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PUBLICATIONS

A111DS blbSSb

NATL INST OF STANDARDS & TECHI R.I.C

Gass Saul I/Expert systems and emergenc QC100 U57 NO 728 V1986;C.2 C.1 NBS-PUBa /

^fAU Of

NBS SPECIAL PUBLICATION 728

U.S. DEPARTMENT OF COMMERCE/National Bureau of Standards

Expert Systems and Emergency

Management: An Annotated

Bibliography

S.I. Gass, S. Bhasker, and R.E. Chapman
m he National Bureau of Standards' was established by an act of Congress on March 3, 1901. The M Bureau's overall goal is to strengthen and advance the nation's science and technology and facilitate their effective application for public benefit. To this end, the Bureau conducts research and provides: (1) a

basis for the nation's physical measurement system, (2) scientific and technological services for industry and

government, (3) a technical basis for equity in trade, and (4) technical services to promote public safety.

The Bureau's technical work is performed by the National Measurement Laboratory, the National

Engineering Laboratory, the Institute for Computer Sciences and Technology, and the Institute for Materials

Science and Engineering

.

The National Measurement Laboratory

Provides the national system of physical and chemical measurement;

coordinates the system with measurement systems of other nations and

furnishes essential services leading to accurate and uniform physical and

chemical measurement throughout the Nation's scientific community, in- dustry, and commerce; provides advisory and research services to other

Government agencies; conducts physical and chemical research; develops,

produces, and distributes Standard Reference Materials; and provides

calibration services. The Laboratory consists of the following centers:

• Basic Standards2

• Radiation Research

• Chemical Physics

• Analytical Chemistry

The National Engineering Laboratory

Provides technology and technical services to the public and private sectors to address national needs and to solve national problems; conducts research in engineering and applied science in support of these efforts; builds and maintains competence in the necessary disciplines required to carry out this research and technical service; develops engineering data and measurement

capabilities; provides engineering measurement traceability services; develops

test methods and proposes engineering standards and code changes; develops

and proposes new engineering practices; and develops and improves

mechanisms to transfer results of its research to the ultimate user. The

Laboratory consists of the following centers:

• Applied Mathematics

• Electronics and Electrical

Engineering2

• Manufacturing Engineering

• Building Technology

• Fire Research

• Chemical Engineering2

The Institute for Computer Sciences and Technology

Conducts research and provides scientific and technical services to aid

Federal agencies in the selection, acquisition, application, and use of computer technology to improve effectiveness and economy in Government

operations in accordance with Public Law 89-306 (40 U.S.C. 759), relevant

Executive Orders, and other directives; carries out this mission by managing

the Federal Information Processing Standards Program, developing Federal

ADP standards guidelines, and managing Federal participation in ADP

voluntary standardization activities; provides scientific and technological advisory services and assistance to Federal agencies; and provides the technical

foundation for computer-related policies of the Federal Government. The Institute consists of the following centers:

Programming Science and

Technology

Computer Systems

Engineering

The Institute for Materials Science and Engineering

Conducts research and provides measurements, data, standards, reference

materials, quantitative understanding and other technical information fundamental to the processing, structure, properties and performance of materials;

addresses the scientific basis for new advanced materials technologies; plans

research around cross-country scientific themes such as nondestructive

evaluation and phase diagram development; oversees Bureau-wide technical

programs in nuclear reactor radiation research and nondestructive evaluation; and broadly disseminates generic technical information resulting from

its programs. The Institute consists of the following Divisions:

Ceramics

Fracture and Deformation 3 Polymers

Metallurgy

Reactor Radiation

'Headquarters and Laboratories at Gaithersburg, MD, unless otherwise noted; mailing address

Gaithersburg, MD 20899.

2 Some divisions within the center are located at Boulder, CO 80303. 'Located at Boulder, CO, with some elements at Gaithersburg, MD.
NBS

RESEARCH

INFORMATION

CENTER

Expert Systems and Emergency Management:

An Annotated Bibliography &uo°

Saul I. Gass

Suneel Bhasker

College of Business and Management

University of Maryland

College Park, Maryland 20742

and

Robert E. Chapman

Center for Applied Mathematics

National Engineering Laboratory

National Bureau of Standards

Gaithersburg, Maryland 20899

Sponsored by:

Federal Emergency Management Agency

500 C Street, S.W.

Washington, DC 20472

and

Center for Applied Mathematics

National Engineering Laboratory

National Bureau of Standards

Gaithersburg, Maryland 20899

U.S. DEPARTMENT OF COMMERCE, Malcolm Baldrige, Secretary

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

November 1986
Library of Congress Catalog Card Number: 86-600591

National Bureau of Standards Special Publication 728

Natl. Bur. Stand. (U.S.), S^< . Publ. 728, 179 pages (Nov. 1986)

CODEN: XNBSAV

U.S. Government Printing Office

Washington: 1986

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, DC 20402
ABSTRACT

This report is the result of an in-depth review of the recent technical

literature on expert systems. The material contained in this report provides

a basis for assessing the potential for using expert systems in emergency

management operations. In choosing the material for inclusion in this report,

special emphasis was placed on those aspects of expert systems which addressed

the types of problems encountered in emergency management operations.

The report is designed for use as a resource document and as a tutorial on expert systems and emergency management. Each chapter consists of a brief

topic essay, followed by a set of references which expand on the main themes

of the essay.

Keywords: Artificial intelligence; emergency management; expert systems.

i i i
Table of Contents

Page

Abstract iii

List of Tables vi

List of Figures vi

1. Introduction 1 1.1 The Purpose of this Report 1 1.2 Literature Survey 1 1.3 Organization of the Report 3

2. The Nature of Emergency Management 4

2.1 Understanding the Nature of Emergencies 4

2.2 The Purpose of Emergency Management 7

2.3 Technology and Non-Human Resources in Emergency Management... 11

3. Introduction to Expert Systems 22

3.1 Selected Expert Systems Currently in Use 22

3.2 The Structure of an Expert System: An Idealization 24

3.3 Tools and Techniques for Building and Testing an Expert

System 27

4. Reasoning and Methodological Issues in the Design and Development

of Expert Systems 39

4.1 Knowledge Representation 39

4.2 Control Structures 40

4.3 The Manipulation of Data 41

5. Tools for Building Expert Systems 63

5.1 General Purpose Tools: LISP and PROLOG 63

5.2 Special Purpose Tools: Shells 65

6. User Interface Considerations 84

6.1 Desired Explanatory Capabilities 84

6.2 Two Approaches for Making Interfaces More User Friendly 85

7. Dealing with Uncertainty in the Design, Development and Use of

Expert Systems 93

7.1 Importance of Dealing With Uncertainty in Expert Systems 93

7.2 The Handling of Uncertainty in Expert Systems 93

8. Applications of Expert Systems Ill

8.1 Types of Problems Amenable to Solutions by Expert Systems.... Ill

8.2 Domains in Which Expert Systems are Being Currently Applied.. Ill

i v
Table of Contents

Page

9. Use of Expert Systems in the Domain of Emergency Management 143

9.1 The Suitability of Expert Systems for Emergency Management... 143

9.2 Expert Systems for Response 143

9.3 Major Expert Systems Research Topics 146

Appendix A: List of Journal and Proceedings Sources 159

Appendix B: Glossary of Technical Terms 166

Appendix C: Some Common Abbreviations 171

v
List of Tables

Table 3.1 Some Existing Expert Systems Classified by

Problem Domain 23

Table 8.1 Expert Systems in Medicine 113

Table 8.2 Expert Systems in Chemistry 115

Table 8.3 Expert Systems in Engineering, Mathematics,

and Other Sciences 116

Table 8.4 Expert Systems in Oil/Mineral Exploration 117

Table 8.5 Expert Systems in Military and Government 118

Table 8.6 Expert Systems in Industry and Business 119

Table 8.7 Expert Systems in Education and Training 121

List of Figures

Figure 2.1 Emergency Dimensions 5

Figure 2.2 Scope of Emergencies 9

Figure 3.1 Schematic of an Ideal Expert System 25

Figure 9.1 Emergency/Expert Systems Areas of Application 148

vi
1. INTRODUCTION

1 . 1 THE PURPO SE OF THIS REPORT

Improving the management of emergency situations at all levels of government

has always been a matter of concern. Thus, emergency managers have been quick

to search out and utilize new equipment and technologies. For example, our

ability to respond to the ever changing requirements of an emergency has

improved greatly due to the use of modern information and communications

systems. Today, with the increasing use of personal and other types of

computers, coupled with recent advances in artificial intelligence, the

emergency manager is faced with the difficult task of determining what aspects

of these advanced technologies should be investigated in terms of their role

in the management of emergencies. Unlike past improvements, the blend of

computers and artificial intelligence, especially the subfield of expert

systems, is not well understood. Also, the development and testing of such

computer-based systems can be quite costly; there is little experience to

guide us. However, as the application of expert systems to emergency

management operations could result in truly major advances, it is important

for the community of emergency managers, administrators and researchers to

become familiar with the potential of expert systems. This annotated

bibiliography is an attempt to provide the necessary information with which

one can review and further pursue the current status of expert systems, with

emphasis on its relation to emergency management.

We have included source material that deals with: (1) the nature of expert

systems; (2) available methodologies and tools for building expert systems;

(3) the logic and reasoning behind the tools used in expert systems; (4) the

approaches used to measure uncertainty of knowledge; (5) the issues of user

interface; and (6) the current and potential emergency management uses of

expert system methodologies. The references will provide the reader with

different views of the theoretical and applications viability of expert

systems. This applies especially to the concepts that form the core of

current expert sytems techniques -- knowledge representation, logic and

reasoning, and uncertainty. It is hoped that this bibliography will aid the

reader to focus on those papers and books that will be of most value with

respect to the reader's background and interests.

1.2 LITERATURE SURVEY

Although it was our aim to find and organize all material on expert systems

and emergency management, this proved to be an impossible task due to the vast

amount of relevant literature. The field of expert systems, in particular,

was demonstrating exponential growth in terms of publications. Also, as we

began gathering and reviewing books and papers, it became clear that much of

the writings on expert systems were short-lived, represented experimental

ventures, and became obsolete rather rapidly. Thus, we found it both

convenient and appropriate to emphasize the inclusion of material published

within the last five years. Care was taken, however, not to overlook seminal

works that by definition have stood the test of time. Further, in many

discussion areas, two or more items that differ only in depth or level of

technical presentation were included so as to make the bibiliography more

useful to the general emergency management community.

1
The actual literature search was done in two stages. First, the computerbased DIALOG Information Retrieval Service at the National Bureau of Standards

was used to retrieve items from the following files:

- NT I S , which is an extensive information base consisting of

governmental -sponsored research, development, and engineering reports plus

analyses, journal articles, and translations prepared by Federal agencies,

their contractors or grantees. This information is produced and maintained by

the National Technical Information Service of the U.S. Department of Commerce.

It is the means by which unclassified, unlimited distribution reports are made

available to the public. The NTIS data base includes material from both the

physical and social sciences, including topics of immediate widespread

interest, such as environmental pollution and control, energy conservation,

technology transfer, health planning, societal problems, and urban and

regional development and planning. The file covers the period from 1960 to

the present. The on-line reviews of NTIS produced nearly 300 references.

The majority of the references produced by this search were theoretical and

technical in scope.

- INSPEC is the largest English language information base in the fields of

physics, el ectrotechnol ogy , computers, and control. It corresponds to the

printed Phys ics Abs

t

r a c

t

s , Electrical and Electro nics Abs tracts, and Computer

and ControJ Abstracts . Non-Englfsh language source material is also included,

but is indexed and" abstracted in English. Journal papers, conference

proceedings, technical reports, books, patents, and university theses are

abstracted for inclusion in INSPEC. The total number of journals scanned is

over 2000; 200 of these are abstracted completely. INSPEC covers citations

from 1969 to the present. During the on-line review of INSPEC, nearly 200

references were produced. This search produced references that were more

varied in the types of issues dealt with.

- The C0MPENDEX data base is the machine-readable version of

Engine ering I ndex; it provides abstracted information from the world's

significant engineering and technological literature. It has world-wide

coverage of approximately 3500 journals, publications of engineering societies

and organizations, papers from the proceedings of conferences, and selected

government reports and books. C0MPENDEX covers citations from 1970 to the

present. More than 50 references were produced during the on-line review of

C0MPENDEX.

- The Ei ENGINEERING MEETINGS is a data base that covers significant

published proceedings of engineering and technical conferences, symposia,

meetings, and colloquia. Ei ENGINEERING MEETINGS covers all disciplines of

engineering including civil engineering, environmental engineering, geological

engineering, bi oengi neeri ng, and electrical engineering. Non-engineering

disciplines covered include electronics, control devices and principles,

applied mathematics, and physics. Many application-oriented references were

obtained through this on-line file search.

2
The second stage of the literature search was a manual one. This enabled us to follow leads generated by the online searches, and to review the many

articles and books not captured by the automated systems. Much of the manual

search was conducted at the National Bureau of Standards Library, supplemented

by the University of Maryland Engineering and Physical Sciences Library, and

the University of Maryland Computer Science Programs Library. The most

productive sources were the International Journal of Man-Machine Studies, the

various Proceedings of the International Joint Conference on Artffi cial

~

Intel l igence , the more recent Procee dings of the American Association "Tor

Arti f fc i a 1 I n t e 1 1 i g_en ce , various Proceedi ngs of the "international Co nference

Cybernetics and Society, and the Proceedings of Trends and Applications 1983

on Automating Intelligent Behavior: Applicati ons and Fr ontiers.

1 . 3 ORGANIZATION OF THE REPORT

Although the chapters in this report address interrelated issues, they have

been designed to be self-contained. The reader is directed to related

discussions by suitable cross-references. Where appropriate, the chapters are

divided into sections, each with a discussion of a significant issue or

subject matter of the chapter. The references and abstracts included in a chapter are the most relevant to the subject matter and issues of the chapter.

The reader should recognize that the placement of a reference (and abstract)

in a particular chapter is a judgment call of the editors, often backed up by

the authors' statements; again, where necessary, items are cross-referenced.

The report also contains a listing of all sources; this should provide a useful starting place for those interested in furthering the search and

obtaining complete copies of an item. We also include a glossary of

artificial intelligence terms; and finally, a table of abbreviations and their

meaning is given.

3
2. THE NATURE OF EMERGENCY MANAGEMENT

2.1 UNDERSTANDING THE NATURE OF EMERGENCIES

Knowing the nature of an emergency is the sine qua non of an effective

response. However, response is only one element, albeit a major one, of the

totality of actions that are generated by an emergency. Understanding the

full implications of an emergency will aid in directing both passive and

dynamic actions that can be applied during the mitigation, preparedness and

recovery phases, as well as during the actual response.

The Many Dimen sions of an Emergency

By dimensions of an emergency we mean the various functional aspects that have

to be considered during an actual emergency situation. These items are given

in Figure 2.1 and range from forewarning to societal response, see FEMA

[1983]. In general, they include the effects and consequences that result

from the emergency. For example, given a major emergency such as the 8.1

Richter scale earthquake that hit Mexico City, we must deal with having no

ample forewarning, widespread damage due to collapsed buildings and fires,

loss of communications, injuries and deaths, loss of homes, and the need to

search and rescue as soon as possible. During such occurrences, normal

activity is at a standstill; business is not as usual. The community must

pull together, be supplied with the essentials, and begin to look to the

future. Other dimensions of the emergency must now be addressed: assessment

of economic losses, restoration and reconstruction, aiding the victims and the

community to return to normal times. Similar functional dimensions apply to

other types of emergencies, as shown in Figure 2.1. The understanding of

these dimensions with respect to their implications for a specific emergency

forms the basis of effective emergency planning.

In our literature review, we noted a number of excellent discussions that

dealt with the many aspects of commonly occurring emergencies, see Bolt

[1978], White and Haas [1975], and Whittow [1979]. We also found material on some often overlooked, but important dimensions that characterize almost every

emergency. We next briefly discuss a few of the more important ones.

With every emergency, there is a need of a societal response, with the

character of such responses varying by emergency. A basic and important type

of societal response is that of citizen volunteer groups. Although these

groups tend to coalesce and operate during and after a disaster, they are also

valuable entities in the mitigation and preparedness phases of an emergency.

Citizen groups have organized to develop local evacuation plans, train and

prepare a community on how to respond to an earthquake, and rebuild damaged

areas. It is important for emergency managers to recognize the importance of

these groups to the overall management of an emergency, and to work with and

cooperate with them so as to be able to channel their activities in the best

direction. An excellent discussion on the role of emergent citizen groups and

their implications for emergency management is given in Stall ings and

Quarantelli [1985].

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The presence of the mass media is to be expected in all emergency situations.

Although the mass media sometimes appear to exacerbate and distort the events

of an emergency, their cooperation and actions can be of great value in the

proper management of all phases of an emergency. For example, the media can

assist in pre-disaster education, serve as an effective part of the early

warning system, provide information and advice to victims, and help stimulate

an effective response to a disaster. However, in contrast, the demands of the

media can interfere with the actions of the emergency managers, may spread

rumors that contradict the reality of the disaster, and can sometimes be an unjust postdisaster critic. Given these observations, emergency planners

cannot neglect the role of the media in all phases of emergency management.

The "act of plugging the media into a disaster plan" involves considerable

planning to achieve the required cooperation. For a discussion on how to cope

with the media based on actual experience (in Canada) see Scanlon et al.

[1985].

The proper financing of emergency management activities is an important

consideration for all levels of government. At the local level, emergency

managers must be knowledgeable about all types of financial assistance. This

is especially important in those situations when little or no State and

Federal aid is forthcoming. Such aid, when available, can take on many forms

and the cognizant emergency managers must be able to interpret the regulations

in a manner that causes the best package of benefits to be applied to their

localities. Although little research has been done in this area, the reader

is directed to Kunreuther and Miller [1985] and Settle [1985].

The final emergency dimension to be discussed is that of the special nature of

the medical response to a disaster. There are many ways in which such medical

activities differ from that of the "usual" daily emergency situations.

Emergency medical personnel have few opportunities to respond to disasters and

thus, are not able to routinize and practice the special needs required by

extreme situations. During a disaster, medical personnel must adapt their

many-on-one training and experience to be able to assess quickly the extent of

an injury, and to render basic emergency care to a large number of persons

under hardship conditions. Also, during a disaster, medical organizations

must work with and coordinate their efforts with outside agencies. These

aspects of emergency medical response emphasize that disasters are not just

intensified everyday emergencies, but must be planned for in a special manner,

see Tierney [1985] for further discussion on this topic.

Nat u ral vs. Man-Made/Te chnol ogi cal. Am_e-r-9-eJTc

} es

In this section we examine some important differences between natural and

technological emergencies and the additional dimensions that need to be

considered in managing the latter type.

6
Man-made/technological disasters such as nuclear reactor accidents, hazardous

chemical spills, and toxic airborne releases have become a major concern for

emergency managers. As compared to natural disasters for which there is a substantial experience on response procedures, emergency managers are often

unfamiliar with specific technological hazards. Although most local emergency

units have received some technological disaster training, hands on experience

is still rare. Technological disasters usually occur quickly and without

warning, for example, a highway chemical spill. The short time between the

onset of the damaging effects of such disasters compounds the problems of the

already difficult response requirements. The resulting hazards may not be

readily observable and may require the expertise of specialists for

identifying, estimating and evaluating the risks.

Citizen response to a technological disaster may differ with respect to the

response given a natural disaster. With a natural disaster (like a flood),

communities are generally familiar with the threat and have experienced actual

incidents. Also, local governments may have instituted controls to prevent or

reduce the damage. Given the low incident and random nature of technological

disasters, a given community, suddenly experiencing such an emergency, would

probably have little knowledge about the possible resulting hazards and proper

response procedures. The dependence on governmental management of an emergency

is far greater in the case of a technological one. Further discussion is

contained in the articles by Kasperson and Pijawka [1985] and Zimmerman [1985].

Common Dimensions Among the Va ri ous Erne r gen ci es

The management of emergencies is a practical matter. It requires emergency

managers to consider their activities in terms of cost, efficiency and

effectiveness. Although each type of emergency tends to be unique, there are

similarities among them, the recognition of which would help to increase the

efficiency of the total emergency management structure. One way of examining

such similarities is through the dimensions of emergency response given in Figure

2.1. For example, although they are required due to different causes, the

response dimensions of hurricanes and earthquakes are identical (except for

forewarning). There are, of course, differences in type and magnitude of

response elements, especially between technological and natural disasters. It is

suggested here that by understanding the similarities and differences in the

response dimensions that exist for the collective set of responses faced by a community, better and more effective use of emergency-related resources can be

made.

2.2 THE PURPOSE OF EMER GENCY MANAG EMENT

Stated very simply, the primary purpose of managing any emergency is to alleviate

and/or prevent the adverse effects and consequences of a disaster. Implied by

this definition is a myriad of response activities to be carried out by an organization that is structured to handle a wide range of disasters. Further, as emergency management must be viewed as an ongoing process, we must include the

organization's activities during the phases of emergency mitigation, preparedness

and recovery.

7
In a typical emergency response, lives and property have to be protected,

communication links established, emergency personnel and supplies transported,

search and rescue operations carried out, medical assistance provided, and so on. Mitigative measures may include hazard analysis, resource allocation, public

information programs, legislating for and implementation of building and land use

codes. The activities of preparedness complement those of mitigation; they

include establishing a warning system, operation of a disaster monitoring center,

organizing and training of emergency response teams, establishing resource

management and evacuation plans. The recovery phase includes the activities of a disaster assistance center, economic assessment, cleanup and disposal,

rebuilding, counseling, review and modification of codes. This totality of

emergency activities demonstrates the complexity of emergency management. And,

when we consider the range of emergencies that may arise, each with its special

dimensions and consequences, we begin to appreciate the full nature of the

emergency management task. See Drabek [1985], Perry and Ni gg [1985], Seigel

[1985], and Cohen and Noll [1977] for discussion of the various phase activities;

and Krzyztofowicz and Davis [1984], Graff [1985] and Cohen, Noll and Weingast

[1977] for discussion on the activities for managing some specific types of

emergencies.

The Sco pe of Emergency Response

An important aspect of emergency response is the amount of time and resources

that must be spent in managing it and the range of agencies involved. This is

usually a function of the size and scope of the emergency. One way of

classifying the magnitude of an emergency is by ranking them according to whether

their scope is local, regional, national, or even international, see Figure

2.2 and FEMA [1983]. This type of categorization only aids in anticipating the

extent of involvement; any individual emergency may not fall into a strict

category. As the actual size and scope of many disasters become apparent only

after they have struck, it is important for local emergency management

organizations to have ongoing communication channels with other local, regional

and Federal response agencies.

Mobilization Cat egories i n Emergen cy Management

Mobilization is an important element of any emergency response. The type of

mobilization is dependent on the type of resources required to manage an

emergency, see FEMA [1983]. Mobilization planning should also take the timing of

management actions into account. The following seven mobilization categories are

based on the type of resources required to manage emergencies:

(1) Military mobilization is the act of preparing for war or other emergencies

through assembling and organizing military resources. It is the process by

which the armed forces or part of them are brought to a state of readiness

for war or other national emergency.

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(2) Industrial mobilization is the process of marshaling the industrial sector

to produce goods and services, including construction, required to support

military operations and needs of the civil sector during domestic or national

security emergencies.

(3) Economic mobilization is the process of marshaling the money, credit, and

taxes needed to finance the management of the emergency, maintain a stable

economy, and stimulate key sectors of the economy.

(4) Infrastructure mobilization is the process of marshaling the output of

infrastructure systems to support the entire mobilization. Infrastructure

systems include transportation, energy, communications, automated information

processing, water, and agriculture.

(5) Human resources mobilization is the process of marshaling people to

provide needed labor. Human resources mobilization involves identifying and

allocating human resources among competing demands (see Siegel [1985] for a discussion on human resource development for emergency management).

(6) Government mobilization is the process of marshaling resources of Federal,

State, and local governments to carry out the tasks required to manage

emergencies. It involves bringing to the appropriate state of readiness the

leadership, policymaking groups, legislative bodies, and courts; and

supporting communications, facilities, procedures, and authorities to manage

the emergency. Government mobilization activates and controls other aspects

of mobilization.

(7) Civil preparedness mobilization is the process of marshaling resources to

provide protection for the people, industry, and institutions of the United

States against the effect of the spectrum of emergencies. Civil mobilization

involves providing for warning and emergency instructions to the public;

relocation of people to safe areas; shelter, food, water, medical care, and

other human needs; and recovery and reconstruction following the emergency.

The timing of management actions for mobilization can be viewed under the

framework of four phases, based on the types of activities predominantly carried

out during each of these phases. These activities correspond to mitigation,

preparedness, response, and recovery, respectively. These stages provide a useful framework for planning mobilization, and, in reality, these stages may not

be as clear cut.

In Phase 1, Normal Operations Phase, there is a base level state of activity

which exists in the absence of an overt decision to take extraordinary measures.

In this phase, mitigation actions may be directed to prevent or reduce the

probability of occurrence of an emergency and minimize the potential for damage

or loss of life.

Phase 2, Preparedness Phase, involves taking preparedness actions to

implement plans, procedures, and programs. It begins with a decision by

appropriate authorities to increase the readiness of the Nation in one or more

areas for a major domestic emergency or during periods of escalating

international tensions.

10
Phase 3, Emergency Phase, involves a domestic or national security emergency,

up to and including conventional or nuclear war. During this phase, response

actions are taken directly before, during, and, after the onset of the emergency,

to end the emergency so as to protect the public, provide critical assistance,

limit damage, and reduce the probability of secondary effects.

Phase 4, Recovery Phase, involves restoring systems to normal. During this

phase, short-term recovery actions are taken to assess damage and return vital

life-support systems to minimum operating standards; long-term actions may

continue for many years.

2.3 TECHNOLOGY AND NON-HUMAN RESOURCES IN EMERGENCY MANAGEMENT

The Cur rent Need for Technology

Each emergency requires a different understanding about the cause-effect

relationships of the resulting situation, specific planning procedures,

involvement of response personnel, and so on. The number of elements that have

to be analyzed and responded to, even for a small-scale emergency, can become

quite trying to an emergency manager. The manager must make decisions that are

based on timely and accurate information; information that is gathered, filtered,

and transmitted by the most suitable technological means. This may include

computer-based communications that gather automatic sensor readings, data

analysis and statistical programs, as well as resource and logistics management

procedures.

The Imp act of Tec h nology on Eme rgency Management

Technological aids to emergency decision making such as early warning systems and

hurricane tracking have improved the emergency management process. Large amounts

of emergency data are now gathered, analyzed and transmitted by electronic links.

Computers have been used for simulations in preparedness training (Clymer [1983]

and Carroll [1983]), for earthquake impact assessment (Russo and Wilson [1983]

and Vance [1983]), for evacuation planning (McLean et al. [1983]), and for

mobilization planning (Balbach, Fittipaldi and Webster [1983]). Various

computer-based management information systems and, more recently, decision

support systems have been developed as aids to emergency management (Wallace and

DeBalogh [1985]). The advent of microcomputers has made the widespread use of

such decision aids possible. This trend will continue in the future and

increases the opportunity for technological improvements in emergency

management.

An important technological possibility is the power of the computer to analyze a diverse set of information with respect to an expert body of knowledge contained

within the computer's memory. This concept is embodied in the term expert

system. An emergency manager -- and even an emergency management team -- cannot

be expected to have the full range of expertise required to manage properly an

emergency situation. Computer-based expert systems, designed to replicate the

information processing and decision structures of emergency management

specialists, represent a possible major improvement in the decision making

abilities of emergency managers.

11
References

Balbach, H. E., J. J. Fittipaldi, and R. D. Webster, "The Mobilization

Facilities Planning System: A Simulation Program for Emergency

Planning," Proceedings of the Con fere nce on Computer Simulation in

Emergency Planning, Vol. if," No. "2, La Jolla, CA: The Society for"

Computer "Simulation, Jan. 1983.

Bolt, B. A., Earthquakes: A Primer, San Francisco: W. H. Freeman and

Company, 19787

This book explores the latest theories of earthquake causes, prediction,

and environmental effects. It also provides a basis for understanding

the attempts to forecast the strength of earthquakes. In addition to

background information on structural geology and faults, the theory of

plate tectonics is presented to help explain the causes and pattern of

earthquakes around the world. The relation between earthquakes and

active geological faults, volcanoes, and tidal waves is discussed. The

author points out the implications of the theory of plate tectonics for

forecasting earthquakes, along with what actually occurs while an earthquake is taking place, and suggestions for reducing earthquake

hazards.

Burby, R. J., and S. P. French, "Coping with Floods: The Land Use Management

Paradox," Journal of Ameri ca n Planning Association , Vol. 3, July 1981.

Butler, J. E., Natural Disasters, Victoria, Australia: Heinemann Educational

Australia, 1979.

This book discusses the scientific explanations of various natural

disasters and their effect on the human and physical environment.

Through investigating causes, it explores means of prediction,

prevention, and control.

Carroll, M. J. "How to Turn an Emergency Simulation into a 'Video Game 1 for

Training," Proceedings of the Con ference on Comput e r Simulat ion in

Emergency Planning, Vol. 11, No." 2, La Jolla, CA: "the Society for

Computer Simulation, Jan. 1983.

Clary, B. B., "The Evolution and Structure of Natural Hazard Policies," Pub! i

Administrat ion Review , Vol. 45, Special Issue, Jan. 1985.

This article discusses the various natural hazard policies and related

actions categorized according to stages of emergency management. Among

the conclusions drawn by the author are: (1) a significant proportion of

actions in recent years at all levels of government has been regulatory

in intent, (2) many states and localities have acted on their own in

developing innovative approaches to hazard control, (3) available

evidence indicates that improvements can be made in emergency preparedness planning, especially where intergovernmental and/or interagency

coordination is required, (4) disaster aid has become part of the

cost-control question, and (5) natural hazards is a policy area in

transition, and much greater emphasis is being given to pre-disaster

planning and mitigation.

12
Cohen, L., and R. Noll, "The Economics of Disaster Defense: The Case of

Building Codes to Resist Seismic Shock," Social Science Working Paper

No. 130, California Institute of Technology, Pasadena, CA, Mar. 1977.

The purpose of this paper is to provide a preliminary exploration into

the problem of applying economic analysis to the design of building codes

in earthquake-prone areas. The purpose of seismic building codes is to

increase the resistivity of buildings and other structures to major

earthquakes. For the most part, seismic codes apply to new buildings and

specify certain minimum design features that new buildings must match or

beat. Usually these codes are designed to enable a structure to

withstand ground motions associated with violent earthquakes without

collapsing. In some instances, new information about the vulnerability

of structures or the nature of the threat of an earthquake in a particular area leads local governments to adopt codes for existing

buildings as well as new structures. For example, the Field Act in

California, passed after the damaging Long Beach earthquake of 1934,

required that all schools in the state, old and new, be built to

withstand a major earthquake. In the aftermath of the 1971 San Fernando

earthquake, measures have been adopted or proposed that would require

retrofitting of particularly hazardous structures, such as dams or public

meeting halls and theaters, to bring them up to the standards required of

new structures.

Cohen, L., R. Noll, and B. Weingast, "Responses to Disaster: Planning for a Great Earthquake in California," Social Science Working Paper No. 131,

California Institute of Technology, Pasadena, CA, Apr. 1977.

Comfort, L. K. , "Integrating Organizational Action in Emergency Management:

Strategies for Change," Public Administration Review , Vol. 45,

Special Issue, Jan. 1985.

This article examines the role of information search processes within and

between organizations as a means of integrating multiple agency and/or

jurisdictional operations into effective emergency response. Creating

effective organizational response under the complex, uncertain operating

conditions of a major disaster poses a sobering challenge to public

service agencies which bear the primary responsibility for emergency

management. The perceived responsibilities of national, state, and local

agencies in a disaster vary with political and economic conditions.

Finding the optimal mix of shared responsibilities within the specific

limitations of time, resources, and professional skills available in any

given emergency, constitutes a continuing task for emergency service

personnel. The author recommends that the following courses of action be

considered: (1) conduct a rigorous research project on the effects of

introducing a preliminary model of concurrent information search

processes into the organizational design of the emergency management

system, (2) revise the proposed model of concurrent information search

processes in emergency management on the basis of the research findings

and to implement this revised model in limited, but carefully monitored

conditions for actual service delivery, (3) review the findings from the

demonstration projects in terms of their applicability to the full range

of interorganizational operations in emergency management, and (4)

continue to refine the model in scheduled practice, with systematic

13
implementation, observation, and evaluation of results to achieve an effective, professional, integrated system of emergency management.

Congressional Research Service, "Information Technology for Emergency

Management, Report Prepared for the Subcommittee on Investigations and

Oversight," Library of Congress, Washington, DC, Oct. 1984.

Drabek, T. E., "Managing the Emergency Response," Public Administration

Review , Vol. 45, Special Issue, Jan. 1985. ~

'

The thesis of this article is that efforts to promote integrated

emergency management information system (IEMIS), or any other disaster

management tool, require explicit recognition of fundamental structural

qualities that characterize emergency responses within American society.

These are discussed under the sections (a) the American scene; (b) who

actually responds?; and (c) strategies for enhancing intergovernmental

coordination. The author offers the following six implications for

managing emergency responses: (1) search and rescue actions following

large-scale disasters in American society reflect the structural

qualities of localism, lack of standardization, unit diversity, and

fragmentation, (2) the demand structures produced by disasters exert

pressures for episodic transformations of their internal operating

systems and their external patterns of relationships with other agencies,

(3) event qualities, type and extent of planning, and the pattern of

interpersonal relationships among the organizational managers form a web

of constraints that structure these emergent response networks, (4) while

local governmental agencies form the core of such response networks,

federal and state agencies perform crucial functions, (5) federal and

state agencies have specialized roles within such response systems to

which they bring unique expertise and resources in many forms, and (6)

much of the strength within the American system stems from its

decentralized quality, and such loosely coupled systems contrast sharply

with certain business firms wherein product standardization derives from

tight control structures, environmental stability, and rigid application

of Taylor's principles of scientific management.

Erley, D., and W. J. Kockelman, Reducing Landslide Hazards: A Guide for

PI anners , Chicago: American Planning Association, 1981.

Federal Emergency Management Agency, Federal Preparedness Circular 2,

Federal Emergency Management Agency, Washington, DC, 1983.

Godschalk, D. R., and D. J. Brower, "Mitigation Strategies and Integrated

Emergency Management," Public Administration Review , Vol. 45, Special

Issue, Jan. 1985.

This article examines present hazard mitigation practices of local

agencies in terms of their potential for developing effective integrated

emergency management systems. It reviews the basic concepts involved in

such systems, mitigation tools available to hazard managers, and multiple

hazard analysis techniques. It argues for packaging mitigation measures

with development management programs into coordinated strategies in order

to address effectively the opportunities and problems of integrated

hazard mitigation.

14
Graff, G., "Beyond Bhopal: Toward a 'Fail-Safe' Chemical Industry," Hi gh

Technol ogy , Apr. 1985.

Helms, J., "Threat Perceptions in Acute Chemical Disasters: Social Aspects of

Acute Chemical Emergencies," Journal of Hazardous Mate rial , Special

Issue, Vol. 4, 1981.

Hewitt, K. , and I. Burton, The Ha zardousness of a Place: A Re gi onal Ecol ogy

of Damaging E vents, Toronto:" University of Toronto Press, 19/1.

Jaffe, M., J. A. Butler, and C. Thurow, Reducing Ea rthquake Risks : A

Planner's Guide, Chicago: Ameri canP~l anni ng Association, 198~1."

Kasperson, R. E., and K. U. Pijawka, "Societal Response to Hazards and Major

Hazard Events: Comparing Natural and Technological Hazards," Publ

i

c Administ ration Rev ie_w, Vol. 4b, Special Issue, Jan. 1985.

This article inquires into the range of problems encountered by society

as it attempts to avoid and respond to the hazardous events rooted in

technology. Two tasks in particular are recognized: first, to

characterize the hazard management process and to highlight the

particularly difficult problems encountered, and second, to assess how

the major hazardous events arising from technology affect people, their

communities, and their institutions. Among the conclusions drawn by the

authors are: (1) the major burden of hazard management in developed

societies has shifted from risks associated with natural processes to

those arising from technological development and application, (2)

technological hazards pose different, and often more difficult management

problems than do natural hazards because of their unf ami 1 i ari ty and

newness, (3) hazards may be conceived as composed of a set of linked

stages begining with human needs and ending in adverse consequences, (4)

in idealized form, hazard management consists of four major activities,

namely, assessment, control analysis, selection of management strategy,

and implementation and evaluation, (5) technological disasters tend to

elicit a different pattern of public response than to natural disasters,

including a greater reliance upon governmental authorities and a reduced

use of community and family social networks, and (6) the emergence of a therapeutic community to ameliorate effects during the post-disaster

period appears substantially less likely for technological than natural

di sasters.

Krzysztofowi cz , R., and D. R. Davis, "Toward Improving Flood Forecast-Response

Systems," Interf aces , Vol. 14, No. 3, May -June 1984.

To save lives and reduce property damage, the National Weather Service

(NWS) provides flood forecasts and warnings serving 3,000 flood-prone

areas. A comprehensive plan for improving this large-scale service has

been formulated by the NWS Office of Hydrology based on several

systems-economic studies. The one described highlights: (1) the concept

of a flood forecast-response system which captures interactions among

15
hydrologic, organizational, behavioral, and economic factors; (2) the

concept of performance measures which capture the interplay between the

quality of forecasts and the quality of floodplain dwellers' response,

and (3) the economic value of flood forecasts and the benefits to be

derived by the general public from the planned improvements.

Kunreuther, H., and L. Miller, "Insurance Versus Disaster Relief: An Analysis

of Interactive Modelling for Disaster Policy Planning," Pub! i

c

Administration Review , Vol. 45, Special Issue, Jan. 1985.

This paper is concerned with ways that policy makers can plan ahead with

respect to dealing with consequences of natural hazards. The use of

decision-aiding approaches to policy planning is illustrated by focusing

on-^rade-off s between insurance and disaster relief programs. The

authors also develop a framework for incorporating a decision support

system as an integral part of the policy-making process, and apply it to

the case of insurance/disaster relief policy options.

Kusler, J. A., "Liability as a Dilemma for Local Managers," Pub! i

c

Administration Review , Vol. 45, Special Issue, Jan. 1985.

The discussion focuses on the broad range of legal issues in hazards

management, which is an increasingly important issue for local

governments. The legal issues and trends in the courts, including

constitutional challenges to government issuance or denial of permits,

planning, zoning, and mitigation measures are also reviewed. Finally,

some strategies for reducing potential liability are suggested.

Martell, D. L., et al., "An Evaluation of Forest Fire Initial Attack,"

Interfaces, Vol. 14, No. 5, Sept. -Oct. 1984.

To help forest fire managers of the Ontario Ministry of Natural Resources

evaluate initial attack resources, a relatively simple simulation model

of the kinds of fires that occur in the province of Ontario and the

resources used to fight them was developed. The model was useful in

planning the acquisition of air tankers and the future use of air

tankers, transport aircraft, and fire fighters, for initial attack

purposes.

May, P. J., "FEMA's Role in Emergency Management: Recent Experience," Pub! i

c

Administrat ion Review , Vol. 45, Special Issue, Jan. 1985.

McElyea, W. D., D. J. Brower and D. R. Godschalk, Before t he St orm: Man agin g

Development to Reduce Hurricane Damages , Chapel Hill, NC: Center for

Urban and Regional Studies, University of North Carolina, 1982.

McLean, M. A., et al., "CLEAR: A Model for the Calculation of Evacuation Time

Estimates in Emergency Planning Zones," Proceedi ngs of the Conference on Computer Simulation in Emergency Planning , Vol. TT,~ No. 2, Jan. f9~8T.

McLoughlin, D., "A Framework for Integrated Emergency Management," Publ i

c

Administration Review, Vol. 45, Special Issue, Jan. 1985.

16
Mushkatel, A. H., and L. F. Weschler, "Emergency Management and the

Intergovernmental System," Public Administration Review , Vol. 45, Special

Issue, Jan. 1985.

This article discusses the importance of the intergovernmental policy

system for emergency management. The authors point out that several

components of the Federal Emergency Management Agency (FEMA) organization

and mission apparently are ill-suited to the challenge the agency faces

within the context of the existing intergovernmental policy system.

Among the issues discussed are the major characteristics of the political

system within which FEMA developed and operates, the intergovernmental

setting and some fiscal considerations affecting emergency management. A

policy matrix is also described which conceptually represents the

challenges to FEMA in implementing an integrated emergency management

system.

National Governors' Association, Comp rehensive Emergency Management: A

Governor's Guide . Washington, D.C.: Center for Policy Research, 1979.

Pasqualetti and D. Pijawka, (Eds.), Nucle ar Power: Assessing an d Managing

Hazardous Technology , Boulder, CO: West view Press, 1984.

Perry, R. W., Comprehensive Eme rgency Management , Greenwich, CT: JAI

Press, 1984.

Perry, R. W., and A. H. Mushkatel, Disa ster Preparedness: Warning, Res ponse

and C ommunity Relocation , Westport, CT: Greenwood Press, 1984.TM

Perry, R. W., and J. M. Nigg, "Emergency Management Strategies for

Communicating hazard Information," Publ ic Admi nstrati on Review , Vol. 45,

Special Issue, Jan. 1985.

The purpose of this paper is to examine the process of communicating

information to the public about environmental risks to increase the

likelihood that citizens will adopt protective measures. To accomplish

this objective, the authors examine three issues in public education.

First, an attempt is made to understand how emergency management agencies

can become identified as credible sources of information within the

community. Second, attention is given to establishing and maintaining

viable communication channels through which local emergency management

personnel can reach the public. The closing section of the paper

discusses strategies for increasing citizen receptivity to officially

designated prospective measures.

Petak, W. J., and A. A. Atkinsson, Natural Hazard Risk Assessment an d Public

Pol i

c

y , New York: Spri nger-Verlag, Inc., 1982.

Petak, W. J., "Emergency Management: A Challenge for Public Administration,"

Public Administration Review , Vol. 45, Special Issue, Jan. 1985.

The author of this article argues that the concept of an integrated

emergency management system is based on the belief that the efforts of

many disciplines are necessary if we are to reduce the consequences of

17
I

natural and man-made disasters. This implies that the public

administrator, as emergency manager, must have the conceptual skill to

understand: (1) the total system, (2) the uses to which the products of

the efforts of various professionals will be put, (3) the potential

linkages between the activities of various professional specialists, and

(4) the specifications for output formats and language which are

compatible with the needs and understanding of others within the total

system. As with any other problem-focused management system, the

emergency management system must give full consideration to the problems

associated with authority relationships, conflict management (i.e., who

is responsible, who is accountable, who pays, and who controls), and

legal authority versus perceived responsibility.

Qu a r a nt e 1 1 i , L . , Delivery o f Emergency Medical Se rvi ce s i n Pi s ast e_rs_: Assu mptions and Realit ies, New York: Irvington Publishers, 1983.

Rubin, C. B., Managing the Recovery From a Natural Disaster , Management

Information Service Report, Volume 14, Washington, DC: International City

Management Association, 1982.

Rubin, C. B., and D. G. Barbee, "Disaster Recovery and Hazard Mitigation:

Bridging the Intergovernmental Gap," Public Administration Re view , Vol. 45, Special Issue, Jan. 1985.

Russo, J. E., and R. R. Wilson, "Earthquake Vulnerability Analysis for Economic

Impact Assessment," Proceedings of the Conference on C omputer Simulation

in Emergency Planning , Vol. 11, No. 2, La Jolla, CA: The Society for

Computer Simulation, Jan. 1983.

Scanlon, J., et al . , "Coping with Media in Disasters: Some Predictable

Problems," Public Administration Review , Vol. 45, Special Issue, Jan.

1985.

The authors of this article propose that the media perform some

very important functions in times of disasters, such as, providing

pre-disaster education, serving as an effective warning system, providing

information and advice to victims and others in the wake of disasters,

activating the local disaster response, and stimulating effective disaster

relief. The authors also develop an effective plan for media relations in

di saster.

Schmidt, W. J., et al., "Tri -purpose Emergency Management Model," Proceedi ngs

of the Conference on Emergency Plannin g, Volume 15, No. 1, La Jolla, CA:

The Society for Computer Simulation, Jan. 1985.

The authors of this article propose that an effective emergency manager

must consider three operational objectives: (1) his plan must provide a logical, flexible progression of response actions, (2) personnel involved

in laboratory execution must understand the logic and be trained in its

execution, and (3) the plan must be tested under a variety of scenarios to

evaluate its effectiveness. They describe an initial version and an improved version of a system model that was developed by the Management

18
Systems Laboratories of Virginia Tech. The initial version was applied

in plan development and analysis for the Department of Energy's Savannah

River Plant.

Selman, V., A. L. Selman, and J. Selman, "Earthquakery/Quackery : The

Science/Art of Predicting Natural Disasters," Pr oceedings of th e Conf erence on Emergency Planning , Volume 15, No. 1, La Jolla, CA:

The Society for Computer Simulation, Jan. 1985.

Settle, A. K. , "Financing Disaster Mitigation, Preparedness, Response and

Recovery," Public Administration Review, Vol. 45, Special Issue,

Jan. 1985.

This paper examines the sources of aid and financial devices that are

available to local governments during disasters. There is a lack of

information on financing emergency management and particularly on funding

mechanisms that can prepare local government to pay for disasters when no

federal or state aid is forthcoming. Because of limited tax base and

reserves, community governments' options of financing disasters is very

restricted. The author provides a matrix on funding alternatives

connected to the different phases of emergency management. The matrix is

particularly helpful in determining what sources and amounts of revenue

would be available to a community to pay the costs of an emergency when

no federal or state emergency is declared.

Siegel, 6. B., "Human Resource Development for Emergency Management," Publ i

c

Admini strati on Review , Vol. 45, Special Issue, Jan. 1985.

Stallings, R. A., and E. L. Quarantelli, "Emergent Citizen Groups and

Emergency Management," Public Administration Review , Vol. 45, Special

Issue, Jan. 1985.

In this paper the authors address three principal questions: (a) What

are the characteristics of emergent citizen groups in disasters? (b)

What roles do they play in the various phases of the emergency

management process? and (c) What implications do such groups have for

emergency management policies and procedures? Drawing primarily from

research by the Disaster Research Center (DRC) on emergent social

groupings in actual or potential disasters, supplemented by the authors'

own interpretation of selected empirical studies of others, they suggest

the following implications for emergency management: (1) the formation

of emergent citizen groups is inevitable before, during, and after

disasters, (2) emergent citizen groups are the outcome of natural social

processes; they do not represent a deviational or abnormal pattern, (3)

emergent citizen groups, while not always functional, are not necessarily

dysfunctional either, (4) emergent citizen groups are not inherently in

opposition to public authorities, and (5) emergent phenomena cannot be

eliminated by prior planning. They also suggest how these

characteristics of citizen emergent groups can be harnessed during

emergency management operations.

Tierney, K. J., "Emergency Medical Preparedness and Response in Disasters:

The Need for Interorgani zati onal Coordination," Publi c Administration

Review , Vol. 45, Special Issue, Jan. 1985.

19
The author of this paper observes that unlike more common medical

emergencies, large-scale disasters are infrequent; therefore, emergency

responders do not have an opportunity to practice and repeat

disaster-related tasks until they become routine. The infrequent nature

of disasters also means that the need for preparedness is obscured;

pressing daily problems get priority. Disasters are also distinctive

because they require health care providers to perform in ways that are

contrary to their training and experience. In providing medical care, it

is suggested that the emergency managers should consider the following

points in attempting to develop more successful disaster emergency

management system (EMS) preparedness programs: (1) the problem of

delivering EMS to victims of disasters and other mass casuality events in

the United States is not a problem of insufficient resources and

technology or an inadequate supply of skilled service providers, (2)

persons with responsibility for emergency management at different levels

of government need to exchange information with local, regional and state

EMS officials and providers, as well as with public and private health

care organizations, (3) medical sector groups need to work more closely

with the major disaster preparedness and response organizations to

develop realistic and compatible disaster response procedures, and (4)

EMS disaster preparedness planning should be an element in comprehensive

local, regional, state, and federal preparedness programs, not a separate

effort.

U.S. Department of Commerce, Invento ry of Nat u ral Hazards Data Resources in

the Federal Gove r nment , Boul der, CO: Nat ional Geophysical and

So far-Terrestrial" Data Center, May 1979.

Vance, E. D., "Earthquake Hazard Assessment Utilizing Geographic Information

System Integration," Proceedings of the Co nference on Computer

Simulati on i n Emergency P lanning, Vol. 11, "No. 2, La Jolla, CA: The

Society for Computer SimuTation, Jan. 1983.

Walker, H., "Spatial Data Requirements for Emergency Response," Proceedi ngs

of the Conference on E mergency Planning, Volume 15, No. 1, La Jolla, CA:

The Society for Computer Simufation, Jan. 1985.

Wallace, W. A., and F. de Balogh, "Decision Support Systems for Disaster

Management," Publi c Admi nistrat ion Review , Vol. 45, Special Issue, Jan.

1985.

In this article the authors focus on an increasingly important component

of information technology, decision support systems (DSS), and describe

its role in disaster management. Specifically, they (1) differentiate

DSS from more traditional management information systems (MIS); (2)

discuss the disaster life cycle and its stages in the context of a typology of decisions -- strategic, tactical, and operational -- found

within those stages; and (3) illustrate the potential of DSS technology

with some selected applications in the area of earthquake mitigation,

crisis response, toxic chemical spill control, and emergency staff

preparedness training. They also review some future technological

developments in this area.

20
White, G. F., and E. J. Haas, Assess ment of Resea r ch on Natural Hazards,

Cambridge, MA: The MIT Press, 1975.

Whittow, J., Disasters: The Anatomy of Environmental Hazard s, Athens,

GA: The University of Georgia Press, 1979.

Zimmerman, R., "The Relationship of Emergency Management to Government

Policies on Man-Made Technological Disasters," Public Administration

Review , Vol. 45, Special Issue, Jan. 1985. ' "~ "

The two main questions addressed in this paper are: (a) To what extent

do emergencies or disasters exist due to man-made technologies involving

hazardous materials? (b) What mechanisms are currently in place to cope

with emergencies, and how well are they adapted to them? To address the

former, the author uses trend data on the origins or releases of

contaminants, health effects, and transport of contaminants to show

whether a gradual or sudden accumulation of adverse conditions exists.

The second question is treated in the framework of one application of an

emergency management typology for natural hazards to existing legislation

and programs for the control of non-natural technological hazards so as to gauge their orientation toward emergencies.

21
3. INTRODUCTION TO EXPERT SYSTEMS

An expert system can be defined as a computer-based model that is able to

replicate the decisions of one or more human experts in a well-defined subject

area. Furthermore, the types of problems addressed by expert systems usually

require significant human expertise for their solution. An expert system

models or mimics the expertise by which the human expert solves the problem.

The major components that serve to model a human decision maker's expertise are

referred to as the knowledge base and the inference procedure.

The knowledge base records rules, facts, and information about the problem that

may be useful in formulating a solution. The inference procedure provides a control structure for analyzing the information contained in the knowledge base

(i .e. , facts and rules)

It is the distinct, separate nature of the knowledge base and the inference

procedure which together captures the human decision maker's expertise. This

separation and its associated flexibility is absent in traditional

decision-aiding models which use a rigidly defined control structure to

manipulate the information contained in a database. Knowledge is the key; the

performance of an expert system is, to a large degree, a function of the size

and quality of its knowledge base. The term knowledge-based expert system is

used to emphasize the importance of knowledge in modeling human expertise.

3.1 SELECTED EXPERT SYSTEMS CURR ENTLY IN USE

Over the past decade, expert systems have been developed for a number of

different problem domains. Several of the better-known applications are listed

in Table 3.1. In all, information on seven problem domains is listed; an example of an expert system for each domain is also given. Table 3.1 is

intended to provide an indication of the variety of such systems currently in

use. Additional information on expert system applications is given in

Chapter 8 and the papers by Gevarter [1982] and Nau [1983].

From Table 3.1, we can see that the problem domain tends to be focused on well-defined subjects (e.g., chemical analysis and computer system design).

With the exception of R1/XC0N, used by the Digital Electronics Corporation for

customizing its VAX computer systems, and DENDRAL, which is used extensively by

researchers in chemistry, most systems have remained in the laboratory stage of

development. Hopefully, this research orientation will change due in part to

the availability of better tools for building expert systems. These tools

should significantly reduce the high cost of system development. Furthermore,

it is likely that many of the second generation of expert systems will be

developed specifically for commercial applications, rather than to explore

topics which are primarily of a research nature.

22
Table 3.1 Some Existing Expert Systems Classified by Problem Domain

Name

Problem

Domai n

DENDRAL Interpretati on

MYCIN Oi agnosi

s

R1/XC0N Desi gn

VM Moni tori ng

Dri 1 ling Advi sor Troubl eshooti ng

Selected System

Purpose

To generate plausible

structural representations of organic

molecules from mass

spectogram data.

To diagnose bacterial

infections and make

recommendations for

antibiotic therapy.

To configure VAX

computer systems based

on a customer's order

of components.

To monitor and

interpret physiological

data of patient

breathi ng.

To determine the likely

causes of drilling

problems and recommend

curative and preventive

treatments.

STEAMER

Spill Crisis

Instructi on

Control

To instruct users how to

operate a steam

propulsion plant.

To interpret, recommend

and monitor crisis

management activities.

23
3 . 2 THE STRUC TURE OF AN EXPERT S YSTEM: AN IDEALIZATION

We next present an overview of an "ideal" expert system. This overview is

abstracted from the text Building Expert Syst ems. The ideal expert system is

shown schematically in Figure 3.1. We first "begin with a capsule summary of the

ideal expert system. The various components of the system are then described in

greater detail and related to specific entries in Figure 3.1. An ideal expert

system contains: (1) a language processor for problem-oriented communications

between the user and the expert system, (2) a global data base for recording

intermediate results, (3) a knowledge base comprising facts, as well as planning

and problem-solving rules, (4) a control structure or inference procedure, which

includes an interpreter that applies the rules, a scheduler to control the order

of rule processing, a consistency enforcer that adjusts previous conclusions

when new data (or knowledge) alter their bases of support, and (5) a justifier

that rationalizes and explains the system's behavior. The text which follows

defines the terms introduced above; interrelationships are shown through

reference to Figure 3.1.

The Language Processor

The user interacts with the expert system via a problem-oriented language. The

language processor mediates information exchange between the user and the expert

system. Typically, the language processor interprets the user's questions,

commands, and volunteered information. Conversely, the language processor

formats and sends to the user information generated by the system, answers to

questions, explanations and justifications for its behavior, and requests for

data. Several key topics governing the user interface are given in Hayes-Roth

[1984].

The Knowledge Base

The knowledge base of an expert system consists of facts and rules. The facts

constitute a body of information that would be generally agreed upon by

experts in the field. The rules are often based upon less well-known

heuristics (rules of plausible reasoning, rules of thumb, rules of good

guessing) that characterize expert-level decision making in the subject area.

Further information on what constitutes a knowledge base and how it is

constructed in practice is given in: Buchanan and Duda [1983], Davis [1982],

and Michaelson, Michie and Boulanger [1985].

The Global Data Base

The global data base records the user's inputs, intermediate hypotheses, and

decisions. Every expert system has some form of intermediate decision

representation. However, only a few explicitly employ all of the components

shown in Figure 3.1. In addition to the user's inputs, three types of

elements are identified as being included in the global data base: (1) plan;

(2) agenda; and (3) solution elements. Plan elements describe the overall or general attack the system will pursue against the current problem, including

current plans, goals, problem states, and contexts. The agenda elements

24
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25
record the potential actions awaiting execution, which generally correspond to

knowledge base rules that seem relevant to some decision previously placed in

the global data base. The solution elements represent the candidate

hypotheses and decisions the system has generated thus far, along with the

dependencies that relate decisions to one another. For a more general

interpretation of the global data base, see Gevarter [1982] and Nau [1983].

Th e Controlle r

The control structure determines how to use the rules contained in the

knowledge base; it contains three elements: (1) an interpreter, (2) a scheduler, and (3) a consistency enforcer. The interpreter executes the chosen

agenda item by applying the corresponding knowledge base rule. Generally, the

interpreter ensures the relevance conditions of the rule, binds variables in

these conditions to particular global data base solution elements, and then

makes those changes to the global data base that the rule prescribes. The

scheduler maintains control of the agenda and determines which pending action

should be executed next. Schedulers may embody considerable knowledge, such as doing the most profitable thing next. To apply such knowledge, the scheduler

needs to give each agenda item a priority according to its relationship to the

plan and other extant solution elements. To do this, the scheduler generally

needs to estimate the effects of applying the potential rule. The consistency

enforcer attempts to maintain a consistent representation of the emerging

solution. Most expert systems use some kind of numerical adjustment scheme to

determine the degree of belief in each potential decision. Such schemes

attempt to ensure that plausible conclusions are reached and inconsistent ones

are avoided. For an indepth discussion of control structures, see Nilsson

[1980] and Buchanan and Duda [1983].

Th e Jus tifier

Finally, the justifier explains the actions of the system to the user. In

general, it answers questions about why some conclusion was reached or why some

alternative was rejected. To do this, the justifier uses a few general types

of question-answer plans. These typically require the justifier to trace

backward along solution elements in the global data base from the questioned

conclusion to the intermediate hypotheses or data that support it. Each step

backward corresponds to the inference of one knowledge base rule. The

justifier collects these intermediate inferences and translates them into

English for presentation to the user.

There are a number of important distinctions between expert systems and

conventional computer-based models which are highlighted by the dashed

rectangle in Figure 3.1. The key distinctions are the inclusion of the

knowledge base and a justifier. Additional comparisons between expert systems

and conventional computer-based models are given in Nau [1983] and Quinlan

[1980].

26
3.3 TOOLS AND TECHNIQUES FOR BUILDING AND TESTING AN EXPERT SYSTEM

One of the primary reasons for the increased use of expert systems is due to

the tools which are available to the analyst. To better understand the

capabilities provided by these tools, it is useful to discuss their role in

the building of an expert system at two levels. The first level focuses on the traditional artificial intelligence programming languages. The second

level focuses on what have become known as shells. A shell may be thought of as a specialty package which facilitates the construction of the knowledge

base and provides the inferencing procedure for exploiting the information

contained within the knowledge base. Both types of tools are discussed in

greater detail in Chapter 5.

If one were to build an expert system, one might choose an artificial

intelligence programming language such as LISP or PROLOG. Within the United

States, LISP (List Processor) is the artificial intelligence programming

language of preference. Since LISP is a symbol manipulation language, it

promotes the representation of knowledge which many authors claim to be

oriented more towards symbolic processing than numeric processing.

PROLOG (Programming in Logic) is widely used in Europe and Japan. The

motivation behind PROLOG is to permit the analyst to specify the tasks in

terms of logic statements, rather than specify how they should be processed

numerically by the computer. One difficulty with the use of artificial

intelligence programming languages is the current lack of standardization.

Consequently, many researchers have been moving toward the use of more

universally accepted languages such as FORTRAN and PASCAL, see Thompson and

Thompson [1985].

Since many applications share common methods for representing knowledge,

performing inference, and maintaining large systems, researchers have designed

shells which facilitate the development of expert systems. The motivation for

developing a shell is to provide support to the analyst in representing

symbolic knowledge. This knowledge can include facts, definitions, heuristic

judgments, and procedures for doing a task or achieving a goal. The shell may

be programmed in an artifical intelligence language so as to exploit its

symbolic manipulation capabilities or a common language. The main benefit in

building an expert system by using a shell is that the analyst is relieved of

the painstaking task of programming in languages such as LISP or FORTRAN.

Consequently, using the shell enables the analyst to spend more time

formulating rules or developing heuristics and less on the production of code.

The use of shells is therefore an important step in reducing the costs of

developing an expert system.

27
Despite the promise of expert systems, the current state-of-the-art limits

their capabilities. The expert systems have rather narrow domains of

expertise, partially because there are no efficient means for building and

maintaining large knowledge bases. Extraction and representation of knowledge

is performed by knowledge engineers. The knowledge engineer must cope with

the expert's inability to impart knowledge in a manner that can be easily

encoded, while the problem domain expert needs to understand the system's

problem solving methods and the representational framework. In almost all

expert systems, the explanation of its line of reasoning is nothing more than

the system retracing its steps to the rules that enabled the system to draw

that conclusion. The expert systems often provide little guidance to the user

about their appropriateness for new problems or about their scope and

limitations. As expert systems are not able to deal with incompatible

knowledge items, expertise is usually limited to that of a single individual.

However, it is often desirable to merge expertise from several experts.

Expert systems are not efficient in handling many types of reasoning concepts

such as causal reasoning, reasoning over time, scene (or 3-dimensional

)

representation, and analogical reasoning. For example, reasoning over time

requires dynamic representation and a mechanism for handling feedback.

The above discussion points out some of the areas in which expert systems must

undergo improvements and characteristics future expert systems must

incorporate. The advent of parallel processing is likely to increase the scope

of expert systems by easing the constraints imposed by the complexity and size

of the knowledge base and the tasks performed. There is also a move toward

building what are called the deep-knowledge systems that reason by using

knowledge of structure, function, and behavior of objects, see Kinnucan

[1984]. These systems are expected to function better than the current expert

systems especially in those domains where novel problems are frequent. As a remedy for the current (preaomi nantly rule-based) knowledge representation

technique's inadequacy in handling concepts such as causal reasoning, scene

representation, etc., there is an increasing trend towards building systems

using semantic networks and frames. Expert systems will also benefit as systems for understanding speech and visual images are perfected. These

advances will open up many problem domains in which expert system technology

can be applied.

28
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The authors describe an expert system as a computer program that provides

expert-level solutions to important problems and is: (1) heuristic, i.e.,

it reasons with judgmental knowledge, as well as with formal knowledge of

established theories, (2) transparent, i.e., it provides explanations of

its lines of reasoning and answers to queries about its knowledge, (3)

flexible, i.e., it integrates new knowledge incrementally into its

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familiarize the readers with the architecture and construction of the

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provide a framework for understanding this advancing frontier of computer

science.

29
Chester, D., "Elements of Knowledge-Based Expert Systems," Proceedings of

Mi cro-Del con '82, The Delaware Bay Computer Conference, March "1982.

Expert systems are built to solve problems in application areas for which

"good" algorithms are not known. These systems consist of a global data

base of assertions, a set of rules that represent small bits of an expert's knowledge, and a control strategy for applying the rules to the

assertions. Agendas are used to make the control strategy more efficient.

Efficiency can be further increased by indexing the rules and assertions

in various ways, one of which is frames. A system for deriving formal

specifications from natural language requirements is presented as an exampl e.

Cohen, P., and E. A. Feigenbaum, (Eds.), The Hand b ook of Artificial

Intelligence (Vol. Ill), Los Altos, CA: Kaufmann, 1982.

D'Ambrosio, B., "Expert Systems - Myth or Reality?" By_te, Vol. 9, No. 1,

Jan. 1985.

David, H., "An Analysis of Expert Thinking," Inte r national Journal of

Man-Machine Studies, Vol. 18, 1983.

Davis, D. B., "Assessing the Strategic Computing Initiative," High Technology , Apr. 1985.

Davis, R., "Expert Systems: Where are We? and Where Do We Go From Here?"

AI Magazin e, Vol. 3, No. 2, 1982.

Duda, R. 0., and E. H. Shortliffe, "Expert Systems Research," Scie nce,

Vol. 220, No. 4594, Apr. 1983.

Research in artificial intelligence (AI ) has several goals. One is the

development of computational models of intelligent behavior, including

both its cognitive and perceptual aspects. A more engineering-oriented

goal is the development of computer programs that can solve problems

normally thought to require human intelligence. This article concerns a class of AI computer programs intended to serve as consultants for

decision-making. They are often called "expert systems" because they

address problems normally requiring human specialists for their solution.

Some of these programs have reached expert levels of performance on the

problems for which they were designed. The authors describe these

accomplishments as well as identify some difficult problems that must be

solved in order to realize their benefits in practice.

Duda, R. 0., and J. G. Gaschning, "Knowledge-Based Expert Systems Come of Age,"

Byte , Vol. 6, No. 9, Sept. 1981.

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Sept. 1984.

30
Feigenbaum, E., and P. McCorduck, "The Fifth Generation, An American Response,"

Compute rworld, Vol. 17, No. 20, May 1983.

The Japanese are at present developing a revolutionary generation of

computers to be known as knowledge information-processing systems, or

kips. The real power of such systems lies in their capacity to reason

with enormous amounts of information. The Japanese expect them to be in

general worldwide use by the 1990s. The authors examine the heart of this

fifth generation, the applied side of artificial intelligence called

knowledge-based systems. They trace their history and describe the

applications of expert systems in fields such as medicine, chemistry, and

defense. The authors stress that it is unacceptable for the USA to have

to depend on Japan for vital defense technology, and summarize the options

open to Americans. They examine in detail one proposal for a National

Centre for Knowledge Technology.

Gaines, B. R., and M. L. G. Shaw, "Expert Systems and Simulation," in

G. Birtwistle (Ed.), Artificia l Intelligence, Graphics, and Simulation,

La Jolla, CA: Simulations Councils, Inc., 1985.

Simulation and expert systems (ES) are contrasted as computer-based

technologies for the support of human knowledge processes. An overview is

given of ES developments emphasizing that an ES is not just a static

encoding of expertise but rather a dynamic process promoting the

acquisition of knowledge. Applications of ES's to industrial control

problems are discussed where the links to classical control and simulation

can be most readily seen. Simulation and ES's are shown to be

complementary technologies. The combination of simulation and ES

technologies is examined and shown to form a basis for very powerful

system designs.

Gevarter, W. B., Intelligent Machines: An In troductory Perspective of

Artificial Intelligence and Ro botics, Englewood Cliffs, NJ:

Prentice-Hall , Inc., 1985.

Gevarter, W. B., "An Overview of Expert Systems," Report No.: NBSIR 82-2505,

National Bureau of Standards, Gaithersburg, MD, May 1982.

This report provides an overview of the rapidly evolving field of expert

systems -- what an expert system is, the techniques employed, existing

systems, applications, who is doing it, who is funding it, the

state-of-the-art, research requirements, and future trends and

opportunities.

Hall, H. K. , J. C. Moore, and A. B. Whinston, "A Theory of Expert Systems,"

Proceedings of the Fi rs t Symposium on the Theory and App lication of Expert

Systems in E'mergency Management Operations , Special Publication 717,

Gaithersburg, MD: National Bureau of Standards, 1986.

Harmon, P., and D. King, Expert Systems, New York: Wiley Press, 1985.

Hayes-Roth, F., "Knowledge-Based Expert Systems: A Tutorial," Computer , Vol,

17, No. 9, Sept. 1984.

31
I

I

This article presents an overview of the field of knowledge engineering.

It describes the major developments that have led to the current great

interest in expert systems, then presents a brief discussion of the

principal scientific and engineering issues in the field, as well as of the

process of building expert systems, the role of tools in that work, how

expert systems perform human-computer interface functions, and the

frontiers of research and development.

Hayes-Roth, F., "Knowledge-Based Expert Systems," Computer , Vol. 17, No. 10,

Oct. 1984.

Problem-solving engines organize the activity of knowledge systems to solve

problems. To understand these engines, we need to relate their

implementation to their design and intended purpose. Today's knowledge

systems aim to solve specific problems. A knowledge engineer analyzes the

problem to be solved and then adopts an overall approach generally

consisting of (1) a problem-solution paradigm, (2) a general

knowledge-system architecture, and (3) a specific problem-solving. Today's

problem-solving engines provide specific devices for implementing the

knowledge engineer's choices.

Hayes-Roth, F., D. A. Waterman, and D. B. Lenat, "An Overview of Expert

Systems," in F. Hayes-Roth, D. A. Waterman, and D. B. Lenat (Eds.),

Buildin gE xpert Systems , Reading, MA: Addi son-Wesley Publishing

Company", 1983.

In this paper the authors trace the principal developments in artificial

intelligence that have led to the current emphasis on knowledge-based

expert systems and the corresponding field of knowledge engineering.

Machines that lack knowledge seem doomed to perform intellectually trivial

tasks. Those that embody knowledge and apply it skillfully seem capable

of equaling or surpassing the best performance of human experts.

Knowledge provides the power to do work; knowledge engineering is the

technology that promises to make knowledge a valuable industrial

commodity.

Keller, R., and P. Townsend, "Knowledge-Based Expert Systems," Computerworl

d

, Vol. 18, No. 49A, Dec. 1984.

An overview of knowledge-based, or expert systems is given. The authors

discuss application generators and the features of knowledge-based

products. An outline of research activities in the business area is

gi ven.

Kinnucan, P., "Knowledge-Based Systems are Tackling Problems that Once Required

Human Expertise," High Technolo gy, Jan. 1984.

Kurzweil, R., "What is Artificial Intelligence Anyway?" Ame rican Scientist , Vol. 73, May-June 1985.

The controversy surrounding AI is evidenced by a certain amount of discord

within the fi eld. Academic researchers have accused some industrial AI

developments of being shallow and giving too high a priority to short-term

32
commercial goals rather than long-term research. Industrial researchers

have accused academic AI of providing superficial demonstrations, of not

developing robust systems that really work. Companies have accused their

competitors of not using real AI techniques and so on. Unfortunately we

are not likely to find published any time soon the definitive work that

will lay this confusion to rest. The author approaches these questions

and the central question "What is artificial intelligence anyway?" by

discussing what the discipline of artificial intelligence is now capable

of achieving, what it will be capable of achieving, and how we can best

reach those goals.

Lee, R. M. , "On Information System Semantics: Expert vs. Decision Support

Systems," Soci ety o f Science and Inf ormat i on St ud i es , Vol. 5, No. 1,

Jan. 1985.

AI will surely have an important impact on the management of future

organizations. The problem now is to make some reasonable assessment of

its promise and limitations in order to direct research and development.

Here we examine what an ideal knowledge-based management information

system could and could not do. The arguments are based on considerations

of formal semantics.

Lee, R. M. , "Expert vs. Management Support Systems: Semantic Issues,"

Cybernetics and Sy stems , Vol. 14, No. 2-4, Apr. -Dec. 1983.

Expert systems hold great promise for technical application areas such as medical diagnosis or engineering design. They are, however, less

promising for management applications. The reason is that managers are

not experts in the sense of possessing a formal body of knowledge which

they apply. The limitations of artificial intelligence approaches in

managerial domains is explained in terms of semantic change, with

attention toward management (decision) support systems.

Martins, 6. R., "The Overselling of Expert Systems," Datamati on , Nov. 1984.

McCartney, J., "Expert Systems: Expertise Captured in a System," Data

Processing, Vol. 24, No. 3, Mar. 1982.

Michaelsen, R. H., D. Michie, and A. Boulanger, "The Technology of Expert

Systems," Byte , Vol. 10, No. 4, Apr. 1985.

This is an introductory level article on expert systems. It also

discusses some methods for building them, including the advantages and

disadvantages of each method. Finally, the article reviews the computer

resources needed to build and run expert systems.

Michie, D., "Expert Systems," The Computer Journal , Vol. 23, No. 4, 1980.

Miller, R . K .

, Artif icia l Intelligence: A New Tool fo r Industry and Business , Volume 1 (Technology and Applications ), Madison, ~GA: SEAI Institute,

"

198"4.

Moore, T., "Artificial Intelligence: Human Expertise from Machines," EPRI

Journa l , June 1985.

33
Nau, D. S., "Expert Computer Systems," Computer , Vol. 16, No. 2, Feb. 1983.

The author points out the main difference between most expert systems and

ordinary computer applications programs: in the former, the model of

problem-solving in the application domain is explicity in view as a separate entity or knowledge base rather than appearing only implicitly as part of the coding of the program. This knowledge base is manipulated by

a separate, clearly identifiable control strategy. Such a system

architecture provides a convenient way to construct sophisticated

problem-solving tools for many different domains. Further, ordinary

computer programs organize knowledge on two levels: data and program.

Most expert computer systems, however, organize knowledge on three levels:

data, knowledge base, and control. Computers organized in this way are

often called knowledge-based systems. This article discusses the

technique used in expert systems on each of these levels. Information on the areas of AI problem-solving and knowledge-representation techniques is

also included.

Naylor, C, Build Your Own Expert System , New York: Wiley Press, 1985.

Nilsson, N. J., Principles of Artifici al Intelligence , Palo Alto, CA: Tioga

Publishing Company, 1980.

The author attempts to fill a gap between artificial intelligence theory

and practice. Unlike many books on artificial intelligence, which divide

the subject into its major areas of application, namely, natural language

processing, automatic programming, robotics, machine vision, automatic

theorem proving, intelligent data retrieval systems, etc., this book is

organized on the basis of general computational concepts involving the

kinds of data structures used, the types of operations performed on these

data structures, and the properties of control strategies used by AI

systems. The important roles played by generalized production systems and

the predicate calculus are stressed. Among the major topics covered are

rule-based systems, robot problem-solving systems, and structured-object

representations.

Quinlan, J. R., "An Introduction to Knowledge-Based Expert Systems," Austr al i an Computer Journal , Vol. 12, No. 2, May 1980.

Discusses the concepts underlying knowledge-based systems, and shows how

they differ from more conventional (algorithm-based) systems. Three

principal dimensions are identified: The representation of knowledge,

architectures for deploying knowledge, and techniques for acquiring

knowledge. Key ideas are illustrated from a selection of current expert

systems covering a wide range of applications.

Rich, E., Artifici a l Intelligence , New York: McGraw-Hill Book Company, 1983.

34
Sauers, R., and R. Walsh, "On the Requirements of Future Expert Systems,"

Proceeding s of the Eighth IJCAI, American Association for Artificial

Intelligence, 1983."

Many artificial intelligence applications require the use of expert

systems. As expert systems move into new domains, several significant

changes can be expected. Among these are an increase in number of rules

in the rule base and in the number of data elements contained in working

memory. Also, many new applications require that expert systems move into

real-time domains. Here, a system must be able to process large

quantities of data which are changing rapidly. Many problem-solving

situations will be time critical, and the system must take into account

the availability and distribution of scarce system resources. For these

reasons, the efficiency of a given expert system design and its ability to

perform complex memory management tasks will become increasingly

important. This will require modifications in the traditional production

system architectures. In this paper, the design requirements of future

expert systems are discussed, and HAPS, a recently implemented production

system architecture designed to address these issues, is presented.

Schindler, M. , "Expert Systems," Electronic Design, Vol. 33, No. 1, Jan.

1985.

This technology forecast discusses the research being undertaken into

artificially intelligent systems. Because qualified personnel are in

extremely short supply, many researchers in artificial intelligence

laboratories are already trying to come up with tools and methodologies to

simplify the development of future AI products. Languages, knowledge base

schemas, user interfaces, and inference paradigms all are undergoing

reevaluation with an eye on efficiency. Another obstacle must be

overcome before a reliable AI industry can develop: The hardware on which

its programs run must become cheaper. That is why artificial intelligence

companies are addressing such mundane problems as execution speed, code

size, and transportability, things that AI workers have always loved to

i gnore.

Sen, A., "Decision Support System: An Expert Systems Approach," Proceedi ngs of

the 16t h Ann ual Meeti ng of the American Institu te for Decision Sciences,

Atlanta"", GAf American Institute for Decision Sciences, Nov. 1984.

Focuses on the design aspects of a decision support system (DSS).

Although DSS have traditionally been designed to address a specific

problem domain, the author believes current expert system design

techniques can be exploited to develop domain-independent, user-friendly

systems. The development of such DSS, called the XDSS, is described.

Shoben, A. M. , "Expert Systems: Newest Brainchild of Computer Science," Report

No.: RAND/P-6552, RAND Corp., Santa Monica, CA, June 1981.

Document discusses an emerging field of computer science called expert

systems. These are computer programs that seek to combine expert

knowledge about a domain (in theory, any domain) with expert methods of

conceptualizing and reasoning about that domain. The decision-making

35
power of such programs rests upon a knowledge base that puts together

factual information about the domain with the heuristics (informal rules

of thumb) experts use to rapidly find solutions to problems. This

specialty in turn is part of artificial intelligence -- the science, as one definition has it, of making machines do things that people need

intelligence of a high order to do. Despite its small size and relative

newness, the expert systems field is attracting widespread interest

because of its demonstrated usefulness in medicine and chemistry, and for

its potential application to such diverse and complex problem areas as structural engineering, military strategy, air traffic control, crisis

management and many others.

Shurkin, J. N. "Expert Systems: The Practical Face of Artificial

Intelligence," Technology Review , Nov. -Dec. 1983.

Sloman, A., "Epi stemol ogy and Artificial Intelligence," in D. Michie (Ed.),

Expert Systems in the Micro-Electroni c Age , Edinburgh: Edinburgh

University Press, 1979.

A favorite area of disagreement among AI researchers concerns the

selection of a formalism for specifying computational mechanisms. Many of

the systems are based on the idea of a set of production-rules, namely

rules with a condition and an action. The underlying mechanism is, in the

simplest cases, a machine which repeatedly looks for a rule whose

condition matches some data-base, and then obeys the action. This paper

attempts to supplement the detailed studies of expertise in the other

conference papers with some general remarks about epistemological issues

arising out of artificial intelligence. The author points out that these

issues are not unique to the design of clever artifacts, but are central

to the study of the human mind.

Stefik, M., et al ., "The Organization of Expert Systems: A Tutorial,"

Artificial Intelligence , Vol. 18, 1982.

Tate, P., "The Blossoming of European AI," Datamati on, Nov. 1984.

Thompson, B. A., and W. A. Thompson, "Inside an Expert System," Byte , Vol.

10, No. 4, Apr. 1985.

In this article the authors describe one way that an expert system can use

a set of rules to conduct a consultation session. A description of the

mechanics of how an expert system operates by creating a "cardboard

inference engine" is given, and some of the programming considerations

for translating the cardboard system into PASCAL are examined.

Vaucher, J. G. , "Views of Modeling: Comparing the Simulation and AI

Approaches," in G. Birtwistle (Ed.), Artifical Intelligence, Grap hics, and

Simulation , La Jolla, CA: Simulations Councils, fnc, 1985".

Researchers and practitioners in the field of simulation and those in

artificial intelligence (AI) have had to face quite similar problems in

creating models of complex and sometimes partially understood systems. To

a large extent, solutions have been developed independently in each area,

leading to techniques and software tools which differ markedly in

36
terminology but often overlap in terms of concepts. The recent stress on knowledge representation in AI has emphasized a common ground, modeling

of reality, but each group maintains a slightly different emphasis:

dynamic behavior for simul ati oni sts and logical inference for AI workers.

In this paper, modeling tools and practice in both areas are contrasted

and useful areas of cross-fertilization are suggested.

Waldrop, M. M. , "The Necessity of Knowledge," Science , Vol. 223, March 1984.

Waltz, D. L. , "Artificial Intelligence," Scient ific American , Vol. 247, No. 4,

Oct. 1982.

Computer programs written by investigators in artificial intelligence have

demonstrated conclusively that in certain activities (including activities

most people would say require intelligence, such as playing games) the

computer can outperform a human being. Recent programs have demonstrated

that the computer can even develop elaborate theories about a limited

domain such as arithmetic from a few simple axioms. At the same time the

understanding of various features of human intelligence has been

considerably enriched by the attempt to describe analogues of those

features in the detail necessary for writing a program. As a result, the

analogy relating the performance of the computer to that of human

intelligence has broadened and matured. This article is a good

introduction to the kinds of results that can be achieved by programs in

artificial intelligence. Most of the programs that are discussed are

well-known approaches to major problem areas in the field. An attempt is

also made to clarify the main contributions of each of these programs.

Waterman, 0. A., A Gu ide to Expert Systems, Reading, MA: Addi son-Wesley

Publishing Company, 1985.

Winston, P. H., Arti ficial Int elligence , Reading, MA: Addi son-Wesley

Publishing Company, 1984.

Yaghmai, N. S. s and J. A. Maxin, "Expert Systems: A Tutorial, " Journal of

American Society for Information Scie nce, Vol. 34, No. 5, Sept. 1984.

Expert systems are intelligent computer applications that use data, a knowledge base, and a control mechanism to solve problems of sufficient

difficulty that significant human expertise is necessary for their

solution. Expert systems use artificial intelligence problem-solving and

knowledge-representation techniques to combine human expert knowledge

about a problem area with human expert methods of conceptualizing and

reasoning about that problem area. As a result, it is expected that such

systems can reach a level of performance comparable to that of a human

expert in a specialized problem area. The high-level knowledge base and

associated control mechanism of expert systems are in essence a model of

the expertise of the best practitioners of the problem area in question

and, hence, human users are provided with expert opinions about problems

in that area. Expert systems do not pretend to give final or ultimate

37
conclusions to displace human decision making; they are intended for

consulting purposes only.

Yazdani, M. , and A. Narayanan (Eds.), Artificial Intellige nce: Human Effects , Chichester, England: Ellis Horwood Limited, "1984.

The main theme of this book is to explore the interaction between the

fast-growing discipline of artificial intelligence (AI ) and other human

endeavors. Among the main topics discussed are the usefulness of AI in

medicine, law, and education. Other topics include intelligent machines

and human society, AI methodology, and philosophical implications of AI.

Taken together, these sections examine the implications that intelligent

computing systems have on our legal system, medical care, and education,

as well as the effects of sharing our environment with such systems. The

conclusion introduces some uncertainties concerning AI, and a postscript

presents an entertaining fictional view of the future with intelligent

machi nes.

38
4. REASONING AND METHODOLOGICAL ISSUES IN THE DESIGN AND DEVELOPMENT OF

EXPERT SYSTEMS

In the previous chapter, the major components of an ideal expert system were

defined. A brief description of the relationship among these components was

used to illustrate how an expert system operated. The purpose of this chapter

is to describe in greater detail several methodological issues which are the

basis of an expert system. The focus of this chapter is on the three

components of an expert system and their associated elements that deal with:

(1) knowledge representation; (2) the control structure; and (3) the

manipulation of data.

4.1 KNOWLEDGE RE PRESENTATION

Knowledge representation in expert systems usually relies on one of four

techniques: (1) production rules; (2) first order logic; (3) frames; or (4)

semantic networks.

Production rules are by far the most common means for knowledge representation

in expert systems. An expert system that uses production rules is termed a rule-based expert system. A production rule consists of two parts, an antecedent and a consequent. The antecedent represents some pattern of

information and the consequent specifies an action to be taken when the data

matches the pattern. The antecedent typically contains several clauses linked

by the logical AND and OR connectors. The consequent consists of one or more

verb phrases that specifies the action to be taken. In the IF-THEN

terminology frequently used to describe production rule sytems, the antecedent

is the IF clause and the consequent is the THEN clause.

First order logic is a formal way of representing logical propositions and

relations between propositions. First order logic is highly useful in

knowledge representation because one can derive any fact that follows

logically from the propositions it represents.

Frames are another technique used in knowledge representation. A frame is a data structure through which all knowledge about a particular object or event

is stored. Frames include attribute data; they can also contain rules to

express more complex facts or relationships, to specify default values for

pieces of information about an object when that information is not explicitly

given, and to invoke or link to other frames when applicable data conditions

or patterns are met. In this method, the organization and structure of the

knowledge is used to constrain and guide the program to a smaller and more

tightly bounded search space of possible candidate solutions.

Semantic networks are another way to represent knowledge. They are similar to

frames in the sense that knowledge is organized around objects and events. In

this case, the objects are represented by nodes in a graph and the relations

among them are represented by labeled arcs.

39
Each of the techniques just described have certain advantages which must be

considered during the design and development of an expert system. Production

rules are especially useful for representing procedural knowledge (e.g.,

methods for accomplishing goals). First order logic supplies a means for

explicitly expressing virtually any type of knowledge. Frame-based sytems can

concisely store an immense amount of knowledge about object properties and

relations. Semantic networks are good for representing relations between

objects.

4.2 CONTROL STRUCTURES

Expert systems employ various control structures or strategies to generate

solutions. The control strategy is often referred to as the "inference

engine." The two-types of control strategies employed are state-space search

and problem reduction. We first describe state-space search procedures,

followed by discussions of the key techniques of backward chaining and forward

chaining. Finally, the pros and cons of problem reduction procedures are

gi ven.

In most expert system applications, the precise series of steps necessary to

solve a problem are generally not known in advance. It is therefore necessary

to search through a space containing many alternate paths, not all of which

lead to solutions. Expert systems employ a variety of techniques to limit and

constrain the number of candidate solutions that are examined. Work in the

areas of artificial intelligence and operations research have produced several

procedural /al gorithmi c approaches (e.g., depth-first, breadth-first, branch

and bound) that can be applied to these search problems. It is important to

point out, however, that these procedural /al gorithmi c-based approaches result

in a certain amount of back-tracki ng, which has an adverse effect on the speed

with which an expert system can establish a solution. This is because

procedural /al gorithmi c approaches explore one or more paths as far as possible

before returning to the search at some other point within the state space.

Because of the extremely large search spaces characteristic of many expert

system applications, it is necessary to further constrain and prune the

search. This is accomplished through the use of heuristics that are based on informal knowledge (e.g., production rules) about the problem to be solved.

These programs or operators are pattern invoked in the sense that they are

not called by other programs in the ordinary way, but instead are activated by

the control structure whenever certain conditions in the data are met.

Backward chaining is the most commonly used control strategy; it explores a series of hypotheses in the order of their prior likelihood. Each hypothesis

is explored in turn, until all hypotheses have been exhausted. The expert

system then ranks the hypotheses by the degree to which they are supported by

the evidence. Backward chaining is also referred to as a goal-driven search,

since the hypotheses described above may be thought of as a hierarchy of

goals, subgoals and data.

40
Forward chaining focuses on the initial evidence contained in the global data

base. The system then considers each item of evidence in turn, attempting to

work forward to a goal. Forward chaining is also referred to as a data-driven

or antecedent-driven search. It is important to note that some types of

forward chaining will not terminate if the initial evidence does not lead to a conclusion. For this reason, many expert systems use backward chaining as a control strategy.

Many expert systems augment the control strategies just described by

propagating constraints. In this approach, the set of candidate solutions

become further and further constrained by rules or operators that produce

local constraints on what small pieces of the solution must look like. More

and more rule applications are made until no more rules are applicable and

only a few candidate solutions are left. This process can be thought of as a type of state-space search that avoids backtracking, since every candidate

solution must satisfy all of the constraints produced by the rule

appl ications.

One alternative to state-space search is an approach known as problem

reduction. Here, the problem to be solved is partitioned or decomposed into

subproblems that can be solved separately, so that the combined solutions to

the subproblems will yield a solution to the original problem. The approach

may be illustrated graphically through reference to a problem-reduction graph

(i.e., an AND/OR graph). All possible decompositions of the problem are shown

on the graph. Within the graph, each OR branch represents a choice of several

alternative decompositions, and each AND branch represents a particular way of

decomposing a problem. Since some decompositions of a problem may lead to

solvable subproblems and others may not, care must be exercised to ensure that

a "feasible" decomposition results. The graph of such a decomposition is

referred to as a solution graph.

4.3 THE MANIPULATION OF DATA

If one thinks of data manipulation within an expert system in the same way as

in a conventional program, one would expect the tools for building expert

systems to provide a variety of data structures (e.g., records, sets and

arrays). As we shall see in Chapter 5, many tools for building expert systems

provide a single powerful representation. This approach permits the user to

focus on the problem without the burden of attending to a variety of syntax

and other regulations which are inherent to conventional data-base management

systems. One should also make a distinction between a knowledge base and a data base. Whereas data bases have a predetermined structure, knowledge bases

consist of a set of unstructured, almost isolated facts and rules. The paths

by which facts and rules are related in a knowledge base are pattern invoked

(i.e., determined as needed to solve a particular problem). The relationships

between data items in relational or network data bases, by contrast, are

designed into the data base. Another critical distinction between the two is

that data bases store all of their information explicitly, whereas the bulk of

the information in a knowledge base is inferred from a few basic facts using

inference rules.

41
References

Aikins, J. S., "Representation of Control Knowledge in Expert Systems,"

Proceedi ngs of the Nati onal Co nference on A rtificial In teVl ij^ence

,

AAAI-80, American As'soci ati on ~f or Artificial Intel 1 i gence, "1980.

Barnett, J. A., "Computational Methods for a Mathematical Theory of Evidence,"

Proce edings of th e Seventh IJCAI , American Association for Artificial

Intelligence, 1981.

Many knowledge-based expert systems employ numerical schemes to represent

evidence, rate competing hypotheses, and guide search through the

domain's problem space. This paper has two objectives: first, to

introduce one such scheme, developed by Dempster and Shafer, to a wider

audience; second, to present results that can reduce the computation-time

complexity from exponential to linear, allowing this scheme to be

implemented in many more systems. In order to enjoy this reduction, some

assumptions about the structure of the type of evidence represented and

combined must be made. The assumption made here is that each piece of

the evidence either confirms or denies a single proposition rather than

disjunction. For any domain in which the assumption is justified, the

savings are available.

Barstow, D. R., "The Roles of Knowledge and Deduction in Algorithm Design," in

J. E. Hayes, D. Michie, and Y. H. Pao (Eds.), Machine Intellig ence 10,

Chichester, England: Horwood, 1982.

In the earliest attempts to apply artificial intelligence techniques to

program synthesis, deduction (that is, the use of a general purpose

mechanism such as a theorem prover) played a central role. Recent

attempts have relied almost exclusively on knowledge about programming in

particular domains, with no significant role for deduction. Even in such

knowledge-based systems, however, there seems to be an important role for

deduction in testing the applicability conditions of specific programming

rules. This auxiliary role for deduction is especially important in

algorithm design, as can be seen in the hypothetical synthesis of a breadth-first enumeration algorithm. The interplay between knowledge and

deduction also shows how one can be motivated to consider the central

mathematical properties upon which particular algorithms are based, as illustrated in the synthesis of two minimum cost spanning tree

al gorithms.

Ben-Bassat, M., "Toward a Theory of Expert System: A Problem-Driven

App roa ch , " Proceedings of the C0MPC0N 83 Fall , Silver Spring, MD

Computer Society" Press, Oct. 1983."

Existing approaches to generalize expert systems, examples EMYCIN and

ARBY, are directed from the technology space to the application space.

That is, given a certain technique (e.g., a rule-based engine) additional

applications are identified which might also be solved by the technique.

This approach forces a given application into a strait-jacket tailored

by tools that were developed for another application. We propose an approach that generalizes from the problem space to the technology space,

42
that is, the selection of a set of techniques to match the unique

requirements of a given class of problems. The abstract problem space is

a collection of abstract problems (e.g., evidence interpretation) which

are part of many real-life applications. This space offers a typology

for classifying the components of real-life applications. By the

problem-to-technology approach, a given real-life application is first

decomposed into its smallest problem solving tasks, which are then

matched with the abstract problems that fit them best. This, in turn,

provides a list of techniques that are applicable with their

corresponding assumptions and limitations. Our approach is illustrated

for a set of abstract problems related to situation assessment tasks

such as battlefield reading, medical diagnosis, corporate assessment,

potential crisis assessment, and weather forecasting.

Ben-Bassat, M., and A. Freedy, "Knowledge Requirements and Management in

Expert Decision Support Systems for (Military) Situation Assessment,"

Report No.: 46/81; ARI-TR-576, Israel Inst, of Business, Tel-Aviv,

Aug. 1983.

Situation assessment tasks (e.g., medical diagnosis) battlefield reading,

corporation assessment for merger or acquisition purposes, are formulated

as a general family of problem solving tasks. We characterize the

generic nature of this family as a multiprespecti ve multimembership

hierarchical pattern recognition problem, identify the types of decision

problems involved in the situation assessment process, and propose a unified approach for the development of situation assessment decision

support systems (DSS). The focus is on knowledge representation and

elicitation, although issues related to inference mechanisms, system

structure and expert-machine-user interface are also discussed. The

presentation is accompanied by examples from military situation

assessment. However, comparable examples from medical and business

applications are also cited. Many of the ideas presented here have

already been implemented in the MEDAS system; and medical DSS for

emergency and critical care medicine.

Bennett, J. S., "On the Structure of the Acquisition Process for Rule-Based

Systems," Machine Intelligence (Infotech State-of -the-Art Report

Series 9, No. 3), Maidenhead, England: Pergamon Infotech Ltd., 1981.

Blanning, R. W., "Knowledge Acquisition and System Validation in Expert

Systems for Management," Huma n Systems Man agement, Vol. 4, No. 4,

Autumn 1984.

The growing number of successes in constructing expert systems for such

recognized professionals and specialists as doctors diagnosing and

treating infectious diseases and mathematicians performing symbolic

integration has given rise to suggestions that expert systems be

developed for managers in order to capture their specialized knowledge in

performing decision tasks in manufacturing, marketing, finance, and

natural language. Concepts at any level of precision or vagueness can be

handled in a consistent way. Fuzzy logic allows appropriate deductions

from rules whose conditions are only partially satisfied, and facilitates

the overlap between rules essential to effective performance of a

43
knowledge-based system. Several preliminary fuzzy systems for management

applications are described, and prospects for future operational systems

are discussed.

Borgida, A., "Knowledge Representation as the Basis for Requirements

Specifications," Computer , Vol. 18, No. 4, Apr. 1985.

For purpose of discussion, we define the requirements phase of the

software life-cycle as the stage that precedes the design of software

system architecture. This phase includes analysis of customer needs, as well as specification of both the functional behavior of the proposed

system and the nonfunctional requirements that must be met. It is clear

that, to carry out these activities, the analyst must gain an understanding of the environment in which the software will be used and

the use to which the software will be put. We contend that this

understanding should be expressed and recorded as a model of the

environment, and that the various requirements should be expressed in

relation to this model. Once we have made a case of this contention, we

present some features that we believe are desirable in any language

intended to support requirements engineering. In the remainder of the

article, we illustrate some features of the language RLL and point out

how it arose from the confluence of two streams of thought:

the representation of knowledge in artificial intelligence and the

traditional concern for abstraction found in software engineering. The

article concentrates on language design issues.

Brent, E., "Consultant: An Expert System Using a Relational Database," AAMS I Congress 83 Proceedings , Bethesda, MD: American Association of Medical

Systems and Informatics, May 1983.

CONSULTANT is a knowledge-based expert program designed to provide a supportive intelligent learning environment for designing research

samples. The paper describes CONSULTANT'S approach to two traditional

problems in AI programming: knowledge representation and explanation.

CONSULTANT uses a relational data base to store and retrieve rules.

Brown, J. S., R. R. Burton, and J. de Cleer, "Knowledge Engineering and

Pedagogical Techniques in SOPHIE I, II, and III, " in D. Sleeman and

J. S. Brown (Eds.), Intelligent Tutoring Systems , London: Academic

Press, 1982.

Buchanan, B. G., "New Research on Expert Systems," in J. E. Hayes, D. Michie,

and Y. H. Pao (Eds.), Machine Intel li gence 10, Chichester, England:

Horwood, 1982.

Bundy, A., et al., "Solving Mechanics Problems Using Meta-level Inference,"

in D. Michie (Ed.), Expert Systems in the Micro-Electronic Age , Edinburgh: Edinburgh University Press, 1979.

In this paper the authors describe a program (MECHO), written in

PROLOG, which solves a wide range of mechanics problems from statements

in both predicate calculus and English. MECHO uses the technique of

meta-level inference to control search in natural language understanding,

common sense inference, model formation and algebraic manipulation.

44
The authors argue that this is a powerful technique for controlling

search while retaining the modularity of a declarative knowledge

representation.

Burton, R. R., "Diagnosing Bugs in a Simple Procedural Skill," in D. Sleeman

and J. S. Brown (Eds.), Intelligent Tutoring Systems , London: Academic

Press, 1982.

Carbonell, J. G., "Experiential Learning in Analogical Problem Solving,"

Proceed in gs of the National Conference on Artif icial Intelligen ce,

AAAI-82, American Association for Artificial Intelligence, 1982.

A computational model of skill acquisition is analyzed based on extensions to an analogical problem solving method and previous AI work

on concept acquisition. The present investigation focuses on exploiting

and extending the analogical reasoning model to generate useful exemplary

solutions to related problems from which more general plans can be

induced and refined. Starting with a general analogical inference

engine, problem solving experience is, in essence, compiled incrementally

into effective procedures that solve various classes of problems in a more reliable and direct manner.

Cendrowska, J., and M. A. Bramer, "A Rational Reconstruction of the MYCIN

Consultation System," International Journal of Man-Machine Studies , Vol. 20, No. 3, Mar. 1984.

This article presents a detailed analysis and partial reconstruction in

POP-2 of the control structure of MYCIN, arguably the best-known and most

significant expert system currently in existence. The aim is to aid the

development of theory in the field and to assist those who wish to build

their own working systems. Attention is focused, inter alia, on the

production rules, the goal -di rected backward chaining of rules that

comprises the control structure and the parameters and context types

employed, together with the data structures created during a consultation, and the system's use of certainty factors to handle

uncertain information. A detailed account is given of how a typical

consultation proceeds and some variants that can occur are considered.

Developments to MYCIN since its original implementation in 1976 and its

generalized version, EMYCIN, are also briefly described. The article

identifies a number of gaps in the original reporting of MYCIN and

presents a critique of a number of its features and an appraisal of the

value of a MYCIN-like approach as a starting point for further expert

systems development.

Chandrasekaran, B., "Expert Systems: Matching Techniques to Tasks," in

W . Re i t ma n (Ed.), Art i f i ci a 1 Intelligence App lication s for Bus i ness

,

Norwood, NJ: Ablex Publishing Corporation, 1984.

In this chapter an attempt is made to relate the architectures and

representations for expert systems to the types of tasks for which they

are appropriate. The author starts with an analysis of the features

that characterize an expert system, and discuss the need for symbolic

45
knowledge structures that support qualitative reasoning in the design of

expert systems. The author considers rules, logical formulas and frames

for representation of expert knowledge. In particular he provides an analysis of the multiplicity of roles that rules have played in different

rule-based systems, and emphasize the need to distinguish among these

roles. The author provides a theory of types of expert problem solving

and argues that such a taxonomy enables one to characterize expert system

capabilities and help match problems with techniques. Throughout

the chapter it is emphasized that the important issue is the nature

of the information processing task in a given task domain, and issues

of formalisms for representation are subordinate to that basic issue.

Chandrasekaran B., and S. Mittal, "Conceptual Respresentati on of Medical

Knowledge for Diagnosis by Computer: MDX and Related Systems," in

M. Yovits (Ed.), Advances in Computers , Vol. 22, New York: Academic

Press, 1983.

The purpose of this article is not to provide another survey of the field

but, rather, to describe and discuss an approach to the design of medical

decision-making systems based on the notion of conceptual structures for

knowledge representation. A collection of related systems that have been

under development in our laboratory exemplifies this approach, but the

ideas are more general than the particular systems to be described. The

central system in this group of systems is called MDX, which is a diagnostic system (i.e., it attempts to classify a given case as an element of a disease taxonomy). This system interacts with two other

systems during its problem solving, PATREC and RADEX, the former a knowledge-based patient data-base system that answers MDX's queries about

patient data, and the latter a radiological consultant which helps MDX in

the interpretation of various kinds of imaging data. Both PATREC and

RADEX are invoked by MDX as needed, but MDX is in control of the overall

diagnostic process.

Chandrasekaran, B., and S. Mittal, "Deep Versus Compiled Knowledge Approaches

to Diagnostic Problem-Solving," Proceedings o f the National Conference

on Artificia l Intelli gence, AAAI-82, American Association for Artifical

Intelligence, 1982.

Chiou, Sr., W. C, "Systems Architecture of a Remote Sensing Expert System,"

Proceedings of the 1983 IEEE Computer Society Work shop on Computer

Architecture for P attern Analysis and Image D atabase Management , Silver Spring, MD: IEEE Computer Society Press, Oct. 1983.

This study presents a preliminary systems architecture of a knowledge-based expert system which provides a powerful approach for

resolving massive remote-sensing data analysis problems. It incorporates

a natural language processor for user interfacing and maintains a standardized global data base. The expert system consists of three major

subsystems: a global spectral database, which acts as a distributed

databank for systems analysis; knowledge-based expert subsystems, which

contain general and specific geological models; and spectral image

processing and understanding, which provides a set of spectral image

analysis methods. An executive consultant system integrates the overall

simulation and synthesis for final systems utilization.

46
Clark, K. L., "An Introduction to Logic Programming," in 0. Michie (Ed.),

I n

t

rodu ct ory Readijngs_ i_n Expe rt Sy st ems , New York: Gordon Breach Science

Publishers, 1982.

Coombs, M. , and J. Alty, "Expert Systems: An Alternative Paradigm,"

International Journal of Man-Mach ine Studies, Vol. 20, 1984.

There has recently been a significant effort by the AI community to

interest industry in the potential of expert systems. However, this has

resulted in far fewer substantial applications projects than might be

expected. This article argues that this is because human experts are

rarely required to perform the role that computer-based experts are

programmed to adopt. Instead of being called in to answer well-defined

problems, they are more often asked to assist other experts to extend and

refine their understanding of a problem area at the junction of their two

domains of knowledge. This more properly involves educational rather

than problem-solving skills. An alternative approach to expert system

design is proposed based upon guided discovery learning. The user is

provided with a supportive environment for a particular class of

problems, the system predominantly acting as an advisor rather than

directing the interaction. The environment includes a database of domain

knowledge, a set of procedures for its application to a concrete problem,

and an intelligent machine-based advisor to judge the user's

effectiveness and advise on strategy. The procedures focus upon the use

of user generated explanations both to promote the application of domain

knowledge and to expose understanding difficulties. Simple database

PROLOG is being used as the subject material for the prototype system

which is known as MINDPAD.

Davis, R., "Amplifying Expertise with Expert Systems," in P. H. Winston and

K. A. Prendergast (Eds.), AI B usiness: The Commercial U ses of Artificial

Intel 1 i genc e, Cambridge, MA: MIT Press, 1984.

In surveying the state-of-the-art of expert systems, two important

calibration points are provided by the magnitude of investment necessary

to build a robust system and the stage of development reached to date by

most expert systems. Data from existing efforts, though meager, seem to

suggest that even in the best of cases, at least five man-years of effort

are necessary before an expert system begins to perform reliably. It is

also revealing to note that most expert systems to date have been

developed only through the stage of construction of the basic knowledge

base. Relatively few so far have progressed to the stage of extended

testing, further development, and documentation.

Davis, R., B. G. Buchanan, and E. H. Shortliffe, "Production Rules as a Representation of a Knowledge-Based Consultation Program," Arti f i ci al

Intelligence , Vol. 8, 1977.

Davis, R., M. Weikert, and M. Shirley, "Diagnosis Based on Description of

Structure and Function," Proceedings of the National Con ference on Artificial Intelligence , AAAI-82, American Association for Artificial

Intelligence, 1982.

47
Defude, B., "Knowledge-Based Systems Versus Thesaurus: An Architecture Problem

About Expert System Design," Proceedings of the Joint BCS and ACM

Symposium on Research and Development in Information Retrieval , Cambridge, England: Cambridge University, 1984.

Duda, R. 0., J. G. Gaschnig, and P. E. Hart., "Model Design in the PROSPECTOR

Consultant System for Mineral Exploration," in D. Michie (Ed.), Expert

Systems in the Micro-Ele ctronic Age, Edinburgh, Scotland, Edinburgh

University Press, 1979.

PROSPECTOR is a computer consultant system intended to aid geologists in

evaluating the favorability of an exploration site or region for

occurrences of ore deposits of particular types. Knowledge about a particular type of ore deposit is encoded in a computational model

representing observable geological features and the relative significance

thereof. The authors describe the form of models in PROSPECTOR,

focusing on inference networks of geological assertions and the Bayesian

propagation formalism used to represent the judgmental reasoning process

of the economic geologist who serves as model designer. Following the

initial design of a model, simple performance evaluation techniques are

used to assess the extent to which the performance of the model reflects

faithfully the intent of the model designer. These results identify

specific portions of the model that might benefit from fine tuning, and

establish priorities for such revisions. This description of the

PROSPECTOR system and the model design process serves to illustrate the

process of transferring human expertise about a subjective domain into a mechanical realization.

Duda, R. 0., and R. Reboh, "AI and Decision Making: The PROSPECTOR

Experience," in W. Reitman (Ed.), Artificial Intelligen ce

Appl i cations for Business , Norwood, NJ: Ablex Publishing Corporation,

1984.

PROSPECTOR is an expert system that was designed for decision-making

problems in mineral exploration. It uses a structure called an inference

network to represent the required judgmental knowledge. Once encoded,

this knowledge can be used in a variety of ways. This chapter describes

the use of inference networks as a language for representing and using

expert knowledge, and presents examples showing its use in three

different kinds of decision-making problem.

Duda, R. 0., et al., "Semantic Network Representations in Rule-Based Inference

Systems," in D. A. Waterman and F. Hayes-Roth (Eds.), Pattern-Pi rected

Inference Sys tems, New York: Academic Press, 1978.

Englemore, R. S., and A. Terry, "Structure and Function of the CRYSALIS

System," Proceedings of the Sixth IJC AI, American Association for

Artificial Intel li gence, 1979.

Feigenbaum, E. A., "Themes and Case Studies in Knowledge Engineering," in

D. Michie (Ed.)., Expert Systems in the Micro-Electronic Age , Edinburgh,

Scotland: Edinburgh University Press, 1979.

48
This paper examines emerging themes of knowledge engineering,

illustrates them with case studies drawn from the work of the Stanford

Heuristic Programming Project, and discusses general issues of knowledge

engineering art and practice.

Feigenbaum, E. A., B. G. Buchanan, and J. Lederberg, "On Generality and

Problem Solving: A Case Study Using the DENDRAL Program," in 6. Meltzer

and D. Michie (Eds.), Machine Intelligence 6 , New York: American

El sevier. 1971.

Findler, V., et al., "A Module to Estimate Numerical Values of Hidden

Variables for Expert Systems," International Journal of Man-Mach ine

Studies , Vol. 18, No. 4, Apr. 1983.

In the area of strategic decision-making, the objective often is to

achieve one's own goals and to prevent the achievement of the

adversaries' goal. To do so, the decision-maker needs to know, as precisely as possible, the values of the relevant variables at various

times. Some of these variables, the open variables, are readily

measurable at any time. Others, the hidden variables, can be measured

only at certain times, either i ntermi ttenti ly or periodically. The

authors have implemented a module that can act as a decision-support

tool for a variety of expert systems in need of estimates of hidden

variable values at any desired time. The estimation is based on generalized production rules expressing stochastic, causal relations

between open and hidden variables. The quality of the estimates improves

through a multi-level learning process as both the number and the quality

of the rules increase. The modularity of these causal relations make

incremental expansion and conflict resolution natural and easy.

Restricting the set and the domain of pattern formation rules to a reasonable size makes the system effective and efficient. Finally, the

system can be easily employed for distributed database applications.

Fox, M. S., "Reasoning with Incomplete Knowledge in a Resource-Limited

Environment: Integrating Reasoning and Knowledge Acquisition," Report

No.: CMU-RI-TR-81-3, Robotics Institute, Carnegie-Mellon University,

Pittsburgh, PA, Mar. 1981.

Fox, M. S., B. Allen, and G. Strohm, "Job-Shop Scheduling: An Investigation in

Constraint-Directed Reasoning," Proc e edings of the National Conference on Artificial Intelligence , AAAI-82, American Association for Artificial

Intelligence, 1982.

Friedland, P., "Acquisition of Procedural Knowledge from Domain Experts,"

Proceedings of the Seventh IJCAI , American Association for Artificial

Intelligence, 1981.

Ganascia, J. G. , "Using Expert System in Merging Qualitative and Quantitative

Data Analysis," International Journal of Man-Mac hine Studies, Vol. 20,

1984.

49
Gaschnig, J. G., et al .

, "Evaluation of Expert Systems: Issues and Case

Studies," in F. Hayes-Roth, D. A. Waterman, and D. B. Lenant (Eds.)>

Bui ld ing Ex p ert Systems, Reading, MA: Addi son-Wesley

Publishing Company, 1983.

Georgeff, M. , and U. Bonollo, "Procedural Expert Systems," Proceedings of the

Eighth IJCAI, American Association for Artificial Intelligence, 1983.

A scheme for explicitly representing and using expert knowledge of a procedural kind is described. The "Scheme allows the explicit

representation of both declarative and procedural knowledge within a unified framework, yet retains all the desirable properties of expert

systems such as modularity, explanatory capability, and extendabi 1 i ty

.

It thus bridges the gap between the procedural and declarative languages,

and allows formal algorithmic knowledge to be uniformly integrated with

heuristic declarative knowledge. A version of the scheme has been fully

implemented and applied to the domain of automobile engine fault

diagnosis.

Genesereth, M. R., "Diagnosis Using Hierarchial Design Models," Proceedin gs of

the Natio nal C onference on Artificial Intelligence , AAAI-82, American

Association for Artificial Intelligence, 1982.

Goldstein, I. P., and B. Roberts, "Using Frames in Scheduling," in P. H.

Winston and D. Brown (Eds.), Artificial Intelligence: An MIT Perspective (Volume I), Cambridge, MA: MIT Press, 1979.

Green, C, and S. J. Westfold, "Knowledge-Based Programming Self-Applied,"

in J. E. Hayes, D. Michie, and Y. H. Pao (Eds.), Machin e Intelligence 10,

Chichester, England: Horwood, 1982.

Hart, A., "The Role of Induction in Knowledge El i citation," Expert Systems , Vol. 2, No. 1, Jan. 1985.

Knowledge elicitation from experts is a major problem in building expert

systems. This paper summarizes some of the difficulties inherent in the

process and suggests that in certain situations induction can help.

Hartley, R. T., "How Expert Should an Expert System Be?" Proc eedings of the

Seve nth IJCAI, American Association for Artificial Intel 1 i gence, 1981.

A computer system which aids computer engineers in fault diagnosis is

described. The system, called CRIB (Computer Retrieval Incidence Bank) is

shown to fit into the class of pattern-directed inference systems.

Emphasis is placed on the before and after phases of system generation

and it is shown why, to be called an expert system, these phases are

important. The forms of knowledge used in CRIB are shown to be adequate

for diagnosis and yet possess little of the structural or functional

knowledge of more advanced expert systems. Summaries are given of the

50
three phases of implementation: elicitation, implementation of knowledge

structures, validation and improvement. The idea of an expert system as a "model of competence" is mentioned and the transference of the system

architecture to software diagnosis, using the same model, is described.

There are short discussions of system performance and the nature of expert

systems.

Jackson, P., and P. Lefrere, "On the Application of Rule-Based Techniques to

the Design of Advice-Giving Systems," Internati onal Journal of Man-Machine

Studies , Vol. 20, 1984.

Jarke, M. , and Y. Vassiliou, "Coupling Expert Systems with Database

Management," in W. Reitman (Ed.), Artificia l Intell i gence Applications

for Bus iness , Norwood, NJ: Ablex Publishing Corporation, 1984.

The combined use of database management systems (DBMS) and artificial

intelligence-based expert systems (ES) is potentially very valuable for

modern business applications. The large body of facts usually required in

business information systems can be made available to an ES through an existing commercial DBMS. Furthermore, the DBMS itself can be used more

intelligently and operated more efficiently if enhanced with ES features.

However, the implementation of DBMS-ES cooperation is not easy. We

explore practical benefits of the cooperative use of DBMS and ES, as well

as the research challenges it presents. Strategies for providing data

from a DBMS to an ES are given. Complementary strategies for providing

intelligence from an ES to a DBMS are also presented. Finally, we discuss

architectural issues such as degree of coupling, and combination with

quantitative methods. As an illustration, a research effort at New York

University to integrate a logic-based business ES with a relational DBMS

is described.

Johannes, J. D., "Representation and Use of Judgmental Knowledge," Proceedi

n

gs

of the IEEE, New York: IEEE, Nov. 1983.

This paper describes the representation and use of judgmental knowledge in

an expert system THYAID. The construction of a judgment -knowl edge space

and procedures used in developing a thyroid disease diagnostic aid are

described. Examples and experimental results are presented as well as the

approach used to provide measure of the system's capability.

Johannes, J. D., "Judgmental -Knowl edge Bases: Problem Solving and Expert

Sy s t ems

, " Proceed ings of the I nternational Con fe rence on Data Eng ineering

,

Silver Spring, MD": IEEE Computer Society Press, Apr. 1984.

A description is given of the representation and use of judgmental

knowledge as applied in the experimental development of a general problem

solver and an expert system. Human judgments on the degree to which

objects are important to processes (solution methods) are factored,

analyzed, and formed into a judgment space of orthogonal vectors. This

space is consulted, in the case of the problem solver, to choose a solution method based on the attributes of the available objects. The

result is a system that decides how to solve a problem in much the same

way a person would. In the expert system THYAID, the attributes of a

51
patient are combined to form a diagnosis. Examples and experimental

results are also given.

Johnson, L., "Inference in the Framework of Expert Systems," IEE Colloquium o n Decision Support As pects of Expert Systems (Digest No. 677", London,

England: IEE, May 1984.

The author reports on the forms of inference employed in the reasoning

components of the majority of the publicized expert systems. The actual

mechanization for implementing these two forms of inference depends on the

scheme(s) used for representing the knowledge captured within the systems.

These schemes are divided into two categories. One category is the

rule-based schemes and the other is a catch-all category (associative nets

and frames). How specific systems perform their inference-making is

di scussed.

Klahr, P., W. S. Faught, and G. R. Martins, "Rule-Oriented Simulation,"

Proceedings of the International Conference on Cyberneti cs and Society,

New York: IEEE, Oct. 1980.

Large-scale simulators have been plagued with problems of intelligibility,

modi f iabi 1 i ty , credibility and performance. Techniques developed in the

area of artificial intelligence, particularly in the development of

knowledge-based expert systems, are relevant to the design of simulators

that model decision-making behaviors. The authors outline the problems

encountered in large-scale simulation and present an approach that

incorporates and extends those techniques. ROSS, a rule-oriented

simulation system that embodies these techniques, is described.

Klahr, P., D. McArthur, and S. Narain, "SWIRL: An Object-Oriented Air Battle

S i mu 1 a t o r

, " Proceedings of the National Conference on Artific ial

Intel 1 i ge nce, AAAI-82, American Association for Artificial Intelligence,

1982.

Kobler, V. P., "Overview of Tools for Knowledge Base Construction," Proceedi ngs

of th e International Conferen ce on Data Engineering , Silver Spring, MD:

IEEE Computer Society Press, Apr. 1984.

The relative merits of a representative sampling of state-of-the-art tools

are described. The tools presented are those that have been publicly

documented and are representative of what is available at the present

time. They range from efforts to provide a better knowledge

representation language to an entire framework with input-output tools.

Kowalski, R., "AI and Software Engineering," Datamation , Nov. 1984.

Kukich, K., "Knowledge-Based Report Generation: A Technique for Automatically

Generating Natural Language Reports from Data Bases," Proceedings of the

Sixth Annual Inter na tional ACM SIGIR Co nference on Research and

Development in Information Retrieval , Baltimore, MD: ACM, June 1983.

Knowledge-based report generation is a technique for automatically

generating natural language summaries from data bases. It is so named

because it applies the tools of knowledge-based expert systems design to

52
the problem of text generation. The technique is currently being applied

to the design of an automatic natural language stock report generator.

Examples drawn from the implementation of the stock report generator are

used to describe the components of a knowledge-based report generator.

Laurent, J. P., "Operation of a Knowledge Base: Choices and Strategies,"

Proceedings of the 4th Con gress on Pattern Recognit ion and Artificial

Intel 1 i gence, Le Chesnay, France: Inst. Nat. Recherche Inf. and Autom.

,

1984.

Lee, R. M., "Information System Semantics (A Logic-Based Approach)," Journ al of

Information Systems , Vol. 1, No. 2, Fall 1984.

Lenat, D. B., "Heuretics: Theoretical and Experimental Study of Heuristic

Rules," Proceedings of the National Conference on Artificial Intelligence , AAAI-82, American Association for Artificial Intelligence," 1982.

Builders of expert rule-based systems attribute the impressive

performance of their programs to the corpus of knowledge they embody: a large network of facts to provide breadth of scope, and a large array of

informal judgmental rules (heuristics) which guide the system toward

plausible paths to follow and away from implausible ones. Yet what is the

nature of heuristics? What is the source of their power? How do they

interrelate; how can/should a large corpus of heuristic rules be

organized? How do heuristics originate and evolve? Heuretics is the

study of heuristics, with an eye toward answering questions such as those.

Two case studies, the AM and EURISKO programs, have led to some tentative

heuretics hypotheses, a dozen of which are presented in this paper. Our

aim is to stimulate future research in this field.

Lenat, D. 8., et al., "Reasoning about Reasoning," in Frederick Hayes-Roth,

D. A. Waterman and D. B. Lenat (Eds.), Building Expert Systems , Reading, MA: Addison-Wesley Publishing Company, 1983.

The authors point out that most knowledge-based systems today employ only

simple kinds of knowledge in simple ways. They investigate meta-knowl edge

and meta-expert systems, which aid in the tasks of constructing,

maintaining, and extending expert systems. Also, the knowledge

engineering and construction methodology issues involved in the building

of meta-knowl edge and meta-expert systems, especially the need for

different kinds of knowledge, are discussed.

Lewis, J. W., "Effective Graphics User Interface for Rules and Inference

Mechanisms," Proceedings of the Joint Services Workshop on Artificial

Intelligence in Maintenance , Boulder, CO, Oct. 1983.

As the technology of rule-based inference mechanisms matures, knowledge

acquisition -- the creation, structuring, and verification of rules -- becomes increasingly important. The accuracy and completeness of the

rules in the knowledge base determine expert system performance, and the

cost of acquiring that knowledge base dominates all other hardware and

software costs in practical systems. To reduce knowledge acquisition time

and error rate, a new interactive graphics interface for rules is being

designed and implemented in GE Corporate Research and Development. In the

53
new system, each set of rules is represented as an AND/OR graph and parts

of the rule base are displayed on a CRT screen as an AND/OR tree. A user

even an unsophisticated user -- can navigate the AND/OR graph, identify

nodes to be modified, analyze the behavior of the graph, verify its

correctness graphically, and follow the execution of inference engines.

Lowrance, J. D., and T. D. Garvey, "Evidential Reasoning: A Developing

Concept," Proceedings o f t he International Conference on Cybernetics a nd

Society, New York: IEEE, Oct. 1982.

One common feature of most knowledge-based expert systems is that they

must reason based upon evidential information. Yet there is very little

agreement on how this should be done. The authors present their current

understanding of this problem and some partial solutions. They begin by

characterizing evidence as a body of information that is uncertain,

incomplete, and sometimes inaccurate. Based on this characterization, the

authors conclude that evidential reasoning requires both a method for

pooling multiple bodies of evidence to arrive at consensus opinion and

some means of drawing the appropriate conclusions from that opinion. This

approach, based on a relatively new mathematical theory of evidence, is

contrasted with those approaches based on Bayesian probability models.

The authors believe that their approach has some significant advantages,

particularly its ability to represent and reason from bounded ignorance.

Masui, S., J. McDermott, and A. Sobel , "Decision-Making in Time-Critical

Situations," Proceedi ngs of the Eighth IJCAI, American Association for

Artificial Intelligence, 1983.

There are a variety of tasks in which the amount of time available to make

decisions does not always allow a careful consideration of all of the

options. AIRPLAN is being developed to assist with one such task,

managing the launch and recovery of aircraft on a carrier. This paper

describes attempts to represent AIRPLAN's knowledge in a way that enables

it to provide as much assistance as possible in the time available.

McDermott, J., "Extracting Knowledge From Expert Systems," Proceedings of the

Eighth IJCAI , American Association for Artificial Intelligence, 1983.

Our understanding of how to use large amounts of knowledge to enhance the

problem solving capabilities of computer programs is quite limited. Over

the past several years a number of knowledge-based systems have been

developed, and this experience has provided us with a handful of

techniques we can apply in a few domains. What we don't have yet is much

of an appreciation of why these techniques work or of the limits of their

usefulness. In order to take this next step, we need more data.

Unfortunately, the analyses of the expert systems currently being built

tend to ignore questions that could provide precisely the data needed.

This paper proposes a few questions that might be worth asking and

shows how answers to those questions could begin to give us the

understanding we lack.

McDermott, J., and B. Steele, "Extending a Knowledge-Based System to Deal with

ad hoc Constraints," Proceedings of the Seventh IJCAI , American

Association for Artificial Intelligence, 1981.

54
Rl is a rule-based program used by Digital Equipment Corporation's

Manufacturing Organization for configuring VAX-11 systems. Since its

inception, Rl has the capability of producing a functional system by

fleshing out an order consisting only of a CPU, some primary memory, and

some devices. Until recently, however, it was not capable of accepting as part of its input a set of ad hoc (customer-specific) constraints. Left

to itself, Rl was capable of producing reasonable configurations. But it

was incapable of modifying its decisions on the basis of information that

others could provide. This paper describes how rules were added to Rl to

take advantage of such information. Rl can now accept as input commands

that specify how particular components are to be configured. Whenever one

of the commands becomes relevant, these rules take control, extend the

configuration in the direction indicated by the command, and then step

aside, allowing Rl 1 s ordinary-case configuration rules to regain control.

McDermott, D., and R. Brooks, "ARBY: Diagnosis with Shallow Causal Methods,"

Proceedi ngs of the National Conference o n Artificial Intelligence , AAAI-82, American As~sociation for Artificial Intelligence, 1982.

Michalski, R. S., and R. L. Chilausky, "Knowledge Acquisition by Encoding

Expert Rules Versus Computer Induction from Examples: A Case Study

Involving Soybean Pathology," International Journa l of Man-Machine

Studies, Vol. 12, 1980.

" '"~ —

In view of the growing interest in the development of knowledge-based

computer consulting systems for various problem domains, the problems of

knowledge acquisition have special significance. Current methods of

knowledge acquisition rely entirely on the direct representation of

knowledge of experts, which usually is a very time and effort consuming

task. This paper presents results from an experiment to compare the above

method of knowledge acquisition with a method based on inductive learning

from examples. The comparison was done in the context of developing rules

for soybean disease diagnosis and has demonstrated an advantage of the

inductively derived rules in performing a testing task (which involved

diagnosing a few hundred cases of soybean diseases).

Michie, D., "The State of the Art in Machine Learning," in D. Michie (Ed.),

Intro ductory Readings in Expert Sy s t e ms , New York: Gordon Breach Science

Publishers, 1982.

Moorthy, V. S., and B. Chandrasekaran, "Representation for the Functioning of

Devices that Supports Compilation of Expert Problem Solving Structures:

An Extended Summary," Proce ed ings of the Joint Servic e s Workshop on Artific ial Intelligence in Maintenance , Boulder, CO .

, ~0ct . 1983

.

Morris, P. A., "An Axiomatic Approach to Expert Resolution," Management

Science , Vol. 29, No. 1, Jan. 1983.

Mylopoulos, J., T. Shibahra, and J. K. Tsotsos, "Building Knowledge-Based

Systems: The PSN Experience," Computer, Vol. 16, No. 10, Oct. 1983.

b5
An overview is provided of the procedural semantic networks (PSN) project,

whose goal is to develop and test linguistic and other tools that

facilitate the construction of large knowledge bases. Two large

knowledge-based systems that use PSN as a knowledge-representation

language are described. They use the PSN formalism to assess

left-ventricular behavior and diagnose cardiac rhythm disorders.

Nau, D. S., J. A. Reggia, and P. Y. Wang, "Knowledge-Based Problem Solving

Without Production Rules," Proceedings of Trends and Applications 1983 on Automating Intelligent Behavior: Applications and F ro ntiers , Silver

Spring, MD: IEEE Computer Society Press, May 1983.

This paper discusses one alternative to production rules which has proven

useful in several medical diagnostic problems and at least one non-medical

non-diagnostic problem. This method, which is based on a generalization

of the set covering problem, provides an intuitively plausible method for

diagnostic reasoning which is sometimes preferable to production rule

techniques.

Pat i 1

, R. S., P. Szolovits, and W. B. Schwartz, "Information Acquisition in

Diagnosis," Proceedings of the National Conference on Artificial

Intel 1

i gence , AAAI-82, American Association for Artificial Intelligence,

1982.

Pau, L. F., "Failure Detection Processes by an Expert System and Hybrid Pattern

Recognition," Pattern Recognition Letters , Vol. 2, No. 6, Dec. 1984.

Pol i tak is, P., and S. M. Weiss, "A System for Empirical Experimentation With

Expert Knowledge," Proceedings of the Fifteenth Hawaii Interna tional

Conference on System Sciences , Vol. 2, Honolulu, HI: Hawaii

International Conference of System Sciences, 1983.

Politakis, P., and S. M. Weiss, "Using Empirical Analysis to Refine Expert

System Knowledge Bases," Artificial Intelligence , Vol. 22, No. 1 Jan. 1984.

SEEK is a system which gives interactive advice about rule refinement

during the design of an expert system. The advice takes the form of

suggestions for possible experiments in generalizing or specializing rules

in a model of reasoning rules cited by the expert. Case experience, in

the form of stored cases with known conclusions, is used to interactively

guide the expert in refining the rules of a model. This approach is most

effective when the model of the expert's knowledge is relatively accurate

and small changes in the model may improve performance. The system is

interactive; the authors rely on the expert to focus the system on those

experiments that appear to be most consistent with his domain knowledge.

The design framework of SEEK consists of a tabular format for expressing

expert-modeled rules and a general consultation system for applying a model to specific cases. Examples are given from an expert consultation

system being developed for diagnosing rheumatic diseases.

Quinlan, J. R., "Discovering Rules by Induction From Large Collections of

Examples," in D. Michie (Ed.), Expert Systems in the Micro-Electronic Age , Edinburgh, Scotland: Edinburgh University Press, 1979.

56
Quinlan, J. R., "Semi -Autonomous Acquisition of Pattern-Based Knowledge," in

D. Michie (Ed.), Introductory Readings i n Ex pert Systems , New York: Gordon

Breach Science Publishers, 1982.

Quinlan, J. R., "Fundamentals of the Knowledge Engineering Problem," in

D. Michie (Ed.), Introductory Readings in Expert Systems , New York: Gordon

Breach Science Publishers, 1982.

Reboh, R., "Extracting Useful Advice From Conflicting Expertise," Proceedings of

the Ei ghth IJCAI , American Association for Artificial Intelligence, 1983.

A method for automatically identifying areas of disagreement and their

sources is presented for multiexpert knowledge-based systems in the context

of the Prospector consultation system. It employs performance evaluation

techniques in combination with the explanatory facilities present in many

expert systems to assist the user of an expert system in deciding which

among several possibly conflicting expert opinions he should choose.

Reggia, J. A., D. S. Nau, and P. Y. Wang, "Diagnostic Expert Systems Based on a Set Covering Model," Internationa l J ou rn al of Man-Machine Studie s,

Vol. 19, No. 5, Nov. 1983.

This paper proposes that a generalization of the set covering problem can

be used as an intuitively plausible model for diagnostic problem solving.

Such a model is potentially useful as a basis for expert systems in that it

provides a solution to the difficult problem of multiple simultaneous

disorders. The authors briefly introduce the theoretical model and then

illustrate its application in diagnostic expert systems. Several

challenging issues arise in adopting the set covering model to real-world

problems, and these are also discussed along with the solutions they have

adopted.

Riesbeck, C, "Knowledge Reorganization and Reasoning Style," Interna ti onal

Journal of Man-Mac hine Studies, Vol. 20, 1984.

Robinson, J. A., "Problems and Trends for the Future of Logic Programming," in

D. Michie (Ed.), Introductory Readings in Expert Systems , New York:

Gordon Breach Science Publishers, 1982.

Robinson, J. A., "Fundamentals of Machine-Oriented Deductive Logic," in

D. Michie (Ed.), Introductory Readings in Expert Systems , New York: Gordon

Breach Science Publishers, 1982.

Saurs, R., and R. Walsh, "On Requirements of Future Expert Systems,"

Proceedings of the Eig hth IJCAI , American Association for Artificial

Intelligence, 1983.

Shaw, M. L. G., "Acquisition of Personal Knowledge," International Journal of

Policy Analysis and Information Systems, Vol. 4, No. 4, Dec. 1980.

Expert systems are increasingly relying on an explicit knowledge base in

which the meaning of the stored information must be machine manipulable.

This paper presents a method for accessing variables (constructs) which a

57
person is using to categorize conceptional entities, and two interactive

computer programs for eliciting these from a human user. The elicitation

may commence from an empty knowledge base (PEGASUS), or be directed

through an expert's knowledge base (PEGASUS-BANK). The interactive

algorithms direct the elicitation of the knowledge base through continuous

on-line analysis of the data structures already entered. A detailed

example is given of the use of the PEGASUS-BANK program in a medical

context.

Slagle, J. R., et al., "An Intelligent Control Strategy for Computer

Consultation," IEEE Transactions o n Pattern Analysis and Machine

Intelligence , Vol. 6, No. 2, Mar. 1984.

Expert consultant systems often perform computations on a directed graph

of associated propositions. Each proposition is represented by a node.

Edges connecting these nodes are associated with rules which organize the

propositions into antecedent/consequent relationships. A node may be

assigned a value through the edges that bind it to its antecedents.

Various strategies are employed to determine assignment sequences that

result in efficient computer consultation. One such strategy, the merit

system, has been successfully implemented in BATTLE, an expert consultant

system for the Marine Crops. The merit strategy enables BATTLE to focus

the consultation process on the most appropriate questions. The merit

system, originally defined for logical functions in the multiple program,

has been extended to the MYCIN style of propagation and to the method of

subjective Bayesian assignments used by PROSPECTOR. A procedure for merit

calculations with any differentiate, real-valued assignment function is

presented.

Smith, R. G., "Declarative Task Description as a User-Interface Structuring

Mechanism," Computer , Vol. 17, No. 9, Sept. 1984.

Sridharan, N. S., and J. L. Bresina, "Knowledge Structures for Planning in

Realistic Domains," Comput ers & Mathematics , Vol. 11, No. 5, 1985.

Sticklen, J., et al., "MDX-MYCIN: The MDX Paradigm Applied to the MYCIN

Domain," Compu te rs & Mathematics, Vol. 11, No. 5, 1985.

Stolfo, S. J., "Knowledge Engineering: Theory and Practice," Proceedings of

Tren ds and Applications 1983 on Au t omating Inte lligence Behavior:

Applications and Frontiers , Silver Spring, MD: "IEEE Computer Society

Press, May f983.

The authors briefly describe ACE, a knowledge-based system for Automated

Automated Cable Expertise, which is designed to provide support for

management analysis, automating decision making for telephone cable

maintenance. The development of ACE, undertaken by Bell Telephone

Laboratories, demonstrates in a forceful way the manner in which Al

techniques can be applied to significant and practical real-world

problems. ACE is a representative of a new technology, the merging of

data base and knowledge-based systems, which may synergi sti cal ly benefit a large number of applications. However, to fulfill their promise for the

very-large-scale embodiment of domain-specific expertise for many

58
real-world problems, systems such as ACE may indeed require an order of

magnitude more knowledge. Thus, a number of scientific questions,

covering a considerable spectrum of AI research, must be addressed and

solved to effect this technology transfer.

Suwa, M., A. C. Scott, and E. H. Shortliffe, "An Approach to Verifying

Completeness and Consistency in a Rule-Based Expert System," AI Magazine , Vol. 3, No. 4, 1982.

Swartout, W., "Explaining and Justifying Expert Consulting Programs,"

Proceedings of the Seventh IJCAI , American Association for Artificial

Intelligence, 1981.

Thorndyke, P. W., and K. T. Wescourt, "Modeling Time-Stressed Situation

Assessment and Planning for Intelligence Opponent Simulation," Report

No.: PPAFTR-1124-84-1, Menlo Park, CA: Knowledge Systems Branch,

Perceptroni cs , Inc., July 1984.

This report summarizes research undertaken to simulate expertise on a real-time time-stressed task requiring situation assessment and planning.

While prior efforts in expert systems development have attacked a variety

of problems either in situation interpretation or planning, none has

attempted to articulate a model that can support both types of activities

simultaneously in a dynamic environment. The present study attempted to

derive such a cognitive model for time-stressed performance in the domain

of Naval surface-warfare tactical decision making. The model extends the

concept of the blackboard architecture -- multiple independent processing

specialists contributing data and results to a common knowledge

base -- previously applied to systems for either signal interpretation or

planning. Their approach treats the time-stressed assessment and planning

task as a hierarchy of blackboard models, with several processing

modules -- each organized as a blackboard model -- interacting through a common knowledge base. Implementation of the model is underway for use as an automated opponent in a Navy tactical battle game. The simulation

will emulate an opposing naval commander and will be embedded in an interactive instructional system providing battle exercise for Navy

tactics trainees.

Tsotsos, J. K., "Cooperative Computation and Time," Proceedings of the Workshop

on Computer Vision: Representation and Control , Silver Spring, MD: IEEE

Comp'uter Society Press, 1984.

van Melle, W., "A Domain-Independent Production Rule System for Consultation

Programs," Proceedings of the Sixth IJCAI , American Association for

Artificial Intelligence, 1979.

EMYCIN is a programming system for writing knowledge-based consultation

programs with a production-rule representation of knowledge. The major

components of the system, including an explanation program and knowledge

acquisition routines, are briefly described. EMYCIN has been used to

build consultation systems in several areas of medicine, as well as in

the engineering domain. These experiences lead to some general

conclusions regarding the potential applicability of EMYCIN to new

domai ns.

59
Walker, A., "Databases, Expert Systems, and PROLOG," in Reitman (Ed.),

Artificial Intelligence Applications for B usiness, Norwood, NJ: Ablex

Publishing Corporation, 1984.

The knowledge needed for an expert system is usually held in the form of

facts and IF-THEN rules. The expert system itself is a program (often in

LISP) which infers advice from the facts and the rules. In the language

PROLOG, a program consists of facts and rules, so the knowledge needed

for an expert system can be written down directly. To get advice, one

can write further rules about how to use the knowledge. The facts in

PROLOG correspond to a relational database, while the rules contain

expertise about how to use the data. This chapter uses specific examples,

taken from three programs, to show that PROLOG is a practical language for

bringing together several apparently diverse techniques in databases and

expert systems.

Wall, R. S., "Industrial Strength Knowledge Representation," Proce e dings of the

Third Annual International Phoenix Conference on Computers and

Communi cati ons , Silver Spring, MD: IEEE Computer Society Press, 1984.

One of the major problems in trying to build knowledge-based systems is

choosing the representation framework that is best suited to the domain.

Traditionally, one selects a single paradigm and then encodes the domain

knowledge in that framework. This approach is troublesome for large or

constantly revised domains. An alternative approach is presented in which

a number of different paradigms were implemented as a set of integrated

tools. The tools were constructed using the object-oriented programming

facility of the LISP machine flavor system. This allows each facet of

the domain to be modeled with its most natural representation. The tools

include partitioned networks, conceptual hierarchies, inference engines,

process simulation and epi stemol ogi cal modeling.

Wall, R. S., and E. L. Rissland, "Scenarios as an Aid to Planning," Proceedi ngs

of t he Natio nal Conference on Artificial Intelligence , AAAI-82, American

Association for Artificial Intelligence", 1982.

In many complex domains, full-scale detailed solutions to a problem -- plans and ways to implement them -- are not possible or practical.

Rather than attempt to produce such detailed solutions, the authors

propose an alternative of using scenarios: a set of "snapshots" of the

future that outline possible courses of action, actions of foreign

processes, and their consequences. In this paper, they present the

scenario idea and discuss its use as an aid to tactical planning.

Finally, they discuss an implementation of these ideas in a Tactical

Assistant to aid planning in the errand running and conflict simulation

game domains and present an example illustrative of its capabilities.

60
Wallis, J. W., and E. H. Shortliffe, "Explanatory Power for Medical Systems:

Studies in the Representation of Causal Relationships for Clinical

Consultations," Methods of Information in Medicine, Vol. 21, No. 3,

July 1982.

This paper reports on experiments designed to identify and implement

mechanisms for enhancing the explanation capabilities of reasoning

programs for medical consultation. The goals of an explanation system

are discussed, as is the additional knowledge needed to meet these goals

in a medical domain. The paper focuses on the generation of explanations

that are appropriate for different types of system users. This task

requires a knowledge of what is complex and what is important; it is

further strengthened by a classification of the associations of causal

mechanisms inherent in the inference rules. A causal representation can

also be used to aid in refining a comprehensive knowledge base so that

the reasoning and explanations are more adequate. The paper describes a prototype system which reasons from causal inference rules and generates

explanations that are appropriate for the user.

Waterman, D. A., "A Rule-Based Approach to Knowledge Acquisition for

Man-Machine Interface Programs," International Journal of Ma n -Machine

Studies , Vol. 10, 1978.

The development of computer programs, called agents, that act as man-machine interfaces for computer users is described. These programs

are written in RITA: the Rule-directed Interactive Transaction Agent

system, and are organized as sets of IF-THEN rules or production systems.

The programs, or personal computer agents, are divided into two main

categories: those that interact the user to computer systems he wishes

to use and those that interact with the user to acquire the knowledge

needed to create these interface programs. The relationship between the

interface program and the knowledge acquisition program is that of

parent-offspring. Three types of parent-offspring RITA agent pairs are

described: (1) an exemplary programing agent that watches a user perform

an arbitrary series of operations on the computer and then writes a program (a task agent) to perform the same task, (2) a tutoring agent

that watches an expert demonstrate the use of an interactive computer

language or local operating system and then creates a teaching agent that

can help naive users become familiar with the language or system

demonstrated by the expert, and (3) a reactive-message creating agent

which elicits text from a user (the sender) and from it creates a new

RITA agent which is a reactive message. The reactive message is sent to

some other user (the recipient) who interacts with it. During the course

of the interaction a record of the recipient's responses is sent back to

the sender.

61
!

!

Weiss, S., et al., "A Model -Based Method for Computer-Aided Medical Decision

Making," Artificial In t elligence , Vol. 11, 1978.

A general method of computer assisted medical decision-making has been

developed based on causal-associational network (CASNET) models of

disease. A CASNET model consists of three main components: observations

of a patient, pathophysiological states and disease classifications. As

observations are recorded, they are associated with the appropriate

states. States are causally related, forming a network that summarizes

the mechanisms of disease. Patterns of states in the network are linked

to individual disease classifications. Recommendations for broad classes

of treatment are triggered by the appropriate diagnostic classes.

Strategies of specific treatment selection are guided by the individual

pattern of observations and diagnostic conclusions. This approach has

been applied in a consultation program for the diagnosis and treatment of

the glaucomas.

Yager, R. R., "Querying Knowledge Base Systems with Linguistic Information via

Knowledge Trees," International Journa l of Man-Machine Studies , Vol. 19, 1983.

The author is interested in finding automated procedures for extracting

information from knowledge bases which contain linguistic information.

The concept of a possibility distribution to represent the information in

a linguistic value is used. The theory of approximate reasoning is used

to provide both a means for translating propositions into a machine-understandable form and a methodology for making inferences from

this information. An algorithmic procedure resulting in the desired

information are presented. This paper is restricted to answering

questions about the value of a variable from a knowledge base consisting

of simple data statements and implication statements.

Yager, R. R., "Measuring the Quality of Linguistic Forecasts," Internati onal

Journal of Man-Machine Studies , Vol. 21, 1984

This paper suggests a method for representing linguistic forecasts via

fuzzy sets. It uses a representation to obtain a measure of quality of

forecasts which takes into account both the validity and the specificity

of the forecast.

Yager, R. R., "Approximate Reasoning as a Basis for Rule-Based Expert Systems,"

Technical Report No. MI I -314, Machine Intelligence Institute, Iona

College, New Rochelle, NY, 1984.

62
5. TOOLS FOR BUILDING EXPERT SYSTEMS

As the computational and analysis requirements of expert systems are different

from those of conventional computer programs, it is not surprising to find that

special tools have evolved for the study and development of expert systems, see

Barstow, et al., [1983], Where the needs of conventional programming helped to

generate special computer languages like FORTRAN and COBOL, the needs of expert

system programming (more accurately, the needs of artificial intelligence) have

produced the LISP and PROLOG languages. Similarly, as many conventional problems

could be solved more efficiently by standardizing major programming elements into

subprograms and subsystems, like file maintenance and retrieval procedures, the

design of expert systems can be facilitated by the use of programming subsystems

termed shells. Shells are specialty packages that construct the knowledge base

and provide an inferencing procedure for exploiting the information contained

within the knowledge base. It is also important to note that many tools are now

available for building expert systems on microcomputers, see Assad and Golden

[1985]. These tools include mi croversi ons of LISP and PROLOG, as well as micro-based shells. It is also possible to build the system on a main frame or minicomputer and down load the production version onto a microcomputer. However,

once the system is down loaded, it may no longer be possible to modify the

knowledge base or control strategy to reflect new information and/or knowledge

about the problem.

5.1 GENERAL PURPOSE TOOLS: LISP AND PROLOG

LISP and PROLOG provide a general framework for non-numeric processing,

especially the heuristics and symbolic manipulation required for knowledge

representation. Dating back to 1957, LISP is one of the oldest programming

languages still in use. The motivation for the language centered on its list

processing capabilities (for which the name LISP is an acronym). LISP has

developed along lines that are quite separate from those of the algorithmic

languages such as FORTRAN and PASCAL.

LISP interpreters and compilers make the computer behave as if it were a symbol -mani pulati on machine. Symbols can be used to represent knowledge in terms

of objects, relationships that establish meaningful links from one object to

another, and processes that govern the creation, destruction, transformation and

other behavior of objects. Objects and their relationships can be represented by

means of symbolic data structures. An object can have various attributes and

their values associated with it by means of a property list. An attribute can

stand for both a descriptive property or it can represent a relationship between

the described object and another object. Relationships can also be defined in

terms of set memberships or predicate functions.

63
There are only two levels of data objects in LISP. The most basic form is the

atom, which is simply a character string used as a symbolic representation. The

higher-level data structure is the S-expression (symbolic expression). An

S-expression is either a single atom or a linked list of S-expressi ons.

S-expressi ons are therefore capable of encoding arbitrary tree-structured data.

Associated with this linked list data type is an unambiguous one-dimensional

surface representation through which the LISP user may communicate.

S-expressions are the only form encountered in pure LISP. All programs, as well

as all data, conform to this structure and are expressible in the surface

representation as parenthesized lists. LISP executes a simple cycle of accepting

input S-expressions, evaluating them, and printing out the results of the

evaluation. This operation is known as the Read-Eval -Pri nt loop.

The programming language PROLOG was a first attempt to design a language that

would enable a programmer to specify tasks in logic, rather than what the machine

should do and when. PROLOG stands for "programming in logic." Resolution and

unification are formal deductive inference procedures that have been used to

prove theorems and to derive information from data that is not explicitly stated,

but which is deducible from the stored data. PROLOG implements a goal-driven

syntactic strategy based upon the resolution principle. Knowledge in PROLOG is

represented in a predicate calculus notation. PROLOG programming differs from

LISP, as well as the algorithmic-based languages, in that the programmer uses

PROLOG to state the problem, but cannot specify the means or procedure that the

system should adopt to achieve a solution.

In PROLOG the control statements are separate from the logic and are invoked in a manner transparent to the programmer. PROLOG also supports arithmetic operators,

structures of sets and lists, and their associated manipulations. PROLOG also

allows one to define new operators, including their position precedence and

associati vity

.

The semantics of PROLOG is that of resolution logic. All PROLOG statements

(axioms) are written in the form "In order to prove B, first prove A]_ and A2 and

... and An ." Such a statement is equivalent to the logical formula

Ai & A2 & •• & An + B

The A's are referred to as the conditions of the clause and the B's are referred

to as the consequences. Knowledge about a problem environment can be stored as axioms in the language. Logical inferences are performed on these axioms to

derive new information.

A PROLOG program involves a sentence consisting of a finite set of Horn clauses.

Syntactically, a Horn clause is one containing at most one consequent. The

method of control used in PROLOG is a restricted form of linear input resolution.

Basically, PROLOG adopts a depth-first interpretation of Horn clauses; it

considers only one alternative at a time, following up the implications under the

assumption that the choice is correct. Programming in PROLOG is not so much like

telling a computer what to do when, but rather telling it what is true and asking

it to draw conclusions. By looking at a PROLOG program, we would know what it

computes rather than how it computes.

64
5.2 SPECIAL PURPOSE TOOLS: SHELLS

Although LISP and PROLOG provide a powerful framework for building expert

systems, the recent rapid growth of the field is due to the variety and

sophistication of shells. Since the list of shells is expanding, especially for

special purpose applications, we shall focus only on five of the better-known

systems: (1) ART (Automatic Reasoning Tool), (2) KEE (Knowledge Engineering

Environment), (3) 0PS5, (4) ROSIE (Rule Oriented System for Implementing

Expertise), and (5) TIMM (The Intelligent Machine Model).

ART is a shell tool that provides the system developer with a great deal of

representational and programming features, see Williams [1984]. Representational

features include: (1) facts, (2) patterns, (3) schemas, (4) rules, and (5)

viewpoints. Programming features include: (1) a help system, (2) menus, (3)

graphics, and (4) a program monitor to facilitate system debugging during

execution.

Propositions in ART express declarative knowledge including general facts and

situation specific facts. A fact in ART consists of a proposition (a recursive

sequence structure) and an extent (a scope of validity). ART differentiates

between facts that are explicitly stated to be false and facts that are unknown

(i.e., not known to be true or false). This is supported by allowing ART

propositions to have truth values associated with them. In ART, patterns are

propositions with variables. Schemas in ART (conceptually similar to frames in

KEE) may be thought of as collections of facts, allowing the representation of

data as conceptual objects.

Rules are used in ART to encode procedural knowledge. ART provides a rich

pattern matching language which is used to create and apply rules. The rule

language includes logical connectives and quantification, as well as procedural

constraints. Furthermore, mixed chaining is possible by combining goals and

facts within a rule. ART differentiates between inference and production rules.

Inference rules add facts to the knowledge base, while production rules change

facts

.

Viewpoints are an interesting feature provided by ART. A viewpoint is a hypothetical world in which ART can pursue a potential course of action to see

where it leads. Hypotheti cal s may be compared and contrasted, merged together or

marked as inconsistent, automatically or by rules. Viewpoints can be used to

represent a point in time, a possible interruption of incoming data, or a potential solution to a problem. Associated with each viewpoint is a confidence

rating that gives a numeric estimate of a viewpoint's validity. Numerical

certainty factors can also be associated with individual facts in the knowledge

base.

KEE is a shell that integrates several artificial intelligence methodologies into

a single system, see Intel 1 i Corp [1984] and Finnucan [1985]. It provides support

for frame-based knowledge representation with taxonomic inheritance, rule-based

reasoning, logic representations, data-driven reasoning, object-oriented

programming, and LISP functional programming. In addition, KEE provides direct

support for various uses of interactive graphics. Important ideas in KEE include

early prototype development, incremental refinement of the problem description,

use of multiple integrated solution methods, and emphasis on visibility of both

the problem-solution process and the explicit description of the problem domain.

65
Application specific knowledge in KEE is called a knowledge base and is loaded

into the KEE system. The knowledge base includes frames that represent or describe objects and classes of objects. Frame systems are inherently

object-oriented as the frames each represent an object (a class is also an object). Behavior is invoked by passing messages to objects. The information

needed to respond to a message is stored in the attribute slots. Storing

behavioral responses directly with an object can facilitate creating more modular

programs

.

Frames in KEE are a data structure consisting of a frame name and a set of

attribute descriptions, each defined by a slot. Associated with each slot is the

attribute's value(s), constraints that the value must satisfy, procedures that

are called whenever the slot is accessed or its values change, and the source of

the inherited values. Attribute values can be restricted to meet constraints

including class, range, cardinality, and data type. Objects include classes,

subclasses, and individuals.

Rule clauses in KEE can include logical expressions with conjunctions,

disjunctions, and negations, as well as arbitrary LISP S-expressions. KEE also

supports backward and forward chaining rule interpreters. The system permits

rules to be used for multiple purposes such as explanation by describing or

displaying a chain of reasoning, as the chain of rules are applied.

User-supplied functions can tailor rule interpretation to specify depth-first,

breadth-first, or other search strategies for backward chaining, and resolve

conflicts in forward chaining when more than one rule is applicable.

0PS5 is a rule-based programming language descended from earlier OPS languages

designed for artificial intelligence and cognitive psychology applications, see

Waterman and Hayes-Roth [1983]. Data elements in 0PS5 are either vectors or

objects with associated attribute-value pairs. Rules in 0PS5 are data-driven and

operate on a single global data base. The rules have an antecedent, a partial

description of data elements, and a consequent, one or more action to be taken if

the antecedent matches the data base.

Control in 0PS5 is governed by the recognize-act cycle, a simple loop in which

rules with satisfied antecedents are found, one is selected (conflict

resolution), and its actions are performed. Most 0PS5 programs make use of goals

to direct the processing, putting them into the data base and deleting them when

appropriate for the current problem-solving strategy. The collection of rules

that are sensitive to the goal for a given task compose the "method" for that

task. Methods and goals are not features of the 0PS5 language; they are simply

convenient organizing principles. The rules associated with a method can perform

tasks directly, or they can create goals that ask other rules to perform the

tasks

.

ROSIE is a general -purpose rule-based programming system suitable for a broad

range of knowledge engineering applications, see Fain, et al., [1982]. Its most

striking feature is its English-like syntax, which facilitates the creation and

manipulation of the ROSIE data base. This data base contains general

relationships with English correspondences in the ROSIE language. To test

relations in the data base, the user writes conditions in English that correspond

66
to the relational forms. Five basic types of English relationships are modeled

by the language: (1) class membership, (2) predication, (3) intransitive verbs,

(4) transitive verbs, and (5) predicate complements. These relationships can be

further refined by the use of prepositional phrases.

ROSIE rules are organized as rule sets, defined to be either procedures,

generators, or predicates. Generators return values of any sort, while

predicates determine proposition truth values. The rule sets operate on a global

data base, but maintain temporary private data bases during execution.

ROSIE supports three types of inference mechanisms: (1) state-driven, where the

state of the system directly causes a rule to fire, (2) goal-driven, where

backward-chaining is used to find rules that will verify predicates in the rule

conditions, and (3) change-driven, where a data-base change causes a rule to

f i re.

TIMM is a general -purpose rule-based programming system, see Craig [1986] and

McWhite [1985]. TIMM is an interactive tool that performs the function of the

knowledge engineer by querying the expert, and constructing and interpreting the

knowledge base. This is accomplished by applying a general method for building

an expert system and solving problems that are independent of the problem type.

TIMM's principal functions are: (1) build, (2) train, and (3) exercise. The

build function enables one to define the decision to be made; possible decision

options (choices), factors affecting the decision, and possible factor values.

The train function is used to define rules, to identify generalities among the

rules, to compress the knowledge base to its most efficient and effective size,

and to identify inconsistencies in the knowledge base. The exercise function is

used to test the effectiveness of the expert system in reaching an appropriate

deci si on.

TIMM reasons by analogical or pattern-directed inference, comparing the current

situation to similar experiences (rules) in its knowledge base. When an exact

match occurs between the antecedent clauses of a rule and the situation, TIMM's

decision is the related consequent clause of the rule. If there is no exact rule

match, TIMM will still provide a decision. For each rule in the knowledge base,

TIMM computes a numerical distance between the rule and the current situation.

If the distance equals zero, then an exact match has occurred; if the distance is

a non-zero number, the distance provides a measure of how well the current

situation is matched by the rule. This is reflected as a reliability measure.

Because of this capability, the knowledge base can be searched for near matches

in response to precise and imprecise questions.

The TIMM software is written in FORTRAN 77. This approach should promote

transportability of the system. In addition, TIMM can be called by another

FORTRAN program.

67
References

Anderson, R. H. , and J. J. Gillogly, "The RAND Intelligence Terminal (RITA) as a Network Access Aid," Proceedings of the 1976 AFIPS National Computer

Conference , Volume 45, Arlington, VA: AFIPS Press, 1976.

ArterrTeva, I. L., et al., "Compiler Generator for Knowledge Respresentati on Languages," Transactions in Programming and Computer Software , Volume 9,

No. 4, 1983.

This article describes a compiler generator for automatic realization of

expert systems. The specific features of the realization process are

described, and a framework is developed for compiling a knowledge base into

an expert system, distinct from the traditional method of knowledge base

i nterpretati on.

Assad, A. A., and B. L. Golden, "Expert Systems, Microcomputers and Operations

Research," Report No.: MS/85-025, College of Business and Management,

University of Maryland, College Park, MO, July 1985.

The goal of this article is to review microcomputer-based software for

building expert systems. There are now at least 15 commercially-available

tools to facilitate the development of expert systems on microcomputers.

First, the field of expert systems is introduced in general terms. Then,

issues relating more directly to microcomputers are studied in detail.

Trends and future directions are also discussed.

Balzer, R., et al., "Hearsay-Ill: A Domain-Independent Framework for Expert

Systems," Proceedings of the National Conference on Artificial Intelligence , AAAI-80, American Association for Artificial Intelligence, 1980.

Barstow, D. R., et al., "Languages and Tools for Knowledge Engineering," in

F. Hayes-Roth, D. A. Waterman, and D. B. Lenat (Eds.), Building Expert

Systems , Reading, MA: Addi son-Wesley Publishing Company, 1983.

In this article, the authors provide a comprehensive survey of tools widely

used in knowledge engineering. Each tool reflects a special emphasis on kinds of problems, kinds of overall designs, or kinds of implementations

deemed desirable. Although none of the tools meets industrial standards for

wide-scale utilization, an order-of -magnitude reduction in development time

can be realized by applying an appropriate tool. Today's tools foreshadow a vast array of future industrial implements. Knowledge engineering means

forming and assembling knowledge, and the tools make this possible.

Brachman, R. J., and H. J. Levesque, "Competence in Knowledge Representation,"

Proceedings of the National Conference on Artificial Intelligence , AAAI-82,

Amercian Association for Artificial Intelligence, 1982.

The range of domains and tasks for "knowledge-based systems" has been

expanding at a furious pace. As we move away from trivial domains, such as the blocks world, the demands on knowledge representation systems used by

expert programs are becoming more extreme. For one thing, the domains

themselves are getting so complex that specialized technical vocabularies

are unavoidable; consequently, the issue of a system talking with an expert

68
in his own language cannot be ignored. For another, tasks such as medical

diagnosis, scene analysis, speech understanding, and game playing all have

as a central feature an incrementally evolving model representing probably

incomplete knowledge of part of the task domain. In this paper, we explore

some of the impact of these two critical issues -- complexity and

incompleteness -- on knowledge representation systems. We review some

aspects of current representation research that offer a foundation for

coping with these problems, and finally suggest a way of integrating these

ideas into a powerful, practical knowledge representation paradigm.

Brooks, R., "A Comparison Among Four Packages for Knowledge-Based Systems,"

Proceedings of the International Conference on Cybernetics and Society , New York: IEEE, Oct. 1981.

Four tools for the construction of knowledge-based systems, EMYCIN, EXPERT,

AMIDS, and the blackboard option in AGE, are compared in regard to their

capabilities for representing three kinds of knowledge: judgmental or

decision-making knowledge, explanatory knowledge, and modeling knowledge.

Buchanan, B. G. , et al., "Constructing an Expert System," in F. Hayes-Roth,

D. A. Waterman, and D. B. Lenat (Eds.), Building Expert Systems , Reading,

MA: Addi son-Wesley Publishing Company, 1983.

Bylander, T., S. Mittal, and B. Chandrasekaran, "CRSL: A Language for Expert

Systems for Diagnosis," Computers and Mathematics , Volume 11, No. 5, 1985.

Charniak, E., C. K. Riesbeck, and D. V. McDermott, Artificial Intelligence

Programmi ng , Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers, 1980.

Clark, K. L., and F. G. McCabe, "PROLOG: A Language for Implementing Expert

Systems," in J. E. Hayes, D. Michie, and Y. H. Pao (Eds.), Machi ne

Intelligence 10 , Chichester, England: Horwood, 1982.

Craig, J. N., "A Tool for Building Expert Systems," Proceedings of the First

Symposium on the Theory and Application of Expert Systems in Emergency

Management Operations , Special Publication 717, Gaithersburg, MD: National

Bureau of Standards, 1986.

TIMM is a flexible and practical system for developing and using expert

systems. TIMM allows experts to interact directly with the program in

constructing knowledge bases by automating the task of knowledge

acquisition. TIMM interviews the expert to define the domain, poses

situations for the expert to consider, induces general rules from specific

cases, and can include expertise from multiple experts. Rapid prototyping

and iterative development of expert systems are supported by TIMM. The

basic TIMM functions can be viewed as a toolbox and each of the tools can be

used to perform a task during system development. The ability to modify and

refine decision domains and rules allows basic systems to be constructed

quickly. By linking multiple expert systems together, both structure and

hierarchy can be improved on the overall system design. TIMM is written in

Fortran and runs in standard computing environments ranging from IBM PC

class computers to large mainframes. No special programming is required to

input expert systems using TIMM. Thus, the critical area of knowledge

acquisition can be addressed directly.

69
I

Duff in, P. and S. F. Lello, "MASES - A PROLOG Expert System," Information Age,

Volume 6, No. 4, 1984.

The development of an expert system using PROLOG is described. The system,

Microcomputer Advice and Selection Expert System (MASES), was intended to

provide advice on the selection of microcomputers. However, unforeseen

limitations of the microcomputer environment in which MASES was developed

prevented achievement of the initial aim of a live, usable system, but a small system, capable of exhibiting some typical features of expert systems,

was devised for use in demonstrations. The construction demonstrates how

additions to or deletions from the knowledge base can be made without

consequent amendment to the set of operating rules. The working model is

limited to three microcomputers (Rainbow, Torch and Videcom) and contains 29

rules and 65 facts about printers, interfaces, software and protocols on four linked knowledge bases. It enables users' requirements to be matched

against the facilities available on the three microcomputers.

Ennis, S. P., "Expert Systems: A User's Perspective of Some Current Tools,"

Proceedings of the National Confere nce on Artificial Intelligence , AAA I - 8 2

,

American Association for Artificial Intelligence, 1982.

The purpose of this paper is to report on one user's experience with several

of the software tools for building an expert system. To the best of the

author's knowledge, this is the first time a number of different expert

system building tools have been applied to a single problem by a single

analyst.

Erman, L. D. and V. R. Lesser, "Hearsay-II: Tutorial Introduction and

Retrospective View," Report No.: CMU-CS-78-117 ; AFOSR-TR-78-1246, Department

of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, May 1978.

The Hearsay-II system, developed at CMU as part of the five-year ARPA

speech-understanding project, was successfully demonstrated at the end of

that project in September 1976. This report reprints two Hearsay II papers

which describe and discuss that version of the system: 'The Hearsay-II

System: A Tutorial', and 'A Retrospective View of the Hearsay-II

Architecture'. The first paper presents a short introduction to the general

Hearsay-II structure and describes the September 1976 configuration of

knowledge-sources; it includes a detailed description of an utterance being

recognized. The second paper discusses the general Hearsay-II architecture

and some of the crucial problems encountered in applying that architecture

to the problem of speech understanding.

Erman, L. D., P. E. London, and S. F. Fickas, "The Design and an Example Use of

HEARSAY-II I," Pr oceedings of the Sevent h IJCAI, American Association for

Artificial Intelligence, 1981.

HEARSAY-II I provides a framework for constructing knowledge-based expert

systems. While HEARSAY-III makes no commitment to any particular applicable

facilities. These include representation primitives and an interpreter for

large-grained, flexible schedulable production rules called knowledge

sources. A detailed overview of the motivations behind HEARSAY-III and the

facilities it provides are presented. Finally, an application of

HEARSAY-III is described.

70
Fahlham, S. E .

, NETL: A System for Repre se nting and Using Re a_l_-_Wor Id Knowledge,

Cambridge, MA: MIT Press, 1979.

Fain, J., et al., "The ROSIE Language Reference Manual," Report No.:

RAND/N-1647-ARPA, RAND Corp., Santa Monica, CA, Dec. 1981.

ROSIE is a programming system designed to support the development of expert

systems and other heuristic programming applications. ROSIE offers great

promise because it combines many modern capabilities within a single system,

including English as a programming language; knowledge representation

capabilities; a built-in relational databases system; flexible string

matching and communication capabilities; highly modular programming

structures; a file package and interactive programming environment. This

note describes in detail the features of the ROSIE environment, and the

syntax and semantics of the ROSIE language. The Reference Manual should be

useful to readers who intend to program in ROSIE or who are interested in

the impl ementati onal details of the ROSIE system.

Fain, J., et al., "Programming in ROSIE: An Introduction by Means of Examples,"

Report No.: RAND/N-1646-ARPA, Rand Corp., Santa Monica, CA, Feb. 1982.

ROSIE is a programming system designed to support the development of expert

systems and other heuristic programming applications. ROSIE offers great

promise because it combines many modern capabilities within a single system,

including English as a programming language; knowledge representation

capabilities; a built-in relational database system; flexible string

matching and communication capabilities; highly modular programming

structures; a file package and interactive programming environment. This

note presents a number of examples of ROSIE programs in such diverse areas

as legal decision making, war gaming, and detecting hazardous chemical

spills. The note should be useful to readers who intend to program in

ROSIE.

Feigenbaum, E. A., "Knowledge Engineering: The Applied Side," in J. E. Hayes and

D. Michie (Eds.), Intel 1 i gent Systems: The Unprecedented Op port unity , Chichester, England: Ellis Horwood Limited, 1983.

This paper discusses the applied artificial intelligence work that is

sometimes called knowledge engineering. The work is based on computer

programs which do symbolic manipulations and symbolic inference, not

calculation. The programs discussed do essentially no numerical

calculation. They discover qualitative 1 i nes-of -reasoni ng leading to

solutions to problems stated symbolically.

Georgeff, M., and U. Bonollo, "Procedural Expert Systems," Proc e edings of the

Eighth IJCAI , American Association for Artificial Intelligence, 1983.

Glasgow, J., and R. Browse, "Programming Languages for Artificial Intelligence,"

Computers and Mathematics, Volume 11, No. 5, 1985.

71
Greiner, R. , "RLL-1: A Representation Language Language," Report No.: HPP-80-9,

Department of Computer Science, Stanford University, CA, Oct. 1980.

The field of AI is strewn with knowledge representation languages. The

language designer typically designs that language with one particular

application domain in mind; as subsequent types of applications are tried,

what had originally been useful features are found to be undersirable

limitations, and the language is overhauled or scrapped. One remedy to this

bleak cycle might be to construct a representation language whose domain is

the field of representational languages itself. Toward this end, the

authors have designed and implemented RLL-1, a frame-based Representation

Language Language. The components of representation languages in general

(such as slots and inheritance mechanisms) and of RLL-1 itself, in

particular, are encoded decl arati vely as frames. By modifying these frames,

the user can change the semantics of RLL-l's components, and significantly

alter the overall character of the RLL-1 environment.

Greiner, R., and D. B. Lenat, "RLL-1: A Representation Language Language,

Supplement, Details of RLL-1," Report No.: HPP-80-23, Department of Computer

Science, Stanford University, CA, Oct. 1980.

This paper includes many implementation level details about the RLL-1

system, described in a companion paper, RLL-1: A Representation Language

Language. The contents are as follows: Special Units, Naming conventions,

Legend, Actual Units, Index of Units, Environment, Top Level Functions,

Functions Needed to Bootstrap RLL-1, Convenience Functions, Advised

Functions, and Global Variables.

Gyanfas, F., and M. Popper, "CODEX: Prototypes Driven Backward and Forward

Chaining Computer-Based Diagnostic Expert System," Proceedings of the

Seventh European Meeting on Cybernetics and Systems Research 2 , Amsterdam,

Netherlands: North-Holland", 1984.

Introduces a medical aid computer-based diagnostic expert system, CODEX. As

it is an empty expert systems requiring an attached knowledge base, its

use is not limited to medicine only. The main principles of the system's

knowledge base design and the system performance are described. Some

implementation details and information on application domains are given.

Hadley, R. F., "SHADOW: A Natural Language Query Analyzer," Computers and

Mathematics , Volume 11, No. 5, 1985.

Harrison, N., "Knowledge Base Builders," Systems International , Volume 12, No. 8,

Aug. 1984.

72
Hayes-Roth, F., et al .

, "Rationale and Motivation for ROSIE," Report No.:

RAND/N-1648-ARPA, RAND Corp., Santa Monica, CA, Nov. 1981.

ROSIE (Rule-Oriented System for Implementing Expertise) is a programming

language and programming system for artificial intelligence (AI)

applications. The ROSIE language is a stylized version of English. The

primary design goal for the language was to achieve exceptional program

readability; a second goal was to support the development of significant

applications. ROSIE allows the programmer to describe complex relationships

simply and to manipulate them symbolically and deductively. In addition, it

supports network communications and patterned reading and writing to other

systems. It also provides for interactive, compiled, and interpreted

computing, with a variety of debugging and programming tools. ROSIE

encompasses many of the capabilities of conventional programming languages.

It is a general -purpose language offering a variety of typical data types

and control constructs found in most high-level languages, together with a few found only in AI languages. Among the features included are: rulesets

that generate sets, predicates that test propositions, propositional data

types, and some limited forms of deduction. Using ROSIE, an AI programmer

can think concretely about the problem domain and translate ideas into a program using substantially the same vocabulary that arises in the English

(non-computational) formulation of the model.

Intellicorp, "The Knowledge Engineering Environment," Knowledge Systems Division,

Mountain View, CA, 1984.

Johnson, W. L., and E. Soloway, "Prost: Knowledge-Based Program Understanding,"

Proceedings of the Seventh International Conference of Software Engineering , New York: IEEE, Mar. 1984.

A program called Proust is described which does on-line analysis and

understanding of PASCAL programs written by novice programmers. Proust

takes as input a program and a nonal gorithini c description of the program

requirements, and finds the most likely mapping between the requirements and

the code. This mapping is in essence a reconstruction of the design and

implementation steps that the programmer went through in writing the

program. A knowledge base of programming plans and strategies, together

with common bugs associated with them, is used in constructing this mapping.

Bugs are discovered in the process of relating plans to the code: Proust

can therefore give deep explanations of program bugs by relating the buggy

implementation steps that the programmer went through in writing the

program. A knowledge base of programming plans and strategies, together

with common bugs associated with them, is used in constructing this mapping.

Kinnucan, P., "Software Tools Speed Expert System Development," High Technology , Mar. 1985.

73
Konopasek, M. , and S. Jayaraman, "Expert Systems for Personal Computers - The TK!

Solver Approach," Byte , Volume 9, No. 5, May 1984.

Although some question the possiblity of implementing expert systems on today's personal computers, the authors of this article wish to present a counter-example. This article shows that many characteristics of expert

systems are present in TK! Solver and describes a framework for

building a variety of expert systems with quantifiable knowledge bases. TK!

Solver is aimed at realizing many concepts expounded in Al research,

human-computer interface design, and human problem solving. It has no built-in knowledge of any particular discipline, but it provides a framework

to make it easier for the user with such knowledge to construct expert

systems. The knowledge engineer - the bottleneck - is eliminated. TK!

Solver was designed to be an expert system primarily in the area of

numerical problem solving. As such it (1) passes entered algebraic

equations and generates a list of variables, (2) solves sets of equations

using the consecutive-substitution procedure, (3) solves sets of

simultaneous algebraic equations by a modified Newton-Raphson iterative

procedure when the consecutive-substitution procedure fails, (4) searches

through tables of data and evaluates either unknown functions, values or arguments when required in the process of (2) or (3), (5) performs unit

conversions, (6) detects inconsistencies in problem formulation and domain

errors, (7) generates a series of solutions for lists of input data and

outputs results tabular and graphic forms.

Kukich, K. , "Design of a Knowledge-Based Report Generator," Proceedi ngs of the

Twenty-first Annual Meeting of the Association for Computational

Linguistics , Menlo Park, CA: Association of Computational Linguistics, June

1983.

Leal, A., "Evaluating the Effectiveness of Military Support Systems: Theoretical

Foundations, Expert System Design, and Experimental Plan (Research note),"

Report No.: ISC-345-3; ARI-RN-83-18, Integrated Sciences Corp., Santa

Monica, CA, Sept. 1982.

The main objective of this program is to construct a flexible testbed for

the evaluation of the effectiveness of computer-based expert systems in

military training and planning. The technical approach consists of

simulating the characteristics of expert systems in a game-like environment.

Such characteristics include friendly system user interaction, system

explanations of rationale about decision recommendations, an ability to make

relevant suggestions and comments about situation assessments and about

plans proposed by the user, and the use of high-level strategic concepts and

terminology. The required software for such a program includes a game

environment simulator called' the Scenario Generator, a simulated expert

system for the game called the Expert Aid, an Optimal ity Algorithm for

computing the best decisions in any situation, and an Evaluation Module for

recording execution histories and performance parameters. The expert system

will monitor the progress of the game and can be interrogated as the user

sees fit. A facility will also be provided for evaluating the user's

performance under different modes of consultation with the expert system.

74
Lenat, D. B., "Computer Software for Intelligent Systems," Scienti fic Amer ican,

Sept. 1984.

McDermott, J., "Rl: An Expert in the Computer Systems Domain," Proceedings of the

National Conference on Artificial Intelligence , AAA I -80, American

Association for Artificial Intelligence, 1980.

McDermott, J., "Rl: The Formative Years," AI Magazine , Vol. 2, 1981.

McDermott, J., "Building Expert Systems," in W. Reitman (Ed.), Artificial

Intelligence Applications for Business , Norwood, NJ: Ablex

Publishing Corporation, 1984.

Though only a handful of expert systems have as yet found a home in

industry, patterns in their development histories are beginning to emerge.

This chapter describes four stages that developers of expert systems

typically pass throuoh and then exemplifies these stages with three case

studies.

McKeown, Jr., D. M. , and J. McDermott, "Toward Expert Systems for Photo

Interpretation," Proceedings of Trends and Applications 1983 on Automating

Intelligent Behavior: Applications and Frontier s, Silver Spring, MD: IEEE

Computer Society Press, May 1983.

McWhite, P., "TIMM: A Fortran-Based Expert Systems Applications Generator,"

General Research Corporation, McLean, VA, Feb. 1985.

This paper presents TIMM capabilities as used in the development of an actual expert system. Examples demonstrate functions which recommend new

rules to make the knowledge base more complete, check for consistency, and

generalize knowledge. Notably, the generalize function helps find the key

factors affecting a decision. TIMM does not require special compilers,

computers, programmers, or personnel. It can be used immediately on most

popular computers and operated by experts with minimal training. As a result, it offers a cost-effective alternative to other methodologies for

building and implementing expert systems. TIMM supports a pattern directed

inference scheme. TIMM can define, modify, exercise, and link expert

systems. It accepts uncertain data, makes decisions in uncertain

situations, and provides a reliability measure for each decision. No

computer programming is required and most of the knowledge engineering

function is automated so that subject matter experts can directly develop

knowledge bases. Advantages over other methods of developing expert

systems, especially those using LISP, include transportability, reduced

memory requirements, and rapid execution.

75
Merry, M. , "APEX 3: An Expert System Shell for Fault Diagnosis," GEC Journal

Research Incorporated Marconi Review , Volume 1, No. 1, 1983.

An expert system is a computer program designed to simulate the behavior of

a human expert in a given field. It consists of a knowledge base of expert

knowledge about the field, and an inference engine, to make deductions from

this knowledge. An expert system shell is an empty expert system; an inference engine requiring a knowledge base to make an actual expert system,

but able to be used with several different knowledge bases to make several

different expert systems. APEX 3 is an expert system shell for building

fault diagnosis expert systems. APEX 3 is described in detail, paying

particular attention to its control strategies, which are designed to enable

the system to make diagnoses by asking the user as few questions as possible. The author describes the commands available to the user, and

gives some notes on the implementation of APEX 3.

Mizoguchi, F., "A Software Environment for Developing Knowledge Base Systems," in

T. Kitagawa (Ed.), Computer Science and Technologies , Amsterdam,

Netherlands: North-Holland, 1982.

This paper describes a software environment for developing knowledgebased systems. The system called Multi -Layered Software Environment (MLSE)

is proposed for providing a designer of an expert system with wide varieties

of design alternatives in software components which derive from artificial

intelligence (AI) technolgoy. The MLSE is a collection of module

packages for building the tools for knowledge-based systems. That is, by

integrating the AI Technology into the MLSE scheme, the author has

emphasized the layered approach to build the software environment as the

basis for developing knowledge-based systems. To illustrate the strategy of

the MLSE scheme the three case studies for designing production systems are

described. Based upon these case studies, a new production system called

ADIPS was developed which was a product of the MLSE scheme. The MLSE has

been implemented in LISP F-3 on M200 of HITAC, IBM 3031 and Inter-LISP of

DEC-20.

Mizoguchi, F., "PROLOG Based Expert System," New Generation Computers, Volume 1,

No. 1, 1983.

A PROLOG based expert system called APLICOT is described. Its basic design

framework is the same as used for other languages implementing expert

systems. This utilization of the same design framework permits a comparison

to be made of system performance obtained within the same domain of

knowledge. The domain chosen involves a reactor's fault diagnostic system

consisting of 76 production rules. The results of this comparison show the

overall performance of APLICOT to be of the same level as that of EXPERT,

EMYCIN and ADIPS, which were developed in the past utilizing different

programming languages. Although its code size is ten times smaller than

that of LISP or FORTRAN based expert systems, APLICOT's backward and forward

reasoning system gives it the same level of system performance and flexible

inference strategy as these other systems. It thus demonstrates the

potential software productivity of PROLOG-based expert systems.

76
Morris, P. A., "An Axiomatic Approach to Expert Resolution," Management Science,

Vol. 29, No. 1, Jan. 1983.

This paper addresses the problem of resolving the differing probability

judgments of a group of experts. Such judgments may be probability

assignments if the uncertainty of interest is on the outcome of an event

such as the occurrence of rain or the winner of an election. Or, the

judgments may be probability densities or distribution functions if the

uncertainty is on a continuous factor, such as the length of the Golden Gate

Bridge or the market share of a new product. The axiomatic approach

proposed here provides a unified Bayesian theory for addressing both types

of problems, and provides a framework for understanding why some common

approaches to the problem are inadequate.

Naylor, C, "ES/P Advisor," Practical Computer , Vol. 7, No. 10, Oct. 1984.

The author reviews ES/P Advisor, an expert system which enables users to

produce a knowledge base, which can then be run with a standard inference

engine. Its prime market is expected to be firms who want to automate

standard procedures and regulations for use by unskilled staff. ES/P

Advisor is available for machines running CP/M-86, MS-DOS and PC-DOS.

Neiman, D., "Graphical Animation from Knowledge," Proceedings of the Nation al

Conference on Artificial Intelligence , AAAI-82, American Association for

Artificial Intelligence, 1982.

Newell, A., "HARPY, Production Systems and Human Cognition," Report No.:

CMU-CS-78-140, Computer Science Department, Carnegie-Mellon University,

1978.

Oliveira, E., "Developing Expert Systems Builders in Logic Programming," New

Generation Compute rs, Vol. 2, No. 2, 1984.

The author develops a set of kits to build expert systems using PROLOG. Two

principal modules, a knowledge base acquisition and consultation subsystems

are presented. Several knowledge representation structures and mixed

inference mechanisms are proposed for the sake of system efficiency.

Finally, some explanation capabilities derived accordingly with used

inference methods are also implemented and presented.

Parker, R. E., and S. J. Kiselewich, "The Modeling of Human Cognitive Decision

Processes in The Intelligent Machine Model (TIMM)," Proceedings of the 1984

American Control Co nference , Vol. 1, New York: IEEE, June 1984.

The Intelligent Machine Model (TIMM) is a software package that enables a user to build an expert system, that is, a system which is capable of

providing expert advice in some well-defined domain of expertise. To

77
accomplish this, TIMM attempts to represent some of the analog character of

the real-world domain, in addition to a standard rule base. This extra

information defines a metric over the rule base. This metric allows TIMM to

model some distinctly human capabilities. TIMM is able to reach decisions

for new, uncertain, and incompletely defined situations. TIMM is able to

examine its own rule base and suggest new rules that seem appropriate for

the domain. These are important capabilities for real-world expert

systems.

People, Jr., H. E., "Knowledge-Based Expert Systems: The Buy or Build Decision,"

in W. Reitman (Ed.), Artificial Intelligence Applications for Business , Norwood, NJ: Ablex Publishing Corporation, 1984.

Reboh, R., "Knowledge Engineering Techniques and Tools in the PROSPECTOR

Environment," Technical Report 8172, SRI International, Menlo Park, CA,

June 1981.

Rich, C, "Knowledge Representation Languages and Predicate Calculus: How to Have

Your Cake and Eat it Too," Proceedings of the National Conference o n Artificial Intelligence , AAAI-82, American Association for Artificial

Intelligence, 1982.

This paper attempts to resolve some of the controversy between advocates of

predicate calculus and users of other knowledge representation languages by

demonstrating that it is possible to have the key features of both in a hybrid system. An example is given of a recently implemented hybrid system

in which a specialized planning language co-exists with its translation into

predicate calculus. In this system, various kinds of reasoning required for

a program understanding task are implemented at either the predicate

calculus level or the planning language level, depending on which is more

natural

.

Richer, M. H., "Evaluating the Existing Tools for Developing Knowledge-Based

Systems," Report No. KSL85-19, Stanford Knowledge Systems Laboratory,

Stanford University, CA, May 1985.

The purpose of this paper is two-fold: (1) to examine the criteria for

evaluating software tools for developing KB systems, and (2) to describe

several existing KB system tools. The reader should take note that the

information gathered here is necessarily limited to a large extent by what

is publicly available. A more complete evaluation must include actual use

of each system, and this was not possible at the time this paper was

prepared. Therefore, no attempt is made to present a complete evaluation of

any one system, and strong comparisons about two or more systems are

avoided. Because this paper is not meant to be tutorial in nature, no attempt is made to systematically explain terms and concepts that are

commonly used in the artificial intelligence community.

78
Sheil, B., "The Artificial Intelligence Tool Box," in W. Reitman (Ed.),

Artificial Intelligence Applications for Business , Norwood, NJ:

Ablex Publishing Corporation, 1984.

Artificial intelligence is often claimed to be applicable to a variety of

commercial applications. The author argues that a significant part of that

applicability derives, not from the AI functionality per se, but from the

underlying software technology. This software technology is examined, its

genesis in the nature of AI programming is described, and the ways in which

it differs from conventional software engineering are explained. The

effectiveness of the AI programming technology for conventional application

development first became clear when it was used to construct the information

management environments in which the first AI applications were packaged.

Building such environments is known to be extremely difficult using

conventional software technology. But, in many ways, AI programming

technology turns out to be ideally suited to this kind of development. The

ability to build highly customized information management systems is itself

an application opportunity of enormous leverage. Thus, in the short run at

least, the application development power of AI's software technology may

turn out to be just as important as the AI techniques for which it is

nominally just a carrier.

Stefik, M. , et al., "The Organization of Expert Systems: A Prescriptive

Tutorial," Xerox Palo Alto Research Centers, Palo Alto, CA, 1982.

Problems in reasoning are diverse. Solving them requires substantial and

varied capabilities of problem solvers. This tutorial is about the design

of expert systems, that is, systems for solving problems that require

substantial expertise. It focuses on principles for matching the

organization of an expert system to the difficulties posed by its problems.

The tutorial begins with a brief review of standard topics from artificial

intelligence, such as the use of the predicate calculus and search

techniques. It also discusses some newer topics such as the use of

abstractions, assumptions, dependency information and meta-level cognition.

These topics are essential for understanding current work on expert systems.

The main section of the tutorial is a discussion of organizational

prescriptions for problem solvers. It begins with a restricted class of

problems that admits a very simple organization. This organization is

appropriate when the input data and knowledge are static and reliable and

the solution space is small enough to search exhaustively. Few real world

problems satisfy all of these requirements. The requirements are then

relaxed, one at a time, yielding ten case studies of organizational

prescriptions. The first cases describe techniques for dealing with

unreliable data and time-varying data. Other cases illustrate techniques

for creating abstract solution spaces and using multiple lines for

reasoning. The prescriptions are compared for their coverage and

illustrated by examples from recent expert systems.

79
Stefik, M. , et al., "Basic Concepts for Building Expert Systems," in

F. Hayes-Roth, D. A. Waterman, and D. B. Lenat (Eds.), Building Expert

Sy stems , Reading, MA: Addi son-Wesley Publishing Company, 1983.

The authors of this chapter provide, in a summary form, the concepts

involved in the general field of artificial intelligence, and several ideas

that are central to knowledge engineering. Knowledge typically derives its

value from its potential to pare down to manageable size problems that

appear impossibly complex. This power comes from its capacity to shrink

large search spaces. Search techniques that cannot apply knowledge will not

succeed at difficult tasks. A description of many new types of search

structures and techniques utilizing knowledge that have evolved is given.

And finally, the usefulness of these in a large number of potential

applications that make use of similar approaches is provided.

Stefik, M. , et al., "The Architecture of Expert Systems," in F. Hayes-Roth, D.

A. Waterman, and D. B. Lenat (Eds.), Building Expert Systems , Reading,

MA: Addi son-Wesl ey Publishing Company, 1983.

Surya, "Expert-Ease," Personal Computer World , Volume 7, No. 6, June 1984.

Expert-Ease is an expert systems generator which enables people with little

or no programming experience to create and interrogate expert models. The

concept of expert systems is discussed and Expert-Ease is reviewed.

van Melle, W., "A Domain-Independent System that Aids in Constructing

Knowledge-Based Consultation Programs," doctoral thesis, Report No.:

STAN-CS-80-820; HPP-80-22, Department of Computer Science, Stanford

University, CA, June 1980.

This thesis demonstrates an effective domain-independent system, called

EMYCIN, for constructing one class of expert computer programs: rule-based

consultants. Such a consultant uses knowledge specific to a problem domain

to provide consultative advice to a client. Domain knowledge is represented

in EMYCIN primarily as production rules, which are applied by a goal -di rected backward-chaining control structure. Rules and consultation

data may have associated measures of certainty, and incomplete data are

allowed. The system includes an explanation facility that can display the

line of reasoning followed by the consultation program or answer questions

from the client about the contents of its knowledge base. Other built-in

human-engineering features allow the system architect to produce, with a minimum of effort, a consultation program that is pleasing in appearance to

the client. To aid the system designer in producing a knowledge base for a domain quickly and accurately, EMYCIN provides a terse, stylized, but easily

understood language for writing rules; performs extensive checks to catch

common user errors, such as misspellings; and handles all necessary

book-keeping chores. To improve efficiency in a running consultation

program, EMYCIN provides a rule compiler that transforms the system's

80
production rules into a decision tree, eliminating the redundant computation

inherent in a rule interpreter. It then compiles the resulting tree into

machine code. The program can thereby use an efficient deductive mechanism

for running the actual consultation, while the flexible rule format remains

available for acquisition, explanation, and debugging.

Waltz, D. L., "New Tools for Emergency Management," Proceedings of the First

Symposium on the Theory and App l ication of Expert Systems in Emergency

Management Operations , Special Publication 717, Gaithersburg, MD: National

Bureau of Standards, 1986.

Waterman, D. A., "Exemplary Programming in RITA," in D. Waterman and

F. Hayes-Roth (Eds.), Pattern-Directed Inference Systems , New York:

Academic Press, 1978.

Waterman, D. A., and F. Hayes-Roth, "An Investigation of Tools for Building

Expert Systems," in F. Hayes-Roth, D. A. Waterman, and D. B. Lenat (Eds.),

Building Expert Systems , Reading, MA: Addi son-Wesley Publishing Company,

1983.

This report describes an investigation into the comparative merits of eight

very high-level programming languages designed for building expert systems

(i.e., programs that embody the expertise of specialists or scholars in some

problem area): E MYCIN, KAS, EXPERT, 0PS5, ROSIE, RLL, HEARSAY-1 1 1 , and AGE.

In the investigation, all eight languages were applied to the same

environmental crisis-management problem which involved the creation of an

on-line assistant to aid in locating and containing an oil or hazardous

chemical spill at the Oak Ridge National Laboratory (ORNL). Such an

assistant must be able to characterize the spill and its hazards, warn

passersby, contain the spill material, locate the spill source to stop the

leak, and notify the appropriate authorities. Since the ORNL complex

contains over 200 buildings connected by a complicated drainage system that

collects all spills, merely locating the spill source is in itself a formidable task.

Waterman, D. A., et al., "Design of a Rule-Oriented System for Implementing

Expertise," Report No.: RAND/N-1158-l-ARPA, RAND Corp., Santa Monica, CA,

May 1979.

This note describes the preliminary design of a Rule-Oriented System for

Implementing Expertise (ROSIE). This system will serve as a tool for model

builders seeking to apply expert knowledge to the analysis of problems and

the evaluation of solutions in complex domains, especially domains for which

useful analytic models are unavailable. Models built within the ROSIE

system constitute simulations in the application domain. Control in these

models is data-directed. That is, actions are specified by sets of rules

81
having the basic form: 'If CONDITIONS then ACTIONS. ' The actions are

executed when data matching the conditions appear in the model's data base.

Major features of ROSIE include: hierarchic structures on data elements and

rules, user selection of rule-invocation strategies, user control of rule

iterations, user control of rule and data activation, and sophisticated user

support tools.

Weiss, S. M.

Models,

Intelligence, 15797

, S. M. , and C. Kulikowski, "EXPERT: A System for Developing Consultation

Models," Proceedings of the Sixth IJCAI, American Association for Artificial

t_j._-i-i.-~ i 7Y-> n

EXPERT is a system for designing and building models for consultation. An

EXPERT model consists of hypotheses (which can be structured into causal and

taxonomic networks); findings or observations; and decision rules for

logically relating these components. A relatively simple language for

describing models is employed. Logical and probabilistic rules are

restricted to particular types which implicitly order the tasks performed by

the control strategies: classifications, question selection, and

explanations. Explicit representations of decision rules are emphasized, as opposed to suboptimal scoring functions. This results in more easily

predictable and correctable performance for a model. The system is

currently being used to develop consultation models in domains such as rheumatology, ophthalmology, and endocrinology.

Weiss, S. M., and C. Kulikowski, A Practical Guide to Designing Expert Systems , Totowa, NJ: Rowman & Allenheld, 1984.

Wescourt, K. T., and P. W. Thorndyke, "Alternative Knowledge Acquisition

Interface Structures," Perceptronics, Inc., Knowledge Systems Branch, Menlo

Park, CA, Dec. 1983.

This research developed a design concept for an interactive system to

acquire domain knowledge from a training expert. Such a system would

facilitate the development of knowledge-based instructional systems by

directly eliciting and encoding from domain experts knowledge needed to

deliver instruction. An analysis of the process by which knowledge-based

systems are constructed indicates that the generality of a knowledge

acquisition system must be limited by domain characteristics and by the

architecture of the system it serves, and the non-sequential, interacting

activities during system development constrain the potential role of

automated knowledge acquisition aids. A feasible concept for knowledge

acquisition technology, building on prior research in artificial

intelligence, involves the notion of class-generic systems for a related set

of domains with a fixed architecture and training capabilities. This

concept is developed and discussed in the context of the proposed Navy

training systems for acquiring models of trainee performance during

learning, rules of behavior for an automated opponent in a tactics trainer,

and a knowledge base of facts to be subsequently presented to trainees for

memori zati on.

82
Williams, C, "Software Tool Packages the Expertise Needed to Build Expert

Systems," Electronic Design , Volume 32, No. 16, Aug. 1984.

It is claimed that the Automated Reasoning Tool, or ART, is the first

comprehensive software package to turn engineers into builders of full-scale

expert systems. As such, it contains an inference engine, which resembles

the operating system of conventional systems; a knowledge base, which

replaces a data base; an editor, which helps create the knowledge base; and

a monitor, which lets users visually follow the tool's reasoning steps.

Unlike other commercial expert system development packages, ART uses more

than one reasoning mechanism. Its language can handle both transformational

and logic programming and can pursue both forward and backward chaining. In

addition, its knowledge base can be organized by schema and propositions,

and through viewpoint mechanisms, the inference engine can consider several

hypothetical solutions to a single problem.

Wong, W. G. , "LISP for CP/M," Microsy stems, August 1983.

Wong, W. G. , "PROLOG: The New AI Language Being Used on 5th Generation Japanese

Computers Can Now be Run Under CP/M," Mi crosy stems , Jan. 1984.

PROLOG stands for PROgramming in LOGic. It is a logical programming

language that has roots in mathematics and logic. Like LISP, it was first

used for artificial intelligence research but has since found wider

acceptance. In fact, it is being used as the programming language for the

Japanese fifth-generation computer project as well as numerous domestic and

European research projects. This article presents some of the ideas behind

PROLOG, as well as the facilities and performance provided by mi cro-PROLOG.

Prolog is a programming language that differs tremendously from

conventional languages such as PASCAL and BASIC because PROLOG is a descriptive language. You tell PROLOG what has to be done, not how it is to

be done. This article presents some of the features of PROLOG, as well as a review of a CP/M-based PR0L0G--cal led mi cro-PR0L0G--avai 1 abl e from Logic

Programming Associates, Ltd.

83
6. USER INTERFACE CONSIDERATIONS

One of the distinct features of an expert system is its ability to explain its

actions and decisions to the user. This is a very desirable feature in an expert system, because, unlike a human expert's advice, machine generated

advice is seldom accepted without doubt and/or questioning; the system's

capacity to explain can help a user to discover when a system is being pushed

beyond the bounds of its domain of expertise; the interaction between the user

and an expert system is far from what might be called a dialogue, as it is

between the user and a human expert; and, since knowledge bases are built

incrementally, one is better able to debug the expert system by monitoring

what the system is doing.

Today's expert systems accomplish the task of explaining their actions and

lines of reasoning through one of the following techniques: (1) By tracing

backward from the conclusion in question to the rules producing it. These

rules (triggering the conclusion) are then reproduced for the user as an

explanation for the action(s) taken or the conclusion(s) drawn. (2) By using

previously prepared text (also called canned explanations), i.e., by

anticipating the questions and storing the answers as a natural language text.

(3) By using the computer code and traces of its execution, and producing

explanations directly from it. Each of the above techniques is deficient in

one way or another. The first method of reproducing the rules that generated

the conclusion can hardly be satisfactory to every user or in every situation.

With canned explanations, one would have to know in advance all the questions

and answers that might be asked by users of the expert system. In the case of

explanations produced from the program code, the quality of the explanations

is largely dependent on how the code is written.

6.1 DESIRED EXPLANATORY CAPABILITIES

Scott et al. [1977] list the following major goals that must be considered in

developing an explanation capability:

(a) The explanation capability must be able to answer all the questions about

its knowledge and actions. These include,

- how it made a certain decision

- how it used a piece of information

- what decision it made about some subproblem

- why it did not use a certain piece of information

- why it failed to make a certain decision

- why it required a certain piece of information

- why it did not require a certain piece of information

- how it will find out a certain piece of information (while the

consultation is in progress)

- what the system is currently doing (while the consultation is in

progress

)

(b) The user must be able to get an explanation that answers the question

completely and comprehensively.

(c) The explanation capability must be easy to use.

84
In addition to the above, consideration must also be given to the question of

how detailed the explanation should be, and to tailoring the explanation to

suit the needs of different users. There are two other areas of development

that promise to make expert systems more user friendly: natural language

interfaces and intelligent front ends.

6.2 TWO APPROACH ES FOR MAKING INTER F ACES MORE USER FRIENDLY

Use of natural language interface (NLI) is a step toward improving the

communication between the user and the expert system. These interfaces allow

a user to build and run an expert system by making use of a subset of a natural language like English. Obviously, the single most attractive aspect

of having a NLI is the ease of learning how to interact with the system.

There are, however, many factors that must be understood before implementing a NLI. First, NLI's are generally more costly to implement because natural

languages are less concise compared to the symbolic languages commonly used in

building expert systems. Second, they are more expensive to run because of

their longer execution times. Third, it is necessary to overcome the

ambiguity caused by the imprecise nature of sentences in natural languages.

Fourth, the type of users expected to interact with the expert system must be

considered; the sophisticated users may be unwilling to compromise with

capabilities of an expert system in order to be able to interact with a natural language. Finally, the NLI's can raise the expectations of users

regarding their capabilities by leading them to believe that these interfaces

are more than just conveniences to the users. For a more detailed discussion

on natural language interfaces see Rich L1984].

An intelligent front end (IFE) can be used to build models of user problems by

allowing the user to express their problems in familiar languages. The system

then translates the resultant problem statement into a language suitable for

the software package. The successful implementation of IFE's requires

development of techniques that will (a) aid in the knowledge representation

(i.e., communication between the user and the software package); (b) solve the

problem of synthesizing the software package instructions from the task

specification (given by the user); (c) translate the results produced by the

software package into the user's language; and (d) carry on a natural language

dialogue with the user. In fact, the IFE's are expert systems by themselves,

requiring various artificial intelligence tools for building them and

implementing them, see Bundy [1984] for further discussion.

85
REFERENCES

Anderson, J. R., B. C. Franklin, and B. J. Reiser, "Intelligent Tutoring

Systems," Science, Vol. 228, Apr. 1985.

Cognitive psychology, artificial intelligence, and computer technology

have advanced to the point where it is feasible to build computer systems

that are as effective as intelligent human tutors. Computer tutors based

on a set of pedagogical principles derived from the ACT (Advanced

Computer Tutoring) theory of cognition have been developed for teaching

students to do proofs in geometry and to write computer programs in the

language LISP.

Brown, J. S., and R. R. Burton, "A Paradigmatic Example of An Artificially

Intelligent Instructional System," International Journal of Man-Machine

Studies, Vol. 10, 1978. '

" "

' — -

This paper describes research directed at understanding and designing

artifically intelligent instructional systems that use their knowledge

bases and problem-solving expertise to aid the student in several ways.

A system is described that derives from the world of manipulatory

mathematics based on attribute blocks and examples are given of

interaction with it. The system can answer student questions and

evaluate his theories as well as critize his solution paths. It can also

form structural models of his reasoning strategies to identify his

fundamental misconceptions and determine when and how to provide

remediation, hints or further instruction.

Bundy, A., "Intelligent Front Ends," in M. A. Bramer (Ed.), Research and

Development in Expert Systems: Proceedings of the Fourth Technical

Conference of the British Computer Society Specialist Group on Expert

Systems , Cambridge, England: Cambridge University Press, 1984.

Carroll, J. M. , "How to Turn an Emergency Simulation Into a "Video Game" for

Training," Proceedings of the Conference on Computer Simul ation in

Emergency Planning , La Jolla, CA: Simulation Councils, Inc., JanT 1983.

Computer simulation can be used to train decision makers. The programs

should be interactive and are readily accepted by trainees if they

resemble video games. Sometimes the structure of the problem already

resides in a simulation program used for planning and resource

allocation. This paper tells how to convert a simulation program for

allocating fire-fighting resources into a microcomputer-based training

exercise for fire dispatchers.

Clancy, W. J., "Extensions to Rules for Explanation and Tutoring," in B. G.

Buchanan and E. H. Shortliffe (Eds.), Rule-Based Expert Systems: The

MYCIN Exp eriments of the Stanford Heuristic Programming P r oject , Reading,

MA: AddiVon-We sley Publishing Company, 198C

86
Clancey, W. J., "Tutoring Rules for Guiding a Case Method Dialogue,"

Internati onal Journal of Man-Machine Studies, Vol. 12, 1980.

The first version of an intelligent computer-aided instruction program

built on MYCIN-like expert systems has been implemented. This program,

named GUIDON, is a case method tutor in which the problem-solving and

tutorial dialogue capabilities are distinct. The expertise to be taught

is provided by a rule-based consultation program. The dialogue

capabilities constitute teaching expertise for helping a student solve a case. This paper describes the rule-based formalism used by MYCIN-like

programs and then argues that these programs are not sufficient in

themselves as teaching tools. We have chosen to develop a mixed-initiative tutor that plays an active role in choosing knowledge to

present to a student based on his competence and interests. It is argued

that is desirable to augment the domain expertise of MYCIN-like programs

with other levels of domain knowledge that help explain and organize the

domain rules. Finally, the author claims that it is desirable to

represent teaching expertise explicitly, using a flexible framework that

makes it possible to easily modify tutorial strategies and communicate

them to other researchers. The design of the GUIDON program is based on natural language studies of discourse in AI. In particular, the

framework used integrates domain expertise in tutorial dialogues via

explicit, modular tutoring rules that are controlled by a communication

model. This model is based on consideration of the student's knowledge

and interests, as well as the tutor's plans for the case session. This

paper discusses interesting examples of tutoring rules for guiding

discussion of a topic and responding to a student's hypothesis based on the evidence he has collected.

Cullingford, R. E., et al., "Automated Explanations as a Component of a Computer-Ai ded Design System," IEEE Transactions on Systems, Man, and

Cybernetics , SMC-12(2), 1982.

Davis, R., "Interactive Transfer of Expertise: Acquisition of New Inference

Rules," Artificial In telligence, Vol. 12, 1979.

Eden, C, "A System to Help People Think About Problems," Computer Weekly,

No. 876, Sept. 1983.

'

For the past five years, a group of academics in the school of management

at the University of Bath has been developing software for combining

expertise. Their focus has been on using computer software to help a team of decision makers work on complicated problems -- the sort of

problems that demand good use of the expertise of each member of the

team, and where that expertise is mostly based on experience rather than

expert knowledge. The author describes how users can argue with an expert system by adding conflicting points of view.

87
Fitter, M. , "Towards More 'Natural' Interactive Systems," Internati onal

Journal of Man-Machine Studies , Vol. 12, 1980.

It is argued that to obtain the maximum benefit from interactive computer

systems, principles of program and dialogue design are needed. It is

unlikely that natural languages such as English will provide a suitable

basis for designing a man-computer dialogue. The objective should be to

model the task domain in a way that will be comprehensible to the user

and to provide an explicit image of the underlying processes. Design

principles are discussed both for general purpose programming languages

and bespoke languages intended as tools for a specific purpose. It is

concluded that AI research may eventually provide intelligent tools with

the inferential powers necessary for a genuine dialogue, but that for the

time being it is better to make the underlying mechanisms and their

limitations as explicit as possible.

Gable, A., and C. V. Page, "The Use of Artificial Intelligence Techniques in

Computer-Assisted Instruction: An Overview," International Journ al of

Man-Machine Studie s, Vol. 12, 1980.

One of the major goals of research in artificial intelligence is the

representation of knowledge so that a computer can solve problems or communicate in a manner which exhibits common sense. Few programs for

computers, including those for education, possess behavior which

approaches any facet of the constellation of human skills and knowledge

which are imprecisely called common sense. However, the revolutionary

decline in hardware costs now makes it possible to consider economically

viable, sophisticated designs for computer-aided instruction systems

possessing some of the common sense attributes of a human tutor. In this

survey, we examine, in depth, techniques from aritifical intelligence

that can be used to endow a Computer-Ai ded Instruction system with

approximations to some of the desirable qualitities of a human tutor. We

consider both techniques which have been proved in prototype systems for

Computer-Ai ded Instruction and some techniques which were originally

developed for other purposes.

Gerring, P., E. H. Shortliffe, and W. Van Melle, "The Interviewer/Reasoner

Model: An Approach to Improving System Responsiveness in Interactive

AI Systems," AI Magazine , Vol. 3, No. 4, 1982.

Grishman, R., "Natural Language Processing," Jou r nal of the American Society

for Information Science , Vol. 35, No. 5, Sept. 1984.

Kidd, A. L., "Human Factors in Expert Systems," Proceedings of the Ergonomics

Society's Conference , London, England: Taylor and Francis, Mar. 1983.

The development of expert or intelligent knowledge-based systems is

raising some new and very important human factor issues in the field of

man-computer interaction. With the advent of thinking, reasoning systems,

the cognitive characteristics of the human mind must become a critical

design constraint in the production of such systems. This paper attempts

to identify the major research questions in this area.

88
Mark, W., "Natural Language Help in the Consul System," Proceedings of the

1982 AFIPS National Computer Conference , Vol. 51, Arlington, VA:

AFIPS Press, June 1982.

If one uses the model of asking an expert, it is fairly clear what users

want from a help system: a service that tells them how to do something

they want to do. But current help systems are not like this. They can

tell the users about system capabilities, but not in relation to what they

want to do. Most help systems are simply databases of on-line

documentation-system manuals, not system experts. Like system manuals,

using them to figure out how to do something or to figure out what went

wrong is a last resort--when no one else is around. Providing real expert

help requires reasoning in terms of models of what the user wants to do

and what the system can do. These models also make it possible to provide

facilities for natural language understanding and generation. Thus, users

can deal with the system in much the same way that they deal with a human

expert: by asking questions and receiving advice in English.

Mikulich, L. I., "Natural Language Dialog Systems: A Pragmatic Approach," in

J. E. Hayes, D. Michie and Y. H. Pao (Eds.), Machine Intelligence 10 , Chichester, England: Norwood, 1982.

An approach is described for the development of natural language

interfaces with data bases. Pragmatic-oriented analysis of utterences

made by casual users is a key concept of the approach. An example of

applying is considered using the DISPUT family of systems.

Miller, P. L., et al., "Teaching With 'Attending': A Practical Way to 'Debug'

an Expert Knowledge Base," AAMS I Congress 83 Proceedings , Bethesda,

MD: American Association of Medical Systems and Informatics, May 1983.

O'Shea, T., "Rule-Based Computer Tutors," in D. Michie (Ed.) Expert Systems

in the Micro-Electronic Age, Edinburgh, Scotland: Edinburgh University

PressrT9T9.

Two important components of any computer tutor are the tutorial strategy

and the student model. Production Rules (PRs) can usefully be employed in

the design and implementation of both these components. The use of PRs

forces careful analysis of the taxonomic aspects of computer tutor design.

PRs are modular, explicit, amenable to automatic manipulation and hence,

applicable to the construction of adaptive tutors. Their use provides a valuable link to work on the analysis of human information processing

strategies.

89
Pollack, M. E., J. Hirschberg, and B. Weber, "User Participation in the

Reasoning Process of Expert Systems," Proceedings of the National

Conferen ce on Artificial Intelligence , AAAI-82, American Association for

Artificial Intelligence, 1982.

The authors argue that expert sytems, if they are to satisfy the

legitimate needs of their users, must include dialogue capabilities as sophisticated as those proposed in current natural language research. In

particular, they must allow the user to direct the flow of the dialogue

and must take into account the user's goals and expectations both in

analyzing the user's statements and in providing appropriate responses.

Their claims are corroborated by an analysis of transcripts of a naturally

occurring expert system, a radio talk show in which callers ask an expert

for financial advice. The authors present data demonstrating that

user-expert dialogues are best viewed as a negotiation process, and

describe the exchanges that compose the dialogue in terms of the

motivations, goals, strategies, and moves of the participants.

Reboh, R., "Using a Matcher to Make an Expert Consultation System Behave

Intelligently," Proceedings of the National Conference on Artificial

Intel 1

i

gence, AAAI-80, American Association for Artificial Intelligence,

1980.

Rich, E., "Natural Language Interfaces," Computer , Vol. 17, No. 9,

Sept. 1984.

Richie, G. N., "ATLANTIS: A Disaster Simulated Exercise for Training

Purposes," Proceedings of the Conference on Computer Simulat ion in

Emergency Planning", La J o 1 la", California: The Society for Computer

Simulation, Jan. 1983.

This paper examines the process of identifying training needs and

methodology, and the selection and development of a computer-based

simulation exercise as a means of meeting these requirements. In doing

so, it seeks to identify guidelines which may be helpful to those involved

in similar projects. These guidelines relate to conception,

identification, and establishment of modeling parameters, project

management and development trials, and the integration of the simulation

exercise into a study program. The paper is based on the experience of

the author in a research project which examined the role and

responsibilities of the administration in maintaining life support systems

in disaster situations in developing countries.

Schachter-Radi g, M. J., "Prospects for the Use of Knowledge-Based Systems - Demands on the User," Proceedings of the COMPAS '84 , Berlin, Germany:

VDE-Verlag, Oct. 1984.

90
Scott, A. C, et al., "Explanation Capabilities of Knowledge-Based Production

Systems," American Journal of Computation al Ling u istics , Microfiche 62,

1977.

Shrager, J., and T. Finin, "An Expert System That Volunteers Advice,"

Procee dings of the National Conference on Artificial Intelligence , AAAI-82, American Association for Artificial Intelligence, 1982.

The system described in this paper essentially encodes the pattern

recognition knowledge of a consultant and applies these patterns to user

input. When inefficiencies are recognized, the automatic observer can

point the way to a more effective use of system capabilities by referring

the user to the help system or some other expert. Otherwise, it can

generate a help message of its own using the context of the particular

sequence to construct useful dialogue. This work represents an attempt to

extend the range of interactive advisor systems. User aids currently in

service help only users who cause errors which would invoke an error

recovery system or those who know how and when to ask for help. The

authors have provided a means by which the system can automatically tutor

intermediate level users who do not make trivial errors but who are not

using commands in an effective way. Therefore, it can bridge the gap

between the introductory user aids and more technically-oriented expert

advisors.

Sleeman, D and J. S. Brown (Eds.), Intelligent Tutoring Systems , London,

England: Academic Press, 1982.

Smith, R. G. , et al., "Declarative Task Description as a User-Interface

Structuring Mechanism," Computer , Vol. 17, No. 9, Sept. 1984.

Swartout, W. R., "Explaining and Justifying Expert Consulting Programs," in W.

J. Clancey and E. H. Shortliffe (Eds.), Readings in Medical Arti ficial

Intelligence: The First Decade , Reading, MA: Addi son-Wesl ey

Publ i shi ng Company, 1984.

Ueno, H., "An End-User-Oriented Language to Develop Knowledge-Based Expert

Systems," Proceedings of the COMPCQN 83 Fall , Silver Spring, MD:

Computer Society Press, Oct. 1983.

A description is given of the COMEX (Compact Knowledge-Based Expert

System) expert system language for application-domain users who want to

develop a knowledge-based expert system by themselves. The COMEX is being

used in several application domains such as medicine, education and

industry.

Wallis, J. W., and E. H. Shortliffe, "Customized Explanations Using Causal

Knowledge," in B. G. Buchanan and E. H. Shortliffe (Eds.), Rul e-Based

Expert Systems: The MYCIN Experiments of the Stanford Heuristic

Programming Project , Reading , MA: Addi son-Wesl ey Publishing

Company, 1984.

91
Waterman, D. A., "User-Oriented Systems for Capturing Expertise: A Rule-Based

Approach," in D. Michie (Ed.), Expert Systems in the Micro-Electronic Age , Edinburgh, Scotland: Edinburgh University Press, 1979.

User-oriented systems for capturing expertise are discussed and

illustrated within the context of RITA and ROSIE, two rule-based

systems developed for building intelligent interfaces and modeling expert

knowl edge.

Woolf, B., and D. D. McDonald, "Building a Computer Tutor: Design Issues,"

Computer , Vol. 17, No. 9, Sept. 1984.

In this article, the authors discuss how a deep understanding of a student

can be constructed in an artificial intelligence program and how this

understanding, coupled with a facility for language generation, can be

used to build a flexible machine tutor. An effective tutor must deal with

a fundamental problem of communication: to determine how messages are

received and understood, and to formulate appropriate answers. A human

tutor, therefore, takes the time to double-check and reviews a student's

knowledge to find out whether or not he understands what the tutor has

said. Compounding the situa + i*n is the fact that a student is generally

unaware of what he does not Know. This means that a tutor, more than a typical speaker, must verify that both parties know what information has

been covered, what is missing, and which communication might be

erroneous.

92
7. DEALING WITH UNCERTAINTY IN THE DESIGN, DEVELOPMENT AND USE OF EXPERT

SYSTEMS

7 . 1 IMPORTANCE OF DEALING WITH UNCERTAINTY IN EXPERT SY STEMS

Expert systems need to be designed to work with data and information that is

uncertain, incompatible, and incomplete. There are several reasons for this.

First, many types of information, some of it inexact, cannot be encoded

precisely in a machine usable form. Second, because of its subjective nature,

the expertise captured from human experts cannot always be faithfully

translated into actionable machine code. Third, users of expert systems are

often unable to give all the information and data required by the expert

system. Fourth, for many decisions, a combination of uncertain data and

information is common. Finally, the value of an expert system would be

considerably diminished if it always required complete and certain data.

7.2 TH E HANDLING OF UNCERTAINTY IN EXPERT SYSTEMS

Expert systems must be able to make acceptable decisions even when there is

uncertainty regarding the data and information, or even the expertise. Human

experts are capable of using a number of different techniques to deal with

uncertainty. In contrast, an expert system is tied to a single method for

handling uncertainty. There are four methods of dealing with uncertainty in

expert systems that have received varying degrees of acceptance. These methods

are based on the following theories: Bayesian probability theory, certainty

theory, possibility theory and fuzzy logic, and the Dempster-Shafer theory of

evidence. Among these, Bayesian probability theory and certainty theory are

the only ones that have been widely used. Although much information is

available on the theory and applicability of fuzzy logic, its usefulness has

not been fully exploited. The Dempster-Shafer theory has only recently

received attention.

Each of these methods differs in the type and amount of data or information

needed to use it. For example, assuming that the knowledge in an expert system

is represented by If -Then rules (as it is in rule-based expert systems), the

Bayesian model would require values for (a) prior probabilities (i.e.,

probabilities without any evidence either supporting or negating some

conclusion), and (b) conditional probabilities (i.e., probabilities associated

with conclusions being true, given some piece of information or evidence) for

all conclusions that can be considered by the system. If the same expert

system were to use the certainty theory, the worth of each piece of evidence

would be expressed as a "measure of increased belief" or a "measure of

increased disbelief" (values between 0 and 1) in a conclusion, depending on whether that piece of evidence increases or decreases the truth of a conclusion. In both instances, the measures would be subjectively assigned by

an expert in the problem domain, see Duda [1983] (Chapter 3) for further

discussion on types of data used by different models in dealing with

uncertainty. We next give a brief discussion of each of these four methods.

93
Bayesian Probability Theory

This theory enables the expert system to express its confidence in any

conclusion or decision made as a conditional probability statement of the

following form:

Given the specified evidence, C can be concluded with a probability of

0.8.

If the conclusion C had been certain (based on the specified evidence), a probability value of 1.0 would have been attached to it. Similarly, if the

evidence had led to concluding 'not C with certainty, a probability value of

zero would have been attached to the above statement. All other probability

values (falling between 0.0 and 1.0) attached to such conditional statements

give the degrees of uncertainty associated with them. Essentially, Bayes'

Theorem can be seen as a mechanism to calculate the probability of a conclusion, in light of specified evidence, from the a priori probability of

the conclusion and the conditional probabilities relating the observations to

the conclusions in which they are expected. Application of the Bayesian theory

involves the following:

(a) Each If-Then rule in the knowledge base is represented in the form of

a conditional probability statement (i.e., a probability value is attached

to every conclusion given a possible evidence).

(b) It is assumed that the portion of evidence (represented as a probability value) that does not support a conclusion, supports its

negati on.

(c) If no information is available regarding a needed piece of evidence,

the probability of a conclusion being true is equal to its prior

probabi 1 i ty

.

(d) The general Bayesian probability rule for computing conditional

probability is applied to combine the various pieces of evidence and for

assigning the conditional probability to the conclusion drawn.

(e) A check for agreement between conditional probabilities derived using

the Bayesian rule and those obtained from the expert, if he were to

independently assess the truth of a conclusion given the various pieces of

evidence, is carried out.

Although computationally simple, the usefulness of Bayesian approach is limited

by practical difficulties, principally by the lack of data adequate to estimate

accurately the prior and conditional probabilities. The conclusions under

consideration are often assumed to be mutually exclusive and exhaustive (i.e.,

only one of the several possible conclusions is entertained, given some set of

evidence). The set of observations is also assumed to have independent

94
elements, although in practice, Bayesian Theory does not require this. The

model does not generally allow for changes in observations (and hence

conclusions) over time. Revisions of prior and conditional probabilities to

achieve consistency between estimates of the expert and those calculated using

Bayesian rule can be quite time consuming. Finally, problems also arise when

two or more pieces of evidence independently point to the same conclusion, but

together rule out the conclusion. An example of this could be the situation

where El suggests C with weight Wl and E2 suggests C with weight W2, but the

presence of both El and E2 rules out C. One way in which such cases are

handled is by using an additional rule of the form El & E2 suggest C with

weight W. See Adams [1984] and Duda, Hart and Nilsson [1976] for more detailed

discussion on how to use Bayesian probability theory in dealing with

uncertai nty

.

Certainty Theory

Certainty theory, developed and first put to use by Shortliffe and Buchanan

[1975] in their MYCIN medical consultation system, is currently the most widely

applied methodology in dealing with uncertainty in expert systems. Central to

this theory is the concept of a certainty factor (CF), a number between -1 and

1 attached to every rule, representing the degree of belief that the rule

implies for its assertion (or the conclusion). The certainty factor itself is

the difference between "the measure of increased belief in conclusion C, given

evidence E" (MB), and "the measure of increased disbelief in conclusion C,

given the same evidence E" (MD). Both MB and MD are numbers between 0 and 1.

The definition of certainty factor brings out the important difference between

this approach and the Bayesian approach. The Bayesian approach specifies that

if we know the value of evidence E in favor of some conclusion C to be x (i.e.,

conditional probability of a conclusion C being true given evidence E), then

the value of the evidence E that does not support conclusion C is given by 1-x

(i.e., conditional probability of a conclusion not being true, given evidence

E). The certainty theory uses the notion that one piece of evidence cannot

both favor and disfavor a single assertion. Thus, if for any piece of evidence

the value of MB is greater than 0, the value of MD is taken to equal 0, and

vice versa. A typical rule in a system using certainty theory is represented

as follows (taken from Shortliffe and Buchanan [1975]):

IF: 1) the stain of the organism is gram negative, and

2) the morphology of the organism is rod, and

3) (a) the aerobicity of the organism is aerobic, or

(b) the aerobicity of the organism is unknown,

THEN: there is suggestive evidence (.7) that the class of the organism is

enterobacteriaceae

Application of certainty theory in expert systems consists of revising or

updating uncertainties as the rules are applied and new evidence becomes

available. Initially, the certainty associated with any assertion is 0. If a rule says El implies C with a certainty factor x, then the certainty of C is

changed to x when evidence El is observed. Finally, as new pieces of evidence

become available, they are combined using the procedures specified by the

theory

.

95
At this point, some important observations regarding this theory are in order.

First, the amount of data required to use this theory is of the same order as

in case of the Bayesian approach. Second, although much of the reasoning in

this theory parallels that of the probability theory, the probability rules

cannot be strictly applied to its concepts. Finally, there are instances

where applications of this theory yield counterintuitive results because of

the way some of its concepts are defined, see Adams [1984] for more details.

More recently, Shortliffe and Buchanan [1984] report changes that have been

introduced in the definitions to correct this deficiency. A careful

delineation of this theory's relationship to conventional probability theory

can contribute to a better understanding of its assumptions and approximations.

Possibility Theory and Fuzzy Logic

The main premise upon which this theory is built may be stated as follows: The

random model of the probability theory is more suited for problems involving

the measure of information, but it is inappropriate for problems where the

meaning of information must be discerned. The problem of dealing with

imprecision is very important in the area of expert systems, as one rarely has

the luxury of being able to work with exact definitions, descriptions, or

assertions. In possibility theory, an attempt is made to formalize vagueness

and imprecision, just as probability theory is used as a formalism in treating

randomness. The theory of fuzzy sets (upon which the possibility theory is

based) allows us to express all kinds of imprecise entities by specifying to

what extent they have membership in suitably labeled fuzzy sets. For example,

if one does not know exactly the probability of an event, (e.g., it will rain

tomorrow) but is able to say that it is either very probable or not very

probable, then we can establish corresponding fuzzy subsets. We can then

decide to what degree we are willing to admit the individual elements

representing evidence into these sets. In other words, a membership function

is established which associates with each element a number in the interval

[0,1] which represents the degree of its membership in the fuzzy set in

question. By introducing the concept of degree of membership, this theory

removes the rigidity of ordinary set theory of either admitting or rejecting an

element (with no possibility of it partially belonging to the set).

The usefulness of fuzzy set theory in dealing with uncertainty in expert

systems arises out of its being able to deal with uncertainty due to vagueness,

where probability theory is inappropriate. Although some efforts have been

made in applying this theory to expert systems, much of it is in the

developmental stage. There is, however, ample literature for the interested

reader (Baldwin [1979, 1983], Bellman and Giertz [1973], Bellman and Zadeh

[1970], Dubois and Prade [1979, 1985], Green [1984], Negoita [1985], Zadeh

[1980, 1982, 1983a, 1983d, 1986], and Zimmerman [1984]).

The Dempster-Shafer Theory of Evidence

The foundations of this theory were laid over two decades ago by Dempster

[1963, 1967, 1968] and extended by Shafer [1975, 1976]. It is only recently

that researchers have shown a renewed interest in this theory and have

investigated its applicability as a model for dealing with uncertainty in

expert systems, see Barnett [1981], Dillard [1983], Friedman [1981], Garvey,

Lowrence and Fischler [1981], Gordon and Shortliffe [1984].

96
The main advantage of the Dempster-Shafer theory is its ability to allow the

system to narrow down the number of hypotheses that must be considered in

decision making. This property is especially valued by builders of diagnostic

expert systems in areas like medicine. In a Bayesian probabilistic model, if

some evidence or data is found that only partially supports a certain

hypothesis being tested, the portion not supporting it is taken as evidence

against the hypothesis being true, which may not be necessarily true. The

Dempster-Shafer theory is not weakened by such an assumption. Further, unlike

the Bayesian approach which does not distinguish between uncertainty, or lack

of knowledge, and equal certainty, this theory more faithfully reflects the

evidence-gathering process by using such evidence to narrow down the original

hypothesis set to a different subset.

In closing, it is useful to view each of these approaches as proposing a tool

for obtaining the desired behavior in an expert system, and not how the

information must be processed.

97
REFERENCES

Adams, J. B., "Probabilistic Reasoning and Certainty Factors," in B. G.

Buchanan and E. H. Shortliffe, (Eds.) Rule-Based Expert Systems:

The MYCIN Experiments of the Stanford Heuristic Programming Project , Reading, MA: Addi son-Wesl ey Publishing Company, 1984.

Baas, M. S., and H. Kwakernaak, "Rating and Ranking of Multiple-Aspect

Alternatives Using Fuzzy Sets," Automatica , Volume 13, 1977.

Baldwin, J. F., "Fuzzy logic and Its Application to Fuzzy Reasoning," in

M. M. Gupta, R. K. Ragade, and R. R. Yager (Eds.), Advances in Fuzzy

Set Theory and Applications , Amsterdam, Netherlands: North-Holland

Publishing Company, 1979.

Baldwin, J. F., "A Fuzzy Relational Inference Language for Expert Systems,"

Proceedings of the Thirteenth International Symposium on Multiple-Valued

Logic, New York: IEEE, May 1983.

A fuzzy relational inference language, FRIL, is discussed in relation to

its application for designing intelligent knowledge bases and expert

systems. It is a high level query language with automatic reasoning and

is based upon fuzzy set and relation theories.

Barnett, J. A., "Computational Methods for a Mathematical Theory of Evidence,"

Proceed i ngs of the Seventh IJCAI , American Association for Artificial

Intelligence, 1981.

Many knowledge-based expert systems employ numerical schemes to represent

evidence, rate competing hypotheses, and guide search through the domain's

problem space. This paper has two objectives: first, to introduce one

such scheme, developed by Dempster and Shafer, to a wider audience;

second, to present results that can reduce the computation-time complexity

from exponential to linear, allowing this scheme to be implemented in many

more systems. In order to enjoy this reduction, some assumptions about

the structure of the type of evidence represented and combined must be

made. The assumption made here is that each piece of the evidence either

confirms or denies a single proposition rather than a disjunction. For

any domain in which the assumption is justified, the savings are

available.

Bellman, R. E. and L. A. Zadeh, "Decision-Making in a Fuzzy Environment,"

Management Science , Vol. 17, No. 4, Dec. 1970.

By decision-making in a fuzzy environment is meant a decision process in

which the goals and/or constraints, but not necessarily the system under

control, are fuzzy in nature. This means that the goals and/or

constraints constitute classes of alternatives whose boundaries are not

sharply defined. Fuzzy goals and fuzzy constraints can be defined

precisely as fuzzy sets in the space of alternatives. A fuzzy decision,

then, may be viewed as an intersection of the given goals and constraints.

A maximizing decision is defined as a point in the space of alternatives

98
at which the membership function of a fuzzy decision attains its maximum

value. The use of these concepts is illustrated by examples involving

multi-stage decision processes in which the system under control is either

deterministic or stochastic. By using dynamic programming, the

determination of a maximizing decision is reduced to the solution of a system of functional equations. A reverse-flow technique is described for

the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by

the conditions that the process stops when the system under control enters

a specified set of states in its state space.

Bellman, R. E., and M. Giertz, "On the Analytic Formalism of the Theory of

Fuzzy Sets," Information Sciences , Vol. 5, 1973.

Bezdek, J. C, "Numerical Taxonomy with Fuzzy Sets," Journal of Mathematical

Biology, Vol. 1, No. 1, 1974.

Bezdek, J. C, Pattern Recognition with Fuzzy Objective Function Alg orithms , New York: Plenum Press, 1981.

Carlson, C, "Tackling an MCDM-Problem with the Help of Some Results from

Fuzzy Set Theory," European Journal of Operational Research , Vol. 10,

1982.

The problem to be addressed and tackled in this paper arose as a byproduct

from some efforts at solving problems involving multiple goals by linking

linear and goal programming models. The critical issue was that some

forms for interdependence among the goals could not be handled in the

programming models. Here the author deals with a set of goals -- with

realistic counterparts in a Finnish plywood industry -- in which a subset

of the goals are (i) conflicting, (ii) unilaterally supporting and (iii)

mutually supporting. It is furthermore observed that the elements of

studied set of goals may be partly independent and partly inter-dependent,

which makes the context a fullfledged MCDM-problem. It is tackled with a technique which is based on the theory of fuzzy sets, the conceptual

framework for fuzzy decisions and the algorithms developed for fuzzy

mathematical programming. The resulting fuzzy mul ti -objecti ve programming

model is simplified and tested with the help of a fairly complex numerical

example.

Chang, C. L., "Decision Support in an Imperfect World," Proceedings of Trends

and Applications 1983 on Au tomat ing Intelligent Behavior: Applications

and Frontiers , Silver Spring, MD: IEEE Computer Society Press,

May 1983.

By a decision support system, the author means an expert system that the

user can use to inquire information to make his decision. Such a system

will be based on an expert knowledge base. It is believed that the

knowledge base is more than facts and rules. It may include less tangible

and less codifiable factors like opinions, judgments, educated guesses, as well as factual information and logic rules for reasoning. That is, the

knowledge may be explicit, logical, heuristic, or fuzzy. This paper

presents some methods to add fuzzy information into a relational

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database. Specifically, it considers the treatments of fuzzy queries. An

answer to a fuzzy query will be a fuzzy set from which a further detailed

study can be made.

Dempster, A. P., "On Direct Probabilities," Journal of the Royal Statistical

Society , Series B, Vol. 25, 1963.

'

The concept of a direct probability argument is introduced. This is a means of assigning distributions a posteriori which satisfies the

requirement that such distributions shall be valid on the observations,

but which differs from the inverse probability argument in that

distributions a priori are not needed. Direct probabilities are closely

related to fiducial probabilities. This theory requires a generalized

concept of sampling a family F of distributions which is discussed in

Section 3. Direct inference from a sample of n is discussed in Section 4

for a single continuous parameter and in Section 5 for non-parametric

cases.

Dempster, A. P., "Upper and Lower Probabilities Induced by a Multivalued

Mapping," Annals of Mathematical Statistics , Vol. 38, 1967.

Dempster, A. P., "New Methods for Reasoning Towards Posterior Distributions

Based on Sample Data," Annals of Mathematical Statistics , Vol. 37,

1967.

This paper redefines the concept of sampling from a population with a given parametric form, and thus, leads up to some proposed alternatives to

the existing Bayesian and fiducial arguments for deriving posterior

distributions. Section 2 spells out the basic assumptions of the

suggested class of sampling models, and Section 3 suggests a mode of

inference appropriate to the sampling models adopted. A novel property of

these inferences is that they generally assign upper and lower

probabilities as given by Bayesian or fiducial arguments. Sections 4 and

5 present details of the new arguments for binomial sampling with a continuous parameter p and for general multinomial sampling with a finite

number of contemplated hypotheses. Among the concluding remarks, it is

pointed out that the methods of Section 5 include as limiting cases

situations with discrete or continous observables and continuously ranging

parameters.

Dempster, A. P., "A Generalization of Bayesian Inference," Journal of the

Royal Statistical Society , Series B, Vol. 30, No. 2, 1968.

Procedures of statistical inference are described which generalize

Bayesian inference in specific ways. Probability is used in such a way

that in general only bounds may be placed on the probabilities of given

events, and probability systems of this kind are suggested both for sample

information and for prior information. These systems are then combined

using a specified rule. Illustrations are given for inferences about

trinomial probabilities, and for inferences about a monotone sequence of

binomial pi . Finally, some comments are made on the general class of

models which produce upper and lower probabilities and on the specific

models which underlie the suggested inference procedures.

100
Dillard, R. A., "Computing Confidences in Tactical Rule-Based Systems by

Using Dempster-Shafer Theory," Report No.: NOSC/TD-649, Naval Ocean

Systems Center, San Diego, CA, Sept. 1983.

This report describes the implementation of confidence computing methods

based on Demspter-Shafer Theory. The theory is applicable to tactical

decision problems that can be formulated in terms of sets of mutually

exclusive and exhaustive propositions. Dempster's combining procedure, a generalization of Bayesian inference, is used to combine probability mass

assignments supplied by independent bodies of evidence. The computing

procedures are implemented in the rule-based system ROSIE, and apply to

all valid mass assignments. An ordering strategy is used to combine

various kinds of assignments by using different procedures that exploit

the special features of each. Applications to platform typing and contact

association are demonstrated.

Dubois, D., and H. Prade, "Outline of Fuzzy Set Theory: An Introduction," in

M. M. Gupta, R. K. Ragade, and R. R. Yager (Eds.), Advances in Fuzzy Set

Theory and Applicat ions , Amsterdam, Netherlands: North-Holland

Publishing Company, 1979.

Dubois, D., and H. Prade, "Decision-Making Under Fuzziness," in M. M. Gupta,

R. K. Ragade, and R. R. Yager (Eds.), Advances in Fuzzy Set Theory and

Appl i cati ons , Amsterdam, Netherlands: North-Holland Publishing

Company, 1979.

Duda, R. 0., P. E. Hart and N. J. Nilsson, "Subjective Bayesian Methods for

Rule-Based Inference Systems," Technical Note No. 124, Stanford Research

Institute, Menlo Park, CA, Jan. 1976.

The general problem of drawing inferences from uncertain or incomplete

evidence has invited a variety of technical approaches, some

mathematically rigorous and some largely informal and intuitive. Most

current inference systems in artificial intelligence have emphasized

intuitive methods, because the absence of adequate statistical samples

forces a reliance on the subjective judgment of human experts. This paper

describes a subjective Bayesian inference method that realizes some of

the advantages of both formal and informal approaches. Of particular

interest are the modifications needed to deal with the inconsistencies

usually found in collections of subjective statements.

Erman, L. D., et al., "The Hearsay-II Speech Understanding System:

Integrating Knowledge to Resolve Uncertainty," Computing Survey s,

Vol. 12, 1980.

Fordon, W. A., and J. C. Bezdek, "The Application of Fuzzy Set Theory to

Medical Diagnosis," in M. M. Gupta, R. K. Ragade, and R. R. Yager (Eds.),

Advances in Fuzzy Set Theory and Applications , Amsterdam: North-Holland

Publishing Company, 1979.

Friedman, L., "Extended Plausible Inference," Proceedings of the Seventh

IJCAI

,

American Association for Artificial Intelligence, 1981.

101
Gaines, B. R. , "Foundations of Fuzzy Reasoning," International Journal of

Man-Machine Stud ies, Vol. 8, 1976.

This paper gives an overview of the theory of fuzzy sets and fuzzy

reasoning as proposed and developed by Zadeh. In particular, it reviews

the philosophical and logical antecedents and foundations for this theory

and its applications. The extension of basic set operations to such fuzzy

sets, and the relationship to other multivalued logics for set theory, are

then outlined. The fuzzification of mathematical structures leads

naturally to the concepts of fuzzy logic and inference, and consideration

of implication suggests Lukasiewicz infinite-valued logic as a base logic

for fuzzy reasoning. The paradoxes of the barber, and of sorites, are

then analysed to illustrate fuzzy reasoning in action and lead naturally

to Zadeh's theory of linguistic hedges and truth. Finally, the logical,

model -theoreti c and psychological derivations of numeric values in fuzzy

reasoning are discussed, and the rationale behind interest in fuzzy

reasoning is summarized.

Gaines, B. R., and L. J. Kohout, "The Fuzzy Decade: A Bibliography of Fuzzy

Systems and Closely Related Topics," International Journal of Man-Machine

Studies, Vol. 9, 1977.

The main part of this paper consists of a bibliography of some 1150 items,

each keyword-indexed with some 750 being classified as concerned with

fuzzy system theory and its applications. The remaining items are

concerned with closely related topics in many-valued logic, linguistics,

the philosophy of vagueness, etc. These background references are

annotated in an initial section that outlines the relationship of fuzzy

system theory to other developments and provides pointers to various

possible fruitful interrelationships. Topics covered include: the

philosophy and logic of imprecision vagueness, other non-standard logics,

foundations of set theory, probability theory, fuzzification of

mathematical systems, linguistics and psychology, and applications.

Garvey, T. D., J. D. Lowrence, and M. A. Fischler, "An Inference Technique for

Integrating Knowledge from Disparate Sources," Proceedings of the Seventh

ICJAI , American Association for Artificial Intelligence, 1981.

Gordon, J., and E. H. Shortliffe, "The Dernpster-Shafer Theory of Evidence," in

B. G. Buchanan and E. H. Shortliffe, Rule-Based Expert Systems: The

MYCIN Experiments of the Stanford Heuristic Pr ogramming Project, Reading,

MA: Addi son-Wesl ey Publishing Company, 1984.

Gougen, Jr., J. A., "Concept Representation In Natural and Artificial

Languages: Axioms, Extensions and Applications for Fuzzy Sets,"

International Journal of Man-Machine Studies , Vol. 6, 1974.

This paper reports research related to mathematics, philosophy, computer

science and linguistics. It gives a system of axioms for a relatively

simple form of fuzzy set theory and uses these axioms to consider the

accuracy of representing concepts in various ways by fuzzy sets.

102
Byproducts of this approach include a number of new operations and laws

for fuzzy sets, parallel to those for ordinary sets, and a demonstration

that all the basic operations are intrinsically determined. In addition,

the paper explores both hierarchical and algorithmic extensions of fuzzy

sets, and then applications to problems in natural language semantics and

combinatorics. Finally, the paper returns to the problem of representing

concepts, and discusses some implications for artificial intelligence.

Green, N. L., "Decision Support With Fuzzy Technology," Proceedings of the

Sixteenth Annual Meeting of the American Institute for Dec i sion Sciences , Atlanta, GA: American Institute for Decision Sciences, Nov. 1984.

In strategic planning, much significant information is imprecise, yet

choosing a strategy is an important decision meriting DSS. Fuzzy sets

technology makes computer support possible using strategic manager's

natural language descriptions of the firm and its environment. An

innovative DSS augments a manager's information processing capability

using a fuzzy expert system to search the database for possible

strategies. It encourages the decision maker's pattern recognition

skills, intuition, and creativity to produce additional strategies

inspired by visual displays. The system leaves to the user's judgement

the choice of a strategy from the alternatives generated by system and

user together.

Humphreys, P., "Knowledge and Probability in Decision Support: Handling

Uncertainty in Structuring 111 -defined Problems," IEE Colloquium on Decision Support Aspects of Expert Systems, (Digest No. 67), London,

England: IEE, May 1984.

The author examines factors which need to be taken into account in

providing decision support for decision makers facing ill-defined decision

problems; that is, those problems where at the outset there is

considerable uncertainty about what is involved in the problem and how to

represent it. It reviews ways in which decision-theoretical models take

four different types of uncertainty into account. Within each of these

structural variants, three levels of problem representation are

identified. But it is shown how at least a further two levels must be

taken into account in handling procedural uncertainty when structuring

ill-defined problems. Operations at these higher levels are characterized

in terms of the development of problem structuring languages and the

exploration of small worlds. A multi-level problem structure generating

scheme is proposed. Examples of typical properties of real -life

decision problems represented at each level are given.

Kohout, L. J., "Fuzzy Decision Making and Its Impact on the Design of Expert

Systems," IEE Colloquium on Decision Support Aspects of Expert Systems , (Digest No. 67), London, England: IEE, 1984.

Some methods for decision making concerned with essentially imprecise data

are discussed. The discussion is illustrated by the examples from those

domains of medical decision making in which classical probabilistic and

statistical methods fail. Fuzzy sets and relations that are the essential

103
tools of this approach have strong impact on inference in expert systems,

as well as on the methods of their design.

Lesmo, L., L. Saitta, and P. Torasso, "Fuzzy Production Rules: A Learning

Methodology," in P. P. Wang (Ed.), Advances in Fuzzy Sets, Possibility

Theory, and Applications , New York: Plenum Press, 1983.

Negoita, C. V., "On Fuzzy Systems," in M. M. Gupta, R. K. Ragade, and R. R.

Yager (Eds.), Advances in Fuzzy Set Theory and Applications , Amsterdam,

Netherlands: North Holland Publishing Company, 1979.

Negoita, C. V., Expert Systems and Fuzzy Systems , Menlo Park, CA: The

Benjami n/Cummi ngs Publishing Company, Inc., 1985.

Nilsson, N. J., "Probabilistic Logic," Artificial Intelligence , Vol. 28,

No. 1, 1986.

Prade, H., "Fuzzy Programming: Why and How? - Some Hints and Examples," in

P. P. Wang (Ed.), Advances in Fuzzy Sets, Possibility Theory, and

Appl i cati ons , New York: Plenum Press, 1983.

Prade, H., "A Computational Approach to Approximate and Plausible Reasoning

With Applications to Expert Systems," IEEE Transactions on Pattern

Analysis and Machine Intelligence , Vol. 7, No. 3, 1985.

The purpose of this paper is twofold: proposing a common basis for the

modeling of uncertainty and imprecision, and discussing various kinds of

approximate plausible reasoning schemes in this framework. Together with

probability, different kinds of uncertainty measures (credibility and

plausibility functions in the sense of Shafer, possibility measures in the

sense of Zadeh) are introduced in a unified way. The modeling of

imprecision in terms of possibility distribution is then presented, and

related questions such as the measure of the uncertainty of fuzzy events,

the probability and possibility qualification of statements, the concept

of a degree of truth, and the truth qualification of propositions, are

discussed at length. Deductive inferences from premises weighted by

different kinds of measures by uncertainty, or by truth-values in the

framework of various multivalued logics, is fully investigated. Then,

deductive inferences from imprecise or fuzzy premises are dealt with;

patterns of reasoning where both uncertainty and imprecision are present

are also addressed. The last section is devoted to the combination of

uncertain or imprecise pieces of information given by different sources.

On the whole, this paper is a tentative survey of quantitative approaches

in the modeling of uncertainty and imprecision, including recent

theoretical proposals as well as more empirical techniques such as the

ones developed in expert systems as MYCIN or PROSPECTOR, the management of

uncertainty and imprecision in reasoning patterns being a key issue in

artificial intelligence.

Ralescu, D., "A Survey of the Representation of Fuzzy Concepts and its

Applications," in M. M. Gupta, R. K. Ragade, and R. R. Yager (Eds.),

Advances in Fuzzy Set Theory and Applications , Amsterdam, Netherlands:

North Holland Publishing Company, 1979.

104
Sanchez, E., "Medical Diagnosis and Composite Fuzzy Relations," in M. M. Gupta,

R. K. Ragade, and R. R. Yager (Eds.)» Advances in Fuzzy Set The ory and

Appl ications , Amsterdam, Netherlands: North Holland Publishing

Company, 1979.

Schefe, P., "On Foundations of Reasoning with Uncertain Facts and Vague

Concepts," Internati ona l Journal of Man-Machine Studie s, Vol. 12, 1980.

Shafer, G., "A Theory of Statistical Evidence," in W. L. Harper and C. A.

Hooker (Eds.), Foun dations and Philosophy of Stat i stical Theories in

Physical Sciences , Hingham, MA: Kluwer Academic, 1975.

Shafer, G., A Ma thematical Theory of Evidence , Princeton, NJ: Princeton

University Pre~ss, 1976.

This book constructs a new theory of epistemic probability. The theory

draws on the work of A. P. Dempster but diverges from Dempster's viewpoint

by identifying his lower probabilities as epistemic probabilities and

taking his rule for combining upper and lower probabilities as fundamental. The book opens with a critique of the well-known Bayesian

theory of epistemic probability. It then proceeds to develop an

alternative to the additive set functions and the rule of conditioning of

the Bayesian theory: set functions that need only be what Choquet called

monotone of order °° , and Dempster's rule for combining such set

functions. This rule, together with the idea of weights of evidence,

leads to both an extensive new theory and a better understanding of the

Bayesian theory. The book concludes with a brief treatment of statistical

inference and a discussion of the limitations of epistemic probability.

Appendices contain mathematical proofs, which are relatively elementary

and seldom depend on mathematics more advanced than the binomial theorem.

Shortliffe, E. H., and Buchanan, B. G. , "A Model of Inexact Reasoning in

Medicine," Mathematical Biosciences, Vol. 23, 1975.

Medical science often suffers from having so few data and so much

imperfect knowledge that a rigorous probabilistic analysis, the ideal

standard by which to judge the rationality of a physician's decision, is

seldom possible. Physicians nevertheless seem to have developed an ill-defined mechanism for reaching decisions despite a lack of formal

knowledge regarding the interrelationships of all the variables that they

are considering. This report proposes a quantification scheme which

attempts to model the inexact reasoning processes of medical experts. The

numerical conventions provide what is essentially an approximation to

conditional probability, but offer advantages over Bayesian analysis when

they are utilized in a rule-based computer diagnostic system. One such

system, a clinical consultation program named MYCIN, is described in the

context of the proposed model of inexact reasoning.

Szolovitz, P., and S. G. Pauker, "Categorical and Probabilistic Reasoning in

Medical Diagnosis," Artificial Intelligence , Vol. 11, 1978.

Tsotsos, J. K. , "On Classifying Time-Varying Events," Proceedings of the IEEE

Comp uter Society Conference on Pattern Recognition and Image Processing , New'~York: IEEE, 1981.

105
Whalen, T., and B. Schott, "Decision Support with Fuzzy Production Systems,"

in P. P. Wang (Ed.), Advances in Fuzzy Sets, Possibility Theory , and

Applications , New York: Plenum Press, 1983.

Whalen, T., "Fuzzy Knowledge-Based Systems in Management," Human Systems

Management , Vol. 4, No. 4, Autumn 1984.

Knowledge-based systems can be an important complement to management

decision making because of their versatility and power in handling

partially-structured problems. The chief bottleneck in developing

knowledge-based systems is acquiring large amounts of knowledge from human

experts. Very closely related is the problem of representing the

knowledge in computer-executable form: A good knowledge-representing

system can make knowledge acquisition much easier. Using fuzzy production

rules with linguistic variables to represent knowledge affords several

advantages. Linguistic variables allow human-computer communication in a form resembling a dialogue. Until recently it has seemed to be a gleam in

the eyes of academics and science fiction writers. However, recent

advances in both hardware and software development means that it is now

possible to develop computers which include aspects of intelligence. This

article looks at two key areas of advance in terms of what has been

achieved and how we can go forward to real applications. The two areas

are natural language and intelligent knowledge-based systems.

Yager, R. R., "A New Methodology for Ordinal Multi objecti ve Decisions Based on Fuzzy Sets," Decision Sciences , Vol. 12, 1981.

Using the structural forms supplied by fuzzy set theory and approximate

reasoning, a new method is presented for solving multiple-objective

decision problems for which the decision maker can supply only ordinal

information on his preferences and the importance of the individual

objecti ves.

Zadeh, L. A., "Fuzzy Sets," Information and Control , Vol. 8, 1965.

A fuzzy set is a class of objects with a continuum of grades of

membership. Such a set is characterized by a membership (characteristic)

function which assigns to each object a grade of membership ranging

between zero and one. The notions of inclusion, union, intersection,

complement, relation, convexity, etc., are extended to such sets, and

various properties of these notions in the context of fuzzy sets are

established. In particular, a separation theorem for convex fuzzy sets is

proved without requiring that the fuzzy sets be disjoint.

Zadeh, L. A., "Fuzzy Logic and Approximate Thinking," Synthese , Vol. 30, 1975.

Zadeh, L. A., "Possibility Theory as a Basis for Information Processing and

Knowledge Representation," Proceed i ngs of the COMPSAC 80 , Oct. 1980.

To enhance the effectiveness of computers as information processing

machines it is necessary to develop a better understanding of ways of

dealing with information which is imprecise, incomplete or not totally

reliable. The recently developed theory of possibility provides a non-statistical basis for the representation and manipulation of uncertain

106
information. The central concept in this theory is that of a possibility

distribution, by which is meant the fuzzy set of all possible values of a variable. With this concept as the point of departure, one can develop a set of translation rules for finding the possibility distribution which is

induced by a given proposition. Such rules form a part of the

representation language, PRUF, which in combination with fuzzy logic,

provides a formal system for the representation and inference from

imprecise knowledge.

Zadeh, L. A., "Possibility Theory and Soft Data Analysis," Proceedings of the AAAS Selected Symposium 54 on Mat eh matical F rontiers of the Social and

Policy Sciences , Washington D.C.: American Association for the

Advancement of Science, 1981.

A thesis advanced in this paper is that much of the uncertainty which is

associated with soft data is nonstati sti cal in nature. Based on this

premise, an approach to the representation and manipulation of soft

data in which the recently developed theory of possibility plays a central role -- is described and illustrated with examples.

Zadeh, L. A., "Fuzzy Probabilities and Their Role in Decision Analysis,"

Proceedings of the Fourth MIT/QNR Workshop on Distributed Information and

Decision Systems , 1982.

The conventional approaches to decision analysis are based on the

assumption that the probabilities which enter into the assessment of the

consequences of a decision are known numbers. In most realistic settings,

this assumption is of questionable validity since the data from which the

probabilities must be estimated are usually incomplete, imprecise or not

totally reliable. In the approach outlined in this paper, the

probabilities are assumed to be fuzzy rather than real numbers. It is

shown how such probabilities may be estimated from fuzzy data and a basic

relation between joint, conditional and marginal fuzzy probabilities is

established. Manipulation of fuzzy probabilities requires, in general,

the use of fuzzy arithmetic, and many of the properties of fuzzy

probabilities are simple generalizations of the corresponding properties

of real-valued probabilities.

Zadeh, L. A., "The Role of Fuzzy Logic in the Management of Uncertainty in

Expert Systems," Fuzzy Sets and Syst ems, Vol. 11, 1983a.

Management of uncertainty is an intrinsically important issue in the

design of expert systems because much of the information in the knowledge

base of a typical expert system is imprecise, incomplete, or not totally

reliable. In the existing expert systems, uncertainty is dealt with

through a combination of predicate logic and probability-based methods. A

serious shortcoming of these methods is that they are not capable of

coming to grips with the pervasive fuzziness of information in the

knowledge base, and, as a result, are mostly ad hoc in nature. An

alternative approach to the management of uncertainty, which is suggested

in this paper, is based on the use of fuzzy logic, the logic underlying

approximate or, equi valently , fuzzy reasoning. A feature of

107
fuzzy logic, which is of particular importance to the management of

uncertainty in expert systems, is that it provides a systematic framework

for dealing with fuzzy quantifiers (e.g., most, many, few, not very many,

almost all, infrequently, about 0.8, etc.). In this way, fuzzy logic

subsumes both predicate logic and probability theory, and makes it

possible to deal with different types of uncertainty within a single

conceptual framework. In fuzzy logic, the deduction of a conclusion from

a set of premises is reduced, in general, to the solution of a nonlinear

program through the application of projection and extension principles.

This approach to deduction leads to various basic syllogisms which may be

used as rules of combination of evidence in expert systems. Among

syllogisms of this type, which are discussed in this paper, are the

intersection/product syllogism, the generalized modus ponens, the

consequent conjunction syllogism, and the major-premise reversibility

rule.

Zadeh, L. A., "A Theory of Commonsense Knowledge," Memorandum No. UCB/ERL

M83/26, Electronics Research Laboratory, College of Engineering,

University of California, Berkeley, CA, 1983b.

The theory outlined in this paper is based on the idea that what is

commonly called commonsense knowledge may be viewed as a collection of

dispositions, that is, propositions with implied fuzzy quantifiers.

Typical examples of dispositions are: Icy roads are slippery, Tall men

are not very agile, Overeating causes obesity, Bob loves women, What is

rare is expensive, etc. It is understood that, upon restoration of fuzzy

quantifiers, a disposition is converted into a proposition with explicit

fuzzy quantifiers (e.g., tall men are not very agile; most tall men are

not very agile). Since traditional logical systems provide no methods for

representing the meaning of propositions containing fuzzy quantifiers,

such systems are unsuitable for dealing with commonsense knowledge. It is

suggested in this paper that an appropriate computational framework for

dealing with commonsense knowledge is provided by fuzzy logic, which, as its name implies, is the logic underlying fuzzy (or approximate)

reasoning. Such a framework, with an emphasis on the representation of

dispositions, is outlined and illustrated with examples.

Zadeh, L. A., "Linguistic Variables, Approximate Reasoning and Dispositions,"

Medical Information , Vol. 8, No. 3, 1983c.

Zadeh, L. A., A Computational Approach to Fuzzy Quantifiers in Natural

Languages," Computer and Mathematics with Applications , Vol. 9,

No. 1, 1983d.

The generic term, fuzzy quantifier, is employed in this paper to denote

the collection of quantifiers in natural languages whose representative

elements are: several, most, much, not many, very many, not very many,

few, quite a few, large number, small number, close to five, approximately

ten, frequently, etc. In our approach, such quantifiers are treated as fuzzy numbers which may be manipulated through the use of fuzzy arithmetic

and, more generally, fuzzy logic. A concept which plays an essential role

in the treatment of fuzzy quantifiers is that of the cardinality of a fuzzy set. Through the use of this concept, the meaning of a proposition

108
containing one or more fuzzy quantifiers may be represented as a system of

elastic constraints whose domain is a collection of fuzzy relations in a relational database. This representation, then, provides a basis for

inference from premises which contain fuzzy quantifiers. For example,

from the propositions "Most U's are A's" and "Most A's are B's", it

follows that "Most U's are B's," where most is the fuzzy product of the

fuzzy proposition most with itself. The computational approach to fuzzy

quantifiers which is described in this paper may be viewed as a derivative

of fuzzy logic and test-score semantics. In this semantics, the meaning

of a semantic entity is represented as a procedure which tests, scores and

aggregates the elastic constraints which are induced by the entity in

questi on.

Zadeh, L. A., "Fuzzy Sets and Commonsense Reasoning" Berkeley Cognitive Science

Report No. 21, Cognitive Science Program, Institute of Human Learning,

University of California, Berkeley, CA, 1984a.

Despite the extensive literature and numerous conferences, there are many

misconceptions about the concept of a fuzzy set which find their

expression in questions such as: What is a fuzzy set? Is there a difference between fuzziness and randomness? Are there significant

applications in which fuzzy set techniques have a distinct advantage over

conventional probabilistic methods? What is the role that the theory of

fuzzy sets can play in artificial intelligence, expert systems, operations

research, process control, medical diagnosis and related fields? These

are some of the questions that the author attempts to answer -- directly

or indirectly -- in this article.

Zadeh, L. A., Decision Analysis and Fuzzy Mathematics , Berkeley, CA:

Computer Science Division, University of California, 1984b.

In sum, the concept of a linguistic variable, in combination with the

other basic concepts of fuzzy mathematics which derive from the notion of

a fuzzy set, make it possible to formulate and solve a number of problems

in decision analysis that are too complex or too ill-defined to be

susceptible to solution by conventional techniques. In this way, the

theory can be brought closer to reality. There will always be many

problems, however, for which no theory, whether based on fuzzy mathematics

or not, could provide a rational basis for solution. This is particularly

true of problems relating to conflict resolution, group decision analysis,

and, more generally, to large-scale systems in which the goals and

horizons are hard to agree upon and not merely hard to quantify. Thus, in

the final analysis, the ultimate limitations of any theory of decision

processes derives not from its mathematical foundations but from our

inability to understand and elucidate the meaning of the rationality of

human behavior. This will always be a controversial issue.

Zadeh, L. A.

, "Management of Uncertainty in Expert Systems," Proceedings of the

First Symposium on the Theory and Application of Expert Systems in

Emergency Management Operations , Special Publication 717, Gaithersburg,

MD: National Bureau of Standards, 1986.

109
Zimmerman, H. J., L. A. Zadeh, and B. R. Gaines (Eds.), "Fuzzy Sets and

Decision Analysis," TIMS Studies in the Management Sciences , Vol. 20,

Amsterdam, Netherlands: North-Holland Publishing Company, 1984.

110
8. APPLICATIONS OF EXPERT SYSTEMS

8.1 TYPES OF PROBLEMS AMENABLE TO SOLUTIONS BY EXPERT SYSTEMS

Recalling the definition of expert systems - a computer program capable of

solving those decision problems that normally require human expertise - we

see that the range of such problems can be rather extensive. A distinction

can be made, however, between those decision problems that have

wel 1 -documented procedures or algorithms for solving them and those that do

not. The latter set of problems is the domain of expert systems.

The problems in this domain are further characterized by incomplete data

and uncertainty regarding the information available to solve them.

Experts, using their experience, represented by rules-of -thumb (heuristics)

and subjective judgments, are able to fill these gaps and, thus, provide

solutions. If expert systems are to solve these decision problems, then

they must mimic the human expert by using the same techniques as the human

expert and/or apply other decision procedures that prove to produce

comparable results. Thus, we would expect expert systems to be able to

represent and manipulate data, deal with incomplete and uncertain data, and

use heuristic rules and make subjective judgments. To form a practical

expert system, it is necessary to extract the knowledge and methodologies

of the human expert; this may not be possible for most problems. As the

art of building expert systems stands today, it is only possible to resolve

those problems that have a narrow domain of expertise.

There are many criteria that may be used to decide whether or not to build

an expert system and for selecting suitable problems. First, if an expert

system is to be worth the effort, its performance must be at least

comparable to that of a human expert in that field. Second, the speed of

response may be important. In such instances, one should be able to take

advantage of an expert system's unique capacity to combine its human

expert-like capabilities with its already existing computing speed.

Third, there are many complex and urgent problems requiring responses when

experts are not at hand. Here, expert systems can be used in aiding the

non-experts to make the necessary responses. Fourth, there are certain

problem areas where even the experts wish they had an intelligent

assistant who can be a source of information and ideas, and with whom they

could interact in making decisions. An expert system can be used for

these purposes. Finally, for many problems, there is an acute shortage of

experts, and new ones cannot be trained very quickly, see Basden [1983]

for further discussion.

8.2 DOMAINS IN WHICH EXPERT SYSTEMS ARE BEING C U RRENT LY APPLIED

Over the past decade or so, expert systems have been built for many problem

domains, the majority of them for problems requiring diagnostic capabilities. They differ in the type of global data base used, the makeup of

their inference engine, as well as in the type of knowledge representations

required to make their decisions (see Chapter 3 for an explanation

of these concepts). Tables 8.1 through 8.7 provide a breakdown of

111
areas into specific problem domains for which expert systems have been

built. Readers interested in the details about a particular system are

directed to the cited references in Tables 8.1 through 8.7.

112
Applications in Medicine

Expert systems in the medical field were some of the earliest (and the

most successful) ones to be built. The majority of these systems aid in

the diagnosis of diseases in a narrowly defined domain of medicine. Many

are also capable of recommending treatments. For example, CASNET aids in

the diagnosis, as well as in the treatment of glaucoma. Table 8.1 lists a number of medical expert systems, along with their areas of expertise.

TABLE 8.1 Expert Systems in Medicine

Expert Systems Problem Domain/Source Article

CASNET Diagnosis and therapy recommendations for glaucoma

Kulikowski and Weiss [1982]

CENTAUR Interpretation of pulmonary function test

measurements from patients with lung disorders

Aikens [1983]

DIGITALIS ADVISOR Treatment recommendation for using digitalis

Swartout [1 977] and Gorry, Silverman and

Pauker [1978]

EMERGE Analysis of chest pain in the emergency room

Hudson and Estrin [1984]

EMYCIN A domain-independent version of MYCIN, usable for

developing rule-based consultation programs for many

fields

van Melle [1979]

HEADMED Diagnostic advice in psychopharmacol ogy

Heiser, Brooks and Ballard [1978]

INTERNIST/CADUCEUS Diagnosis in internal medicine

Pople [1 973] and Pople [1982]

KARDIO-E Diagnosis of cardiac arrhythmias using

electrocardi ographs

Lavroc [1985]

KMS Diagnosis of medical problems

Reggia et al. [1980]

113
TABLE 8.1 Expert Systems in Medicine (Continued)

Expert System Problem Domain/Source Article

MDX

MYCIN

ONCOCIN

PATREC

PIP

PUFF

RADEX

RX

TIERESIAS

VM

Diagnosis of causes for cholestasis

Chandrasekaran and Mittal [1983]

Diagnosis and treatment recommendations for certain

bacterial infections

Shortliffe [1976]

Assistance in the management of cancer patients on chemotherapy protocols for forms of lymphoma

Shortliffe et al. [1981]

Intelligent medical data base needed for MDX

Mittal, Chandrasekaran and Stickler [1984]

Clinical diagnosis

Parker et al . [1976]

Analysis of data from pulmonary function tests

Atkins et al. [1982]

Diagnostic advice in radiology through

interpretation of various kinds of imaging data

Chandrasekaran, Mittal, and Smith [1983]

Determination of causal relationships in medicine

Blum [1982]

Generation of explanations of MYCIN'S diagnoses and

treatment recommendations

Davis [1976, 1982]

Monitoring of patient's respiration during intensive

care

Fagan [1980]

114
Applications in Chemistry

Many of the problem domains in chemistry also require diagnostic skills.

However, unlike medical diagnosis, where medical data and evidence are

generally used to draw conclusions that already exist in the knowledge

base, many chemistry expert systems generate new possibilities for the

user to examine. This capability is extremely useful to the chemist

trying to identify the molecular structure of a chemical compound. Table

8.2 gives a representation of the range of problems in the area of

chemistry for which expert systems have been built.

TABLE 8.2 Expert Systems in Chemistry

Expert System

CONGEN

CRYSALIS

DENDRAL/METADENDRAL

Problem Domain/Source Article

GA1

GAMMA

MOLGEN

SECS

SYNCHEM

Construction of chemical structures

Cahart [1979]

Identification of structure of a protein from

electron-density maps

Engelmore and Ni i [1977]

Generation of structural representations of organic

molecules using mass spectroscopic data for their

identification

Buchanan and Feigenbaum [1978]

Inference of a complete molecular structure by

generating molecular pieces

Stefik [1978]

Interpretation of gamma ray activation spectra

Barstow [1 979]

Designing of molecular genetic experiments

Friedland [1980] and Martin et al . [1977]

Generation of schemes for synthesis of organic

compounds

Wipke et al. [1977]

Generation of schemes for synthesis of organic

compounds

Gelernter et al. [1977]

115
Applications in Engineering, Mathematics, and other Sciences

The expert systems in engineering, mathematics and other sciences have

been built to solve difficult problems requiring complex representation

and manipulation. Many of the applications attempted in the engineering

domain are similar to those in chemistry -- structural analysis, circuit

analysis and design, etc. Some of the better-known expert systems in

these areas are given in Table 8.3.

TABLE 8.3 Expert Systems in Engineering, Mathematics, and Other Sciences

Expert System Problem Domain/Source Article

AM

ASA

EL

EURISKO

MACSYMA

MECHO

REX

SACON

STATPATH

SU/X

Discovery of mathematical concepts

Lenat [1982]

Analysis of experiments using non-parametric

statistical tests

O'Keefe [1982]

Analysis of electrical circuits

Sussman and Stallman [1975]

Generation of solutions through the use of

heuri sti cs Lenat [1983]

Symbolic mathematics

Moses [1971]

Problem-solving in mechanics

Bundy et al. [1979]

Performance of regression analysis

of results, and tutoring

Gale and Pregibon [1982]

interpretation

Advice in the use of large, general -purpose

structural analysis programs to engineers

Bennett and Engelmore [1979]

Identification of a statistical analysis technique

appropriate for the data in hand

Portier and Lai [1983]

Understanding signals (represented as descriptions

of spectral lines) for identification, location, and

determination of velocity of objects in a physical

space

Ni i and Feigenbaum [1978]

116
Applications in Oil/Mineral Exploration

This is another area in which expert system methodology has been

successfully applied. Here, the expert system serves the same purpose as an expert by using the same data and reasoning to analyze and predict the

chances of successful oil strikes or mineral finds. Some of the better

known expert systems in this area are given in Table 8.4.

TABLE 8.4 Expert Systems in Oil /Mineral Exploration

Expert System Problem Domain/Source Article

DIPMETER ADVISOR Analysis of dipmeter logs to determine the

subsurface tilt in oil drilling operations

Davis et al. [1981]

DRILLING ADVISOR Determination of cause and possible resolution of

problems encountered in the course of drilling

production oil wells

Hollander et al. [1983]

ELAS Well-log (various electromagnetic, sonic, and

nuclear signals) analysis through multilevel

reasoni ng

Weiss et al . [1982]

KAS Acquisition of knowledge needed for PROSPECTOR

Reboh [1982]

PROSPECTOR Aid in mineral exploration

Gaschnig [1982] and Duda, Gaschnig and Hart

[1977]

117
Applications in Military and Government

Applications in these areas represent some of the more practical uses for

which expert systems have been applied. In many of these systems, the

expert system technology is often combined with other tools to process the

necessary data. Efficiency and speed have been the most important gains

in using expert systems in these problem domains. Many of the systems

listed in Table 8.5 are still in the testing stages.

TABLE 8.5 Expert Sytems in Military and Government

Expert System Problem Domain/Source Article

AIR ID

AIRPLAN

BATTLE

HASP/SIAP

KNOBS

SPAM

SPERIL

STAMMER2

TAC II

TATR

Aircraft identification

Aldridge [1984]

Assistance in launch and recovery of aircraft on aircraft carriers

Masui, McDermott and Sobel [1983]

Battlefield weapons assignment

Slagle et al. [1984]

Ocean surveillance through si gnal -processi ng

Nii et al. [1982]

Tactical mission planning consultant

Engelman, Berg and Bischoff [1979]

Interpretation of high resolution aerial photographs

of commercial airports

McKeown and McDermott [1983]

Earthquake damage assessment for structures

Ishiguka, Fu and Yao [1981]

Tactical situation assessment

McColl [1979]

Tactical Decision Making

Geschke, Bullock and Widmaier [1983]

Tactical targeting

Callero, Jamison and Waterman T1982]

118
Applications in Industry and Business

Industry and business seems to have been the main beneficiary of expert

systems, if the number of application areas in which they have used this

technology is any indication. One of the most frequently cited success

stories is that of Rl (also called XCON) used by the Digital Electronics

Corporation to configure its computers. Table 8.6 lists problem domains

for which expert systems have been constructed.

TABLE 8.6 Expert Systems in Industry and Business

Expert System Problem Domain/Source Article

ACE

ACRONYM

APRIKS

AQ11

CATS-1

CRIB

DART/DASD

GETREE

HARPY

HEARSAY

Generation of trouble-shooting reports and

management analyses for telephone cable maintenance

Vesonder, Salvatore and Zielinski [1983]

Visual perception through analysis of photographs

Binford [1981]

Diagnosis and treatment of plant diseases, and

crop pest control

Tou and Cheng [1983]

Diagnosis of plant diseases

Michal ski and Chilansky [1980]

Diagnosis of diesel locomotive malfunctions and

demonstration of repair procedures

Kinnucan [1984]

Computer fault-finding through knowledge

engi neeri ng

Hartley [1984]

Diagnosis of computer system failures

Bennett and Hollander [1981]

Knowledge management through interactive graphics

in building expert systems

Lewis and Lynch [1983]

Speech understanding

Newell [1978]

Speech understanding

Hayes-Roth and Lesser [1977]

119
TABLE 8.6 Expert Systems in Industry and Business (Continued)

Expert System Problem Domain/Source Article

NDS

NOAH

PTRANS

Rl (XCON)

REACTOR

STEAMER

STRIPS

VENTILATION EXPERT

XSEL

Isolation and repair of multiple faults in

electronic systems

Williams, Orgren and Smith [1983]

Use of robot in problem solving

Sacerdoti [1974]

Assistance in processing customer orders for

computer systems

McDermott [1983]

Configuration of computer systems based on customer

orders for components

Shannon [1984]

Diagnosis and treatment of nuclear reactor accidents

Nelson [1982]

Determination of causes and remedies for mechanical

problems

Forbus [1981] and Stevens et al. [1981]

Use of robot in problem solving

Nilsson [1980]

Management of the operation of coal mine ventilation systems

Kohler [1986]

Assistance to salespeople in tailoring computer

systems to fit the needs of the customer

McDermott [1982]

120
Applications in Education and Training

The earliest systems in this domain were simple programs with limited

tutoring capabilities. Today's expert systems not only tutor the students

on how to solve complex problems, but also assess their understanding and

adjust the tutoring approach based on the student's sophistication. Many

successful expert systems in this area are really descendents of expert

systems originally built to perform certain specific tasks. Table 8.7

gives the names and domains of some expert systems in this area.

TABLE 8.7 Expert Systems in Education and Training

Expert System Problem Domain/Source Article

ACE

BUGGY

EXAMINER

GUIDON

LOGO

NEOMYCIN

RESEDA

SCHOLAR

SOPHIE

WUMPUS

Environment allowing students to explore algorithms

Miller et al . [1983]

Determining a student's arithmetic misconceptions

Brown and Burton [1982]

Tutoring in the use of contextual knowledge in the

analysis of diagnostic behavior

Olesson [1977]

Tutoring through case-method for improving a student's ability to diagnose complex problems in

medicine and science

Clancey [1979]

Learning environment for student initiated learning

of a broad range of subjects

Howe and O'Shea [1978]

Interpretation of student behavior and teaching

diagnostic strategy in medicine

Clancey and Letsinger [1981]

Biographical data management

Zarri [1984]

Tutoring in geography of South America

Carbonell[1970]

Tutoring in electronic trouble shooting

Brown, Burton and de Kleer [1 982]

Tutoring in logical and reasoning skills

Goldstein [1982]

121
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reconnaissance craft is straightforward. Two models permit exploration of

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Man-Machine Studies , Vol. 19, 1983. ~

This article seeks to bring together a number of issues relevant to the

application of expert systems by discussing their advantages and

limitations, their roles and benefits, and the influence that real-life

applications might have on the design of expert systems software. Part of

the expert systems strategy of one major chemical company is outlined.

This system is described briefly and used to illustrate much of the later

discussion. It is of the plausible-inference type and has application in

the field of materials engineering. The article is aimed both at the

interested end-user and those working in the field of expert systems.

122
Basden, A., and B. A. Kelly, "An Application of Expert Systems Techniques in

Materials Engineering," Proceedings o f the Colloquium on Application of

Knowledge-Based (or Expert) Systems , London, England, 1982.

Bennett, J. and R. Engelmore, "SACON: A Knowledge-Based Consultant for

Structural Analysis," Proceedings of the Sixth IJCAI , American Association

for Artificial Intelligence, 1979.

This paper presents an application of Artificial Intelligence methods to

the engineering domain of structural analysis. The authors have developed

and partially implemented an "automated consultant" called SACON

(Structural Analysis CONsultant), using the E MYCIN system as its frame

work. SACON advises engineers in the use of a large, general -purpose

structural analysis program. The structure of the knowledge base,

including the major concepts used and inferences drawn by consultants, is

presented. Some observations in the light of this application about the

EMYCIN system as a representational vehicle are given.

Bennett, J. S., and C. R. Hollander, "DART: An Expert System for Computer

Fault Diagnosis," Proceedings of the Seventh IJCAI , American Association

for Artificial Intelligence, 1981.

This paper describes an application of artificial intelligence techniques

to computer system fault diagnosis. In particular, the authors have

implemented an automated consultant that advises IBM field service

personnel on the diagnosis of faults occurring in computer installations.

The consultant identifies specific system components (both hardware and

software) likely to be responsible for an observed fault and offers a brief explanation of the major factors and evidence supporting these

indictments. The consultant, called DART, was constructed using EMYCIN,

and is part of a large research effort investigating automated diagnosis

of machine faults.

Bennett, S. W. and A. C. Scott, "Computer-Assisted Customized Antimicrobial

Dosages," American Journal of Hospital Pharmacy , Vol. 37, 1980.

Blidberg, D. R., A. S. westneat, and R. W. Corel 1, "Expert Systems, a Tool for

Autonomous Underwater Vehicles," Proceedings of Trends and Applications

1983 on Automating Intelligent Behavior: Applications and Frontie rs,

Silver Spring, MD: IEEE Computer Society Press, May 1983.

The need for guidance and control of an unmanned untethered underwater

automaton calls for a knowledge-based expert as pilot. This paper

considers the expert system in a time-constrained environment. It

concludes that substantial improvements in reliability, in performance of

the mission, in coping with unexpected events should be achievable with

on-board microcomputer capabilities.

Blum, R. L., "Discovery, Confirmation, and Incorporation of Causal

Relationships From a Large Time-Oriented Clinical Data Base: The RX

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Decision-Making in General Practice," Medical Information , Vol. 9, No. 1,

1984.

Recent advances in expert systems and statistics make the development of

decision-support systems for clinical use a real possibility. One natural

location for such systems is with the general practitioner, as the general

practitioner has to manage a wide range of problems covering all

specialties, and often has to make decisions without the necessary

knowledge at hand. However, if decision-support systems are to gain wide

acceptance, a number of criteria must be met. They must be easy to use

and not interfere with the consultation. More important, they must

provide information about important problems which clinicians actually

face in their consulting rooms, and they must provide help in ways which

are compatible with the way doctors think and work. The investigation of

decision-making in general practice is therefore one of the necessary

prerequisites of any successful developments of the next generation of

computer systems for clinica. jse.

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The DENDRAL and Meta-DENDRAL programs are products of a large

interdisciplinary group of Stanford University scientists concerned with

many and highly varied aspects of the mechanization of scientific

reasoning and the formalization of scientific knowledge for this purpose.

An early motivation for our work was to explore the power of existing AI

methods, such as heuristic search, for reasoning in difficult scientific

problems. Another concern has been to exploit the AI methodology to

understand better some fundamental questions in the philosophy of science,

for example, the process by which explanatory hypotheses are discovered or

judged adequate. From the start, the project has had an application

dimension. It has sought to develop "expert level" agents to assist in

the solution of problems in their discipline that require complex symbolic

reasoning. The applications dimension is the focus of this paper. In

order to achieve high performance, the DENDRAL programs incorporate large

amounts of knowledge about the area of science to which they are applied,

structure elucidation in organic chemistry. A "smart assistant" for a chemist needs to be able to perform many tasks, as does an expert, but

124
need not necessarily understand the domain at the same theoretical level

as the expert. The over-all structure elucidation task is described

followed by a description of the role of the DENDRAL programs within that

framework. The Meta-DENDRAL programs use a weaker body of knowledge about

the domain of mass spectrometry because their task is to formulate rules

of mass spectrometry by induction from empirical data.

Cahart, R. E., "CONGEN: An Expert System Aiding the Structural Chemist," in D.

Michie (Ed.), Expert Systems in the Micro-Electronic Age , Edinburgh,

Scotland: Edi nburgh University Press, 1979.

CONGEN (for CONstrained GENerator) is a complex symbol -manipulation

program for the construction of chemical structures. Its input consists

of a molecular formula (i.e., a list of atoms to be used as building

blocks) together with restrictions (constraints) on the possible

interconnections among the atoms. Using a variety of sophisticated

graph-theoretic algorithms, CONGEN finds all possible ways of assembling

the atoms into molecular structures which satisfy the specified

constraints. Aside from a description of CONGEN and some examples of its

contributions to structure analysis, the discussion focuses upon two

issues which are important in any application of expert systems. One

relates to the amount of human engineering needed to overcome the

psychological barriers to the use of complex computer programs. This

issue not only encompasses the design of a smooth and helpful

user-interface, but also of basic algorithms to reformulate the user's

input. There are often several alternative ways of expressing a problem

to a complex expert system, and what seems to the user to be a perfectly

logical statement of the problem may be far from optimum from the

program's standpoint. The other issue involves the choice between

different representations of the expert knowledge contained in the sytems:

What parts of the knowledge should be hand coded into the system as highly

efficient, special -purpose algorithms, and what parts should be left in a more flexible, external form?

Callero, M. L. Jamison, and D. A. Waterman, "TATR: An Expert Aid for Tactical

Air Targeting (Interim report)," Report No.: RAND/N-1 796-ARPA, RAND

Corp., Santa Monica, CA, Jan. 82.

This report describes an initial version of TATR (Tactical Air Target

Recommender) , a prototype expert system being developed at RAND to assist

tactical air targeters in selecting and prioritizing targets. TATR

applies a knowledge engineering problem-solving approach in which human

domain knowledge is essential, and judgment plays a larger role than

mathematical algorithms and stochastic formalisms. Under interactive user

direction, TATR preferentially orders enemy airfields, determines targets

on those airfields to attack, and identifies the most effective weapons

systems against those targets. TATR is programmed in the ROSIE language,

that has an English-like syntax that facilitates nonprogrammer

125
comprehension and program verification. The program applies predetermined

planning heuristics to generate an airfield attack plan. It then replans,

incorporating user modifications, and projects a series of plans over

several days. TATR interactively maintains databases by requesting and

processing updates from the user and provides detailed information about

plans, friendly force capability, and enemy force posture and status.

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Chandrasekaran, B., and S. Mittal, "Conceptual Representation of Medical

Knowledge for Diagnosis by Computer: MDX and Related Systems," in

Marshall C. Yovits (Ed.), Advances in Computers , Vol. 22, New York:

Academic Press, 1983. ~"~ "TM

The purpose of this article is to describe an approach to the design of

medical decision-making systems based on the notion of conceptual

structures for knowledge representation. A collection of related systems

that have been under development in the authors' laboratory exemplifies

this approach, but the areas are more general than the particular systems

to be described. Section 2 provides an overview, from a theoretical

viewpoint, of the conceptual structure methodology. Later sections

describe the functioning of the systems being developed to give

concreteness to the theoretical ideas. The central system in this group

of systems is called MDX, which is a diagnostic system (i.e., it attempts

to classify a given case as an element of a disease taxonomy). This

system interacts with two other systems during its problem solving, PATREC

and RADEX. The former is a knowledge-based patient data base system that

answers MDX's queries about patient data, and the latter is a radiological

consultant which helps MDX in the interpretation of various kinds of

imaging data. Both PATREC and RADEX are invoked by MDX as needed, but MDX

is in control of the overall diagnostic process.

Chandrasekaran, B., S. Mittal, and J. W. Smith, "RADEX: Towards a Computer-Based Radiology Consultant," in E. S. Gelesma and L. N. Kanal

(Eds.), Pattern Recognition in Practice , Amsterdam, Netherlands:

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Chang, C. L., "Decision Support in an Imperfect World," Proceedings of Trends

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Clancey, W. J., "Tutoring Rules for Guiding a Case Method Dialogue,"

International Journal of Man-Machine Studies , Volume 11, 1979.

Clancey, W. J., and R. Letsinger, "NEOMYCIN: Reconfiguring a Rule-Based Expert

System for Application to Teaching," Proceedings of the Seventh IJCAI , American Association for Artificial Intelligence, 1981.

NEOMYCIN is a medical consultant system in which MYCIN'S knowledge base is

reorganized and extended for use in GUIDON, a teaching program. The new

system constitutes a psychological model for doing diagnosis, designed to

provide a basis for interpreting student behavior and teaching diagnostic

strategy. The model separates out kinds of knowledge that are

procedurally embedded in MYCIN'S rules and are inaccessible to the

teaching program. The key idea is to represent explicitly and separately:

a domain-independent diagnostic strategy in the form of meta-rules,

knowledge about the structure of the problem space, causal and

data/hypothesis rules, and world facts. As a psychological model,

NEOMYCIN captures the forward-directed, compiled association mode of

reasoning that characterizes expert behavior. Collection and

interpretation of data are focused by the differential or working memory

hypotheses. Moreover, the knowledge base is broadened so that GUIDON can

teach a student when to consider a specific infectious disease and what

competing hypotheses to consider (i.e., essentially the knowledge a human

would need in order to use the MYCIN consultation system properly).

Clancey, W. J., and E. H. Shortliffe (Eds.), Readings in Medical Art ificial

Intelligence: The First Decade , Reading, MA: Addi son-Wesl ey Publishing

Company, 1984.

Davis, R., "TEIRESIAS: Applications of Meta-Level Knowledge," in

R. Davis and D. B. Lenat (Eds.), Knowledge-Based Systems in Artificial

Intel 1

i

g ence , New York: McGraw-Hill Book Company, 1982.

Davis, R. , et al., "The DIPMETER ADVISOR: Interpretation of Geologic Signals,"

Proceedings of the Seventh IJCAI , American Association for Artificial

Intelligence, 1981.

de Mori, R., et al., "An Expert System for Speech Decoding," Proceedings of the

ECAI-82, 1982.

Dillard, J. F., and K. Ramakrishna, "Knowledge-Based Expert Computer Systems

for Accounting Related Tasks: The Case of Price Analysis," Proceedings of

the Sixteenth Annual Meeting of the Ame rican Institute for Decision

Sciences, Atlanta", GA: American Institute for Decision Sciences,

Nov. 1984.

Artificial intelligence (Al) technology is applied in constructing a prototype system to assist in price analysis decisions. This is

accomplished by using indepth interviews, verbal protocols and available

authoritative documentation. The resulting knowledge base is used to

design the architecture of a prototype, which in turn provides the basic

structure for building more intelligent expert computer systems in this

and other related decisions situations.

127
Duda, R. 0., and T. D. Garvey, "A Study of Knowledge-Based Systems for Photo

Interpretation," Artificial Intelligence Center, SRI International, Menlo

Park, CA, 1980.

Dutta, A., and A. Basu, "An Artificial Intelligence Approach to Model

Management in Decision Support Systems," Computer , Vol. 17, No. 9, Sept.

1984.

Decision support systems, or DSS's, are computer-based systems that can be

used directly by decision makers who are not sophisticated programmers to

solve semistructured or unstructured problems. Such problems are

characterized by the lack of quantitative descriptions, well-defined

goals, or prescribed algorithms for their solution. As a result, the

distribution of effort in solving them differs significantly from that

required for structured problems. Since many important management

problems, especially strategic problems, tend to be unstructured, or

semistructured at best, the development of effective DSS's is an active

field of research. Generically, a DSS consists of three major subsystems;

the dialog, data, and the models. Due to the characteristics of DSS

applications, the solution process usually involves appreciable

trial-and-error, and data is transformed in various ways through a diverse

collection of program modules (models). It is therefore necessary to have

not only a comprehensive collection of such models, but also suitable

mechanisms to use and control these models to solve problems that are

usually phrased as queries to the system. In other words, the design of

the model -management subsystem has a major impact on the interaction

between a user and the DSS. This article addresses two key issues in

model -management system design: machine representation of models and

development of mechanical methods for their manipulation.

Engelman, C, C. H. Berg, and M. Bischoff, "KNOBS: An Experimental Knowledge

Based Tactical Air Mission Planning System," Proceedings of the Sixth

I JCA I , American Association for Artificial Intelligence, 1979.

KNOBS (from KNOwledge Based System) refers to an experimental system under

development at MITRE corporation to explore the applicability of

artificial intelligence techniques to the implementation of an automated,

extremely flexible tactical mission planning consultant. This paper

discusses that system and another one which supports the rule-based

simulation of aircraft identification strategies.

Ercegovac, Z., "Knowledge-Based Expert Systems: A Profile and Implications,"

National Online Meeting Proceedings , Apr. 1984.

The purpose of this article is to discuss some of the implications of

knowledge-based expert systems in general, and specifically the

applications in library-related practice. The main foci of the article

are: general profile of research and development in knowledge-based

expert systems; implication of knowledge-based expert systems in the

context of library practice, and the systems' role for the future

management of information resources, services, and products; and impact of

artificial intelligence on professional goals and values for excellence in

library services, particularly in view of the financial pressures that

face libraries today. It is anticipated that this article will be useful

128
library services, particularly in view of the financial pressures that

face libraries today. It is anticipated that this article will be useful

to libraries and users of library services who will be affected by the AI

technology as it unfolds.

Fallon, R., "Rule-Based Modeling as an Analysis Tool: Implications for

Resource Allocation Within the Strategic Air Command," Report No.:

N-1489-AF, RAND Corporation, Santa Monica, CA, 1980.

Fieschi, M. , "Expert Systems: A New Look at Computers in Medical Education,

Meeting the Challenge: Informatics and Medical Education," Proceedings of

the IFIP-IMIA Working Conference , Amsterdam, Netherlands: North-Holland,

1983.

The classic medical education aid programs are directed mainly towards

teaching objectives aimed at the acquisition of knowledge; they are rigid

and inflexible where the acquisition of ability is concerned. Artificial

intelligence methods and expert systems make it possible to contemplate

the construction of systems which have knowledge of the field being

studied, of the pedagogical data, of the student's level, and knowledge

allowing a man-machine communication very close to natural language.

Moreover, by their construction, these systems present considerable

advantages with regard to the modularity and easy up-dating of the

knowledge bases. These facilities, in addition to the high level of

expertise they present on the subject dealt with, allow one to try

different explicit teaching strategies and to provide explanations adapted

to the student.

Forbus, K. D., "Qualitative Reasoning about Physical Processes," Proceedi ngs of

the Seventh IJCAI , American Association for Artificial Intelligence, 1981.

Fox, J., and A. Rector, "Expert Systems for Primary Medical Care?" Automedi ca , Vol. 4, No. 2/3, 1982.

Fujita, T., and S. Goto, "A Rule-based Routing System," Proceedings of the IEEE

International Conference on Computer Design: VLSI in Computers , New York:

IEEE, 1983.

Gale, W. A., and D. Pregibon, "Artificial Intelligence Research in Statistics,"

AT&T Bell Laboratories, Murray Hill, NJ, 1984.

Gale, W. A., and D. Pregibon, "An Expert System for Regression Analysis,"

Proceedings of the Fourteenth Symposium on the Interface of Computer

Science and Statistics , New York, NY: Spri nger-Verlag, 1982.

Gaschnig, J., "Prospector: An Expert System for Mineral Exploration," in D.

Mi chi e (Ed.), Introductory Readings in Expert Systems , New York, NY:

Gordon Breach Science Publishers, 1982.

Gaschnig, J., "Application of the Prospector System to Geological Exploration

Problems" in J. E. Hayes, D. Michie, and Y. H. Pao (Eds.), Machi ne

Intelligence 10, Chichester, England: Horwood, 1982.

129
A partial criterion for the success of a knowledge-based problem-solving

system is its usefulness as a tool to those working in its specialized

domain of expertise. Here we describe several applications of the

PROSPECTOR consultation system to mineral exploration tasks. One was a pilot study conducted for the National Uranium Resource Estimate program

of U.S. Department of Energy. This application estimated the favorability

of several test regions for occurrence of sandstone uranium deposits. For

credibility, the study was preceded by a performance evaluation of the

relevant portion of PROSPECTOR'S knowledge base, which showed that

PROSPECTOR'S conclusions agreed very closely with those of the model

designer over a broad range of conditions and levels of detail.

Gelernter, H. L., et al., "Empirical Explorations of SYNCHEM," Scien ce,

Vol. 197, 1977.

Gershman, A., "Building a Geological Expert System for Dipmeter

Interpretation," Proceedings of the ECAI- 82, 1982.

Geschke, M. J., R. A. Bullock, and L. E. Widmaier, "TAC II: An Expert

Knowledge-Based System for Tactical Decision Making," Master's thesis,

Naval Postgraduate School, Monterey, CA, June 1983.

There exists a genuine need for a tactical decision-making system within

the Department of Defense for the small scale environment tactical

decision maker. To this end, we propose TAC II, a prototypical system for

tactical decision making, to be implemented as a distributed system on microcomputers. TAC II is a redesign and partial implementation of an expert Artificial Intelligence system proposed by previous Naval

Postgraduate School students. The system receives preprocessed sensor

inputs, determines what contacts are present, and suggests the best

possible actions to take. It performs target analysis and correlation

based on the current tactical situation. Production rules are used to

discover which actions have been established by higher authority for the

current tactical situation. A pattern matching algorithm provides a heuristic means of identifying similar known situations, and suggests

actions to take based on those situations.

Goldstein, I. P., "The Genetic Graph: A Representation for the Evolution of

Procedural Knowledge," in D. Sleeman and J. S. Brown (Eds.),

Intelligent Tutoring Systems , New York: Academic Press, 1982.

Gorry, G. A., H. Silverman, and S. G. Pauker, "Capturing Clinical Expertise: A

Computer Program that Considers Clinical Responses to Digitalis,"

American Journal of Medicine, Vol. 64, 1978.

130
Groundwater, E. H., "A Demonstration of an Ocean Surveillance Information

Fusion Expert System," Proceedings of the SPIE International Society of

Optical Engineering , Vol. 485, May 1984.

The expert system described here models the thought processes of an ocean

surveillance watch analyst attempting to assess vessels' missions and

destinations, given their correlated tracks, history, and location/status

of other vessels in the domain of interest. The expert system has been

developed using the 0PS5 production system and the Franz LISP programming

language on a VAX/VMS computer system. The architecture and design of the

expert system are presented, implementation issues are discussed, and

progress and future plans are reviewed.

Hahn, G. J., "More Intelligent Statistical Software and Statistical Software

and Statistical Expert Systems: Future Directions," The American

Statistician , Vol. 39, No. 1, Feb. 1985.

Stati sti cal computer programs are becoming increasingly accessible to

people with limited statistical training. More intelligent statistical

software is clearly needed. In this article, new or improved

offerings -- ranging from computer-based indexes of the literature to

expert statistical systems -- are discussed and illustrated. Three general

levels of statistical software are differentiated: computerized

statistical answering and referral services, expert guidance embedded in

statistical programs, and automated statistical consultation and data

analysis. The first of these is already feasible and is being implemented

in various ways. Embedding expert guidance in statistical programs is a technically challenging, but highly worthwhile undertaking. Automating

statistical consulting and data analysis in the form of a statistical

expert system is fraught with difficulties; the best chances of success

appear to be in researching specialized modules. Possible features of an

expert system module for product-life data analysis are briefly described.

Harandi, M. T., "A Knowledge-Based Programming Support Tool," Proceedings of

Trends and Applications 1983 on Automating Intelligent Behavior:

Applications and Frontiers , Silver Spring, MD: IEEE Computer Society

Press, May 1983.

This paper presents an overview of a knowledge-based programming support

tool. Although the system would not synthesize programs automatically, it

has the capability of aiding programmers in various phases of program

production such as design, coding, debugging and testing. The underlying

design principles of this system are similar to those governing the

implementation of knowledge-based expertise in other domains of human

mental skill. The system is composed of several major units, each an expert system for a sub-domain of program development process. It

implements various elements of programming expertise by an interactive

system which allows the domain specialist to easily and effectively

transfer to the system the knowledge it needs for its decision making.

Harandi, M. T., "Knowledge-Based Program Debugging: A Heuristic Model,"

Proceedings of the Conference on Software Development Tools,

131
Techniques, and Alternatives , Silver Spring, MD: IEEE Computer Society

Press, July 1983.

Program debugging is a time and resource consuming task. The fact that

there are a large number of human experts in this domain indicates that

this is an area where knowledge-based techniques can be applied. This

paper discusses a knowledge-based program debugging system which embodies

heuristics dealing with a majority of compile time and certain run time

errors for which sufficient explicit symptoms are present. The paper

includes a brief description of expert systems, an overview of the

properties of the domain of program debugging, and a presentation of the

structure and function of the debugging system.

Hartley, R. T., "CRIB: Computer Fault-Fi ndi ng Through Knowledge Engineering,"

Computer , Vol. 17, No. 3, Mar. 1984.

Hayes-Roth, F., and V. R. Lesser, "Focus of Attention in the Hearsay-II Speech

Understanding System (Interim report)," Report No.: AFOSR-TR-77-0326,

Dept. of Computer Science, Carnegie-Mellon Univ., Pittsburgh, PA, Jan.

1977.

Hearsay-II is a complex, distributed-logic system for speech understanding

developed at Carnegie-Mellon University. Processing is performed by

independent, data-directed knowledge source processes that examine and

alter values in a global data base representing hypothesized phonetic

segments, syllables, words, and phrases, as well as the hypothetical

temporal and logical relationships between them. The question of how the

numerous potential activities of the knowledge sources should be scheduled

to complete the interpretation of an utterance in minimal time is called

the focus of attention problem. Near optimal focusing is especially

important in a speech understanding system because of the potentially very

large solution space that needs to be searched. This focus of attention

problem is representative of general resource allocation problems

involving cooperative and competitive processes. A general attention

control mechanism is developed that facilitates the experimental

evaluation of a variety of specific attention control policies (such as best-first, bottom-up, and top-down search strategies) and allows the

modular addition of specialized heuristics for the speech understanding

task. Empirical results demonstrate the effectiveness of the focusing

principles. Possible directions for future research are considered.

Hertz, D. B. , "Artificial Intelligence and the Business Manager,"

Computerworld , Vol. 17, No. 43, Oct. 1983.

The Fortune 1000 executive may have too much data too soon. His problem

lies in turning it into usable information. Artificial intelligence (AI)

may promise one resolution of the executive's dilemma. Knowledge resides

in the facts; mining that knowledge for more effective competition,

smoother operations and better planning is a major goal of AI knowledge

scientists and engineers. They believe the right kind of computer

132
programs will turn the tide of data from an uncontrollable flood to an

orderly, productive stream.

Hollander, C. R., et al., "The Drilling Advisor," Proceedings of Trends and

Applications 1983 on Automating Intelligent Behavior: Applications and

Fronti ers , Silver Spring, MD: IEEE Computer Society Press, May 1983.

This paper discusses the Drilling Advisor, an interactive computer-based

consultant whose goal is to provide advice on how to resolve problems

encountered in the course of drilling production oil wells. At present,

only problems which manifest themselves as striking of the drilling

mechanism within the bore-hole are considered. The Drilling Advisor is an example of a knowledge-based expert system in which a superior level of

task performance is achieved by representing, in data structures, a huge

amount of expert knowledge obtained from a human specialist.

Horn, W., W. Buchstaller, and R. Trappl, "Knowledge Structure Definition for an Expert System in Primary Medical Care,

Proceedi ngs of the Seventh IJCAI , American Association for Artificial Intelligence, 1981.

Hudson, D. L., and T. Estrin, "EMERGE - A Data-driven Medical Decision Making

Aid," IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. 6, No. 1, Jan. 1984.

EMERGE is an expert system designed as a medical decision-making aid. It

is machine-dependent, and is implemented in standard PASCAL. It has

modest memory requirements and can operate on a microcomputer. EMERGE is

rule-based and its initial application is the analysis of chest pain in

the emergency room. The knowledge base is maintained separately from the

consultation program. Thus the application area can be changed without

any modification to the software. This paper describes the control

structures and rule searching procedures used in EMERGE.

Hughes, P., "Keyword Access System," Creative Computing , Vol. 6, No. 3, March

1980.

The Keyword Access System (KAS) is a set of programs designed to store

information about magazine articles and allows this information to be

retrieved in various ways. The data in the file used by KAS consist of

article name, author, magazine name, date, page number, and up to ten

keywords or descriptors. The purpose of KAS is to access information more

easily either by an online inquiry using a terminal, or by looking through

a printed report. The online inquiry program allows the user to qualify a search by any or all of the following: magazine name, author, up to 10

keywords. A printed report of all the data is available in the order the

information is stored in the file and, also, sorted by keyword. KAS is

implemented on a SWTPC MP-68 computer using Basic 3.0.

Jones, M., "Applications of Artificial Intelligence Within Education,"

Computers and Mathematics , Volume 11, No. 5, 1985.

133
Jones, M. J., and D. T. Crates, "Expert Systems and Videotex: An Application

in the Marketing of Agrochemicals, " Proceedings of Videotex '84

Internati ona l , London, England: Online Publications, 1984.

The development of a computer-based agricultural advisory system is

described bringing together the technologies of expert systems and

videotex. The factors leading to the choice of these media are discussed

in the context of the role of the system as a marketing aid.

Johnson, W. L., and E. Soloway, "PROUST: Knowledge-Based Program

Understanding," Report No.: YALEU/DCS/RR-285, Department of Computer

Science, Yale University, New Haven, CT, Aug. 1983.

This paper describes a program called PROUST which does on-line analysis

of PASCAL programs written by novice programmers. PROUST takes as input a program and a nonalgorithm description of the program requirements, and

finds the most likely mapping between the requirements and the code. This

mapping is in essence a reconstruction of the design and implementation

steps that the programmer went through in writing the program. A

knowledge base of programming plans and strategies, together with common

bugs associated with them, is used in constructing this mapping. Bugs are

discovered in the process of relating plans to the code. PROUST can give

explanations of program bugs by relating the buggy code to its underlying

i ntentions.

Kaihara, S., et al., "A Rule-Based Physicians' Consultation System for

Cardiovascular Diseases," Proceedings of the International Conference on Cybernetics and Society , New York: IEEE, Oct. 1978.

Kohler, J. L., "The VENTILATION EXPERT for Underground Coal Mines," Proceedings

of the First Symposium on the Theory and Application of Expert Systems in

Emergency Management Operations , Special Publication 717, Gaithersburg, MD

National Bureau of Standards, 1986.

Kulikowski, C. A., "Artificial Intelligence Methods and Systems for Medical

Consultation," IEEE Transactions on Pattern Analysis Machine Intelligence , Vol. 2, No. 5, Sept. 1980.

The major artificial intelligence (AI ) problems that arise in designing a consultation program involve choices of knowledge representations,

diagnostic interpretation strategies, and treatment planning strategies.

The need to justify decisions and update the knowledge base in the light

of new research findings places a premium on the modularity of a representation and the ease with which its reasoning procedures can be

explained. An important insight that has resulted from the design of

several artificial intelligence systems is that robustness of

performance, in the presence of many uncertainty relationships, can be

achieved by eliciting from the expert a segmentation of knowledge that

also provides a rich network of deterministic relationships to interweave

in the space of hypotheses. A number of knowledge-based AI

representational schemes that generalize the results of the early

consultation programs have emerged recently (EMYCIN, EXPERT, AGE). They

provide techniques and tools for system building that promise to be very

versatile in helping to design new consultation programs.

134
Kulikowski, C. A., and S. M. Weiss, "Representation of Expert Knowledge for

Consultant," in P. Szolovits (Ed.), Artificial Intelligence in Medicine , Boulder, CO: Westview Press, 1982.

Lavrac, N., "KARDIO-E - An Expert System for Electrocardiographic Diagnosis of

Cardiac Arrhythmias," Expert Systems , Vol. 2, No. 1, Jan. 1985.

KARDIO-E is an expert system for the electrocardiographic diagnosis of

heart arrhythmias. At the present stage, the system can be used as a helpful diagnostic tool in the routine assessment of ECG recordings in

preventive or systematic examinations as, by a cardiologist's estimation,

its performance is equivalent to that of a specialist of internal medicine

(non-cardiologist) highly skilled in the reading of ECG recordings. The

system is also useful for instruction in electrocardiography education.

This paper presents the ECG diagnosis problem, the automatic generation of

the system's knowledge base, and a detailed description of the system.

Results of an assessment study are also presented.

Lenat, D. B., "EURISKO: A Program that Learns New Heuristics and Domain

Concepts," Artificial Intelligence, Volume 21, 1983.

Lewis, J. W., and F. S. Lynch, "GETREE: A Knowledge Management Tool,"

Proceedings of Trends and Applications 1983 on Automating Intelligent

Behavior: Applications and Frontiers , Silver Spring, MD: IEEE Computer

Society Press, May 1983.

In practical expert systems, regardless of inference technology, the

accuracy and completeness of the knowledge base determine expert system

performance, and the cost of acquiring that knowledge base tends to

dominate all other hardware and software costs. To reduce knowledge

acquisition cost and error rate, GETREE a new interactive knowledge

management system -- is being designed and implemented in GE Corporate

Research. In the new system, the knowledge base is represented as an

(almost tree-like) network of nodes which can be displayed and manipulated

on a personal computer workstation. Users -- even unsophisticated

users can build and navigate these networks, modify nodes and connecting

arcs, verify correctness visually, follow the execution of inference

engines, and generate equivalent code to be run in more constrained target

environments. Consequently, with GETREE, the user-organization can have

full responsibility for and control of the knowledge base.

Lumley, J. W., "Using Expert Systems for Planning," IEE Colloquium on Decision

Support Asp ects of Expert Systems (Digest No. 67), London, England: IEE,

May 1984.

Planning is an important part of many decision-making processes involving

generation of a sequence of actions, prediction of the effect of these

actions, and modifications of the plan in the light of these predictions.

As expert systems begin to be used for more demanding decisions, the

ability to plan becomes essential. This article considers the problems

involved with using expert systems in this planning role.

135
McDermott, J., "XSEL: A Computer Salesperson's Assistant," in J. E. Hayes, D.

Michie, and Y. H. Pao (Eds.)» Machine Intelligence 10 , Chichester,

England: Horwood, 1982. * "

Rl, a knowledge-based configuere of VAX-11 computer systems, has been used

by Digital Equipment Corporation's Manufacturing Organization. The

success of this program, and the existence at DEC of a newly formed group

capable of supporting knowledge-based programs, has led other groups at

DEC to support the development of programs that can be used in conjunction

with Rl. This paper describes XSEL, a program being developed at

Carnegie-Mellon University that will assist salespeople in tailoring

computer systems to fit the needs of customers. XSEL will have two kinds

of expertise: it will know how to select hardware and software components

that will fulfill the requirements of a particular set of applications, and

it will know how to provide explanations in the computer system sales

domai n.

McKeown, Jr., M. D., and J. McDermott, "Toward Expert Systems for Photo

Interpretation," Proceedings of Trends and Applications 1983 on Automating

Intelligent Behavior: Applications and Frontiers , Silver Spring, MD:

IEEE Computer Society Press, May 1983.

This paper describes some preliminary results in the design and

implementation of a system for semi-automatic photo-interpretation of high

resolution aerial photographs. The system, SPAM, consists of three major

components, an image/rnap database, a collection of image processing tools,

and a rule-based system whose domain of expertise is commercial airports

in general, and National Airport (Washington, D.C.) in particular.

Discussed are the design rationale and implementation. Applications for

such photo-interpretation systems include cartography and

decision-support systems for situation assessment.

Michalski, R. S., and R. L. Chilausky, "Learning by Being Told and Learning

from Examples: An Experimental Comparison of Two Methods of Knowledge

Acquisition in the Context of Developing an Expert System for Soybean

Disease Diagnosis," International Journal of Policy Analysis and

Information Systems , Volume 4, No. 2, Dec. 1980.

Mill, J., "Expert Systems Made Simple," Computer Magazine , Feb. 1985.

Miller, F. D., et al., "ACE (Automated Cable Expert) Experiment: Initial

Evaluation of an Expert System for Preventive Maintenance," Proceedings of

the Joint Services Workshop on Artificial Intelligence in Maintenance , Boulder, CO, Oct. 1983.

ACE is a knowledge-based system designed to direct preventive maintenance

activities in the local telephone network. ACE was developed at AT&T Bell

Laboratories to demonstrate and evaluate the potential for using expert

systems technology in cable maintenance. This paper describes the role of

ACE in the maintenance process and discusses the methods used to evaluate

ACE's performance in the field. Some general issues for the evaluation of

expert systems are also considered.

136
Mittal, S., B. Chandrasekaran, and J. Sticklen, "PATREC: A Knowledge-Directed

Database for a Diagnostic Expert System," Computer , Vol. 17, No. 9,

Sept. 1984.

PATREC is one component in a cluster of knowledge-based medical expert

systems developed at Ohio State University. This system holds extensive

knowledge on relevant medical data entities; it uses this knowledge to

acquire and organize data about patients and to answer questions about

patient data needed for diagnostic reasoning. The most important feature

of Patrec is its use of inferential knowledge, embedded in the underlying

knowledge base, to generate answers when corresponding data are not

explicitly stored in the database. In addition, PATREC can handle some

temporal aspects of the data and answer questions regarding the temporal

relationships of events. While Patrec is an experimental vehicle using a variety of state-of-the-art AI representational techniques, it is, in

fact, extensively used by an automated diagnosis system called MDX.

Negoita, C. V., "Management Applications of Expert Systems," Human Systems

Management , Vol. 4, No. 4, Autumn 1984.

Only recently have expert systems advanced to the point that management

applications are promising to accomplish practical results. Most of these

results can be attributed to the fact that they can have natural language

front ends and accept imprecise data. Internalizing a model of the

external milieu, they avoid interfaces and permit a real dialogue with the

users.

Nelson, D. E., "Combat Battle Damage Assessor Expert System," Report No.:

SBI-AD-F650 094, Air Force Wright Aeronautical Labs., Wright-Patterson

AFB, OH, May 1984.

Newell, A., et al., Speech Understanding Systems: Final Report of a Study

Group , New York: American Elsevier, 1973.

Newell, A., "HARPY: Production Systems and Human Cognition," Report No.

CMU-CS-78-140, Computer Science Department, Carnegie-Mellon University,

Pittsburgh, PA, 1978.

Nii, H. P., and E. A. Feigenbaum, "Rule-Based Understanding of Signals," in D.

Waterman and F. Hayes-Roth (Eds.), Pattern-Directed Inference Systems, New

York: Academic Press, 1978.

Nii, H. P., et al., "Si gnal -to-Symbol Transformation: HASP/SIAP Case Study,"

AI Magazine, Volume 3, No. 2, 1982.

O'Keefe, R. M. , "Expert Systems and Operational Research - Mutual Benefits,"

Journal of Operational Research Society , Vol. 36, No. 2, Feb. 1985.

The aim of much operational research and expert systems work is similar

(i.e., helping a decision maker do his job). Liaison will produce mutual

benefits. Expert systems development will benefit from the experience of

operational research. The operational research community is well placed

to support and develop the use of such systems by management. In this

137
paper the author considers: knowledge as a model, expert systems in the

OR domain, knowledge acquisition, and decision support systems.

O'Keefe, R. M. , "An Expert System for Statistics," Technical Conference on Theory and Practice of Knowledge Based Systems , England: Brunei

University, 1982.

Oleson, C. E., "EXAMINER: A System Using Contextual Knowledge for Analysis of

Diagnostic Behavior," Proceedings of the Fifth IJCAI , American Association

for Artificial Intelligence, 1977.

Pauker, S. et al .

, "Toward the Simulation of Clinical Cognition: Taking the

Present Illness by Computer," American Journal of Medicine , Volume 60,

1976.

Pople, H. E., Jr., "Heuristic Methods for Imposing Structure on II 1 -Structured

Problems: The Structuring of Medical Diagnosis," in P. Szolovits (Ed.),

Artificial Intelligence in Medicine , Boulder, CO: Westview Press, 1982.

Portier, K. M., and P. Y. Lai, "A Statistical Expert System for Analysis

Determination," Proceedings of the Statistical Computing Section , American

Statistical Association, 1983.

Reboh, R., "Knowledge Engineering Techniques and Tools in the Prospector

Environment," SRI Technical Note 243, AI Center, SRI International, Menlo

Park, CA, May 1982.

Reggia, et al., "Towards an Intelligent Textbook of Neurology," Proceedings of

the Fourth Annual Symposium on Computer Applications in Medical Care , 1980.

Rosenberg, S., "Knowledge-Based System for Providing Intelligent Access to a Petroleum User Database," Dept. of Energy, Lawrence Berkeley Laboratory,

University of California at Berkeley, 1979.

Rychener, M. D., "Expert Systems for Engineering Design," Expert Systems , Vol. 2, No. 1, Jan. 1985.

An expert system embodies a human expert's domain-specific knowledge and

skill, acquired and refined over years of experience. A number of

problems in engineering design can be solved by using current expert

system techniques. This paper enumerates the main components of such

problems and the steps that are taken in solving them. A few prototypical

artificial intelligence systems embody techniques that can be applied to

engineering problems. These are surveyed, and their relevance to

components of design problems is discussed. Some expert systems in design

domains are summarized, with emphasis on aspects that can illustrate wider

applicability of the techniques. The area of engineering design offers

rich opportunities for advancing the state-of-the-art in expert systems.

An annotated bibliography is included.

138
Scarl, E. A., J. Jamieson, and C. Delaune, "Knowledge-Based Fault Monitoring

and Diagnosis in Space Shuttle Propel lant Loading," Proceedings of the

1984 IEEE National Aerospace and Electronics Conference (NAECON

)

, Vol . 2,

New york: IEEE, 1984.

LES (LOX Expert System) is a knowledge-based system being applied to a semi -real -time system application, monitoring the loading of a cryogenic

fuel for the space shuttle. The system's design and fault isolation

techniques are described.

Schutzer, U., "Applications of Artificial Intelligence to Military

Communications," Proceedings of the 1983 IEEE Military Communications

Conference , Vol. 3, New York: IEEE, 1983.

This paper explores the field of artificial intelligence with respect to

its application to military communication design problems. In particular,

it is shown how natural processing languages and knowledge-based system

technologies can be used to reduce the required capacity, and to improve a communication system's robustness and tolerance of errors by trading off

computation for communication. These technologies are also shown to

improve the security of a military communications system. Other

applications of artificial intelligence technology include the use of

expert systems in the operation, control and maintenance, and training

areas.

Shannon, T. C, "XCON - DEC's Artificial Intelligence VAX Configurer," VAX/RSTS

Prof., Vol. 6, No. 5, Oct. 1984.

Shaw, R., "Artificial Intelligence and Personnel Planning," Proceedings of

the Thi rd National Conference and Exhibition on Computers in Per sonnel:

Making Manpower Profitable , London, England: Institute of Personnel

Management, June 1984.

Shortliffe, E. H. , "MYCIN: A Knowledge-Based Computer Program Applied to

Infectious Diseases," Proceedings of the First Annual Symposium on Computer Application in Medical Care, New York: IEEE, Oct. 1977.

A rule-based expert system is described which uses artificial intelligence

techniques and a model of the interaction between physicians and human

consultants to attempt to satisfy the demands of a user community that is

often reluctant to experiment with computer technology. Experience to

data has demonstrated that the program is efficient, relatively easy to

use, and reliable in the domain of bacteria therapy selection. Future

work will involve broadening and evaluating the program's expertise in

other areas of infectious disease therapy. To that end, rules regarding

diagnosis and treatment of meningitis have been written and are under

evaluation.

139
Short! iffe, E. H., et al. "ONCOCIN: An Expert System for Oncology Protocol

Management," Proceedings of the Seventh IJCAI , American Association for

Artificial Intelligence, 1981.

Shortliffe, E. H., Computer- Based Medical Consultations: MYCIN , New York:

American Elsevier, 1976.

Shortliffe, E. H., B. G. Buchanan, and E. A. Feigenbaum, "Knowledge Engineering

for Medical Decision Making: A Review of Computer-Based Clinical Decision

Aids," Proceedings of the IEEE , Vol. 67, No. 9, New York: IEEE, Sept.

1979.

Shubin, H., and J. W. Ulrich, "IDT: An Intelligent Diagnostic Tool,"

Proceedings of the National Conference on Artificial Intelligence , AAAI-82, American Association for Artificial Intelligence, 1982.

Sumner, G. C, "Knowledge-Based Systems Maintenance Applications (ATE),"

Proceedings of the Autotestcon '82: the 1982 IEEE International Automatic

Testing Conference , New York: IEEE, Oct. 1982.

Taylor, J. H., and D. K. Frederick, "An Expert System Architecture for

Computer-Ai ded Control Engineering," Proceedings of the IEEE , Vol. 72,

No. 12, New York: IEEE, Dec. 1984.

Expert or knowledge-based systems are defined as software environments

designed to aid in solving problems that require high levels of expertise,

'reasoning' (inference), and heuristics. The authors propose the

development of a rule-based expert system to create a third-generation

man/machine environment for computer-aided control engineering (CACE).

The focus is primarily on the high-level requirements for an improved CACE

environment, and on the expert system concepts and structures that have

been conceived to fulfill these needs. The chief goal is to determine

what artificial intelligence has to contribute to such an environment, and

to provide as definite and as credible a vision of an expert system for

CACE as possible. The main product of this effort is an expert system

architecture for CACE. The authors close with a brief status report.

Thomas, R. C, "Knowledge-Based Techniques for Decision Support for

Management," IEE Colloquium on Decision Support Aspects of Expert Sy s tems

(Digest No. 67 ), London: IEE, May 1984.

Tou, J. T., and J. M. Cheng, "Design of a Knowledge-Based Expert System for

Applications in Agriculture," Proceedings of Trends and Applications 1983

on Automating Intelligent Behavior: Applications and Frontier s, Si 1 ver

Spring, MD: IEEE Computer Society Press, May 1983.

This paper presents the design of a computer-based knowledge system,

APRIKS, for application in agriculture. The APRIKS system is a pilot

system which demonstrates the use of a minicomputer in agricultural

knowledge transfer and utilization for diagnostic consultation, crop pest

control, plant disease treatment, and management recommendations. The

major components of this expert system consist of a knowledge base and a recognition/inference scheme. The design makes use of pattern-directed

140
approach, as well as rule-based approach. The system can respond to both

menu-selection inputs and simple natural language inputs. It performs

three modes of operations: interactive browsing, diagnostic consultation,

and question-answering.

Tsukiyama, M., and T. Fukuda, "An Application of Knowledge Base to Control

Systems," Proceedings of the International Conference on Cybernetics an d

Society , New York: IEEE, Oct. 1981.

This paper discusses the knowledge base of plant operations and the design

of kndwledge base control system (KBCS) to realize an expert system for

plant control. The structure of the KBCS is represented in a network

organization of modules which comprise knowledge and calculation tools.

The total structure is constructed by a set of rules describing the

relation between two modules. An illustrative example is shown.

Underwood, W. E., "A CSA Model-Based Nuclear Power Plant Consultant,"

Proceedings of the National Conference on Artificial Intelligence , AAAI-82, American Association for Artificial Intelligence, 1982.

van Melle, W., "MYCIN: A Knowledge-Based Consultation Program for Infectious

Disease Diagnosis," International Journal of Man-Machine Studies , Vol. 10,

1978.

MYCIN is a computer-based consultation system designed to assist

physicians in the diagnosis of and therapy selection for patients with

bacterial infections. In addition to the consultation system itself,

MYCIN contains an explanation system which can answer simple English

questions in order to justify its advice or educate the user. The

system's knowledge is encoded in the form of some 350 production rules

which embody the clinical decision criteria of infectious disease experts.

Much of MYCIN'S power derives from the modular, highly stylized nature of

these decision rules, enabling the system to dissect its own reasoning and

allowing easy modification of the knowledge base.

van Melle, W., "A Domain-Independent Production-Rule System for Consultation

Programs," Proceedings of the Sixth IJCAI , American Association of

Artificial Intelligence, 1979.

Vesonder, G. T., et al., "ACE: An Expert System for Telephone Cable

Maintenance," Proceedings of the Eighth IJCAI , American Association for

Artificial Intelligence, 1983.

ACE, a system for Automated Cable Expertise, is a knowledge-based expert

system designed to provide trouble-shooting reports and management

analyses for telephone cable maintenance. Design decisions faced during

the construction of ACE were guided by recent successes in expert systems

technology, most notably R1/XC0N, the Digital Equipment Corporation VAX

configuration program. ACE departs from standard expert system

architectures in its use of a conventional data base management system as its primary source of information. Its primary sources of knowledge are

the users of the database system and primers on maintenance analysis

strategies.

141
Weiss, S., C. A. Kulikowski, and A. Safir, "A Model -Based Consultation System

for the Long-Term Management of Glaucoma," Proceedings of the Fifth IJCAI,

American Association for Artificial Intelligence, 1977.

Weiss, S., et al., "Building Expert Systems for Controlling Complex Programs,"

Proceedings of the National Conference on Artificial Intelligence , AAAI-82, American Association for Artificial Intelligence, 1982.

Wesson, R. B., "Planning in the World of the Air Traffic Controller,"

Proceedings of the Fifth IJCAI , American Association for Artificial

Intelligence, 1977.

An enroute air traffic control (ATC) simulation has provided the basis for

research into the marriage of discrete simulation and artificial

intelligence techniques. A program which simulates, using real world

data, the movement of aircraft in an ATC environment forms a robot's world

model. Using a production system to respond to events in the simulated

world, the robot is able to look ahead and form a plan of instructions

which guarantees safe, expedient aircraft transit. A distinction is made

between the real world, where pilots can make mistakes, change their

minds, etc., and an idealized plan-ahead world which the robot uses. The

overall simulation alternates between updating the real world and planning

in the idealized one to investigate the robot's ability to plan in the

face of uncertainty.

142
9. USE OF EXPERT SYSTEMS IN THE DOMAIN OF EMERGENCY MANAGEMENT

9.1 THE SUITABILITY OF EXPERT SYSTEMS FOR EMERGENCY MANAGEMENT PROBLEMS

Researchers have recognized that many of the problems of emergency

management can be viewed as data management decision problems like those

found in other applications areas. Thus, we have computer-based decision

systems for emergency management utilizing the standard information and

decision support procedures as tools for disaster policy analysis

(Kunreuther et al . [1978]) and disaster management and support (Wallace

and de Balogh [1985]; Belardo and Karwan [1984]; Belardo et al. [1982]).

With the advent and popularization of expert systems, many of the

decision requirements of emergency management were found to fit the

philosophy and capabilities of current expert systems technology. In

particular, expert systems have been developed for hazardous materials

spills (Allen et al. [1982]; Johnson and Jordan [1983]), coal mine

ventilation (Kohler [1986]), reactor accident (Nelson [1982, 1984]) and

earthquake early-warning and assessment (de Balogh et al. [1983], Russo

and Wilson [1983]). Although the applications and the end uses of the

associated expert systems are few in number, these attempts testify to

the suitability of expert systems to address a variety of emergency

management problems. In general, however, it is felt that expert systems

can be of most value in the response phase of emergency situations. We

expand upon this view in the next section.

9.2 EXPERT S YSTEMS FOR RESPONSE

In Figure 9.1, we list a number of emergency problems and the expert

system area of application for the response phase of each problem. It is

in this phase that we feel expert systems have the greatest potential.

Most of the response-oriented expert systems would be operated by State

and local agencies, while at the national level, we would have FEMA

expert systems for mobilization and enemy attack and recovery.

With reference to Figure 9.1, response expert systems appear to be

applicable to the following areas:

1. The problems of moving people, material and equipment into and out of

an emergency zone (evacuation routing)

2. The location and assignment of evacuees to emergency shelters

(shelter allocation)

3. The determination that an emergency situation is likely to occur

(early warning)

4. The systemic and decision processes by which a determination is made

that an emergency is occurring and automatic controls are to be

initiated to shut down or degrade essential equipment and services

(automated response/shutdown)

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5. The problems associated with marshaling of the full range of

resources necessary for the proper response to an emergency (resource

management)

6. The analysis and determination of the source, type and extent of

certain emergency situations (fault analysis)

We next discuss items (1), (2) and (5) in terms of possible activities

and research in expert systems. As noted below, expert system

requirements for these areas are interrelated with shelter allocation

dependent on routing decisions, and resource management dependent on both

shelter allocation and routing decisions.

The problem of evacuation routing is critical in those emergencies that

involve the general public to a high degree. Recent chemical spills and

toxic airborne releases have captured the headlines and have raised

serious questions as to whether communities that live in the shadow of

chemical plants are in a position to respond to a serious event. Thus,

there is a need to establish and test contingency plans for notifying the

surrounding community that an emergency has occurred, with the initiation

of specific plans based on an expert system's analysis of the emergency

(e.g., weather, time of day and other critical factors). The development

of a prototype expert system for evacuation routing, designed and tested

for a specific community, should be investigated. The extensive

literature in routing procedures would be of value here.

For those emergencies in which evacuation is required, shelter allocation

must be coordinated with routing assignments. Hence, a more general

expert system that integrates the emergency management functions of

escape routing and shelter allocation would be of value. The

requirements of a prototype expert system that includes both routing and

allocation should be studied for a high-risk community.

As evidenced by Figure 9.1, the problem of general resource management

(personnel, equipment, communications, information processing) is one

that cuts across all emergencies; it represents a fruitful area for

investigation. As a generic expert system for resource management that

is applicable to the full range of emergencies is not possible, attention

should be focused on resource problems associated with evacuation type

problems (e.g., toxic release, hurricane). For these problems, we feel

that the expert system requirements of resource management and those of

routing and shelter allocation can be combined into a powerful emergency

control tool. Further, such an integrated system, when coupled with

local and regional emergency information and data systems, would be able

to assess changing conditions and thus, be an extremely valuable decision

aid to local emergency managers.

In line with the above discussion, we cite the following bibliographic

items: Belardo et al . [1984] and de Balogh [1986] for managing disasters

using decision support systems and microcomputers; Petri e and Hathaway

[1984] for automated response to high-speed rail accidents; Ferrell and

Krzysztofowicz [1983] and Cuena [1983] for flood warning and dam control,

and Heaton [1985] and Moore et al . [1985] for earthquake warning and

145
assessment. Specifications for such an expert system based on the

requirements of a specific community should be developed.

Within the other phases of emergency management -- mitigation,

preparedness and recovery — expert systems can also be of value. For

example, expert systems can play an important role in preparedness if

they can be integrated as decision aids into training exercises, with the

expert systems being those that the trainee would actually use in the

field. During a recovery phase, expert systems can be used to analyze

eligibility for loans and government reconstruction programs.

MAJOR EXPERT SYSTEMS RESEARCH TOPICS

In this section, we describe expert system applications in areas that

stem from FEMA's operational and educational activities; these areas are

of broad scope and/or more technically oriented than the ones described

above. In particular, we address how expert systems can be used by FEMA

in the analysis of computer-based model outputs, automated early warning,

training and research, and information retrieval; what areas of expert

system research would be of most value to FEMA; and conclude with a short

list of general expert system activities that should be considered by

FEMA.

• Mode l Output Reviewer

FEMA uses many large-scale econometric, input-output and other

models to analyze impacts of emergencies on the nation's

industrial and transportation system. Such analyses are often

based on changing scenarios and require the review of a great deal

of computer output. It has been proposed that FEMA investigate a computer-based process by which such outputs can be reviewed

automatically, and the essence of the data be processed and

formatted by an expert system that replicates what the human

expert analysts would accomplish. At a minimum, such an expert

system would be able to organize the model outputs into tables and

graphs; at a maximum, it would be able to find, filter and adjust

conflicting data and to make recommendations for future analyses

and specific actions. Output analysis programs have been used for

quite some time, but here we are proposing a major advance in such

post-processor programs. Issues that would have to be resolved

include the specification of the models, the associated expert(s)

to be used to build a knowledge base, and the types of modeling

exercises to be reviewed.

• Automated Early Warning/Response

Some emergencies such as earthquakes or nuclear power-plant

accidents require a means for early detection and, given that

there is no means of stopping the incident, a quick response

(e.g., within 100 seconds) is required to shutdown or softly

degrade critical systems and to warn emergency managers. It has

been suggested that an expert system, tied into a detection

146
network, would be able to automatically shutdown critical parts of

the infrastructure and issue the appropriate warnings. The expert

system would be designed to interpret the information produced by

the detection network and to make the "call" that a destructive

incident is about to occur. There are a number of issues here

that go beyond just being able to build such an expert system.

For example, our present ability to differentiate earthquake

signals is not perfected and false alarms would trigger needless

actions (although the expert system triggering mechanism could be

designed not to be activated unless an earthquake of sufficient

Richter magnitude was actually happening). Furthermore, critical

facilities may not consent to be tied into such an automatic

system. From an expert system point of view, the logic design

would need a proper research base and an ability to be tested and

val i dated.

Expert System for Training and Research

It has been proposed to investigate how expert system concepts can

be integrated within the present training program of the FEMA

National Emergency Training Center (NETC). The current Central

City Training exercise could be augmented by expert system

decision aids for evacuation routing, allocation of resources, and

shelter allocation. Using the NETC would enable FEMA to test out

the utility of expert systems, and if successful, initiate

development of operational systems for transfer to field

locations. Research would be required to scope the application

areas, and to design the decision rules and algorithmic processes

for evaluation of the exercise information. This concept can also

be extended to FEMA Headquarters' exercises in which expert

systems can be used as decision aids and for automating certain

exercise functions.

Inf

o

rmati on/Knowl edge Base Access

Any emergency, especially at the national level, requires access

to a variety of data bases and, given expert systems will be used

in the emergency management area, access to many types of

knowledge bases. The proper use of such information can be a complex matter and could possibly require an expert system, or

certainly an advanced information retrieval system. At issue here

is the structuring and availability of the data and knowledge

bases, the usages and analyses to be made of the information, and

the availability of the proper development tools.

Related Res earch Topics

As noted, the expert system/artificial intelligence (ES/AI ) field

has not reached its full potential and is under constant

development. A number of ES/AI research topics that would

hopefully be of benefit to the emergency management field have

been proposed and are cited next.

147
Natural Language Dialogue Interface

Research in this area is needed to improve the ability to use

natural English and the emergency management jargon for those

who interface with computer and related information systems.

The hope is to ease the use of computers by non-technical

emergency management personnel by enabling them to hold a dialogue with the computer system to retrieve information and

initiate analyses.

Decis ion Rules in Expert _Sy_sjtems_

There are a number of basic areas of interest here such as:

(1) The manner in which an expert system analyzes uncertainty

to determine certainty factors.

(2) The ability to state a full set of contingencies and

decision rules.

(3) The way partial and imprecise information is interpreted.

(4) The means by which an expert system "learns" during its

1 i f etime.

(5) The use of decision models, such as the Analytic Hierarchy

Process, within an expert system.

Validati on of Expe rt Systems

As with all decision models, there is a need to validate the

recommendations of an expert system in terms of their

consistency and acceptability by emergency managers, and their

ability to replicate expert decisions. The process of

validation is extremely important with respect to FEMA expert

systems, as the automation of decisions that impact human

lives is always open to second guessing and the clarity of

hindsight. Thus, for an expert system, FEMA must establish a process with which it is validated by the associated experts

and field-tested. The design of validation processes and the

conducting of associated experiments are expert system aspects

of prime concern to FEMA.

Expert Sy s t ems She lis an d PC-Based Expert Systems

The building of an expert system can be facilitated greatly by

using commercially available software and/or specialized

equipment. Expert system shells (i.e., software that can

handle the logic and construction of decision rules and is

independent of the applications) need to be evaluated in terms

of FEMA requirements. In addition, with the expected heavy

use of personal computers (PC's) by field personnel, FEMA

should evaluate commercial PC-based expert systems on field

requirements, as well as FEMA Headquarters' applications.

148
References

Allen Jr. J. D., et al., "Spills Problem and Applied Artificial Intelligence,"

Report No.: C0NF-820418-23, Oak Ridge National Lab., Oak Ridge, TN,

1982.

This paper describes an Artificial Intelligence program designed for

assisting in the location, assessment, and cleanup of hazardous spills

and for the training of spills personnel who must deal with these

matters. It includes a discussion of expert systems, a description

of the system used, Carnegie-Mel 1 on ' s 0PS5, and illustrates a variety of

rules, some taken directly from the SPILLS program.

Belardo, S., K. R. Karwan, and W. A. Wallace, "Managing the Response to

Disasters Using Microcomputers," Interfaces, Vol. 14, No. 2, 1984.

The authors describe the microcomputer-based decision aids they developed

that assisted four different disaster management organizations: the

American Red Cross, the United States Coast Guard, a regional emergency

medical organization, and the New York State Office of Disaster

Preparedness. The four systems emphasize different components of a typical disaster response DSS.

Belardo, S., K. R. Karway, and W. A. Wallace, "An Investigation of System

Design Considerations for Emergency Management Decision Support," IEE E

Tra nsaction s on Syst ems, Management, and Cybern eti cs , Vol. S MC - 1 4 , No". 6

,

New York: 1TEE7T9~8T.

Simulation tests carried out to evaluate DSS design factors in the

context of emergency management decisionmaking are reported in this

paper. In particular, the effects of two basic considerations are

examined: 1) the availability of a microcomputer for enhancing

data display during an emergency, and 2) the stage or severity of the

emergency. The simulation was conducted at the training facility of the

Federal Emergency Management Agency in Emmitsburg, MD with county civil

preparedness personnel from around the country. The exercise involved

the response to a nuclear generating facility accident, a situation that

typically would require various inputs from public officials to protect

the local community from potential radiation exposure. The results of

the experiment indicate: 1) clear support for computerization of

emergency management decision support systems, and 2) an apparent need to

focus attention of further development of these systems to improve the

decision-making capabilities of public managers during the later

less-structured stages of an emergency.

149
Belardo, S., K. R. Karwan, and W. A. Wallace, "DSS Component Design Through

Field Experimentation: An Application to Emergency Management,"

Proceedings of the Third Int e r natio nal Conference on In fo rmation Sy s terns

,

Ann Arbor, MI, 1982.

This paper describes the use of field experimentation in the development

of a particular Emergency Management Decision Support System (EMDSS).

The model component of this sytem, which contains a bi -objective

transportation algorithm, was field tested with prospective users from a regional emergency medical organization. The results of the test are

presented and implications for the use of field experimentation as a design strategy are discussed.

Berlin, 6. N., "A Simulation Model for Assessing Building Firesafety,"

Model Systems, Inc., Boston, MA, undated.

This paper presents a computer simulation technique for analyzing

emergency evacuation and building firesafety. The systematic, objective

procedure includes mathematical modeling, graph theory and simulation to

represent and analyze the building layout. Examples illustrating the

application of this model to a mobile home are presented.

Brady, J., J. K. Bradford, and J. Mirolla, "Computer Applications for

Emergency Management," Monograph No. 85-1, California Specialized

Training Institute, San Luis Obispo, CA, 1985.

The purpose of this paper is to examine the use of computers in Emergency

Management/Emergency Services. It analyzes the current and future use of

computers in this field. The following questions are discussed: (1) Is

the government using computers effectively? (2) Is the government

spending a large amount of money on computers? (3) What kind of hardware

and software is the government using?

Buzzell, C. A., et al., "Using Interactive Color Graphics to Model the

Command Control Function in a Battlefield Simulation," in 6. Birtwistle

(Ed.), Artif ic ial Intelligence , Graphic s, and Simulation, La Jolla, CA:

Simulation Councils, Inc., 1985.

The authors describe Janus, an event-driven, stochastic computer program

simulating ground combat, in which players model the command/control

function using interactive color graphics. Giving players the

command/control function allows us to evaluate commanders' control

measures and to understand their requirements for information. Players'

actions and reactions are meant to simulate actual decision making on the

battle field. As part of the simulation, the players may experiment with

new methods; examine the effectiveness of their decisions; and study

tactics, information flow, and weapons systems in various conflict

scenarios. In the interactive mode, the simulation lacks repeatability.

To offset this, players can war game to gain insight about a plan or

weapon system and then run segments of the battle in a batch mode without

interacting. This allows them to observe the sensitivity of the

simulation to specific parameters.

150
Chartrand, R. L., "Information Technology for Emergency Management: The Many

Potentials," CRS Review, 1984.

There is growing concern in many sectors of government and society that

we as a nation -- at the federal level, as well as in the states and

localities -- are inadequately prepared to cope with the technological

and natural emergencies which affect people and property. The very

complexity of our societal structure, as reflected in jurisdictional

conflicts over responsi bl i ty and the accelerated development and use of

new energy sources, pesticides, and fertilizers with toxic side effects,

have caused lawmakers and program managers to reexamine the effectiveness

of our crisis (or emergency) management capability. One critical facet

of improving this potentiality -- through improved mitigation,

preparedness, response, and recovery mechanisms and procedures -- is the

development of advanced information systems for acquiring, storing,

processing, retrieving, and sharing essential data that may be used by

emergency managers.

Chartrand, R. L., "Optimum Emergency Management: The Effective Use of

Information Technology," Proceedin gs of the First Symposium on the Theo ry

and App lication of Expert Sy stems "in Emergency Ma nagement Operations , Special Publication 717, Gafthersburg, MD: Natfonal Bureau of Standards,

1986.

Cuena, J., "The Use of Simulation Models and Human Advice to Build Expert

System for the Defence and Control of River Floods," Proceedings of th e Eighth IJCAI, American Association for Artificial Intelligence", 1983.

de Balogh, F., "Management Science Models and Disaster Management: Problems

and Prospects for Capacity Building," Decision Analysis Series, Institute

of Safety and Systems Management, University of Southern California,

San Diego, CA, Mar. 1985.

This paper is designed to offer some observations as to the current

status of model building in disaster management and the problems and

prospects of such processes in enhancing the capacity of American society

to deal with man-made and natural emergencies. Its perspective is based

on the research being conducted by the Risk and Emergency Management

Laboratory (REML) and Decision Support Systems Laboratory (DSSL) of the

Institute of Safety and Systems Management of the University of Southern

California (USC) in several types of disaster areas to include

earthquakes, hazardous materials spills and discharges, and emergency

evacuation. Specifically, this paper: (1) describes modeling and

simulation approaches to two disaster types -- earthquake and hazardous

materials spi 1 1 /di scharge -- currently being followed at USC, (2) uses

these two examples of research to illustrate the problems associated with

modeling these two types of disasters, and, (3) generalizes about the

prospects of developing reliable and accurate models to enhance the

disaster management capacity of governments. The paper concludes that,

to date, management science models have not had a significant impact on the capacity of American society to deal with disasters. The problems of

unreliable hazard risk estimation, complex and incomplete methodology,

fragmented modeling research efforts, significant and costly data

151
requirements, lack of transferability, imbalance in research resources

committed to risk estimation models as opposed to models managing the

response and training, and lack of a common program and plan of action to

promote capacity building among public jurisdictions by federal research

sponsoring agencies all combine to ensure that progress at best will

continue to be atomized and incremental. It recommends some strategies

for overcoming these problems.

de Balogh, F., "Decision Support and Expert Systems for Emergency Management

Operations: A Microcomputer Approach," Proceedings of the First

Symposi um on t he Theory a nd Application of E xpert Systems in Emer gency

Management Operations, Special Pubfi cation 717, Gaithersburg, ML):

Nati onaOureau of"Standards , 1986.

de Balogh, F., W. Petak, and J. Sessler, "A Computerized Demonstration Model

for Earthquake Mitigation," Decision Support Systems Laboratory,

University of Southern California, San Diego, CA, Feb. 1983.

This paper focuses on the potential power of utilizing computers for

improved earthquake mitigation management within the framework of a new

class of information system: decision support systems (DSS). A

computerized demonstration system -- EDSS -- is described which is

designed to extend the capability of planners and public officials to

make effective decisions in earthquake mitigation. It consists of a micro-computer system which employs several data files on building

structure inventories by planning areas and a damage calculation

(modeling) component. The decision maker provides ground shaking

intensity data for each planning area which is used by EDSS to calculate

percentage of square feet damage and percentage of assessed value lost

for each building classification and age group. The system permits "what

if" analysis and is used interactively. A project of USC's Decision

Support Sytems Laboratory (DSSL), EDSS is at present a demonstration

model and not a comprehensive, working prototype DSS. Future plans call

for expanding EDSS into a fully operational prototype available for

testing by public officials and planners.

Ferrell, W. R., and R. Krzysztof owi cz , "A Model of Human Response to Flood

Warnings for System Evaluation," Water Resources Research, Vol. 19,

No. 6, Dec. 1983.

A behavioral model of human response to flood warnings is developed as a component of a methodology for evaluation of the performance of flood

forecast-response systems. A floodplain dweller responds to a sequence

of flood warnings by taking protective action (such as evacuation, flood

proofing, shutdown of a facility) in order to reduce his loss. The model

is meant to mimic the actual response behavior of the floodplain dweller

or to predict such behavior under future system conditions. It is

building of mathematical representations of four interconnected cognitive

elements of response: (1) uncertainty about flooding and loss prior to a flood, (2) sequential interference based on warnings during a flood, (3)

response strategy, and (4) learning after a flood. Results of an application and qualitative verification of the model are discussed.

152
Gaffney, J. E. , <Jr, and I. R. Racer, "A Learning Interpretive Decision

Algorithm for Severe Storm Forecasting Support," Proce edings o f Trends

and Applications 1983 _on _Au_t omati ng Intel 1 i gent Be havfor: Appl i ca ti o ns and Fro ntiers, Si 1 ver ~Spri ng, Mb: fEEE Computer Society Tress, May "1983.

As part of its ongoing program to develop new and better forecasting

procedures and techniques, the National Weather Service has initiated an effort in "interpretive processing". Investigation has begun to

determine the applicability of Artificial Intelligence (AI)/expert system

algorithm that is being investigated to support the forecasting of severe

thunderstorms

.

Gaschnig, J., R. Reboh, and J. Reiter, "Development of a Knowledge-Based

Expert System for Water Resource Problems," Technical Report SRI Project

1619, SRI International, Menlo Park, CA, Aug. 1981.

Gottingger, H. W., "HAZARD: An Expert System for Screening Environmental

Chemicals on Carcinogenicity," Expert Systems, Vol. 1, No. 2, Oct. 1984.

The purpose of this paper is to design an expert system or an intelligent

procedure to screen hazard potentials of environmental chemicals on the

basis of structure-activity relationships in the study of chemical

carcinogenesis. An analysis of a computerized database of known

carcinogens (knowledge base) is being performed using the

structure-activity trees in order to test the validity of the tree as a classification scheme (inference engine) and to evaluate trends or patterns that may exist between chemical structure and specificity for

target tissue, route of administration, and animal species. Practical

applications of the structure-activity tree depend on its eventual

validation as a predictor of carcinogenic activity.

Heaton, T. H. , "A Model for a Seismic Computerized Alert Network," Scienc e,

Vol. 228, May 1985.

In large earthquakes, damaging ground motions may occur at large

epicentral distances. Because of the relatively slow speed of seismic

waves, it is possible to construct a system to provide short-term warning

(as much as several tens of seconds) of imminent strong ground motions

from major earthquakes. Automated safety responses could be triggered by

users after receiving estimates of the arrival time and strength of

shaking expected at an individual site. Although warning times are

likely to be short for areas greatly damaged by relatively numerous

earthquakes of moderdte size, large areas that experience very strong

shaking during great earthquakes would receive longer warning times.

153
Hunt, A. R. , and R. K. Mullen, "The Development of an Expert System to Assess

the Vulnerability of Assets to Subnational Adversaries," Proceedin gs of

the 1984 Carn ahan Conference on Security Techn ology , New York: IEEE, May

1984.

Ishizuka, M. , K. S. Fu, and J. T. P. Yao, "Rule-Based Damage Assessment System

for Existing Sturctures," Report No.: CE-STR-82-14; NSF/CEE-82020,

School of Civil Engineering, Purdue Univ., Lafayette, IN, Mar. 1982.

The state-of-the-art of damage assessment is reviewed and SPERIL version

I, a rule-based damage system of existing structures, particularly those

subjected to earthquake excitation, is described. The problem of damage

assessment is considered in terms of the theory of pattern recognition

and an expert system approach is used to develop the computer-based

systems. In SPERIL version I, separate evidential observations are

integrated on the basis of the extended Dempster and Shafer theory for

subsets. Useful information is collected and expressed in a stylized

rule format in the knowledge base. The current rules are expected to be

updated by more specific ones for better performance.

Ishizuka, M., K. S. Fu, and J. T. P. Yao, "SPERIL I: Computer Based Structure

Damage Assessment System," Report No.: CE-STR-81-36, Purdue University,

Lafayette, IN, 1981.

Johnson, C. K. , and S. R. Jordan, "Emergency Management of Inland Oil and

Hazardous Chemical Spills: A Case Study in Knowledge Engineering," in F.

Hayes-Roth, D. A. Waterman, and D. B. Lenat (Eds.), Building Expert

Systems , Reading, MA: Addi son-Wesl ey Publishing Company, 1983.

The authors of this article describe in some detail a typical problem in

knowledge engineering: an expert system is needed to help consult with

regular workers or to augment the limited experience of off-shift workers

facing a difficult task. Like several other applications, this belongs

to the class of crises-management problems. The oil and hazardous

chemical spi 1 1 -management task demands a system that can integrate

diverse sources of knowledge, reason heuri sti cal ly with incomplete and

errorful data, accept data and advice continuously as they become

available, and allocate limited resources to various tasks in a reasonable order.

154
Kohler, J. L., "The VENTILATION EXPERT for Underground Coal Mines,"

Proceed i ngs of the First Sympo sium on the Theory a nd Applicat i on of

Expert" Sy stems "in Emergency Management Operati ons," Special Publication

717, Gaithersburg, MD: Nat'fohTHBureau of StandTrds, 1986.

The underground coal mine environment does not present health or safety

risks to workers when it is maintained to specific standards. Mining

activity, as well as unforeseen events, can result in sudden and

dangerous levels of explosive gas and dust. A mine may have hundreds of

miles of passages spread over ten or more square miles. Control devices

for maintaining the environment are distributed throughout the

infrastructure of the mine. Many of the decisions which have to be made

to maintain a safe environment are based on judgment, rather than

documented facts or empirical procedures. All of these characteristics

of the system compound to make it difficult to manage ventilation under

routine conditions. When a fire or explosion occurs, the management

problem escalates dramatically. The development of a prototype expert

system to manage the operation of coal mine ventilation systems is

described in this paper. The characteristics of the mine, as they affect

the expert system design, are presented. The knowledge engineering

aspects of this system, as well as implementation issues, are examined.

The paper concludes with some observations on the requirements of a future version of the VENTILATION EXPERT.

Kunreuther, H., et al., "An Interactive Modeling System for Disaster Policy

Analysis, Program on Technology, Environment and Man," Monograph #26,

Institute of Behavioral Science, University of Colorado, 1978.

McColl, D. C, et al., "STAMMER2 Production System for Tactical Situation

Assessment," Report No.: TD 298, Naval Ocean Systems Center, San Diego,

CA, 1979.

Meitzler, W. D., and R. T. Jaske, "Exercise Evaluation and Simulation

Facility," in G. Birtwistle (Ed.), Artificial Intell igence, Graphics,

and Simulat ion, La Jolla, CA: Simulation Councils, Inc. 1985.

The authors describe the Integrated Emergency Management and Information

System (IEMIS), a minicomputer based system that serves as a tool to

assist FEMA in the evaluation of emergency plans and preparedness for

natural and technological hazards. As an IEMIS subsystem, the Exercise

Evaluation and Simulation Facility (EESF ) provides capabilities speciifc

to emergency exercises and plan development for application near

commercial nuclear power facilities. EESF integrates the following

resources: a meteorology model, evacuation model, dose model, maps, and

exercise information into a single operational package. The user may

assess these various resources, and on completion, display the results on

a color graphic display or hardcopy unit. Key to the capability provided

by EESF are the ease of use and quick comprehension of results presented

via the graphic displays.

155
Mick, S., and W. A. Wallace, "Expert Systems as Decision Aids for Disaster

Management," Report No.: 36-84-P6, Statistical, Management, and

Information Sciences Department, Rensselaer Polytechnic Institute, Troy,

NY, undated.

Moore, D., et al., "The FEMA Earthquake Damage and Loss Estimation System

(FEDLOSS)," Procee dings of the Conference on Emergen cy Plan n in_g , Vol. 15,

No. 1, La Jol 1 a, CA: ~~The Society for Computer Si mu fat i on, "Jan. 1985.

This paper describes a computerized methodology to estimate earthquake

damages, casualties, and dollar losses in the event of damaging

earthquakes. The type of damage/loss estimates the FEDLOSS model

provides are: (1) the expected physical damage caused by ground shaking,

(2) the expected percentage of loss of function or usability, (3) the

expected percentage of population killed and injured, and (4) expected

losses due from collateral earthquake hazards such as ground failure,

fire and inundation. The output of the FEDLOSS model can be used to

drive other models such as the FEMA Earthquake Impacts Modeling System

(FEIMS).

Nelson, W. R., "Reactor: An Expert System for Diagnosis and Treatment of

Nuclear Reactor Accidents," Proceedings o f the Na tional Conference on Artific ial Int el ligence , AAAI-82, American Association for Artificial

Intelligence, "1982.

REACTOR is an expert system under development at EG&G Idaho, Inc. that

will assist operators in the diagnosis and treatment of nuclear reactor

accidents. This paper covers the background of the nuclear industry and

why expert system technology may prove valuable in the reactor control

room. Some of the basic features of the REACTOR system are discussed,

and future plans for validation and evaluation of REACTOR are presented.

The concept of using both event-oriented and function-oriented strategies

for accident diagnosis is discussed. The response tree concept for

representing expert knowledge is also introduced.

Nelson, W. R., "Response Trees and Expert Systems for Nuclear Reactor

Operations," Report No.: EGG-2293, Department of Energy, Washington, DC,

Mar. 1984.

Ogawa, H., K. S. Fu, and J. T. P. Yao, "An Expert System for Structure Damage

Assessment," Pattern Recogn it ion Lett. , Vol. 2, No. 6, Dec. 1984.

An expert system called SPERIL-II is introduced for the damage assessment

of existing structures using the knowledge of experienced structural

engineers. Fuzzy sets and Dempster and Shafer's Theory are used in this

inexact inference method.

156
Petrie, J. F., and W. T. Hathaway, "Development of an Automated Emergency

Response System (AERS) for Rail Transit Systems," Report No.:

UMTA-MA-06-0152-84-4, U. S. Department of Transportation, UMTA, Office of

Technical Assistance, Washington, D.C., Oct. 1984.

This report presents the results of an effort to deploy and evaluate

automated emergency response systems (AERS). Developed initially by a train controller at the Bay Area Rapid Transit District (BART), the AERS

is a computerized data bank containing equipment and facilities location

information and predetermined response actions. Its purpose is to

provide controllers, dispatchers and supervisors with a quick and

accurate information retrieval system. In the development of UMTA's

Recommended Emergency Preparedness Guidelines for rail transit systems,

the AERS was identified as a decision-making aid that would be of value

to the transit industry.

Russo, E. J., and R. R. Wilson, "Earthquake Vulnerability Analysis for

Economic Impact Assessment," Proceedings of the Conference on Computer

Simul ation in Emergency Plan ning, La Jolla, CA: The Society for Computer

Simulation, Jan. 1983.

This paper describes research to develop multidiscipl inary techniques for

a comprehensive damage and economic and industrial impact evaluation for

catastrophic California earthquakes. The earthquake damage ; eval uati on technique is patterned after damage evaluation techniques for nuclear war

that have evolved over the past several decades. The economic impact

evaluation techniques are based on a small area input-output modeling

system in which the damage evaluations are used to generate evaluations

of the economic dislocations. The damage evaluation technique integrates

three components including geocoded seismic intensity simulations;

geocoded inventories of housing, buildings, and facilities; and damage

functions relating seismic intensity and structural characteristics of

buildings and facilities to damage estimates. The damage evaluation

technique is designed to simulate damages from hypothetical earthquakes

and to provide rapid first damage estimates in the event of actual

catastrophic earthquakes in California. The economic impact evaluation

technique is designed to employ results of the damage evaluation

methodology in conjunction with a recently developed interindustry

capability. The new interindustry system provides for the evaluation of

disaster economic impacts with an unprecedented geographic and economic

sector detail. With this system, county, state, regional, and national

economic impacts of catastrophic events can be evaluated for up to 470

economic sectors.

157
Wallace, W. A., and F. de Balogh, "Decision Support Systems for Disaster

Management," Publ ic Admin is tration Re view, Vol. 45, Special Issue, Jan.

1985.

This paper provides a conceptual framework for the employment of decision

support systems (DSS) in the field of disaster management. This

framework incorporates the following dimensions: (1) definition of DSS

and its differences from MIS in the public sector, (2) stages in the life

cycle of a disaster and the types of events occurring within stages, (3)

a framework for conceptualizing decision making in disaster management at

various levels -- operational, tactical, and strategic, (4) a matrix of

information and modeling needs of planners and decision makers within

each stage, and (5) selected examples of DSS applications and prototypes,

developed on microcomputers, that serve to illustrate the role of DSS in

the disaster management.

Waltz, D. L., "New Tools for Emergency Management," Proceedings of the First

Symposium on the Theory and Appl ic ation of Expert Sys tems in Emergency

Management Ope rations, Special Publication 717, Gaithersburg, MD:

National Bureau of Standards, 1986.

This paper explores a number of possible applications of AI to the

problems of emergency management. First, it presents some far-future

scenarios, illustrating the kinds of aid that AI could ultimately supply.

While many of the applications will require many years of research before

they can be realized, there are some near-term applications, especially

for new natural language processing technologies; a few interesting

possibilities are discussed. There are marked similarities between the

problems of emergency management and the three key problems being

addressed by the U.S. Department of Defense. These similarities are

identified, and projections made about trickle-down effects of this DoD

research. Finally, the paper discusses some obstacles to achieving the

goals of automated emergency management systems and aids, and presents

some ideas on how we can achieve these goals.

Williams, T. L., P. J. Organ, and C. L. Smith, "Diagnosis of Multiple Faults

in a Nationwide Communications Network," Proceedings o f the Eighth IJCAI , American Association for Artificial Intelligence, 1983.

The Network Diagnostic System (NDS) is an ARBY based expert system for

fault isolation in a nationwide communications network (COMNET). Due to

both the structure and function of the network, failures in COMNET are

often multiple component failures (either dependent or independent) or intermittent failures. The maintenance procedure for isolating and

correcting faults in COMNET exploits multiple types of knowledge,

including the topological structure of COMNET, geographic organization,

and frequency of failure information. Fault isolation in NDS is

represented as a heuristic search through a space of hypotheses. The

available diagnostic tests impose a refinement hierarchy on the space of

hypotheses, enabling the exploitation of hierarchical search. Back links

to more general hypotheses at higher levels in the refinement hierarchy

are introduced to ensure the isolation and repair of multiple and

intermittent failures. The NDS currently performs at the level of an intermediate COMNET diagnostician.

158
APPENDIX A

LIST OF JOURNAL AND PROCEEDINGS SOURCES

AAMSI Congress 83 Pro ceedin gs, Bethesda, MO: American Association of Medical

Systems an<TTnfdrmati cs, May 1983.

ACM SIGEUE Bulletin, Vol. 12, 1978.

Advances in Computers, Vol. 22, New York: Academic Press, 1983.

AI Magazine , Vol. 2, 1981; Vol. 3, 1982.

American Journa l of Hospital Pharmacy , Vol. 37, 1980.

American Jou rnal of Medicine, Vol. 60, 1976; Vol. 64, 1978.

American Scientist, Vol. 72, Sept. -Oct., 1984; Vol. 73, May -June 1985.

The American Sta ti stician , Vol. 39, No. 1, Feb. 1985.

Annals of Mathematical Statistics , Vol. 37, 1967; Vol. 38, 1967.

Artifi cial In telligence, Vol. 5, 1971; Vol. 8, 1974; Vol. 11, 1978; Vol. 12,

1979; Vol. 18, 1982; Vol. 20, 1983; Vol. 21, 1983; Vol. 22, 1984.

Australian Computer Journal, Vol. 12, No. 2, May 1980.

Automat i

c

a, Vol. 13, 1977.

Automedica, Vol. 4, No. 2/3, 1982.

Byte , Vol. 6, No. 9, Sept., 1981; Vol. 9, No. 5, May 1984, Vol. 10, No. 2,

Jan. 1985; Vol. 10, No. 4, Apr. 1985.

Communications ACM 8 , 1971.

Computer , Vol. 16, No. 2, Feb. 1983; Vol. 16, No. 10, Oct. 1983; Vol. 17, No.

^7~March. 1984; Vol. 17, No. 9, Sept. 1984; Vol. 17, No. 10, Oct. 1984;

Vol. 18, No. 1, Jan. 1985; Vol. 18, No. 4, Apr. 1985.

Computers and Mathematics, Vol. 9, No. 1, 1983; Vol. 11, No. 5, 1985.

The Computer Journal , Vol. 23, No. 4, 1980.

Computer Magazine , Feb. 21, 1985.

Computer Weekly , No. 876, Sept. 1983.

Computing Surveys , Vol. 12, 1980.

Computers and Biomedical R esearch , Vol. 15, No. 2, 1982.

159
Computer-world , Vol. 17, No. 20, May 1983; Vol. 17, No. 43, Oct. 1983;

Vol. 18, No. 49A, Dec. 1984.

Creative Comp u ting , Vol. 6, No. 3, Mar. 1980.

CRS Review, Nov. /Dec. 1984.

CSELT Rapp. Tec, Vol. 11, No. 3, June 1983.

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Da ta Proces sing, Vol. 24, No. 3, Mar. 1982.

Datamati on , Nov. 1, 1984.

Decision Science s, Vol. 12, 1981.

Electronic Design, Vol. 32, No. 16, Aug. 1984; Vol. 33, No. 1, Jan. 1985.

EPRI Journal , June 1985.

European Jou rnal of Operational Research , Vol. 10, 1982.

Expert Systems, Vol. 1, No. 2, Oct. 1984; Vol. 2, No. 1, Jan. 1985.

Fuzzy Sets and Systems , Vol. 11, 1983.

High Technology , Jan. 1984; Mar. 1985; Apr. 1985.

Human Systems Management, Vol. 4, No. 4, Autumn 1984.

ICL Technical Journal , 1980.

IEE Colloquium on Decision Support Aspects of Expert Sytems (Dige st No. 67) , Condon, England: IYE, May 198T.

IEEE Tra nsactions on Circu its and Systems, Vol. CAS-22, No. 11, 1975.

IEEE Tran sactions on Man-Machine Sys tems, MMS -11, 1970.

IEEE Transacti ons on Pattern Analysis and Machine Intelligence , Vol. 2, No. 5, " Sept. 1980; Vol. 6, No. 1, Jan. 1984; Vol. 6, No. 2, Mar. 1984; Vol. 7,

No. 3, May 1985.

IEEE T ransactions on Systems, Mana gement, and Cybernetics , Vol. SMC-12, No. 2,

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Information Age , Vol. 6, No. 4, Oct. 1984.

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160
Interfaces, Vol. 14, No. 2, Mar. /Apr., 1984; Vol. 14, No. 3, May/June 1984;

VoTT"14, No. 5, Sept. /Oct. 1984.

International Journal of Man-Ma chi ne Studies , Vol. 6, 1974; Vol. 8, 1976;

Vol. 9, 1977; Vol". 10, 1978; Vol. 12, 1980; Vol. 18, 1983; Vol. 19, 1983;

Vol. 20, 1984; Vol. 21, 1984.

International Journal of Policy Analysis and Info rma tion Systems , Vol . 4,

No. 4, Dec. 1980.

Journal of American Planning Association , Vol. 3, July 1981.

Journal of American Society for Information Science , Vol. 35, No. 5,

Sept. 198T:

Journal of Hazardous Material , Special Issue, Vol. 4, 1981.

Journal of Information Systems , Vol. 1, No. 2, Fall 1984.

Journal of Mathematical Biology , Vol. 1, No. 1, 1974.

Journal of Operational Research Society , Vol. 36, No. 2, Feb. 1985.

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Machine Intelligence 9 , Chichester, England: Horwood, 1981.

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Management Science , Vol. 17, No. 4, Dec. 1970; Vol. 29, No. 1, Jan. 1983.

Mathematical Biosciences , Vol. 23, 1975.

Medical Information , Vol. 8, No. 3, July/Sept., 1983; Vol. 9, No. 1,

Jan. /Mar. 1984.

Methods of Information in Medicine , Vol. 21, No. 3, July 1982.

Microsystems , Aug. 1983, Jan. 1984.

National Online Meeting Proceedings , Apr. 1984.

New Generation Computers , Vol. 1, No. 1, 1983; Vol. 2, No. 2, 1984.

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Personal Computer World , Vol. 7, No. 6, June 1984.

Practical Computer , Vol. 7, No. 10, Oct. 1984.

161
Proceedings of the AAAS Selected Symposium 54 on Mathematical Frontiers of the

Social and Policy Sciences , Washington, DC; American Association for

the Advancement of Science, 1981.

Proceedings of the AFIPS National Computer Conference , Arlington, VA: AFIPS

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Proceedings of the 1984 American Control Conference , Vol. 1, New York: IEEE,

June 1984.

Proceed i ngs of the ASME Symposium on Engineering Databases: Software for

On"-Line Applications , San Antonio, TX: ASME, June 1984.

Proceedings of the Autotestcon '82: the 1982 IEEE International Automatic

Testing Conference , New York: IEEE, Oct. 1982.

Proceedings of the 1984 Carnahan Conference on Security Technology , New York:

IEEE, May 1984.

Proceedings of the Colloquium on Application of Knowledge Based (or Expert)

Systems

,

London, England, 1982.

Proceedings of the CQMPAS '84 , Berlin, Germany: VDE-Verlag, Oct. 1984.

Proceedings of the COMPCQN 83 Fall , Silver Spring, MD: Computer Society

Press, Oct. 1983.

Proceedings of the COMPSAC 80 , Oct. 1980.

Proceedings of the Conference on Computer Simulation in Emergency Planning , La Jolla, CA: The Society for Computer Simulation, Vol. 11. No. 2,

Jan. 1983.

Proceedings of the Conference on Emergency Planning , La Jolla, CA: The

Society for Computer Simulation, Vol. 15, No. 1, Jan. 1985.

Proceedings of the Conference on Software Development Tools, Techniques, and

A1 ternati ves , Silver Spring, MD: IEEE Computer Society Press, July 1983.

Proceedings of the ECAI-82 , 1982.

Proceedings of the Eighth IJCAI , American Association for Artificial

Intel 1 i gence, 1983.

Proceedings of the Eleventh Collegium Internationale

Neuro-Psychopharmacol ogi cum , Vienna, 1978.

Proceedings of the Ergonomics Society's Conference , London, England: Taylor

and Francis, Mar. 1983.

Proceedings of the Fifteeenth Hawaii International Conference on System

Sciences , Honolulu, Hawaii: Hawaii International Conference of

System Sciences, Vol. 2, 1983.

162
Proceedings of the Fifteenth Pittsburgh Modeling and Simulation Conference , Pittsburgh, PA, Aug. 1984.

Proceedings of the First Annual Symposium on Compute r Application in Medical

Care, New York: IEEE, Oct. 1977.

Proceedings of the First Symposium on the Theory and Application of Expert

Systems in Emergency Management Operations, Special Publication 717,

Gaithersburg, MD: National Bureau of Standards, 1986.

Proceedings of the Fourth MIT/ONR Workshop on Distributed Information and

Decision Systems , 1982.

Proceedings of the Fourteenth International Conference on Fault-Tolerant

Computing , Silver Spring, MD: IEEE Computer Society Press, 1984.

Proceedings of the Fourteenth Symposium on the Interface of Computer Science

and Statistics, New York: Spri nger-Verl ag, 1982.

Proceedings of the Fourth Annual Symposium on Computer Applications in Medical

Care , 1980.

Proceedings of the Fourth Congress on Pattern Recognition and Artificial

Intel 1 igence , Le Chesnay, France: Inst. Nat. Recherche Inf. and Autom.,

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Proceedings of the IEEE , Vol. 67, No. 9, Sept. 1979; Vol. 71, No. 11,

Nov. 1983; Vol. 72, No. 12, Dec. 1984.

Proceedings of the IEEE Computer Society Conference on Pattern Recognition and

Image Processing , New York: IEEE, 1981.

Proceedings of the 1983 IEEE Computer Society Workshop on Computer

Architecture for Pattern Analysis and Image Database Management , Silver Spring, MD: IEEE Computer Society Press, Oct. 1983.

Proceedings of the IEEE International Conference on Computer Design: VLSI in

Computers , New York: IEEE, 1983.

Proceedings of the 1983 IEEE Military Communications Conference , New York:

IEEE, Vol. 3, 1983.

Proceedings of the 1984 IEEE National Aerospace and Electronics Conference

INAECQN ), New York: IEEE, Vol. 2, May 1984.

Proceedings of the IFIP-IMIA Working Conference , Amsterdam, Netherlands:

North-Holland, 1983.

Proceedings of the Internat ional Conference on Cybernetics and Society , New York:—IEEE, Oct. 1978; Oct. 1980; Oct. 1981; Oct. 1982T

Proceed ings of the International Conference on Data Engineering,

Silver Spring, MD: IEEE Computer Society Press, Apr. 1984.

163
Proceedings of the International Conference on Data Engineering , Silver Spring, MD: IEEE Computer Society Press, Apr. 1984.

Proceedings of the Joint BCS and ACM Symposium on Research and Development in

Information Retrieval , Cambridge, England: Cambridge Univ.: 1984.

Proceedings of the Joint Services Workshop on Artificial Intelligence in

Mai ntenance , Boulder, CO, Oct. 1983.

Proceedings of the Micro-Del con '81 , The Delaware Bay Computer Conference,

Mar. 1982.

Proceedings of the National Conference on Artificial Intelligence , American

Association for Artificial Intelligence, AAA I -80, 1980; AAA It 82, 1982.

Proceedings of the Seventh IJCAI , American Association for Artificial

Intelligence, 1981.

Proceedings of the Seventh International Conference on Software Engineering , New York: IEEE, Mar. 1984.

Proceedings of the Sixteenth Annual Meeting of the American Institute for

Decision Sciences , Atlanta, GA: American Inst, for Decision Sciences,

Nov. 1984.

Proceedings of the Sixth Annual International ACM SIGIR Conference on Research

and Development in Information Retrieval , Baltimore, MD: ACM, June 1983.

Proceedings of the Sixth IJCAI , American Association for Artificial

Intelligence, 1979.

Proceedings of the SPIE International Society of Optical Engineering , Vol. 485, May 1984.

'

Proceedings of the Statistical Computing Section , American Statistical

Association, 1983.

Proceedings of the Third Annual International Phoenix Conference on Computers

and Communications , Silver Spring, MD: IEEE Computer Society Press,

T98X

Proceedings of the Third IJCAI , American Association of Artificial

Intelligence, 1973.

Proceedings of the Third International Conference on Information Systems , Ann Arbor, MI, 1982.

Proceedings of the Third National Conference and Exhibition on Computers in

Personnel: Making Manpower Profitable , London, England: Institute of

Personnel Management, June 1984.

Proceedings of the Thirteenth International Symposium on Mutiple-Valued Logic , New York: IEEE, May 1983.

164
Proce e dings of Trends a n d Applicat ions 1983 on Automating Intelligent "TehavioTT Applications and Fro ntier's ,~ Si I ver Spring, MD: IEEE Computer

Society Press, May 1983.

Proceedings o f the Twenty-first Annual Meeting of the Association for

Computational Linguistics , Menlo Park, CA: Association of Computational

Linguistics, June 1983.

Proceedings of Videotex '84 International , London, England: Online

Publications, 1984.

Proceedings of the Workshop on Computer Vision: Representation and Control , Silver Spring, MD: I EEE "Computer Society Press, 1984.

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Science , Vol. 197, Sept. 1977; Vol 217, Sept. 1982; Vol. 220, Apr.

1983; Vol. 223, Mar. 1984; Vol. 228, May 1985; Vol. 228,

Apr. 1985.

Scientific American , Vol. 247, No. 4, Oct. 1982.

Society of Science and Information Studies , Vol. 5, No. 1, Jan. 1985.

SIGART Newsletter , No. 87, Jan. 1984.

Systems International , Vol. 12, No. 8, Aug. 1984.

Technology Review , Nov. /Dec, 1983.

TIMS Studies in the Management Science s, Vol. 20, 1984.

Transactions in Programming and Computer Software , Vol. 9, No. 4,

July-Aug. 1983.

Water Resources Research , Vol. 19, No. 6, Dec. 1983.

VAX/RSTS Prof., Vol. 6, No. 5, Oct. 1984.

165
APPENDIX B

GLOSSARY OF TECHNICAL TERMS

Algorithm - A well-defined computational procedure for solving a problem in a finite number of steps.

AND/OR Graph - A tree-like structure with two types of nodes: AND nodes for

which several successors of a node have to be accomplished (or

considered), and OR nodes for which only one of several of the node's

successors are to be considered.

Artificial Intelligence - A discipline devoted to developing and applying

computational approaches to intelligent behavior.

Atom - An indivisible element. A logical proposition that cannot be broken

down into other propositions.

Backtracking - Describes a return to an earlier point in a search space,

usually due to a depth-first search failure. Also a name given to

depth-first backward reasoning.

Backward Chaining - Describes a system that begins a solution search by

examining a limited set of production rules with defined goals as its

starting point.

Bidirectional Search - A solution search procedure which proceeds

simultaneously from both the start node and from a set of goal nodes.

Blackboard - A common working data storage with which the various system

elements communicate with each other.

Breadth-First - A search technique in which, starting with the root node, the

nodes in the search tree are generated and examined level by level before

proceeding deeper. This approach is guaranteed to find an optimal

solution if it exists.

Combinatorial Explosion - Describes the rapid growth of possibilities as the

search space expands. If each branch point (decision point) has an average of n branches, the search space tends to expand as n^, as the

depth of search, d, increases.

Computer Architecture - Describes the way in which the various computational

elements are interconnected to produce a computational function.

Computer Vision - The symbolic description of a scene depicted in an image

developed by a computer using visual sensory inputs such as photographs.

Connectives - Describes the operators (such as AND, OR, etc.) connecting

statements in logic so that the truth-value of the composite is

determined by the truth-value of the components.

166
Consistency Enforcer - An element in expert systems responsible for

maintaining consistent representation of the emerging solution.

Controller - Refers to a distinct entity in an expert system, consisting of an interpreter, a scheduler and a consistency enforcer.

Control Structure - That aspect of an expert system which determines how to

use the rules contained in the knowledge base.

Data Base - Describe an organized collection of information on some subject.

Data-Driven - A problem solving technique which uses forward reasoning.

Declarative Knowledge - Facts and assertions about a problem that is

represented in the global data base.

Depth-First - A search technique in which one proceeds from the root node to

one of the successor nodes and then to one of that node's successor

nodes, etc., until a solution is reached or the search is forced to

backtrack

.

Domain - The range of the problem area of interest.

Event-Driven - A problem solving procedure that uses forward-chaining driven

by the current status of the problem.

First-Order Predicate Calculus - A system of logic in which variables

representing predicates are not admitted.

Forward Chaining - Describes a system that starts with a subset of evidence

and proceeds to invoke the production rules in a forward direction,

continuing until no further production rules can be invoked.

Frame - A knowledge structure (or database) used to describe the attributes

that an object possesses.

Generate and Test - A problem solver consisting of two basic modules: one

that enumerates (generates) possible solutions and the other that

evaluates (tests) each proposed solution, either accepting it or

rejecting it.

Global Data Base - The name given to the data base containing information on the specific problem, its status, and the solution process, and is

accessible to all the components of an expert system.

Goal Driven - A problem solving technique that uses the goal of the problem to

guide the solution search using backward chaining.

Heuristic - A technique that human experts use to improve the efficiency of a problem-solving process, even though its use cannot be justifiied

theoreti cal ly

.

167
Inference Engine -That part of an expert system responsible for applying the

appropriate rules and stopping when a termination condition has been met;

also called the "control structure."

Intelligent Front-End - Used to describe that aspect of the user interface

which makes a system more flexible and user-friendly.

Interface - The system by which a user interacts with the computer; in

general, the connection between two components.

Interpreter - That element of the controller which executes rules according to

an agenda.

Invoke - To put a rule into action, usually by satisfying a precondition.

Irrevocable Control Strategy - A control strategy in which an applicable rule

once applied is not allowed to be reconsidered later.

Justification - A statement about the line of reasoning used by an expert

system in drawing a conclusion or for demanding some additional piece(s)

of information.

Justifier - An element of the expert system that performs the justification

task

.

Knowledge Base - The collection of facts and rules resident in the data base

of an expert system.

Knowledge Engineer - A person whose task is to extract knowledge from an expert and represent that knowledge in a computer.

Logical Representation - Knowledge representation using logical formulas that

capture a partial description of the world.

Meta-Knowledge - The knowledge which allows a system to reason about actions,

processes and plans.

Meta-Probl ems - These are higher level problems involving a system's own

actions, processes and plans.

Meta-Rules - Higher level rules used to reason about lower level rules.

Minimaxing - A search procedure in which a system determines the score at each

node in a game tree by either picking the minimum or maximum scores.

Model Driven - A problem solving technique that uses top-down approach and

generates inferences to be verified based on the domain model present in

the system.

Modus Ponens - A logical rule of inference which has the form:

If Ei is true, then E2 is true.

Ei is true.

Therefore E2 is true.

168
Modus Tolens - A logical rule of inference which has the form:

If Ei implies E2, then not Ei implies not E2.

Natural Language - A term used to refer to languages like English.

Parallel Processing - The simultaneous processing of data, as opposed

to the usual sequential processing done by conventional computers.

Parser - Refers to the syntactic program that segments sentences into

meaningful phrases.

Pattern Recognition - A term given to describe the task of classifying a set

of data into predetermined categories.

Predicate Calculus - A modification of propositional calculus to allow the use

of variables and functions of variables.

Problem State - The condition of a problem at any given instant.

Procedural Knowledge - The knowledge about a problem that is represented in

the rules.

Production Rules - Rules used in production systems, that have the general

form:

If: Logical conditions are satisfied

Then: Take the indicated action.

Production System - A system based on a collection of production rules.

Propositional Calculus - An elementary logic that uses argument forms to

deduce the truth or falsehood of a new proposition from known

propositi ons.

Rule-Interpreter - see Interpreter.

Rules - Elements in the knowledge base of an expert system that tell the

system which direction to take or what to do next.

Scheduler - That element in the control structure that maintains control of

the agenda and determines which pending action should be executed next.

Script - A frame-like knowledge structure used to represent related sequence

of events.

Search Space - The implicit graph representing all the possible states of the

system that may have to be searched to find a solution. In many cases

the search space is infinite. The term search space is also used for

non-state-space representations.

Semantic Net - A directed graph of nodes and connecting links. The nodes

represent objects, events, concepts, situations or actions and the links

represent properties and relationships between them.

169
Solution Path - A successful path through a search space.

Speech Recognition - Recognition by a computer of spoken words or sentences.

State Space - A space in which the nodes represent the system state and the

connecting arcs represent operators that can be used to transform the

state from which the arcs emanate to the state at which they arrive.

Symbolic Reasoning - Use of abstract representations or symbols (for concrete

objects) and reasoning methods in arriving at the goal state from the

initial state, or vice versa.

Top-Down Approach - A problem solving technique which is goal directed or

expectation-guided based on models or other knowledge; also referred to

as "hypothesize and test" approach.

Truth Value - One of the two values associated with propositions in logic - True or False.

User Interface - see "interface."

170
APPENDIX C

SOME COMMON ABBREVIATIONS

AAAI - American Association for Artificial Intelligence

ACE (expert system) - Automated Cable Expertise

AERS - Automated Emergency Response System

AGE (expert system) - Attempt to Generalize

AI - Artificial Intelligence

ART (shell) - Automatic Reasoning Tool

CAI - Computer Assisted Instruction

CASNET (expert system) - Causal Associated Network

CF - Certainty Factor

COMEX (expert system) - Compact Knowledge-Based Expert Systems

COMNET - Communications Network

CONGEN (expert system) - Constrained Generator

CRIB (expert system) - Computer Retrieval Incidence Bank

DSS - Decision Support Systems

ES - Expert System

IEMIS - Integrated Emergency Management and Information System

IFE - Intelligent Front End

IJCAI - International Joint Conference on Artificial Intelligence

KAS (expert system) - Keyword Access System

KB - Knowledge Base

KEE (shell) - Knowledge Engineering Environment

KNOBS (expert system) - Knowledge Based System

171
LISP - List Processor

MB - Measure of Belief

MCDM - Multiple Criteria Decision Making

MD - Measure of Disbelief

MLSE - Multi -Layered Software Environment

NAECON - National Aerospace and Electronics Conference

NLI - Natural Language Interface

PR - Production Rule

PROLOG - Programming in Logic

RLL (shell) - Representation Language Language

RITA (shell) - Rule-directed Interactive Transaction Agent

ROSIE (shell) - Rule Oriented System for Implementing Experti

SACON (expert system) - Structural analysis consultant

TATR (expert system) - Tactical air target recommender

TIMM (shell) - The Intelligent Machine Model

VLSI - Very Large Scale Integrated Circuits

172
NBS-114A (rev. 2-eo

' U.S. DEPT. OF COMM.

didi inrpADuip n/VTA blDLIUuKArnlO UM 1 M

SHEET (See instructions)

1. PUBLICATION OR

REPORT NO.

NBS/SP-728

2. Performing Organ. Report No. 3. Publication Date

November 1986

4. TITLE AND SUBTITLE

Expert Systems and Emergency Management: An Annotated Bibliography

5. author(S) Saul I. Gass, Suneel Bhasker, and Robert E. Chapman

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions) 7. Contract/Grant No.

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE 8. Type of Report & Period Covered

Gaithersburg, MD 20899 Final

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City. State, ZIP)

Federal Emergency Management Agency

500 C Street, SW

Washington, DC 20472

10. SUPPLEMENTARY NOTES

Library of Congress Catalog Card Number 86-600591

fJJ Document describes a computer program; SF-185, FlPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a significant

bibliography or literature survey, mention it here)

This report is the result of an in-depth review of the recent technical literature

on expert systems. The material contained in this report provided a basis for

assessing the potential for using expert systems in emergency management operations 0 In choosing the material for inclusion in this report, special emphasis was placed

on those aspects of expert systems which addressed the types of problems encountered

in emergency management operations.,

The report is designed for use as a resource document and as a tutorial on expert

systems and emergency management., Each chapter consists of a brief topic essay

followed by a set of references which expand on the main themes of the essay

.

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolons)

artificial intelligence; emergency management; expert systems

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