

function in the absence of the other. For example, if a car’s 

sensor should fail, the segment controller will be aware of 

the discrepancy between what one vehicle is reporting and 

what another vehicle is reporting. Once aware, the segment 

controller is capable of guiding a car to the nearest exit by 

relaying information that vehicles are reporting through the 

segment controller or, depending on the existence of humans 

in the segment, merely instruct the car to pull over or for the 

human driver to assume control. If a segment controller 

should fail, the simulation controller has the ability to 

function as a temporary segment controller. The vehicles 

attempt communication with the simulation controller 

automatically whenever failure with the segment controller 

occurs. This also allows for segment controllers to be 

changed without affecting the simulation. If both the 

simulation and segment controllers should fail, the vehicles 

will still be capable of navigating the highway, but they will 

be unable to platoon since the existence of human drivers is 

unknown. They will be unaware of advanced traffic 

situations. 

As shown above in Figure 2, three control types are 

available. Traffic 1 represents a car that is unaware of its 

environment and is only there to be an obstacle to other 

vehicles. Traffic 1 never makes a lane change, however, 

Traffic 2 randomly changes lanes. It will not change lanes 

into other cars, but provides an advanced challenge to the 

computer-controlled cars. The computer-controlled car is 

capable of joining or leading platoons, and it is capable of 

intelligently navigating around traffic and human-controlled 

cars. Human-controlled cars are completely driven by an 

operator at the terminal. The car controls are shown in the 

mid-right section and are capable of allowing lane changes 

or speed changes. The speedometer shows the current 

speed, and the bottom scrollbar shows the current lane. 

III. IMPLEMENTATION

Figure 2. Four types of cars in simulation 

Figure 3. Links and Visualization 

192.168.0.4:5004

192.168.0.4:5003

192.168.0.4:5005

192.168.0.4:5005

192 1. 68.0 4. :5003

192.168.0.4:5004

192.168 0. .4 5: 004

Figure 4. Internal representation of links 
Effective networking, avoiding delays and redundancy, 

is critical to this project because of the representation of 

vehicle communication between each other, between 

segment controllers and between simulation controllers. To 

simulate real-world radio/wireless communications, the 

simulation uses TCP/IP sockets to communicate with each 

other. Necessary sensors are also represented as sockets in 

the implementation. 

Inter-vehicle communication is represented as a series 

of six sockets, called a six-way dynamic socket mesh, or 

DSM. Each direction represents what the vehicle would see 

at a given location, specifically forward, forward-right, 

backward, backward-right, backward-left, and forward-left. 

This is a linked structure that forms a graph, and each car is 

a point on the graph. This represents sensory information 

gathering in a real-world implementation, so they are able to 

get basic operational data from these connections. 

However, using the mutual resolution paradigm allows for 

cars to communicate with each other to exchange awareness 

of their current goals – this will need more than just sensing 

capabilities; i.e. intelligent message passing, if implemented 

in a real-world scenario. However, to mimic expected 

reality, this is assumed to only happen between two 

computer-controlled cars. 

Automatic testing programs were written to ensure 

proper functioning of the simulation. In addition to testing 

programs, a rudimentary visualization was implemented to 

analyze the simulation. Java was the language of choice for 

the implementation, since it aids in portability and simplifies 

the distributed networking implementation. Figure 3 and 

Figure 4 demonstrate how the links are represented. The 

right hand side of Figure 3 shows the visualization 

application. 

The dialog boxes shown in Figure 3 surrounding the 

speedometer represent the data stored by each car, which 

can be retrieved by double clicking on either the individual 

listing of the car in the main control application or by double 

clicking the list in the Visualization. This shows that each 

car identifies and locates others by storing IP addresses and 

sockets. Therefore, in the simulation, computer-controlled 

cars have the same link maintenance logic as human and 

traffic. The green cars in Figure 3, (and Figure 5, Figure 6) 

are traffic cars while the red car is a human-controlled car. 

It is to be noted that in the visualization, cars are traveling 

downward, so in this case the forward link is actually down, 

right is left, vice versa. In Figure 4, the circles represent 

cars and their unique nodes on the network, and the arrows 

pointing to other cars represent the connections on the 

graph. This concludes that a link is represented by two 

TCP/IP socket connections, which is similar to sensors 

mounted on the two cars. 

In Figure 5 and Figure 6, the continuing validity of the 

links is illustrated. It shows that as the red car passes the 

first green car, it looses its forward right connection to it and 

gains a backward right. Conversely, the green car gains a 

forward left and looses a backward left. The front green car 

gains a backward left. Note that the backward left link was 

invalid to have by definition of the graph until the first green 

car had been passed. 

As mentioned earlier, the segment controller is in 

charge of all vehicles within a small portion of the highway. 

When a segment controller is launched, it connects to the Figure 5. Passing the first green car 

Figure 6. Passing the second green car Figure 7. Simulation and segment controllers 
simulation controller to determine what area of influence it 

has. If the simulation controller is unavailable, it reads from 

a file what section it is supposed to be controlling. This 

allows functionality without the simulation controller, but 

allows the simulation controller to assign new areas if 

change is necessary. Unlike the segment controller, each 

vehicle first attempts to connect its last known segment 

controller for the area it is currently located. If no segment 

is available, the car then attempts to connect to the 

simulation controller to locate another segment controller. 

If the simulation controller does not have a predefined 

segment for the vehicles location, the car is informed. If a 

segment is predefined but a controller does not exist, the 

simulation controller launches a segment controller for it to 

connect to. This means that the simulation controller 

actually runs segment controller logic if a segment is absent, 

and tells the car to connect to it. 

Figure 7 shows two segment controllers executing and 

the simulation controller at the top of the screen. The three 

lines (of text) at the top of the simulation controller 

represent a predefined segment. The first two are active, 

and the last is inactive. The three white areas in the middle 

of the segment controllers (no cars are located in either 

segment) represent each lane of the highway. The change 

right and change left buttons allow for the movement of cars 

by the segment controller for testing purposes, as does the 

set speed. 

A useful application of the simulation controller can be 

seen in Figure 8. Figure 9 and Figure 10 show the 

corresponding implementation for the scenario depicted in 

Figure 8. Here, a computer-controlled vehicle – an 

ambulance, gets priority in using a lane and all computercontrolled cars are instructed to move to a different lane. 

The ambulance will use either the segment controller or the 

simulation controller to clear its lane. In the case illustrated 

in Figure 9, an ambulance vehicle has just been informed of 

an emergency, but two cars (a red human controlled car – 

immediately upfront and a green computer-controlled car 

before the human controlled car) are in the way. The 

human-controlled car (line of sight communication) may not 

immediately move over while the other car is a computercontrolled car. Since the ambulance is unable to directly see 

the computer-controlled car, it would be unable to inform 

the car to move out of the way. However, with the segment 

controller, the ambulance sends a message to the segment 

controller, which, in turn, sends a message to every 

computer or traffic-controlled car in the lane, within a 

predefined range, commanding it to move out of the way. 

Figure 10 shows the segment controller’s ability to request 

the green car (computer-controlled) out of the ambulance’s 

way, even though the human car (one right before the 

ambulance in the middle lane) is blocking its view. 

Segment 

Controller 

Simulation 

Controller 

Cars on 

Highway 

Traffic cars 

Computer-controlled cars 

Human-controlled cars 

Client-Server message passing 

Peer-to-peer message passing 

Emergency vehicle 

1a 

2a 

1b 

2b 

Figure 8. Communication Flow in an Emergency 

Situation

Figure 9. Ambulance wishes to pass 

Figure 10. Ambulance can only move computer 

cars out of its way 
The segment controller increases network efficiency in 

cases with more cars because each node on the graph does 

not have to be traversed to relay messages. However, a 

computer-controlled car would only be able to dodge the 

ambulance if it was directly in front of it. 

More importantly, a lane lock has occurred in the 

middle lane. Set by the segment controller, computer 

controlled cars are unable to switch back into the middle 

lane until the ambulance is out of the way. To increase 

network efficiency, vehicles are unaware of the lane lock 

until attempting to switch into the lane. This allows cars to 

switch lanes without informing the segment controller. 

Without the segment controller, cars would have to check 

their diagonal links for situations like an ambulance, but due 

to the functionality of the links demonstrated in Figure 3, 

Figure 5, and Figure 6, they might still not have seen the 

ambulance coming until it began to switch lanes. 

IV. CONCLUSION

This paper presented the implementation of a flexible 

and robust three-tier communication and control structure 

applied to a distributed simulation of an Automated 

Highway Simulation. Vehicles can be controlled by humans 

or be logic driven. This paper captures the most likely 

scenario of any future AHS as a result. The Java platform 

provides adaptability and object-oriented structure that 

allows for modularization for easier future modification and 

maintenance, and allows portability. The paper has also 

shown that while external communication is not necessary 

for functionality, efficiency is greatly increased and safety in 

the face of failure is ensured. 

V. REFERENCES

[1] R. E. Fenton and R. J. Mayhan, “Automated Highway Studies at Ohio 

State University- An Overview,” IEEE Trans. on Vehicular 

Technology, vol. 40, no. 1, pp. 100-113, Feb. 1991. 

[2] C. Unsal, P. Kachroo and J. S. Bay, “Multiple Stochastic Learning 

Automata for Vehicle Path Control in an Automated Highway 

System,” IEEE Trans on Systems, Man and Cybernetics, vol. 29, no.1, 

pp. 120-127, Jan. 1999. 

[3] D.N. Godbole, J. Lygeros, E. Singh, A. Deshpande, A.E. Lindsey. 

“Communication Protocols for a Fault Tolerant Automated Highway 

System”. Dept of Elec. Engg. and Comp. Sciences. Univ. of CA. 

[4] R. Donath, April Dean, D. Girardi and S. Ramaswamy, " Distributed 

Simulation of an Automated Highway System with Intelligent 

Vehicles ", 2001 Summer Computer Simulation Conf, July 2001, 

Orlando, Florida. 

[5] Luke D. Postema, “Automated Highway Systems: Incremental 

Deployment as a Solution for The Future of Transportation”. 

Washington internships for Students of Engineering. August 1998 
Tessellating and Searching Uncertain State Spaces

A. Meystel, A. Bathija

Department of Electrical and Computer Engineering, Drexel University

Philadelphia, PA 19104

Abstract

Multiresolutional S3-search generated a need to properly

tessellate spaces and efficiently searching them. Drexel

University has introduced MR-methodology such as uniform

and non-uniform space tessellation and efficient algorithms

of searching within ressellated state space. This methodology

for solving planning and control problems is successfully

applied in autonomous vehicles, industrial robots and power

stations. This paper focuses on computational phenomena

characteristic for randomized tessellation and affecting the

results of S3-search.

Keywords: bias, Dijkstra algorithm, envelope,

multiresolutional, randomized tessellation, shaking the grid,

search in the state space, stripe, uniform and non-uniform

tessellation, variable traversability

Introduction

To “tessellate" to form or arrange

elementary units of space in a mosaic fashion so

that all selected space be covered. Tessellation of

a closed polytope means decomposing it into

non-overlapping sets of tessellata (granules,

boxes or tiles). These subsets form an

equivalence class in which all the points within a

tile are identified with a single label. The natural

interpretation is that each of the labels represents

all points of a single tessellatum while its

generalized location is in the center of the

tessellatum. There are three basic types of

tessellations:

1. Regular Tessellation

2. Semi Regular Tessellation

3. Irregular Tessellation

Regular Tessellation: This means a tessellation

made up of congruent regular polygons. Regular

means that the sides of the polygon are all the

same length. Congruent means that the polygons

that you put together are all the same size and

shape. Only three regular polygons tessellate in

the Euclidean plane: triangles, squares or

hexagons. Regular tessellation are shown in

figure 1.a.

Semi-Regular Tessellation: A variety of regular

polygons is used to make semi-regular

tessellations. Examples of this type of

tessellation are shown in Figure 1.b. This

tessellation has two properties:

1. It is formed by regular polygons.

2. The arrangement of polygons at every

vertex point is identical.

Irregular Tessellation: In this case points are

sprinkled randomly. This randomity is equivalent

to the uncertainties o sampling the state space.

There are quite a few methods of generating

random tessellations.

Generating randomized tessellation.

S

3

-search requires generating random

points in the state space [1]. The law of

distribution that characterizes coordinates of the

random points describes the uncertainty of the

state space taken into consideration. There are

tessellation

by triangles

tessellation

by squares

tessellation

by

hexagons

(a)

 

(b)

Figure 1: Different types of tessellation

many methods of generating randomized graphs

(Irregular tessellation). A few of them are listed

below.

1. Random generation of grid-points

positions: Random points in the state space can

be generated by a standard random number

generator. A deficiency of this method is in the

fact that “random” points may not be evenly

distributed. In other words, the density of the

particular set of points in all regions of the state
space may not be the same. This may occur

because of dealing with imperfect random

number generators. However, this shortcoming

can be dealt with by multiple running the

algorithm that is being tested.

2. Randomizing by “shaking the grid”: In

this method we use a grid to generate the basic

set of points. The grid points are considered to be

at the intersection of a row and a column. Then

we introduce a random shift to these coordinates

of points: shake the grid. Thus, the points move

away from the initial positions into random

positions.

 

The shift is assigned in the form of a

fraction (percentage) of the interval between the

rows and the columns (Δ). Figure 2 explains the

generation of the randomized tessellation using

the grid method. The advantage of this method is

that it ensures that the overall density of the

points is the same in all the regions of the graph.

But this true only if the shift in the points is less

than or equal to 50% (.5Δ). If the shift exceeds

50% the distribution gets similar to the one

observed in the random number generation

method. This comparison is shown in Figure 3.

3. Concentric circle non-uniform grid

generation: This method is similar to the grid

generation method. In the latter we used straight

lines to generate the grid. But in this case we use

concentric circles instead of the rows and the

radius replaces columns. The specifics of this

method is that there are many points generated

close to the center of the circle and as we move

away from the center the density of the points

decrease. This method is useful in the cases

when a variable resolution is attempted within

one level of representation like in page 4 of [2],

where the fine motion planning is obtained for a

few steps in the vicinity and the further we move

from the acting robot the lower the resolution is.

Certainly, any law of gradual decrease of

resolution can be assigned. In Figure 4: we

illustrate how the random points are generated by

this method.

Randomized Grid Generation

The algorithm of randomized grid

generation is described by this pseudocode:

1. Start.

2. Obtain the parameter of the grid

a. Total number of points

b. Total number of rows

c. Total number of columns

d. Check if the product of the rows and the

columns is equal to the total number of

points. It true then continue else go to

step 2.a

e. Obtain the starting coordinate of the

row and the column.

f. Obtain the delta for the row. Check if

the obtained value is valid.

Grid Shaken

Figure 2: Shaken grid method of

generating random points

Shaken grid

method with

50% shift

Random

number

generator

method

Shaken grid

method with

150% shift

Figure 3: Comparison of the various

methods for generating the grid

Figure 4: Non-uniform Random

points generation using concentric

circles
g. Obtain the delta for the column. Check

if the entered value is valid

h. Obtain the shift percentage of the

points.

i. Calculate the max possible shift of a

point with respect to the row and the

column delta.

3. Repeat the following for total number of

rows (columns).

i. Assign a random sign (+ or -)

ii. Obtain a random value with the

max possible shifts for each of the

row and column.

iii. Add this shift to the corresponding

row and column coordinate.

4. Store this information

5. Stop.

Randomized graph using different

distributions.

In the above pseudocode, we assign the

“sign” that a random generator uses to obtain the

random shift within the maximum limits.

Different distributions can be used to obtain this

random value. Uniform distribution is the

distribution that we have used in our

experiments. In uniform distribution the random

point obtained has equal probability of falling

anywhere in the space specified for it. While if

we use the Guassian Distribution then the points

have a greater probability of falling close to the

center of the shift compared to the probability of

falling at the edge of the maximum shifts.

Searching on the Grid Problem: Why

Bias?

This is a useful advantage and the

disadvantage of a grid that from any node in the

grid to another there are many equal (or

approximately equal) paths. This can be seen in

Figure 5.

In the above Figure, it is clear that there are 3

possible equivalent paths from node A to node

B. On using the new algorithm on the grid, it

tends to give the full line path (in Figure 5) as

the optimal solution. It turns out to be that if the

starting node is on the left hand side and the

destination point is on the right hand side and

there are many possible paths from the starting to

the destination then the left most path will be

given as the optimal solution. Similarly if the

starting point in the right hand side and the

destination on the left hand side, then the right

most path among the possible paths will be given

as the optimal one. For a single run of the mincost search see Figure 6.

The concept of Vicinity

Unlike the standard Dijkstra search

algorithm, our search in the randomized grid

does not have any graph prepared for exploration

in advance for exploration. The recommended

algorithms builds the graph as it explores the

graph. The concept of a vicinity is introduced.

The present “standpoint” is being surrounded by

a “vicinity” that can have a “radius” of 1, 2, (or

more) of average edge length (v1, v2, v3). Edges

are constructed to all nodes in the vicinity. After

the current “cheapest” node is found, it becomes

the standpoint node and is being surrounded by

its vicinity. Certainly, the solutions of 3-vicinity

lead to “straighter” trajectories, but it takes more

complexity to compute them.

Superimposing Multiple Search Results

A better way of finding the generalized

result of searching in the randomized graph is

running search in multiple random grids and

superimposing their solution. In Figure 6, we

show the results of such superimposing of

several min-cost paths for a dynamic system of

Initial point

Front of the

search

Trajectory

Final point

 Figure 6: Velocity vs Distance with Friction (v1)

A

Figure 5: Three possible paths from node A to

node B in the grid
the first order (the acceleration and deceleration

of a constant mass on a surface with friction.

One can see that acceleration is not as rapid as

deceleration. (This test further develops our prior

results from [3]). It is important that in a

multiplicity of superimposed random search

results all stochastic components compensate for

each other, and the envelope (the stripe for the

subsequent higher resolution search) has smooth

edges (as seen in Figure 9).

Figure 7: A single search performed on a grid with a shift of

30% and vicinity 1.

Solution to the grid problem

To avoid getting the extreme paths as

the solutions we have to make sure that there

aren’t any “equidistant paths” from the starting

to the destination point. This can be achieved by

increasing the shift in the points there by

eliminating the distorting effect of the grid.

(a) (b)

Figure 8. (a) Super imposed image of 30 runs of 30% shift

(b) Super imposed image of 30 runs of 60% shift. The above

runs were for performing a search of optimal path from the

(0,0) location in the grid to (29,7) location

A comparison of the search with shift of 30%

and 60% is shown in Figure 8 as the results of

searching optimal path from the (0,0) location in

the grid to (29,7) location It is clear from the

Figure 8, the phenomenon of Bias is due to the

grid effect. It was found that 50% shift is the

most beneficial for removing the distorting grid

effect. In other words, by shifting from a regular

tessellation to a irregular tessellation we can

reduce the bias problem substantially. Large bias

is shown in Figure 6, a reduced case in Figure 8.

Figure 9. Envelope (“stripe”) for the subsequent search of

higher resolution obtained by superimposing multiple results

of randomized searching.

Using randomization of the grid for

virtual modeling the uncertainties

While using the tessellation for any

particular purpose, 3 components affect actively

the results of the execution.

1. Grid Law: The law is determined by the

technique that we use to generate the tessellation.

2. Grid Density: This is computed as a

ratio: the quantity of points located in the state

space over its volume (area).

3. Shift of the randomization: This is the

Results of “shaking” the grid (percentage)

introduced to generate the effect of

randomization.

Among these 3 components, the grid

density and the shift of randomization create an

intrinsic error of the path. When solving the

same problem analytically, the solution (path)

obtained has a stochastic component in it, which

is perceived as a noise, a part of the uncertainties

of the sources. This stochastic component is

equivalent to the intrinsic error. Hence, if we

know the sources of the uncertainty in the system

(e. g. in the form of the values of Expectation
and the Variance of this uncertainty) we can

assign the grid density and the shift and the

results of randomization will be equivalent to

using the models of stochastic components, since

we obtain the same Expectation and the Variance

as the model would produce. In other words,

instead of modeling the uncertainties we are can

build the tessellation in such a manner that is

produces this statistical truth.

Randomization in the Case of Multiple

Traversabilities Segments

It is instructive to consider a case of

finding the minimum cost path for a case of

having multiple traversabilities of the state space.

In Figure 10, a space segmented in multiple

zones of traversbility is shown and the results of

v1-searching for the case when traversability

gradually reduces clockwise.

Figure 10. A single run of the S3-search algorithm and a

randomized grid

A question that should be answered: how to

determine the width of a stripe (envelope) within

which the search should be executed at a higher

level of resolution. We exercised computing the

boundaries for a stripe with 3σ width on the right

and on the left around the average approximation

of the path trajectory. This is a pretty

cumbersome computation that includes the

following stages: a) finding the approximation of

the single path known, b) assuming that this

approximation can be considered “an average

recommended trajectory,” c) computing the

value of 3σ from the information of uncertainty,

d) constructing piecewise boundaries around the

average trajectory.

The following technique was tested and

seems to be more practical. The search is being

run many times, and the results of this multiple

search are collected together and are considered

a “stripe” (envelope).

In Figure 11, the results of this

experiment are brought together for the case

shown in Figure 10.

Figure 11. The cumulative results of conducting multiple

search for the case shown in Figure 10.

One can see that a stripe emerges that

demonstrate the statistical representation of the

zone that is preferential for the subsequent higher

resolution searching.

Interesting observations can be made: a)

the stripe is narrowing as the traversability of

tthe path is reducing, b) the stripe is narrowing in

the areas of leaving IP and reaching the GP, c) in

some areas the distribution gravitates to the

unimodal law (like in Figure 8b); however in

many areas the distribution is similar to the

uniform one

References:

[1] A. Meystel, J. Albus. Intelligent Systems:

Architectures, Design, Control, Wiley, NY,

2002, Pages 381-387

[2] A. Meystel, “Learning-Planning-Control

Continuum,” Ed. by H. S. Sarjoughian, et al,

2000 AI, Simulation and Planning in High

Autonomy Systems, SCS, San Diego, CA, 2000,

pp. 3-23

3. A. Meystel, A. Lacaze, “Introduction to

Integrated Planning-Learning Paradigm”, in

Proceedings of the 1997 International

Conference on Intelligent Systems and

Semiotics, Gaithersburg, MD, 1997, pp. 522-530
Autonomy and Socialization

Abstract

Kirstie L. Bellman, Ph.D.

AISC

The Aerospace Corporation

For quite some time it has been popular to study intelligence within the context of

autonomous systems. When studying autonomous systems, the focus is often on the

adaptive perceptual, behavioral and cognitive capabilities of an individual within some

operational environment. However, if we look at the behavior of biological systems, we

see dependency on other individuals as well as autonomy, and capabilities for coping as a

member of a population as well as coping as an individual. Of course, there is enormous

diversity in how frequently or in what manner the members of a given biological

population interact.

In the past, many researchers, while they recognized the importance of animal

populations and group behaviors, took the scientific strategy of focusing first on

discovering the mechanisms for the individual’s perception and behavior. They assumed

that such behaviors were somehow more fundamental than group behavior. The strategy

assumed that they could later tackle the additional capabilities needed for social,

cooperative, or interactive behaviors. This paper presents the view that social behaviors

are as fundamentally part of the construction of intelligent behavioral capabilities and as

essential for survival as any individual’s perceptual, cognitive or behavioral capabilities.

This paper also presents our reasons for believing that some type of analogues of social

capabilities are necessary to all autonomous constructed systems, such as “agents” or

robots, if we want intelligent, independent behavior in real world environments. We will

describe the types of capabilities necessary for adaptive behavior in both individual and

social behavior. We will then discuss how social behaviors, cultures, and cooperative

behaviors enhance the capabilities for individual autonomous behavior.
1

A Sketch of

Multiresolutional Decision Support Systems Theory

A. Meystel

Department of Electrical and Computer Engineering, Drexel University

Philadelphia, PA 19104

Abstract

Multiresolutional Decision Support Systems gain better

performance and higher accuracy by the virtue of building

highly efficient multiresolutional representation and

employing multiscale Behavior Generation Subsystem

(Planning and Control ). The latter are equipped by devices for

unsupervised learning that adjust their functioning to the

results of self-identification. We show planning and learning to

be joint processes.

Keyswords: behavior generation, control, decision support,

generalization, knowledge, learning, instantiation,

multiresolutional, multiscale, planning, randomized,

representation, resolution, search

1. Introduction

Multiresolutional Representation (MR)

of the World should be considered one of the tools

in the arsenal of Knowledge Management [1]. It is

the tool that is widely applied but is scarcely

noticed, probably because of its overwhelming

omnipresence. The concept of MR can be

illustrated by the series of pictures shown in

Figure 1 (a - f). The enhanced set of these pictures

with much more details can be seen in [2]. The

resolution of each subsequent image is increased

by an order of magnitude while the are of

observation is simultaneously reduced by two

orders of magnitude. It is not difficult to deduce

that as far as the underlying knowledge is

concerned, the objects in the image f are contained

in the image e [(skin texture)⊃(hand)], the objects

of the image e are contained in the image d

[(hand)⊃(sleeping person)], the objects of the

image d are contained in the image c [(sleeping

person)⊃(picnic)], the objects of the image c are

contained in the image b [(picnic)⊃(green lawn)],

and the objects of the image b are contained in the

image a [(green lawn)⊃(part of the city)].

This MR nestedness of sub-processes

and sub-systems of the overall processes and

system in not obvious in a standard cursory

analysis, it can be discovered only as a result of

special observations (computer vision equipment)

and investigative analysis. More importantly, it is

not obvious that this nestedness of entities and

their properties is important (if necessary at all)

for supporting the decision making activities at

each level of resolution. Yet, all images in Figure

1 are tightly linked by the prior cognitive

activities that are unified by identical processes of

generalization performed upon higher resolution

images to obtain a lower resolution image. Similar

processes of instantiation allow for receiving each

higher resolution image from the lower

Resolution. Actually, not the process of sensing or

the process of image edges detection and

segmentation (and others) determine further

image understanding and interpretation but rather

the joint processes of generalization and

instantiation that are executed upon these images

top-down and bottom-up.

As the signals measuring and

processing is conducted, at each particular level of

resolution they contain a different package of

frequencies (Figure 2). It demonstrates that the

granularity of representation is correlated with the

bandwidth of the signals at a level.

Why do we encounter this phenomenon:

multiresolutional knowledge representation? Why

the mechanisms emerged of generalization and

instantiation? The reduction of complexity via

reduction of “multiplicity” could only be done by

the virtue of grouping and representing the group

by a single symbol. This semiotic principle

emerged because of the need to reduce

computational burden. Computational benefits for

a particular example of knowledge representation

associated with planning is given in [8, 9].

The system of representation based upon

recursive grouping/decomposition incorporates

and uses the algorithms of generalization and

instantiation in different incarnations that depend

on circumstantial factors as for example, the

information we are dealing with, or the

subsystem of the world where the results are

applied. Thus, the learning system must employ

the same tools: labeling the entities in order to

deal with concise notations (symbols), grouping

the entities, decomposing them if information

details are required. Learning systems use the

same computational mechanisms.
2

a b

c e

e f

 Figure 1. Consecutive increasing the resolution of representation

The decision support system (DSS)

treats knowledge as an entity suggested in [1]: it

employs the awareness of familiarity gained by

experience for storing experiences as well as for

constructing decisions (including plans and

controls) that ensure functioning of a goaloriented system with increased performance

index. MR gives an opportunity to minimize the

value of computational complexity in a subset of

DSS that organizes knowledge by joint processes

of generalization and instantiation and use nested

MR-search for converging to a recommended

solution. This concept was introduced for

planning and control purposes in 1986 [2] and

explored in depth in subsequent works [3-10].
3

 Figure 2. Multiresolutional representation (alow resolution, b-mid-resolution+low

resolution, c-the sum of signals of all levels)

All these processes dwell on the processes of

learning employed by MR-DSS.

2. Decision Support of Behavior Generation

The structure of power station control

system shown in Figure 3 was successfully

tested at Delmarwa Power Station, DE, USA

[11]. is required in all faculties of a system

shown in Figure 3. Three levels of resolution are

demonstrated Low (“Task Level”), Middle

(“Component Level”) and High (“Actuation

Level”). Each level forms a loop closed through

connection 1. Each of these loops is a loop of

“closure” [9] and is equivalent to the Elementary

Loop of Functioning (ELF) described in [6, 7, 8].

The vertical subsystem 5 (Plant) from

Figure 3 is equivalent to subsystem Sensors,

World and Actuators from the Elementary Loop

of Functioning that is described in [8]..

Figure 3. Architecture of the Power Station Multiresolutional Decision Support System for energy efficient

Planning/Control

1

2 4 3 6 5

7

8

9

1

1

1

1

1

LEARNING

DISPLAY

PLANNING

(SEARCH)

ARBITRATION PLANT PREPROCESSIN REPRESENTATION

STABILIZATION/

SAFETY OVERRIDE

PREDICTION

ASSIGNMENT

ASSIGNMENT

TASK

LEVEL

COMPONENT

LEVEL

ACTUATION

LEVEL

TASK

LEVEL

COMPONENT

LEVEL

ACTUATION

LEVEL

TASK

LEVEL

COMPONENT

LEVEL

ACTUATION

LEVEL

TASK

LEVEL

COMPONENT

LEVEL

ACTUATION

LEVEL

TASK

LEVEL

COMPONENT

LEVEL

ACTUATION

LEVEL

TASK

LEVEL

COMPONENT

LEVEL

ACTUATION

LEVEL

1
4

Figure 3 contains several subsystems

that should be added to the standard ELF if there

is a need to equip the system by

Multiresolutional Decision Support: these are the

systems of Learning and Prediction. Learning

enhances Representation, while Prediction,

together with Stabilization arrives at the

subsystem of Arbitration (6). Standard ELF is

the core structure demonstrating the important

property of any functioning system, including

Intelligent Systems: it shows the property of

closure. The meaning of closure is in the fact

that the proper functioning requires the loop of

information flows to be closed.

What happens in the subsystem of

Behavior Generation? The latter has two

mechanisms: 1) maintenance of the ontology that

organizes the inventory of the known symbols

and their definitions and keeps relationships of

nestedness with ontology subsystems of other

resolution levels, 2) combinatorial search engine

that performs planning i. e. creates alternatives

of imaginary (possible and desirable) worlds,

and 3) simulator engine that explores expected

behaviors of the alternatives of the imaginary

worlds and/or monitors the execution processes.

The goal of functioning to be achieved

by the system arrives at the subsystem of

Behavior Generation [9] that is equipped by

mechanisms of planning and execution. At the

present time, these mechanisms cannot be

considered as thoroughly studied, and the general

theory of planning can hardly be attempted. We

will discuss a subset of problems in which the

goal is defined as the attainment of a particular

state or a particular string of states. Other types

of problems can also be imagined: in chess the

goal is clear -to win but this goal demand

achieving a special configuration (matesituation) but it cannot be achieved by arriving at

a particular pre-determined position in a space

(even in a descriptive space.) Most of the

problems related to the theory of games and

linked with pursuit and evasion are characterized

by a similar predicament and are not discussed

here.

Planning is understood as searching for

appropriate future trajectories of motion leading

to the goal. Searching is performed within the

system of representation (simulation) that gives a

tremendous advantage in comparison with

searching via trying.

3. Planning in a Representation Space with a

Given Goal

The world is assumed to be judged upon

by using its Space of Representation, or its State

Space which is interpreted as a time tagged

vector space with a number of properties. Any

activity in the World (State Space or

Representation Space) is called motion. It can be

characterized by a trajectory of motion with the

“working point” or “present state” (PS) that is

traversing the space from one point (initial, or

state, IS) to one or many other states (goal states,

GS.) The goal states are given initially from the

external source as a “goal region”, or a “goal

vicinity” in which the goal state may not be

completely defined in a general case. This vision

of the problem of Behavior Generation was

dominating in the area of Control Systems.

Planning was unified with Control only recently

when it became clear that both Planning and

Control are involved into anticipation of the

preferable motion (off-line) with some

appropriate correcting activities (on-line).

One of the stages of planning (often the

initial one) serves for defining where exactly is

the GS within the “goal region.” In this paper,

we will focus upon a subset of planning

problems where one or many GS remain

unchanged through all period of their

achievement. Traversing from IS to GS is

associated with consuming time, or another

commodity (performance index, or cost.).

Planning Problems in Behavior

Generation is frequently associated with the

domain of robotics or automated control systems

although it is absolutely equivalent to planning

in all other domains. Robotics became the

integrated research domain that provided for

blending the goals and testing the means of

achieving them, i.e. a domain with a direct need

for planning. In 1983, T. Lozano-Perez has

introduced the idea of search in "configurations

space". From the experience of using this search,

it became clear that the exhaustive search would

be computationally prohibitive if the

configuration space is tessellated with the final

accuracy required for motion control. The theory

of configuration space made one important thing

obvious: planning is searching for admissible

alternatives. This development helped to realize

that planning should combine the exhaustive

(often meaningfully complex) search off-line,

and an efficient algorithm of an off-line control.
5

At this period of time the engineering

community stopped talking about control of

actions and introduced a more balanced term of

Behavior Generation

The overview of the situation in the area

of planning and control can be found in [9]. The

recommended algorithm should be aligned with

the following suggestions. Consider the Ω state

space in which the start and final points SP and

FP are given. The minimum cost path from SP to

FP is to be found with the final accuracy ρ. Let

us consider particular cases Ω=Ω1 and ρ=ρm. To

declare the final accuracy is equivalent to

applying some mechanism od space tessellation.

One of mechanisms of tessellation is distributing

discrete points in the state space. We will

distribute them in a random fashion and then,

will determine the minimum cost path while

considering these points as vertices of an

imaginary graph. The condition of constructing

random tessellation reflects uncertainties of the

system that should be available for evaluation

from the existing representation. In Figure 4,a

the random points are distributed in the state

space with obstacles. An example of the result of

running a minimum-cost algorithm in the

tessellated state space with obstacles is shown in

Figure. 4,b.

Since the graph is randomized, the

trajectory is a random one, too. If one runs the

search algorithm a number of times, we receive

the results of searching as a “stripe of solution”

as shown in Figure 5,a. Then, we get a privilege

to continue with constructing tessellations of

higher resolution only within this stripe as shown

in Figure 5,b. Then, when we run the minimum

cost search-algorithm only within this “finely”

tessellated stripe with high resolution density of

tessellata, we receive a high resolution plan. This

process can be repeated recursively within as

many levels as necessary.

We will introduce three operators that

describe the above computations.

I. Operator of Representation (Â)

R:(Ω,ρ)→M, or M=R(Ω,ρ)

, (1)

where M- is the map representing the statespace Ω, ρ is the level of resolution of this map

determined by the density of the search-graph that we

intend to run at this particular level of resolution

(determined by the accuracy ρ). This is a non-trivial

operator because it presumes discovery of entities,

putting them into relationships with each other,

generalization, instantiation and measuring

relationships (including costs).

a

b

Figure 4 Randomized tessellation of the state space (a)

and a single running of the minimum cost trajectory

algorithm (a rectangle and a trapeze – obstacles)

II. Operator of state space search (S3)

S

3: (M, SP, FP, J, ρ)→P , (2)

or P=S3(M),where P- is the optimum path connecting

the start point SP and the finish point FP with

tessellation constructed for the accuracy ρ. J- is the cost

of operation which should be minimized as a result of

search S3. This operator should be based upon one of

the minimum-cost algorithms (e.g. Dijkstra) and

tailored for specifics of the problem.

III. Operator of space contraction (C)

which determines the width of stripe and the new final

goal for the algorithm of search.

C:(P, w)→Ω, or Ω=C(P), (3)
6

where w- is the width of the “stripe” obtained

after several runs of the search algorithm. Instead of

“stripe” one can use the term “envelope”, (e.g. the

“width” is the parameter of the envelope).

a

b

Figure 5 Multiple running of the minimum cost

trajectory algorithm (a) and the uncertainty stripe

obtained as a result of the multiple running (b)

The hierarchical control algorithm can be

described as follows:

for k=1, ..., m do the following string of procedures:

a) Ω k=C(Pk-1), or at k=1 assume Ω k=Ω,

b) Mk= R(Ω κ, ρk),

c) Pk= S3(Mk).

The algorithm of control can be represented as a

diagram

w ρk SP, FP, J

 ↓ ↓ ↓

Pk-1 →Χ → Ρ → Σ3 →Pk(4)

 

↑ Ω k ↑ Mk

or a recursive expression

Pk=S3(R (C (Pk-1, w), ρk ) SP, FP, J) (5)

The algorithm (5) has proven to be good

for off-line search in the state space. In the class

of on-line problems the process of control is to

be described by the trajectory of “working point”

moving in the state space.

4. Learnable Representations

All Representation Spaces are acquired

from the external reality by the processes of

Learning. Many types of learning are mentioned

in the literature (supervised, unsupervised,

reinforcement, dynamic, PAC, etc.). We will

focus primarily on processes of unsupervised

learning [12]. Before classifying the needs for a

particular method of learning and deciding how

to learn, we would like to figure out what should

we learn. Now, it is not clear whether the

process of learning can be separated

algorithmically into two different learning

processes: a) of objects representation, and b) of

the rules of action representation, or are these

two kinds of learning just two sides of the same

core learning process. In both cases, learning is

storing and generalizing information of

experiences with their values associated with

achieving particular goals.

The following knowledge should be

contained in the Representation Space. If no GS

is given, any pair of state representations should

contain implicitly the good rule of moving from

one state to another. In this case, we consider any

second state as a provisional GS.

 We will call “proper” representation a

representation similar to the mathematical

function and/or field description: at any point of

the space, the derivative is available together

with the value of the function. The derivative can

be considered an action required to produce the

change in the value of the function.

We will call “goal oriented”

representation a representation in which at each

point a value of the action is given required for

describing not the best way of achieving an

adjacent point but the best way of achieving the
7

final goal. Both “proper” and “goal oriented”

representations can be transformed in each other.

Neither is mandatory for functioning: valued

memories of experiences (ME) are sufficient.

5. The Artifacts of Representation Space:

The Phenomenon of “Sea Weeds”

Representation as sets of valued ME is

characterized by the following artifacts:

 • existence of states with its boundaries

determined by the resolution of the space

each state is presented as a tessellatum [9], or

an elementary unit of representation, the

smallest discernible unit of attention)

 • characteristics of the tessellatum which is

defined as an indistinguishability zone; we

consider that resolution of the space shows

how far the “adjacent” tessellata (states) are

located from the “present state” (PS)

• lists of coordinate values at a particular

tessellatum in space and time

• lists of actions to be applied at a particular

tessellatum in space and time order to achieve

a selected adjacent tessellatum in space and

time

• existence of strings of states intermingled

with the strings of actions to receive next

consecutive tessellata of these strings of

states

• boundaries (the largest possible bounds of

the space with similar properties, i. e. the

obstacles

• costs of traversing from a state to a state and

through strings of states.

Figure 6. The sea weeds

When ME are clustered into classes of

similarity (e.g. of adjacency) they remind

visually masses of “sea weeds”. In many cases,

the states contain information pertaining to the

part of the world beyond our ability to control it,

and this part is called “environment.” The part of

the world to be controlled is the system for

which we plan often referred to as “self.” Thus,

the representation is a part of “self” including

knowledge about actions that “self” should

undertake in order to traverse the environment.

Plans are formed as strings of preferable “sea

weeds” combined together.

6. Planning in Redundant Systems

Non-redundant systems have a unique

trajectory of motion from one state to another.

Redundant systems are defined as systems with

more than one “the best” trajectory of motion

from initial (IS) to final states (FS)

These systems contain a multiplicity of

alternatives of space traversal. Redundancy

grows when the system is considered to be a

stochastic one. The number of available

alternatives grows even higher when we consider

also a multiplicity of goal tessellata at a

particular level of resolution. This happens when

the goal is being assigning at a lower resolution

level which is the fact in multiresolutional

systems (such as NIST-RCS [8, 9])

In non-redundant systems, there is no

problem of planning. The problem is to find the

unique trajectory and to provide tracking of it by

an appropriate classical control system.

7. Learning as a Source of Representation:

Storing and Clustering “Sea Weeds”

Learning is defined as knowledge

acquisition via experience of functioning. Thus,

learning is development and enhancement of the

representation space. The latter can be

characterized in the following ways:

• by a set of paths (to one or more goals)

previously traversed

• by a set of paths (to one or more goals)

previously found and traversed

• by a set of paths (to one or more goals)

previously found and not traversed

• by a totality of (all possible) paths

• by a set of paths executed in the space in

a random way.

One can see that this knowledge

contains implicitly both the description of the

environment and the description of the actions

required to traverse a trajectory in this

environment.

All information arrives in the form of

experiences. The “learned” representation as a

set of strings of valued “sea weeds” is equivalent

to the multiplicity of explanations how to

traverse, or how to move.
8

8. Types of Problems of Planning

Any problem of planning is associated

with

 • actual existence of the present state

 • actual, or potential existence of the goal state

 • knowledge of the values for all or part of the

strings of executable states as far as some

particular goal is concerned.

Any problem of planning contains two

components: to refine the goal (bring it to the

higher resolution) and to determine the path to

this refined goal. They are performed together, or

separately and can be formulated as follows:

a) given PS, GS and KS find the subset

of KS with a minimum, or a prearranged cost, or

with a cost in a particular interval.

b) given PS, Gs from the lower

resolution level and KS (all paths) find the GS

with a particular value

Finding solutions for these problems is

done by a process of planning. In other words,

planning is a construction of the goal states,

and/or strings of preferable states connecting the

present state with the goal states. There is a

striking similarity and interrelatedness between

planning and learning, actually their

inseparability.

In order to do this, we must learn where

the goal is located by consecutive refinement of

the initial coarse information. In all cases it is

associated with reduction of the

indistinguishability zone and the size of the

tessellatum associated with a particular variable,

i.e. the accuracy of representation grows. We

plan and learn by testing: in the representation,

for planning, and in the reality, for learning.

Learning via testing simulated systems is

becoming more and more wide spread.

The second component is the simulation

of all available alternatives of the motion from

the initial state. Procedurally, this simulation is

performed as a search, i.e. via combinatorial

construction of all possible strings (groups). To

make this combinatorial search for a desirable

group more efficient we reduce the space of

searching by focusing attention.

The need in planning is determined by

the multialternative character of the reality The

process of planning can be made more efficient

by using appropriate heuristics which are

available via processes of learning.

9. The Unified System of Planning and

Learning: A Subsystem of MR DSS

The process of searching for plans is

associated either with collection of additional

information about experiences, or with extracting

from KS the implicit information about the state

and moving from state to state, for the purpose of

learning. In other words, planning is inseparable

from and complementary to learning.

This unified planning/learning process

is always oriented toward improvement of

functioning in engineering systems

(improvement of accuracy in an adaptive

controller, improvement of efficiency in energy

consuming devices) and/or toward increasing of

probability of survival (emergence of the

advanced viruses for the known diseases that can

resist various medications, e.g. antibiotics.)

This joint process can be related to a

system as well as to populations of systems and

determines their evolution.

Figure 7. On the relations between planning and

learning

10. Planning, Learning, and Control: A

Unified Theory

Learning/Planning Automaton. The

joint Planning/Learning process is studied by

using a tool of Learning/planning automata

(LPA) is a tool that allows for jointly exploring

these two fundamental processes of intelligent

systems. Naturally, it becomes a component of

the Multiresolutional DSS.

Elementary Computations. Search (S)

is always performed by constructing feasible

combinations of the states within a subspace

(“feasible” means: satisfying a particular set of

conditions or constraints.) As many as possible

alternatives of feasible motions should be

explored and compared. If search is combined

with formation of alternatives, we call this

procedure combinatorial search (CS).
9

Usually, grouping (G) presumes

exploratory construction of possible

combinations of the elements of space and as one

or many of these combinations satisfy conditions

of “being an entity”, this group generates a new

symbol with subsequent treating it as an object.

The larger the space of search is, the

higher is the computational complexity of search.

This is why a special effort is allocated with

reducing the space of search, i.e. focusing

attention (FA) upon reduced sub-spaces. FA

results in determining two conditions of

searching, namely, its upper and lower

boundaries:

a) the upper boundaries of the space

where the search is to be performed, the scope

b) the resolution of representation (the

lower boundaries, the tesselatum)

Via exploring these experiences in

planning and learning we arrive at a conclusion

that they are always employ these three

procedures: grouping, focusing attention and

combinatorial search (or subsets of them).

The property of Intelligence. Forming

multiple combinations of entities (combinatorial

search, CS) satisfying required conditions of

transforming them into new entities (grouping,

G) within a bounded subspace (focusing

attention, FA) is frequently performed as a

fundamental set of procedures. Since these three

procedures work together we will talk about

them as about a triplet of computational

procedures (the abbreviations GFACS or CFS

are used.) Notice, that in learning it creates lower

resolution levels out of higher resolution levels

(bottom-up) while in planning it progresses from

lower resolution levels to higher resolution levels

(top-down). This algorithmic triplet emerges as a

tool of multiresolutional representation and/or

for the purposes of generating goal-oriented

behaviors.

This triplet of computational procedures

is characteristic for intelligence of living

creatures and constructed systems, and probably

is the elementary computational unit for

The need in GFACS is stimulated by

the property of knowledge representations to

contain a multiplicity of alternatives of space

traversal (i. e. a property of any representations

to be redundant.) Representations reduce the

redundancy of reality. This allows for having

problems that can be solved in a closed form (it

is a form when no combinatorics is possible

and/or necessary).

At each level of resolution, planning is

done as a reaction for the slow changes in

situation which invokes the need in anticipation

and active interference

a) to take advantage of the growing

opportunities, or

b) to take necessary measures before the

negative consequences occur.

The deviations from a plan are

compensated for by the compensatory

mechanism also in a reactive manner. Thus, both

feedforward control (interpreted as planning at

all levels of resolution but the highest one) and

feedback compensation of deviations are reactive

activities. Both can be made active in different

implementation approaches in control theory.

Examples: a) Classical control systems are

systems with no redundancy, they can be solved

in a closed form without searching.

 b) Any stochastic condition, any type of

uncertainty introduced to a control system

creates redundancy and requires either for

elimination of redundancy or performing search.

 c) Optimum control allows for the degree of

redundancy that makes searching feasible.

In Figure 8, the process of

multiresolutional planning via consecutive

search with focusing attention and grouping is

demonstrated for the control problem of finding

a minimum-time motion trajectory. From Figure

8, one can judge the processes that are performed

during the single level S

3

-search in the

randomized tessellated state space. The reader

can identify the processes because a trivial

example is considered: minimum time

functioning of the dynamic system. The

operation won’t change if one is dealing with

higher order and/or non-linear system.

intelligence. Its purpose is transformation of

large volumes of information into a manageable

form that ensures the success of functioning.

This explains the pervasive character of

hierarchical architectures in all domains of

activities including Decision Support Systems.

The space is learned and encoded in

advance by multiple testing, and its

representation is based upon knowing that the

distance, velocity and time are linked by

nonredundant expression. Several methods of

constructing attention envelopes are applied.
10

Figure 8. S3-Search in the state space for the minimum tyme dynamic trajectory

Conclusions

We have demonstrated the advantages

of Multiresolutional Decision Support Systems that

can be listed as follows:

1. Multiresolutional System of Knowledge

Organization allows to reduce complexity and

increase efficiency of representation.

2.Most of the Planning/Control problems are being

solved via S3-search (Search in the State Space). The

latter requires performing randomized state space

tessellation with density of points that reflects the

uncertainty of information. Multiresolutional S

3

-

search allows for stochastic optimization of systems.

3. Representations at each level of resolution are

organized as memories of experiences and do not

require constructing any analytical model: this

system plans and controls with no model required.

4. This representation supports processes of

unsupervised learning that contains self-oriented

information; no special self-identification is required.

5. The MRDS system was tested in applications to

power station energy efficient planning/control

system, for planning/control of an unmanned

autonomous mobile or spray-casting robots.

References

1. A. Satyadas, U. Harigopal, N. P. Cassaigne,

“Knowledge Management Tutorial: An Editorial

Overview,” IEEE Transactions on Systems, Man, and

Cybernetics, Part C, Vol. 31, No. 4, November 2001,

pp. 429-437

2. P. Morrison and P. Morrison, and the Office of C. and

R. Eames, Powers of Ten, Scientific American Library,

A division of HPHLP, NY, 1982

3. A. Meystel, "Planning in a hierarchical nested

controller for autonomous robots," Proc. IEEE 25th

Conf. on Decision and Control, Athens, Greece, 1986,

pp. 1237-1249

4. A. Meystel, "Theoretical Foundations of Planning and

Navigation for Autonomous Mobile Systems",

International Journal of Intelligent Systems, Vol.2,

No.2, 1987, pp. 73-128

5. C. Isik, A. Meystel, “Pilot level of a hierarchical

controller for an unmanned mobile robot," IEEE J. of

Robotics & Automation, Vol. 4, No. 3, 1988, pp. 244-

255

6. A. Meystel, Autonomous Mobile Robots, World

Scientific, World Scientific, Singapore, 1991

7. A. Meystel, “Nested Hierarchical Control,” in Eds. P.

Antsaklis, K. Passino, An Introduction to Intelligent

and Autonomous Control, Kluwer Academic, Boston,

1993, pp. 129-161

8. A. Meystel, “Architectures, Representations, and

Algorithms for Intelligent Control of Robots”, in

Intelligent Control Systems: Theory and Applications,

eds. M. M. Gupta, N. K. Sinha, IEEE Press, New York,

1996, pp. 732-788

9. A. Meystel, J. Albus, Intelligent Systems: Architecture,

Design, Control, Wiley, NY., 2001

10. J. Albus, A. Meystel, Engineering of Mind: An

Introduction to the Science of Intelligent Systems,

Wiley, NY, 2001

11. C. Corson, A. Meystel, F. Otsu, S. Uzzaman “Semiotic

Multiresolutional Analysis of a Power Plant”, ”, in

Architectures for Semiotic Modeling and Situation

Analysis in Large Complex Systems, Proc. of the 1995

ISIC Workshop, Monterey, CA, pp. 401-405

12. A. Meystel, “Learning Algorithms Generating

Multigranular Hierarchies”, in Mathematical

Hierarchies and Biology, Eds.: B. Mirkin, F. R.

McMorris, F. S. Roberts, A. Rzhetsky, DIMACS Series

in Discrete Mathematics, Vol. 37, American

Mathematical Society, 1997
Motivated Metamodels

Paul K. Davis

Senior Scientist, RAND and Professor,

RAND Graduate School

1700 Main St. Santa Monica, CA 90407

(pdavis@rand.org)

Santa Monica, CA 90407

(pdavis@rand.org)

James H. Bigelow

Senior mathematician, RAND

1700 Main St., Santa Monica, CA

90407-2138

(James_Bigelow@rand.org)

ABSTRACT

A metamodel is a relatively small, simple model that approximate

the “behavior” of a large, complex model. A common way to

develop a metamodel is to generate “data” from a number of largemodel runs and to then use off-the-shelf statistical methods without

attempting to understand the model’s internal workings. It is much

preferable, in some problems, to improve the quality of such

metamodels by using various types of phenomenological

knowledge. The benefits are sometimes mathematically subtle, but

strategically important, as when one is dealing with a system that

could fail if any of several critical components fail. Naïve

metamodels may fail to reflect the individual criticality of such

components and may therefore be misleading if used for policy

analysis. By inserting an appropriate dose of theory, however, such

problems can be greatly mitigated. Our work is intended to be a

contribution to the emerging understanding of multiresolution,

multiperspective modeling.

Keywords: metamodel, multiresolution modeling, model

abstraction, response surfaces, repro models, statistics,

regression, intelligent machines, robotics, machine planning

INTRODUCTION

A metamodel is a relatively small, simple model intended to

mimic the “behavior” of a large, complex model. Two

reasons for wanting to build metamodels are.[1]:

- Cognitive. We want to “understand” why the large

model behaves as it does. This will enhance the

model’s meaningfulness and credibility with ourselves,

other analysts, and with whomever we seek to

influence.

- Exploratory analysis. We often want to explore the

behavior of a model over a large part of its domain. A

metamodel with only, e.g., 5-10 (rather than hundreds

or thousands) of variables makes comprehensive

exploratory analysis feasible and comprehensible[2],

[3].

Sometimes, it is possible to build models using

multiresolution modeling design principles [1], in which case

the low-resolution versions (the more abstracted or aggregate

versions) already have these virtues. Often, however, the

baseline situation is that a subject area has been modeled

only in detail. Often, the detailed model is old, opaque, and

difficult to work with. A metamodel, then, is an attempt to

generate a low-resolution version after the fact.

Consider first two extreme approaches to building a

metamodel. For the statistical approach, one runs the large

model many times for a variety of inputs; one then collects

the inputs and outputs in a big dataset. A statistician

analyzes these data as he would data collected in any series

of experiments. He seeks a statistical model that does a good

job of estimating outputs, while using as few input variables

as possible so as to keep the model simple and so as to avoid

“overfitting” his initial data.

An idealized phenomenological (or theory-driven) approach

starts with the most exact theory available and derives a

simplified model by rigorously aggregating (as in replacing

integrals over volume with a representative value times the

volume), rearranging and combining terms and factors, and

so on—using physical insights wherever possible (e.g.,

recognition of some conservation principles or of being able

to view a factor as an idealization times an efficiency factor).

Some statisticians, operations researchers, and computer

scientists prefer the first approach and want to know nothing

about the “innards” of the model whose behavior they are

attempting to replicate. They may have a purist philosophy

of “allowing the data to speak,” without “contaminating it”

with theoretical assumptions. Or they may simply prefer not

having to deal with the complexities of the model’s innards:

they may wish to turn the problem over to automated

software. At the other extreme, some theoretically inclined

academicians clearly prefer the second approach because it

allows rigorous tying together of phenomena at different

levels of detail (as when classical thermodynamics is

understood from quantum mechanics). These, then, are the

extremes. Most scientists, engineers, and analysts, however,

should prefer something in between. Often, the “inbetween” amounts to an analyst postulating some simple

scaling relation and using data to calibrate that scaling
relation. The scaling relation may be naïve and the

calibration may use only the crudest of statistics.

Our interest in this research was to clarify principles for

doing better. We had some ideas for how to do so, based on

theoretical reasoning, but we preferred where possible to test

and iterate ideas by experimenting with specific well-posed

problems. With that in mind, we used a well-documented

model that we could consider “large and complex model”

and applied our ideas in stages, starting with pure statistical

metamodeling and moving toward more theory-informed

work. In what follows, we first discuss how one should

judge the quality of a metamodel. We then describe our

experiments and conclusions. The discussion adds to an

earlier preliminary discussion [4] and is drawn from a longer

and more technical piece [5].

WHAT MAKES A METAMODEL GOOD

We suggest five criteria for assessing the goodness of a

metamodel, heroically assuming for the sake of this

discussion only that the base model is in fact accurate:

Goodness of fit. Obviously, we want the metamodel’s

predictions to be reasonably consistent with those of the

baseline model. A measure of this is the root mean square

error (or fractional error) of predictions across the domain of

input values. This is superior to the commonly used R2.

Parsimony. For purposes of both cognition and exploratory

analysis, a good metamodel will have relatively few

independent variables, which ideally, would also be

meaningful. Achieving parsimony may be accomplished by

omitting some of the baseline model’s inputs (i.e., treating

them as constant) or by combining several base model inputs

into a smaller number of intermedate variables. The set of

independent variables should be rich enough to represent the

issues being addressed with the model. Beyond that, the

fewer extra variables, the better.

Identification of “critical components.” Our third criterion

seems new and we believe it to be crucial. Many uses of

models in analysis involve systems or strategies, the failure

of which is to be very much avoided. We suggest that a

metamodel should highlight all of the input variables that are

essential to success—especially when troublesome values of

those variables are plausible. The model should not give the

impression that one can compensate for a weak component

of the system by improving some other component (if such

substitution is in fact inadequate). This is a significant

consideration in metamodeling, because standard statistical

methods lead to linear sums that imply substitutability. We

refer to components that must individually succeed (have

values above or below an appropriate threshold) as critical

components. If critical components in this sense exist, the

metamodel should be appropriately nonlinear.

Reasonable depiction of relative “importances.”

Metamodeling can generate statistical measures of the

significance or importance of candidate variables. In

stepwise regression, the less significant variables are

dropped. That, in turn, could mean that someone using the

metamodel for resource allocation would consider the

dropped variables as unimportant. A good metamodel would

give no misimpressions on this score.

A good storyline. Without a story, a model is just a “black

box.” A story explains why the model behaves as it does.

More, it relates the model to the real world, telling us why

the model should behave as it does. We use the term

“storyline” because all models are a simplification of reality,

but we intend no cynicism. Said differently, the model

should be “physically (or otherwise phenomenologically)

meaningful and interpretable,” not just a math formula.

With this background defining what we mean by a good

metamodel, let us now describe the analytical experiments

we conducted to illustrate and sharpen our understanding of

ways to improve metamodeling.

THE EXPERIMENT

Our experiment was to begin was a relatively large and

complex model and to develop a series of metamodels to

represent it. For the first metamodel we relied almost entirely

on statistical methods, uninformed by phenomenology (i.e.,

our knowledge of the workings of the base model). With

each successive metamodel, we took advantage of

progressively more phenomenology.

The Large Model

Our “large, complex model” (i.e., our baseline model) was

EXHALT-CF [5],[6], which treats the so-called halt phase of

a military operation. Although much simpler than real base

models of interest, it has scores of variables and a great many

nonlinearities. It seemed complex from its documentation

and program.

In its simplest version, the halt phase is a mere race. An

attacking force (Red) is advancing on an objective while the

defenders (Blue) interdict its armored vehicles with longrange fires. Red will halt when he reaches his objective (a

Red win) or when Blue has killed a specified number of

vehicles (a Blue win), whichever comes first. EXHALT-CF,

however, adds many embellishments relevant to current

strategic concerns about real-world military operations,

especially in the Persian Gulf.

First, the model must represent Blue deployments. Some

number of shooters may be stationed in theater in peacetime.

Depending on strategic warning, diplomatic relations, Red’s

deceptiveness, and Red’s ability to threaten bases in theater

(e.g., with weapons of mass destruction), Blue may or may

not be able to augment this number before Red begins his

advance. Once Red’s advance begins, Blue will deploy more

shooters into the theater, up to a theater capacity, which

reflects logistical shortcomings.
The effectiveness of Blue shooters is measured by kills per

shooter-day. Early in the campaign, Blue may be unable or

unwilling to attack the Red column because of Red air

defenses. After a period of air-defense suppression, Blue’s

attacks will start. Even then, however, sortie rates may be

reduced because of a continued threat of attack with massdestruction weapons, which would force Blue personnel to

work in protective gear or would force Blue to operate from

more distant, and more poorly prepared bases.

The weapons and strategy Blue selects will also influence

Blue shooter effectiveness. Blue may select an area weapon,

capable of killing several Red armored vehicles per shot. To

counter this, Red may space his vehicles more widely. Or

Blue may select a point weapon, which kills no more than

one vehicle per shot, and is unaffected by Red’s vehicle

spacing. Also, Blue will likely have limited supplies of his

best weapons, and revert to lesser weapons when his best are

exhausted. Blue may attack the entire Red column in depth

(the “In Depth” strategy) or focus his attack on the leading

edge (the “Leading Edge” strategy). If Blue does the latter,

his attack may slow Red, but each sortie may be less

effective due to deconfliction problems.

These and other complications of the halt problem are

represented in EXHALT-CF and the simulation version,

EXHALT. They are implemented in Analytica‰, a graphical

modeling environment for the personal computer.

EXHALT-CF has 63 inputs, 8 switches to turn features on or

off (the model has a multiresolution, multiperspective

design) , three indexes, and 451 variables that are calculated

directly or indirectly from inputs. For our purposes, we

focused on a subset of cases, which reduced to 25 the

number of input variables affecting the problem. This

seemed adequately complex to illustrate our points—or,

more accurately, to allow us to experiment. The experiments

in question were experiments of discovery and learning, not

rigorous hypothesis testing.

The Experimental Data

We selected statistical distributions, mostly uniform

distributions, from which to generate the 25 variable-value

inputs. We then ran EXHALT-CF to generate a Monte Carlo

sample of 1000 cases from the overall input space. We did

not weight one or another region of the input space because

we were seeking a broadly good fit of behavior over the

entire domain of interest.

METAMODELING

Metamodel 1

In our first experiment, we acted as though we had handed

the dataset to a statistician (or statistically oriented

operations researcher or computer scientist), and

commissioned him to develop an estimator for the halt

distance that Blue could achieve using his best strategy and

weapon type for the circumstances of the case. A good

statistician would insist on discussing the problem. He

would want to know which data elements to use as

independent variables, which are outcome variables, and so

on. Even if he preferred to operate as thought the original

model is a “black box,” he would probably want at least

some interpretation of the variables’ meanings. This would

permit him to do some data manipulation that would simplify

his analysis.

For his initial analysis, our simulated statistician specified a

linear model with 25 independent variables. Because he

knew that we wanted a parsimonious metamodel, he ran a

stepwise linear regression procedure in which the

independent variables were added to the model one by one in

the order of decreasing explanatory power. That is, the first

variable considered yielded the largest reduction of the root

mean square of the residual error (RMS Error. After the first

six or seven variables, further additions didn’t improve the fit

very much. Actually, the fit wasn’t very good no matter

how many variables were included (the standard error was

on the order of 25%, which in this problem is large).

In any case, our simulated statistician stopped with 14

variables, all coefficients of which were significant at the

0.05 level.

How good was metamodel 1? Earlier we identified five

features that make a metamodel good. The performance was

not impressive, although, in our experience with

metamodeling pure statistical approaches such as this not

uncommonly do quite well by the average goodness-of-fit

criterion. Still, in this case, there were 421 cases in which

Red actually reached his objective and the model estimated

that Red was halted short of his objective in 92 percent of

them.

Parsimony was our second criterion for a good metamodel.

This model had 14 variables. We would like fewer, but this

was perhaps a marginally acceptable number.

Identification of critical components was our third criterion.

Here Metamodel 1 performed very poorly. Such metamodels

are linear in the variables identified as significant by the

statistical analysis. Thus, when used, metamodel 1 failed to

identify and highlight critical components. For example, the

model would predict that by merely improving munitions

sufficiently, Blue could guarantee a small halt

distance—independent of the other variables. That is flatly

wrong. The model also gave a very misleading sense of the

relative importance of variables. After stepwise regression,

one variable had dropped out, while another—which entered

the problem in precisely the same way (if the variables were

X and Y, they entered the problem only through the product

XY)—was retained as significant. This could be a very

serious shortcoming if the model were used to inform

resource-allocation decisions. Also, as discussed earlier, ur

final criterion for a good metamodel was that it have a good

storyline. This metamodel had no storyline at all.
Metamodel 2

A statistician will often try to improve his model by

introducing transformations of the independent variables,

such as exponentials, powers, and products of variables.

That is, he will still use linear regression, but with some of

the variables of that regression being nonlinear composites of

others. So many possible transformations of variables are

possible that the statistician may need some guidance

selecting which ones to try. Brute force (e.g., considering all

of the quadratic combinations of elementary variables as

new, composite variables) can result in a good fit, but usually

with even more statistically significant variables and no

“story.”

Phenomenology (i.e., the “innards” of the baseline model)

can suggest what transforms to try, including transforms that

statisticians do not generally consider. These include

transforms that use the MAX and MIN operators. Indeed, a

number of transformations are built into EXHALT-CF. We

designed it as a multi-resolution model, to permit the user to

specify inputs at different levels of detail. Even if the

metamodeler finds EXHALT-CF as a whole to be big and

complex, even early chapters of documentation, which deal

with various idealizations, are sufficient to highlight natural

composite variables. They may not fully substitute for the

more elementary variables, because the “real” EXHALT-CF

(as distinct from the simplified versions discussed in early

documentation chapters) includes more complex interactions.

Nonetheless, we thought that the suggested composite

variables (“aggregation fragments”) might go a long way.

For Metamodel 2, then, we looked at a number of such

composites.”

The simulated statistican then defined a linear model with far

fewer variables, many of them the composites.

How good was metamodel 2? The performance, while

considerably better than Model 1, was still not impressive.

The standard error was perhaps 80 km, rather than 140 km

(with interesting values being in the range 0-600).

On grounds of parsimony the model was better, since only

ten independent variables proved to be statistically

significant.

The good news was that the model did predict the criticalcomponent phenomenon: we had identified enough of the

key composite variables so that we could see importaant

nonliearities. In particular, to obtain a good halt distance,

Blue had to address three issues simultaneously (with no

substitution). These involved the number of initial shooters,

the earliest time at which Blue could begin attacking Red

effectively, and the number of “shooter days” required for

success (a functon of Red’s size and Blue’s effectiveness per

attack mission). This important “system feature” stood out.

However, this metamodel still did not have a storyline,

although the variables at least had more physical

significance.

Metamodel 3

So far the simulated statistician had been combining the

original, low-level variables into intermediate variables that

we thought were reasonable on phenomenological grounds.

We might characterize this as a “bottom-up” strategy. Now

we turned to a “top-down” strategy. We viewed this as

explicitly building in a storyline. It depended on an

understanding of phenomenology, but it not require that the

theory for describing that phenomenology be analytically

tractable (e.g., solvable in closed form).

One piece of knowledge used was inferrable from even

minimal documentation of EXHALT-CF, to wit that the

model considered two different Blue strategies and took the

better of the two results as the answer. The model did

similarly in comparing two different classes of weapons. In

the earlier metamodels, these branches and use of

MIN/MAX operators was all buried, but in Metamodel 3 we

built that same macro logic in. This meant that Metamodel 3

actually involved four metamodels, plus logic to compare

results from each.

More important, we inserted physical reasoning in simplified

terms. Upon thinking about the problem physically, we

could reason that the halt time was just the time required to

kill the requisite number of Red targets. However, that

depended on the number of Blue shooters, which increased

linearly with time (subject to some further constraints in the

full model), the size of the Red force, and the per-shooterday effectiveness of Blue. We could write a simple

analytical equation for this—so long as we glossed over

details and inserted averages. We estimated an integral by

the product of a time duration and the average number of

shooters within the interval (without knowing precisely how

to calculate that average).

We then made a very crude estimate of this and other

averages. The result was dimensionally correct and not

absurd, but it was not intended to be accurate.

What we were looking for was structural form. This we

postulated as the basis for Metamodel 3, although building in

fudge factors to measure error in the form assumed.

How good was metamodel 3? Metamodel 3 fitted the data

much better than either of the two previous metamodels. It

was also much more parsimonious than the previous

metamodels. It had only five significant variables and the

fudge factors proved to be not very large. Thus, the “story”

understandable from the highly simplified model that did

violence to mathematics by, e.g., treating an integral as a

product of a duration and an average value during the period,

was largely correct.
The model also did well in predicting the critical-component

phenomenon. The composite variables that made ths

possible in Metamodel 2 were also present in Metamodel 3,

but now with a better story. Nor were there any serious

errors with respect to the relative importance of variables.

All in all, results were rather good: building in the

approximate structure had paid off handsomely.

Metamodel 4

The last metamodel that we considered pushed the analytic

work further. Upon reflection, it was possible—in this

particular problem—to do a much better job of estimating

the average number of shooters present during the halt phase.

This required nothing more profound than simple integration

and relatively simple algebra. A good student of first year

calculus would be able to do the problem without difficulty.

If we inserted this knowledge, the resulting metamodel was

exceedingly accurate—so much so that it was embarrassing.

The results demonstrated that the complexity of EXHALTCF was the result of essentials having been obfuscated by the

programming. The subtleties and complications were simply

not necessary when the numbers were crunched.

Interestingly, however, Metamodel 4 was not better than its

predecessor by our criteria. Why? Because, in the process

of inserting the improved solution, the structural form

became more complicated and non-transparent. This

obscured the story, making it impossible to actually put the

model up on viewgraph and explain what was going on as,

e.g., a product of three meaningful variables divided by a

fourth, with a small error term reflecting the many

simplifications involved. If we wanted clarity and insight,

then Metamodel 3 was arguably better. We say “arguable,”

because with clever presentation one could hide some of

Metamodel 4’s complications.

SUMMARY AND LESSONS LEARNED

Our experiments confirmed our belief that much could be

gained by combining virtues of statistical and

phenomenological (theory-informed) approaches. They

confirmed and give more precise arguments to our

skepticism about approaching metamodeling as an exercise

in pure data analysis, with the baseline model merely being a

black-box generator of data. Although the experiment dealt

with only a single baseline model, the insights appeared to us

to be relatively general—at least for purposes of suggesting

general cautions and approaches to consider. We intend no

grand claims here, but those were (and are) our impressions.

In our experiments, the application of statistical methods

uninformed by phenomenology did not produce a good

metamodel. In part it failed because the data we were fitting

describe a highly nonlinear surface. A linear model might fit

locally, but never globally. Moreover, there was no

guarantee that the regression coefficients would be a good

guide to the relative importance of the independent variables.

It was necessary to introduce nonlinear combinations of the

low-level inputs in order to obtain a good fitting metamodel.

Phenomenology could motivate the construction and

selection of the appropriate nonlinear combinations. It

would be very difficult to discover them from the data alone.

A linear model also failed because the data did not describe a

smooth surface. Like many models, EXHALT-CF makes

liberal use of MAX and MIN operators to make either-or

choices. So the data described a surface with “kinks” in it.

When one fits a kinky surface with regression, the

coefficients obtained from regression don’t need to make

sense. The regression results tell us the average importance

of inputs, but not when each one is important. We found it

necessary to use phenomenology to separate the smooth

segments of the surface, after which we could do a good job

of fitting each smooth segment with just plain statistics.

As we introduced more and more phenomenology, we

obtained successively better fitting models. We would argue

that in addition, Model 1 had very little cognitive value (i.e.,

it didn’t tell a story) and Model 2 had only a little more.

Model 3, however, did tell a coherent story, one that could be

related persuasively to the client. With Model 4 it could be

argued that we began to lose cognitive value. The

phenomenology became more complex and less transparent.

All the equations began to obscure the story.

Particularly important also is that by inserting

phenomenologically motivated structure one can avoid

certain important blunders of system depiction. In particular,

one can preserve and even highlight the role of critical

components—components that enter the problem more

nearly as products than as sums, or components that must

individually have threshold values to avoid system failure.

This is particularly important if metamodels are to be used in

policy analysis or design. We would expect it to be quite

important in design of intelligent systems, because we would

expect it to be normal, not unusual, for designers to be

worried about numerous independently critical factors.

ACKNOWLEDGMENTS

This research was sponsored by the United State Air Force

Research Laboratory.

REFERENCES

[1] Davis, P. K. and J. H. Bigelow, Experiments in

Multiresolution Modeling, RAND, Santa Monica, CA, 1998.

[2] Davis, P.K., J. H. Bigelow, and J. McEver, Exploratory

Analysis and a Case History of Multiresolution,

Multiperspective Modeling, RAND RP-925, Santa Monica,

CA. Reprinted from Proceedings of the 2000 Winter

Simulation Conference, J. A. Joines, R. R. Barton, K. Kang,

and P.A.. Fishwick (editors), December, 2000 and

Proceedings of the SPIE, Vol. 4026, 2000.

[3] Davis, P. K., J. McEver, and B Wilson, Measuring

Interdiction Capabilities in the Presence of Anti-Access
Strategies: Exploratory Analysis to Inform Adaptive Strategy

for the Persian Gulf, RAND, Santa Monica, CA MR-1471-

AF, 2002.

[4] Davis, P.K. and J. H. Bigelow, “Meta Models to Aid

Planning of Intelligent Machines,” Proceedings of the 2001

PerMIS Workshop, September 4, 2001 (NIST Special

Publication 982), Gaithersberg, MD.

[5] Bigelow, J.H. and P.K. Davis, “Developing improved

metamodels by combining phenomenological reasoning with

statistical methods,” Proceedings of the SPIE, Vol. 4716,

2002 (A. Sisti and D A. Trevasani, editors).

[6] Davis P.K, J.H. Bigelow, and J McEver, EXHALT: An

Interdiction Model for Exploring Halt Capabilities in a

Large Scenario Space,” The RAND Corporation, MR-1137-

OSD, 2000
On the Role of Quality Attributes in Specifying Software/System Architecture

for Intelligent Systems

Hessam S. Sarjoughian

Computer Science & Engineering

Arizona State University

Tempe, AZ 85287-5406

sarjoughian@asu.edu

www.acims.arizona.edu

Abstract

In recent years, researchers in software engineering have been exploring a range of issues on software and system

architectures. One area that directly affects intelligent system is software and system architecture. Recent findings

offer evidence that software architecture can be developed based on a set of well-defined concepts and methods. In this

paper, we examine this line of research and its role in the field of intelligent systems. We suggest using architecture

lifecycle as an approach to deriving architectures for intelligent systems. We introduce intelligence quality attribute

and propose using it with a set of existing quality attributes to support incremental, evolutionary specification and

evaluation of intelligent software/system architectures.

1 Introduction

The architecture of a computing system can be defined to be one or more structures composed of software/hardware

components and their connections. A variety of architectural structures – conceptual, physical, control flow, data flow,

module, call, process or coordination, and class – serve a collection of complementary, and sometime necessary,

blueprints of complex systems. For example, a data flow structure allows one to trace satisfiability of functional

requirement based on its constituting components and connections which represent programs and send data

relationships respectively. These structures are of particular importance since they can serve as the first set of formal

specifications suitable for “predicting” the desired behavior of intelligent systems. That is, a subset of such structures

capture a system’s behavior in terms of primitive artifacts that can be specified and qualified/quantified over a

system’s lifetime.

2 Software/System Architectures

The role of an architecture is to put in place a set of blueprints guiding the development of a system that can satisfy

some desired behavior. The system’s desired behavior can be formally specified in terms of a set of quality attributes

such as performance and reusability. Many system architecture definitions, descriptions, and specifications have been

proposed and employed from fields such as control theory, artificial intelligence, and software engineering (e.g., [1-

9]). Bass, Clemens, and Kazman [4] offer a comprehensive account for the software/system architecture. Their

treatment is founded on fundamental concepts (e.g., hierarchical structure) and techniques (e.g., modularization)

proposed by Fred Brooks, Edsger Dijkstra, Kevin Iverson, and David Parnas among others. They present the

architecture of a computing system as a set of specifications capturing its key elements in a systematic fashion. The

approach allows one to specify and assess an architecture in a step-wise fashion – i.e., moving from domain

independent to application-specific. The architecture, therefore, can be said to provide a set of artifacts that can be

evaluated in terms of how well it supports or enables a system’s desired external (e.g., availability and performance)

and internal behavior (e.g., testability and modifiability).

More formally, an architectural specification of a computing system is defined as a structure or structures composed of

software components, the externally visible properties of the components, and the relationships among them [4]. Each

structure can be viewed as an skeleton of a system in terms of its components and connections. Each component has a

type (software, hardware, or mixed), behavior (static, dynamic, or mixed), and roles (processing, control, mediating,

etc.). The connections specify the type (communication, control, send/receive) and nature (e.g., asynchronous) of

connections among the components. The structure itself specifies the topology of components and the kinds of

interactions enabled – e.g., hierarchical, flat, layered, and mixed such as layered hierarchical.
2

The software/system architecture lifecycle 1 is shown in Figure 1a. These architectures can be derived in terms of three

more primitive artifacts: architectural style, reference model, and reference architecture (see Figure 1). These artifacts

provide “early design decisions”. An architectural style specifies which quality attributes it supports based on generic

description of component types and their patterns of runtime control and/or data transfer. The architectural style also

specifies general constraints placed among components and types of interactions among them. For example, layered

architecture defines how a layer may interact with layers below or above it and supports reusability quality attribute.

Reference Model

Architectural Style

Reference

Architecture

Software

Architecture

System

Architecture

Abstract Quality Attributes

• performance

• availability

• security

• usability

• modifiability

• integrability

• reusability

• testability

• intelligence

• functionality

Concrete Quality Attributes

• performance

• availability

• security

• usability

• modifiability

• integrability

• reusability

• testability

• intelligence

• functionality

Architecture Lifecycle

Figure 1: Architecture Lifecycle and Quality Attributes

The reference model, unlike architectural style, specifies a standard decomposition of a known problem (domain) into

parts that cooperatively provide a suitable solution (e.g., Model-Viewer-Controller). A reference model represents a

division of functionality together with data and control flow between them. The reference architecture is a reference

model mapped onto components 2 along with their data and control flow where components cooperatively implement

the functionality defined in the reference model. Next, we describe in some detail architectural style and quality

attributes.

2.1 Architectural Styles

Architectural styles play a major role in the development lifecycle of intelligent systems since they can serve as the

first step toward specifying and examining quality attributes (see Section 2.2). Unlike a software or system

architecture, an architectural style specifies a family of architectures3 – i.e., a set of components, and connectors along

with a topology and its semantic constraints. In an architectural style, the topology specifies the layout of components

and connectors as well as their collective interactions. Each component is designated to carry out some functions (e.g.,

a semi-autonomous vehicle processing sensory inputs). The connectors (e.g., procedure call, multicast, etc) are

responsible for mediating communication, coordination, or cooperation among components. The architectural style,

 

1

 Lifecycle is iterative and evolutionary.

2

 Mapping of reference model to reference architecture is not necessarily one-to-one.

3

 It is important to note that architectural style not a kind of (software or system) architecture. Architectural style (a) does not

specify the number of components, (b) dos not provide domain-specific, detailed functionality of the components and (c) does not

give precise details for the interactions that may take place among components.
3

therefore, is an abstraction of a set of software/system architectures capturing their abilities and limitations from a

domain-independent viewpoint. The importance of an architectural style is that it can be examined in terms of how

well it enables some quality attributes (e.g., performance and integrability) and possibly impeding others (e.g.,

modifiability and security) in early stages of design.

2.1.1 Quality Attributes

Establishing quality attributes is an essential step toward specifying a system architecture which can facilitate or

hinder some desired/necessary behavior. Quality attributes can serve as elementary artifacts for specifying and

measuring a software/system architecture from distinct and complementary viewpoints. The quality attributes are

categorized as runtime observable and non-runtime observable [4]. Common runtime observable attributes are

performance, availability, security, and usability. Modifiability, integrability, reusability, portability, and testability

quality attributes are common non-runtime observable. To account for system’s behavior, there exist also the

functionality quality attribute. The collection of runtime and non-runtime quality attributes serve as a basis toward

specifying alternative architectures where each architecture is an instantiation of quality attributes tradeoffs. The

tradeoffs capture interaction among quality attributes and their ranking with an underlying assumption that there can

be no architectural specification that can satisfy all quality attributes4.

To exemplify the interplay of architectural style and quality attributes, we consider a university library and its on-line

system where patrons have access to hardcopy materials such as books and archival periodicals as well as softcopy

materials and other on-line information via a web portal. Suppose we are asked to recommend an architectural style 5

satisfying availability and portability quality attributes. One recommendation may suggest the base architectural style

to be a layered and transactional database-repository with call-and-return and client-server architecture flavor (see

Table 2). The layered style provides portability for the client-side and if necessary for the server-side. To support high

degree of availability (e.g., > 99.9%), we can use multiple servers either in centralized or decentralized fashion. The

centralized approach suggests having two or more identical servers in one physical location where the system can

automatically switch from one server to another as necessary (replace data storage unit.). In a decentralized setting,

servers are dispersed in different locations thus ensuring availability – for example network (e.g., router/switch) or

power failure. Client/server fits the general pattern of use of services offered by university library on-line system and

dispersed patrons. It supports the main scheme of many users accessing the on-line system. Data-centered style is

useful and important for satisfying the functionality requirement. The server-side data (e.g., library holdings) needs to

be stored in a centralized database for frequent access by large number of patrons.

Architectural Control Issues Data Issues

Styles Topology Synchronicity Binding Time Topology Synchronicity Mode

Call-based client/server star synchronous wt, ct, rt star spor., lvol. passed

Layered hierarchical any wt, ct, it, rt hierarchical spor., lvol., cont. any

Decentralized servers arbitrary asynchronous wt, ct, rt arbitrary spor., lvol. passed

Table 1: Candidate Architectural Style for University Library On-line Example

It is important to note the data/control issues as they show how static and dynamic aspects of each architectural style

relates to satisfying some desired quality attributes. The control topology is principally of star pattern between library

patrons and the library on-line system. Given the layered architectural style, control topology is hierarchical for a

single server and arbitrary if multiple servers are used (set of decentralized servers). Control is asynchronous since

there is no need to synchronize users – users interactions with the system are independent of one another. The overall

data topology is arbitrary and hierarchical with multiple servers. Data continuity is also frequently sporadic and low

volume (small amount of data is transmitted from client to server). Binding time is of all types – wt(write-time),

 

4

 There are three complementary higher level categories of quality attributes referred to as system, architecture, and business [4].

5

 It is important to note that the above recommendation is subject to business quality attributes – use of cost, market, tools, etc. may

result in other architectural styles!
4

ct(compile-time), it(invocation-time) and rt(run-time). Data to and from clients are primarily “passed” although the

database content is “shared”. Table 1 shows typical settings for data and control for the architectural styles [4].

3 Intelligent Systems Lifecycle Architecture

Given the foregoing discussions, we can consider the role of architectural style as the first step toward specifying the

architecture of an intelligent system. Before we proceed further, we briefly discuss performance and intelligence

attributes which are considered as necessary for any intelligent system6.

3.1 Performance Attribute

Performance attribute represents how well (response time) a system responds to stimulus both internally and externally

(e.g., a robot decision to abandon a subgoal and pursue another; heed a warning send by a remote sensor). An

architecture specification can satisfy performance quality by controlling the communications among components. For

example, the number of interactions and the size and number of data/control can be minimized to increase

performance. Of course, performance also depends on how fast each component can carry out its responsibilities,

interdependencies of a component’s processing on other components, hardware components (e.g., processing speed of

a router and network bandwidth) and architectural topology (e.g., physical separation between computing nodes), etc.

The key point is that the architectural style is the first design artifact which can be used to specify and assess

performance attribute in a formal setting. Furthermore, since architectural style design decisions feed into the software

and system architecture, we are able to track it from its abstract to its concrete specifications. This ability has a

profound consequence – i.e., formally trace the satisfiability of performance attribute from its inception to its

realization within the architecture as shown in Figure 1.

3.2 Intelligence Attribute

Now, we introduce intelligence as one additional quality attribute to those given in [4]. Treating intelligence as a

quality attribute is based on the concept that it is distinct from the functionality quality attribute in the sense that a

system may produce some desired behavior without necessarily using its intelligence faculty7. Consider the definition

of intelligence as proposed by [7] – “intelligence is the ability of a system to act appropriately in an uncertain

environment, where an appropriate action is that which increases the probability of success, and success in the

achievement of behavioral subgoals that support the system’s ultimate goal”. This definition suggests, among other

things, the notion that an intelligent system may achieve its goal but not necessarily with the same success rate as

compared with one that is considered intelligent. Thus, we suggest the intelligence quality attribute to be distinct from

the other quality attributes. Clearly, intelligence considered as a quality attribute implies that it plays a key role in the

architecture lifecycle – i.e., some architectural styles may provide for intelligence while others may not and therefore

some software/system architecture may or may not be categorized as “intelligent” by examining its intelligence quality

attribute. Similar to other quality attributes, intelligence quality attribute interacts with other quality attributes (e.g.,

intelligence and security quality attributes may interact with another).

3.3 Use of Quality Attributes

As we have discussed, software/system architectures increasingly play a central role in the development of large-scale

complex intelligent systems. This is primarily due to the fact that architectural specifications collectively contain the

necessary knowledge required for describing a system’s performance, availability, testability, functionality, etc.

Intuitively, performance and intelligence of an intelligent system should therefore be traceable to its architecture. For

example, for an semi-autonomous vehicle to be able to restructure itself in pursuit of achieving its objectives, it may

need to account for quality attributes such as intelligence. The architecture of a semi-autonomous vehicle navigating

itself to its destination through an unknown environment must account for the intelligence and performance quality

attributes. Concrete realization of these quality attributes may be arriving at the destination within 5% of a specified

time period with high-degree of intelligent decision-making to avoid collision – e.g., 98% collision avoidance rate.

 

6

 Reference to performance and intelligence as “attributes” may not necessarily be in agreement with the characterizations given in

[6] and [7].

7

 Similar arguments can be given for other quality attributes.
5

This view suggests abstract and concrete quality attributes where architectural style accounts for the former and

software/system architecture accounts for the latter. Given abstract and concrete quality attributes, we may ask the

following questions in order to substantiate the premise that intelligent systems can be specified with the architecture

lifecycle using quality attributes (see Figure 1).

1. do abstract quality attributes form a primitive basis for specifying the architecture of intelligent systems?

2. can abstract quality attributes be evolved to their concrete counterparts through the architecture specification

lifecycle?

We answer our questions informally. An affirmative response to the first question implies that architectural style is the

most suitable specification to capture quality attributes. Architectural style by definition is void of domain-specific

knowledge. Furthermore, architectural style is sufficiently rich to account for quality attributes – its structure,

components, and interactions can account for quality attributes in an abstract setting – independent of explicit

knowledge for a system under consideration. Consider, a layered architectural style. This style supports dividing

tasks into those aimed for a specific application and those generic to many applications. This accounts for the

hierarchical decision-making. The layering approach also allows lower levels to be specialized for distinct computing

platforms and thus supports the portability quality attribute. Intelligence and portability attributes are possible without

domain specific knowledge for a specific application. The satisfiability of these attributes can be shown by examining

the constituent parts, control issues, and data issues of the architectural style8 (refer to Table 2). For example,

hierarchical data and control topologies play major roles for satisfying the modifiability and portability attributes – i.e.,

the control and data issues are of direct importance to allow components interact via their connectors in an abstract

setting.

Constituents Parts Control Issues Data Issues

Components Connectors Topology Synchronicity Binding time Topology Synchronicity Mode

various various hierarchical any wt, ct, it, rt hierarchical spor., lvol., cont. any

Table 2: Layered Architectural Style Specification

With an affirmative response to the first question, we now turn to the second question. The answer to this question is

also affirmative but perhaps less evident. In the architectural lifecycle, reference model accounts for known

(empirically proven) ways of partitioning a class of systems (e.g., Model-View-Controller) into components and

interactions using a set of unit operations [4] such as abstraction, compression, and part-whole and is-a

decompositions. These unit operations, of course, underlie the quality attributes – for example, the degree to which

performance and availability quality attributes can be supported by the reference model. Furthermore, unit operations

support reasoning about architectural styles and thus allowing measurements in qualitative/quantitative forms. This

suggests that reference model serves as a vehicle to transform abstract quality attributes to those that are grounded in a

particular class of systems. Therefore, the combination of the reference model and architectural style (i.e., reference

architecture) serve as the intermediary for deriving the concrete quality attributes for a software/system architecture.

Quality attributes as defined within the architecture lifecycle can be used as a means to formalize varying levels of

intelligence from the architectural specification point of view. For example, intelligence can be said to range from

selection rules (most primitive) to synthesis of paradigm (most advanced) [7]. The intermediary levels of intelligence

are combinations of selection rules, new rules, grouping of rules, synthesis of states, and synthesis of context. That is,

the ability to begin with abstract quality attributes and arrive at their concrete counterparts can serve a means to

formalize increasingly higher levels of intelligence.

4 Other Approaches for Developing Intelligent System Architectures

A large body of research has been devoted to the study of intelligence and intelligent systems. Many algorithms,

techniques, and methodologies have been developed for intelligent systems (e.g., [7, 10-13]). Some of these have

 

8

 Control/data interaction for the layered architectural style is specified as having often isomorphic shapes with flow directions

either being the same or opposite [4].
6

become the foundations for system architectures such as NIST-RCS [2], subsumption [3, 5], InteRRAP [14]. Some

architectures (e.g., subsumption) emphasize lower-level information processing and evaluation (e.g., obstacle detection

and motion control) while others (e.g., InteRRAP) focus on higher-level knowledge processing and decision-making

(e.g., planning and model-based prediction). The latest NIST-RCS reference architecture described by Meystel and

Albus [7] account for both low- and high-level processing and decision-making. They suggest sensors, actuators,

sensory processing, behavior generation, world model, and value judgment to account for the RCS class of reference

architectures.

5 Conclusions

We have presented an architecture lifecycle for the design of intelligent systems. We described the importance of

architectural styles as the basis toward specification and development of software and architectures. We showed the

key role quality attributes play and their importance in specifying and measuring an intelligent system’s performance,

intelligence, modifiability, etc.. An important aspect of the quality attributes is how they evolve from their abstract

form (captured in an architectural style) to their concrete realizations (represented in a system architecture). Our

proposition rests on the premise that an incremental and evolutionary architectural specification lifecycle can lend

itself best for selecting, specifying, developing, and measuring quality measures and ultimately system architecture.

Furthermore, with this approach, tradeoffs among quality attributes can be systematically accounted for and analyzed.

References

1. Albus, J.S., M. H.G., and L. R., NASA/NBS Standard Reference Model for Telerobot Control System Architecture

(NASREM). 1987, Robot Systems Division, Center for Manufacturing Engineering, National Technical

Information Service: Gaithersburg, MD.

2. Albus, J.S., A Reference Model Architecture for Intelligent Systems Design. 1994, NIST: Gaithersburg, MD.

3. Brooks, R.A., Cambrian Intelligence: the early history of the New AI. 1999: MIT Press.

4. Bass, L., P. Clements, and R. Kazman, Software Architecture in Practice. The SEI Series in Software

Engineering. 1998: Addison Wesley.

5. Brooks, R.A., A Robust Layered Control System for A Mobile Agent. IEEE Journal of Robotics and Automation,

1986. 2(1): p. 14-23.

6. Meystel, A.M. and E.R. Messina. Measuring the Performance and Intelligence of Systems. Proceedings of

PerMIS. 2000. Gaithersburg, MD: NIST.

7. Meystel, A.M. and J.S. Albus, Intelligent Systems Architecture, Design, and Control. 2001: John Wiley & Sons,

Inc.

8. Müller, J.P. and M. Pischel, An Architecture for Dynamically Interacting Agents. International Journal of

Intelligent and Cooperative Information Systems, 1994. 3(1): p. 25-45.

9. Sarjoughian, H.S., B.P. Zeigler, and S.B. Hall, A Layered Architecture for Agent-based System Development.

IEEE Proceeding, 2001. 89(2): p. 201-213.

10. Genesereth, M., N.J., Nilsson, Logical Foundation of Artificial Intelligence. 1987, San Mateo, CA: Morgan

Kufmann. xviii+405.

11. Saridis, G.N., Intelligent Robotic Control. IEEE Trans. Auto. Cont., 1983. 28(5): p. 547-557.

12. Zadeh, L.A., Fuzzy Logic, Neural Networks and Soft Computing. Comm. of ACM, 1994. 37(3): p. 77-84.

13. Zeigler, B.P., Object-Oriented Simulation with Hierarchical, Modular Models: Intelligent Agents and

Endomorphic Systems. 1990, New York: Academic Press.

14. Müller, J.P., The Design of Intelligent Agents: A Layered Approach. Lecture Notes in Artificial Intelligence. 1996:

Springer.
1

Uncertain Predictions of Flow and Transport in Random Porous Media: The

Implications for Process Planning and Control

Shlomo Orr, MRDS, Inc.

Traditional predictions of flow and transport in

porous media are based on mass balance

equations in the form of partial differential

equations (PDEs), where the flux at every point

is defined by Darcy’s law, q = -K∇h, i.e., the

flux is proportional to hydraulic head gradient,

where K is the hydraulic conductivity of the

medium (a tensor or a scalar; essentially, a

material property); it is further assumed that

Darcy’s law applies to transient multiphase flow

in three dimensions [14,26,36,55]. The solutions

of these PDEs constitute groundwater models,

oil reservoir simulators, geothermal models, and

models of flow and transport in soils/vadosezone. Due to the similarity between the linear

Darcy’s law and Ohm’s law in electricity,

Fourier law in heat conduction, and Hooke’s law

in elasticity, such models (or PDE solutions) are

similar and commonly interchangeable between

these fields.

Natural porous formations are heterogeneous,

and display spatial variability of their geometric

and hydraulic properties. This variability is of

irregular and complex nature. It generally defies a

precise quantitative description because of

insufficient information on all relevant scales

[9,18,26, 29,30,32,33,86,91]. In practice, only

sparse measurements are available (limited by cost

of drilling and monitoring). Under lack of

exhaustive information, the higher the variability,

the higher is the uncertainty. Geostatistics is

commonly used to analyze and interpolate

between measurements in mining and oil

explorations, as well as hydrology and soil

sciences, using methods such as “kriging”, where

the uncertainties in “krigged” values are also

quantified [33,35,36,42,56-58,76,77,81,83,84,87,

104,105]. Frequently, these data are collected on

different scales that may differ from the required

scale of predictions. The task of quantitatively

relating measurements and properties on different

scales is difficult and intriguing [4,5,7,13,27,29,

30,38-40,46,59,78,86,91,101,108,109]. Lack of

information in both observed results (output) and

measured material properties (parameters) causes

uncertain predictions. Spatial variability and

uncertainty have lead engineers and geologists to

use probabilistic theories that translate the

uncertainty to a random space function (RSF) or a

random field, consisting of an ensemble of

(infinite number of) equally probable

“realizations” of parameter values, all having the

same spatial statistics, particularly correlation

structure [107,76,77,23,33,35,36,42,56,58,83,84,

85,87,91]. Imbedded in this approach is a

geostatistical model of an assumed joint pdf. In

practice, only the first two moments are

considered, with an underlying assumption of

multivariate normal distribution; in particular, the

theoretical semi-variogram (or simply,

“variogram”) - the reciprocal of the covariance

function, and the mean and variance of the pdf.

Since these joint moments are inferred from

spatial data, the assumption of ergodicity (i.e.,

assuming that the ensemble and spatial statistics

are identical – a theorem that cannot be proven on

real data) must be invoked, which, in turn, implies

some kind of stationarity (or statistical

homogeneity) [33,35,36,49]. Further, in order to

determine the variogram model from available

spatial data, an inverse method has to be used to

estimate the parameters of this variogram;

sensitivity to data errors on one hand, and

identifiability problems (of model parameters) on

the other hand [81,87,104,105] lead to uncertainty

in the geostatistical model itself, which is usually

ignored (in fact, the common practice is to fit the

variogram model to the experimental variogram

by eye and by subjective judgement of model

type, degree of stationarity (drift), and statistical

anisotropy). Another ignored uncertainty is in the

“measured” hydraulic conductivity value that are

actually inferred from hydraulic tests interpreted

by simplistic models that assume local

homogeneity, which is somewhat inconsistent

with the RSF approach.

The use of RSF to predict behavior of

uncertain systems is not limited to flow in porous

media; great efforts have been devoted in all

science and engineering fields to (a) estimate or

predict mean behavior of the system under

different stresses, and (b) compute the uncertainty

associated with these predictions (expressed by the

variance-covariance of the solution), i.e., the first

two statistical moments of system output

[4,5,7,10-12,15,16-22,25,28,31-33-35,44,47-51,

57,62,63,68-71,73-78,82-93,97,99-101,103,106,

107,110,111,112,114]. The resulted approximate

solutions are usually limited to simple geometry
2

and boundary conditions, and to moderate to low

variability, as they mostly rely on variations of

small-perturbation methods. Recently developed

approximations for higher variability in material

properties using integro-differential

representations have been limited to simple 2D

geometry and simple boundary conditions

[91,114], with little use, yet.

With respect to mean behavior, it is especially

desired to define effective properties of

heterogeneous media [1-3,7,8,25,33,37-41,43,45,

52,54,60,61,64-66,71-73,75,77,78,91,93,97,98,

102,108,109,114]. With the two statistical

moments of system output, one hopes to

optimize and control systems such as oil

production, groundwater remediation, irrigation,

leaching, etc. However, approximating the

statistical behavior of a complex system of flow

in random porous media based on the statistics of

the hydraulic parameters is a formidable task, at

best [46,114, 91-93], because this implies

solving the stochastic flow and transport

equations (analogous to the Heat/Diffusion

Equation) or other stochastic PDEs in other

fields (ibid). Since a direct, explicit (closedformed) solution to the problem of random

parameters (or coefficients) is practically

impossible1 [74,93,112,114], only approximate

solutions have been reported in the literature for

relatively simple cases [ibid,28,31-33,49-50,83-

93,100-101]. Interest in this class of stochastic

differential equations has its origins in quantum

mechanics, wave propagation, turbulence theory,

random eigenvalues, and functional integration

[8,20,51,48,62,63,68-71,74,78,99,103,111]. Due

to the limited types of problems that can be

tackled by stochastic theories (closure

approximations), in practice, numerical

approximations in the form of high-resolution

 

1

 A solution to a stochastic PDE consists of

specifying the (joint) probability density function

(pdf) of the response, h(x), given those of K(x)

(and forcing functions and boundary conditions).

Unfortunately, one cannot obtain the joint

cumulative distribution function (CDF) of the

random response at all (infinite number of)

points. Even for a finite set of points, one cannot

obtain closed-form equations for a finite number

of moments. This problem can be circumvented

by either approximations (e.g., perturbation

methods, Neumann series) or by numerical

approximations, i.e., Monte Carlo simulations

(MCS).

Monte Carlo simulations (MCS) are used;

however, MCS require ample computer power

and CPU time [46,114,88-89] Orr [114]

describes and analyzes other difficulties and nonquantifiable uncertainty associated with MCS,

particularly the generation of correlated random

fields that are faithful to the geostatistical model,

and simulations of flow in highly

heterogeneous/erratic media.

The geostatistical model provides the statistics

of the parameters, particularly the permeability; in

MCS, it provides the spatial distribution of

parameter values for each realization. Based on

these values and assumed model structure (i.e., the

conceptual model of flow and transport, including

large geologic features, boundary- and initialconditions, sources and sinks), stochastic solutions

are approximated (analytically or numerically).

Since the model structure itself is frequently

uncertain due to (a) unknown boundary and initial

conditions, (b) extent of large-scale geologic

features, and (c) information pertaining to

geochemical reactions and phase transition, such

sets of 500-1000 MCS need to be repeated for

several, if not many alternative conceptual models

or equally probable model structures [116].

Subsequent optimization requires many repetitions

of each Monte Carlo simulation in order to build

the search space (i.e., generate sufficient number

of scenarios or trajectories); hence, rigorous

optimization under uncertainty is prohibitive in

terms of computer power and time for most

practical applications. In an attempt to optimize

best new well placement in an oil reservoir,

Guyaguler and Horne [53] were forced to perform

optimization on only 23 randomly selected,

“history matched” realizations in order to

overcome the obstacle of prohibitive computer

power and time, while continuously verifying their

results against a “truth” model (apparently based

on extensive calibration and some “effective”

properties). Indeed, the authors concluded that “a

decision based on a single realization (though with

perfect history match) may differ substantially

from the true optimum”[53, p.4]. As was shown

theoretically by Neuman and Orr [91], unique

effective properties (of random media) that are

data independent do not generally exist except for

a few special cases. This explains why parameter

estimates obtained by traditional inverse methods

tend to vary as one modifies the database and/or

the imposed stress [113,115]; consequently,

calibration of deterministic models may be

meaningless in term of predictive power.

Thus, on the way to optimal solutions using

stochastic predictions we already encounter
3

significant theoretical and practical barriers,

particularly, uncertain interpretations, model

limitations, prohibitive computer power, and nonexisting effective parameters – all of which render

these predictions highly uncertain, while only part

of this uncertainty is being quantified. Our

presentation will discuss these barriers, and will

bring two examples where traditional stochastic

approximations (i.e., approximate solutions of the

governing stochastic PDEs) are unable to provide

reliable practical solutions. One example deals

with the difficult problem of finding the best

location of the next well in an oil reservoir [53].).

The following simplified block diagram describes

the work flow as described by Guyaguler and

Horne [53]. Note that “data” are inferred values

from field tests. Note also that at each step, there

are inherent errors, inconsistencies, and

unaccounted (as well as counted) uncertainty.

Another example involves optimal control of

heap leaching in the mining industry [94,95].

In both cases:

1. Rigorous MCS is already prohibitive in

terms of computing resources

2. Models cannot capture the full complexity;

hence, predictions are unreliable

3. Rigorous optimization is impossible due to

time and computer limitations

In each of these cases, decisions have been made

at every step of the solution. Many of these

decisions are made subjectively, based on

experience, knowledge, thoroughness,

understanding (or conceptual models of the

process), computer resources, and time limitation,

possessed by the modeler. These factors affect and

are being affected by the degree of belief (by the

modelers) in each decision made.

Initially, sampling (network) design

decisions have to be made re sampling locations

(an optimization procedure on its own that

depends on the end results as well, i.e., a feedback

mechanism with the goal of minimizing prediction

uncertainty [119]). Then, the following decisions

(and sub-decisions) have to be made: (a) type of

model and/or curve fitting to use to determine the

hydraulic conductivity or permeability (a miniinverse model that could be ill-posed); in the case

of two-phase flow (e.g., oil-water in a reservoir,

water-air in a heap), several other decisions (or

assumptions) have to be made, and an ill-posed

inverse procedure must take place [117-121, and

author’s personal experience]; (b) determining and

eliminating outliers; (c) determining optimal lag

distance for the experimental variogram; (d)

determining the pdf, variogram model, and model

parameters; particularly, choosing between

Gaussian and Indicator models, judging between

drift and/or anisotropy, and determining the drift

(requires a complex inverse procedure, with

typical ill-posed cases; see [36,81,104,105]); (e)

determining dimensionality, domain size, and

mesh resolution with respect to correlation scales,

measurement scales, and property/parameter

representation scales; (f) determining conceptual

model (or model structure) of flow and transport,

including the parameters to be treated as random,

Collect “data”

and construct

a geostatistical

model

Generate a random

field with many

realizations, and

run MCS

Select several calibrated

simulations/realizations

and perform optimization

on each (for max NPV)

Compare

w/”truth”

model

Collect historical

data on heap

construction, and

construct a

geostatistical model

Generate a random

field with many

realizations, run

MCS, and compute

“ensemble” statistics

I. Perform optimization on

the ensemble mean output

(predicted production) for

max NPV

II. Perform optimization

on selected realizations

that resemble production

behavior (for max NPV)
4

the governing equations (PDEs, including reactive

transport and multiphase flow), and uncertain

boundary- and initial-conditions, boundary

locations, zones of specific character/features

(based on geologic and hydraulic information) –

i.e., the simulator (these decisions should be made

first, and re-evaluated as more information is

being analyzed); (g) for MCS: random number

generator (RNG; portable or not; RNG type; seed,

etc.); (h) random field generator (RFG), including

type and RFG parameters (in addition to the

variogram model and pdf); (i) number of

simulations (should be based on the behavior of

point output variance as a function of the number

of simulations, i.e., a trial & error procedure which

usually is not being done, including in the above

two cases), given the limited computer power); (j)

number of realizations required for optimization;

ideally, all (hundreds of) realizations are used; one

compromise may be reducing the number of

realizations [53]; another compromise would

consider mean system behavior (using the

resulting ensemble mean results, and optimize that

mean behavior (a major uncertain decision); (k)

type of optimization/search algorithms (l) number

of simulations (per realization) for constructing a

sufficiently dense search state-space; (m)

objective function and cost variables. The last

three decisions have to be made by all

optimization procedures.

In the well placement problem [53], due to

limited computer power, a decision was made to use

only 23 realizations (while 500-1000 simulations

are typically needed to provide meaningful

ensemble statistics [114,46]), and a decision was

made to calibrate randomly selected random

realizations, individually, which contradicts the

concept of random fields (or RSF), but may have

served a practical purpose (i.e., to find the

maximum NPV). Similar prohibitive computer

power problem prevails in the heap leaching

simulations. While unstable oil-water fronts in the

well placement case cannot be captured by the

simulator, unstable wetting fronts during heap

leaching cannot be captured even by high-resolution

two-phase models (like the one used by Orr and

Vesselinov [95]). In the latter case, lack of

information on essential reactive transport

properties, and unmonitored dynamic changes in

heap structure (particularly sealing of pore space by

clays and erosion products) cannot be determined

and modeled. Consequently, the simulators are

weak, missing on mean system behavior (or

predictions) with unaccounted uncertainty, resulting

in weak optimization and control.

We see that along the track of approximate

solutions and partial optimization of oil reservoirs

and heap leaching operations based on

predetermined stochastic PDEs, the confidence of

modelers and decision-makers is being eroded

with each decision being made, depending on their

knowledge and degree of belief at each decision

point. By the end of this process, decision-makers

find themselves with very little confidence and

very little decision power. Commonly, in this

stochastic approach, the degree of belief is not

quantified, though it contributes to the total

uncertainty. Alternative fuzzy logic techniques do

quantify the uncertainty associated with the degree

of belief.

We therefore propose to replace these

formidable stochastic approaches by a simpler

yet intelligent stochastic control, particularly, the

multiresolution decision support system (MRDS,

which includes fuzzy logic as one of its

components) in order to reach more reliable and

efficient optimal solutions, with reduced,

accountable uncertainty, in real time, with

minimal computer resources. Moreover, unlike

the rigid stochastic PDEs, MRDS can be

naturally extended to optimal control of linked

processes. In the oil field case, this includes

exploration, all surface installations and

operations, delivery system, and distribution. In

the heap leaching case, this includes subsequent

solvent extraction and electrowinning, as well as

all antecedent processes – from exploration and

blasting to transportation, crushing,

agglomeration, conveying, placement, and

design of the irrigation systems.

REFERENCES

1. Ababou, R., Identification of effective conductivity tensor

in randomly heterogeneous and stratified aquifers, 5th

Canadian-American Conference on Hydrogeology,

Sept. 18-20, Calgary, Alberta, Canada, 1990.

2. Ababou, R. and E.F. Wood, Comment on ``Effective

groundwater model parameter values: Influence of

spatial variability of hydraulic conductivity, leakance,

and recharge" by J.J. Gomez-Hernandez and S.M.

Gorelick, Water Resour. Res.} 26(8), 1843-1846,

1990a.

3. Ababou, R. and L.W. Gelhar, Self-similar randomness and

spectral conditioning: analysis of scale effects in

subsurface hydrology, Dynamics of fluids in

hierarchical porous media, edited by J.H. Cushman,

Academic Press, 393-428, 1990.

4. Adomian, G., Stochastic Systems, Academic Press, New

York, 1983.

5. Ahmed, N.U., Semigroup Theory with Applications to

Systems and Control, Longman Scientific & Technical

with John Wiley & Sons, New York, 1991.

6. Albus, J., A. Meystel, S. Uzzaman, “Nested Motion

Planning for an Autonomous Robot”, Proc. IEEE

Conference on Aerospace Systems, May 25-27,

Westlake Village, CA, 1993
5

7. Auriault, J.L., Heterogeneous medium. Is an equivalent

macroscopic description possible? Int. J. Engng. sci.

29(7), 785-795, 1991.

8. Avellanda M., and A.J. Majda, Mathematical models with

exact renormalization for turbulent transport, Comm.

Math. Phys., 131, 381- 429, 1990.

9. Bachu, S., and D. Cuthiell, Effects of core-scale

heterogeneity on steady-state and transient fluid flow in

porous media: Numerical analysis, Water Resour. Res.

26(5), 863-874, 1990.

10. Bakr, A.A., Effect of Spatial Variations of Hydraulic

Conductivity on Groundwater Flow, Ph.D. dissertation,

N.M. Inst. of Min. and Technol., Socorro, 1976.

11. Bakr, A.A., L.W. Gelhar, A.L. Gutjahr, and J.R.

MacMillan, Stochastic analysis of spatial variability in

subsurface flows 1. Comparison of one- and three

dimensional flows, Water Resour. Res., 14(2), 263-271,

1978.

12. Batchelor, G.K., Transport properties of two phase

materials with random structure, Annual Reviews of

Fluid Mechanics, 6, 227-254, 1974.

13. Baveye, P. and G. Sposito, Macroscopic balance equations

in soils and aquifers: The case of space- and timedependent instrumental response, Water Resour. Res.,

21(8), 1116-1120, 1985.

14. Bear, J., Dynamics of Fluids in Porous Media, American

Elsevier, 1972.

15. Bellman, R.E., Stochastic transformations and functional

equations. In Stochastic Processes in Mathematical

Physics and Engineering, edited by R.E. Bellman,

Vol.XVI, 171-177, Amer. Math. Soc., 1963.

16. Benaroya, H. and Rehak, M., The Neumann series/born

approximation applied to parametrically excited

stochastic systems. Probabilistic Engineering

Mechanics, 2(2), 1987.

17. Benaroya, H. and Rehak, M., Finite element methods in

probabilistic structural analysis: A selective review,

Applied Mechanics Reviews, 41(5), 201-213, 1988

18. Beran, M.J., Statistical Continuum Theories, Interscience,

New York, 1968.

19. Bharucha-Reid, A.T., Random Integral Equations,

Mathematics in Science and Engineering, Vol.96,

Academic Press, 1972.

20. Brenner, H., A general theory of Taylor dispersion

phenomena, II, An extension. PCH, Physicochem.

Hydrodyn., 3(2), 139-157, 1982.

21. Cheng, A.H.-D. and O.E. Lafe, Boundary element solution

for stochastic groundwater flow: Random boundary

condition and recharge, Water Resour. Res., 27(2),

231-242, 1991.

22. Christakos, G., Random Field Models in Earth Sciences,

Academic Press, 1992.

23. Clifton, P.M. and S.P. Neuman, Effects of Kriging and

inverse modeling on conditional simulation of the Avra

Valley aquifer in Southern Arizona, Water Resour.

Res., 18(4), 1215-1234, 1982.

24. Corson, C., A. Meystel, F. Otsu, S. Uzzaman, “Semiotic

Multiresolutional Analysis of a Power Plant”, in

Architectures for Semiotic Modeling and Situation

Analysis in Large Complex Systems, Proc. of the 1995

ISIC Workshop, Monterey, CA, pp. 401-405

25. Cushman, J. H., Volume averaging, probabilistic

averaging, and ergodicity, Adv. Water Resour., 6, 182-

184, 1983.

26. Cushman, J.H., On unifying the concepts of scale,

instrumentation, and stochastics in the development of

multiphase transport theory, Water Resour. Res.,

20(11), 1668-1676, 1984.

27. Cushman, J.H., On measurement, scale, and scaling,

Water Resour. Res., 22(2), 129-134, 1986.

28. Cushman, J.H., Development of stochastic partial

differential equations for subsurface hydrology,

Stochastic Hydrology and Hydraulics, 1(4), 241-262,

1987.

29. Cushman, J.H., An introduction to hierarchial porous

media, Dynamics of fluids in hierarchical porous

media , edited by J.H. Cushman, Academic Press, 1-5,

1990.

30. Cushman, J.H., On the inseparability of the measurement

process from theories of transport in heterogeneous

materials, in Concise Encyclopedia of Environmental

Systems, edited by P. Young, in press, 1991.

31. Dagan, G., Models of groundwater flow in statistically

homogeneous porous formations, Water Resour. Res.,

15(1), 47-63, 1979.

32. Dagan, G., Statistical theory of groundwater flow and

transport: Pore to laboratory, laboratory to formation,

and formation to regional scale, Water Resour. Res. ,

22(9), 120S-134S, 1986.

33. Dagan, G., Flow and Transport in Porous Formations,

Springer-Verlag, New York, 1989.

34. Dederichs, P.H., and R. Zeller, Variational treatment of

elastic constants of disordered materials, Z. Phys., 259,

103-116, 1973.

35. Delhomme, J.P., Spatial variability and uncertainty in

groundwater flow parameters: A geostatistical

approach, Water Resour. Res., 15(2), 269-280, 1979.

36. de Marsily, G., Quantitative Hydrogeology, Academic

Press, 1986.

37. Desbarats, A.J., Numerical estimation of effective

permeability in sand-shale formations, Water Resour.

Res., 23(2), 273-286, 1987.

38. Desbarats, A.J., Spatial averaging of hydraulic

conductivity in three-dimensional heterogeneous

porous media, Math. Geol.}, Vol. 24(3), 249-267,

1992a.

39. Desbarats, A.J., Spatial averaging of transmissivity in

heterogeneous fields with flow toward a well, Water

Resour. Res., 28(3), 757-767, 1992b.

40. Desbarats, A.J. and R. Dimitrakopoulos, Geostatistical

modeling of transmissibility for 2D reservoir studies,

SPE Formation Evaluation ,5(4), 437-443, 1990.

41. Deutsch, C., Calculating effective absolute permeability in

sandstone/shale sequences, SPE Form. Eval., Sept.

343-348, 1989.

42. Deutsch, C.V., and A.G. Journel, GSLIB: Geostatistical

Software Library and User Guide, Oxford University

Press, 1992.

43. Dykaar, B.B. and P.K. Kitanidis, Determination of the

effective hydraulic conductivity for heterogenous

porous media using a numerical spectral approach 1.

Method, Water Resour. Res., 28(4), 1155-1166, 1992a.

44. Elishakoff, L., Probabilistic Methods in the Theory of

Structures, John Wiley \& Sons, New York, 1983.

45. El-Kadi, A.I. and W. Brutsaert, Applicability of effective

parameters for unsteady flow in nonuniform aquifers,

Water Resour. Res., 21(2), 183-198, 1985.

46. Follin, S., Numerical Calculations on Heterogeneity of

Groundwater Flow, Ph.D. dissertation, The Royal Inst.

of Technol., Stockholm, 1992.

47. Freeze, R.A., A stochastic-conceptual analysis of onedimensional groundwater flow in nonuniform

homogeneous media, Water Resour. Res., 11(5), 725-

741, 1975.

48. Frisch, U., Wave propagation in random media, in

Probabilistic Methods in Applied Mathematics, edited

by A.T. Bharucha-Reid, 1, 75-198, Academic Press,

New York, 1968.

49. Gelhar, L.W., Stochastic subsurface hydrology. From

theory to applications, Water Resour. Res., 22(9),
6

135S-145S, 1986.

50. Gelhar, L. W., Stochastic analysis of solute transport in

saturated and unsaturated porous media, in Advances in

Transport Phenomena in Porous Media , NATO ASI

Ser., edited by J. Bear and M. Y. Corapcioglu, 657-

700, Martinus Nijhoff, Dordrecht, Netherlands, 1987.

51. Ghanem, R.G., and P.D. Spanos, Stochastic Finite

Elements: A spectral Approach. Springer-Verlag, New

York, 1991.

52. Gomez-Hernandez, J.J. and S.M. Gorelick, Effective

groundwater model parameter values: Influence of

spatial variability of hydraulic conductivity, leakance,

and recharge, Water Resour. Res., 25(3), 405-419,

1989.

53. Guyaguler, B. and R.N. Horne, Uncertainty Assessment of

Well Placement Optimization, SPE 71625, 2001.

54. Hashin, Z., and S. Shtrikman, A variational approach to the

theory of effective magnetic permeability of multiphase

materials, Jour. Of Appl. Physics, 33, 3125-3131, 1962.

55. Hassanizadeh, M. and W.G. Gray, General conservation

equations for multi-phase systems, 1, Averaging

procedure, Adv. Water Resour., 2, 131-144, 1979.

56. Isaaks, E.H. and R.M. Srivastava, An Introduction to

Applied Geostatistics}, Oxford University Press, 1989.

57. Johnson, M.E., Multivariate Statistical Simulation, John

Wiley & Sons, New York, 1987.

58. Journel, A.G. and Ch.J. Huijbregts, Mining Geostatistics,

Academic Press, 1978.

59. Kadanoff, L.P., Scaling and Universality in statistical

physics, Phisica A, 163, 1-14, 1990.

60. Kasap, E., and L.W. Lake, An analytical method to

calculate the effective permeability tensor of a grid

block and its application in an outcrop study, paper

presented at the SPE Symp. on Reser. Sim., Soc. of Pet.

Eng. Houston, 1989.

61. Kasap, E., and L.W. Lake, Calculating the effective

permeability tensor of a grid block, SPE Form. Eval.,

June, 192-200, 1990.

62. Keller, J.B., Wave propagation in random media, Proc.

Symp. Appl. Math ., 13, 227-246, Amer. Math. Soc.,

Providence, Rhode Island, 1962.

63. Keller, J.B., Stochastic equations and wave propagation in

random media, Proc. Symp. Appl. Math ., 16, 145-170,

Amer. Math. Soc., Providence, Rhode Island, 1964.

64. Keller, J.B., Effective conductivity of reciprocal media, in

Random Media, edited by G. Papanicolaou, The IMA

Vol. in Math. and its Appl., Vol.7, Springer-Verlag,

New York, 1987.

65. King, P.R., The use of renormalization for calculating

effective permeability, Transport in Porous Media , 4,

37-58, 1989.

66. Kitanidis, P.K., Effective hydraulic conductivity for

gradually varying flow, Water Resour. Res., 26(6),

1197-1208, 1990.

67. Kohler, W.E., and G. Papanicolaou, Bounds for the

effective conductivity of random media, in

Macroscopic properties of Disordered Media , edited

by R. Burridge, S. Childress, and G. Papanicolaou,

Lecture Notes in Physics, 154, Springer-Verlag, New

York, 1982. 111-130

68. Kraichnan, R.H., Dynamics of nonlinear stochastic

systems, J. Mathematical Phys., 2, 124-148, 1961.

69. Kraichnan, R.H., The closure problem of turbulence

theory, Proc. Symp. Appl. Math ., 13, 199-225, Amer.

Math. Soc., Providence, Rhode Island, 1962.

70. Kraichnan, R.H., Eddy viscosity and diffusivity: exact

formulas and approximations, Complex Systems, 1,

805-820, 1987.

71. Kröner, E., Bounds on effective elastic moduli of

disordered materials, J. Mech. Phys. Solids, 25, 137-

155, 1977.

72. Landau, L.D. and E.M. Lifshitz, Electrodynamics of

Continuous Media , Pergamon Press, Oxford, England,

1960.

73. Landauer, R., Electrical conductivity in inhomogeneous

media, in Electrical Transport and Optical Properties

of Inhomogeneous Media , edited by J.C. Garland and

D.B. Tanner, AIP Conference Proceedings, No.~40,

American Institute of Physics, 1978.

74. Lumely, J., and A. Panofski, The Structure of Atmospheric

Turbulence. John Wiley \& Sons, New York, 1964.

75. Markov, K.Z., Application of Volterra-Wiener series for

bounding the overall conductivity of heterogeneous

media. I. General procedure, SIAM J. of Appl. Math., 4,

831-849.

76. Matheron, G., The intrinsic random functions and their

applications, Advan. Appl. Prob., 5, 439-468, 1973.

77. Matheron, G., The Theory of Regionalized Variables and

its Applications, Les cahiers du contre de morphologie

mathematique de Fontainbleau, No.5, 1971.

78. Mei, C.C. and J.-L. Auriault, Mechanics of heterogeneous

porous media with several spatial scales, Proc. R. Soc.

London, Ser. A, 426, 391-423, 1989.

79. Meystel, A. and S. Uzzaman, “Multiresolutional Decision

Support System”, US Patent, No. 6102958 of August

2000

80. Meystel, A. and J. Albus, Intelligent Systems:

Architectures, Design, Control, Wiley, NY 2001

81. Myers, D.E., A series of lecture notes on conditional

simulations, in Advanced Geostatistics course,

University of Arizona, 1990.

82. Nashed, M.Z., and H.W. Engle, Random generalized

inverse and approximate solutions of random operator

equations, in Approximate Solutions of Random

Equations, edited by A.T. Bharucha-Reid, North

Holland, New York, 1978.

83. Neuman, S.P., Statistical characterization of aquifer

heterogeneities: an overview. In Recent trends in

hydrology, edited by T.N. Narasimhan, Special Paper

189, 81-102, Geol. Soc. Am. Spec. Pap. 1982.

84. Neuman, S.P., Role of geostatistics in subsurface

hydrology, in NATO ASI Series, Geostatistics for

Natural Resources Characterization, edited by G.

Verly, M. David, A.G. Journel, and A. Marechal, D.

Reidel, Hingham, Mass., 2, 127-140, 1984.

85. Neuman, S.P., Stochastic continuum representation of

fractured rock perrmeability as an alternative to the

REV and fracture network concepts, 28th US

Symposium on Rock Mechanics, edited by I.W. Farmer,

J.J.K. Daemen, C.S. Desai, C.E. Glass, and S.P.

Neuman, Balkema, Rotterdam, The Netherlands, 533-

561, 1987.

86. Neuman, S. P., Universal scaling of hydraulic

conductivities and dispersivities in geologic media,

Water Resour. Res., 26(8), 1749-1758, 1990.

87. Neuman, S.P. and E.A. Jacobson, Analysis of nonintrinsic

spatial variability by residual kriging with application

to regional groundwater levels, Math. Geol., 16(5),

499-521, 1984.

88. Neuman, S.P., S. Orr, O. Levin, and E. Paleologos, Theory

and high-resolution finite element analysis of 2-D and

3-D effective permeabilities in strongly heterogeneous

porous media, in Computational Methods in Water

Resources IX, edited by T.F. Russell, R.E. Ewing, C.A.

Brebbia, W.G. Gray, and G.F. Pinder, Elsevier Applied

Science, New York, 1992.

89. Neuman, S.P., O. Levin, S. Orr, E. Paleologos, D. Zhang,

and Y.~-K. Zhang Nonlocal representations of

subsurface flow and transport by conditional moments,

Computational Stochastic Mechanics, edited by A.H.D.
7

Cheng, and C.Y. Young, Computational Mechanics

Publications, 1992.

90. Neuman, S.P., Eulerian-Lgrangian theory of transport in

space-time nonstationary velocity fields: 1. Exact

nonlocal formalism by conditional moments and weak

approximation, Water Resour. Res., 1993.

91. Neuman, S. P., & Orr S., Prediction of steady state flow in

non uniform geologic media by conditional moments:

Exact non local formalism, effective conductivities,

and weak approximations, Water. Resour. Res., 29 (2),

341-364, 1993.

92. Neuman, S. P., Tartakovsky, D. M., Wallstrom, T. C. &

Winter, C. L., Correction to “Prediction of steady state

flow in nonuniform geologic media by conditional

moments: Exact nonlocal formalism, effective

conductivities, and weak approximation”, Water

Resour. Res., 32(5), 1479-1480, 1996.

93. Orr, S. and S.P. Neuman, "Operator and IntegroDifferential Representations of Conditional and

Unconditional Stochastic Subsurface Flow". An invited

paper, Journal of Stochastic Hydrology and Hydraulics,

Vol 8(2), pp. 157-172, 1994.

94. Orr, S. "Enhanced Heap Leaching - I. Insights”, ME/SME

Journal Vol. 54, No. 8, 2002.

95. Orr, S. and V. Vesselinov, "Enhanced Heap Leaching – II.

Applications", ME/SME Journal, Vol. 54, No. 8, 2002.

96. Papoulis, A., Probability, Random Variables, and

Stochastic Processes, McGraw-Hill, 1984.

97. Paleologos, E., Effective Hydraulic Conductivity of

Bounded, Strongly Heterogeneous Porous Media,

Ph.D. dissertation, University of Arizona, 1993.

98. Poley, A.D., Effective permeability and dispersion in

locally heterogeneous aquifers, Water Resour. Res., 24,

1921--1926, 1988.

99. Roberts, P.H., Analytical theory of turbulent diffusion,

Jour. Fluid. Mech., 11, 257-283, 1961.

100. Rubin, Y. and G. Dagan, Stochastic analysis of

boundaries effects on head spatial variability in

heterogeneous aquifers 1. Constant head boundary,

Water Resour. Res., 24(10), 1689-1697, 1988.

101. Rubin, Y., and Gomez-Hernandez, J.J., A stochastic

approach to the problem of upscaling of conductivity in

disordered media: theory and unconditional numerical

simulations, Water Resour. Res., 26(4), 691-701, 1990.

102. Saez, A.E., C.J. Otero, and I. Rusinek, The effective

homogeneous behavior of heterogeneous porous media,

Transport in Porous Media , 4, 213-238, 1989.

103. Saffman, P.G., On the boundary condition at the surface

of a porous medium, Studies in Applied Math ., L(2),

93-101, 1971.

104. Samper, F.J. and S.P. Neuman, Estimation of spatial

covariance structures by adjoint state maximum

likelihood cross validation, 1. Theory, Water Resour.

Res., 25(3), 351-362, 1989a.

105. Samper, F.J. and S.P. Neuman, Estimation of spatial

covariance structures by adjoint state maximum

likelihood cross validation, 3. Application to

hydrochemical and isotopic data, Water Resour. Res.,

25(3), 373-384, 1989b.

106. Schetzen, The Volterra and Wiener Theories of Nonlinear

Systems, John Wiley \& Sons, New York, 1980.

107. Shvidler, M.I., Filtration Flows in Heterogeneous Media

(in Russian), Izv. Akad. Nauk SSSR Mech., 3, 185-190,

1962.

108. Tartar, L., Remarks on homogenization, Homogenization

and Effective Moduli in Materials and Media , edited by

J.L. Ericksen, D. Kinderlehrer, R. Kohn, and J.L.

Lions, The IMA Vol. in Math. and its Appl., Vol.7,

228-246, Springer-Verlag, New York, 1986.

109. Tartar, L., Nonlocal effects induced by homogenization,

in Partial differential equations and the calculus of

variations, Vol. 2, Essays in honor of Ennio De Giorg i,

edited by F. Colombini, A. Marino, L. Modica, and S.

Spagnolo, Progress in nonlinear differential equations

and their applications, 925-938, Birkhauser, Boston,

1989.

110. Tsokos C.P., and W.J. Padgett, Random Integral

Equations with Applications to Life Sciences and

Engineering, Mathematics in Science and Engineering,

108, Academic Press, New York, 1974.

111. Van Kampen, N. G., Stochastic Processes in Physics and

Chemistry, North-Holland, Amsterdam, 1981.

112. Vanmarcke, E.H., Random Fields: Analysis and

Synthesis, MIT Press, Cambridge, Mass., 1983.

113. Carrera, J., J. Heredia, S. Vomvoris, and P. Hufschmied,

Fracture flow modelling: Application of automatic

calibration techniques to a small fractured monzonitic

gneiss block, in Hydrogeology of Low Permeability

Environments, edited by S.P. Neuman and I.

Neretnieks, Hydrogeology, Selected Papers, Intern.

Assoc. Hydrogeol., Verlag Heinz Heise, Hannover,

Germany, 2, 115-167, 1990.

114. Orr, S., Stochastic Approach to Steady State Flow in

Nonuniform Geologic Media, Ph.D. dissertation,

University of Arizona, 1993.

115. Neuman, S.P., and A. Guadagnini, A new look at

traditional deterministic flow models and their

calibration in the context of randomly heterogeneous

media. MODELCARE –1999.

116. James, A.L., and C.M. Oldenburg, Linear and Monte

Carlo uncertainty analysis for subsurface contaminant

transport simulation. Water Resources Research, Vol.

33, No. 11. pp. 2595-2508. 1997.

117. Carrera, J., Neuman, S.P, Estimation of aquifer

parameter under transient and steady state conditions :

1. Maximum likelihood method incorporating prior

information. Water Resources Research, v. 22, 2, p.

199-210, 1986a.

118. Carrera, J., Neuman, S.P., Estimation of aquifer

parameter under transient and steady state conditions :

2. Uniqueness, stability, and solution algorithms.

Water Resources Research, v. 22, 2, p. 211-227,

1986b.

119. Carrera, J., Neuman, S.P., Estimation of aquifer

parameter under transient and steady state conditions :

3. Application to synthetic and field data. Water

Resources Research, v. 22, 2, p. 228-242, 1986c.

120. Carrera, J., State of the art of inverse problem applied to

the flow and solute transport equations. Groundwater

Flow and Quality Modeling, Custodio, E. et al., eds.,

NATA ASI Series, Serie C : Mathematical and

Physical Sciences, v. 224, D. Reidel Pub. Co.

Dordrecht, The Netherlands, p. 549-583, 1987.

121. Gh. de Marsily, J.P. Delhomme, A. Coudrain-Ribstein,

A.M. Lavenue, Four Decades of Inverse Problems in

Hydrogeology, in celebration of S.P. Neuman’s 60th

Birthday, 30 pp., 1998.
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