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.

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norms ...values

roles

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percept

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plan

Intentions

beliefs

Desires

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Belief

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