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/Theory and application of expert system QC100 .U57 N0.717 V1986 C.2 C.1 NBS-PUB- % T

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NBS SPECIAL PUBLICATION 717

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

Theory and Application

of Expert Systems in

Emergency Management

Operations

Gass and Chapman, Editors
Tm 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

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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

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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

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'Headquarter! and l aboratories al Gaithersburg, MD, unless otherwise noted; mailing address Gaithersburg, MD 20899.

: Some divisions within the center are located at Boulder, CO 80303.

'l (x-.ii'.'cl ai Boulder, CO, with some elements at Gaithersburg, MD.
Theory and Application of Expert Systems in

Emergency Management Operations

Proceedings of a Symposium

Held at the Department of Commerce

Washington, DC

April 24-25, 1985

Edited by:

Saul I. Gass

College of Business and Management

University of Maryland

College Park, Maryland 20742

Robert E. Chapman

National Engineering Laboratory

National Bureau of Standards

Gaithersburg, Maryland 20899

Sponsored by:

Federal Emergency Management Agency

500 C Street, S.W.

Washington, DC 20472

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

Issued November 1986

NBS

RESEARCH

INFORMATION CENTER

(Xuoo

,uXi

12,

and

and
Library of Congress Catalog Card Number: 86-600546

National Bureau of Standards Special Publication 717

Natl. Bur. Stand. (U.S.), Spec. Publ. 717, 264 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
Federal Emergency Management Agency

Washington, D.C. 20472

Foreword

by

The Honorable Julius W. Becton, Jr.

Director

Federal Emergency Management Agency

As the recently appointed director of the Federal Emergency Management Agency

(FEMA), I would like to reinforce the thought-provoking material contained in

these Proceedings of the Symposium on the Theory and Application of Expert

Systems in Emergency Management Operations. As pointed out in the symposium,

expert systems hold unique advantages in the arena of emergency management.

These systems can give you advice quicker, better and more extensively than

panicked human beings. Quicker, because they can be consulted at any hour by

using a personal computer. Better, because they contain the distilled opinion

of a wide range of expertise. More extensively, because the computer can simultaneously consider a wider range of input factors than any human being.

And the computer never panics, seldom sleeps, and has never been known to

be absent.

In the time that has passed since the symposium, FEMA has taken some of the

first steps indicated by the symposium. We now have available on our computer

or under development expert systems in such diverse areas as notification of

emergency weather conditions and estimation of facility damage in earthquakes.

The technology is being used to codify emergency expertise, and to improve

our emergency management capabilities.

The future is limited only by our imagination. One can imagine the ability

to call a number and to be answered by a computer that can understand your

speech. This computer would have a suite of expert systems which it would

relate to your description of your problem, and give you the best advice

available. The ability to perform smarter with less is and will continue to

be a driving force. The publication of this document is evidence of our continued commitment to excellent research devoted to fulfilling human needs.

i i i
ABSTRACT

The First Symposium on The Theory and Application of Expert Systems in

Emergency Management, held at the Department of Commerce, Washington, DC

(April 24 and 25, 1985) was funded by the Federal Emergency Management Agency

and organized by the National Bureau of Standards' Operations Research

Division. The purpose of the symposium was to bring together researchers in

expert systems, artificial intelligence, and emergency operations in a forum

to review the concepts of expert systems and the problems of emergency

management, with the objective of determining how expert systems can be used

to augment the experience of local, State and Federal emergency managers faced

with the difficult tasks of determining the best response to an emergency

situation. Speakers addressed the following areas: The theory and

uncertainty aspects of expert systems, artificial intelligence's future role

in emergency management, technology for building and using expert systems,

emergency management decisions and information needs and uses, applications of

expert systems in the management of chemical spills and shipboard and coal

mine fires, and the role and use of simulation in emergency management expert

systems.

Keywords: Artificial intelligence; chemical spills; coal mine fires; decision

support systems; emergency management; expert systems; fire

management; shipboard fires; simulation; uncertainty

Note: The paper "Emergency Management for Chemical Spills" by C. K. Johnson

that was presented at the Symposium is not included in this volume.

The interested reader should consult "Emergency Management of Inland

Oil and Hazardous Chemical Spills: A Case Study in Knowledge

Engineering," Chapter 10 in Bui ldi ng Expe r t System , F. Hayes-Roth,

D. A. Waterman and Douglas B. Lenat (eds."J"» Addi son-Wesl ey Publishing

Company, Inc., 1983.

i v
TABLE OF CONTENTS

Page

Foreword -- Julius W. Becton, Jr iii

Abstract iv

Welcome Louis 0. Giuffrida 1

Introductory Remarks — Raymond G. Kammer 4

A Theory of Expert Systems — Andrew B. Whinston, Keith Hall and

James Moore 6

Management of Uncertainty in Expert Systems — Lotfi A. Zadeh 22

New Tools for Emergency Management — David L. Waltz » 40

TIMM - A Tool for Building Expert Systems -- Joseph N. Craig 57

Decision Support Systems and Expert Systems for Emergency Management

Operations: A Microcomputer Approach — Frank de Balogh 86

Optimum Emergency Management: The Effective Use of Information

Technology — Robert Lee Chartrand 109

Roles of Simulation in the Application of Expert Systems in

Emergency Management Operations — A. Ben Clymer 124

Expert System for Fire Emergency Management — Geoffrey N. Berlin 158

The VENTILATION EXPERT for Underground Coal Mines — Jeffrey L. Kohler.. 183

Emergency Management Decisions at the Local Level —

Robert S. Wilkerson 203

The SMOKEY Project: An Intelligent Sensor-Based Fireman's

Assistant — Jaime G. Carbonell 211

Symposium Program 223

Speakers Names and Addresses 226

Appendix I: Expert Systems and Emergency Management: Background Paper

Saul I. Gass and Robert E. Chapman 227

Appendix II: Expert Systems and Emergency Management: Research

Directions -- Saul I. Gass and Robert E. Chapman 250

v

WELCOME

Honorable Louis 0. Giuffrida

Director

Federal Emergency Management Agency

Good morning, ladies and gentlemen. I would like to welcome you to the

first "Expert Systems in Emergency Managment Symposium." One of the questions

that you could ask is: "Why should the Federal Emergency Management Agency

co-sponsor a symposium on expert systems now, especially during a time of

budget constraints?" Because of a large and expanding deficit we're having to

cut back funding in many areas. Yet, we are funding this symposium.

There are several good answers to this question. The first answer is

that this technology is just beginning to emerge from the laboratory into

practical application. We need to plan this type of information system now in

order to obtain good performance later.

However, that is not the only reason for beginning the planning now. As

I'm sure many of you know the common definition of an expert is an ordinary

drip under pressure. However, it has been shown in field after field, from

quarterbacking to fighter pilots to heart surgery that the true definition of

experts are those 20 percent of the people who do fifty percent of the

successful work. Whether that success is measured in terms of touchdown

passes thrown, or in terms of patients surviving heart surgery, twenty percent

of the people have fifty percent of the total successes.

Just think about it, what if we could make all our people, all our emergency managers, equivalent in their expertise over the next few years so they are at the top twenty percent. We would certainly find that we could do

far more with fewer resources. It seems now that because of the budget

deficit the next few years are likely to be times when we have to manage our resources more carefully than ever. This is the right time to look at expert

systems. Expert systems will help us husband those resources.

Another legitimate question is: "Why should FEMA look at expert systems

in emergency management?" Emergency management can be considered to be nearly

an ideal field for the use of expert systems. The Department of Defense

realizes this fact. They are leaders in this field, building a network of

expert systems which they are calling their battlefield manager. They

anticipate using expert systems in many ways to support their tactical and

strategic systems in the future.

Fortunately most emergencies in the United States are on a much lower

scale of intensity than those that the Department of Defense plans to face.

But many of our problems are similar in nature. First of all, when an emergency occurs, it usually pays little attention to the expert's location or the time of day in which it occurs. Very often the person that is facing the

emergency is not going to be an expert, nor will he always have direct access

1
to the needed expertise. Often we find that the person who is an expert is

located in industry and is not immediately available to the government

emergency manager. Expert systems may be able to fill these gaps in field

operations

.

In the area of emergency management we've taken the first steps towards

identifying those functions for which we require expert systems. Since I began my tenure as the Director of the Federal Emergency Management Agency, a new policy has been adopted called the integrated emergency management system.

Under this policy we no longer base all our planning on individual emergency

situations. Rather we have identified certain functions which are common to

any emergency.

These functions may be those of an emergency transportation manager, an emergency evacuation expert, or a warning expert. No matter what the

emergency is, people have to be removed from the area of danger or protected

in place. The injured have to be cared for. The temporarily homeless have to

be housed and fed. The economic base of the affected region has to be

rebuilt. These functions require high levels of expertise which are not

always immediately or locally available. Therefore, the creation of expert

systems in these various areas should help emergency managers to manage the

limited resources available in a much more efficient and effective manner.

Another reason for investigating the utilization of expert systems in

emergency management is that the environment is now right. As you look around

you, you can find seven year olds using computers. Computer skills and the

computers themselves are becoming more and more available. No longer do you

have computer center gurus running large mainframe computers who talk to

themselves and no one else. Rather you have many people that have the skills

and abilities needed to interface with the computer. The technology itself is

growing to the point that it is becoming natural to use computers.

Furthermore, as more and more people are using computers, the information

flow available to the emergency manager is increasing. We in FEMA have been

accommodating and encouraging this increase with the investment we've been

making in computers and communications. Computer-based expert systems are a natural extension of this trend. And finally, no small factor in these days

of tight budgets, Congress itself has expressed an interest in improving

utilization of computers, communications, and other modern high technology

techniques, in responding to emergencies.

So what is it that the Federal Emergency Management Agency expects from

this symposium? First of all we want to listen to our emergency managers, to

hear what they are saying, to hear their problems, to hear them tell us about

those problems that they believe expert systems may be able to help. We also

want to hear the experts on the development of expert systems. We want to

hear what they say the solutions are, what they say they can co. We want to

hear how they would respond to the problems that these emergency managers have

advanced. From listening to these two groups we hope to develop a strategy

for the future implementation of expert systems.

2
In essence, we want to develop a list of projects for the priority

implementation of expert systems. We don't expect this conference to produce

a panacea for emergency management. Nor do we expect a multitude of expert

systems to spring up overnight. FEMA recognizes that this is a development

project. However, we believe that the time to listen is now, the time to

begin is today, the place to begin is here, listening to what you have to say.

Thank you for your participation and your advice.

3
INTRODUCTORY REMARKS

Raymond G. Kammer

Deputy Director

National Bureau of Standards

It is with great pleasure that I welcome you on behalf of the National

Bureau of Standards and our parent organization, the Department of Commerce,

to this first symposium on the theory and application of expert systems in

emergency management operations.

The challenge associated with the rapidly emerging field of expert

systems is reminiscent of the beginnings of the information revolution. More

than 30 years ago applied mathematicians at NBS used the SEAC — Standards

Eastern Automatic Computer — to solve problems previously thought to be

unsolvable. The first successful application of the now famous simplex

algorithm was performed on the SEAC as a part of NBS's defense-related

activities.

One of the first demonstrations of the value of computers is associated

with research which led to the development of the hydrogen bomb. While a team

of renowned physicists at Princeton were responsible for calculating the yield

of the device, two NBS mathematicians, who were performing the back up

calculations on the SEAC, produced results of startling accuracy. What is

perhaps most remarkable is that this was done with a machine which had about

the same capability as a sophisticated desk top personal computer.

As computers came of age and the mathematical models built to run on them

became more complex, the role of measurement and standards for information

exchange came to occupy a central position. The Federal Information

Processing Standards, promulgated under the provisions of the Brooks Act,

comprise key components of NBS's technical and coordinating role for improving

the utilization of computers and their associated software within the Federal

Government

.

Both pure and applied research scientists were quick to realize that

mathematical models are laboratories for the study of complicated scientific

and engineering phenomena. Although mathematical models have always been a part of the scientific endeavor, the advent of computers changed forever the

way in which models are used. Mathematical models have become important

tools, not only for scientists and engineers, but for decision makers at all

levels of government and industry.

The practical experience of our applied mathematicians and many of their

colleagues with computer-based models points towards a need for guidelines in

the planning, development, testing and appropriate use of such models. These

practical concerns led to research on the subject of model evaluation. The

research on model evaluation, for which NBS has been a focal point over the

past decade, is complementary to the emerging field of expert systems. We at

NBS, as all of you here today, are striving to explore the potential of these

new techniques.

4
When we first began organizing this symposium, our goal was to establish

a forum through which researchers in artificial intelligence could meet and

interact with specialists in emergency management. As the symposium

approached and our staff responded to the numerous inquiries, it became

evident that many participants were interested not only in procedures for

developing expert systems but for evaluating them as well.

NBS' work in this area should provide insight into the utility of expert

systems in emergency management. These insights may reduce the challenge

facing us, and they may increase the speed with which these new tools become a part of the decision-making process. In view of the urgency and complexity of

the tasks confronting us, we anticipate that the work which will be described

to you by our distinguished speakers over the next two days will be both

stimulating and thought provoking.

5
A THEORY OF EXPERT SYSTEMS

H. Keith Hall, James C. Moore and Andrew B. Whinston

Krannert Graduate School of Management

Purdue University, West Lafayette, IN 47907

Introduction

The expert system approach to computer-based problem solving has received a great deal of

attention in the last few years. Expert systems have been developed and implemented in such

diverse areas as medical diagnosis, oil exploration, computer configuration, and logical data base

design.

1 The most popular approach of current expert systems has been to represent expert

knowledge within the system as a set of production rules that describes the process of decision

making and characterizes the process that human experts go through in acquiring information and

making decisions under uncertainty. Although there have been a number of different areas of

research relating to expert systems including the theoretical basis for many of the the computational techniques used, little research has been done on the decision theoretic basis for such problem solving systems in general. That is, they have not generally attempted to characterize an

"ideal" process by which a decision could be made. Classical decision theory as used in economics, however, has typically characterized "best" decisions or decision strategies that may be for- mulated by a rational decision-maker. The basic goal of this paper, therefore, is to briefly discuss

the decision-theoretic approach to decision-making under uncertainty and to compare this with

that taken in many current expert systems. The major difference is the focus on the "correctness" of a decision and the efficiency of the decision process. An extension of the decision-theoretic

approach to allow more complex information gathering strategies where the tradeoff between the cost of acquiring further information and the potential benefit from a better (more informed) decision made under less uncertainty may be found in Moore, Hall, and Whinston (1985).

The first section of the current paper presents the basic approach to acquiring information

and making decisions under uncertainty taken in economics. The second section discusses

current expert systems and presents a simple theoretical reformalization of this approach that is seen to be logically consistent in decision-making under uncertainty in economics. Both

approaches employ information gathering strategies that generate an information structure upon

which a decision strategy is based. In the last section, the expert knowledge necessary to construct an "ideal" expert system as would be suggested by the decision theoretic approach to decision-making under uncertainty is briefly discussed.

1. The Economics of Information

The economic approach to decision making has typically been concerned with the efficient choice of an alternative from a limited set of alternatives. The term efficiency has been broadly

defined as the "best" decision made by a rational, utility maximizing decision-maker. When the decision is to be made under uncertainty, the decision-maker must make inferences about the

relevant portions of his environment (the decision problem domain) after obtaining information

1. See Gevarter (1983) or Nau (1983) for briefs discussions of the techniques and many of the application

areas of existing expert systems. Also see Bonczek, Holsapple, and Whinston (1981) for an exploration of management applications of expert systems.

6
about certain parameters that affect the outcome of his/her decision. These inferences are based

upon a set of beliefs about the likelihood of the various possible states of the environment as embodied in a subjective probability distribution. The information that may be acquired prior to the decision, such as running experiments or test, asking questions, etc., generally reduces

uncertainty by altering this set of beliefs (likelihoods). The major difference in this approach

from the current view of expert systems, however, is the focus on the "correctness" of a decision

(finding the "best" solution) provided by the development in economics of the theory of prefer- ences under uncertainty and the recognition that there is a tradeoff between correctness and the cost of acquiring the information necessary to improve the decision. In general, economic models of decision-making under uncertainty by a single decisionmaker may be placed into four of the following five categories. The fifth represents the extension

suggested by the authors cited in the introduction above.

1 ) Passive Response

a) A decision is made without any information gathering, usually to maximize the

expected payoff of the decision.

b) A decision is to be made after receiving an (exogenous) information signal, so that a decision strategy (the decision made is contingent upon which signal is received) is formulated, usually to maximize the expected payoff of the decision.

c) A number of decisions are to be made, each after receiving an information signal, so

that a sequential decision strategy must be formulated, usually to maximize the

expected utility of the decisions.

2) Active Response:

a) A single information gathering action is chosen which generates an information signal. The action is chosen and a decision strategy is formulated usually to maximize

the expected net payoff of the decision (taking the cost of the action into account).

2

b) A number, r> 1 , of information gathering actions may be chosen, each generating an

information signal. An information gathering strategy and a decision strategy are for- mulated. Each action and decision is contingent upon the signals received from previously executed actions in the sequence. Again, the objective is to maximize the

expected net payoff of the decision.

1.1. The Basic Model of Decision Making

The environment is comprised of a finite set of states denoted by X = {x}. The decisionmaker is assumed to have a set of feasible (final) decisions, D, and to receive a (gross) payoff

which depends upon the state of the environment and the decision chosen. There is a deterministic relationship between decisions, states of the environment, and a set of outcomes (or results),

O, such that there exists an outcome function, o(x, d) mapping the set X x D into the outcomes.

If the decision-maker's preferences over the outcomes and the cost of the decision may be

represented by a real valued utility function, u(o, d), for all o e O and d e D, then the (gross)

payoff function may be defined by

co(x, d) = u[o(x, d), d]. For the remainder of our theoretical discussion we will take the (gross) payoff function, ©(•)> as

2. This may also be interpreted as the choice among various information gathering strategies, each of which

generates a sequence of information signals. Under this interpretation, this type of model would represent the "normal" form of the decision model in 2b) (the extensive form).

7
given.

While the payoff function alone may be used to order decisions according to the decisionmaker's preferences given the knowledge of the true state of the environment, the connection

between preferences and decisions under uncertainty is made by the "expected-utility" theorem of von Neuman and Morgenstern (1944). According to the theorem, a consistent decision-maker

will not prefer decision d' e D to d e D if X cq(x, d) Pr{x} > £ ©(x, d') Pr{x}. xe X xe X

Example. We will now use a simple example to illustrate the concept of expected utility

(payoff). Let a decision-maker be choosing from among five different decisions and let there be

eight different possible states of the environment Further, there is the following gross payoff

function

Payoff Function

States decisions x, x2 X3 X4 x5 Xfi x7 x8 di 32 28 24 20 28 24 16 12

d2 28 24 32 30 24 20 12 12

d3 24 16 20 16 32 24 32 30

d4 24 24 24 32 16 32 12 32

d5 16 32 12 28 8 28 0 20

and probability distribution

Pr{X1 } Pr{X2} Pr{X3 } Pr{X4> Pr{X5} Pr{X6} Pr{X7 } Pr{X8 }

0 3/8 1/16 1/16 3/16 1/16 1/4 0

In comparing the five different decisions, an ordering may be formed with the equation

£ (0(x, d) Pr{x} as follows:

x e x

di d3 d2 d4 d5 24 23 3/4 21 5/8 20 1/2 17 3/4

1.1.1. Information Acquisition

Associated with the states of the environment is a finite set of information gathering actions,

which may be denoted without loss of generality by A = {0, 1, n} and a cost function c: A —

>

R. Associated with each element of A is a set of n(a) potential information messages which may,

without loss of generality, be written Ya={l, 2, n(a)}. Each is seen to create a partition on X

3. For a brief discussion of the expected-utility theorem and decision making under uncertainty see

Hirshleifer and Riley (1979) pp. 1379-1381.

a
and hence there implicitly exists the functions TJa : X —> Ya for a e A that may be used to define

those states of the environment consistent with a given information signal y e Ya

. 4

Definition 1. The function rj a: X -> Ya

is called an information structure on X imposed

by a. We will let a=0 denote no additional information collection so that its associated partition is the

entire state space. Instead of the choice of a single information action, the decision maker may instead be

choosing from among different information gathering strategies. That is, during the decision pro- cess a sequence of information gathering steps is taken, each contingent upon the results of previ- ous steps, so that prior to the decision process a strategy must be formulated.

5 To distinguish

between the choice of single information gathering actions and the choice of different information strategies we will use a e A to denote a strategy and continue to use a e A to denote a sin- gle action. For example, in figure 1 below, the decision tree representation of an information

strategy, (Xj, results in one of four different sequences of actions and seven different associated

sequences of information signals. Each node represents a single information gathering action and

each branch from a node represents a different possible signal. Also, each of the seven paths

through the tree is associated with a sequence of signals.

Figure 1: Information Strategy otj

Each step in the sequence of information actions is the continued partitioning of the state space into two subsets. One subset is known to contain the "true" state of the environment and

the other is known not to. As more information is collected, the subset that contains the true state gets smaller and therefore uncertainty is reduced. For example, if a medical test is run by a physician, then the result is a signal that is known to be associated with certain diseases and not with

others. Further, if a second medical test is run, then the second signal is consistent only with

4. An alternative definition of an information structure based on an underlying matrix of conditional

probabilities relating the states to the signals and the marginal probabilities of the signals has been also used. See Marschak and Miyasawa (1968).

5. See 2a) and 2b) above in section 1.

9
portions of the current "possible" subset of the state space and therefore the addition of the new

information reduces uncertainty as to the true disease. New updated likelihoods of the various

states of the environment are formed using some known underlying probability distribution.

Each strategy is therefore related to an information structure on X, r\ a : X —» Ya, where Ya is the

set of the possible sequences of information signals. This choice between different information

strategies is the "normal" form of the decision problem (which may be thought of as a game

against nature). In an "extensive" form of this same decision problem, found in Moore, Hall, and

Whinston (1985), the focus is on the generation of an information structure rather than the choice

of one. This would represent what classical decision theory might suggest is an "ideal" model of an expert system.

6

1.1.2. Comparison of Information Structures

The value to a decision-maker of an information strategy and therefore its resulting information structure comes from the effect it's sequence of signals has on the choice of a final decision. The ex post value of an information strategy may therefore be measured by the change in

the payoff due to the information minus the cost of the sequence of actions actually taken during

the decision process

co(x, da) - (o(x, do) - cost where da is the decision made after receiving the sequence ye Ya of information signals and do

is the decision that would have been made without additional information.

Since an information strategy must be chosen before a sequence of signals is received and a

final decision is made, the decision-maker must rely instead upon the expected (a priori) value of an information strategy. Since a decision is contingent upon the sequence of information signals, a decision function must be first specified and we will denote this by

5:Ya ->D.

Following the expected-utility theorem the following definitions may then be made.

Definition 2. The maximum expected gross payoff of sequence y, the optimal decision rule for sequence y, and the maximum expected gross payoff of strategy a are defined by

v(y) = max £ Pr(x | y) co(x, d)

d6 D xe x = I Pr(x| y) co[x, 5*(y)]

X € X

5*(y) = argmax £ Pr(x | y) (u(x, d)

X € X

C2(oc) = £ Pr(y)v(y)

y e Y0

Since each strategy is associated with its resulting information structure, T| a( ), Q(a) may be used

to compare information structures. That is, T| a is said to be more valuable than rj^ if Q(a) > Q(a).

Example cont. We now wish to illustrate the concept of the value of an information struc- ture building upon our earlier example. Suppose the decision-maker may now gather information

6. Sec Owens (1983) pp. 1-6 for a general discussion of the normal and extensive forms of a game.

10
using one of five different information strategies and is therefore choosing from five different

information structures and sequences of signal sets:

Ti0(X)=l & Y0 ={1};

Th(xi) = TU(x2)= l,ti 1 (x3) = ...=r| 1 (x8) = 2 & Y1 = {1,2};

Tl2(xi) = rh(x2) = 1. "n2(x5) = ThCxe) = 2, -n 2(x3) = r| 2(x4) = r| 2(x7) = rj 2(x8) = 3

& Y2 = {1,2,3};

r|3(xi) = r| 3(x2) = 1, T|3(x3) = r| 3(x4) = ti 3(x7 ) = r| 3(x8) =2, r|3(x5) = ri 3(x6) = 3

& Y3 = {1,2,3};

T|4(xi) = ru(x2) = 1, T\4(*3) = T\4(*4) = 2, rj 4(x5) = - = "H^s) = 3 & Y4 = {1, 2, 3}.

The information structure r| 0 represents no information collection, rjj partitions X into two sets, {xh x2} and {x3, x4, x5, x6, x7, x8}, r| 2 partitions X into {xlt x2}, {x5 , xe), and {x3 , x4, x7 , x8 }, rj 3 partitions X into {x^ x2}, {x3, x4, x7, x8}, and {x5 , xg}, and ri 4 partitions X into {xls x2}, {x3, x4},

and {x5 , xg, x7, x8 }.

Given the probability distribution for X, the marginal probability of each signal, Pr{y}, and

conditional probability of each state given each possible sequence of signals, Pr{x| y}, may be

calculated

7 as well as an optimal decision rule for each sequence of signals. The optimal decision rules for each strategy are:

Tl0 Til Tl2 T|3 T|4

8*(l) = d, 8*(1) =d5 S*(2) = d38*(l) = d5 8*(2) = d3 8*(3) = d3S*(l) = d5 8*(2) = d3 5*(3) = d3

8*(l) = d5 8*(2) = d2 8

+ (3) = d3

In comparing the five different information structures, an ordering may be formed as follows:

Q(4) Q(l) fi(2) Q(3) Q(0)

31 3/8 29 3/4 29 3/4 29 3/4 24

2. Rule-Based Expert Systems

Expert systems are designed to apply specialized or expert knowledge in performing

difficult problem solving tasks. They differ from other computer-based systems in that they are designed to solve problems that normally require human expertise and intelligence. While there

are rule-based, frame-based, and logic-based expert systems and most of our discussion may be

applied to all three types of knowledge representation, we will focus on the first type that represents knowledge mainly as production rules.

8 Rule-based expert systems are comprised of

three basic components: a set of rules, a data base, and a rule interpreter. The data base temporarily holds the facts or assertions about a specific problem during the solution process. The

rules of the rule set contain the domain-specific or expert knowledge. And the rule interpreter

7. See Marschak and Miyasawa (1968) pp. 140-142 for a discussion of the general properties of the standard

probability measure and the calculation of conditional probabilities.

8. Frame-based systems tie knowledge objects and classes of objects connected by semantic links. Logic- based systems represent knowledge as assertions in logic, usually first order predicate logic and inference procedures are usually based on theorem proving.

11
contains the general problem-solving knowledge of the system that combines with the expert

knowledge to solve problems. The interpreter functions by repeatedly matching production rules against data in the data base, selecting one or more of the rules with antecedent conditions met by

the data (and therefore applicable to the particular problem being solved), and then executing the

rule(s) which affects changes in the data. A solution is reach when an acceptable value is found

for the variable or variables that represent the problem solution.

DATABASE RULES

INTERPRETER

Figure 2: Components of a Rule-Based System

A production rule may be viewed as a conditional statement that relates factual statements with one another and are of the general form

IF <antecedent condition> THEN <action> WITH <CF>

The <antecedent condition> of a production rule is basically a conjunction of clauses that are

either true or false. If the conjunction of clauses in the antecedent condition is currently true according to the database, then the <action> is executed which adds or replaces data in the database. When there is uncertainty in the validity of the rule, the <CF>, which stands for "certainty

factor", represents the strength of belief in the rule. The knowledge embedded in the set of pro- duction rules are domain-specific. An example of a production rule from the MYCIN expert sys- tem isANTECEDENT CONDITION:

(1) the infection is primary-bacteremia

and

(2) the site of the culture is one of the sterilesites and

(3) the suspected portal of entry of the organism is the gastro-intestinal tract

ACTION:

there is suggestive evidence that the identity of the organism is bacteroides

12
WITH CF=0.7.

Since a solution is found by transforming the data in the database sequentially, the set of

production rules may be formed into an AND/OR tree and the solution process represents the

search of this tree for an acceptable solution. The path that the system takes through this tree represents the sequence of rules that are executed as determined by relevant data from the database. The rule interpreter controls this search for a solution so that it, with a given set of rules,

defines a solution strategy that includes a strategy for acquiring information on each particular

problem (information strategy) and for choosing a solution (decision strategy). The information

and decision strategies may be sequential or nonsequential or both. The MYCIN medical diagnosis and treatment system, for example, goes through the following steps in interacting with the

physician using the program:

1) The program begins with a set of questions about the patient. Since this same set of questions is asked regardless of the answers they may be asked in any order. This information

acquistion is therefore nonsequential.

2) The program then asks for data on the patient as it is needed during the solution process to reduce the range of possible diseases. The information collected depends upon previously

acquired information in step 1 and earlier in step 2 and is therefore collected in a specific

order. The information acquired in step 2 is therefore collected sequentially. In applications to different patients, different sequences of questions will be asked in this step,

depending upon the characteristics of the problem.

3) The program asks for the data on the patient needed to formulate a suggested treatment. This information is also sequential since it depends upon the information collected in step

2.

4) The program recommends treatment.

2.1. A Formal Representation

To discuss the solution strategy of an expert system we need to more formally present the

basic expert system model. The data in the database may be represented by a set of parameters

each with a number of possible values. In the example of the production rule from the MYCIN

expert system presented in the previous subsection there are three parameters referred to in the

antecedent condition and one in the action, each of which is assigned a value or range of values.

If in the database the parameter representing infection is assigned the value denoting primary- bacteremia, the parameter representing the site of the culture is one of the sterilesites (i.e. the

parameter falls within a range of values), and the parameter representing the portal of entry is assigned the value denoting the gastro-intestinal tract, then the value for the parameter presenting

the identity of the organism is assigned the value that represents bacteroides. With the assumption that a production rule only refers to parameters in the database the parameters are of three

types: those that may be given a value only by collecting information directly from a problem

addressed by the system, those that may be assigned a value only by executing a production rule (therefore indirectly from a problem), and those that may be assigned a value either way (directly

usually being the preferred way).

We will group these three types of parameters into two different sets. First, let there be a

finite number K of parameters that represent those parameters that may be directly observed or measured from a problem. A state of the observable environment is therefore an assignment of a value to each of these parameters and we will represent this set of states by

Z = Zi x Z2 x • • • x where Zj is a set of possible values for the i* observable parameter.

13
Second, let there be a finite number m of parameters that represent those parameters that may not be directly observed from a problem. A state of the unobservable environment is therefore an

assignment of a value to each of these parameters and we will represent the set of states by

0 = ©! x 02 x • • • x 0m where 6, is a set of possible values for the 1th unobservable parameter.

An expert system, such as MYCIN, typically functions by sequentially reducing the range of possible observable states of the environment and then at some point in the sequence, selecting a

final solution. During its solution process the MYCIN expert system uses a threshold of 0.2 (the

likelihood that a particular state of the environment holds). Also, an expert system will, under

certain values of the parameters in the database, collect a certain piece of information on a problem and under other values stop collecting information and select a certain problem solution.

Since information collection involves only parameter values that may be directly observed, we

may formulate two different sets of conditional statements or rules (not production rules) that are logically equivalent to the original set of production rules and the control strategy of the rule interpreter but involve only the observable states of the environment. First, there are decisionmaking rules which are are conditional statements in which the conclusions assign a value to the parameter or parameters that represents a problem solution. Let there be a number Ki of these

rules and b, represent a set of possible values for the data in the data base. We shall express these

rules as: Y",:

"

IF bi THEN di fori=I, 2, Ki.

The antecedent condition bj of each conditional statement may be used to define a corresponding

"truth set", which we will represent by Bv This truth set is the set of all the possible states of the environment (i.e. Bj £ Z) under which the conjunction of clauses in bi (representing states of the

database) are all true. Therefore, the antecedent condition bj is seen to partition the states of the observable environment into two subsets: B4 and those states not in B,.

It is important that an expert system correctly compute solutions for all problems in its problem domain. Knowledge base evaluation is the process of testing that a knowledge base is both complete and consistent. If a rule set is complete, then the rule processor may find a solution for all problems in the problem domain. If a rule set is consistent, then a consistent rule compiler will find the same solution each time a particular problem is solved by the system (or

two different problems with the same relevant characteristics). 10 Much of the research on expert

systems has dealt with trying to insure consistency in rule-based expert systems where rules may

be easily added or removed from a rule set and the rule interpreter must therefore often choose

between several applicable rules during the solution process. For example, rule compilers are used frequently in expert systems and are designed to transform a rule set into decision tree form.

The EMYCIN system, for example, has a rule compiler that transforms blocks of rules with com- mon clauses into decision trees which evaluate several rules in parallel. For example, given the

rules AandB -»X

A and not B -> Y

not A and C -> Z

the decision tree in Figure 3 below would be formed by the rule compiler.

1

1

9. See pp. 225 in Davis, Buchanan, and Shortliffe (1977).

10. See Suwa, Scott, and Shortliffe (1984) for a general discussion of consistency and completeness in a rule

set. 11. Taken from van Mellc (1980) chapter 5.

14
Figure 3: A Compiled Block of Rules

Executing the decision tree representation is logically equivalent to interpreting the original block

of rules but is more efficient since the redundancy in the antecedent conditions of the rules is removed and each clause is evaluated only once. Using the above, we may more formally state the following two definitions of completeness

and consistency.

Definition 3. The decision strategy of an expert system is complete if the union of the truth

sets Bj, defined for each of the Kj decision-making rules, equals the entire set of possible

states of the environment. That is, the following holds:

K, Z = UBi.

i=l

Definition 4. The decision strategy of an expert system is consistent if no two of the truth

sets contain the same state of the environment. Or more formally, for any i, j e {1,2,

Ki>, i * j we have:

IF B, p| Bj * 0 THEN dj = dj.

If one or both of the above conditions fail, then the rule set of the expert system is not

correct. If a system is not complete, then there will be problems in the domain for which the sys- tem can reach no decision. Clearly, new decision-making rules need to be added until the system

is complete. If a decision strategy is not consistent, then there will be problems in the domain for

which the system reaches either more than one decision or different decisions on different applications to the same problem. The antecedent conditions of the existing rules need to be

strengthened until the truth sets no longer overlap or a new rule needs to be added of the form:

IF bj and bj THEN dKl+1 and the overlapping rules must be modified to: IF bj and not bj THEN dj

If the decision strategy of an expert system is complete and consistent it is therefore seen to be equivalent to a decision function on the partition {B l5 B2 , BKl } of the observable state space Z. Second, there are information-gathering rules which are conditional statements in which die

conclusions are in the form of a recommendation for acquiring further information on a specific

problem. Let there be a number K2 of these rules and we shall express them as:
IFbjTHENaj foi i=l,2,

K

2

. where b, represents a set of possible values for the data in the database and a; is a request of

further information on a problem. If the expert system were a medical diagnosis program, then

this would be the recommendation that additional information on a patient be acquired by run- ning an additional test(s) or asking an additional question(s).

If the information acquisition strategy of an expert system is consistent then the same

sequence of actions will be executed for the same specific problem (with the same value of the

observable critical parameters).

12

If the information strategy is complete then the sequence of

actions always results in a decision. If an information strategy is both complete and consistent,

then a decision tree may be formed with the actions and final decisions that represents the infor- mation and decision strategies of the system. To illustrate, in Figure 4 below the first tree represents both complete and consistent information and decision strategies, the second

represents incomplete strategies, and the third represents inconsistent strategies. Each terminal

node represents a decision (labeled with a d) and each of the other nodes represents an information action (labeled with an a). Each branch from a node represents a different possible result of

the information action.

Figure 4: Solution Strategies

The set of information-gathering rules of an expert system is therefore seen to create the

partition {B 1( B2, BKl } upon which the decision-making rules form their decisions. Since

each Bj c Z of a decision-making rule is associated with a sequence of information gathering

steps (and therefore sequences of information signals) an information structure, r\ a : Z —> Ya, is generated by the information-gathering rules.

12. If the information strategy is nonsequential then this is trivially true since the same actions are executed

for all problems.

16
2.2. Choice and Generation of Information Structures

Since a complete and consistent solution strategy generates an information structure dif- ferent solution strategies represent possibly different choices of an information structures upon

which to base a decision. The value of this choice is therefore measured by the correctness of the

solutions generated by the system. In addition to the correctness of the solutions, the solution

strategy also determines the efficiency of an expert system. A basic goal of expert system

development is that this search for a solution be carried out selectively and intelligently. The

concern with efficiency stems from the recognition that the solution process, particularly information collection, is costly and therefore costs should be taken into account. When a human expert

goes through the process of collecting information prior to making a decision he/she implicitly

makes judgements about the desirability of the possible outcomes and the cost of information.

For example, when diagnosing and treating a patient, a physician will often decide that more tests should be run to find out if a patient has a relatively rare disease that, if incorrectly diagnosed and

treated, is potentially very harmful. On the other hand, the same physician may decide not to collect further information on a patient even if he still has little idea which disease the patient suffers

from, if he has eliminated all but those with the same best treatment or those that are not very

harmful if misdiagnosed.

Example cont. Adding more detail to our earlier example, suppose the state of the environment consistents of two different parts: the observable portion and the unobservable portion (i.e. X = Z x 0). Let Z = Zx x Z2 = {0, 1} x {0, 1}, 0 = {Qh 82}, 13 and the following partitions be

created by each information action, Ma = {Mal , Ma2} for a = 0, 1, 4, where:

Mn = {ze Z| zj+z2 < 1}

M21 = {ze Zj z2 = 0}

M31 = {ze Z| zj-z2 < 1}

M41 = {z e Z I z x = 0}

Now consider the following four strategies:

M12 = {z e Z | Zi + z2>l}

M22 ={ze Z| z2 = 1}

M32 = {z e Z \ z1 -z2 = 1}

M42 = {z e Z | zj = 1}.

cii:

13. Note that xt = (0, 0, Bi), x2 = (0, 0, 9^, .... x7 = (1, 1, fy), x8 = (1, 1, 82).
Figure 5: Decision Tree Representations

and note that each generates one of the information structures, r^, T)4, described earlier. Let

us further suppose that each information action has an associated cost, c : A —» R, so that each

18
strategy now has an expected cost, 7 : A —> R. which may now be calculated as follows

Y«Xi) = c(l)

y(a2) = c(2) + Pr(y=l)c(3) = c(2) + 3/8c(3)

yCa3) = c(3)+Pr(y=l)c(l) = c(3) + 3/8c(l)

y(a4) = c(4) + Pr(y=l)c(l) = c(4) + 3/8c(l)

The maximum expected net payoff of strategy a is then defined as - y(a) and given that c(0)=0, c(l)=3, c(2)=4, c(3)=3, and c(4)=6, may be used to create the following ordering on information structures:

^(1) -7(1) "(3) - y(3) Q(2)-y(2) Q(4) - y(4) ft(0) - 7(0)

26 3/4 25 5/8 24 5/8 24 1/4 24

3. The Acquisition of Expert Knowledge

There are three types of expert knowledge that must be acquired to implement an efficient

expert system as described above. First is the knowledge of the problem domain that is necessary

to identify a set of relevant parameters (and therefore the states of the environment) that describe

each problem, identify a set of possible problem solutions (decisions), and calculate the a priori

likelihoods of each of the various states of the environment. Second is the knowledge of the

information gathering abilities of the system or system user which, combined with knowledge of

the problem domain, is needed to update the likelihoods of the various possible states of the

environment given the evidential support of one or more of the information actions. And lastly,

knowledge of the desirability of the outcomes (and therefore the correctness) of the possible final decisions is needed to be weighed against the computational costs in guiding the solution process. In the creation of an expert system, the acquisition of knowledge from an expert source generally begins with the conceptualization and structuring of knowledge of the domain. This is essentially the identification of many of the parameters and parameter values on which the pro- duction rules operate. Similarly, in classical decision theory under uncertainty a decision-maker

is seen to make inferences about an uncertain environment after obtaining information about certain critical parameters that affect the consequences of his decisions. The importance of identifying these environmental parameters lies in their effect on the outcome of the decision and, more

importandy, since the correctness of a decision is variable, in their effect on the desirability of the

outcome. The identification of these parameters and their values cannot, therefore, be separated

entirely from the identification of the different possible outcomes of a given solution and some

sort of measure of their desirability.

The ease with which the environmental parameters may be identified in the construction of an expert system will vary with different problem domains. Generally, one would expect that expert knowledge that may easily be represented in productions rule form will also have a fairly

developed set of critical parameters already identified by expert sources. An important distinction between a problem domain whose expert knowledge is easily represented by production rules and one whose expert knowledge is not, is whether it is best conceptualized as consisting of

several independent states or by a single unified theory. Many examples of the former case may

be found in medical diagnosis where expert sources have typically already identified and

classified diseases, their associated symptoms, and possible treatments. The MYCIN expert

19
system, for example, diagnoses and suggests treatment for the class of infectious diseases. Problem domains that do not lend themselves well to representation by production rules are often best

conceptualized not as various possible states but as a general unified theory that describes the

relationships between possible states. Mathematics, for example, in representing knowledge

about an operation such as addition, will focus on the property of commutativity that all operations called additivity share.

14

If expert sources have already identified the parameters, then they will have done so with

the set of possible decisions, the potential outcomes of the decisions, and the desirability of these

outcomes at least implicitly in mind. In medical diagnosis, for example, it is obvious that distin- guishing between two different diseases that typically either affect any given patient the same way no matter what the treatment (i.e. have the same effect on his/her well being) or have the same best treatment is less important to a physician than distinguishing between two other

diseases with different effects on a patient or with different best treatments. Expert knowledge of the domain must also be used to formulate a priori likelihoods (a fre- quency distribution) of the various states of the environment. An estimation of the likelihoods of

the possible states is necessary because solutions are generally made without complete

knowledge of the values of the critical parameters (i.e. without knowing the true state of the

environment). The necessary data will normally be available in the form of frequency distributions over intervals and the intervals generally used in the presentation of the data in expert sources such as text books and papers will have an influence on the form of the specification of

the environmental parameters. In the case of medical diagnosis, the incidence, within the general

population to which the expert system is to be applied, of diseases of the various types which are

to be diagnosed could be used. One useful expert source would be published public health data. Knowledge of the system or system user's abilities to collect information on a specific decision problem is the second type of expert knowledge needed for an expert system. This, together

with knowledge of the domain, allows the identification of a set of available information gathering actions and the calculation during the solution process of the likelihoods of the various states of the environment given the possible states of evidential support provided by previously exe- cuted information gathering actions. This knowledge is likely to be available from the same

expert sources as knowledge of the domain. Therefore, if there is already a fairly well developed

set of critical parameters for a particular domain, there will likely be a fairly developed set of

information gathering actions. In medical diagnosis, for example, the standard medical tests used

by physicians and data on the incidence within the population of the diseases in the domain associated with the different test results as well as estimates from human experts is generally available. The last type of knowledge needed to implement our model as an expert system is knowledge of the desirability of the outcomes of the different solutions. The identification of the

set of critical environmental parameters, set of possible solutions, and set of information actions

already involves some of this knowledge because elements of these sets are important only

through their effect on the outcome of the decision or more precisely on the desirability of the outcomes. The importance of using knowledge of the desirability of the possible outcomes to guide the solution process comes from the desire to efficiently reach problem solutions. In real applications, this is the recognition that collecting information is costly.

14. Sec pp. 29 Davis and King (1977)

20
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Buchanan, B.G. and R.O. Duda (1982). "Principles of Rule-Based Expert Systems", to appear in

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Davis, R., B.G. Buchanan, and E.H. Shortliffe (1977). "Production Rules as a Representation of

a Knowledge-Based Consultant Program", Artificial Intelligence, 8, 15-45.

Davis, R. and J.J. King (1984). "The Origin of Rule-Based Systems in AI", in Rule-Based Expert

Systems, (B.G. Buchanan and E.H. Shortliffe, eds.), Addison-Wesley.

Davis, R. et al., (1981). "The Dipmeter Advisor: Interpretation of Geological Signals", Proc.

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Rule Based Expert Systems, (B.B. Buchanan and E.H. Shortliffe, eds.), pp. 272-292.

Hirshleifer, J. and R.G. Riley (1979). "The Analytics of Uncertainty and Information—An Expository Survey" , Journal ofEconomic Literature, XVII, 1375-1421.

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Design", International Journal Information Systems, Vol. 7, no. 3, March, 281-296.

Ishizuka, M., K.S. Fu, and J.T.P. Yao (1982). "Inference Procedures under Uncertainty for the

Problem-Reduction Method", Information Sciences, 28, 179-206.

Marschak, J. (1971). "Economics of Information Systems", in Frontiers of Quantitative Economics edited by M.D. Intriligator, Amsterdam: North-Holland, 32-107.

Marschak, J. and K. Miyasawa (1968). "Economic Comparability of Information Systems",

International Economic Review, 9, 137-174.

McDermott, J. and B. Steele (1981). "Extending a Knowledge-Based System to Deal with Ad

Hoc Constraints", Proc. Seventh Int'lJoint Conf. Artificial Intelligence, 824-828. van Melle, W. (1980). "A Domain-Independent System That Aids in Constructing KnowledgeBased Consultation Programs", Ph.D. Dissertation, Computer Science Department, Stanford

University.

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unpublished manuscript.

Nau, D.S. (1983). "Expert Computer Systems", IEEE, February, 63-85. von Neumann, J. and O. Morgenstern (1944). Theory ofGames and Economic Behavior, Prince- ton University Press, Princeton.

Owen, G. (1983). Game Theory second edition, Academic Press, Inc.

Shortliffe, E.H. and B.G. Buchanan (1975). "A Model of Inexact Reasoning in Medicine",

Mathematical Biosciences, 23, 351-379.

Shortliffe, E.H., R. Davis, S.G. Axline, B.G. Buchanan, C.C. Green, and S.N. Cohen (1975).

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Shortliffe, eds.), pp. 159-170.

21
MANAGEMENT OF UNCERTAINTY IN EXPERT SYSTEMS

L. A. Zadeh*

Abstract

During the past several years, the question of how to deal with uncer- tainty in the context of expert systems has attracted a great deal of attention because much of the information which is resident in the knowledge base

of a typical expert system is imprecise, incomplete or not totally reliable.

The existing approaches to the management of uncertainty in expert systems

are based for the most part on probability theory or its variants. However,

it may be argued, as it is done in this paper, that probability theory is not

sufficiently expressive as a language of uncertainty to represent the meaning

of the imprecise facts and rules that form the knowledge base of a typical

expert system. In an alternative approach which is outlined in this paper,

fuzzy logic forms the basis for both meaning representation and inference.

In particular, syllogistic reasoning is used to formulate a collection of rules

for combination of evidence, with fuzzy quantifiers replacing probabilities

and certainty factors as indicators of the degree of uncertainty.

1 . Introduction

The issue of uncertainty plays an important role in the design and

operation of expert systems because much of the information which is resident

in the knowledge base of a typical expert system is imprecise, incomplete or not totally reliable.

The traditional probability-based methods of dealing with uncertainty

do not work well in the case of expert systems because in practice it is

usually infeasible to determine all of probability distributions which are needed to compute the conditional probability of a hypothesis given the evidence. This makes it necessary to employ ad hoc techniques of analysis in

which many implicit and hard to verify assumptions regarding the underlying

probability distributions are made.

Among the best known systems for dealing with uncertainty in the context

of expert systems are those of MYCIN [11] and PROSPECTOR [18]. In MYCIN, for

example, a typical rule is associated with a certainty factor , CF, which is a number in the interval [-1,1]. If positive, this number serves as a measure

of the relative increase in the probability of a hypothesis given the evidence.

If negative, its magnitude measures the relative decrease in the probability

of the hypothesis given the evidence. The MYCIN rules of combination provide

a system for computing the certainty factor of the conclusion from the cer- tainty factors of the primary sources of evidence.

Although the MYCIN system works reasonably well in practice, it has a number of serious shortcomings which are analyzed in detail in a recent paper

by Heckerman [23]. In his paper, Heckerman proposes several modifications

_Computer Science Division, Department of Electrical Engineering and Computer

Science, University of California, Berkeley, CA 94720. Research supported

in part by NASA Grant NCC2-275 and National Science Foundation Grant

ECS-82 09679.

22
to the MYCIN system which make it much less ad hoc and are more solidly based

on probability theory. However, there are some important sources of uncertainty

in expert systems which are not addressed by the techniques based on probability

theory and first-order logic. Among the more important of these sources are the following [52].

(1) The fuzziness of antecedents and/or consequents in rules of the form

(a) If X is A then Y is B,

(b) If X is A then Y is B with CF = a,

where the antecedent, X is A, and the consequent, X is B, are fuzzy propositions, and a is a numerical value of the certainty factor, CF. For example [11]

(a) If the search space is moderately small then exhaustive search is feasible .

(b) If a piece of code is called frequently then it is worth optimizing .

(c) If large oil spill or strong acid spill then emergency is strongly

suggested.

(d) If X is small then Y is large with CF = 0.8.

(e) If the route of the administration of the penicillin is oral, and there

is a gastrointestinal factor which may interfere with the absorption

of the penicillin, then there is suggestive evidence (0.6) that the

route of administration of the penicillin is not adequate .

In these rules, the italicized words are labels of fuzzy predicates

(e.g., small , large , emergency ) or fuzzy predicate modifiers (e.g., moderately )

.

This implies that the antecedents and consequents in the rules in question

are fuzzy propositions or, equivalently, fuzzy events.

In the existing expert systems, the fuzziness of antecedents and consequents is ignored or treated incorrectly because neither probability-based

methods nor bivalent logical systems provide a computational framework for

dealing with it. As a consequence, fuzzy facts and rules are generally

manipulated as if they were nonfuzzy, leading to conclusions of doubtful

val idity.

To illustrate this point, consider a rule of the general form [52]:

If X is A then Y is B with probability 3,

where X and Y are variables, A and B are fuzzy predicates and B is a fuzzy

probability expressed as a fuzzy number, e.g., about 0.8, or as a linguistic

probability, e.g., very likely . For example

If Mary is young then Mary is healthy is likely,

where X = Age(Mary), Y = Health(Mary) , A = young, B = healthy and 3 = likely .

Expressed as a conditional probability, the rule in question may be

written as PHY is B | X is A} is 3- (1.1)

In the existing expert systems, such a rule would be treated as an ordinary

conditional probability, from which it would follow that

23
Pr{Y is not B | X is A} is 1-3 (1.2)

However, as shown in [52], this conclusion is, in general, incorrect if A is

a fuzzy set. The correct conclusion is weaker than (1.2), namely,

with the understanding that the probabilities in question may be fuzzy numbers

The above example points to two essential shortcomings of classical

probability theory as a tool for dealing with uncertainty in expert systems.

First, it makes no provision for fuzzy events; and second, it does not provide

a mechanism for computing with fuzzy probabilities. Thus, the problem with

the use of probability theory as a basis for the management of uncertainty in

expert systems stems not from any flaws in its axiomatic foundations but from

its lack of expressive power in representing the meaning of facts and rules

which contain fuzzy predicates and/or fuzzy probabilities (or, equivalently

,

fuzzy quantifiers). Thus, viewed as a language, probability theory does not

offer an appropriate framework for inference from propositions of the general

form X is A, if X is A then Y is B, X is A is A, if X is A then Y is B is A,

and QA's are B's, where A and B are fuzzy predicates (e.g., smal

1

, tall ) ; A is a fuzzy probability (e.g., 1 ikely , very unl ikely )

, and Q is a fuzzy

quantifier (e.g., most , many )

.

A view articulated in [52] is that the expressiveness of probability

theory and predicate logic may be enhanced through the employment of fuzzy

logic--a logical system which allows the use of fuzzy predicates and fuzzy

quantifiers and thus subsumes both probability theory and predicate logic.

In particular, through the use of fuzzy syllogisms, fuzzy logic provides an effective framework for the formulation of rules of construction of evidence

in expert systems. In what follows, we shall present a summary of the basic

concepts in fuzzy logic based on [50,52,53] and outline the rules of inference

which are needed for dealing with uncertain facts and rules in the knowledge

base of an expert system.

Like most logical systems, fuzzy logic has two components: (1) a representational component which deals with the representation of meaning of

predicates, connectives, predicate modifiers and propositions; and (2) an inferential component , which, as its name implies, is concerned with the deduc

tion of a conclusion from a set of premises. The representational component

is bcS'^d on PRUF [46] and test-score semantics [51], while the inferential

component involves, for the most part, the manipulation of possibility distributions and fuzzy quantifiers.

Mere specifically, the first step in inference in fuzzy logic involves

a translation of the premises p-j,...,p into PRUF--a meaning representation

language which is based on the concept of a possibility distribution [46,49].

Thus, if p is a proposition and P is its translation into PRUF,

Pr{Y is not B | X is A} + Pr{Y is B | X is A} > 1

,

(1.3)

2. Fuzzy Logic

P (2.1)

24
then P may be viewed as a procedure which acts on a collection of relations

in a database--or, equivalently, a possible world—and returns a possibility

distribution which represents the information conveyed by p.

If X is a variable taking values in U, then the possibility distribution

of X, denoted by Ity, is the fuzzy set of possible values of X. The membership function of Ity is referred to as the possibility distribution function

and is defined by

ir x (u) = Poss{X = u} (2.2)

where the right-hand member of (2.2) should be read as: "The possibility

that X can take u as a value," with the understanding that 0 < ttJu) £ 1.

More generally, if X is an n-ary variable

X = (Xr...,X

n

) (2.3)

where X. takes values in U . , i = l,...,n, then n# Y

„ \ is a fuzzy subset

1 I \ A] , . . . , Aft ) of the cartesian product U = U-|X-..xU

n

, and the projection of

^ ^ on

u"(s) = Uii*. • .xU-j^ is defined by the possibility distribution function

V"x V(Xi —• xn )(Ui r"'V

= su p

u , ,...,u.

1T (x

1 ,...,xn

) (ur--- u

n

) {2A)

where X^ -(X-j-j ,. . . is a subvariable of X - (X-|,...,X

n

) and

A(s') - (Xj-j , . . . ,Xj^) is the complementary subvariable, i.e., =

{"I.,... ,h} - {t, . >\h

An n-ary possibility distribution is particularized by forming the con- junction of the propositions "X is F" and "%U) is G," where X(

s ) is a subvariable of X and F and G are fuzzy subsets of U. Thus,

nY [nY = G] - F n g (2.5)

X X

(s)

where the right-hand member denotes the intersection of F with the cylindrical

extension of G, i.e., a cylindrical fuzzy set defined by

Vg(u.|,...,u

n

) = y

G

(u

i ,...,u

i ), (u1 ,...,u

n

) € U-| x ... x(Jn (2.6)

1 k

where denotes the membership function of G.

There are many cases in which the operations of particularization and

projection are combined. In such cases it is convenient to use the simplified

notation

x. x...xx. nC\

, =G] (2 - 7)

25
to indicate that the particularized possibility distribution (or relation)

n ^ nx (s)

= G ^

^ s P r°J ected on u i-| x " ,xU i|<" For examPl e >

x

1 xx3

n[n

(x

3 ,x4

) =G] (2 - 8)

would represent the projection of n[n^ ^ j = G] on U-|XU 3

. Informally, (2.7)

may be interprested as: Constrain the X/^ by nx(

s

) = G and read out the X^

.

In particular, if the value of X(

s )--rather than its possibility distribution

--is set equal to G, then (2.7) becomes

X. x-xX.

*X(t)"«- < 2 - 9 >

n

l \

Now let p be a proposition of the form

p

- N is F (2.10)

where N is the name of an object, a variable or a proposition, and F is a fuzzy subset of a universe of discourse U. For example:

p - Sheila is very intelligent

q

= X is small

r = (X is small) is more or less true

A translation of p may be focused or unfocused . By a focused translation in PRUF is meant a translation of the form

P^X,....^) = F < 2 -">

where the X-j are variables which are explicit or implicit in N and F is a fuzzy subset of U]X...xUn

. A translation of this form serves to (a) identify

the variables in N whose possibility distribution is determined by p, and

(b) specify the possibility distribution in question. To reflect this aspect

of the translation process, the right-hand member of (2.11) is referred to as the possibility assignment equation , with F representing the possibility

distribution induced by p.

As a simple illustration of (2.11), consider the proposition

p - Mary has blue eyes. (2.12)

In this case, (2.11) becomes

Mary has blue eyes -> nColor(Eyes(Mary)

}

= BLUE (2.13)

in which X = Color(Eyes(Mary) ) and BLUE, the denotation of blue, is the fuzzy

subset of colors which are perceived as blue.

Turning to the concept of an unfocused translation, let V = {D} denote a collection of databases, with D representing a generic element of V. For

26
the purposes of our analysis, D will be assumed to consist of a collection of

possibly time-varying relations. If R is a constituent relation in D, then

by the frame of R is meant the name of R together with the names of its

columns (i.e., attributes). For example, if a constitutent of D is a relation

labeled POPULATION whose tableau is comprised of columns labeled Name and

Height, then the frame of POPULATION is represented as POPULATION [Name, Height].

In relation to £>, the unfocused translation of p can assume one of two

equivalent forms:'

(a) p —* a procedure which yields for each D in V the possibility of

D given p, i.e., Poss{D|p}

(b) p —* a procedure which yields for each D in V the truth-value of

p relative to D, i.e., Tr{p|D}

The equivalence of (a) and (b) is a consequence of the way in which the

concept of truth is defined in fuzzy logic [53]. Thus, it can readily be

shown that, under mildly restrictive assumptions on D, we have

Tr{p|D} = Poss{D|p} (2.14)

which implies the equivalence of (a) and (b). Furthermore, it should be noted

that (2.11) is a special case of (a), so that the concept of a focused translation is subsumed by that of an unfocused translation. In essence, our motivation for introducing the concept of a focused translation is to provide a mechanism for enhancing the explanatory effectiveness of a translation. In

the case of a focused translation, this is accomplished by focusing on those

and only those variables in D whose possibility distribution is constrained

by the meaning of p.

If n*

5 and jfi are the possibility distributions induced by p and q, respectively, then p and q are semantically equivalent if and only if nP = n^. 2

In symbols

p q iff n

p = TIq (2.15)

For example, it can readily be shown that

X is small is true X is not small is false

provided the linguistic truth- value fal se is taken to be the antonym of true ,

Wfalse

(v) = W(l-v), 0<v<l (2.16)

where and ytrue

are the membership functions of false and true ,

^It should be noted that (a) and (b) are in the spirit of possible-world seman- tics and truth-conditional semantics, respectively. In their conventional

form, however, these semantics have no provision for fuzzy propositions and

hence are not suitable for use in fuzzy logic.

2

To be more precise, we have to differentiate between the concepts of semantic

equivalence and strong semantic equivalence. The latter concept reduces to

that of semantic equivalence in predicate logic when p and q are nonfuzzy

propositions.

27
respectively, More generally, q is semantically entailed by p if and only if

nP c n

q . In symbols

p h> q iff n

p c nq

. (2.17)

For example,

X is very small I— X is small (2.18)

if very is defined by

,2 u very s maii<

u

> <>WU » > ueu- < 2 - 19 >

We shall make use of the concept of semantic entailment in Section 3.

An essential component of a fuzzy logic is the set of translation rules,

which may be categorized into four basic types: Type I - rules pertaining to

modification; Type II - rules pertaining to composition; Type III - rules

pertaining to quantification; and Type IV - rules pertaining to qualification.

For our purposes, it will suffice to state the rules of Type II, which are:

If X is F -> n

x

= G and Y is G -> nY = G (2.20)

where F and G are fuzzy subsets of U and V, respectively, then

(a) X is F and Y is G —> n^

x

Y j = F x G (2.21)

where Vip xG

(u,v) = y

p (u) ~y

Q

(v). (2.22)

(b) X is F or Y is G —* n^

x Y j = FUG (2.23)

where F = F x V, G = U x G (2.24)

and ypJg(u,v) = y

p (u) - y

G

(v) . (2.25)

(c) If X is F then Y is G —> n(

Y | X ) = ^' ® G (2.26)

where n

( y | X

) denotes tne conditional possibility distribution of Y given X,

and the bounded sum © is defined by

y

F

, e g(u,v) = 1 ~(1 -y

G

(u) +y

G

(v)). (2.27)

It should be noted that the compositional rules in FL are defined in a way that makes them consistent with the valuation rules in Lukasiewicz's

'"Alephl

^°5"' c - I n particular, if X = Y then it follows at once from (2.27)

that the proposition

r = If X is F then X is G (2.29)

conveys no information concerning X (i.e., is a tautology) if and only if

F c G. This conclusion serves as a basis for the entailment principle which

is stated in the following section.

28
3. Rules of Inference

The rules of inference in fuzzy logic are semantic rather than syntactic

in nature and are based, in the main, on two principles: (a) the entailment

principle, and (b) the extension principle.

The entailment principle, which is based in turn on (2.28), may be

expressed in a schematic form as

nS = F (3.1)

n^ = G: F c G

which means that if the possibility distribution induced by p is contained in

that induced by q, then q may be inferred from p. (The symbol +— stands for

"retranslation. ") In other words, from p we can infer any proposition which

is semantically equivalent to p or, more generally, semantically entailed by p.

To state the extension principle, let f be a function from U or V and

let F be a finite fuzzy subset of U expressed as F-u,/6

l + — + W

Then, the extension of f to the set of fuzzy subsets of U is defined by*

f(F) = f{^/u

} +

•

= Uj/fdij) +

+ Vu

n

}

•+y

n /f(un

).

(3.2)

(3.3)

More generally, if the support of F is a continuum, i.e.,

then

F =

f(F) =

y c (u)/u

u

y c (u)/f(u).

u

(3.4)

(3.5)

Furthermore, if U is a cartesian product of U,,...,U_ and f is a mapping from

U,x...xu to V, then 1 n

n f(F) = y

F (u1 ,...,un )/f(u-| ,...,u

n

) (3.6)

In particular, if we have only partial information about up, e.g., the knowledge of its projections on U-j,...,U , then

f(F) = u-|(u-|) -y

p (u

n )/f(u1 ,un

) (3.7)

The notation F = y]/u-| + - • -+un /un signifies that F is a collection of fuzzy

singletons y-j/u-j, i = l,...,n, with u-j representing the grade of membership

of iu in F. More generally, F may be expressed as F = E. y^/u. or

F =

J y

p (u)/u.

29
where u., i = 1 n, is the membership function of the projection of F on .

A more explicit representation of f(F) which is equivalent to (3.7) is

the following, (u - membership function of f(F).)

f(F) =

f u(v)/v (3.8)

where y ^ v ^ " max

Ul , . . . ,u y

l

u

l " ' " y

n^

u n^ ^ 3 ' 9 ^

subject to the constraint

v = f(u

] ,. .. ,un

) .

In this form, the extension principle serves to reduce an inference

process to the determination of the solution of a variational problem in

nonlinear programming.

Among the rules of inference which are based on the entailment principle

are the following.

Projection rule . Stated in a schematic form, this rule may be expressed

as P^ n

(x r

...,x

n

) = F

(3.10)

n

(X. ,...,X. ) =

X. x...xX. f

n l \ M \

where

x x-.-xX- F denotes the Projection of F on Uj-jX- • -xU^ (see (2.4))

In other words, (3.10) asserts that if p induces the possibility distribution

ny Y w \> then from p we can infer any proposition q which induces the lA],...,An j projection of IT on U-j-jX. . .xU-j. .

As a simple illustration of (3.10), consider the proposition

p - John is big

and assume that BIG, the denotation of big, is a binary fuzzy relation which

may be represented as the cartesian product of the unary fuzzy relations TALL

and FAT. Then

TALL Height616

and hence we can assert that

John is big

John is tall

Conjunction rule . Let X, Y and Z take values in U 2 V and W, respectively;

let F and G be fuzzy subsets of U x V and VxW; and let F and G be the cylindrical extensions of F and G in UxVxW. Thus,

30
F = FxW

and G = U x G.

The conjunction rule is defined by the following scheme

[ (X,Y) P — n>? v vN = F (3.11)

n

(Y,Z) = G

r "- n

(X,Y,Z) * Fng

In particular, in the special case where

P - n$ = f

q —> = G

(3.12)

(3.11) implies that from p and q we can infer r, where

n£ = fog.

Compositional rule . On combining the projection and conjunction rules, we obtain the compositional rule of inference (3.13) which includes the

classical modus ponens as a special case.

More specifically, on applying the projection rule to (3.11), we obtain

the following inference scheme

P -> "(X.Y)

= F

q -» I^

Y>Z)

= G (3.13)

r r "(X.Z) F ° G

where the composition of F and G is defined by

y poG (u,w) = sup

v (yp

(u,v) -yQ(v,w)). (3.14)

In particular, if p is a proposition of the form "X is F" and q is a proposition of the form "If X is G then Y is H," then (3.13) becomes

p - n

x

= F

q H(

Y

. X ) = G' ® H (3.15)

r +- n

y

= Fo(G' • H)

The rule expressed by (3.15) may be viewed as a generalized form of modus

ponens which reduces to the classical modus ponens when F = G and F, G, H are nonfuzzy sets.

31
Semantic equivalence . An immediate consequence of the entailment principle is that from p we can infer any proposition q which is semantically

equivalent to p.

An important special case of this rule is embodied in the truth-qual ification principle which may be stated as follows.

Let x be a linguistic truth- value which is characterized by its membership function y^ and assume that

p = X is F. Then

X is F X is G is x (3.16)

where the membership functions of F, G and t are related by

u

F

(u) = y

T (yG

(u)). (3.17)

Thus, from the proposition q = X is G is x we can infer p = X is F, and

vice-versa, with the understanding that yFis given by the composition of y

and yg.

T

As an illustration, consider the proposition

p - Lynn is young is very true

in which « _1

and V"ng(u) =

• " e [0,100]

Vue(v) " v2 > v S CO' 1 *

Then, we can infer from p that

u 2

" 4

^AgeUynn)^

= {1 + ( 20 } )

which upon retranslation may be expressed as

q - Lynn is very very young.

Transformational rule . This rule is implied by the extension principle

and may be expressed in a schematic form as

p

= X is F

q

= Y is f(X) (3.18)

r = Y is f(F)

where F is a fuzzy subset of U, f is a mapping from U to V, and f(F) is a fuzzy subset of V defined by (3.8).

As a simple illustration of (3.18), assume that X is a fuzzy number which

is expressed as "approximately 5" or, more explicitly,

X = 0.2/2 + 0.5/3 + 0.8/4 + 1/5 + 0.8/6 + 0.6/7 + 0.2/8

32
and Y = X . Then

Y = 0.2/4 + 0.5/9 + 0.8/16 + 1/25 + 0.8/36 + 0.6/49 + 0.2/64.

4. Syllogistic Reasoning

In addition to the rules described in the preceding section, there is a class of rules of inference in fuzzy logic which are of particular relevance

to the combination of evidence in expert systems. The rules in question may

be viewed as fuzzy syllogisms [54] in which the premises are propositions of

the form QA's are B's, where A and B are fuzzy predicates and Q is a fuzzy

quantifier.

In its generic form, a fuzzy syllogism may be expressed as the inference

schema

Q-jA's are B's

Q 9 C's are D's (4.1)

Q

3

E's are F's

in which A, B, C, D, E and F are interrelated fuzzy predicates and Q, , and

are fuzzy quantifiers.

The interrelations between A, B, C, D, E and F provide a basis for a classification of fuzzy syllogisms. The more important of these syllogisms

are the following. = conjunction, v = disjunction).

(a) Intersection/product syllogism: C = A~B, E = A, F=C~D

(b) Chaining syllogism: C=B, E=A, F=D

(c) Consequent conjunction syllogism: A = C = E, F = B~D

(d) Consequent disjunction syllogism: A = C = E, F = B v D

(e) Antecedent conjunction syllogism: B = D = F, E = A~C

(f) Antecedent disjunction syllogism: B = D=F, E = A~C.

In the context of expert systems, these and related syllogisms provide

a set of inference rules for combining evidence through conjunction, disjunction and chaining [52].

One of the basic problems in fuzzy syllogistic reasoning is the following. Given A, B, C, D, E and F, find the maximally specific (i.e., most

restrictive) fuzzy quantifier Q3 such that the proposition Qi E's are F's is

entailed by the premises. In the case of (a), (b) and (c), this leads to the

following syllogisms.

Intersection/product syllogism

Q, A's are B's

Q

2 (A and B) 's are C's (4.2)

(Q-, ®Q9

) A's are (B and C) 's

33
where ® denotes the product in fuzzy arithmetic [25]. It should be noted that

(4.2) may be viewed as an analog of the basic probabilistic identity

p(B,C/A) = p(B/A)p(C/A,B).

A concrete example of the intersection/product syllogism is the following

most students are young

most young students are single (4.3)

most students are young and single,

2

where most denotes the product of the fuzzy quantifier most with itself.

Chaining syllogism

Q

1 A's are B's

Q

2

B's are C's (4.4)

(Q

1 <SQ2

) A's are C's.

This syllogism may be viewed as a special case of the intersection/product

syllogism. It results when B c A and Q-j and Q2 are monotone increasing, i.e.,

>_ Q-] = Qi , and < Q2 = Q2> where > Q-j should be read as at least Q] , and 1 ikewise for Q

2

- A simple example of trie chaining syllogism is the following:

most students are undergraduates

most undergraduates are single

most^ students are single

Note that undergraduates c s tudents and that in the conclusion F = single , rather than young and single , as in (4.3).

Consequent conjunction syllogism . The consequent conjunction syllogism

is an example of a basic syllogism which is not a derivative of the intersection/product syllogism. Its statement may be expressed as follows:

Q

1 A's are B's

Q

2

A's are C's (4.5)

Q A's are (B and C) 's,

where Q is a fuzzy quantifier which is defined by the inequalities

0 © (Q-, ® Q2 Gl) < Q < Q

1 © Q2 (4.6)

in which ©, Q, © and 0 are the operations of v (max), * (min), + and - in

fuzzy arithmetic.

An illustration of (4.5) is provided by the example

34
most students are young

most students are single

Q students are single and young

where 2most©l £ Q < most. (4.7)

This expression for Q follows from (4.6) by noting that

most ©most = most

and

0 © (2most e 1) = 2most 0 1

The three basic syllogisms described above are representative of a class

of rules of inference in fuzzy logic in which fuzzy quantifiers play a role

analogous to that of probabilities in probabilistic reasoning. Through the

use of such rules, probabilistic reasoning becomes a part of fuzzy logic. In

this way, it becomes possible to employ a single conceptional framework for the

management of uncertainty in expert systems in which both probability theory

and possibility theory play central roles.

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35
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36
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38
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.

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39
New Tools for Emergency

Management

David L. Waltz

Thinking Machines Corporation

Cambridge, MA

and

Brandeis University

Waltham, MA

ABSTRACT

This paper explores a number of possible applications of AI (Artificial

Intelligence) 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.

40
1. Far Future Emergency Management Scenarios

Projecting current research in AI, we can envision emergency management methods of the future that are dramatically different from

today's practice. For example, consider the following possible scenarios (also see Figure 1):

Forest Fire: an automated monitoring system, using satellites and

a network of ground-based TV cameras networked with a state-wide

emergency management coordination system, catches a forest fire in

its early stages. Responsible state officials are called at home and

notified of the fire by the system, and the system informs the gover- nor of the plan that it has devised, including transport of firefighting

equipment and support supplies from cities A, B, and C to the site

of the fire. The governor gives the OK, and the system begins calling

the transport drivers to report to work. It issues orders to the robot

firefighters, actually bulldozers equipped with vision and on-board intelligence, built to withstand very high temperatures, some equipped

with tanks and nozzles for water or chemicals, others equipped with

blades and cutters for clearing forest firelines, still others equipped for

refueling or servicing. The robot firefighters begin loading themselves

onto the appropriate trucks to await the arrival of their drivers.

Drone aircraft are automatically called by the system and given instructions. They begin runs over the fire, dropping water and chemicals well before the bulldozers arrive. They drop their loads on spots

picked out in part by the overall system (based on its assessment of

the weather, direction of the fire spread, endangered buildings, natural firebreaks, etc.) and in part on the on-board intelligence of each

aircraft, which pinpoints the most appropriate exact point to drop its load so that it will have the maximum retardant effect.

The central system periodically informs the governor of progress.

Meanwhile, electronic fund transfers are begun to pay for fuel and

supplies used, and orders are placed to replenish stocks of spare parts.

Any planes or bulldozers lost are reordered. (They are quite inexpensive: they are built in totally automated factories, do not have to

41
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42
make provision for protecting human operators, and the several powerful computers on board add only a small amount to the total cost

of the vehicles.) As the fire is brought under control, another fire

breaks out in another part of the state, and some of the vehicles are

instructed to go to the new site. They have been working 24 hours a day for three days; the robots firefighters that have sustained damage

"volunteer" to go to the other site, and are automatically repaired

enroute. Since they were designed to be repaired automatically, they

have on-board self-diagnosis facilities, and repair robots can remove

and replace most components as entire modules to effect repairs.

Tornado: When a tornado is sighted by a distributed computer vision system, the automated centralized regional emergency system

(containing map, resident, and phone number data) calls the residents in and near its path, causing a special emergency ring on their

phones, and interrupting calls in progress. Following the tornado, the

robot rescue and clean-up cousins of the firefighters are deployed to

the scene of the damage. Meanwhile, the system prepares detailed

applications for federal aid, based on an automated damage assess- ment system that gets its data from the robots at the scene, as well

as from real estate value data bases. The same system will later file follow-up reports automatically. As regulations change, federal computers transmit program alterations to state and regional systems,

and the changes are reflected in the operation of these systems with

little or no need for human intervention. When it becomes clear that

the current number of emergency vehicles was inadequate for dealing

with the tornado emergency, the system helps to generate a system

upgrade request to the state legislature, providing detailed justification in terms of the savings in lives, property, and recovery that

could have been realized if the requested equipment had been available. This justification is backed up by a computer simulation of the

event, illustrating the effects of a variety of possible changes in the

emergency aid delivery system, and incorporating long-term weather

norms to assess expected costs and benefits.

Other emergencies: The same technologies could generate robot

emergency handlers for dam repair; bomb defusing; mine rescue (or

43
mining itself, eliminating mine rescue); cleanup of railroad or truck ac- cidents involving toxic chemicals; cleanup of hazardous waste dumps;

repair or cleanup of nuclear reactors; early warning, evacuation and

rescue, and cleanup from earthquakes and floods; and so on. Skill and

intelligence could be brought to bear on problem situations without

endangering the lives of the rescuers. In many situations, this might

allow us to avoid emergencies: for example, the quick righting of a

derailed tank car by a fearless, strong, expendable robot might prevent the need for evacuation; early personalized warning of tornados

or floods could save many lives; and so on.

2. What problems need to be solved to achieve this future?

The systems described above are large, costly, and very complex by

today's standards. Many components of the system are little more

than small-scale laboratory experiments today. However, there is good reason to believe that this future is possible, perhaps even likely.

Some investment in and encouragement of the dream now could help

bring this future about sooner.

First, consider some of the technical advances that need to made:

(a) Centralized knowledge bases must be built up, along with mechanisms to keep them up-to-date. Such knowledge bases must

contain information on residents within a jurisdiction, phone

numbers, map and route data; emergency equipment and its lo- cation, condition, abilities, support needs; local, regional, state

and federal authorities responsible for making and carrying out

decisions; histories of past emergencies for aid in understanding needs and strategies for dealing with future ones in a better

manner; rules, regulations, reporting and request procedures and

contacts, and so on.

(b) Fast, cheap, reliable, and smart sensors (optical, seismic, thermal) must be devised and deployed.

44
(c) A vast communications system must be constructed. It must

be capable of high data-rate transmission and sufficiently re- dundant or rugged to operate continuously, even under extreme

emergency conditions.

(d) Intelligent systems must be devised that can:

• analyze, sort, and integrate incoming messages and data

from people, robots, distributed sensors, and satellites, to

form an accurate assessment of any given situation.

• form plans for allocating resources, deploying equipment,

informing the appropriate persons (potential victims as well as decision-makers), gathering data needed to comprehend

a situation, and keeping costs as low as possible.

• give detailed direction to the actual emergency handlers, human and robot, revising directions as each situation changes.

• formulate plans for improving preparedness, based on ad- vances in technology and shifts in demographics, and help

prepare requests and justifications for these plans.

• prepare emergency aid requests, submit aid applications,

help in distributing aid, file follow-up reports, suggest procedure revisions, keep histories.

(e) Robot emergency handlers must be developed. These systems

must have

• good sensory abilities;

• the ability to take general instructions and adapt them to an actual situation;

• the ability to construct and send accurate, appropriate re- ports to the central emergency handling system;

• and, of course, the manual or physical abilities and dexterity

to handle particular emergencies.

Some of the special technical complications involved include:

• the inherent unpredictability of emergencies; the great diversity of types of data and communications systems that

must be integrated;

45
• the problems of making assessments from incomplete or conflicting data;

• the very large amounts of computing power that must be

deployed at many levels: small, powerful, rugged, portable

units for robots; powerful, reliable units for distributed sensors; and ultrapowerful units for centralized data integration, situation assessment, communications handling, plan

generation, and detailed direction of emergency handlers;

• the abilities of all systems to withstand extremes of temperature and lighting, precipitation, power interruption, the

shocks of earthquakes and explosions; and so on. Some other special problems include:

• the planning for and gradual integration of such systems

into our existing infrastructure. Some acceptable balance

must be struck between completely autonomous systems,

and systems which offer possible courses of action for human

approval - each type of system may have its own range of

applicability.

• the sensible assignment of responsibility for various types of

emergencies in various locations - there are problems both

with jusidictional gaps that aren't covered at all, and with

overlaps of jurisdiction where systems may work at cross- purposes;

• and, of course, the cost of constructing such systems, which

will clearly be very high, even if much of the hardware

needed continues its steep long-term historical price decline.

What reason do we have to suppose that this future is possible?

Virtually every one of the problems that needs to be solved is a cur- rent active research area, though the applications envisioned for the

research are for the most part completely outside the domain of emergency management.

46
In particular, in preparing this paper, I was struck by the very close

similarities between the types of problems that need to be solved to

realize the far-future emergency scenarios, and the three problems

that form the core of DoD's current AI research effort. The three

DoD problems are:

• Battlefield management;

• Autonomous vehicles; and

• Pilots' associates.

Of these, the first two are the most relevant. The "Battlefield Manager" , like the emergency manager, is expected to integrate data from

remote sensors, voice reports, etc., and form some overall assessment

of a threatening situation. Both systems must plan, deploy and direct humans and robots. In both cases, response speed is critical.

Both must deal with incomplete and conflicting information: in the

emergency case, we assume that nature is capriciously unpredictable,

while in the battlefield, an enemy may deliberately try to mislead and

confuse the system.

The "Autonomous Vehicle" is a robot tank, reconnaissance, or supplycarrying vehicle. Many of the problems for an emergency-handling

vehicle are analogous to the DoD autonomous vehicle: both must

operate in all types of weather and terrain, and under adverse additional conditions: hostile enemy action in the DoD case; fire, flood,

toxic chemicals, landslide, earthquake, explosion, and so on in the

emergency case. While less relevant, research on a pilot's associate might be a help in

developing drone emergency handling aircraft, and certainly, civilian

pilots might also benefit in avoiding or handling emergencies from

having pilots' assistants.

In addition, much of the commercial work on expert systems for handling electronic communication, financial planning and transaction

processing, decision-making, and the simulation of large-scale systems will be of benefit in the emergency management realm, as will

47
research on industrial automation and robotics, signal processing, and

computer vision.

4. What can be done now?

Some current research projects and commercial products can already

be of aid to emergency managers. I will mention a few examples in

the areas of expert systems and natural language processing. For a much more detailed assessment of these two areas see [Waltz 1983a]

and [OTA 1985]. 1 Expert systems: most expert systems built to date have 1000

rules or less. Nearly all successful applications have been for wellcircumscribed, fairly well-understood areas. Such systems are already

quite hard to devise; generally a human expert and one or more programmers must spend a substantial amount of time (on the order of a year) to build one, though there are a number of expert systembuilding tools on the market that promise to cut that time, as well as ease the problems of getting a system to the point where one can begin

to trust its reliability. There is general agreement that the bottleneck

in developing expert systems is the problem of knowledge acquisition

[Davis 1982]. A fair amount of work has gone into trying to speed

up expert systems; for an assessment of the limits of speed-ups, see [Forgy et al 1984].

Natural language: a number of tools now exist, ranging from text

preparation aids (for checking spelling, grammar, and style of text);

to data base question-answering systems that allow a user to phrase

questions in English [Waltz 1978, Hendrix 1978, Harris 1977, Woods

et al 1972]; to automatic indexing systems for helping to organize

documents and messages, and for helping to generate indexes for proposals or reports. Again there are tools available for building custom

NL applications. 2

1 The November 1983 issue of the IEEE Spectrum also is a good source for information on knowledge acquisition [Hayes-Roth 1983], natural language processing [Waltz 1983b],

speech understanding [Reddy 1983], and vision [Kanade 1983).

2 For example, the IRUS system from Bolt Beranek and Newman, Inc., Cambridge, MA,

or the KLAUS system from SRI International, Menlo Park, CA.

48
Table I shows a very abbreviated summary history of natural language

projects, both laboratory experiments and applications.

In the next section, I give a little more detail on what natural language

systems can offer to emergency management systems in the near and

far term. In part this is because natural language systems are my own

interest, and the area I know best; while they are also quite relevant,

I will not treat expert systems in much more detail, because other

papers in this volume are already devoted to them.

5. Role of natural language processing systems

Natural language processing (NLP) systems can be useful because:

• any database items are in natural language (NL) already;

• Emergency handlers and the general public already know NL

(e.g. English);

• Existing NL tools can provide immediate help for a variety of

problems;

• Experience with interim NL systems can help in developing ultimate expert systems, by helping to build up the knowledge bases

that will be needed.

Figure 2 shows the places where NL will be needed in a full-scale

emergency management system. NL is needed to interpret reports

phoned or typed into the system from the field; to route this information to the right parts of the database; to handle questions in NL

posed by users of the system; to process regulations and fill in report

forms (while following regulations); to prepare proposals and requests

for state and federal aid; and so on. The TMC Indexer 3

is an example of a product that can be useful right

away. It is a system that takes text, and automatically generates a back-of-the-book like index for the text. It can be used to keep related

topics together: it can organize messages, proposals, reports, plans,

3 The TMC Indexer is available from Thinking Machines Corp., Cambridge, MA.

49
LABORATORY APPLICATION

1960

* Experimental

machine translation

* Al: question-answering;

problem-solving;

robot control

* DB keyword search

* Word processing systems

1970

1980

1985

LUNAR (Woods)

LIFER (Hendrix

PLANES (Waltz)

* Story understanding (Schank)

*/FRUMP (DeJong)

N\iews story summarizer

* NL Understanding

Stories

Dialogues

Articles

* NL Generation

* Theory

* Spelling correctors

* DB question-answering

(Harris: INTELLECT, Al Corp.)

* Isolated-word speech recognition

* Message handling (Hardt)

* Expert system explanation generators

* NL menu (Tennant: Tl)

taiiorable NL interface

* Limited machine translation

* Style checker (Heidorn: IBM)

* Automatic subject index generator

(Mott, Waltz, ResnikofF: TMC Indexer)

* On-line help systems

Table 1

50
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histories, etc. by finding key terms, and generating an alphabetic

index for any or all of these items. The Indexer contains a handy

interface that allows a user to see the index in one screen window, and

the original text in the other window. When the user picks a term

in the index (using either a mouse or a key) the text in which the

term occurs pops up in the text window, and the term is highlighted

in both windows, as illustrated in figure 3. (See [TMC 1985]) An

Indexer would be a useful component at each of the sites marked

"NL" in figure 2. The Intellect system 4

is a commercially available NL database question answering system; it allows a user to ask questions in relatively

free English [Harris 1977]. English language database "front ends" can be very useful for providing casual or infrequent users with ac- cess to information in a data base.

When coupled with a speech understanding system of the sort ex- pected to become widely available within the next ten years, and an

automatic document reader (such as the Kurzweil reader), systems

like the TMC Indexer and Intellect can form a potent, useful core for

emergency management aid systems of the future.

6. Other issues

There are political and ethical problems with constructing systems of

the sort described in the far-future scenarios. I will just mention two

here, though I suspect many more issues will surface over the next

few years.

• Ethical considerations: in some circumstances, a system may

have to decide to meet one of two or more simultaneous emergencies first, because of resource limitations. The system will

thus potentially make decisions to let one group suffer or die in

order to save another. In a similar case, a poorly designed system might fail to prevent or defuse an emergency. In such cases, who is responsible? The designers of the system? The operators of the system? The agency that contracts for its design?

4 The Intellect system is available from AI Corp., Waltham, MA.

52
lext File: INDEXES: DOC; SAMPLE. TEK! ( NORMAL, 6156 bytes, 121 lines) Index File: indexer :doc; senple . text (65 entries fron 1 files) [Read-Only]

TflC lnd*»*r I .P

,

' top of Text Description of the Connection Machine Conputer

Dr. David L . Ualtt

1. The H1ll1s Connection Machine Concept

The Connection Machine 1s a conputer for concurrently

nanipulating large enounts of Infornatlon, which nay be stored 1n any convenient forn. For a broad range of problens, the Connection Machine provides processing power proportional to the site of a problen. Each

data elenent or structure of data elenents can be assigned Its own

sinple processor 1n the Connection Machine , up to perhaps a nil Hon

processor/nenory cells. Each processor can be dynanlcally connected to any other processor, so that the geonetry of a problen can be nlrrored

by the interconnection structure of the processors. If the Interconnection structure consisted of physical wires, the nacMne

would have to be rewired for every problen. Since this 1s 1npract1cel,

the processor cells are connected through a Packet Switched Network . They connunicate by sending nessages. Receiving a nessege causes a Mort At low Top of Irxttx Advantages , Connection Machine 852 ( Inversions

1 below) advantages , Connection Machine offers 742 ( Inversions: 1 below)

AI 512 area , chip 872 Artificial Intelligence , field of 502 ( Inversions: 2 below) Artificial Intelligence , problens in 622 ( Inversions: 2 below)

Central Processing Unit 222 Codd's relational algebra 922 conputer , serial 382 ( Inversions: 1 below) 482 ( Inversions: 1 be 1 ow

)

conputers , serial 362 < inversions

1 below) 452 ( Inversions: 1 below) 632 ( Inversions: 1 below) Conputing Needs 482 Connection Machine 22 ( Inversions: 1 below)

42 ( Inversions: 1 below)

172 ( Inversions: 1 below) Connection Machine , features of the 862 ( inversions: 2 below) Connection Machine , processor 1n the 72 ( Inversions: 2 be 1 ow

)

Connection Machine Advantages 852 ( Inversions: 1 above

)

• '" '

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53
Who will decide the policies to be built into such systems? How

will they weight the costs and benefits of various courses of action, e.g. what are the relative values of life and property? Will

the public take part in the design of policies for such systems?

Should it?

• There are real dangers to centralized systems that know a great

deal about citizens, and have distributed information- gathering

potential; they have potential for misuse as "Big Brother"; there

may be political resistance to deploying such systems.

It is not too early to consider and prepare to deal with such problems.

In fact, Isaac Asimov, George Orwell, Robert Heinlein, and other sci- ence fiction writers have considered such issues in considerable depth

over the last 40 years.

However, we should not lose sight of the fact that such systems can

save lives and property that are now lost, without risking the lives of

rescuers. Such systems should in the long run make us all relatively

free of fear and danger from natural and man-made catastrophes.

54
References

[Davis 1982] Davis, R. and D. Lenat. Knowledge-Based Systems in

Artificial Intelligence. New York: McGraw-Hill, 1982.

[DoD 1983] Strategic Computing, "New generation computing technology: a strategic plan for its development and application to critical problems in defense", Defense Advanced Research Projects Agency, Oct. 28,

1983, p. vii (citation from [OTA 1985]).

[Forgy 1984] Forgy, C, A. Gupta, A. Newell, and R. Wedig. "Initial

Assessment of Architectures for Production Systems" Proc. AAAI-84 (1984

National Conference on Artificial Intelligence) Austin, TX, August 1984,

116-120.

[Harris 1977] Harris. L. "User oriented data base query with the ROBOT

natural language query system", International Journal of Man-Machine

Studies 9, 697-713.

[Hayes-Roth 1983] Hayes-Roth, F. "Codifying knowledge for machine

reading" IEEE Spectrum 20, 11, 79-81, November 1983.

[Hendrix 1978] Hendrix, G. G., E. D. Sacerdoti, D. Sagalowicz, and J. Slocum. "Developing a natural language interface to complex data" ACM

Transactions on Database Systems 8, 2.

[Kanade 1983] Kanade, T., and R. Reddy, "Computer vision: the challenge of imperfect inputs" IEEE Spectrum 20, 11, 88-91, November 1983.

[OTA 1985] Information Technology R & D: Critical Trends and Issues.

Washington, DC: U.S. Congress, Office of Technology Assessment, OTACIT-268, February 1985.

[Reddy 1983] Reddy, R. and V. Zue, "Recognizing continuous speech

remains an elusive goal" IEEE Spectrum 20, 11, 84-7, November 1983.

55
[TMC 1985] The TMC Indexer Manual. Thinking Machines Corp.,

Cambridge, MA.

[Waltz 1978] Waltz, D. L. "An English language question answering

system for a large relational database" Communications of the ACM 21, 7,

526-39.

[Waltz 1981] Waltz, D. L. "The State of the Art in Natural-Language

Understanding", in W. Lehnert and M. Ringle (eds.) Strategies for Natural

Language Processing, Hillsdale, NJ: Lawrence Erlbaum Associates, 3-32.

[Waltz 1983a] Waltz, D. L., M. Genesreth, P. Hart, G. Hendrix, A.

Joshi, J. McDermott, T. Mitchell, N. Nilsson, R. Wilensky, and W. Woods.

"Artificial Intelligence: An assessment of the state-of-the-art and recom- mendation for future directions" A I Magazine 4, 3, 55-67, Fall 1983.

[Waltz 1983b] Waltz, D. L., "Helping computers understand nataral

languages" IEEE Spectrum 20, 11, 81-4, November 1983.

[Woods 1972] Woods, W. A., R. M. Kaplan, and B. Nash-Webber. 1972.

The lunar sciences natural language information system: final report. Report No. 2378, Bolt Beranek and Newman, Inc., Cambridge, MA.

56
TIMMTM - A TOOL FOR BUILDING EXPERT SYSTEMS

Joseph N. Craig

General Research Corporation

7655 Old Springhouse Road

McLean, Virginia 22102

INTRODUCTION

TIMMTM, the Intelligent Machine Model, is a computer program that is

used to build expert systems.

Feigenbaum [1] has defined an expert system as follows:

"An expert system is an intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their

solution. The knowledge necessary to perform at such a level, plus

the inference procedures used, can be thought of as a model of the

best practitioners in the field.

The knowledge in expert systems consists of facts and heuristics. The 'facts' constitute a body of information that is widely

shared, publicly available, and generally agreed upon by experts in

a field. The 'heuristics' are mostly private, little-discussed

rules of good judgment (rules of plausible reasoning, rules of good

guessing) that characterize expert-level decision-making in the

field. The performance level of an expert system is primarily a function of the size and quality of the knowledge that it

possesses.

Typical expert systems consist of two primary components.

(1) a knowledge base of domain facts and heuristics associated

with the problem, and

(2) an inference procedure (or control structure) for using the

knowledge base in the solution of the problem.

Frequently expert systems are augmented by a variety of "user

friendly" functions that ease communication with and understanding of

the expert systems. Additions include "natural language" interfaces and

"explanation facilities." The latter permit a user to challenge or examine the reasoning process that leads to the system's decision.

The development of domain specific knowledge bases is the key to

expert systems technology. In fact, an entire field, "knowledge engineering," has developed. Knowledge engineers are artificial intelligence and computer specialists who interview experts and translate their

expertise into the set of "rules" that constitute a knowledge base.

57
Because knowledge engineers are a scarce and expensive resource, the

development of expert systems has tended to be an expensive proposition.

Recently, several products have appeared on the market which

provide software environments in which expert systems can be developed

and used. TIMMTM is such a product. TIMMTM provides all of the tools

needed by an expert to define, train, and refine an expert system.

TIMMTM also provides an environment in which a non-expert can use expert

systems, ask questions, and obtain expert-level advice.

TIMMTM has advantages over other products that make it attractive.

First, TIMMTM is highly portable and is available on a wide variety of

standard computer systems. TIMMTM is written in FORTRAN so it can be

interfaced or embedded in other programs relatively easily. Second,

TIMMTM provides an English-like interface permitting experts to develop

expert systems directly. Tools available within TIMMTM allow the expert

to define an expert decision structures, provide rules to the system,

customize the user interface, and iterate and modify the system until

acceptable performance has been achieved. All of these can be accom- plished without any programming effort. Third, TIMMTM employs a unique

analogical inferencing procedure to reach decisions. Finally, a multiple expert system can be linked together.

58
2. TIMMTM PHILOSOPHY

Automated, rapid acquisition and representation of knowledge is a desirable goal for expert system development. In the development of

TIMMTM , three problem areas that exist in the traditional approach to

expert system development have been addressed specifically. These

problems are: the time and cost to develop knowledge bases is high; the

constructive aspect of problem solving is often hidden; and reconciling

the continuously varying nature of our experience with the exact logical

modeling of standard inference schemes is difficult. The first of these

problems is a practical one. The others, involving constructive and

analogical thought, directly apply to the modeling of human problem

solving.

We describe these problems below and show how TIMMTM deals with

them.

The Time and Cost to Develop Knowledge Bases is High

Knowledge acquisition and representation is hard and slow [2, 3,

4]. Davis has observed that "... even for the best-understood problems,

experienced researchers using the best-understood technologies still

require at least five man-years to develop a system that begins to be

robust" [5]

.

The basis of this problem is the need for specific world knowledge

for intelligent behavior. It is now recognized that the power of expert

systems comes from large amounts of knowledge [5, 6]. Two needs of

knowledge acquisition stand out. First, a way is needed to acquire and

represent knowledge in a way congenial to human understanding (as distinguished from representational convenience). Second, we need to automate the process of knowledge acquisition, to simplify and accelerate

the process and to remove the need for specialized engineering skills to

achieve usable knowledge representation.

Construction in Problem Solution Is Hidden

Most expert systems approach problem solving through the repetitive

reading of lists of rules, terminating when a satisfactory candidate

solution is isolated. It is essentially a process of elimination. But

human problem solving proceeds by constructing and modifying models [7,

8, 9]. Structure is important in human problem solving.

• Meaning guides convergence on problem solutions. But meaning is a product of context, which, in the importance of order in

creating meaning, entails structure [10].

• Aesthetics in problem solution allows construction of elegant

solutions. Elegance is defined by what is known of past solutions, compared with whatever partial solution is known to exist

for the current situation. This is a structural relation [7].

59
• Analogies, metaphors, cues, paradigms, and expectations are used

in constructing solutions and explanations. All of these

involve problem structures.

In most expert systems, structure and construction are not modeled

explicitly [11]. The A.I. community has become more aware of the

importance of separating declarative and procedural knowledge [12], and

frame systems have been developed to reconcile the need for cooperation

between declarative and procedural knowledge in mapping the structures

of a problem domain [13, 14, 15]. The fluidity of human problem solving

has not however been captured.

Continuously Varying Experience Doesn't Fit Exact Inferencing

The importance of explanation is impressing itself upon workers in

expert systems [10, 16]. The psychological underpinnings of expert

systems should be such that explanations flow from a problem solving

model compatible with that of the system's users.

Exact match inferencing may be too rigid a model for human problem

solving [17]. Work has been done on partial match rules to allow

recognition of incomplete matching. Still, partial match inferencing is

organized around exact matching of partial groupings of decision criteria, rather than on inexact or analogous matching.

People use knowledge of continuously varying natural phenomena in

problem solving. The world of our perceptions — a world of sizes,

colors, sounds — is one of continuous variation. It is sensible to

talk of "like", "almost", "very similar". Knowledge acquisition and

representation schemes which fail to take this into account, and

inference schemes which are rigid, do not mirror human problem solving.

Rapid Development of Procedural Knowledge

TIMMTM is a computer program that incorporates approaches to many of

the ideas expressed above. It is a domain-independent tool for building

expert systems.

TIMMTM separates knowledge into declarative and procedural sections,

much like frame-based systems which allow attached procedures. Somewhat

surprisingly, the frame paradigm, which organizes knowledge in a set of

slots (attributes) rather than in IF-THEN rules, has only been minimally

applied to expert systems development.* Use of a frame-like approach

*KN0BS is a frame-based system, but variations of the production rule

paradigm are much more common in expert systems. In Building Expert

Systems [6], there are only three references to frames in the index,

each reference being minor.

60
allows easy characterization of the stable aspects of the domain. TIMM

maintains separation of control knowledge from domain facts and relations [10], and follows completion of declarative knowledge definition

with a decision-oriented approach to procedural knowledge acquisition.

This decision-oriented approach supports rapid development of

procedural knowledge, and is based on the simple observation that

experts are better at doing than at describing. The stable knowledge

about a problem domain in the declarative knowledge section lays the

groundwork for the system's synthesis of realisic problem descriptions.

Using these, an interactive dialog can be entered with the expert,

oriented toward making decisions (doing), rather than explaining how

decisions are made (describing). The system, originating questions and

monitoring answers, can create rules based on observed expert behavior

(which can then be offered to the expert for annotation.) This process

greatly speeds the development of rule bases, and a knowledge engineer

is not required.

Modeling Constructive Problem Solving

TIMMTM models constructive problem solving in two ways. First, it

allows explicit representation of domain paradigms to guide making

individual decisions. Second, it models the expectations of the expert

in its domain representation.

TIMMTM allows incorporation of expert-defined paradigms in con- structing a problem's search space. Both declarative and procedural

knowledge representation allow nesting of varying levels of definition,

from the very general to the very specific. Further, overlapping of

definition is allowed in the knowledge base. In this way, overlapping

high-level structures can represent multiple paradigms valid in the

problem domain. These can be used to guide search — the lower level

specifics are not instantiated if there is not a good match with the

high-level paradigm. Efficiency of search is aided by guiding the

search from the earliest opportunity, as applicable paradigms are discovered and inapplicable paradigms eliminated, so that only useful

structures are instantiated. Moreover, explanatory power is gained by

having the problem domain paradigms explicitly defined and available for

inspection by users.

Expert's expectations about normal domain behavior are incorporated

by extending the implementation of the frame paradigm to include a measure of the typicality of the values associated with slots in

individual frames. This expands the idea of default slot values to let

each slot have a graded set of 'expectations', rather than only one. By

also providing a measure of salience among slots within a frame, the

varying importance of different aspects of a situation can be conveniently modeled. Typicality and saliency increase the flexibility of

frame structures. Since expected (i.e. typical) values are specified,

an expectation-guided problem solving strategy is possible. By com- bining the program's expectations with the gradual instantiation of

61
paradigms, each succeeding step in problem solution is dependent both on the current state of the system's understanding and on the system's

expectations. This is a much more satisfactory model of human problem

solving than the stimulus/response model most often used in expert

systems.

i Analogical Inferencing and Knowledge Generalization

TIMMTM approaches the problem of reasoning with incomplete, uncertain, and ambiguous situation knowledge through use of analogical

inferencing. A side effect of this technique has been the ability to

generalize procedural knowledge.

Reasoning with uncertainty is often necessary. TIMMTM combines

partial match inferencing with an ability to measure the similarity of a perceived situation to known situations within individual rule clauses.

Partial match inferencing is an important tool for dealing with uncertainty, but as F. Hayes-Roth points out, "... representations should be

sought that support the use of proximity measures or directionality to

identify good partial matches" [18]. In TIMMTM we have used proximity

and directionality — in general, context-sensitive similarity — as well as partial matching, in the operation of our inferencing procedure.

This approach uses the ranges of values specified for entities in the

declarative knowledge section to provide a metric across the problem

domain. Similarity of situations can be estimated by their closeness in

the representational space. Analogical inferencing is a combination of

partial match inferencing with a similarity measure on rule clause

matching.

The use of continuously varying knowledge and analogical inferencing allows TIMMTM to generalize procedural knowledge. Because TIMMTM

can gauge the similarity between rules , groups of similar rules can be

experimentally reduced to single, more general rules. Such rules, if

meeting the approval of the expert training the system, can then replace

their precursors. While this process sounds simple, some sophistication

is necessary to avoid 'stupid' simplifications. In the current version

of TIMMTM, the salience and typicality of rule clauses and values is

implicit rather than explicit. Thus, TIMMTM must examine the content of

the rule set to discover which simplifications are based on important

similarities, and which are spurious. We hope that the ability to

generalize will produce views of the problem domain both unanticipated

by, and useful to, the human expert.

62
A HIGH LEVEL VIEW OF TIMfT

TIMM

Division

3. FUNCTIONAL ELEMENTS OF TIMMTM

TIMMTM is a domain-independent, knowledge-based program which permits a user to construct, exercise, modify, and interrogate a body of

knowledge whose subject matter is selected by the user. TIMM

ra, s knowledge base contains rules, definitions, etc. about a particular

domain. Initially, the knowledge base is empty and an expert adds data

to the knowledge base by carrying out a dialog with the "knowledge

engineer" that is built into TIMMTM. When the knowledge base is developed, a non-expert user can interact through the expert system interface

to obtain answers to questions. Both interfaces to TIMMTM are "friendly", providing spelling correction, help facilities, and three

levels (terse, verbose, and help) of interaction.

63
TIMM Implementation and Capabilities

The TIMMTM knowledge base is implemented as a data file that is

accessed when TIMMTM is exercised. Therefore, multiple experts can be

maintained in the computer environment. The user has the option of

specifying which expert should be used at a given time. In a multi-user

environment, such as VMS on a VAX computer, multiple copies of TIMMTM can run concurrently permitting simultaneous development and/or exercise of

multiple expert systems.

The expert system building phase of using TIMMTM centers around dialogue with an expert. First, the expert is led through a definition of

the problem. What must be considered in coming to a decision? What

decisions are possible? Then, the expert is questioned about specific

problem examples. What should be done if this combination of events

occurs?

TIMMTM transforms answers to such questions into rules. When the

expert is satisfied, TIMMTM can

• eliminate redundant rules (compress the knowledge base),

• discover and ask about inconsistencies,

• say where it doesn't know enough about the problem (check for

completeness), and

• look for deeper, simpler rules (generalize), using a pattern

recognition procedure).

The problem definition and problem solution dialogues can be reopened,

and the process repeated, until the expert is satisfied and system

building is complete.

Problems, and our understanding of them, are rarely static. A

useful expert system should have the capacity to grow as understanding

of the problem grows. TIMMTM makes it easy to add or modify rules when

new knowledge comes to light.

The rules generated by TIMMTM are in English. Expert systems built

using TIMMTM can explain decisions by displaying the rules used in making

the decision. Additionally, the English form of the rules makes inspection of the system's 'expertise' possible by people with no knowledge or interest in programming.

64
rmm

TIMM" MAIN MENU

(c) Copyright 1983 General Research Corporation

TIMM" The Intelligent Machine Model

Version 2-0

Good morning^

•MAIN MENU*

Type letter

+ file name and returnSelection?

I'm TIMM' Let's beginB Build T Train

I Inquire G Generalize

M Modify N Check consistency

R Recover S Stop

E Exercise

C Compress

P Check completeness

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TIMMTM is capable of supporting nine different operations at the

user's request. These operations are presented to the user as options

from TIMMTM^ main menu or as options to functions that can be selected

from the main menu.

65
rysm> oi \

A LINKED KNOWLEDGE BASE

DEMONSTRATION

ORGANIZATION

MAINTENANCE

BENCH

STOCK

REPAIR EQUIPMENT

1

f

VENDORS REPAIR TECHNICIANS

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J

To illustrate the use of TIMM , we present a partial description of

the construction of a prototype expert system for "Organization

Maintenance." The expert system will help a maintenance activity decide

whether to repair a failed part or issue a replacement part from

stock. As shown, the system implements expertise in logistics support,

inventory control, equipment repair, and vendor selection. Each of

these areas is implemented as a separate expert system, linked together

using TIMM^'s hierarchical structure.

We focus here on the expert system called "MAINTENANCE ACTIONS."

It decides whether to refer to a knowledge base on repair or one on stock management. The figures represent what will appear on the user's

terminal screen during a TIMMTM session. User inputs are identified by

an arrow ( ^ )

66
nmm

TIMM" BUILD FUNCTION

(c) Copyright 1983 General Research Corporation

TIMM" The Intelligent Machine Model

Version 2-0

Good morning- I'm TIMM". Let's begin-

•MAIN MENU*

Type letter

+ file name and returnSelection?

IM

R

B ORGMAIN

Build T Train E Inquire G Generalize C Modify N Check consistency P Recover S Stop

Exercise

Compress

Check completeness

What would you like to call this new expert system: ORGANIZATION MAINTENANCE

What decision will I be learning how to make: REPAIR OR REPLACE

Tell me the possible choices- Enter one per line, and enter a "/" as the last

LINE:$FIX

SBSTOCK

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3.1 BUILD

TIMM^'s BUILD function guides the user through the process of

building a new knowledge base. It first asks the user to specify the

file name for the knowledge base and the name of the decision that will

be made. Next, the user specifies the possible decisions that can be

made. In the example, the two possible decisions are to refer the

decision to a repair specialist (FIX) or to an inventory specialist

(BSTOCK). The dollar sign ($) tells TIMMTM that these are the names of

other expert systems that should be called when a decision is reached.

67
nmmT 1 MM" BUILD FUNCTION (CoNT-)

NOW TELL ME THE NAME OF EACH FACTOR THAT INFLUENCES THE DECISION- ENTER ONE PER LINE, AND ENTER A "/" AS THE LAST LINE: PART REPAIRABLE -<-

PRIORITY

REPAIR COST

CUMULATIVE REPAIR COST

TIME IN SERVICE

PART

ARE THE VALUES OF THIS FACTOR NUMBERS (N) OR DESCRIPTIVE PHRASES (P) ? P ARE THESE VALUES UNORDERED (U), LINEARLY-ORDERED (L), OR CIRCULARLY-ORDERED

(O? U ~Mr- TELL ME THE POSSIBLE VALUES- ENTER ONE PER LINE, AND ENTER A "I" AS THE LAST

LINE:

B

D

G ^

/ Advanced

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TIMM next asks for a list of factors that influence the decision,

and for attributes of each factor. The factors can be described as numeric or phrases; and the values can be unordered, linearly ordered,

or circularly ordered. If the factor values are unordered, TIMMTM's

inferencing procedure will function in a standard forward-chaining

manner. If a particular ordering is specified, TIMMTM is able to make

use of this knowledge in reaching a decision. [As an example, the days

of the week are specified as circularly ordered; TIMMTM would know that

Saturday and Monday are equally close to Sunday.

)

68
Please give me a verbose description for each of the factors- Terminate

each description with a "/":

PART

WHICH PART FAILED?/

REPAIRABLE

IS THE FAILED PART REPAIRABLE?/

Please give me help information for each of the choices- Terminate each

entry with a

"/":

INVOKE FIX

/

INVOKE BSTOCK

Please give me help information for each of the factors- Terminate each

entry with a "/":

PART

A-AXLE, B-BEARING, C-COUPLER, D-D I AGONAL , E-EAR, F-FLANGE, G-GEAR, I-IBAR

/ REPAIRABLE

IS THE FAILED PART REPAIRABLE LOCALLY?/ Advanced

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J

Help and Verbose prompts are used to provide additional information to

the user. When the expert system is being used, the user has the choice

of seeing terse or verbose prompts. If HELP is typed at any time, the

appropriate help prompt will be displayed.

69
r

•MAIN MENU*

Type letter

+ file name and returnSelection? T

B Build T Train E Exercise

I Inquire G Generalize C Compress

M Modify N Check consistency P Check completeness

R Recover S Stop

For training, 1 need a definite value or range of values for every FACTOR

you've defined- Either you pick them, or I will- Then I'll ask you for your

decision, given those valuesSpecify values or ranges of values for the FACTORS LIKE THIS:

HEIGHT = 70

WEIGHT = <180 (EQUAL TO OR LESS THAN 180) RANK = >C0L (EQUAL TO OR MORE THAN COL)

SCORE = A:C (BETWEEN A AND C INCLUSIVE)

AGE = * (ANY VALUE — "IRRELEVANT" OR "DONT CARE")

FOR THE DECISION, YOU CAN SPECIFY JUST ONE CHOICE, OR SPECIFY UP TO TEN CHOICES WITH CERTAINTY NUMBERS (WHOLE NUMBERS P TO 100), LIKE THIS:

PROMOTABLE = YES (67)

= NO (33)

= / (to indicate that's all)

Specify the decision as "$" to mean "That combination of factor values isn't

possible, or isn't allowed " to stop, enter a factor value or choice as "$"•

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3.2 TRAIN

The TRAIN function permits the expert to enter knowledge regarding

particular decisions once the domain of the decisions has been defined.

TIMMTM provides several ways to enter this information: specific

scenarios can be generated by TIMMTM, or the expert can enter specific

scenarios or more general rules.

70
r

Shall I pick the training cases for you (Y or N)? Y IS IT OK IF I DUPLICATE OLD TRAINING CASES (Y OR N)? N Another (Y or N)? Y PART = E REPAIRABLE = YES

PRIORITY = 2 REPAIR COST = 195

CUMULATIVE REPAIR COST = 237 TIME IN SERVICE = 165

REPAIR OR REPLACE = $BST0CK

Another (Y or N)? Y PART = a REPAIRABLE = YES

PRIORITY = 4 REPAIR COST = 145

CUMULATIVE REPAIR COST = 395 TIME IN SERVICE = 15

REPAIR OR REPLACE = $FIX

ANOTHER (Y OR N)? Y

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When TIMM specifies scenarios, factor values are generated

randomly and the expert is prompted for a decision.

71
Shall I pick the training cases for you (Y or N)? N PART = a REPAIRABLE = YES

PRIORITY = 2 REPAIR COST = 500

CUMULATIVE REPAIR COST = 500 TIME IN SERVICE =30 -<-

REPAIR OR REPLACE = $F I

Another (Y or N)? Y PART = • REPAIRABLE = NO -<-

PRIORITY = • REPAIR COST = • CUMULATIVE REPAIR COST =

* TIME IN SERVICE = •

REPAIR OR REPLACE = $BST0CK

Advancad

Technologies •* Division

When the expert provides examples or rules, TIMMTM prompts for each

factor value and the appropriate decision. Here, the second example is a "rule" rather than a specific case. It should be read "If the part is

not repairable, then invoke the BSTOCK expert system."
rmm

•MAIN MENU*

Type letter

+ file name and return

Selection? I •INQUIRE*

Type letter

and returnSelection? N

B Build T Train E

I Inquire G Generalize C M Modify N Check consistency P R Recover S Stop

Exercise

Compress

Check completeness

D Show decision structure R Return to

K Show knowledge base Main Menu

N Show number of rules

Shall I copy the information into a file in your directory as I display

it (Y or N)? N SIZE OF THE KNOWLEDGE BASE FOR THE EXPERT SYSTEM "ORGANIZATION MAINTENANCE"

Primary knowledge base: 18 rules

Secondary knowledge base: 0 rules

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3.3 INQUIRE

The INQUIRE function permits the user to examine the knowledge base

in any desirable level of detail* The user can examine the decision

structure (the informatin entered during the BUILD function), examine

any or all rules, or obtain a summary of the rules in the knowledge

base.

73
rmm-

•MAIN MENU*

Type letter

+ file name and return

Selection? P

B Build T Train E

1 Inquire 6 Generalize C M Modify N Check consistency P

R Recover S Stop

Exercise

Compress

Check completeness

The following case is a distance of 0-452 from the nearest entry

in the knowledge base: PART = B REPAIRABLE = YES

PRIORITY = 4 REPAIR COST = 782

CUMULATIVE REPAIR COST = 1561 TIME IN SERVICE = 12 DO YOU WANT TO ADD THIS CASE AS A NEW TRAINING CASE (Y OR N)? Y REPAIR OR REPLACE = SBSTOCK

DO YOU WANT TO CONTINUE (Y OR N)? Y

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3.4 CHECK COMPLETENESS

This function allows the expert to identify areas in the domain for

which TIMMTM has no rules that could be applied. Using these attributes

of the decision factors, TIMMTM identifies specific situations that are "most dissimilar" from the rules in the knowledge base. These are presented as training examples for the expert.

74
rmm-

•MAIN MENU*

Type letter

+ file name and return

Selection? 6

B Build T Train

I Inquire G Generalize

M Modify N Check consistency

R Recover S Stop

E Exercise

C Compress

P Check completeness

i will now attempt to formulate general rules, based on the knowledge in the

current knowledge baseDo YOU WANT ME TO PRESENT THE POTENTIAL NEW RULES TO YOU FOR APPROVAL OR

DISAPPROVAL (Y OR N)? Y

OK, let's begin- To stop for any reason, enter "$"•

Consider the following rule:

If: PART REPAIRABLE

PRIORITY

REPAIR COST = >101

CUMULATIVE REPAIR COST = >660 TIME IN SERVICE = >74

Then;

REPAIR OR REPLACE = $BST0CK(100)

IS THIS RULE CORRECT (Y, N, OR ?)? N Advanced

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3.5 GENERALIZE

The GENERALIZE function searches the existing knowledge base and

attempts to formulate general rules from the more specific examples and

rules in the knowledge base. This is one of the more powerful functions

available in TIMMTM making it possible to induce general rules from

specific examples.

The rules are presented to the expert for acceptance or rejection.

If the expert is not sure of a rule (?), TIMMTM will not add the rule to

the knowledge base, but searches for similar rules that might be more

acceptable.

75
rmm

•MAIN MENU* : B Build T Type letter : I Inquire G

+ FILE NAME ! M MODIFY N AND RETURN • ! R RECOVER S Selection? C My attempt to compress the knowledge base is complete

Train

Generalize

Check consistency

Stop

E Exercise

C Compress

P Check completeness

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3.6 COMPRESS

The COMPRESS function is used after GENERALIZing . It deletes

specific examples that are covered by more general rules. This keeps

the knowledge base as small as possible (for efficiency) without losing

information. The expert can use INQUIRE in conjunction with COMPRESS to see which rules were deleted from the knowledge base.

76
rmm

•MAIN MENU* : B Build T Train E Exercise

Type letter : I Inquire G Generalize C Compress

+ FILE NAME : M MODIFY N CHECK CONSISTENCY P CHECK COMPLETENESS

AND RETURN- : R RECOVER S STOP

Selection? N

i will now list for you all rules and pairs of rules in my knowledge base

which can lead to inconsistent decisions- if you have not already done so, i suggest that you print the knowledge base after i am finished so you can refer

to it- Shall I COPY THE INFORMATION INTO A FILE IN YOUR DIRECTORY AS I DISPLAY IT (Y OR N)? N INCONSISTENCIES IN THE KNOWLEDGE BASE FOR THE EXPERT SYSTEM ORGAN I ZT I N MAINTENANCE"

The knowledge base appears to contain no inconsistenciesAdvanced

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3.7 CHECK CONSISTENCY

The CHECK CONSISTENCY function examines the knowledge base for

rules that would result in contradicting decisions in a specific case. Rules that have more than one choice or rules with overlapping IF

clauses and distinct THEN clauses are identified. The expert has the

option of deleting rules or preserving the "inconsistency" if it is

appropriate.

Inconsistency often arises when multiple experts train an expert

system. This function identifies where the experts disagree. In some

cases, reasonable men may reasonably disagree, and the expert system can make a decision that includes multiple choices.

77
rmm

Build

Inquire

Modify

Recover

•MAIN MENU* : B Type letter : I + FILE NAME : M AND RETURN- : R Selection? M •MODIFY* : AF Type letters : DF AND RETURN DR Selection? DR Tell me the rule you want to delete

Rule 65

Train E Generalize C Check consistency P Stop

Exercise

Compress

Check completeness

Add a factor AC Delete a factor DC Delete a rule N

65

Add a choice

Delete a choice

Change a name

Return to Main Menu

If:

Then:

PART REPAIRABLE

PRIORITY

REPAIR COST

CUMULATIVE REPAIR COST TIME IN SERVICE

i:REPAIR OR REPLACE

CYES

2m

1459

181

$BST0CK(100)

Is this the rule you mean (Y or N)? N Advanced

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J

3.8 MODIFY

The MODIFY function allows the user to make additions, deletions,

and changes to the decision structure and to the knowledge base. Decision choices and factors can be added, deleted, or given new names. Rules can be deleted. Spellings and prompts can be changed.

78
Build T Train E Exercise

Inquire 6 Generalize C Compress

Modify N Check consistency P Check completeness

Recover S Stop

TO EXERCISE THE EXPERT SYSTEM, I NEED VALUES FOR THE FACTORS YOU'VE DEFINEDEither you pick them, or I will- If you pick them, you may give a single

VALUE FOR A FACTOR, OR AS MANY AS TEN VALUES WITH CERTAINTY NUMBERS (WHOLE

NUMBERS UP TO 100), LIKE THIS:

TEMPERATURE = HIGH (50)

NORMAL (50)

/ (to indicate that's all)

you can also give the value of a factor as "*" for "irrelevant" or "?" for

"don't know"- Ranges of values aren't allowed- To stop, enter a value as "$"•

If you ask me to narrow down the choices as I go, I'll list my choices after

each factor value is specified, revising my list with each new factor valueOtherwise, I'll wait for all the factor values and then make a decisionI'll show my choices with certainty numbers, and also a reliability number to

let you know (on a scale of 100) how confident i am in my decision-

•MAIN MENU*

Type letter

+ file name and returnSelection? E

After each test case, I'll ask you if my decision was correct (Y, N, or E)- "E" means EXPLAIN -- I'll show you which rules I used to make my decision- Then

I'll ask if you want another test case (Y, N, or A). "A" means ADD the previous

test case to the knowledge base as a new rule- I'll give you the ADD option

only if all factors were given definite valuesAdvanced ———— Technologies

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3.9 EXERCISE

The EXERCISE function allows a non-expert user to access the

knowledge base, ask questions, and receive answers. Several options are available.

79
T 1 MM" "EXERCISE" FUNCTION (ContO

Shall 1 pick the test cases for You (Y or N)? Y SHALL 1 NARROW DOWN THE POSSIBLE CHOICES AS I 60 (Y OR N)? N Do you want me to use terse (T) or verbose (V) mode? T FOR THIS CASE, SHALL I EXCLUDE ANY CHOICES (Y OR N)? N PART = a REPAIRABLE = YES

PRIORITY = 1 REPAIR COST = 755

CUMULATIVE REPAIR COST = 1104 TIME IN SERVICE = 24 REPAIR OR REPLACE = $BST0CK(100)

(Reliability = 100)

Does this conclusion seem reasonable (Y, N, H, or E)?

Advanced

Technologies

Division

The first example shows a case in which TIMM has generated a situation at random and given a decision. TIMMTM's reliability factor

tells, on a scale of 0 to 100, how well the example matches the rule

employed to make the decision. In this case, the test case exactly

matches a rule.

Several options are available. The user may ask for an explanation

(E) in which case the rules used to make the decision will be displayed.

If the user answers No to the question, TIMMTM can add the case to the

knowledge base and prompt the user for the correct decision. Entering

(H(elp) will cause TIMMTM to display the help prompt for the decision.

80
Shall I pick the test cases for You (Y or N)? N Shall I narrow down the possible choices as I go (Y or N)? N

1)0 YOU WANT ME TO USE TERSE (T) OR VERBOSE (V) MODE? V For this case, shall I exclude any choices (Y or N)? N For this case, do you want to specify any factor values first (Y or N)? N WHICH PART FAILED?

Answer: A -<-

IS THE FAILED PART REPAIRABLE?

Answer: Yes

WHAT IS THE PRIORITY OF THE FAILED PART?

Answer: 3 WHAT IS THE ESTIMATED COST TO REPAIR THE FAILED SUBASSEMBLY?

Answer: 585

HOW MUCH HAS BEEN SPENT IN PRIOR REPAIRS FOR THE FAILED PART?

Answer: 585

HOW LONG HAS THE FAILED PART BEEN IN SERVICE?

Answer: 140

REPAIR OR REPLACE:

DETERMINE IF A REPLACEMENT FOR THE FAILED PART IS IN BENCH STOCK

(100)

Reliability = 77 Advanced

:hn«

Division

Technologies

If the user selects the verbose mode of operation, the verbose

prompts are used by TIMM1" to solicit factor values. In this case, the

specific situation is not exactly like the closest rule, so TIMMTM

reports a reliability less than 100.

81
4 . SUMMARY

TIMMTM is a flexible and practical system for developing and using

expert systems. TIMMTM allows experts to interact directly with the

program in constructing knowledge bases by automating the task of

knowledge acquisition. TIMMTM 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 TIMMTM. The basic TIMMTM 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.

TIMMTM is economical. TIMMTM 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 TIMMTM. Thus, the critical area of knowledge acquition can

be addressed directly.

82
ACKNOWLEDGMENTS

The author is indebted to many individuals at GRC who have con- tributed to the development of TIMM: Mr. Dennis Cooper, Dr. Lester

Ford, Dr. Stephen Kiselewich, Mr. Jim Kornell, Dr. Richard Parker, Dr.

Ralph Pennington, Mr. Phil Sewell, Mr. Randall Urban, and Mr. Rollin

Weeks. Dr. Kiselewich is the principal author of the TIMMTM program.

The Organizational Maintenance expert system described in this paper was developed by Mr. Gerald Paul and Dr. Peter McWhite.

83
REFERENCES

1. Fiegenbaum, E. A., "Knowledge Engineering for the 1980's," Computer

Science Department, Stanford University, 1982.

2. Schank, R. , "The Current State of AI: One Man's Opinion," AI

Magazine , Vol. 4, No. 1, Winter /Spring 1983.

3. Norman, D. , "Twelve Issues for Cognitive Science," Cognitive

Science , Vol. 4, No. 1, January-March 1980.

4. Feigenbaura, E. A., P. McCorduck, Fifth Generation , Addison-Wesley

,

Reading, Mass., 1983, pp. 80-84.

5. Davis, R. , "Expert Systems: Where Are We?" And Where Do We Go

From Here?", AI Magazine , Vol. 3, No. 2, Spring 1982.

6. Hayes-Roth, F, D. Waterman, and D. Lenat, Building Expert Systems , Addison-Wesley, Reading, Mass., 1983.

7. Wertheimer, M. , Productive Thinking , Harper Torchbooks, New York,

NY, 1959.

8. Lakoff, G. and M. Johnson, "The Metaphorical Structure of the Human

Conceptual System," Cognitive Science , Vol. 4, No. 2, April-June

1980.

9. Centner, D. , "Structure-Mapping: A Theoretical Framework for

Analogy," Cognitive Science , Vol. 7, No. 2, April-June 1983.

10. Wittgenstein, L. , Philosophical Investigations , trans. G.E.M.

Anscombe, Blackwell, Oxford, England, 1953.

11. Aikens, Jr., "Prototypical Knowledge for Expert Systems," Journal

of Artificial Intelligence , Vol. 20, No. 2, Feb. 1983.

12. Friedland, P., "Acquisition of Procedural Knowledge from Domain

Experts," Computer Science Department, Stanford University,

Stanford, CA 94305.

13. Minsky, M. , "A Framework for Representing Knowledge," in The

Psychology of Computer Vision , P. Winston, Ed., McGraw-Hill, New

York, NY, 1975.

14. Schank, R. , R. Abelson, Scripts Plans Goals and Understanding , Lawrence Erlbaum Associates, Hillsdale, NJ, 1977.

15. Winograd, T. , "Frame Representations and the Declarative/Procedural

Controversy," in Representation and Understanding , D. Bobrow, A.

Collins, eds. , Academic Press, New York, NY, 1975.

84
16. Clancy, W. , review of States of Mind by Jonathon Miller, AI

Magazine, Vol. 4, No. 4, Winter 1983.

17. Pentland, A. and M. Fischler, NA More Rational View of Logic," AI

Magazine , Vol. 4, No. 4, Winter 1983.

18. Hayes-Roth, F. , "The Role of Partial and Best Matches in Knowledge

Systems," in Pattern-Directed Inference Systems, D. Waterman, F.

Hayes-Roth, Eds., Academic Press, New York, NY, 1978.

19. Kornell, J., "Embedded Knowledge Acquisition to Simplify Expert

Systems Development," Proceedings of the 1984 Conference on Intelligent Systems and Machines . Detroit. Mich., 1984.

20. Hendrix, G. G», E. D. Sacerdoti, D. Sagalowicz, and J. S locum,

"Developing a Natural Language Interface to Complex Data," ACM

Transactions on Database Systems. Vol. 3, No. 2, June 1978, pp.

105-147.

85
DECISION SUPPORT AND EXPERT SYSTEMS

FOR EMERGENCY MANAGEMENT OPERATIONS: A MICROCOMPUTER APPROACH

By

FRANK DE BALOGH, Ph.D.

Director, Decision Support Systems Laboratory

Institute of Safety and Systems Management

UNIVERSITY OF SOUTHERN CALIFORNIA

Los Angeles, California 90089-0021

1 . INTRODUCTION.

Using micro-computer technology to improve the productivity

and capacity of various facets of our economy and government

infrastructure is now a major thrust in many professions

including emergency management. This paper is designed to: (1)

provide an overview of some of the activities of the University

of Southern California (USC) in using micro-computers in

emergency management to develop greater capacity in decision

support, and (2) to make recommendations to the Federal

Emergency Management Agency (FEMA) as to how it should seek to

take advantage of the opportunity offered by a new class of

information technology - Expert Systems (ES) - to advance the

field of emergency management.

Since the two USC research examples to be described in this

paper are incorporated in a particular class of information

system - Decision Support Systems (DSS) - this concept will be

addressed first. The term "DSS" is today widely employed in

financial and business circles and is slowly finding its way into

the vocabulary of emergency management. A Decision Support

System (DSS) is a collection of computational, analytical and

decision- theoretic models together with a data base which reside

in an interactive computer environment. Its express objective

is to improve the effectiveness of what have been termed

knowledge workers. A DSS consists of three major components:

(1) the Data Base, (2) the Model Base, and (3) the software

system which contains the Data Base Management System, the Model

Base Management System and the Dialog Management System. The

computer hardware and software, which make up these components,

(as a system) is called the DSS Generator (DSSG) since it is the

vehicle used to develop (generate) specific applications in

emergency operations (Sprague and Carlson, 1982).

86
To provide a clear understanding of the focus of USC's

research program, which also underlies this paper the following

summary is provided. The majority of USC's research effort is

based on the premise: CAPACITY BUILDING THROUGH WIDESPREAD

TECHNOLOGY TRANSFER. That is why we are employing widely

available commercial hardware and software such as the IBM PC

AT, LOTUS* 1-2-3, Microrim's CLOUT, etc., in applications

development. FEMA has stated that it is turning to the off-theshelf commercial sector to meet some of its hardware and software

needs (Giuffrida, 1985; Campbell, 1984).

Organizationally, USC has the following components dealing

with emergency management:

* DECISION SUPPORT SYSTEMS LABORATORY (DSSL)

The function of this laboratory, one of the few such university

facilities in the United States, is to conduct state-of-the-art

research as well as to develop prototypes in the field of

decision support systems (DSS). This research effort is

designed to develop DSS applications and programs for a wide

range of sponsors and users such as government agencies,

aerospace companies, business, and industrial organizations.

Such DSS can help managers make better decisions in complex,

unstructured, or semi-structured situations. Current research

projects focus on the development of (1) an Earthquake Decision

Support System to support earthquake management efforts and

projects designed to study the impact of hardware and software

configurations and presentations in decisionmaking capabilities;

(2) a decision support system to aid in the management of

hazardous materials spills or discharges, and, (3) computerized

management aids for emergency evacuations of smaller cities or

localities. The DSSL is currently working on non-disaster

related systems as well.

* RISK AND EMERGENCY MANAGEMENT LABORATORY (REML) : Work to date by this laboratory has included projects done

in (dam safety) risk assessment and addressing the needs of

special populations during a disaster, etc. REML and others at

USC have also been involved in a project to assist public

officials (specifically, EPA policymakers) on how to communicate

risks to public officials and the public at large.

Also noteworthy is the SPECIAL ISSUE ON EMERGENCY MANAGEMENT

in PUBLIC ADMINISTRATION REVIEW (PAR) published in January 1985

funded by FEMA, edited by William Petak, REML director. The

article on decision support systems in that special issue,

written by William Wallace of Rensselaer Polytechnic Institute

and this author, reflect the same premise that capacity building

requires ubiquitous hardware and software (Petak, 1985; Wallace

and de Balogh, 1985).

87
COMEX PROJECT:

This is a nationally-recognized computer simulation and

training laboratory for public administrators organized within

the USC School of Public Administration with support from IBM.

It teaches the dynamics of policymaking and planning by using a

model of a medium-sized city which is driven by a data base on a minicomputer. Prospective policies ("What Ifs") are tried out

through role playing at terminals using various scenarios.

The next section will focus on USC research efforts underway in

two areas: (1) EARTHQUAKE DECISION SUPPORT SYSTEM (EDSS) - a two

year project funded by the National Science Foundation using two

main-frame software packages (ADMATCH 80 and DATAPLOT). This is

being carried out in conjunction with USC's Social Science

Research Institute (SSRI) and DSSL, and (2) HAZARDOUS MATERIALS

(TOXDSS) - a DSSL project using the LOTUS 1-2-3 software and

microcomputers.

2. DECISION SUPPORT SYSTEMS ARCHITECTURE FOR EARTHQUAKE

MANAGEMENT.

2 . 1 INTRODUCTION.

The EDSS prototype is designed to extend the capability of

planners and public officials to make effective decisions in

mitigation and other stages of the earthquake life cycle. EDSS

is a combination of data base files, decision aiding models, and

user communications which facilitate employment by a variety of

users (See Figure 1).

It consists of several basic files on building

characteristics, population distribution, quake intensity, and

upgrade costs. These are used by various mathematical models

that generate damage assessments as well as the costs of

upgrading building stock by regional (planning) areas which

divide a locality. The user employs a series of help menus as

well as the ad hoc reporting capabilities of the system

(Schuerman, Petak and de Balogh, 1982).

2.2 GENERAL EDSS FUNCTIONAL GOALS.

EDSS is capable of aiding in the following mitigation

planning functions:

* ASSESS THE IMPACT OF AN EARTHQUAKE ON:

+ Present buildings in a planning area

+ Proposed buildings

+ Buildings with proposed upgrading

* ESTIMATE THE COST OF:

+ Proposed improvements in present buildings to 1934

standards (in the Los Angeles city prototype)

88
Figure 1

EDSS CONCEPTUAL FRAMEWORK

DATA BASE MODEL BASE

Building

Characteristics

Population

Distribution

Quake Intensity

Upgrading Cost

Working Files —

Output Files —1

Data Extraction

Files Creation

Computational Models

Report Models

AD HOC DSS Models

CP/M

OS

CONDOR

DBMS

EDSS

HELP MENUS

User Dialogue

User

Sample EDSS Display Output of Simulated Earthquake Damage by Planning Area

(City of Los Angeles)

Quake Damage Assessment Using the Current Building Stock

Central Business District Quake Magnitude = 6.3 Date:

Results for Structure Class 3 and Age 3

Square Feet Damaged and Replacement Cost in Each Planning Area

Sq. Ft. Replacement*

Damaged Cost

Planning Area (in thousands) (in millions)

VALL1 VALLST

PLNAREA NAME (Subtotal) (Subtotal)

103 SOUTHEAST LA 0 0

104 W.ADAMS, LEIMERT, BALDWIN 25 21

105 S.CENTRAL LA 0 1 106 WILSHIRE 58 107

107 HOLLYWOOD 35 12

109 WESTLAKE 2 0

110 CENTRAL CITY 100 192

326 WESTWOOD 45 14

327 W.LA, CENTURY-CITY, R.PK 21 38

328 PALMS, MAR-VISTA, DEL-REY 4 3

330 WESTCHESTER, PLAYA-D-REY 8 3

Total 298 391

*Based upon assessed value.

89
+ To 1974 standards (Los Angeles prototype)

* ESTIMATE THE POTENTIAL SAVINGS FROM IMPROVEMENTS TO:

+ Present buildings

+ Future Buildings

* PROVIDE SUMMARY STATISTICS ON:

+ Total damage

+ Impacted geographic areas

+ Building categories

* CONDUCT 'WHAT IF' ANALYSIS ON THE ABOVE BY VARYING:

+ Cost per square foot to upgrade buildings

+ Earthquake intensity

For policy simulation purposes, the decisionmaker 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 (de Balogh, Petak and Sessler,

1985).

2.3 FUNCTIONS OF SELECTED EDSS MODULES.

The system contains two major modeling components:

* DAMAGE ASSESSMENT - this permits the calculation of

damages to certain building categories within a planning area

given certain earthquake intensities which are input into the

system by the user.

* BUILDINGS UPGRADING - this calculates the cost of

upgrading certain age classified buildings within a planning

area from one standard to another. The cost per square foot of

upgrading a particular category is entered prior to any

calculations by the user.

Both models permit "before" and "after" building upgrade damage

assessment calculations and loss value estimation. Several of

the key externally-provided data items that the EDSS user may

define and vary are:

> COST PER SQUARE FOOT TO UPGRADE BUILDINGS - this can range from $2.00 to $15.00 depending on building

category

> SPATIAL INTENSITY OF THE EARTHQUAKE - this can range from 5.0 through 9.0 on the Richter scale.

The model calculates percent loss according to a Modified

Mercalli Index (EDSS takes this from Steinbrugge but without the

"knee" shown in his vectors) (Algermissen and Steinbrugge, 1980).

90
3. DECISION SUPPORT SYSTEMS ARCHITECTURE FOR HAZARDOUS MATERIALS

MANAGEMENT

3.1 INTRODUCTION.

The following describes a prototype Hazardous Materials DSS

(TOXDSS) which focuses both on preparedness (through training

support) and actual response to a chemical spill of limited size

either in a city/county or on a military base while the substance

was in transport. The purpose of TOXDSS is to aid managers in

configuring the best composition of a response team in terms of

specialist staffing, vehicles, equipment, protective clothing,

clean-up and neutralization chemicals and other requirements.

It consists of (1) a data base of emergency personnel and their

qualifications, the types of toxic chemicals and where they are

stored, the characteristics of these chemicals, and, the types of

requirements associated with spill management, (2) a model base

which includes predetermined mixes of personnel, equipment, etc.,

needed to field a response team for a given type and magnitude of

spill, and (3) a menu-driven system for developing a response

team based upon input provided by the user as to the type, size,

location, etc., of the spill.

This DSS is a demonstration model first developed at USC

(Germany campus) in Stuttgart, West Germany by a team including

engineers and programmers for potential application in U.S. Army,

Europe (USAREUR) , and later expanded in Los Angeles.

3.2 GENERAL TOXDSS FUNCTIONAL GOALS.

The prototype incorporates a number of unique features as

well as some of the functions that are planned for future

modules. (Hearne, et al., 1984); (See Figure 2).

These functions are:

+ Determine the composition of the incident response team

to include in-house as well as contract resources;

+ Assist in providing immediate health and safety

information to those reporting a spill as necessary;

+ Notify appropriate agencies about the spill, its

environmental impact, potential for migration, and other

consequences and requirements;

+ Identify possible disposal sites given the specific

characteristics of the spill;

+ Aid in determing spill responsibility;

+ Estimate immediate spill response costs for budgetary

planning and cost recovery.

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3.3 FUNCTIONS OF SELECTED TOXDSS MODULES.

The three TOX DSS components - data base, models, and user

dialog - for this module are discussed in more detail. However,

since it is a prototype the hazard migration models referred to

are not fully developed (de Balogh, 1985).

TOXDSS incorporates a model similar to that found in

the National Oil and Hazardous Substances Contingency Plan (NCP).

(See Environmental Protection Agency, CFR, in references,) It

employs a system for ranking hazardous materials based on a

mathematical model with simulation capabilities that uses the

following variables:

+ CHEMICAL CHARACTERISTICS VALUES (CHEMCHAR) - the weighted

values are taken directly from the NCP and include ignitability,

reactivity, and toxicity/per sistency;

+ INCOMPATIBILITY SCORES (INVNTORY) - weighted values taken

from the NCP;

+ ACCESSIBILITY SCORES (INVNTORY) - weighted values taken

from the NCP;

+ POTENTIAL MIGRATION SCORES (INVNTORY) - given the

complexity of modeling this phenomenon, this contains only

limited estimates based on a 20 point scoring system (American

Petroleum Institute, 1972).

In determining the most cost-effective combination of

response resources for a specific incident the module calculates

hazard scores for the spill for: (1) fire and explosion; (2)

direct human contact; (3) potential migration for ground water,

surface water and air. Once the hazard associated with a

specific incident has been established the CRTEAM data base is

queried to select recommended response staff, equipment,

vehicles, etc. The selection is based on a calculated hazard

score that is less than the values listed in the CRTEAM data base

for the particular item of response equipment or specialist.

Size of the spill is also taken into consideration in determining

the composition of the incident response team.

The prototype is geared to be used by non-specialist

personnel with a minimum of training. This is especially

important during the response phase of an incident. Therefore,

it has been designed to be menu driven. It prompts the user to

enter the chemical's National Stock Number (NSN) , quantity

spilled, and location. After accessing a number of data bases

and assessing hazard scores, the module automatically presents

the suggested response team composition. Actual field testing

of this module has been initiated by the County of San Diego,

California, with the intention of DSSL improving the prototype

based upon results from that test.

93
TOXDSS in its broadest design concept will incorporate a number of additional functional capabilities including a

knowledge base (ES) component. A cost estimation and

forecasting capability will be developed in the near future.

This interfaces with the previous module and takes the per hour

cost of all response resources involved in a specific incident

plus other cost categories to develop the cost of a spill. This

can be used later to recover costs from parties responsible for

the spill. An aggregative cost model permits "what if" analyses

by varying cost parameters. It sensitizes all those involved in

spill management as to the immediate dollar costs of specific

incidents and resources used in response.

Modeling the hazard is to be accomplished by a component

that will estimate the migration of a hazardous material spill or

discharge in terms of its migration potential on land, water or

air environments in a specific incident. This is likely to be

the most difficult to develop since it is dependent on detailed

data for a specific geographic area which in many cases is hard

to obtain before an incident.

4. THE ROLE OF EXPERT SYSTEMS IN DSS ARCHITECTURE.

From an architectural point of view there are three basic

forms of relationships between ES and DSS in the foregoing

disaster situations and most others:

4.1 ES AS A SEPARATE COMPONENT FOR EACH DSS COMPONENT.

Under this concept each DSS component - data base, model base and

dialog - would have a separate expert system component

incoroporated into its design or externally interfaced for quick

call-up (See Figure 3).

4.2 ES AS A FOURTH MAJOR COMPONENT IN DSS.

This would be a single, integrated and highlycomplex component

that would interface with the other major components of DSS. It

could be accessed from each of the three DSS components (See

Figure 4)

The two primary functions of ES in such DSS would be: (1) improve

the operation of the DSS and make it easier to use, and (2) bring

expertise to bear on the emergency management life cycle.

4.3 REAL-TIME ES SYSTEM WITH ARTIFICIAL INSTINCTS.

Specialized forms of expert system architectures for narrow

but highly important problems such as early warning and early

response to catastrophic natural or manmade disaster are also

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forthcoming. These forms are designed for the highly timedependent, real-time critical applications that are likely to be

sensor based. The study of these architectures is drawn from

military research and development (R & D) and is termed the

(AI)2 concept. This stands for "artificial intelligence - artificial instinct" whose application to emergency management is

described in a later section (Gaertner, 1985).

The following section provides more detail about the nature

of the DSS G architecture:

i 5. DSS DIALOG MANAGEMENT SYSTEM.

This is of primary importance since to the user this is "the

system." Namely, what the user perceives he is interacting with

and how he controls it.

Currently, there are a number of alternative hardware and

software modes for controlling a computer system which include:

* MENU - standard and icon (symbols)

;

* COMMAND LANGUAGE

;

* INPUT IN CONTEXT OF OUTPUT (Spreadsheet);

* SYMBOLIC REPRESENTATION (ICON such as the operating

environment of the Apple Mac Intosh or Digital

Research's GEM)

;

* NATURAL LANGUAGE;

* WINDOWING (Multi- tasking)

.

Certain hardware capabilities are also desirable such as:

* MOUSE OR "NUDGER";

* COLOR;

* VOICE

;

* TOUCH

* JOYSTICK;

* OPTICAL CHARACTER RECOGNITION (OCR).

The application of artificial intelligence methodology to

facilitate the control (dialogue) of information systems in

emergency operations by persons who have little or no formal

knowledge for using computers and interacting with complex

systems should be a high priority. Improving the ability to use

natural English and the specialized terms (jargon) associated

with this field to formulate even complex inquiries and better

97
manage (input/update/merge) emergency related data and files are

vital. Special emphasis should be placed on making the dialog

with a computer system as simple as possible during the emergency

response phase when untrained volunteers may, in some cases, be

pressed into service (de Balogh, 1985).

Several micro-computer software products (e.g., Microrim's

CLOUT, Excalibur's SAVVY, etc.) are commercially available for

this purpose which can now accomodate up to 500 specialized words

(synonyms). These can form the basis for further research

(Bond, 1985).

A combination of natural language and icon-based interactive

dialog environment is essential to assure some user friendlyness

of such systems (Foster, 1985).

6.0 SMART DATA BASE MANAGEMENT FOR DSS.

An ES component to make data base management systems smart

to deal with would include the following functions: (1)

simplifying the complexity posed by a large number of emergency

operations related files; (2) automatically establishing logical

relationships between numerous data variables; (3) assist in

determining which files are relevant dependent on the particular

stage of an emergency; and, (4) smart updating (self-updating) of

selected files in the emergency data base through periodic links

with outside systems (e.g., county assessor file, building

permits file, etc. ) via communication links (Ulmen, 1985).

See Figure 5 for an illustration of the complexity

associated with an "ideal" DSS for the earthquake problem.

7. SIMULATION MODELING AND EXPERT SYSTEMS.

Clearly providing support in the third DSS component for

emergency contexts - modeling the hazard and the response - is

where ES may have its biggest role in helping to deal with

complexity, uncertainty and incomplete methodology.

The following sections discuss the research problems and

need for ES within the context of the two previous disaster

types.

7.1 EARTHQUAKE IMPACT MODELING.

EDSS illustrates some of the difficulties of modeling in

this area especially in terms of the state-of-the-art of current

methodology for understanding and forecasting earthquake impact

on various categories of buildings. At present, EDSS employs

only a simplistic categorization of 5 building types. However,

the latest data available on this comes from the work of Applied

Technology Council, Inc., which used a Bayesian approach to

quantify expert opinion on earthquake impact at different levels

on over ninety separate types of buildings (Rojahn, et al, 1984;

and Donald Moore,et al., 1985). The accuracy and reliability of

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99
that methodology remains to be demonstrated (Shah and Dong,

1984).

Clearly, as such methodology becomes more complex so does

the modeling problem, especially the quality of the data

requirements of such models when applied to a particular

geographic area such as a medium-to-large size city.

One of the research tasks of the EDSS project is to survey

the literature as to the number and types of models found

addressing the earthquake problem (Rao, 1985 forthcoming). A

finding of the survey is that there are about two dozen such

models dealing with seismic risk, mostly on mainframe computers.

They have been developed from many different perspectives:

earth sciences, geophysics, geotechnical engineering, structural

engineering, urban planning and land use. These separate

development efforts show no integration among models. The

models are fragmented by discipline and also differ considerably

in the level of detail they address. Very few if any can be

called user friendly and thus most are not ready for operational

use in support of emergency management. At present there is no

validated prototype system that is general izable to public

jurisdictions.

Thus, the prospect for capacity building in the earthquake

area is not good for the immediate future. It is true that

finer estimation methods for seismic risk are emerging and this

is where ES may provide opportunities for meaningful advances in

the field.

7.2 HAZARDOUS MATERIALS SPILL/DISCHARGE MIGRATION MODELING.

The problem of complexity found in the seismic risk

modeling for earthquakes is at least equal if not greater in the

hazardous materials area. It is estimated that there are 19,000

separate hazardous materials in existence and a 1000 new

materials being added to this number every year. Therefore, the

problem of hazard migration modeling is not only horrendous but

is dynamic in nature. Any system designed to provide decision

support in this area would require a continuous, systematic

updating effort of major proportions because it would have to

deal with many new substances each year.

The prospect for modeling having a signficant impact in the

hazardous materials problem is likely to be limited not only for

the above reason but also because it is a much newer topic of

research interest as compared to the earthquake problem. The

most comprehensive ES work relating to the hazardous materials

area has been done by the Oak Ridge National Laboratory (Carroll

Johnson and Sara Jordan, 1983). That type of capability needs

to be made available on a micro-computer before society's

capacity to deal with this problem is impacted.

100
8. THE ARTIFICIAL INTELLIGENCE - ARTIFICIAL INSTINCT (AI)2

CONCEPT.

Recent developments in military R&D, specifically through

projects funded by the U.S. Army Signal Warfare Laboratory,

have led to the emergence of what may be termed an automated,

"real-time" expert system, a specialized Artificial Intelligence

architecture. Specifically, this has important implications for

automated emergency indicators, warning and response systems that

either are or will in the next few years become technologically

feasible.

Such systems are designed to be sensor-based to monitor

conditions within a complex environment and communicate a warning

plus initiate immediate societal response within seconds after

such a condition has been identified. They incorporate the

expertise of decisionmakers within the system. They rely on a

combination of specialized ES software and very fast computer

processers linked with reliable telecommunications to a

jurisdiction's command and control or emergency operations center

and numerous other potentially affected organizations.

Such (AI)2 systems are expected to execute actions within

milliseconds (thousandths of a second) by relying on precomputed

full or partial response arrays which in turn set into motion a

series of automated warning and response mechanisms at various

levels of governments involved in the crisis.

The nature of (AI)2 architecture differs somewhat from

those previously discussed because: (1) it is much more costly,

requiring significant hardware, software, and staffing

investments, and (2) contains both real-time and non-real-time

components (See Figure 6).

It consists of:

1. KNOWLEDGE BASE - the basic data, inference rules, and

response procedures and frameworks associated with the particular

environment. This artificial component of (AI)2 is considered

"slow" or non-real-time because of the large size of the data

base and the undesir abil ity of attempting to process such a

volume in a crisis where response must take place in a millisecond

environment. This ES knowledge base, however, is the foundation

for the next component;

2. ARTIFICIAL INSTINCT NODULE - this contains a super high

speed processing capability for taking hazard condition

indicators (search keys) and searching for an applicable set of

prestored responses which are retrieved (there is a "hit") and

then implemented for action in real-time to reduce the

consequences of impending disaster.

The technical requirements for such a (AI)2 system are

considerable (Gaertner, 1985) :

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- specialized hardware to include fast circuitry with

clock rates up to 100 MHZ and large amounts of main memory (up to

64 Megabytes) for the artificial instinct component (Kimmel,

1985).

- a tested and reliable knowledge base with a good

inference engine which can update both the knowledge base and, as

a result, the artificial instinct date base as well;

- a reliable network of sensors to monitor the given

environment;

- a fast, reliable telecommunications system linking

various communities, organizations and levels of government via a

wide area network (WAN) with linkages to other wide area nets

(e.g., law enforcement, etc.) and key local area nets (LAN).

9. RECOMMENDED APPROACHES TO ES DEVELOPMENT IN EMERGENCY

OPERATIONS.

The following are some suggestions and observations to the

conference participants and FEMA about the potential advantages

associated with various research options for ES prototyping and

testing of concept.

9.1 DEVELOP ES PROTOTYPE AT FEMA TRAINING CENTER.

Use of the FEMA National Emergency Training Center as a base

for development of a demonstration project in expert systems

employing the training scenario of "Central City" which is

currently the basis of exercises is highly desirable. Such a

demonstration project (a prototype for training purposes) should

be modular in scope and use simulated (non-real) data to develop

quick, early ES versions which can be tested and evaluated by

practitioner students who come for training to this center.

Revisions can then be made and new versions issued for further

evaluation. Widely available hardware and a suitable expert

system generator software should be employed.

This approach would be similar to one being employed by the

Defense Systems Management College (DSMC) which is developing

its Program Management Support System (PMSS) which will have a

knowledge base component. PMSS is designed to become the microbased computer information system to be used by all major weapons

acquisitions program management offices of the Department of

Defense. The concept behind PMSS is modular, prototype

development using private consulting firms. Both a training and

a field prototype are to be developed. A similar approach should

be taken by FEMA with regard to ES.

9.2 DEVELOP FIELD PROTOTYPES VIA DEMONSTRATION PROJECTS

FOR SPECIFIC DISASTER TYPES.

Three possible applications to the emergency management area

deserve FEMA research attention:

103
9.2.1 EARTHQUAKE WARNING AND RESPONSE.

Current discussions at the California Institute of

Technology and other research institutions about the technical

possibility of building a sensor-computer-communications network

to give Southern California, for example, a 100 second warning of

an imminent, disastrous earthquake clearly call for ES

application. Significant response to such a short-lead time

warning can only be done through plans requiring automated

execution. ES would have a major role in determining not only

whether a warning is justified but, after giving the warning,

carrying out a series of actions (e.g., fail safe of commercial

and other computers centers; securing of life lines such as

transport, utility generation etc.) to reduce the destructive

impact on the public and the infrastructure. This type of

split-second response, if successfully carried out, could save

numerous lives and reduce the necessary time for economic and

other recovery by weeks if not months.

9.2.2 TOXIC CHEMICAL DISCHARGE WARNING AND RESPONSE.

This involves a series of complex models or a knowledge base

designed to simulate/estimate the migration of specific

hazardous materials on land, water, and other environments in a specific incident. This is likely to be the most difficult to

develop since it is dependent on detailed data for a specific

geographic area which in many cases is not available before an

incident.

An ES component to such a system would be very valuable in

those industrially vulnerable population centers where timely

warning, containment and evacuation would not be otherwise

possible.

9.2.3 NUCLEAR POWER PLANT ACCIDENT WARNING AND RESPONSE.

Work is already proceeding to integrate ES into the fail

safe systems of this particular industry. In the response area

especially, concurrent processing by ES based systems at several

levels of government simultaneously would be especially desirable

(Comfort, 1985).

Any program by FEMA to address the issue of how ES can be

best applied to the emergency management area must include in its

goals research in the (AI)2 area (de Balogh, 1985).

10. NATIONAL CLEARING HOUSE FOR TECHNOLOGY TRANSFER IN DECISION

SUPPORT AND EXPERT SYSTEMS.

Finally, there is a definite need for a national repository

of research findings and a clearing house for computer hardware

and software information (including but not restricted to

decision support and expert systems) in emergency management.

It could provide software and even hardware at cost to

prospective user agencies. The "computer store" concept now

104
used by the General Service Administration (GSA) is a good

example.

An on-going program carried out through FEMA, via a number

of universities with recognized research specializations in

emergency management (e.g., by type of disaster) and significant

library holdings on their particular topic is the answer.

11. SOME CONCLUDING OBSERVATIONS.

11.1 PROBLEMS WITH THE CONSORTIUM APPROACH TO ES DEVELOPMENT.

Since FEMA plans to proceed in ES evaluation primarily

through work that the Department of Transportation will do with

the recently acquired TIMM software from General Research

Corporation, it is important to understand that this approach is

not necessarily desirable or effective especially when

development is by another organization.

To recall a bit of data processing history, the USAC

experiment in MIS in the early 1970's had similar objectives and

resulted in very little if any capacity building. Then the

intent by a team of federal agencies with several million dollars

to spend over several years was to develop a prototype MIS

designed to be applied by any medium-sized American city. Both

major functional (e.g., public finance, law enforcement, human

recources, etc.) and integrated approaches to such development

were undertaken with six cities being involved as test bed

environments. This effort failed because it was technically not

possible to transfer the results of that effort nor were the

cities themselves interested in accepting concepts they had no

hand in developing. This was a costly failure and an important

lesson in technology transfer which must not be repeated with ES.

11.2 EMPLOYING USER FRIENDLY ES DEVELOPMENT TOOLS.

It should be noted that there are now other federal efforts

underway to evaluate ES software which should be actively

followed by FEMA. Foremost among them is the Department of

Defense which has chosen the KEE (Knowledge Engineering

Environment) software as its main ES developmental tool and has

awarded Intellicorp, KEE's manufacturer, a million dollar

contract to tailor KEE to DOD needs. KEE is a very user

friendly system which merits close scrutiny by the emergency

management community (Kinnucan, 1985; Univ. of Miami ICS, 1985).

TIMM, on the other hand, does not have the same level of user

f riendlyness.

A multiple research and evaluation approach conducted in

parallel by FEMA using several excellent ES development

(skeleton) software packages such as KEE, in several emergency

contexts, is more appropriate. The time to find the right

vehicle for capacity building, via ES, would then be considerably

shortened.

105
11.3 PLAN NOW FOR TECHNOLOGY TRANSFER.

Based on our work at USC in decision support systems for the

emergency management community, one conclusion is manifestly

clear. Unless we systematically and purposively build into any

expert systems research program a technology transfer mechanism

which will build capacity of the emergency management

infrastructure, we will not make a difference any time soon.

Such a program must include: (1) clearly defined objectives and

schedule for evaluation; (2) a workable organizational vehicle

that will lead to information sharing within the emergency

management community; (3) reliance on "ubiquitous" computer

systems (hardware and software) that are widely available; and

finally (4) a commitment to provide for technology transfer, even

if that transfer meets only partial needs, NOW.

SELECTED REFERENCES

American Petroleum Institute "The Migration of Petroleum

Products in Soil and Ground Water - Principles and

Countermeasures," Publication No. 4149, (1972).

Bond, George "Clout and Salvo," BYTE, Volume 9, No.ll, pp.279-

286. Callamaras, Peter V. "Savvy," BYTE, Volume 9, No. 2,

pp.303-306. Duffy, Richard "CLOUT Narrows PC Language Gap," PC

WEEK, Volume 1, No.17, pp.l,6_. Eisenberg, Jane and Hill, Jeffrey

"Using Natural-Language Systems on Personal Computers," BYTE,

Volume 9, No.l, pp.226-238. Mace, Scott "Can Natural Language

Sell?," InfoWorld, Volume 6, Issue 46, pp.36-41. Perrone,

Giovanni "Clout," PCWeek, Volume 1, No.31, pp.74-75.

Campbell, Bruce. "FEMA: Information Processing in Emergency

Preparedness", SIGNAL, Vol. 38, No. 11 (July 1984), pp. 35-37.

Comfort, Louise K. "Information Search Processes in Emergency

Management: Computer Simulation as a Means of Improving

Organizational Decision-Making Capacity," EMERGENCY PLANNING, San

Diego, Ca. : Society for Computer Simulation, 1985, pp.41-45.

de Balogh, Frank. EXPERT SYSTEMS AND ARTIFICIAL INSTINCTS: A NEW

CONCEPT FOR REAL-TIME WARNING AND RESPONSE FOR EMERGENCY

MANAGEMENT, Los Angeles, Cal.: Decision Support Systems

Laboratory, 1985 (In Preparation).

de Balogh, Frank. DECISION SUPPORT FOR OIL AND TOXIC CHECIMCAL

SPILL RESPONSE MANAGEMENT, Los Angeles, Ca. : Decision Support

Systems Laboratory, January 1985 22p.

de Balogh, Frank. FUNCTIONAL AND TECHNICAL REQUIREMENTS FOR A

PROTOTYPE EARTHQUAKE DECISION SUPPORT SYSTEM: A SURVEY REPORT,

Los Angeles, Ca. : Decision Support Systems Laboratory, February

1985, 58p (Developed under NSF Grant CEE-8217406).

106
de Balogh, Frank. EXPERT SYSTEMS AND ARTIFICIAL INSTINCTS: A NEW

CONCEPT FOR REAL-TIME WARNING AND RESPONSE FOR EMERGENCY

MANAGEMENT, Los Angeles, California: Decision Support Systems

Laboratory, University of California, DSSL-85-3 (In Preparation).

de Balogh, Frank, Petak, William, and Sessler, John. "Earthquake

Decision Support System: A Computerized Demonstration Model for

Earthquake Mitigation," Proceedings of the First International

Earthquake Conference, Los Angeles, Ca.: February, 1985; pp.

273-293.

Foster, Edward. "Artificial Intelligence: Beyond the Buzzwords,"

Personal Computing, April 1985, pp. 62-69.

Gaertner, W.W. "(AI)2 For Real-Time." Signal, April 1985, pp.

55-61.

Giuffrida, Louis 0. "FEMA Information Systems: Key to Effective

Emergency Management," SIGNAL, Vo. 39, No. 9 (May 1985), pp.

147-156.

Hearne, Stephen, Hearne Laura, Mason, Sara, and Vesay, Anthony.

TOXIC SPILL CONTAINMENT (USING LOTUS 1-2-3), Stuttgart, Germany:

Institute of Safety and Systems Management, University of

Southern California, 1984, Research Report.

Johnson, Carrol K. and Jordan, Sara. "Emergency Management of

Inland Oil and Hazardous Chemical Spills: A Case Study in

Knowledge Engineering". In, Hayes-Roth, Frederick (ed.),

BUILDING EXPERT SYSTEMS. Reading, Mass.: Addison-Wesley, 1984.

pp. 3 49-3 97.

Kimmel, Steven. "Using Very High Speed Integrated Circuits for

Army Systems," SIGNAL, Vol. 39, No. 5 (January 1985), pp. 21-

24.

Kinnucan, Paul. "Software Tools Speed Expert System

Development," High Technology, March 1985, pp. 18-20.

Moore, Donald, Okamoto, Tatsuo, Russo, Joseph, Wilson, Robert;

and Rojan, Christoper. "The FEMA Earthquake Damage and Loss

Estimation System (FEDLOSS)," In, Carroll, John M. (ed.),

EMERGENCY PLANNING, San Diego, Ca. : Society for Computer

Simulation, 1985, pp. 118-124.

Petak, William (ed.). "Emergency Management: A Challenge for

Public Administration." PUBLIC ADMINISTRATION REVIEW, Vol. 45,

(Special Issue January 1985), pp. 172.

Rao, J. K. MODELS OF SEISMIC RISK AND DAMAGES FOR EARTHQUAKE

DECISION SUPPORT SYSTEMS, Los Angeles, Ca. : ISSM, University of

Southern California, June 1985 (forthcoming). (Part of the NSF

funded EDSS project.)

107
Rojahn, C. and R.L. Sharpe, R.E. Scholl, A.S. Kiremidjian,

R.V. Natt, R.R. Wilson. "Development of Earthquake Damage

and Loss Estimates for Facilities in California," Eighth World

Conference on Earthquake Engineering, Vol. 1 SEISMIC RISK AND

HAZARD, Englewood, CI if f s, N. J. : Prentice-Hall, 1984, San

Francisco. (Basis of unpublished 1984-FEMA/ATC-13 report).

Schuerman, L., Gardiner, P., Petak, W., and, de Balogh, F.,

"Decision Support System for Earthquake Mitigation and Response

Management." (A proposal submitted to the National Science

Foundation by U.S.C.'s Social Systems Research Institute [SSRI]

and the Institute of Safety and Systems Management [ISSM], Los

Angeles, California; July 1982). This contains a more detailed

presentation of the EDSS concept.

Shah, H.C. and Dong, W.M. "A Re- Evaluation of the Current

Seismic Hazard Assessment Methodologies," Eighth World Conference

on Earthquake Engineering, Vol. 1 SEISMIC RISK AND HAZARD,

Englewood Cliffs, N.J. : Prentice-Hall, 1984, San Francisco.

Sprague, Ralph and Carlson, Eric. BUILDING EFFECTIVE DECISION

SUPPORT SYSTEMS, Englewood Cliffs, N.J.: Prentice-Hall, 1982.

University of Miami, Intelligent Computer System Research

Institute. "DOD: $1 Million to Intellicorp: Company Will

Develop Expert System Tool Kit Based on KEE," ICS Applied

Artificial Intelligence Reporter, Vo. 2, No. 7 (April 1985), p.

1, 5.

Wallace, William A. and de Balogh, Frank. "Introduction to

Decision Support Systems in Disaster Management." Public

Administration Review (Janaury 1985) Special Issue on Disaster

Management, pp. 134-146.

108
OPTIMUM EMERGENCY MANAGEMENT: THE EFFECTIVE USE OF

INFORMATION TECHNOLOGY

Robert Lee Chartrand

Senior Specialist in Information

Policy and Technology

Congressional Research Service

Library of Congress*

The contribution that the emergency management

community can expect from new information

technology will not come from better or faster

gadgets, but from greater understanding of the

group dynamics of crisis situations, from a greater ability to prepare for such situations

through sophisticated simulations and

training, and from a finer analysis of their

qualitative and quantitative characteristics. (1) Dr. Jacques P. Vallee

Throughout countless generations mankind, from its origins

in primeval forests, atop stilted lake dwellings, or in nomadic

travail amid the inhospitable barrenness of desert regions, has

coped with crises of its own making or caused by nature. The

often murky chronicles of disparate civilizations tell the

stories of such awesome happenings as the Great Inundation, the

engulfment of Atlantis and Antilla, the slashing devastation

wrought by massive meteors, and the inexorable glacial

onslaughts. In many instances, the reportial accuracy of the

observer on the scene may be somewhat distorted, which once led

Evelyn Waugh to note that we live in a world "in which

perceptions count more than facts." (2)

Philosophically, our Nation prides itself on systematically

analyzing situations and marshalling the requisite responsive

resources —human, technological, budgetary—to plan for or cope

with perceived or occurring crises. Information technology,

through its spectrum of sophisticated mechanisms and

methodologies, may offer an amplified context for those who must

delineate a pragmatic phi l osophy of true emergency management.

The intrinsic nature of human beings, acknowledged experts in

some cases, reflects an awareness of the criticality of

stretching their horizons" which in this modern age may include

the use of technology-supported outreach systems. This is

indicative of their awareness that change is constant and

requires a sustained effort on the part of institutions and

individuals to adjust their infrastructures and goals.

There is a growing sense of urgency within both the public

and private sectors that we as a Nation—at the Federal level as

well as in the States and localities —remain inadequately

*The views expressed are those of the author and are not

necessarily those of the Congressional Research Service

nor the Library of Congress.

109
postured to cope with the spate of natural and technological

crises of these complex times. Our societal structure and the

governmental apparatuses conceived of over the years increasingly

are hard-pressed to anticipate, much less respond effectively to,

some of the disasters which have begun to impact lives and

property. Surely those officials faced with the devastation

wrought by the Mount St. Helens* eruption or on a lesser scale

the scatter-shot tornadoes which ravaged the Carolinas early in

1984 would argue that a rethinking of certain priorities and

processes should be a paramount consideration.

One critical facet of strengthening the emergency management

capability in the United States—whether the focus is on

mitigation, prevention, response, or recovery-- is the

development of advanced information systems for collecting,

storing, processing, retrieving, and sharing essential data and

"value-added" information that may be used by emergency managers.

The introduction of advanced technology into any information

handling environment is fraught with imponderables. The

emergency management (EM) community is no exception. The growing

panoply of sensors (aloft and aground), processors, and

disseminators —the result of human inventiveness —instigates the

rethinking of former, established positions. Included among the

organizations in the United States with recognized roles and

responsibilities in emergency management who share such concerns

are: (2)

Federal agencies, such as FEMA, VA, USGS, NWS, NOAA,

DOD, and many more; State and local governments (including task

forces); regional commissions; private sector consultants and

information services; information "clearinghouses";

organizational "watch centers"; national coordinating groups;

and private sector contractors—corporate, university not-for

profit

Today, there is a sizable cadre—more than 12,000 designated

"emergency managers" —responsible for anticipating and coping

with crises of natural or man-caused origins ranging from

tornadoes and floods to terrorism and toxic spills. These forces

have learned that James Michener was correct when he pointed out

that: (3)

...[our] balance in life consists of handling

in real time those problems which cannot be

delayed, then recalling more significant data

during periods of reflection, when long-term

decisions can be developed.

no
Congressional Insight and Initiatives

Congressional cognizance of this increasingly vital area has

been on the upswing, and not surprisingly encompasses all four

phases of emergency management, whether occurring before, during,

or after a disaster. Among the many issues coming under initial

legislative scrutiny are these of a higher priority.(4)

* Is there a current, valid long-range plan addressing

the role of communications networks in emergency

situations?

* Has the optimum use of advanced information

technologies in various disaster scenarios been

studied, and plans for their operational utilization

developed?

* Have priorities been determined for the creation,

maintenance, and use of those essential information

files which may be available to decision makers during

emergencies?

* Will secure communications be available in contingency

situations?

* Are the advantages and disadvantages of various

technologies employed in anticipating or responding to

natural or man-caused disasters understood by those

managers or operators?

* Is there a need to review present emergency management

concepts and plans, particularly those concerning the

roles of "watch centers," networks, and key human

resources?

During the 97th and 98th Congresses, the Subcommittee on

Investigations and Oversight, under the leadership of

Representative Albert Gore, Jr., of the House Committee on

Science and Technology, undertook a multi-faceted exploration of

the role of information technology in emergency management.

Early on, the Chairman noted that: (5)

The subject of disasters is not one that

many of us care to dwell on. Earthquakes,

fires, assassinations, terrorist attacks and

nuclear melt-downs are the stuff of Hollywood

and we like to keep it that way. As a result

of this "out of sight, out of mind" ethic, our

society is often ill-equipped to deal with

emergencies when they do arise.

Present and potential uses of computers and

telecommunications received primary attention in the 97th

Congress opening series of events as the Subcommittee considered

their value in preventing or coping with technological or natural

ill
disasters. Following a roundtable discussion led by

Representative Gore and Dr. Richard Beal of the White House, two

days of hearings were held on September 29 and 30, 1981, with

expert testimony by acknowledged governmental and private sector

leaders in the field. Subsequently, a technical forum was

sponsored by the Subcommittee on November 23, 1981, featuring

participation by 17 senior individuals who engaged in structured

discussions and witnessed demonstrations of technology-supported

information systems.

The second phase of Subcommittee activity in this focal area

occurred in the first session of the 98th Congress, with the

convening of a combined two-day hearing and workshop, on November

\6 and 17, 1983. The highlights of these various Subcommittee

initiatives along with interpretive commentary and summary

recommendations are contained in a committee print entitled

"Information Technology for Emergency Management," prepared by

the Congressional Research Service. The essence of the thematic

goals enunciated and pursued by the Gore Subcommittee throughout

its investigation is found in these four areas: (6)

1. a grasp of the full range of natural and technological

disasters;

2. what technology can and cannot do;

3. the overt and subtle interaction between human beings

and their innovations; and

4. the value of incremental improvements, when sweeping

policy and program revamping is not possible.

A Range of Crises

Experience has shown that information is critical in

anticipating or dealing with many types of disasters, and as

improved technologies have become available they have been added

to the inventory of equipment and related methodology which can

be applied to EM tasks. A selective recital of disaster types

and the havoc ensuing from their occurrence is instructive: (7)

• Boating and aircraft emergencies — several hundred

persons died in 1982 off-shore sinkings: and in FY

1982, 6,414 fishing vessels required SAR (search and

rescue) assistance, plus 49,834 such incidents

involving recreational boats. In addition, the FAA

reported (in FY 1982) that 3,394 air carrier and

general aviation accidents took place.

• Floods — During 1983, according to the National

Oceanic and Atmospheric Administration (NOAA) 204

persons died and property losses exceeded $4 billion as

a result of floods caused by accumulated snowfall

runoff, dam breakage, or intensive rainfall.

112
• Hazardous materials spills (in transit) — The National

Transportation Safety Board (NTSB) estimates that

250,000 hazardous materials shipments are made daily.

Transportation accidents--train, truck, barge--

involving such cargoes have accounted for about 25

deaths per year over the past decade, in interstate

accidents alone. In 1971 there were 2,225 hazardous

releases, but by 1980 the statistic had increased to

16,115.

• Nuclear powerplant accidents — Although Three Mile

Island is the only "major" accident in recent years,

there were 3,804 LFP (Licensee Event Reports)

documented instances (in 1980) where performance

exceeded technical design parameters. Filed with the

Nuclear Regulatory Commission (NRC) , these reports came

from 69 commercial nuclear plants: 753 accidents were

attributed to human errors and 2,174 to equipment

malfunction.

• Tornadoes — These destructive weather aberrations

account for millions of dollars in damage and numerous

deaths each year. The "super outbreak" in a 10 State

area in 1974 involved 148 reported tornadoes which

caused 315 deaths and $600 million in property loss.

In FY 1982, FEMA responded to 181 tornado warnings or

touchdowns

.

Other types of emergencies, while happening with less frequency,

are still of high concern to Federal, State, and local officials

alike. Earthquakes, volcanic eruptions, and hurricanes can be

highly destructive and while infrequent when compared with some

other forms of disasters, can wreak severe damage. Domestic

terrorism, still modest in numbers, remains a menace of serious

concern to authorities. In 1981, 31 terrorist incidents were

reported in the United States, compared to 52 in 1982.

The Many Facets of Emergency Management

Even the most cursory examination of this responsible field

of authority oversight and action provides an insight into the

kaleidoscopic nature of emergency management which, it should be

understood:

• occass iona 1 1 y brings about awesome but largely

anonymous "miracles:

• has its share of impresarios, and sometimes such skills

and talents are desperately needed

• often must be hurriedly established in "oases" of

power, light, and other sustainers amid desolation

• usually is comprised of major episodes accompanied by

singularly vital byplays

113
• may be represented, in more cases than usually

imagined, by only the most embryonic "systems."

Decisionmakers at all levels of the emergency management

structure have to understand the benefits and limitations of

using information technology. Their questions include: What can

technology do to better manage vital information? Can technology

enhance the determination of information validity, including

dealing with misinformation or "disinformation?" Should smallscale experiments and practical case studies be undertaken to

better understand the man-machine interfaces involved in advanced

systems?

A few important studies and related documentation have been

produced in the past few years which represent a first look at

this complex topic:

1979-1980 A series of papers on a proposed Crises and

Emergency Management Information System (CEMIS),

prepared by The MITRE Corporation^)

1982 The Role of Science and Technology in Emergency

Management . a project report by the National

Research Council (9)

1982 PEMA Da£ab_as e_ Regui r eme_nts Assessment a_nd

Resource Directory Model, a project report by

the Information Retrieval Research Laboratory(lO)

1983 Computer S imu l ati on in Eme_r_g_e_ncy_ Planning.,

conference proceedings (11)

1984 Remote S_e_nsing_ __ad £r_iy_a£e_ S_e.cto.r_, a

technical memorandum prepared by the Office of

Technology Assessment (12)

Other means exi3t, in literature and the media, for

broadening both professional and public understanding of

disasters and existing or projected coping mechanisms. Veteran

observers still recall, perhaps with bemusement, the reaction to

Orson Welles' Martian invasion broadcast or the delight

registered by those reading James Thurber's "The Day the Dam

Broke. "(13) Recent media offerings attracting widespread

attention and comment have included such "docudramas" as "The Day

After" (ABC, November 20, 1983), "The Crisis Game" (ABC, November

22-25, 1983), "Special Bulletin" (NBC, March 20, 1983), and "If I Were President" (ABC, August 6, 1980).

114
Retaining __]_& Paramountcy Q& thS. Sllfflaa Sfilfi While most will aver that order and change need to be better

synthesized within the emergency management environment, there

are still compromises to be struck as to how such a meld is best

achieved. Dr. Robert F. Kahn, long a moving force in his

leadership role with the Defense Advanced Research Project Agency

(DARPA) , reminds us that "Technology for emergency management

should be usable. ..a natural part of the working

environment. ..dependable. ..ubiquitous... [and] interoperable with

other systems." (14) This is a large order and may be seen by

many at the local level, with limited resources, as unattainable.

Yet, much has been done through the exercise of ingenuity, as in

Vermillion County, Illinois, where a formidable emergency

response force has been built on a "shoestring." (15)

There is a great deal that may be learned by civil sector

authorities from their defense establishment and intelligence

community counterparts. Time and again during the recent

congressional hearings and workshop allusions were made to tools

and techniques developed by these emergency-oriented groups. In

many instances, it has been imperative to involve both military

and civilian forces in responding to certain types of

emergencies. It has been learned that not only is the sharing of

resources during a disaster incident often required, but prior

joint planning of such manpower and materials 1 disposition can be

most beneficial. Another major contributor to coping with crises

is the National Voluntary Organizations Active in Disaster

(NVOAD) comprised of key private sector groups.

Collaborative effort is often impeded by bureaucratic

obstacles or personality frictions, but the overriding need to

mount interjurisdictional emergency response mechanisms —either

informal or fully legalized—has resulted in impressively mounted

action on a growing scale. Maximum communication between

authorities at various levels must be achieved, not only to

ensure protection of life and property, but because as Alexander

M. Hunter has pointed out local emergency managers must "embark

upon vulnerability analyses and a prioritization of hazard

problems within their own communities. ...What happens if

[they ] ...f ai 1 to take advantage of information that is

avai lable...to deal with the matter of saving lives and saving

property?. ...they are looking at serious liability problems." (16)

Thus, with all of the incredible advances gained as a result

of the inventiveness of man, the human factor remains paramount.

Working to maintain the stability which underlies the general

welfare, while striving concurrently to ensure sufficient

emergency preparedness and plan perceptively for "continuity in

government" under unsought ±_a e_xt.rein.is conditions, the

leadership's dilemma is very real as it strives to initiate a new

115
order. The reverberations of such change are manifest in the

evolution of financial, commercial, and social service, as well

as emergency preparedness, systems. Robert F. Littlejohn of the

Pinkerton organization broadens our context of consideration

regarding the human role by stressing that: (17)

Crisis management. ..is actually the antithesis

of mismanagement and it is something that has

to be really ^fine-tuned: identifying crisis

issues; forecasting what an impact would be on

a company politically, socially, and

economically; and looking at what is the

probability of the crisis taking place.

Issues and Opportunities

As noted earlier, human industry and ingenuity have combined

to provide an ever-broadening array of technology-supported

emergency handling capabilities. Illustrative of the major uses

of these so-called "wizard machines" are: (18)

• 800 minicomputer warning systems in use throughout the

country;

• a variety of airborne platforms such as the NASA U-2

and NOAA Flying Laboratories, along with sophisticated

satellites featuring multi-sensor collection systems;

• the large masses of data stored in computerized

or microfilm files (e.g., National Hurricane Center);

• the rapid retrieval of key data utilizing on-line

access systems, by users located in emergency

operations centers (EOCs), mobile units, or other

remote (local EH) sites: and

• the varied communications conduits--landlines,

airwaves, including satellite systems (e.g.,

Inmarsat) —for transmitting key data.

In the case of aerial data collection (infrared, radar,

conventional photography), the possibilities for use in advance

crisis condition detection are many. Arthur C. Lundahl and Dino

A. Brugioni, pioneer practitioners in the arts of photographic

interpretation and photogrammetry, offer the opinion that: (19)

The future portends even greater opportunities because

imagery can now be digitized, and the combining of

imagery interpretation expertise with computer

technology. ..provides us with numerous innovative

applications. The enormous volume of imagery-derived

data now under computer control provides untold

opportunities for utilization by analysts in Emergency

Operation Centers.

116
One valuable resource for many emergency managers is

geographical data which can be instrumental in presenting, and

helping analyze, potential impact areas, transportation routes,

and population placement. Dr. Jerome E. Dobson, drawing upon his

experience at Oak Ridge in creating automated geographical

systems, comments that the primary effect of this system would

(20)

" not be to supplant human intelligence and

decisionmaking, but rather to speed up many

calculations and judgments that are

already being made in emergency situations.

The greatest advantage would be a better

understanding of where the impacts can be

expected to occur ....this understanding would

be shared through a common set of information

among planners and decisionmakers at all

levels of authority."

Another notable application of information technology, observes

Dr. Marilyn C. Bracken, is that associated with hazardous waste

management. In that connection, she emphasizes the "enormous

potential" of its use: "Obtaining the best information

possible—being able to assess the uncertainty and limitations

surrounding the data, in a critical time frame—is the objective

of all...involved in emergency management." (21)

These commentaries are indicative of the breadth and depth

of this topic, and as the investigation by the Gore Subcommittee

progressed, an identifiable plan of action emerged which could be

undertaken by those responsible for emergency management in this

country(22)

.

Firstly, the Federal Emergency Management Agency (FEMA) was

urged to consider such actions as:

• Creation of a uniform disaster reporting system.

• Establishment of a National Assistance Program Index.

• Expansion of present orientation and training

capabilities and programs, including multi-sensor work.

• Development of a permanent simulation capability,

including models to exercise a wide variety of data.

• Preparation of a five-year plan, featuring interagency

information handling capabilities.

Secondly, other Federal agencies, either in collaboration

with FEMA or through unilateral action, should:

• Create a "core crisis management mechanism," preferably

within the Executive Office of the President.

117
• Reexamine the present role of the Federal

Communications Commission in emergency communications.

• Establish a focal point in a designated agency to study

the application of technology to non-DOD problems.

• Direct Federal technology providers to review new

information technologies for possible EN use.

• Undertake the establishment of a civil sector

communications network with qualities of flexibility

and durability.

Thirdly, State and local emergency handling authorities were

encouraged to:

• Establish or upgrade mutual aid assistance agreements,

with particular attention to technological interactive

support

• Standardize communications frequencies at least within

an identifiable potential disaster area.

• Develop "liability" groundrules to clarify

decisionmaking protocols and priorities.

• Optimize their EM capabilities in the interest of

"self-sufficiency," a prerequisite for certain types of

disasters

• Begin to prepare that "secondary layer" of EM

applicable statutes and regulations, to augment or

serve in the absence of Federal laws.

Fourthly, the Congress through its committee structure, was

asked to contemplate:

• Further revision of the Communications Act of 1934.

• Intensified oversight regarding a range of emergency

management activity areas, both pre- and post-disaster.

• Creation of a "national emergency communications

network ."

• Establishment of a requirement for the "dual use of

technology" in funding new procurements.

• Delineation of a new "clearinghouse" activity which

would collect, store, and make available prioritized EM

data

This spectrum of recommendations goes far toward

establishing a viable course of action for the foreseeable

future. The sequence and speed with which these are accomplished

118
may be affected, in some instances, by factors beyond the control

of the implementing institutions.

Effective Crisis Management; Aq. Unflagging Need

Thus, it is difficult to foresee when this Nation will not

have to confront Mencken's "great problems that heave and

lather," but in the meantime every attempt must be made to attain

a posture of preparedness and response that utilizes all possible

resources. If a "systems approach" is a viable means to that

end, then the lessons learned within the aerospace, military, and

intelligence establishments must be heeded. The importance of

this strategem was heralded in 1966 during the deliberations of

the National Commission on Technology, Automation, and Economic

Progress, which asserted that: (23)

In short, what a systems approach implies is

comprehensive planning so that we can trace

out the effects, progressive and regressive,

of any set of choices and decisions upon all

other relevant decisions.

Although there is an understandable and necessary emphasis

on the ways in which officialdom collects, processes, and uses a great variety of information, the needs of the public-at -large

for certain enlightenment —whether on television or radio, in

written publications, or through lectures and seminars—also must

be considered and met.

The durabi 1 ity and fl exibi l ity of emergency management

systems are critical parameters for functional effectiveness.

There is an upswing in simulations and actual exercising of

certain crisis handling systems to test their execution of vital

operations under stress. This constitutes one facet of "quality

control" which can be a crucial underpinning for any such system.

To many, if not most, emergency managers, the criterion is the

_l_Ll.ive_r.y_ _>£. ne_ede_d inf.o_r_IS.at.lo_a that is accurate, timely,

comprehensive (where possible), and relevant to the challenge at

hand. The "system" or "tool" may be incidental, and if its

performance is unreliable—whether in terms of linking networks

or simply retrieving a piece of key data—then the responsible

office may opt to return to a simpler, more trustworthy

capability.

The acquisition, verification, and transmittal of

information has always been critical in meeting a variety of

crises ranging from small-scale localized disasters to larger

emergency situations affecting a wide geographic area.

Information technology as never before has come to offer a wide

range of potentials for enhancing the effectiveness of crisis

organizations —both governmental institutions and designated

authorities within the private domain—responsible for emergency

warning and notification, situation assessment, decisionmaking

during crises, and dissemination of essential information for

responsive action.

119
If the challenges of the past are any indication of the

pressures to be borne by the emergency management community in

the future, those who shape key policies and direct the resultant

programs roust continue to seek out all potential resources to

meet those forthcoming confrontations. And implicit in this

challenge is the opportunity for optimum utilization of

innovative technology which when combined with masterful group

dynamics will ensure our survival and well-being as a Nation.

120
FOOTNOTES

(1) U.S. Congress. House. Committee on Science and Technology.

Subcommittee on Investigations and Oversight.

Emergencymanagement information and technology. Hearings.

97th Congress. 1st Session. September 29, 30, 1981.

Washington, D.C., U.S. Government Printing Office, p. 150.

[Commentary by Dr. Jacques S. Vallee]

(2) Chartrand, Robert L. Information Technology for Emergency

Management: The Many Potentials. CRS Review.

Congressional Research Service. U.S. Library of Congress

(A.A. Hoehling, ed.) Vol. 5, #10. November/December 1984,

p. 23

(3) Michener, James A. Space. New York, Random House, 1982.

p. 622

(4) U.S. Library of Congress. Congress Research Service.

Information Technology in Emergency Management." Pamphlet

produced by Robert L. Chartrand, Clark F. Norton, Nancy R.

Miller, and Madeline Seidner for hearings and work shops

held by the Subcommittee on Investigations and Oversight.

House Committee on Science and Technology, November 16-17,

1983. "Prologue" by Albert Gore, Jr.

(5) U.S. Congress. House. Committee on Science and Technology.

Subcommittee on Investigations and Oversight. Information

Technology for Emergency Management. 98th Congress, 2nd

Session. October 1984. Washington, D.C., U.S. Government

Printing Office, p. 123

(6) Ibid., p. 10

(7) Chartrand, Robert L. Information for Emergency Management:

The Many Potentials. CRS Review. Congressional Research

Service. U.S. Library of Congress. (A.A. Hoehling, ed.)

Vol. 5, #10, November/December 1984, p. 22

(8) Stauch, B. Information needs of a Pilot crisis and

emergency management information system (CEMIS). The MITRE

Corporation [McLean, Virginia] July 1980. p. 59 and

Janicik, E. Bounds for the pilot crisis and

emergency management information system (CEMIS). The MITRE

Corporation [McLean, Virginia] October 1979. p. 33

(9) National Research Council Committee on Emergency Management.

The role of science and technology in emergency management.

National Academy Press. Washington, D.C., 1982, p. 90

(10) Tenopir, Carol, and Martha E. Williams. FEMA data base

requirements assessments and resource directory model.

Washington, D.C., May 1982. p. 123

121
(11) Society for Computer Simulation. Computer simulation for

emergency planning. (Proceeding of conference, January 27-

29, 1983 San Diego, California] Ed. by John M . Carroll.

La Jolla, California. January 1983. p. 115

(12) U.S. Office of Technology Assessment. Remote Sensing and

the Private Sector: Issues for Discussion — Technical

Memorandum. Washington, D.C. March 1984. p. 41

(13) Thurber, James. The Thurber Carnival. Franklin Center,

Pennsylvania. The Franklin Library, 1980. p. 360-366

(14) U.S. Congress. House. Committee on Science and Technology.

Subcommittee on Investigations and Oversight. Emergency

management information and technology. Hearings. 97th

Congress 1st Session. September 29, 30, 1981. Washington,

D.C, U.S. Government Printing Office, p. 180

[Testimony of Robert E. Kahn]

(15) U.S. Congress House. Committee on Science and Technology.

Subcommittee on Investigations and Oversight. Information

technology for emergency management. 98th Congress. 2nd

Session. October 1984. Washington, D.C, U.S. Government

Printing Office, p. 345-356, 383-384

(16) Ibid., p. 358. [Commentary by Alexander M. Hunter]

(17) Littlejohn, Robert F. "Crisis Management: A Team

Approach." Remarks during the AAAS Symposium session on

"Information Technology for Emergency Management." New

York, Sheraton Centre, May 27, 1984

(18) U.S. Congress. House. Committee on Science and Technology.

Subcommittee on Investigations and Oversight. Information

technology for emergency management. 98th Congress. 2nd

Session. October 1984. Washington, D.C, U.S. Government

Printing Office, p. 5

(19) Lundahl, Arthur C, and Dino A. Brugioni. Aerial

Photography: World Class disaster fighter. An article to

appear in the forthcoming issue [1985] on "Information

Technology for Crisis Management" of The Information Society

journal

(20) U.S. Congress. House. Committee on Science and Technology.

Subcommittee on Investigations and Oversight. Information

technology for emergency management. 98th Congress. 2nd

Session. October 1984. Washington, D.C, U.S. Government

Printing Office, p. 320 [Testimony of Jerome E. Dobson]

122
(21) Bracken, Marilyn F. "Chemical Emergency Response: Risk

Assessment and Information Resources." Remarks during the

AAAS Symposium session on "Information Technology for

Emergency Management." New York. Sheraton Centre. May 27,

1984

(22) U.S. Congress. House. Committee on Science and Technology.

Subcommittee on Investigations and Oversight. Information

technology for emergency management. 98th Congress. 2nd

Session. October 1984. Washington, D.C., U.S. Government

Printing Office, 1984. p XV-XVI

(23) Technology and the American Economy. Report of the National

Commission on Technology, Automation, and Economic Progress.

Vol 1. February 1966. p. 100

123
ROLES OF SIMULATION IN THE APPLICATION OF

EXPERT SYSTEMS IN EMERGENCY MANAGEMENT OPERATIONS

by A. Ben Clymer

Clymer Technology

Ocean, New Jersey 07712

1. INTRODUCTORY CONCEPTS

1 . 1 Concepts Used

In this paper an approach is used that is rather different from the

usual. The following assumptions have been applied:

1. The bionics approach to artificial intelligent systems design

can be productive (i.e., drawing upon how various concepts of

the intelligence of organisms, including man, are organized).

2. Block diagrams are a fruitful way to communicate many of the

concepts

3. The human intellect is highly parallel, not just serial like a classical Von Neumann computer.

4. It is necessary to deal with levels of abstraction and hierarchies of meaning, in order to understand some of the fundamental intelligent functions on both the afferent (input) and

the efferent (output) side.

5. Analogies enable a further exploitation of the bionics approach, if a number of systems analogous to the system of concern are known.

1 . 2 An Approach to Human and Artificial Intelligence

The perceptual side of the central nervous system may be conceived to be

essentially hierarchies of recognition devices, with at least one hierarchy of each sensory modality. Each recognition device ("recognizer")

has an output at the top only when its set of inputs at the bottom meets

certain criteria that correspond to some concept. The output is more abstract than the inputs. A hierarchy of recognizers can recognize very

abstract concepts. See Figure 1-1.

The inputs to a recognizer need not be "on" (i.e., high or signaling)

all of the time in order for a recognition to be achieved. Any uncer- tainty shown by inputs that are on intermittently (like blinking lights)

will be shown in the same manner and to the appropriate degree also in

the output. Thus, fuzziness in the inputs produces fuzziness also in

the output. The higher the percentage of the time an output is on, the

greater the certainty of its recognition.

124
OUTPUT = f (INPUTS).

BOOLEAN a/OR ALGEBRAIC VARIABLES.

THE OUTPUT IS MORE ABSTRACT THAN THE INPUTS.

IF INPUTS ARE TRAITS, OUTPUT IS A GENERAL CONCEPT.

FIG. l-h RECOGNIZER

ito OUTPUT= fj (INPUT).

THE INPUT IS MORE ABSTRACT OR GENERAL THAN THE OUTPUTS.

IF INPUT IS A GENERAL COMMAND, OUTPUTS ARE ORDERS.

FIG. 1-2= ELABORATOR

125
Some of the visual and tactile recognizer hierarchies are organized to

identify patterns in a perceived field of two dimensions. It is con- ceivable that this process takes place by pattern matching, in which the

system finds the figures that have to be synthesized and superposed in

order to match the perceptual field. If there is a residual difference

between the synthesized and perceived fields, figures are sought in the

residual field and then are matched, until the residual field is a uniform or random background. This process requires that each synthesized

figure be changed in size, location and orientation until it cancels out

the corresponding figure in the perceived field.

A set of recognizers need not be a strict hierarchy (like an organization chart in which each person has only one boss). A recognized con- cept can flow upward to two or more recognizers in parallel. In principle there need be no fewer outputs at the top than there are inputs at

the bottom. A recognizer can have more than one abstract concept in its

top layer. In fact, as in some optical illusions, the concept recognized can alternate between two possibilities.

One of the benefits of having a field broken down into a small number of

figures is that they can be stored much more efficiently than if every

bit in the field had to be stored. For recall of the field it would be

necessary only to call out the figures and superpose them.

On the neuromuscular side of the central nervous system there are hierarchies of command elaboration units ("elaborators") , each of which

converts a single input into a number of distinct outputs. The input is more abstract or general than the outputs. See Figure 1-2.

A hierarchy of recognizers is itself a recognizer. Therefore, the

graphic symbol for a recognizer (Figure 1-1) can be used to denote

either a recognizer or a hierarchy of recognizers. This convention can

save a lot of detail in diagrams. See Figure 1-3.

Similarly, a hierarchy of elaborators is itself an elaborator. The

graphic symbol in Figure 1-2 can be used for either. See Figure 1-4.

Another component of the central nervous system is the arousal system

(reticular system). It dictates the functional level that must be

aroused and involved at the present moment; higher levels can be es- sentially at rest. It takes its input from the hierarchies of recognizers for all sensory modalities. It sends an arousal signal upward,

and it sends commands for increased gain coefficients in all recognizers

and elaborators that are appropriate to the arousal.

Yet another component of the central nervous system is a reflex unit.

It has one or more inputs from recognizers, and it has an output to an elaborator. Thus, it can cause an action appropriate to the recognition, which is a reflex action.

There are also lower level components, such as control (homeostatic)

loops, which adjust physiological variables when there are changes in

126
RECOGNIZERS ARE BUILDING BLOCKS.

THEY CAN BE PUT TOGETHER IN A HIERARCHY:

THIS HAS A TRIANGULAR ENVELOPE, SO A

RECOGNIZER SYMBOL CAN BE USED ALSO

TO REPRESENT A HIERARCHY OF

RECOGNIZERS.

FIG. 1-3= RECOGNIZER HIERARCHY

127
ELABORATORS ALSO ARE BUILDING BLOCKS.

THEY TOO CAN BE PUT TOGETHER IN A HIERARCHY:

/. ML \

THIS TOO HAS A TRIANGULAR ENVELOPE, SO AN

ELABORATOR SYMBOL CAN BE USED TO

REPRESENT A HIERARCHY OF ELABORATORS.

FIG. 1-4= ELABORATOR HIERARCHY

PROBLEM

SOLVER

FIG. 1-5= INTELLIGENT SYSTEMS

128
the body's boundary conditions. These lower levels are not of concern

in artificial intelligence.

All of the foregoing components working together cannot duplicate the

capabilities of the human brain (or any other animals' brains). One of

the missing capabilities is problem solving, planning, decision making,

etc., which can be dealt with only partially in today's artificial intelligent systems. These activities are among a manager's functions.

One of the key functions of a manager is planning. A way to conceptualize planning is in terms of pattern recognition in an abstract sense,

using some appropriate and familiar conceptual scheme to provide structure. For example, the plan needed might be an internal portion of a Pert chart, given the rest of the chart. As in pattern recognition,

building blocks would have to be "seen" that together would fill the gap

and meet the requirements, then directing attention to any requirements

not met. Some heuristic algorithms for tasks of this type are well

known in artificial intelligence.

1 . 3 Some Systems Analogous to the Nervous System

These systems have analogous structures:

1. A business manager.

2. Expert system.

3. Battlefield officer.

4. Management support system.

5. A full-blown intelligent robot.

6. A comprehensive AI system.

7. A hierarchy of managers and/or expert systems in any mix.

8. A hierarchy of agencies in emergency management.

These systems have an analogy also with the central nervous system. It

might help in thinking about these systems to portray them all as shown

in Figure 1-5.

However, there are some distinct differences between the nervous system

and an Emergency Management (EM) system. For example, in the EM system:

1. There are no clearcut hierarchies of recognizers and elaborators. The elements of the system, mainly people, have both in

each person.

2. There is no specialized arousal system in many EM systems.

3. The EM systems do not have characteristic signal frequencies

like the beta, alpha, gamma, and delta rhythms.

4. There is very little proprioception.

5. There is some sensorimotor action (as in redirecting the direction of gaze), such as when a person is sent to a particular area to inspect and report.

129
6. Uncertainty in EM messages is not manifested in the transmission.

1 . 4 Computer Implementations

Some of the functional systems discussed above can be rendered with

various kinds of computer hardware. The implementations with LISP programs and AI serial digital computers are well known and so will not be

discussed here.

Parallel computers have aroused some strong support recently in artificial intelligence circles (see References 1 and 2), partially as a re- sult of the Japanese national computer project. Generally, there is a trend in artificial intelligence to look more closely at any commercial

parallel computer or parallel arrays of computers or both in one system.

One rationale is to increase speed; another is to emulate how the brain

is believed to work, which is surely largely in parallel. One purpose

is to simulate an expert system, and another purpose is to have an ex- pert system.

It might be helpful conceptually to think of the parallel type of com- puter processing repetitively a hierarchy of recognitions, row by row,

from bottom to top, starting over as often as input changes warrant.

One could think of each recognizer that is actually recognizing some- thing as lighting up. The pattern of lights would show what is being

recognized. Similarly, a hierarchy of elaborators would show what

elaborations it is producing in this pattern of lights. Blinking lights

would signify uncertainty.

Another type of implementation that is of interest in artificial intelligence is a robot. Conventional robots are programmed to perform re- petitively a useful motion in an open-loop manner (no proprioceptive

feedback). A hierarchy of elaborators could, in principle, generate the

detailed motions appropriate to a general command, yet be able to re- spond to other commands also.

2. SIMULATION IN EMERGENCY MANAGEMENT

2 . 1 Meanings of "Simulation"

"Simulation" has come to have several meanings in the EM field. One

meaning is that of a game or exercise, played for training, research, or plan development, having participants who play roles of emergency man- agement personnel according to a preestablished scenario. This meaning

is sometimes subdivided according to whether a computer is used in an important role. With a computer involved, one has a "technologysupported simulation," as opposed to a "traditional simulation" if a computer is not involved (Reference 3).

A totally different meaning of "simulation" is taken from the field of

simulation, namely, the capability or activity of generating, by means

of a computer, the behavior of a system by progressively solving in time

130
the dynamic equations comprising a mathematical model of the system.

Often this is called "computer simulation," a term which, unfortunately,

can mean the simulation of a computer, which is an important branch of

simulation. As applied in EM, this meaning of "simulation" is the simulation of an emergency itself, usually without real or simulated individual people. However, as discussed in Section 2.5, simulation of

people in emergencies is under development and will appear more and more

in EM simulations.

A "simulation," in the sense of a capability of a computer program,

generates an approximate version of the simuland's (of the system to be

simulated) behavior in specified situations and for specified purposes.

Cases outside the region of the specified situations and purposes are usually not rendered with much realism.

2 . 2 Functions of Simulation for EM

Simulation has valuable uses in all four phases of EM (mitigation,

preparation, response, and recovery):

o Mitigation

1. Simulations of emergencies and mitigation efforts to determine

the best allocation of resources among emergencies of different kinds to get the best return on the EM investment by re- ducing the incidence and/or impact of certain emergencies.

2. Detailed simulations of a particular class of emergency to

determine for it the best allocation of resources among mitigation activities that might be effective, and to evaluate the

cost/benefit ratio.

3. Simulations of compound emergency causation, hazard probability, vulnerability, etc., for local situations such as chemical or power plants.

o Preparation

1. By means of simulations of emergencies and responses to them,

it is possible to determine the best plans for given contingencies. Some of these studies could well be done by local

industry, which has a real concern for emergencies.

2. Simulation is a powerful tool for R&D on emergency responses

by the various people involved, particularly in matters of

psychology and group dynamics. Real people would be used in

this research.

3. Simulation of EM management processes with real people included is potentially valuable for testing particular organizations and for evaluating types of organizations, particularly under stress of a realistic exercise.

131
4. Simulation is the tool of choice for most studies in system

engineering to improve the performance of systems for communication, information processing, etc.

5. Simulation of certain emergencies for prediction of where and

when the threat is to be expected, for the purpose of shortterm planning and blending into the response phase.

6. Simulations can be used to generate dramatic demonstrations of

emergencies and the consequences of various public responses,

good and bad, for public education presentations.

7. Simulations of all types can be used effectively in training

professionals in EM or affording them practice. The whole

continuum of degree of stress is available for use in particular cases.

o Response

1. Simulation can be used as a manager's analytical tool in support of decisions during an emergency, such as to project the

future severity of the emergency, including the effects of the

planned response.

2. Simulation can be used to determine unknown parameters of an emergency (such as release quantity or rate, or wind speed and

wind direction averaged over a plume) by matching iteratively

the predicted plume to the field data concerning the plume.

o Recovery

1. Simulation can be used to compare the benefits of alternative

plans for recovery.

2. Simulation is often used as a tool for reconstructing what

must have happened in an emergency, step by step, matching

recorded information, such as to ascribe the cause, and learn

whatever is possible for next time.

The foregoing applications of simulation to EM would normally be performed locally for local purposes. However, the national EM organization has echelons, each of which has reason to do its own simulation

studies. For example, on the national level one could single out:

1. Simulation of the human ecology of the country, with emphasis

on its vulnerabilities and risks (Reference 3)

2. A national facility for EM simulations of all kinds on a re- gional or national scale (Reference 3).

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2.3 Simulation of Emergencies: State of the Art

There has been fairly general agreement in EM on the scope of the meaning of "emergency." However, in recent years there has been arising a good case for extending the time scale of "emergency" to include very

slow processes that pose severe threats to man.

Examples would be the threat to the ozone layer by aerosols, the greenhouse effect due to increased production of carbon dioxide, the opposite

effect due to processes acting in the other direction, the progressive

exhaustion of forests and other resources, acid rain, the theorized

poisoning of the Romans by lead, the multiple consequences of exceeding

"the limits to growth," etc., etc. Many of these slow emergencies have

been simulated already. Some papers of this type will be presented at

the Conference on Simulation at the Frontiers of Science, sponsored by

the Society for Computer Simulation, Norfolk, Virginia, March 1986.

Even without including these slow emergencies, there is a considerable

body of past simulations of emergencies. The longest bibliography known

to the writer is in Reference 4. This bibliography lists 45 fairly re- cent papers under the following headings:

1. Simulation of Fires and Firefighters.

2. Simulation of Emergencies in Plants.

3. Simulation of Spills and Decontamination.

4. Simulation of Structural Emergencies.

5. Simulation for Water Management.

6. Simulation of Deployment of Specialized Vehicles.

7. Simulation of Emergency Medical Services.

8. Simulation of Evacuation of Buildings and Cities.

The frequency distribution of publication dates indicates a steady

growth in the number of papers per year.

Many applications and potential applications were absent from the foregoing bibliography: wind storms over land or water, ocean waves and

surges, avalanches and mudslides, nuclear weapon effects, storage tank

accidents, outdoor fires, vehicle fires, building bombings, civil defense systems, and chained multiple emergencies (e.g., an earthquake

breaking a dam, which floods its plain, or a storm affecting transportation and communications). Most of the omissions were due to the selectivity in the data base that was searched.

The Proceedings of the Conferences on Computer Simulation in Emergency

Planning, sponsored by the Society for Computer Simulation, San Diego,

January 1983, and January 1985, include some of the areas omitted above.

A bibliography on evacuation research has been compiled by John Sorenson,

of Oak Ridge National Laboratory. Some of these papers report on the

use of simulation as a tool for evacuation research.

133
2.4 Simulators for EM

2.4.1 Introduction

Definitions and meanings of the word "simulator" were the subject of an entire panel discussion at the SCS Simulators Conference, Norfolk, Virginia, March 1985. Roughly speaking, however, for the present purpose,

a "simulator" will be a special-purpose simulation capability implemented on a dedicated computer. It might or might not have a videodisk

or graphics as an adjunct.

Here some actual and some possible future simulators are presented to

illustrate what simulators can do for EM. Since simulators form a $2

billion per year industry which is eyeing EM as a market, emergency

management leaders might well now look seriously at simulators as potentially valuable tools.

2.4.2 Puff and Plume Prediction

The largest and most advanced facility for simulating the transport and

spread of a plume or puff of radioactive pollutant is ARAC (Atmospheric

Release Advisory Capability) , at Lawrence Livermore National Laboratory

(References 5 and 6). It provides calculated consequences of releases

of radioactivity into the atmosphere, such as level of surface contamination or cumulative dose at a point, resulting from a plant or transportation accident. Current and predicted values are available in tens

of minutes from Lawrence Livermore National Laboratory. Over 50 facilities of DOE and DOD are served by ARAC.

The simulation programs used by ARAC cover a wide range of needs. In a site system at each facility of concern there is a personal computer

which uses a Gaussian plume model for prompt information to EM personnel. The site computer is also tied to the main computer at LLNL, where

the model used (ADPIC) is 3D particle-in-cell fluid transport and diffusion. The wind field, which is consistent with the actual detailed

topography of the site, is calculated by the MATHEW program and is used

as input to ADPIC. The central computer also provides graphic displays

of isolines for the plume, dose, and surface contamination. It is

probably only a matter of time until ARAC will be extended to include

toxic chemical or biological releases, volcanic dust clouds, etc.

A recent survey (Reference 7) of computer systems for assessing plumes

turned up quite a few that are on the market: the Safer System, Chemical Emergency Evaluation System and Community Assurance Program, Hazardous Assessment System for Toxic Emissions, Chemical Hazardous Area

Release Model and EMERGE.

The least well known parameter is the magnitude of the release expressed

as a release mass or a release rate, depending on the accident. It is

possible to determine the release magnitude or rate with the puff or plume, respectively, simulation by iteratively adjusting it to give the

best fit to whatever downwind measurements of pollutant are available.

134
The same principle can be extended to determine source location or wind

parameters, if they are unknown or not known accurately. Figure 2-1

shows the principle.

2.4.3 Flood Prediction Systems

The same principle can be applied to the flooding of a river. Rain

gages and water depth gages in the tributaries report automatically to a central computer, which is able to interpolate any missing data and then

solve the flow equations to forecast flood levels for the entire river

basin. The technology has already been demonstrated by International

Hydrological Services, and it was reported at the Emergency 85 conference in Washington, D.C., in May 1985. It was predicted that there will

be 1,000 automated flood computer systems in the U.S. by the year 2000.

2.4.4 Nuclear Power Plant Simulators and Analyzers

For the past 20 years there have been, all over the world, an increasing

number of multi-million-dollar simulators of nuclear power plants for

training operators. The trend is for all but the smallest of plants to

have their own simulator. These simulators can give training in a wide

range of situations arising from malfunctions or operator errors, including severe emergencies. However, in general, the simulators are unable to provide training in a situation requiring action by more than

one or two operators in the control room. Only their own actions are noted by the simulator. This is one future direction of evolution of

simulators

.

The Three Mile Island incident stimulated the development of another

class of simulators called "analyzers," which are capable of running a plant simulation faster than real time for research, for on-the-spot

planning, or for later reconstruction of an accident. Analyzers are only now becoming able to meet the stringent requirements on speed of

calculation of a huge model.

2.4.5 Outdoor Fire Simulators

In fighting a large outdoor fire, it is valuable to have a portable

simulator for planning. The U.S. government funded development of a simulator called "BEHAVE" for this purpose (Reference 8), among others.

2.4.6 Some Future Simulators

One can imagine a dozen or more distinct types of simulators that would

be valuable in EM. Their time will come.

For example, one elaborate type of simulator, which has been described

by Clymer and Perley (Reference 9), would contain three levels of the

overall EM organization associated with a nuclear power plant: the

control room operators, the utility management, and the outside agencies. Emphasis of the uses (R&D, planning, and training) would be upon

the processes of interaction among the three organizational echelons.

135
STARTING

ESTIMATES FOR

PARAMETERS

BEST-FIT

PARAMETER

VALUES

OBSERVED

DATA

1

FAST- TIME

SIMULATION OF

PROPAGATION

PREDICTIONS ERRORS ALGEBRAIC

ANALYSIS

OF ERRORS

ADJUSTMENTS TO UNKNOWN PARAMETERS J

FIG. 2-1 ITERATIVE DETERMINATION OF

RELEASE PARAMETER VALUES

136
Therefore, the simulator need not include all of the details of each

person's environment or task.

Another type of simulator would be highly generic but flexible. That

is, it would be designed to represent any of a set of plants. The con- trol panels would be replaced by generic CRT displays and keyboard com- mands. The software would enable the user to construct a new plant by

drawing the flow diagram on the CRT screen, the software producing the

appropriate equations for the model from a modular data base. Such a simulator would be useful for a state or regional agency concerned with

several plants.

The other end of the market is a large number of agencies having small

budgets. Thus, there is an opportunity for development of simulators on personal computers. Among the possible uses would be planning and

training. Each agency would simulate mainly its own operations, plus

simplified interactions with superior, inferior, and lateral organizations in the EM hierarchy.

2.5 Modeling People for EM Simulations

At present there are three distinct approaches to the modeling of people, such as those in EM organizations and citizens in an emergency.

The oldest was continuous dynamic modeling of variables in simple situations, such as a single homeostatic loop in physiology or the tracking

of a target by a gunner. The second has been the use of logic and discrete simulation technology in a digital computer for prediction of se- quences of events, such as the motion of a pilot's line of sight over an instrument panel in flight situations. The third is the use of expert

systems to stand in place of people for restricted expert functions.

These three approaches are illustrated below with some examples.

The continuous dynamic approach gives a transfer function in the case of

gunner or pilot, or a set of differential equations in the case of a physiological system. These models do not reach very far upward in the

human functional hierarchy, but they do provide a foundation. Continuous models succeed significantly in describing these systems, which

themselves have a continuous character.

There are other systems, however, which are apparently not continuous

but which offer promise of being modeled by differential equations

nevertheless. An example is the "position" of a hostage while captured

by terrorists. The intermittent and sudden changes of position are similar to the motions of a block of wood on a tabletop with static

friction and two opposed springs pulling on it. One spring represents

the force applied by the hostage's traditional beliefs, and the other

spring represents the force applied by the terrorists. This mechanical

model can be described "exactly" by differential equations. On the

whole, however, systems involving beliefs, feelings, decisions, etc., are not able to be modeled well by differential equations.

137
Discrete models are preferable for accounting for a series of discrete

purposeful actions. There are several types of human actions that might

be or have been modeled this way:

1. What an operator of a plant will do in a particular situation

could be modeled in terms of three, say, actions that might be

taken, each with a given probability each time. Two of the

three would be "human error," each with a small probability.

The probabilities could be made functions of the stress level.

2. A similar model might be applied to members of the public in an emergency in following evacuation directions and road

signs, or trying to dial a phone.

3. Likewise, the model might apply to many aspects of the work of an emergency manager.

4. Another type of model deals with a human trying to keep track

of many variables, such as a pilot scanning an instrument

panel or plant operator scanning a whole control room. Depending on the previous rate of change of a variable, its

proximity to a critical value, and the variance of the variable, he will return to a certain variable within a time period estimated to be safe, attending to more urgent variables

meanwhile. Work of this kind was pioneered at Bolt Beranek

and Newman.

5. When a human operator is to be modeled in a task network, the

SAINT language has been used (Reference 10)

The third approach, using artificial intelligence to simulate some human

ability, has been evolving rapidly. The first major breakthrough was symbol processing for problem solving (Newell, Shaw and Simon). Recently, the most promise has been shown by expert systems in situations

requiring complicated decisions, diagnoses, etc., which will be discussed herein in Section 3. An alternative approach has been to apply

fuzzy logic in simulating human controllers, such as operators forced to

go into the manual mode of control systems in an emergency. See References 11 and 12. It is interesting to note that the fuzzy controller

tries to satisfy each of several conflicting guiding principles, com- promising the deviations, a process that is reminiscent of and partially

analogous to the foregoing spring-and-mass model of a hostage's position.

All in all, human simulation has a long way to go, but some of the roads

are passable. Sorenson, of Oak Ridge National Laboratory, has stated

that the best theories and models of behavior account for only 40 to 50%

of the variance (Reference 13)

3. EXPERT SYSTEMS IN EMERGENCY MANAGEMENT

The desirability of applying expert systems to emergency management was recognized by Frederick Hayes-Roth in Reference 3. The first major

138
public exploration of the possibilities has been the First Symposium on

the Application of Expert Systems in Emergency Management Operations , of

which this paper and these Proceedings are fruits.

In this section a brief treatment is given of the topic of the Symposium, not so much for its own sake as for providing entree to the subject

promised by the title of the paper, which is treated in Section 4.

3 . 1 A Concept of Expert Systems

Expert Systems as a discipline is considered to be a subset of Knowledge

Engineering, which is a subset of Artificial Intelligence (AI), Reference 14. The functions of Knowledge Engineering Systems include problem

solving, search, and symbolic programming, and these subsume the functions of expert systems (see Section 3.3).

Jay Liebowitz has defined expert systems as "computer programs that

mimic the behavior of a human expert in a specific domain of knowledge."

Thus, expert systems are simulations in a sense. However, they bring a new class of models to the simulation field.

Expert systems have some sort of logic in their core, where inferences

are produced. The outermost layer is a natural language interface for

bilateral communication between the spoken language of the user and the

formal language in the core.

The core, where knowledge is stored, data are brought in, and inferences

are made, can be strictly logical and deterministic. However, it is

also possible for an expert system to work with fuzzy logic and to generate probabilistic or equivalent semi-quantitative expressions of natural language.

Another parameter in the meaning of "expert system" is the degree of

expertise. Some expert systems exceed the ability of a human expert,

some match it, but even less ability would be noteworthy and useful,

particularly early in the development of a new application area. It

would not be appropriate to apply the term "expert system" to a much

less impressive degree of expertise, such as the models of people that

are typical of the field of human simulation today (see Section 2.5), or

an automated reference librarian or file clerk.

3 . 2 State of the Art of Expert Systems

The telling and retelling of the successes achieved by expert systems

has been the chief fuel rocketing artifical intelligence into prominence. Here only a brief summary is given.

The spearhead function for expert systems, with which the first major

breakthroughs were made starting in the 1960's, is diagnosis and

troubleshooting of disease, malfunctioning hardware or software, etc.

Many other functions and applications have followed, as is discussed in

Sections 3.3 and 3.4. However, Dixon (Reference 15) argues that only

139
about four expert systems could be considered operational in 1983, although the number has been growing. The first two expert systems to

equal and then surpass human expert capability were Dendral and Macsyma

(Reference 14) . Another well publicized success has been the system to

plan the manufacturing of VAX computers of varied configurations.

Expert systems are growing also in complexity. The current upper limit

on program size is 1,000 to 2,000 rules.

Emergency management is only lately becoming a recipient of the benefits

of expert systems. Among the current developments is an expert system

designed by Radian Corporation to assist in making decisions during

emergencies (Reference 7).

3 . 3 Functions of Expert Systems

3.3.1 Input Data Processing

The functions of expert systems are classified here in terms of three

categories

1. Input data processing

2. General data processing

3. Output data processing

The first category consists of convergent-upward hierarchical data processing, as described for recognizers in Section 1. Similarly, the

third category consists of divergent-downward hierarchical data processing, as described for elaborators in Section 1. The second category

consists of processing that includes both the first and third but also

characteristically a very general set of operations such as problem

solving, designing, planning, etc.

Input data processing involves six or more media in which the input data

might be expressed, as shown in Table 3.3-1. In each medium the processing is most often and perhaps most successfully arranged hierarchically, as suggested by the column headings of the table. The analogies

among these operations in different media are recognized in the generic

row at the bottom of the table which encompasses them all.

The process shown in the first row of Table 3.3-1 is called pattern

recognition, machine vision, computer vision, automated perception, etc.

An expert system for object recognition has been the subject of a paper

(Reference 16) . The same process includes inspection of manufactured

parts for defects. A recent review covers today's technology (Refer- ence 17) .

The second row of the table addresses a similar but more complicated

case in which the perceiver is moving relative to the objects in the

field of view. Before the visual field can be analyzed it must first be

rid of the effects of the observer's motion, by determining what that

motion is. Then the process proceeds as in the static image case.

140
Table 3.3-1

Input Data Hierarchical Processing

Information

Medium

Static

Image

Preliminary

Processing

Edge Detection,

Image Processing,

Enhancement

Progressive

Recognition

2%D

Pattern Recognition

2 . Moving

View

3. Numerical

Data

4. Natural

Written

Language

5. Medical

Language

6. Natural

Spoken

Language

7. Any or All

of the Above

Data

Moving Edge Detection, SelfMotion Identification and Correction

Data Processing

and Storage/

Retrieval

Word-Level

Recognition

Symptoms

Identification

Frequency and/or

Analyses of

Phonemes or Words

Intermedium

Translation and

Merge

Pattern Recog

nition

Parameter

Severity

Identification

Message/Passage

Interpretation

Syndrome Determination

Word-Level

Pattern Recognition

Abstract

Pattern Recognition

Top-Level

Recognition

3D

Object Recognition by

Matching

Object Recog

nition

Situation

Recognition

Gist Recognition

Diagnosis of

Disease

Gist Recognition

Concept

Recognition

141
The medium in the third row is numerical data. An example could be a situation the severity of which is being assessed by frequently measuring and communicating some variables of the situation. A system to

monitor these data and interpret them progressively in a hierarchical

manner for severity would be used also to store the data in a data base

and retrieve any needed data. This would be an expert system in the

management and analysis of data about a situation. For example, the

situation could be a fire that has been "put out" in a building, and the

expert system could be left in the building at a key location to watch

for signs of the fire breaking out again.

Written language, the medium of the fourth row, subsumes all processing

of natural language in written form as input data. The desired outcome

can be identification of the gist of a message, or it could be an as- sessment of a situation described in words.

A special case is diagnosis of a disease described in medical language,

as shown in the fifth row of Table 3.3-1. The processing deals progressively with more general sets of symptoms, culminating in the syndrome of the disease finally diagnosed. It is not true that all diagnosis systems work this way, but they could. An analogous problem is

diagnosis of a fault in an electrical or mechanical system, given a set

of facts in written language.

The corresponding problem stated in spoken language is shown in the

sixth row. It too involves analyses and recognition of progressively

larger units of thought. A recent review of these systems has been

given in Reference 18. The present art is compelled to make compromises

such as requiring a limited vocabulary, or limiting how many different

speakers can be dealt with, or forcing the user to separate his words

with pauses, or otherwise falling short of the desired capabilities.

Expert systems are expected to have a major role in upgrading speech

recognition system performance.

Since all of the foregoing media applied similar hierarchies of progressive processing, it is possible, as shown in the seventh row, to

generalize to the processing of any given mixture of the media in a hierarchy of successively larger recognitions.

Nothing has been said specifically about emergency management applications of these recognition systems. Yet applications are possible with

hierarchical processing in any of the media shown in Table 3.3-1.

3.3.2 Problem-Solving Expert Systems

The term "problem solving" is intended here to embrace all human intellectual effort except pure recognition and pure elaboration. This

category includes designing and planning as well as recreational or practical solving of problems of any type. In Figure 3-1, a problem

solver block is shown acting in a closed loop with the outside world,

making use of a recognizer and an elaborator, to solve a problem.

142
EXPERT SYSTEM

SENSORY

INPUTS

OUTSIDE

WORLD ACTIONS

FIG.3-1: CONFIGURATION OF EXPERT SYSTEM

INTERACTING WITH OUTSIDE WORLD

143
A rather artificial classification of problem solving systems is made

here into "knowledge systems" and "action systems." In the special case of knowledge systems, the only outside world of concern is a computer or specialized tool for knowledge processing, and its output is knowledge.

The output of an action system is action directed at the outside world

in the unrestricted sense.

The following examples of expert system functions that can be classified

as knowledge systems are given in approximate increasing order of difficulty:

1. An artificial instructor, such as a computer program and interactive video, could be developed to teach a student a specified body of knowledge. In the simplest case the only

feedback from the student is a map of knowledge and ignorance

as he progresses. (Question answering is deferred to Item 6

of this list.

)

2. An expert system during an emergency can manage on-line analyses, predictive simulations, and probabilistic forecasts of

the future developments in an emergency. These services on behalf of a decision maker can significantly increase the effectiveness of decisions relating to emergencies. Simulations

can be run by an expert system also between emergencies for

such purposes as research, training, and long-range planning.

3. A somewhat similar capability is to reason about a causality

network, such as to determine the probable origin of a fire

from indirect information from remote sensors.

4. Another function that could be undertaken by a knowledge system is machine learning or knowledge acquisition. Today's

expert systems are given their rules in advance.

5. Expert systems are being developed to do mechanical design

(Reference 15 and references therein)

6. Another function of a knowledge system is development of a plan, which is an iterative process that ultimately leads to a plan meeting all requirements. Simulation might function as a step in the design loop.

7. An expert system can give computerized support to an emergency

manager in connection with decision making. These services

include consulting, advice giving, question answering, etc.

It is a short step from there to actual decision making in

lieu of a person, such as in early or remote aspects of emer- gencies when emergency managers are scarce.

Action systems have much more interaction with the outside world than do

the foregoing examples of knowledge systems. Several types can be distinguished :

144
1. Troubleshooting and diagnosis, on engineered systems or human

bodies, alternating with tests whose outcomes divide the alternatives roughly in half (as in "20 Questions"), are such

functions

.

2. An area in which there has been intensive development is

automatic picking of parts from a randomly loaded bin (Refer- ence 19). Both machine vision and part gripping are involved.

3. An even more complicated function is autonomous remote fighting of an emergency under conditions too demanding for a human. Examples of such emergencies would be a radioactive

spill to be picked up and removed or a fire generating very

high temperature.

4. Still more complicated would be backup replacement of an emergency manager by an expert system, including the functions

of decision making (within some limited domain) and crisis

management.

3.3.3 Output Data Processing

The output data processing functions are pure command elaboration in a hierarchy driving a battery of effectors (muscles in the nervous system

paradigm). As with input data processing there are several distinct

modalities (media) in which the commands can be rendered: spoken language, computer code, graphics, written words, etc.

The following list of examples is indicative of the great potential variety:

1. Elaboration of alphanumeric graphic commands to a printer.

2. Progressive elaboration of computer code into more detailed

languages , even for expert systems

.

3. Progressive allocation of resources or deployment of entities

to agencies of smaller scope.

4. Elaboration of repair instructions.

5. Automatic speech generation from symbolic words.

6. Progressive elaboration of commands down to a number of parallel orders or messages, to coordinate emergency fighters.

7. Hierarchical organization of a program elaboration function

for a generic robot (instead of humans writing code directly

for the lowest level of detail).

145
3 . 4 Applications of Expert Systems

Each application of expert systems is a function of expert systems as implemented in a particular field. Thus, expert systems constitute a large matrix. Such a matrix is given in Reference 20. Only a brief

illustrative list is included here:

o Equipment Operation and Maintenance

1. Decision support for well drilling when problems are encoun- tered (Reference 14) .

2. Equipment failure diagnosis (Reference 21), e.g., for locomotives, turbines, programmable controllers, computers, jet en- gines, cars, and telephone cables.

3. System debugging (Reference 22).

o Medical Diagnosis

1. INTERNIST and CADUCEUS (Reference 23).

2. Mycin, Emycin, Puff, etc. (Reference 14).

o Industrial Plant Control

1. Several companies are developing expert advisors for nuclear

power plant operators (References 23 and 24)

2. Expert system for control system design (Reference 25).

3. Expert system for monitoring and interpretation of a multiplicity of alarms (References 25 and 26).

4. Expert system for process control (Reference 27).

o Vehicle Control

1. Navigation by an autonomous vehicle (Reference 28).

2. Expert system for emergency procedures for an aircraft (Ref- erence 29).

3. Pilot's assistant,

o Automated Programming

1. Simulation programming tools (Reference 24).

2. Expert system for program writing (Reference 30).

146
o Military

1. Battlefield command decisionmaking support.

2. Surveillance of ships with automated analytic aids (Refer- ence 31).

o Financial

1. Legal tax advice.

2. Fuzzy rules and logic for stock market advice,

o Emergency Management

1. Structural damage assessment (Reference 32).

2. Risk management analyses.

3. Fire detection and assessment (Reference 33).

The foregoing list is, as mentioned above, only indicative of the variety of expert system applications today. Some additional commercial

expert systems are listed in Reference 34.

3 . 5 Development and Implementation Methodology

Expert system methodology has been evolving from its beginning in the

LISP language and the Von Neumann serial digital computer. The evolution has led to quite a few distinct methodologies.

One of the branch points has been away from Boolean logic into fuzzy

logic. "Reveal" can be used to program an expert system in fuzzy logic

(Reference 35). Reveal is a decision support program.

Another branch point is the method used to obtain the rules that underlie an expert's thinking in his domain of expertise. The traditional

method is to interview an expert interminably until the rules are com- plete. Recently there has appeared an alternative method which uses a program written for a personal computer (Reference 36).

Expert system architectures are in ferment. The classical architecture

consisted of a data base for rules and data, an inference engine, and

perhaps a natural language interface. There are now several strategies

implemented in inference engines (Reference 14)

.

There is a ferment also in the approaches being used for expert system

development. The conventional computational logic approach, which

processes "what ifs" in LISP, on a serial computer, is finding growing

competition from approaches which are naturally suited to the speedier

parallel computers (Reference 37). Kurzweil feels that "...parallel

architectures are the wave of the future" (Reference 37).

The concepts in Section 1, such as recognizers, lend themselves to the

parallel approach. A recognizer may be regarded as a forward inference

147
engine operating bottom-up on a hierarchy of rules to recognize a hierarchy of concepts. If implemented on a parallel computer, a recognizer

would be traversed a row at a time at the computer cycle rate.

Some of the other approaches that are currently being developed or used

are

1. Decision theory, combining some ideas from statistics, operations research and economics.

2. Using simulation as a stepping stone (Reference 38).

3. Factor correlation as a method for determining domain rules

from data for particular cases, avoiding rule programming.

One of the explosive trends is the appearance of a diversity of software

packages that facilitate or obviate the programming of rules for expert

systems. Many of these new software tools have been written for personal computers. The following list is a sampling:

1. GCLISP (Golden Common LISP) is an IBM PC implementation of

LISP. (See critique in Reference 39).

2. Knowledge Engineering Environment (KEE) , from IntelliCorp, is one commercial "shell" which saves programming time (Refer- ence 40).

3. Personal Consultant, from Texas Instruments, based on the

EMYCIN shell, runs on the TI personal computer (Reference 40).

4. M.l, from Teknowledge, runs on the IBM PC (Reference 40).

5. Insight Knowledge System, from Level 5 Research, also runs on

the IBM PC (Reference 40)

6. SMALL-X (Reference 41).

7. Exsys , from Exsys , is for creation of expert systems on an IBM

PC (Reference 42).

8. Expert Choice, from Decision Support Software, Inc., runs on

the IBM PC or PC XT (Reference 43).

9. Expert Ease, from Human Edge Software Corp., also runs on the

IBM PC or XT (References 36 and 44) . It lets the user build

his/her own rules by learning from experience.

10. Expert Systems Development Tool (ESDT) , being developed for

DARPA by IntelliCorp, is based on KEE.

148
11. The Intelligent Machine Model (TIMM) , from General Research

Corp.'s Advanced Technologies Division, runs on an IBM PC. It

asks questions and reconciles the answers in order to obtain

rules (Reference 45).

Most of what has been written about expert systems implicitly assumes

that they will last forever. However, there will always be need for

maintenance, upgrading, and updating, so the life cycle viewpoint is

appropriate

.

3.6 Future Evolution of Expert Systems

Expert systems will evolve in many dimensions such as:

1. New applications, e.g., management of calculations, and EM

applications

.

2. Improvements in performance by orders of magnitude, as for

example in the areas of speech recognition and language translation.

3. Enormous growth in scale of effort, as illustrated by the 1984

to 1989 projected market growth in artificial intelligence

from $147 million to $1.1 billion (Reference 46).

4. Expansion into larger and faster computers, which will accom- modate more elaborate programs in expert systems.

5. Combinations of expert systems into more complex and capable

systems, not only in integrated products but also in the separate but interactive mode of organization, leading to hierarchies of expert systems.

6. More flexible relationships between expert systems and people.

4. SIMULATION AND EXPERT SYSTEMS IN EM

The union of the fields of simulation and artificial intelligence has

been called "Intelligent Simulation" (Reference 47). It is the main

subject of Section 4, although some particular known applications to EM

are mentioned.

4. 1 Simulation in an Expert System

Simulation can be useful when it is a block within an expert system, as shown with two examples in Figure 4-1. To be sure, simulations are not

normally parts of expert systems, but such a relationship is a possibility. One such case has been mentioned in Reference 48, which involves a simulation within an expert system.

Another possibility is an iterative organization of an expert system for

design, in which one block could be a simulation of the performance of

149
EXPERT SYSTEM

SIMULATION

EXAMPLES^

I. EXPERT SYSTEM CAN GET NEEDED DATA

FROM SIMULATION.

2. KNOWING WHAT THE USER WANTS, EXPERT

SYSTEM CAN RUN SIMULATION.

FIG. 4-h SIMULATION IN AN EXPERT SYSTEM

SIMULATION OF EM SYSTEM

EXPERT SYSTEMS

EXAMPLES^

I. EXPERT SYSTEMS CAN STAND IN FOR PEOPLE

IN EMERGENCY MANAGEMENT SIMULATIONS.

2. SIMULATION PROVIDES A REALISTIC

ENVIRONMENT IN WHICH EXPERT SYSTEMS

CAN BE TESTED.

FIG. 4-2: EXPERT SYSTEM IN A SIMULATION

IbO
the system being designed. Also in future one could expect to see human

operator or emergency manager models inside an expert system. In fact

an entire EM hierarchy might be simulated within a multiagency EM expert

system

.

4.2 Expert System in a Simulation

Figure 4-2 shows examples of the opposite case, in which an expert system is one block inside a simulation. The first example is based on the

fact that any expert system is, in a way and to a degree, a discrete

simulation of a thinking person or team of persons or agency in EM, for

instance. In Figure 4-3 there is shown an expert system used as a high

level part of a simulation of a person. In the second example of Figure 4-2 a simulation of a domain provides an interactive environment for

testing and development of an expert system.

An expert system could appear also inside simulation software for decisionmaking or planning for a model or run campaign. The simulation

could be related to EM.

Expert systems are entering simulation. As put by Oren, "The question

is not whether or not to have artificial intelligence in simulation, but

rather how to have it? at which level? how reliably? how soon? and

above all how intelligently?" (Reference 49).

4.3 Other Relationships of Simulation and Expert Systems

Rather than within or encompassing a simulation, an expert system can be over a simulation in a managerial sense. In an emergency, or in case there are to be thousands of runs (another type of emergency) , it is

valuable to have some automation.

Simulation has been found to be a good tool for use in evaluating and

researching EM decision support systems (Reference 55). Similarly, as shown in Figure 4-4, simulation has been demonstrated to be a useful

stepping stone in the development of an expert system for plant control

(Reference 36)

.

Expert systems have been proposed or applied for the design of a computer simulation (References 51 and 50). Also, artificial intelligence

has been examined for applicability to development of a complete simulation environment (Reference 52).

Simulation of computer cognition has been discussed (Reference 53)

.

The ROSS language has been developed for defining simulations, such as of large combat situations (Reference 54).

4 . 4 Future Developments

Simulation in relation to expert systems or artificial intelligence is

the subject of several conferences in 1985, so it is a very active area

151
EXPERT SYSTEM

FOR DECISIONMAKING

SIMULATION OF MANAGER

OR HUMAN OPERATOR

ROLE OF EXPERT SYSTEM IN UPPER ECHELONS

OF A HUMAN SIMULATION.

FIG. 4-3= EXPERT SYSTEM IN A

HUMAN SIMULATION

00 SIMULATION

OF PROCESS

AND CONTROLS

DO EXPERT

SYSTEM

DESIGN

ELMAGHRABY'S APPROACH TO EXPERT SYSTEM

DESIGN VIA SIMULATION FOR PROCESS CONTROL

(PROC. SCS CONFERENCE ON Al AND

SIMULATION, NORFOLK, MARCH 1985).

FIG. 4-4= SIMULATION AS A STEPPING STONE

TO AN EXPERT SYSTEM

152
of research. One of those conferences that is not mentioned in the

References section of this paper is the conference on Artificial Intelligence in Simulation, University of Ghent, Belgium, February 25-27,

1985.

Reference 51 is indicative of the far-reaching thinking that has already

been done toward further harnessing AI in the service of simulation.

One sees less about the benefits that simulation will offer to artificial intelligence, but it should not be underestimated.

153
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1. Waldrop, M. Mitchell, "Artificial Intelligence in Parallel," Science, Volume 225, 10 August 1984, pp 608-610.

2. Feldman, Jerome A., "Connections," Byte, April 1985, pp 277-284.

3. Congressional Research Service, Library of Congress, "Information

Technology for Emergency Management," U.S. Government Printing Office, Washington, D.C., October 9, 1984.

4. Clymer, A. Ben, "Simulation in Emergency Management in the Year

2000," Proc. International Conference and Exhibition for Emergency,

Disaster Preparedness and Relief (Emergency 85), Washington, D.C.,

May 21-24, 1985.

5. Dickerson, M.H. , et al., "ARAC Status Report: 1985," Lawrence

Livermore National Laboratory, University of California, Livermore,

California, Report UCRL-53641, May 1985.

6. Sullivan, Thomas J., "Modeling, Simulation and Emergency Response,"

Proc. 1985 SCS Conference on Computer Simulation in Emergency

Planning, January 1985, San Diego.

7. Basta, Nicholas, "U.S. CPI Start to Feel Effects of Bhopal Tragedy," Chemical Engineering, March 18, 1985, pp 27-33.

8. Andrews, Patricia L. , "A System for Predicting the Behavior of Forest and Range Fires," Proc. 1983 SCS Conference on Computer Simulation in Emergency Planning, San Diego, January 1983, pp 75-78.

9. Clymer, A. Ben, and Perley, Daniel, "Simulators for the Coordination of Nuclear Power Plant On-Site and Off-Site Radioactivity

Emergencies," Proc. SCS Conference on Computer Simulation in Emergency Planning, San Diego, California, January 1983.

10. Laughery, K. Ronald, "Modeling Human Operators on a Microcomputer:

A Micro Version of SAINT," Simulation, January 1985, pp 10-16.

11. Zadeh, Lotfi A., "Making Computers Think Like People," IEEE Spectrum, August 1984, pp 26-32.

12. Zadeh, Lotfi A., "A Fuzzy-Set-Theoretic Approach to the Compositionality of Meaning: Propositions, Dispositions and Canonical

Forms," Journal of Semantics, December 1983, Volume II, No. 3/4,

pp 253-272.

13. Sorensen, John, "Public Perspectives on Nuclear Power," Emergency

85, Washington, D.C., 21-24 May 1985.

14. Hayes-Roth, Frederick, "The Knowledge Based Expert System: A Tutorial," Computer, September 1984, pp 11-28.

154
REFERENCES (Continued)

15. Dixon, J.R. , and Simmons, M.K. , "Computers that Design: Expert

Systems for Mechanical Engineers," Computers in Mechanical Engineering, November 1983, pp 10-18.

16. Kim, J.H., Payton, D.W. , Olin, K.E., and Tseng, D.Y. , Hughes Aircraft Company, "An Expert System for Object Recognition in Natural

Scenes," First Conference on Artificial Intelligence Applications,

Denver, Colorado, December 5-7, 1984, sponsored by IEEE Computer

Society and American Association for AI

.

17. Aldersey-Williams , Hugh, "Machine Vision Finds New Applications,"

Digital Design, June 1985, pp 40-46.

18. Meng, Brita, "Speech Recognition: Not a Typical Engineering Program," Digital Design, June 1985, pp 49-57.

19. Edson, Daniel, "Bin-Picking Robots Punch In," High Technology, June

1984, pp 57-60.

20. Frenkel, Karen A., "Toward Automating the Software-Development Cycle," Communications of the ACM, Volume 28, Number 6, June 1985,

pp 578-589.

21. Herrod, Richard A., and Papas, Barbara, "Artificial Intelligence

Moves into Industrial and Process Control," I&CS, March 1985,

pp 45-49.

22. Gupta, N.K. , and Seviora, R.E., "Expert System Approach to Real

Time System Debugging," First Conference on Artificial Intelligence

Applications, Denver, Colorado, December 5-7, 1984.

23. Anon., "Artificial Intelligence," Science, Volume 223, February 24,

1984, pp 802-804.

24. Murphy, Thomas E., "Setting Up an Expert System," I&CS, March 1985,

pp 54-60.

25. Foxboro, "Expert System Solves Control Problems," I&CS, March 1985,

p 53.

26. Andow, Peter, "Alarm Systems and Alarm Analysis," Plant/Operations

Progress, Volume 4, Number 2, April 1985, pp 116-119.

27. Moore, R.L, et al., "A Real-Time Expert System for Process Control," First Conference on Artificial Intelligence Applications,

Denver, Colorado, December 5-7, 1984.

28. Parodi, A.M., "A Route Planning System for an Autonomous Vehicle,"

First Conference on Artificial Intelligence Applications, Denver,

Colorado, December 5-7, 1984.

155
REFERENCES (Continued)

29. Lineberry, M. , et al. , "Intelligent Automation of Emergency Procedures in Advanced Fighter Aircraft," First Conference on Artificial

Intelligence Applications, Denver, Colorado, December 5-7, 1984.

30. Haradvala, Sam, et al., "Expert Systems for High Quality Code Generation," First Conference on Artificial Intelligence Applications,

Denver, Colorado, December 5-7, 1984.

31. Groundwater, Elizabeth H. , "A Demonstration of an Ocean Surveillance Information Fusion Expert System," Proc. Artificial Intelligence and Simulation Conference, Norfolk, Virginia, March 1985.

32. Ogawa, H. , et al., "An Expert System for Damage Assessment of Existing Structure," First Conference on Artificial Intelligence Applications, Denver, Colorado, December 5-7, 1984.

33. Carbonell, Jaime, "Sensor-Based Diagnosis in the SMOKEY System,"

these Proceedings.

34. Anon., "I&CS Guide to Artificial Intelligence Products," I&CS,

March 1985, pp 50-52.

35. Okuma, Amy, "Software and 'Fuzzy' Logic Let Any Good Programmer Design an Expert System," Electronic Design, April 4, 1985, pp 173-

184.

36. Crabb, Don, "Expert Ease 1.11 Reconsidered," InfoWorld, April 1,

1985, pp 46-50.

37. Kurzweil, Raymond, "What Is Artificial Intelligence Anyway?,"

American Scientist, Volume 73, May-June 1985, pp 258-264.

38. Elmaghraby, A.S., Jagannathan, V., and Ralston, P., "An Expert

System for Chemical Process Control," Proc. Al and Simulation Conference, Society for Computer Simulation, Norfolk, Virginia, March

1985.

39. Amsterdam, Jonathan, "GCLISP," Popular Computing, May 1985, pp 116-

118.

40. Kinnucan, Paul, "Software Tools Speed Expert System Development,"

High Technology, March 1985, pp 16-20.

41. Kaplan, Randy M. , "SMALL-X: An Environment for Constructing Expert

Systems on a Microcomputer," ACM Sigsmall Symposium on Small Systems, May 1985.

42. Heite, Ned, "Exsys," InfoWorld, January 28, 1985, pp 43-44.

43. Beechhold, Henry F. , "Expert Choice," InfoWorld, January 28, 1985,

pp 45-50.

156
REFERENCES (Continued)

44. Milman, Jeffrey 0. , "Do-It-Yourself Expert Systems with Artificial

Intelligence on a Microcomputer," Proc. IEEE Compcon '84 Fall Conference on the Small Computer (R)Evolution, Arlington, Virginia,

September 16-20, 1984, pp 20-26.

45. Elphick, Art, "TIMM—Expert System Builder," Simulation, June 1985,

p 306.

46. Anon., "Artificial Intelligence: Current and Future Commercialization," Business Communications Co., Inc., Stamford, Connecticut,

December 1984.

47. Anon., "Call for Papers," 1984 Conference on Intelligent Systems

and Machines, Oakland University, Rochester, Michigan, April 24-25,

1984.

48. Kohler, J.L. , "The VENTILATION EXPERT for Underground Coal Mines,"

these Proceedings.

49. Oren, Tuncer I., "Intelligence in Simulation," Simuletter, ACM

Sigsim, January 1985, Volume 16, Number 1, p 3.

50. Vansteenkiste, Ghislain, summary of paper given at conference on Frontiers in Simulation Hardware and Software, Scottsdale, Arizona,

April 11-12, 1985.

51. Shannon, Robert E., Mayer, Richard, and Adelsberger, Heimo H. , "Expert Systems and Simulation," Simulation, June 1985, pp 275-284.

52. Reilly, Kevin D. , Jones, Warren T. , and Dey, Pradip, "The Simulation Environment Concept Artificial Intelligence Perspectives,"

Proc. AI and Simulation Conf . , Norfolk, Virginia, Holmes, Willard

M. , Ed. , March 1985.

53. Clancy, William J., and Shortliffe, Edward H. , Eds., "Readings in

Medical Artificial Intelligence: The First Decade," Addison

Wesley, 1984.

54. Klahr, Phil, report on paper presented to Fourth UKSC Conference on Computer Simulation, Bath, England, Simulation , February 1985,

p 97.

55. Bellardo, Salvatore, Karwan, Kirk R. , and Wallace, William, "An

Investigation of System Design Considerations for Emergency Management Decision Support," IEEE Trans, on Systems, Man, and Cybernetics, Volume SMC-14, Number 6, November/December 1984, pp 795-

804.

157
Expert System for Fire Emergency Management

Geoffrey N. Berlin

Modeling Systems, Incorporated

1718 Peachtree Street

Atlanta, Georgia 30309

Emergency management is a highly inter-disciplinary field concerned

with the arrangements and functional capabilities available to mitigate the

injury and property damage due to natural and man-made disasters. The

purpose of this paper is to discuss the application of an "expert systems"

formulation to fire emergency management and to illustrate how local, state

and federal officials can utilize such a capability to react to a variety

of fire emergencies.

The proper management of emergency resources to combat fire can prevent thousands of injuries and millions of dollars in property damage.

A typical fire grows at an ever-increasing rate in intensity and area. Since time is critical and the available resources are limited, the rapid

deployment of firefighting resources is essential.

Fire certainly poses a major challenge to local, state, regional and

federal emergency agencies. Exhibit 1 illustrates the number of major

conflagrations that have occurred within the past eighty years. Fires have

been major problems following military actions, earthquakes, civil

disturbances and chemical accidents.

While we have established that fire definitely presents a significant

social and economic threat, the question to be addressed here is whether an expert system can aid fire emergency managers in more effectively utilizing

available resources to mitigate the consequences of such fires and to

diminish the likelihood of a conflagration all together. The challenge is

to help fire managers get ahead of the fire, NOT chase it, by anticipating

the condition of the fire over time and analyzing the effectiveness of

alternative firefighting tactics.

To build an expert system, it must be possible to adequately describe

fire behavior and the effectiveness of fire suppression efforts. While

there are a number of influencing factors that effect fire behavior, fire

is a reasonably predictable phenomenon. As is documented in research

reports on the theory of fire and in experimental tests, there is a significant knowledge base and modeling experience to suggest that under

certain known conditions, fire spread and growth is predictable to the

degree of precision required by emergency managers although there remain

some yet unexplained variation [2],

For instance, there have been apparent "discrepancies" in the damage

due to fire following earthquakes. While some significant earthquakes such

as the 1906 San Francisco quake generated great number of fires, other

major tremors such as the 1971 San Fernando quake resulted in minimal

damage. At this time, it is unclear whether these d iscrepenc ies are just

statistical variation, differences in firefighting tactics or underlying

differences in the built environment in the two cities.

158
Exhibit 1

Selected US Configurations (Source - NFPA and others) »[9]

Date City Buildings Destroyed Loss (millions 1984 $)*

Feb 7 1904 Baltimore 80 city blocks 500

Oct 23 1978 Malibu 230 60

Jul 26 1978 Santa Barbara 239 55

Oct 23 19^7 Maine 1200 120

Jul 31 1979 Houston 26 25

Mar 21 1916 Paris TX 1440 80

Sep 17 1923 Berkeley CA 640 24

Oct 20 1941 Cleveland 89 30

Oct 23 1935 Los Angeles 222 20

May 15 1933 Auburn NY 250 12

* very approximate, intended for comparitive purposes only

159
Although fire managers are frequently called upon to perform in

emergency situations, their response efforts are largely confined to a single working fire. While an expert system can be of benefit in such

situations, especially when the fire involves a complex building design or requires occupant evacuation, the value of an expert system increases as the number and magnitude of working fires increases such as may occur following an earthquake or in the case of forest and grasslands fires [83.

Here, the fire manager must make decisions regarding a relatively large

geographic area and resource allocation with limited, and possibly,

inaccurate information. The following description of the fire-related

aspects of the 1983 Coalinga quake are included here to illustrate the

emergency environment.

The following material is from a chapter in the Earthquake Engineering

Research Institute report on the Coalinga Earthquake entitled "Fire-Related

Aspects of the Coalinga Earthquake" [10].

"This chapter discusses the fires and related aspects of the Coalinga

Earthquake of Kay 2,1983. First reports from Coalinga indicated that

fires caused by the earthquake were of conflagration proportions.

Television news bulletins in San Francisco the evening of May 2

reported fires with "flames 40 and 50 feet in the air," a sight

resembling "Tokyo in the great fire bombing." The San Francisco

Chronicle of May 3 spoke of "...nearby oil pipelines ablaze,...

buildings burst into flame." These reports exaggerated greatly the

fire-related events of May 2, 1983. Nevertheless, events of that day

contain valuable lessons with respect to the post-earthquake fire

problem, which may be of importance in a similar or larger earthquake

in a large metropolitan region. This chapter presents a narrative of

the Coalinga earthquake, followed by a discussion of important

observations and issues.

Fire-Related Events of May 2, 1983

The Coalinga Fire Department (CFD) consists of 30 personnel

(11 paid, 19 volunteers). Equipment consists of two 1250 gpm and one

1000 gpm pumpers, one hose wagon and two ambulances, (the only

ambulances in Coalinga, next nearest ambulances being Avenal which

is 17 miles distant in the next county). Also located in Coalinga is

a California Division of Forestry (CDF) forest battalion, equipped

for brush fires. Located about 5 miles away is the Westside Fir(

Stftion. Following the earthquake, there were about 15 grass fires

in the surrounding countryside, caused by downed or arcing power

lines. These grass fires arc- not reported' rs hsvirg rjtscd

extensive damage. There were no reported oil-related fires.

At the time the earthquake occurred (4:42 pm, PDT), there were four

personnel in the fire station (Capt., Lt., firefighter and ambulance

attendant). When the shaking began, they exited the building and saw collapses of nearby buildings. After the shaking had stopped,

they moved the CFD equipment out of the fire station and issued a radio All Call ( ie , all CFD personnel report to the fire station).

The Captain (John Donelan) proceeded in his radio-equipped car to

perform a reconnaissance of the Central Business District (CED), also

known as the Coalinga Plaza and located two blocks away. Large

160
amounts of dust had been raised, which obscured the CBD from the

view of the fire station, so that the extent of the damage was unrealized. Arriving at the Plaza, Donelan radioed to the station,

declaring a disaster and requesting notification of OES and mutual

aid fire companies. At this time he was located at 5th and Elm,

surrounded by brick buildings, when an after-shock struck (3 min.,

46 sec, after the main shock, times taken from the CFD radio log).

He then relocated to 5th and Durian, a safer location, and

continued setting up a command structure and notification of outside

authorities (relayed by radio). By this time, many brick buildings

had collapsed, with consequent brick debris as much as 20' into the

street on either side, measured from the building face. However,

Coalinga building-to-building street width is 80' (wider than average)

so that streets were still passable in the center.

About 8 minutes after the main shock, as dust was clearing, smoke was observed from the CBD and CFD responded with two engines, with

however only one man on each. Hydrants and hose layouts were

assigned, downed power lines identified and fire fighting commenced.

The fire was in 260 Coalinga Plaza, the Coalinga Inn, a two story

brick building which had partially collapsed and on arrival resembled

a "bonfire" rather than a building fire. Due to the shortage of CFD

personnel, citizen volunteers manned hose lines with a professional

firefighter at the nozzle. Immediately after the main shock, Calif.

Div. Forestry (CDF) personnel and trucks (which are equipped for bush

fires) were dispatched for triage within Coalinga. CDF personnel

decided to respond to Coalinga rather than to the above-mentioned

grass fires based on likelihood of injuries in Coalinga and relatively

low fire hazard in the grass in early May. Their officers assisted in

mutual aid coordination. About 20 minutes after the main shock

Westside fire units arrived and assisted CFD (Westside Fire District

has three fire stations within twenty miles of Coalinga). The fire,

although not especially large, proved extrememly difficult to

extinguish. At the time of this writing, CFD judgement is that the

fire was fueled by high-proof liquor from broken bottles in a storeroom on the first floor, which also contained a water heater.

This is based on interviews of the owner and review of the debris.

The persistence of the fire is attributed to its being a flammable

liquid fire, to hose streams being deflected by the debris, and to

the "open-ness" of the partially collapsed structure. The Coalinga

Inn is a relatively small building (20 feet by 90 feet) and efforts

were also hampered by aftershocks and collapse of buildings. There

was a standing order not to enter buildings for fire fighting purposes, due to the danger of collapse. After 4 hours, the fire

had spread to the contiguous building (Petty' s Jewelry Store),

threatened the next building (Pressy's, housing most of Coalinga'

s

insurance records), and had twice ignited the Webb building (both

times extinguished). The Webb building is a two-story brick

building separated from the Coalinga Inn by an alley (20 foot width).

It had damaged but still intact walls during these events (the second

floor walls were down the next day). Due to the stubbornness of the

fire, it was finally decided, 5-1/2 hours after the main shock, to

demolish Petty' s Jewelry Store. That is, removal of the debris

would constitute a fire break. This was initially attempted using

a crane, which proved too slow, and finally accomplished using a large

161
front end loader and dump trucks.

During these 5-1/2 hours there also occurred:

- Chemical spills at the West Hills Community College and the

Coalinga High School and a report of chemical spill at a dry

cleaning establishment.

I - Fire alarms (but no fire) at the Coalinga Convalescent Center

and the Coalinga Hospital.

- Several attempts, finally successful, to verify that the town's

gas had been shut off (Coalinga has a municipal gas system).

Verification of this took 3-1/2 hours. Interviews with

Coalinga residents indicate that many people on their own initiative turned their own and neighbors' gas off.

- There was an electrical outage caused by the main shock. By

and large, residental electricity was not turned off house-byhouse at personal initiative however, in contrast to the

experience with gas.

- CFD and other vehicles sustained flat tires.

- Shortages of fuel for CFD vehicles, and shortages of drinking

water at the fireground.

- A second fire, at 205 South Joaquin St. This was a residential

kitchen fire, reported to CFD by telephone at about 8 pm. The cause is attributed to the following: shelf items were thrown

by the earthquake around the kitchen, striking the electric

stove controls and also landing on the heating elements. Electric power was later restored without notifying CFD. Damage

was largely confined in the kitchen.

A third fire (510 South Joaquin St.) also occurred at about 8 pm.

The cause was very similar to the fire at 205 South Joaquin but

people were present and quickly extinguished the fire, with minimal

damage. A fourth fire at 1080 Joaquin St., also a residential

kitchen fire, occurred at about midnight. The cause was an electrical

appliance (a can opener) being turned on by the flying debris,

overheating and setting debris on fire. Damage included the entire

kitchen and heat and smoke damage throughout the house. This house

belonged to a police officer who was responding to the earthquake

emergency at the time. It is significant that all these fires on Joaquin and South Joaquin Sts. had similar causes. Another potential

ignition source was, six hours after the earthquake, down and arcing

wires reported behind the Eank of America in the Coalinga Plaza."

This description provides an illustrative example of the need to

integrate and interpret data from many sources and to make decisions based

on erroneous information. The potential benefit of an expert system is

also obvious.

162
II. Expert Systems Design

How can a computer system can help? In general, an expert system is

not only to retrieve and integrate information but also to analyze and

interpret it. An expert systems approach involves the definition of the

possible decisions, the identification of influencing factors, the creation

of a knowledge base and the application of one or more mechanisms for

drawing inferences. For instance, decisions can involve evacuation, fire

suppression tactics and fire containment. Fire loss is influenced by

ignition frequency, conflagration potential and fire suppression resources.

Finally, the creation of a knowledge base may combine results from

laboratory test data, fire experience and fire models.

The following list presents the information that would assist fire

managers at both the local and regional levels. At the regional level, an expert system could provide the following information:

1. Identify urban areas in the United States that are prone to

conflagration under the prevailing weather conditions.

2. Evaluate the effectiveness of hardening buildings and the water

supply in conflagration prone areas.

3. Evaluate tactics that break (interrupt) the growth and spread of

fire when Forest Service personnel and/or the National Guard are available to aid local fire companies.

4. Review alternative tactical responses to firefighting under

various resource allocation schemes.

5. Determine alternative mechanisms for either reducing average fire

loss or minimizing the likelihood of a conflagration, such as the

installation of sprinkler systems.

At the local level, an expert system could provide the following

information

1. Integrate data provided by a large number of widely dispersed

sources.

2. Display confirmed ignitions and their magnitude and project the

fire growth profile.

3. Display likely ignitions sources such as locations of known open

flames and chemical mixing.

4. List the closest available fire fighting equipment and suppression

agents for each confirmed ignition.

5. Display inaccessible areas.

6. Forecast (predict) possible fire spread scenarios.

163
7. Identify locations of likely ignitions that will be isolated by

major roadway failure.

This information is essential for supporting the decision process at the

fire scene. With respect to a specific emergency, there are the following

questions regarding firefighting tactics:

1. What resources should be dispatched to each fire over time?

2. What is the proper fire fighting task—contain the fire spread,

extinguish all burning, or perform rescue activities?

When considering these items, most involve either the integration of

information from several different sources and/or the interpretation of

information. As a result, it seems that an expert system, with components

that may vary in the degree to which "artificial intelligence" is used,

would be of benefit to the fire manager.

The critical requirements for developing an expert system concern the

existence of a knowledge base and an inference mechanism. The knowledge

base can be constructed from several sources including fire tests,

historical data and fire models. Fire protection as an area of scientific

study and engineering practice provides many of the theoretical

relationships. There are major laboratories such as the National Eureau of

Standards, Factory Mutual Research Corporation, Sandia Laboratory and

Lawrence Livermore that have developed considerable understanding of the

physics and chemistry of fire [1,7 and 11], This paper will present

several models that use a concept of fire development realms to describe

the phases of fire behavior. The fire data along with the modeling

abstraction of realms provide the elements for developing a knowledge base.

Drawing a conclusion about the effectiveness of alternative courses of

action is very complex and requires an assessment of both fire behavior as well as suppression activities. A reasonable model of fire emergency

management might follow the same format as the "RUN CARD" as illustrated in

Exhibit 2 that typically prescribes the response for five alarm levels.

Each alarm level reflects the known hazards along with the risks associated

with events not yet realized because of inaccurate or incomplete

information. The following factors are among the most influential:

1. Expected fire size,

2. Number of persons endangered,

3. Potential property loss,

*J. Environmental conditions, and

5. Conflagration potential.

For instance, fire departments may make major commitments of manpower to a grassland fire because of the high possibility of unbridled spread even though the fire might be thought to be very small. In a specific

situation, the tactical decisions are complicated by the following

164
Exhibit 2

RESPONSE CARD

Cooper River Drive 1121 to 1184

| Engineers Trucks

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! Chief | Special i Move-Up !

| 6 4 ! T4

! 3-2-4-1 |

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! 1C 103 ! T10

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165
uncertainties:

1. Number, size and locations of fires known at this time,

2. Accessibility to the identified fire locations,

3. Adequacy of fire suppression agents (water, chemicals, etc.), and

4. Sufficient fire personnel for the assigned tasks.

The correct assessment of these uncertainties is critical to the choice of

firefighting tactics, which in turn requires the rapid integration and

interpretation of data for disparate sources.

Now the question concerns the most appropriate design of an inference

mechanism. It appears that fire, like many other real problems, requires a hybrid of the major approaches to expert systems design. There are a number of questions that fit the "IF-THEN" model such as "If there are persons trapped, then initiate evacuation" or "If the chemical is chlorine,

then do not use water." However, the identification of the appropriate

evacuation routes must be determined by a mathematical model that considers

the status of the street network at present and in light of the potential

fire spread. Also, decisions based on the rate and size of the fire in the

next ten minutes are best approached using a simulation model.

III. Fire Modeling Elements of an Expert System

There are computer tools presently available to assist fire managers.

For illustrative purposes, three different models are briefly discussed.

The first is FIREMOD which simulates grassland fire spread. The second is

EMBER which models fire spread and occupant movement within a building.

Finally, FILO is an urban fire spread model. In many ways the models are similar in the mechanisms used to describe fire growth and spread. None of

these models is designed to tell a fire manager how to fight a fire.

FIREMOD - Wildlands Fire Spread

FIREMOD is a computer program designed by the U.S. Forest Service to

project wildland fire spread potential and to assess the probable success

of initial attack forces. The projection of forward and lateral fire

spread can be obtained for one-hour increments without recognizing any

suppression activity. FIREMOD is not designed to tell a fire manager how

to fight a fire but the direction and speed of spread. FIREMOD uses the

following information:

1. Fuel type (grass, chamise or brush)

2. Slope

3. Age of fuel

166
4. Fine fuel moisture

5. Wind speed

Based on these input data, the elliptical spread model displays the

following information:

1. Expected forward rate of spread.

2. Expected ellipse dimensions.

3. Flame length.

4. Heat energy output of the fireline.

The effectiveness of alternative firefighting tactics are displayed in

Exhibit 3.

Because of the uncertainty in the number and location of spot fires, some departments faced with grassland fires will commit a major part of the

firefighting resources to a suspected fire. In this way, the precise

deployment of units may not be critical since there will be sufficient

manpower to survey the entire area. Furthermore, there is likely to be

sufficient firefighting power to extinguish quickly the incipient fires.

Time is of utmost importance for these types of fires since there are few

natural barriers.

167
Exhibit 3: FIRE SUPPRESSION INTERPRETATIONS

I. Fire Line Intensity:

Flame Lengths:

100 BTU/SEC/FT

H Feet

Firefighting Tactics:

Fire can generally be attacked at the head using hand tools.

II. Fire Line Intensity: 100-500 BTU/SEC/FT

Flame Lengths: 4-8 Feet

Firefighting Tactics:

Fires are too intense for direct attack on the head

by persons using hand tools.

Hand lines cannot be relied on to hold fire.

Engines, dozers and retardant aircraft can be effective.

III. Fire Line Intensity: 500-1000 BTU/SEC/FT

Flame Lengths: 8-11 Feet

Firefighting Tactics:

Fire may present serious control problems, i.e. crowning

and spotting.

Control efforts at the fire head will probably be ineffective.

IV. Fire Line Intensity: 1000 BTU/SEC/FT

Flame Lengths: 11 Feet

Firefighting Tactics:

Crowning, spotting

Control efforts at

and major fire runs are probable,

the head of the fire are ineffective.

168
EMBER - Building Firesafety Model

EMBER was designed to analyze the hazards of fire in a structure.

Here the fire manager is often faced with the dilemma either to extinguish

the fire thereby both protecting the building occupants and saving the

structure itself or to assist occupants in reaching a location of safety

and then to extinguish or confine fire spread. Unlike the wildland fire

situation, the building structure provides barriers to fire spread that

enables the fire manager to consider alternatives that require less

resources.

A preliminary version of this computer model was initially developed

with support from the Department of Housing and Urban Development. This

model can be used to determine the available escape routes, the expected

time for evacuation and the anticipated extent of fire spread and toxic

levels of the combustion gases.

EMBER is a computer model that simulates the movement of smoke and

occupants as though an experiment were being conducted [3 3. Fire

development within a building is described in terms of "realms." A fire

realm describes a phase in the development of a fire with regard to its

heat release and flame size as illustrated in Exhibit 4. Exhibit 5

presents data depicting the average time in several realms obtained from

full scale fire tests for two dwelling types—mobile home and single family

design. Because these data were drawn from fire tests for which is was desirable to save the structure, all fires were extinguished at the last

moment. As a result, data were not available for the higher realms. Based

on a description of the building, fire profile and occupant locations, the

model provides a continuous record of temperature and carbon monoxide

levels in each building space as well as the occupant locations and their

exposure to carbon monoxide.

The purpose of this model is to describe the potential for normal and

handicapped persons to escape or be rescued in the event of a fire or similar emergency. The modeling approach involves the construction of an egress and airflow network for a building and computer simulation. The

"egress network" is a graphic representation which describes the

connections among adjacent spaces. The "airflow" network adds channels for

airflow to the egress network. Based on this combined network, a computer

analysis is used to evaluate the potential for occupants to escape.

The evacuation analysis of two prototypical transit stations is used

to illustrate the application of EMBER. The focus is on the identification

of those elements of station design that are essential for rapid and

reliable evacuation. The analysis involves the following four steps:

Step 1: Obtain Station Designs

Step 2: Construct Egress and Airflow Networks

Step 3: Distribute Occupant Load

Step 4: Run EMBER

Exhibit 6 illustrates the longitudinal section for two prototypical

transit stations that are approximately 40 feet below the surface with

platforms 450 feet long and 30 feet wide. The first station is a double

169
Exhibit 4: REALM III

Critical Events

Indicators Value

Flame Height .3 m

Flux to Fuel

from flame 20 kW/m 2

from ceiling 1 kW/m 2

Power Level 5 kW

Remote Flux 0

Gas Temperature 15° k rise

Exhibit 5: REALM Profile

Duration (seconds)

i

i

! II III IV

Mobile Home | 200 251 105

i

i Single Family ! 262 1 041 ?

170

end-loaded design such that evacuation could be along two stair towers at

opposing ends of the platform and on the two combination stair and

escalator paths from the platform through the mezzanine to the surface.

The second is a single end-loaded station. In this design evacuation is

through the single stair tower at the end of the platform or the tandem

escalator and stair arrangement at the other end.

Exhibit 7 and 8 illustrate a portion of the network for the station

platform. Here, the use of nodes to represent a train car as well as the

adjacent platform area is illustrated.

To evaluate the dynamic aspects of occupant evacuation, EMBER is run with three different occupant scenarios: (1) a light occupant load of 640

persons distributed on two trains and the platform; (2) a heavy load of

3142 persons distributed among a single train, the platform and mezzanine;

and (3) a crush load of 4188 persons divided among two trains, the platform

and mezzanine. To illustrate the potential impact of disruptions to the

circulation system, the stair tower and two interior paths are blocked.

Table 1 illustrates selected results from these EMBER simulation

experiments

The following observations concern the escape potential for these two

station designs based on the simulation results:

1. The major bottleneck in both designs is the stair tower. V/hile it

is required to comply with the fire code provisions regarding

remoteness of exits, the stair is actually detrimental at the

present dimensions. To provide both quality and reliability, the

stair and door widths should be increased.

2. Stair tower (s) alone are insufficient to provide for the rapid

evacuation of the platform should the mezzanine be blocked which

diminishes the value of these stairs as a reliable, separate means

for evacuation.

3. All escape routes are well used making it very difficult for

transit personnel and firefighters to travel in the opposite

direction to reach the fire or control equipment located inside

the station.

4. Stair towers will not provide a smoke-free environment in the

event of a fire since the platform doors remain open during most

of the evacuation.

5. The percentage of persons that are closer to a stair tower under

crush load conditions for the double loaded station plan is 37T.

This proximity to such a large proportion of the occupants

explains, in part, why the stairs play such a vital role in the

rapid evacuation process.

6. There is a potential for misdirected or dead-end travel by having

doors opening into the ancillary rooms at the end of the platform

located near the platform entrances to the emergency stair towers.

172
173
174
Table 1: Simulated Evacuation Times

Evacuation Times (seconds)

Light Load Crush Load

425

253

1414

1341

346

298

313 1240

226 1086

Station Design

Single End Loaded

Double End Loaded

Single End Loaded

with Blocked Stairway

Double End Loaded

with Blocked Stairway

Single End Loaded

with Wider Stairway

Double End Loaded

with Wider Stairway

175
Table 1 also presents the impact of various disruptions to the

circulation system such as when the stair tower is blocked and

alternatively, increased in width. When the single stair tower is blocked,

evacuation time is increased approximately 19? for the double end-loaded

station but decreased for the single end-loaded station. The decrease in

evacuation time that resulted from the blockage of an exit illustrates the

complexity of the evacuation dynamics. When the stair tower is blocked,

occupants do not commit themselves to using the nearby stair which rapidly

becomes congested. Instead, occupants follow a path through the mezzanine

despite the fact that it seems at first to be a longer route, but because

of the larger capacity, actually results in a more rapid evacuation.

This type of model can be used to analyze different strategies for

gaining access to the fire without seriously disrupting the occupant

evacuation and for identifying critical locations that should be monitored

so that additional congestion does not occur.

FILO - Urban Fire Loss Simulation

This is a discrete event simulation based on a description of the

built environment and climatic conditions. This computer model was initially developed with support from the National Science Foundation.

Initial fires are located according to a probability distribution that is

determined by the magnitude of the causal condition such as the intensity

of an earthquake. The FILO simulation then describes fire development

within a building and its spread from a building to its neighbors.

For each building, the following data elements are required:

1. The Building Number

2. Building Type and Location

3. Realm Profile for each Euilding Type

M. Location of Active Fire Companies

Exhibit 9 illustrates the type of information that can be assembled for a typical building type.

In addition, two global quantities will be specified, namely the wind

velocity and humidity. These quantities have a dramatic influence on impingement possibilities and radiation effects.

Given the above data, the program will:

1. Calculate the distances between adjacent buildings.

2. Compute the expected times to enter the next fire realm.

3. Compute the time of spread to an adjacent building.
Exhibit 9 : TYPICAL SAN PIEGO SINQUE FAMILY RESIDENCE

-M-H

1 1 1 J if rLTLTLr

e 7rr

ROOMS : FIRE SOURCES : 6 Bath 1. Gas-fired water heater (unanchored)

K Kitchen 2. Air conditioning unit

L Living Rm 3. Shelf storage of possibly

D Dining Rm flammable liquids

BR Bedroom 4. Automobiles

E Entry Hall 5. Electric space heaters

G Garage 6. Wood burning fireplace

U Utility Rm 7. Electric clothes dryer

8. Gas range

9. Outdoor gas grill

CONSTRUCTION : Windows: Double glazing in andoized

aluminum sash and frame

Exterior Walls:

Wood studs with 5/8" gyp. fin on interior .Exterior finished

with 3/1*" stucco over plywood

sheathing

Insulation: ^"fiberglass batt in

walls 8" in ceiling

Floor : 6" concr. slab 8" slab in garage

Fl.Fin: carpet or vinyl asbestos tile

Ceiling: 5/8" gypsum board

Roof: Plywood on wood trusses or wd. rafters

Interior Walls: 5/8" gypsum over wood studs

At Utility Rm: 2 layers of 5/8" gyp.over studs

on kitchen side; and 1 layer on utility room side

At Garage: 8" concr. masonry wall between garage and

entry 4 utility rm fin at entry with 5/8" gyp. on furring

177
4. Assign the closest available fire companies to each fire.

Taken from a similar model developed by Scawthorn [9], Exhibit 10

illustrates a CRT screen display and Table 2 illustrates the tabular report

on the fire spread.

IV. Implementation of an Expert System Approach

These three models can be used to respond to a wide range of

hypothetical and actual situations. However, each provides results that

are dependent upon a number of specific conditions. Since there are an infinite number of input values for each of the pertinent parameters, there

is potentially an infinite number of different outcomes. However, the

analysis procedure remains the same, and hence, amenable to an expert

systems approach.

Thus, an expert system for fire emergency management will rely heavily

on mathematical modeling because of the dynamic and spatial aspects of fire

growth and resource allocation. It will also embody both the concepts and

elements of a Decision Support System and Artifical Intelligence. This

structure also suggests that the expert system shell should enable

rule-based, frame-based and object-oriented representation as well as forward and backward logical chaining. For instance, backward chaining

would be used to identify the fire site for several observations of smoke

while forward chaining would be used to describe the expected resource assignment for a confirmed ignition.

Furthermore, the inference mechanism provided by existing software

such as the Automated Reasoning Tool (ART) from the Inference Corporation

is advertised as being as to process "viewpoints". Viewpoints allow ART to

consider potentally conflicting alternatives such as the conflict between

extinguishment (Defend in Place) and evacuation (Move the Exposed)

strategies toward lifesafety. The capabilities of this particular expert

system shell also enables the system to explore many aspects of one problem

as well as to evaluate a scenario over time.

This application focuses on the acquistion, display and intrepretation

of data on the potential number of persons effected, the location and

magnitude of the fire and the availability of firefighting resources. Fortunately, much of the the required data and technology are readily

available to support this application of an expert system.

1. DIME File. This is a data base originally designed by the Census

Eureau that describes the road system for most urban areas in the

United States. The DIME file provides the means of calculating

the possible paths between one location and other. Used in

conjunction with information on road segments that are impassable

provides a mechansim for calculating the difficulty to reach a particular area if a fire does occur.

2. Census Demographic Data provides information on the number of

persons, number of housing units and average value.

178
Exhibit 10: San Francisco Fire Department Fire Station Locations

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180
3. Weather conditions regarding wind direction and speed, temperature

and humidity are readily available.

4. Radio and remote phone equipment is available to enable

communications among all personnel.

5. Affordable large format CRT terminals and large screen projection

systems enable the implementation of multi-dimensional graphical

displays.

6. Fire realms and "alarm level" abstractions provide a manageable

mechanism for describing fire behavior and suppression response

requirements.

These elements provide the mechanics required for the practical aspects of

implementing an expert system.

Since emergency fire management is often carried out under hectic

conditions with limited information and scarce resources, mistakes in early

tactics and resource deployment can be costly. The three models presented

here illustrate techniques that can be employed by personnel lacking formal

fire engineering training to participate in emergency management in order

to assess the risks and allocation resources.

181
V. References

[I] Alpert R.L., A.T. Modak and J.S. Newman, "The Third Full-Scale

Pedroom Test of the Home Fire Project," Volume 1, Factory Mutual

Research Corporation, Norwood, Massachusetts, 1975.

[21 Berlin, G.N., "Managing the Variability of Fire Behavior", Fire

Technology, Volume 16, No. 4, 1980.

[3] Berlin, G.N., "A System for Describing the Expected Hazards of

Building Fires," Fire Safety Journal, Volume 2, 1980.

[4] Berlin, G.N. and William T. Hathaway, "Transit Station Evacuation,"

National Bureau of Standards, Gaithersburg , MD, 1985.

[5] "Fire Experience and Exposure in Fixed-Guideway Transit Systems,"

Report written by Schirmer Engineering Corporation and Rolf

Jensen & Associates, Inc. and prepared for the American Iron and

Steel Institute, December, 1980.

[6] Hathaway, V.'illiam T. , "Fire Safety in Transit Systems," Paper

presented at the Fifth International System Safety Conference,

Denver, Colorado, July, 1981.

[7] Quintiere, James, "Some Observations on Building Corridor Fires,"

Fifteenth International Symposium on Combustion, The Combustion

Institute, August 197**.

[8] Scawthorn, C. , Y. Yamada, H. Iemura, "A Model for Urban

Post-Earthquake Fire Hazard," DISASTERS, Volume 5, No. 2,

London, England, 1981.

[9] Scawthorn, C. , "Post-Earthquake Fire Considerations in Urban

Earthquake Policy and Planning," U.S. -Japan Workshop on Urban

Hazards Reduction, July, 1984.

[10] Scawthorn, C, and John Donelan, Research Institute report on

the Coalinga Earthquake entitled "Fire-Related Aspects of the

Coalinga Earthquake," Earthquake Engineering Research Institute,

Berkeley, 1984.

[II] Tewarson, A. "Final Report - Experimental Evaluation of Flammability

Parameters of Polyment Materials," Factory Mutual Research Corporation,

Norwood, Massachusetts, 1979.

Acknowledgement

I wish to express my appreciation to Dr. Charles Scawthorn of Dames 4

Moore for his many contributions to this paper and to John Gerard of the

National Fire Protection Association.

182
The VENTILATION EXPERT for Underground Coal Mines

by

Jeffery L. Kohler

Assistant Professor of Mining Engineering

The Pennsylvania State University

Department of Mineral Engineering

University Park, PA 16802

ABSTRACT

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 is concluded with some observations on the requirements of a future version of the VENTILATION

EXPERT.

INTRODUCTION

The Pennsylvania State University, Department of Mineral

Engineering was funded by the Bureau of Mines to perform a conceptual

investigation of a management information system for coal mine ventilation systems (1). Initial work under this contract suggested that an expert system implementation would be most appropriate. Although the

actual construction of an expert system was not called for under the

contract scope-of-work, it was decided that a small prototype system

should be built and evaluated. This paper is a summary of the ongoing

effort to build and evaluate expert system structures for the coal mine

domain. The effort described here is also one in which the domain

experts performed the development work, rather than knowledge engineers.

The design objectives of the management information system are presented in the next section of this paper; then the coal mine domain

is briefly reviewed as it relates to the expert system. This is

followed by a discussion of the selection criteria applied to the

183
selection of the expert system tool. The general aspects of the

selected architecture are then reviewed as they relate to the coal mine

problem. The paper is concluded with a summary of the experiences,

including the strengths and weaknesses of the selected control

structure; planned changes for a future implementation are also given.

DESIGN OBJECTIVES

The objective of building a prototype was to achieve a better

understanding of what a coal mine expert system should be capable of

performing; how it should be structured; and the software and hardware

constraints that might be applicable. Since the prototype system would

only be used by the researchers and not mine personnel, some additional

flexibility existed in areas such as "user friendliness," throughput

rate, and so forth. Notwithstanding, the objectives of the future

system were carefully defined to allow for a more realistic formulation

of the prototype system.

The objectives of the system generally correspond to removal of

deficiencies in the operational methodology of many existing mine

ventilation systems, and more specifically, perceived deficiencies as computerized monitoring systems become more commonplace in mining. The

types of information-management problems which will become more common- place as monitoring systems become integrated into mine operation can already be observed in the approximately 56 mines where monitoring

systems have been installed.

The most notable problem is the large amount of data generated by

the monitoring system and other sources, and the relatively small amount

of information contained in that data. The sheer bulk of data

discourages personnel from using it, thereby negating many of the

benefits of monitoring; a more serious consequence is the possibility

that important information within the data will not be noticed. Thus an important objective of the proposed management information system is to

sift and sort the data for information useful to specific tasks and

individuals. Secondarily, this information should be automatically

routed to the appropriate personnel.

An examination of decision making, with regards to mine

ventilation, reveals other needs which should be incorporated into the

design objectives for the system. The first aspect of decision making

is the time frame. Some decisions must be made in a matter of minutes

or even seconds, as during a fire or after an explosion in the mine.

Others can be made over weeks or months; for example long term

improvement of ventilation system performance. Still others fall

somewhere in between these two extremes. However it is the extremes

which illustrate the need for a system which can provide not just

information in short time, but also advice based on an intelligent use

of the information. In the one case there is so little time to make the

decision that all resources must be brought to bear, while in the other

extreme so much time has elapsed that information may have become

scattered or lost. Thus, another objective of the system is to utilize

the available Information to provide advice to management.

184
The resources available to support the decision-making process

include mathematical relationships such as the fan laws, computer

simulations, statuatory requirements, and the judgment and experience of

the decision maker. An obvious objective of the system is to embody

these same resources. Embodying the system with the capability to

utilize heuristical information is particularly important since many

mining decisions have to be based on heuristics. Furthermore, the

people with the experience are not always available at a local level to

participate in the decision-making process. It would therefore be very

desirable if this experience could be extracted from the expert,

encoded, and then made available to everyone through a knowledge-based

expert system.

Given the minimal time to make certain decisions, and the severity

of the consequences if they are not made promptly and correctly, it

would appear desirable to allow the system to assert control as appropriate and necessary. For example if the system's advice is to

change fan speed, and mine personnel have not responded, then it may be

feasible to allow the system to change the speed through a control loop.

This would be illegal under current law and raises other issues, but is

a germane issue at this stage of the research.

The aforementioned objectives can be summarized as a set of

functional goals; the system should have the capability to:

-manage the information flow to personnel,

-advise management on problems and solutions, and

-make decisions and assert control.

The nature of the information, the functional goals, and the need

to allow for the addition of user-site-specific knowledge in the system

suggest that a knowledge-based expert system implementation would be

appropriate. Figure 1 illustrates the system concept. The actual

implementation of the knowledge-based expert system (ES) is dependent

upon the domain. A precise definition of the domain is not only necessary to structure an ES, but is useful in forcing a more precise understanding of the domain's interaction with the ES. The coal mine domain

is briefly reviewed in the next section.

COAL MINE DOMAIN

An underground coal mine can be described in terms of several

attributes; many of which are shown in Figure 2. These are discussed

briefly in this section, with an emphasis on those which have a significant relationship to the ventilation system.

The infrastructure of a mine consists of a complex network of

openings which have been excavated to facilitate access to the deposit,

provide routes for men, materials, and ore, and to provide airways. The

excavated openings are known as entries or crosscuts, depending on their

orientation and function. Solid pillars of coal are left unmined to

support the roof. Typically the width of an entry or crosscut is

16-20 ft., with entries being on 40-100 ft. centers, and crosscuts on

185
USER SENSORS

1

KNOWLEDGE-BASED

EXPERT SYSTEM

SYSTEM RESPONSE MODE

MANUAL AUTOMATIC

I

REPORTING

ADVISOR

CONTROLLER

FAN

REGULATORS

Figure 1. Ventilation Expert - System Concept

186
COAL MINE

INFRASTRUCTURE

DEPOSIT

VENTILATION

MATL HANDLING

PRODUCTION

POVER

DRAINAGE

MANAGEMENT

Figure 2. Coal Mine Attributes.

187
60-150 ft. centers, depending on the pillar requirements. The height of an opening is usually the same as the thickness of the coal seam, and

ranges from an average of 32 in. to 72 in. , and even greater in the

thick seams found in western U.S.

The entries are given specific names based on their function as airways and production areas, or their use for materials handling. For

example if an entry is a major path to and from many parts of the mine

it may be called a main entry. If that particular entry also carries

intake air (fresh air) to the working place it would be known as a main

intake entry. An example of the entry types is shown in Figure 3.

The deposit, an attribute of the mine, has its own attributes. Two

of interest here are methane content of the coal and the structural

integrity of the rock immediately above and below the coal seam. The

methane content will determine the amount of methane liberated when the

deposit is mined, and will affect the amount of gas which "bleeds" into

the mine atmosphere. The structural characteristics are important

insofar as the probability of a roof fall or floor heave is concerned;

either a fall or heave will result in a blockage of the aircourse. The

first attribute will tend to be similar throughout the mine, while the

second may tend to exhibit more local variation.

The attributes of materials handling, production, power, and

drainage constitute important subsystems within the mine. However,

their import insofar as the expert system for mine ventilation is

concerned, is minimal; accordingly they will not be discussed. The

management attribute is important here because it defines the scope of

the expert system and how it must interact with mine management to

achieve the desired design goals.

The structure of mine management is determined by both statuatory

requirements and company organization. One example of the organization

follows. At the local level a face boss is in charge of a discrete

production unit, within the mine, known as a section. An assistant mine

foreman has responsibility for a number of sections, and the mine

foreman has responsibility for all the sections at the mine. The

maintenance superintendent oversees all maintenance activities, and may

have a foreman and other lower level managers reporting to him. The

mine superintendent is in charge of the mine and has responsibility for

all that occurs there. The mine engineer, safety engineer, and

ventilation engineer report to the mine superintendent. Depending on

the size of the company, the mine superintendents from several mines

will report to a district manager, who reports to a vice-president for

production.

The management attribute of the mine penetrates virtually every

aspect of the expert system. The information available to the expert

system, the interactive operation of the expert system, and so forth

depends upon the management structure. A detailed account of this has

been prepared, and is documented in reference 2. For the purposes of

this paper, however, it is worth noting that the complex management

structure serves to complicate the issue of information management as

far as the monitoring system is concerned.

188
VENTILATEJ iiijijijilijijiiiiiiijj!

ill l!

FAN

REGULATORS TT

STOPPINGS

ENVIRONMENT f*T

1

n 1 1

n 1

Figure 3. Entry Types.

189
The ventilation attribute is central to the development of this

expert system, and in its own right is one of the more important

subsystems of the mine. The purpose of a coal mine ventilation system

is to dilute, render harmless, and carry away all noxious and toxic

gases and dusts. In some mines air conditioning is also performed to

provide better temperature and humidity levels. The ventilation

consists of entries carrying fresh air, i.e. intake entries, entries

carrying spent air, i.e. return entries, a fan and other devices.

The importance of the ventilation system necessitates that it be

examined in greater detail. Figure 4 illustrates the attributes of the

ventilation system. For technical accuracy in the domain, it must

be noted that face ventilation has not been included in this first

generation of the Ventilation Expert.

The first three attributes of Figure 4 are control devices within

the system. The f an is located on the surface and is the prime source

of fresh air for the mine. A mine fan may have a capacity of 500,000

cfm at 10 inches water gauge for example. Large mines may have multiple

fans. The operating point for quantity and pressure of the fan is

adjusted to provide adequate air to the mine. Under current law this

point cannot be changed by fan adjustment while workers are in the mine.

For the purposes of this project it is assumed that the point can be

changed at any time, to provide only needed air, thereby substantially

reducing power costs, while always providing adequate ventilation.

Regulators are devices placed in certain aircourses to introduce a shock loss in the airflow, thereby increasing the resistance of that

airway. These devices are used to control the ratios in which the air

splits among a large number of entries. The technology has been

demonstrated to control regulators remotely, and it is assumed that this

capability will be available to the expert system.

Stoppings are walls placed in crosscuts between certain entries to

separate different types of flows, for example intake from return.

Their importance lies in their behavior and the attendant consequences

when they begin to leak.

The environmental attribute of Figure 4 can be further quantified

by illustrating its attributes, as was done in Figure 5. Each of these

attributes can be sensed, and by monitoring them at appropriate points

throughout the mine, the performance of the ventilation system can be

precisely defined. Similarly, when problems arise, these parameters are useful for diagnostic purposes. Some examples of the significance

attached to these measurable attributes follow.

Pressure and quantity measurements define flows throughout the

mine. Besides comparing these to expected values, anomalies can be

diagnosed by examining specific changes, or values, at different

locations. Methane concentrations are significant with respect to

preventing fires and explosions; additionally the concentrations in

various locations are limited by statuatory requirements. Carbon

monoxide measurements are used to detect and to identify the source of

combustion. In general the other attributes shown in Figure 5 would not

190
ENVIRONMENT

PRESSURE TEMF'ERATURE

QUANTITY "T^ HUMSDJTY

1 CH 4 1 TTT [ o THER ] |

Ceo 1 tt TT ~ —

1

I DUST 1 "7T i'-l- T^T

I C02 ] £| :::!:::'!:!:•:!::: 7

I o2 1 ""T^;:;:;;;;;:;:;-*^

Figure 4. Ventilation System Attributes.

191
AGE

RAIL

Figure 5. Environmental Attributes.

192
be measured, unless there was a site-specific reason. For instance if

spontaneous combustion was a problem, then carbon dioxide might be

sensed in gob areas for early warning of an incipient problem.

A properly designed and maintained ventilation system will provide

a safe and healthy working environment without wasting energy. The mine

is very dynamic, and as the infrastructure changes so does the

ventilation system and the ventilation requirements. Ventilation

requirements are also a function of production activities. In addition

to changes in ventilation requirements for the aforementioned reasons, a number of other events can alter system performance. Examples include

roof falls which may block aircourses, equipment moves which will

obstruct aircourses, gas outbursts, and failure of labor to implement

certain changes in the system such as regulator adjustment. Management

response to these problems, as indicated earlier, may need to occur

within minutes or over months, depending on the nature and severity of

the problem.

For the purposes of this project, problems which the expert system

will need to identify and react to have been divided into two classes:

those occurring on a routine basis are grouped as normal and those which

are catastrophic are grouped as emergency. "Normal" problems include

inadequate air quantity, excess methane, and inadequate air velocity,

among others. "Emergency" problems include fire, explosion, and gas

inundation. It is important to note that the so-called "normal"

problems are quite significant in that a failure to correct them can result in an explosion or fire. Additionally, there can be an adverse

affect on production if they are not quickly corrected.

The requirements of the expert system and the domain in which it

will be used have been summarized. With that as background, the

criteria utilized for selecting an expert system tool will be described.

TOOL SELECTION CRITERIA

The primary objective of building the Ventilation Expert was to

facilitate a more accurate specification of an expert system which would

be available to mining companies at some future time. Since the

objective was not to develop a system for general use, the criteria set

for selecting a tool are somewhat different than might be the case otherwise. The applied criteria were:

-existing and demonstrated tool,

-readily available at low cost, and

-satisfy the intent of the research project.

The desire for an existing tool is obvious. The requirement for a demonstrated tool stemmed from the fact that we did not want to take

time to debug a new tool; also, it was felt that a demonstrated tool

would be more fully documented. The requirement on availability and

cost was a reflection of the resources available for the effort.

Finally, it was important that the selected tool be capable of doing the

types of things which were perceived as important for the ventilation

application. Satisfying this requirement was problematic, since an

193
implicit objective of building the prototype was to get a better idea of

just what type of expert system architecture would be best suited to the

ventilation problem.

The types of expert system tools available can be grouped into

generic categories, for example: (3)

-interpretation, in which situation descriptions are inferred from

sensor data;

-prediction, in which consequences of given situations are

inferred;

-diagnosis, in which malfunctions are inferred from observables;

-monitoring, in which observations are compared to plan

vulnerabilities;

-debugging, in which remedies are prescribed for malfunctions;

-repair, in which a plan to administer a prescribed remedy is

executed;

-control, in which system behavior is interpreted, predicted,

repaired, and monitored.

Utilizing these generic groups, the Ventilation Expert needs to

first perform interpretation, then diagnosis, followed by debugging, and

then prediction to verify the proposed remedy, prior to implementing the

repair. The generic category of control essentially satisfies the

Ventilation Expert's needs. Unfortunately, the control category

probably has received the least amount of attention from those who

develop expert system tools, and at the time this effort was begun a suitable expert system tool for control was unavailable. Consistent

with the project goals and the other constraints on tool selection, a decision was made to use a production system known as mini-MYCIN. This

particular system is quite similar to EMYCIN.

EXPERT SYSTEM IMPLEMENTATION

MYCIN is a rule-based expert system for providing diagnosis and

recommended therapy for certain classes of infectious blood diseases.

It is used in a consultation mode and has well-developed explanation

capabilities. The solution method employed by MYCIN is backward chaining, from a diagnostic hypothesis to data, under the guidance of

inferential rules. Knowledge is represented as productions, i.e. rules.

Another important characteristic of MYCIN is its ability to reason under

uncertainty. (3)

EMYCIN is the MYCIN expert system without the domain specific

knowledge. Mini-MYCIN, developed at MIT, has the essential

characteristics of EMYCIN, and was used to develop the Ventilation

Expert. The development of an expert system using mini-MYCIN involves

the development of the domain-specific knowledge base. The knowledge

base consists of two major components: the structural component which

defines the organization of the knowledge, and the problem solving

component which provides the necessary knowledge to develop a solution

method and make inferences or use other resources to solve problems.

Specification of the structural component essentially means defining the

194
context types and parameters; specification of the problem solving

component requires the development of rules which define the behavior of

the domain, by capturing an expert's expertise about the domain.

Structural Component of the Knowledge Base

The ventilation expert uses three context types: mine, entry, and

location. The relationship between context types is described by the

static context tree which is shown diagrammatically by Figure 6. During

execution of the Ventilation Expert, the context types are instantiated,

as necessary, to form the dynamic context tree, which describes the

relationship between specific instances of the context types for that

particular execution (consultation)

.

MINE

ENTRY

LOCATION

Figure 6. Static Context Tree for the Ventilation Expert.

Parameters are characteristics of the context types. Representa

tive parameters for the MINE context type are:

-Fan Characteristic Curve

-Fan Status

-Methane Liberation Rate

-Monitoring System

-Management

-Significant Problem

-Causes

-Symptoms

-Solutions

The context type ENTRY has the parameters:

-type

-production status

195
The context type LOCATION has the parametersm among others:

-Station ID

-Pressure

-Quantity

-Carbon monoxide concentration

-Methane concentration

-Velocity

-Resistance

-Roof

-Devices

-Events

-Obstructions

-Inby relation

-Outby relation

-Split relation

Taken in the aggregate, the context types and their associated

properties completely describe the domain, and provide the necessary

building blocks to reason about the domain. Before any additional

discussion on the parameters, some properties of all parameters will be

noted, specifically the "type," "expects," and "prompting-option"

properties.

One of three values for the "type" property of parameters is

allowed: "yes-no," "single-valued, "and "multiple-valued." The "expects"

property defines the allowable values of the parameter; "NUMBER" or a list of specific numbers, a list of specific entities, or "ANY" are examples of acceptable definitions for the "expects" property. The

"prompting-option" property gives the system builder control over the

means by which the expert system arrives at values for each parameter.

For example this property can be set so that the system always infers

the value, or always asks the user for the value, or asks the user for

verification of an inferred value, or only asks the user after a failure

to definitely establish a value through inference.

Table 1 lists examples of parameters and certain properties.

Table 1. Examples of Property Specification for Parameters.

Property

PARAMETER TYPE EXPECTS PROMPTING OPTION

Monitoring system single-valued OK, Not OK,

Questionable

Inferred (if

possible)

Significant Problem multiple-valued Inadequate

Quantity, Roof

Fall, Excess

Methane, Fire

Inferred (always)

Pressure Measurement

single-valued Any Number

1 User

(Cont'd.)

196
Table 1. Examples of Property Specification for Parameters (Cont'd).

PARAMETER

Property

1 1 r Ei PPnMPTTWf jrKUiiir J. ±iNij nPTTHTJ L/Jrii-UlN

Event single-valued Roof Fall, Verify

Equipment Move

Production Status single-valued Abandoned, idle, User

full

Note 1. In the future system, measured values will be obtained

directly from the monitoring system.

Another property of each parameter, known as "main-prop," allows

the system builder to determine when the values for certain parameters

will be obtained. This is an expedient feature since the system can be

forced to first obtain values for fundamental parameters before extensive tree growth has occurred.

An examination of parameters, and their property specifications can provide insight into the workings of the domain. For the purposes of

this paper such an examination is unwarranted, but some general comments

about the structural component of the knowledge base may be useful.

First, it is important to describe the domain with the least number of

context types as possible, particularly in a small prototype system. As

the number of context types increases, tree growth (dynamic context tree)

increases geometrically, and user confusion increases exponentially.

Memory size and increased execution time can also be problematic.

Second, the EMYCIN inference structure is a tree, not a network.

Accordingly, it is necessary that contextual relationships be "one to

many" and not "many to one." This sometimes presented a problem in

structuring the Ventilation Expert, but in all cases appropriate kludges

were developed, with only a small decrease in utility. Third, the

capability to control how the expert system obtains information is quite

useful. It makes little sense to burden the system with complex

inferences which the user could easily provide. Similarly, the system

should not bother the user for information which could be easily

inferred, especially in light of the fact that the user may provide

erroneous information.

Problem Solving Component of the Knowledge Base

The rule base represents the knowledge, including the problemsolving expertise of the human expert. Development of the rule base

requires an examination of three general aspects of the knowledge:

-types,

-representation, and

-acquisition.

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Other specific issues are then associated with each of these aspects.

The first two general aspects are examined in more detail.

The types of knowledge available to the Ventilation Expert arej

-Statuatory requirements, e.g., required air quantities and

velocities at specific locations, allowable practices, etc.;

-Ventilation plan, MSHA approved plan for operation of the

ventilation system at the specific mine;

-Analytical models, e.g., fan laws, Atkinson's law, etc.

-Computer simulations, e.g., iterative routines such as PSU Mine

Ventilation Simulator (MVS);

-Sensor measurements, i.e. inputs from the monitoring system;

-Heuristics, e.g. operational "rules-of- thumb

,

11 judgement, etc.

Except for the heuristical information, it is a straightforward, if

somewhat tedious, task to represent these knowledge types. Also,

obtaining knowledge from computer simulations represents a special

problem which will be discussed later.

There are essentially three ways of representing knowledge in miniMYCIN; they are rules of the form;

-causal,

-self-referencing, and

-meta-level.

Additionally certain rules can be streamlined using knowledge tables.

Causal rules take the form,

If premise 1 , and

premise 2, and

premise n

Then conclusion 1, and

conclusion 2, and

conclusion.

The number of premises does not have to match the number of conclusions.

If each premise is true, then the conclusions are affirmatively

asserted; otherwise the conclusions are not asserted. The logical

"else" operator may be used in premises or conclusions, as may be the

logical "or" operator. An example of a causal rule from the Ventilation

Expert is (parameters are in upper case)

If SIGNIFICANT PROBLEM Inadequate Quantity is suspected and

Qmeas < Qthreshold and

FANS STATUS is definite

Then MONITORING SYSTEM NOT OK is suspected.

Self-referencing rules are used to improve or reduce the certainty

of a piece of knowledge in light of another piece of knowledge. For

example it may be known a priori that a roof fall will obstruct an air

198
course, thereby reducing the quantity of air moving past the fall. If

reduced flow is certain, but the cause is unknown, an expert would

select a specific cause out of several, based on prior experience. In

this case, roof fall as the cause, would be ranked as low probability.

On the other hand, if the expert was told that the roof in the vicinity

of the detected flow problem was an incompetent shale with closely

spaced joints, then the probability of roof fall as the cause would be

dramatically increased. Without getting into the probability aspects at

this point, one example of a self-referencing rule is:

If EVENT roof fall is suspected and

ROOF is jointed shale

Then EVENT roof fall is definite.

Self-referencing rules are said to reason and conclude about themselves.

Meta-level rules are currently not used in the Ventilation Expert.

This rule formulation allows strategic information to be imbedded in the

rule base. For example given the presence of certain knowledge, the

problem solving strategy could be modified in some fashion.

A backward chaining system such as mini-MYCIN requires a goal rule

which represents the starting point of the expert system at execution

time. The goal rule for the Ventilation Expert is:

If there is a SIGNIFICANT PROBLEM, and

" the CAUSE is known

Then find a SOLUTION.

The evaluation of this goal rule can be summarized without examining the

reasoning network which will result from the evaluation.

First, the system will try to determine that a significant problem

does exist. To do this it will look for all rules which conclude that a significant problem exists. Each of these rules will be evaluated until

the premise, that a significant problem exists, is established, or until

the relevant rules are exhausted. When attempting to evaluate a rule

which concludes that a significant problem exists, the system will have

to establish the truth of that rule's premise. In so doing, it will

look for other rules which conclude about the truth of the premise in

question. This backward chaining will continue until the validity of

the original premise, in this case that a significant problem does

exist, can be established. If this premise can be established as true,

then the system will attempt to establish the truth of the next premise

in the goal rule in a similar fashion. Otherwise execution is

terminated. Once all premises have been satisfied the conclusions will

be asserted. In the case of the goal rule, the conclusion "find a solution," will require additional reasoning.

One observation which can be made, based on the foregoing, is

premises should be ordered with the least probable one given first down

to the most probable one given last. Thus excessive time will not be

wasted on rules which have premises which will seldom be true.

199
The foregoing discussion on rules has not considered the certainty

with which premises or values are known, or to which conclusions can be

asserted. The reasoning of human experts utilizes qualitative measures

of certainty such as likely or more likely, and so forth. This presents

somewhat a challenge to expert-system builders, since a more

quantitative description of certainty provides a more familiar basis for

analysis. However much theory has been developed to support reasoning

under uncertainty.

EMYCIN utilizes a certainty factor concept which has been found to

be satisfactory for the prototype Ventilation Expert. The certainty

factor is associated with each instance of a parameter and reflects the

degree to which the value assigned to that parameter is believed to be

true (or false). The certainty factor can range from -1, corresponding

to definite disbelief, to +1, corresponding to definite belief.

Additionally the MYCIN developers formulated a conversion scale between

the qualitative descriptors, e.g. not likely, and their numeric certain

factor.

During the reasoning process the certainty factors which are propagated are not combined in some ad hoc fashion. Rather combination

occurs through a carefully defined and tested set of procedures.

Essentially measures of belief (MB) and disbelief (MD) , for each

hypothesis are updated each time a new piece of evidence is acquired.

After all of the relevant rules have been evaluated a cumulative

certainty factor is computed for each hypothesis by subtracting MD from

MB. The hypothesis with the highest cumulative certainty factor is then

asserted. Additional information on the concept and the procedures is

found in reference 4.

The knowledge table is a useful tool to eliminate multiple

articulation of certain knowledge. For example the following rule,

If it is definite that the SENSOR ID is nnn Then it is definite that the OUTBY RELATION is xxx, and

it is definite that the INBY RELATION is yyy, and

it is definite that the SPLIT RELATION is zzz,

would have to be rewritten for each possible value of SENSOR ID.

However, by using a knowledge table, the foregoing rule could be written

as,

IF the value of SENSOR ID is known with certainty

Then it is definite that these parameters-OUTBY RELATION, INBY

RELATION, SPLIT RELATION should be transferred from the

knowledge table,

thereby allowing for a more efficient formulation of the rule base.

200
IMPLEMENTATION OF THE VENTILATION EXPERT

The knowledge-based expert system tool known as mini-MYCIN is

written in NIL (New Implementation of Lisp) and will operate on a Vax in

a VMS environment. The Ventilation Expert is currently being used on a microVAX 1, in a microVMS environment which has been tuned for NIL. The

system is running with 2 megabytes of memory and 30 Mb of disk. This is

approximately the smallest system configuration which will support

mini-MYCIN. In fact, an additional 2 megabytes of memory is very

desirable to reduce excessive paging and to increase the speed of the

system. It would also be desirable to increase the disk space by at

least another 20 Mb. However, as implied before, the important issue is

building a small prototype system as early in the design process as possible, even with marginal facilities.

SUMMARY OF EXPERIENCES WITH THE VENTILATION EXPERT

Based on the experience derived from developing the Ventilation

Expert certain observations can be made which may be useful to other

domain experts contemplating the development of an expert system. One

general observation is that it is very worthwhile to build a small

prototype, even if compromises must be made. This effort will provide a clearer understanding of what is really needed, than would be possible

otherwise. Despite compromises and shortcomings, mini-MYCIN was a very

useful tool. Some other specific comments follow, primarily related to

shortcomings which will (hopefully) be overcome in the next Ventilation

Expert.

The backward chaining structure was useful for determining causes

of problems and finding solutions, but quite cumbersome for determining

if there was a significant problem. In the "real world" the monitored

information would be checked for alarm states (values too high or low)

.

If a sensor went to an alarm condition the Ventilation Expert would be

invoked. If forward chaining were used, the expert system would know

not only that a problem existed, but also where it existed. The

reasoning network could then be developed using this initial knowledge.

Currently, the control structure instantiates the location context for

every sensor, which results in a massive tree with the attendant space

and time problems. In a future system forward chaining should be used

to establish the existence of a significant problem, and backward

chaining should be used to determine the cause and find a solution.

The use of an expert system with a real-time monitoring system

requires an expert system control structure which is cyclic. EMYCIN is

not. It operates on one snapshot in time, and then must be restarted

from scratch. This was not a serious problem for the prototype system,

but would have to be resolved for an operational system. Ventilator

Manager, which is a real-time version of MYCIN (5) , demonstrates the

feasibility of a real-time and cyclic system.

A more serious problem which plagued the development of the

Ventilation Expert was EMYCIN' s inability to work with spatial

201
relationships. The ventilation system of a coal mine is a complex

network of aircourses. During the reasoning process it is often

necessary to consider the variation of events at different points within

the ventilation network. There was no straightforward way to do this,

so a kludge using inby, outby, and split relationships was born, to

permit inferences based on events at spacially related points. At

this time an alternative does not seem to be available, although the use

of a semantic network structure appears promising.

The nature of the reasoning process within the coal mine

ventilation system domain frequently requires numerical calculations or

the use of computer simulators. Numerical calculations might be used to

compute trend information or determine the amount of proportional

control to assert on a regulator, for instance. A simulator can be used

to verify a hypothesis or predict the impact of a proposed solution for

certain situations. Currently the calculations are handled with user

defined functions (written in NIL); a software interface is being

considered to allow information to pass from the Ventilation Expert to

the FORtran-based simulator, MVS.

The use of the blackboard control structure with several

independent knowledge sources is also being considered as a possible

solution to the aforementioned problems. This seems particularly

attractive in light of our conclusion that we seem to need a different

type of expert system structure for each of the different aspects of the

Ventilation Expert!

ACKNOWLEDGEMENT

The author gratefully acknowledges the patience, advice, and

support provided by Prof. Peter Szolovits of the MIT Laboratory for

Computer Science.

REFERENCES

1. USDI, Bureau of Mines, "Conceptual Investigation of a Management

Information System for Underground Coal Mines," Contract No.

J0348005, 10/31/83 - 1/30/86.

2. Ramani, R.V., Frantz, R.L., and R. Bhaskar, "An Analysis of the

Decision-Making Processes in Mine Ventilation System Management,"

Internal Report prepared as part of BOM Contract J0348005 (Ref . 1 above), March 1985, 67 pgs.

3. Hayes-Roth, F.H., Waterman, D.A., and D.B. Lenat, ed., Building

Expert Systems , Addison-Wesley Publishing Company, Inc., 1983,

pp. 13-56.

4. Buchanan, B.G., and E.H. Shortliffe, ed., Rule-Based Expert Systems , Addison-Wesley Publishing Company, Inc., 1984, pp. 209-294.

5. Fagan, L.M., VM: Representing Time-Dependent Relations in a Clinical Setting . , Ph.D. Thesis, Stanford University, 1980.

202
EMERGENCY MANAGEMENT DECISIONS AT THE LOCAL LEVEL

Robert S. Wilkerson

Federal Emergency Management Agency

Washington, D.C.

We are today often concerned with expert systems and systems in general. We

are constantly concerned with the general guest ion of the application of

computers to the emergency management field.

I think it's very important we consider for a moment, who are the experts

and what expertise we're proposing to integrate into such systems.

Generally, I feel we will find that emergency management experts fall into

two categories: 1) those people who are experts because of their experience,

generally in a limited number of instances. There are probably three or four

clients of yours vrtio have direct experience with an emergency situation and

have developed some degree of expertise because of that; and, 2) those of us

who are emergency management professionals at some other level who basically

study that first group. We are the anthropologists of the emergency management world; the people who look at how others have made their decisions. I hope I'm already getting a message across: the proper focus of expert systems

applications is at the local level.

Two years ago, I was asked to talk about the application of science and

technology to State and local emergency management operations and decisionmaking. That venerable discussion in the halls of Congress deliberated the

application of third generation computers, remote sensing devices, and

telemetry devices, to a trade or profession which was operating at that point

in the "technology of a legal pad." That situation has changed somewhat. I also argued that there was a problem particularly in emergency operations

with the practitioners' receptivity to science and technology. There was difficulty in understanding how such tools might be applied. I have recently

203
become aware that this situation has changed dramatically. Let me give you an example of the change. I think it bears consideration during your

deliberations as to when to apply expert systems, how long will it take, and

what's the true market place.

Over the last several months we've asked a number of respondents about their

application of computers, particularly to the evacuation problem. We asked

a series of questions, hierarchial in nature, as to whether or not they had

computers or had access to computers. Did they in fact use those computers

in planning? Did they use those computers in operations? All the outputs

are very interesting, but I want to quote one set of statistics that I found

amazing. Of the respondents who said: a) that they didn't have a computer; b) that they had no current interest or intention of trying to access a computer for use in a planning and operations environment: almost half of

them answered that they were interested in attending training or discussions

on how the computer or technology might be applied to their situation. That

represents to me a dramatic turnaround in the receptivity for the type of

tools that you are discussing.

But what are we trying to apply them to? I said a "legal pad." Vhat is the

local environment? I think it is very important that we as Federal employees

or you as technical experts always keep mind: there is no local environment.

Now local government people, I didn't just sell the farm to the Feds. What

I 'm saying is that you can not homogenize the local government situation.

You can't design an expert system in my opinion, one system that will apply

to the legal structure and the decision making structure that exists in all

of the States, and certainly can't design one that fits the siutation in all

of the local governments.

Let me deviate from the background of computer science into political science.

We have a system in this country of strong governors and weak governors. We

have at least three distinct groupings of government form at the local level.

204
The people who make decisions and therefore, would potentially use those

expert systems are generally a group, not an individual, and they have

varying powers and varying values. I think you have to bear that in mind.

But how do those decisions get made? Let's look at it first from an

individual decision process. Individual expertise is often characterized

as a person who can arrive at a decision, a sound decision, but can not

generally explain precisely the variables considered.

That's especially true at the local government level where a wide variety

of values are brought closer to the problems and decisions. At the local

level, those decisions-makers, those users of the expert systems and also

the experts, change frequently. Design of a static expert system, one that

does not allow for frequent adjustment values for application to emergency

management by the local government may not be cost effective.

We said we didn't have a homogeneous local government situation. You also

can't consider emergencies as if they are homogeneous. The expert systems

and the speed with which they need to be applied will vary based on the

characteristics of the emergency. There's a lot more time to access expert

systems in a hurricane than there is during a hazardous material emergency.

One is a slow developing, fairly long forecast, even fairly predictable as opposed to the others which happens—now! Even hazardous materials

incidents vary widely. A train derailment is dramatically different from a tank truck spill.

Experts systems can assist local decision-makers in making decisions

ahead of time, and forecasting and outlining parameters and values. They

can also provide interactivity and branching to other areas of expertise

during emergencies. There is no reason why you shouldn't be able to ask

a question then be branched elsewhere to expertise; or set your community

decision parameters and recognizing a norm, from the decisions that a

205
collaborative group of experts have said they think they would make under

similar situations, expect at least a flag saying to him; "you've gone

outside those parameters; deviated from the norm; have you considered why

you're doing this?"

The local government official's situation during an emergency, is a rags to

riches information situation. There's either no information or there's more

data than you can deal with. Clearly the expert system has to structure

the information, has to assist the emergency manager in: 1) dealing with

decisions when he has too much data, and 2 ) accessing sources of information

when there is none.

We talked about the question of whether or not local governments are ready

to use conputers; whether they're ready for expert systems. I'd like to

make a blanket statement: they are ready for the use of extensive

technological tools that are presented to them in an understandable form, if they allow for the input of their values , if they allow for the sharing

of data bases, if they do not create a command structure and remote decision

making process. In short, they are ready for decision-making assistance

tools. They are not ready for decision makers that are conputers.

We sometimes talk about a five-year development process. The market for

these types of systems is not five years down the road for two reasons:

1) the demand is now, and 2) there is a need to continue the process of

building comfort with these complicated and sophisticated tools.

We can start today with basic decision trees that are constructed on an individual local government basis, forming the foundation for amalgamation

into a larger decision or expertise sharing system. That market place is

there today; the receptivity is there today; and we need to start. Cost

benefit is going to be a continuing issue in this process. True cost

206
benefit will be found where the research and the development process renders

a benefit today, incorporating user needs tomorrow, all as the product is

being developed.

Receptivity to computers, involves incremental adjustment to the capabilities of a machine. A word processor replacing a typewriter has shown

some resistance on the part of the user in corporation after corporation.

Dropping an expert system without forewarning, without training, and without

the experience that will came from participation in development, on an emergency manager will result in but one thing: an expert system that stays

in the closet. It may "played with" on a day-to-day basis, but certainly in

the heat of the moment, unless he, she, and they as a group are familiar with

it, it will not be effectively used.

Simple decision assistance tools, the clear message to local government

that what you're trying to provide is a tool, will not become a remote

decision maker, cannot become a remote decision maker by the shear guest ion

of legal authority. If we're going to talk about the bulk of emergency

management situations, you are not talking about a national command structure.

Thus, the system has to be distributed, decision parameters have to be put

in at the local level, and the data has to be shared. There is also a need

within the national command structure. Both local and national needs can

be supported. The development of the data base and a growing familiarity

with the use of expert systems will build a tool that will fit the national

emergency environment and yet at the same time provide a cost effective

capability for day-to-day emergency management use.

Three or four strong points; we're talking about a group user not an

individual; we're talking about masses of group users not a single group.

We can develop a decision support system; an expert system to use at the

national level; but without the local input, without the local parameters,

207
it will be of limited, if any, use. So what I'm advocating is a building

block concept that starts at the local level and builds up to the national

level. If you will, an expert system that's based on a multitude of experts

as opposed to a select few. We've got to incorporate the experience and

the risk parameters of a number of people.

Are we talking about a centralized system? If the definition for a centralized system is a main frame structure, into which every local user must

go each time then the answer is no. If we're talking about a centralized

system, better defined as an interconnected system which ties a number of

experts together, a number of users together, then we are talking about a centralized system. We desperately need to move from the individual

experiential mode into that experience sharing process, possible through

interconnectivity

If the system is structured so that it interacts with the decision-maker

in a manner that asks guest ions that lead to deliberate answers and then

analyzes the answer against what would have been the expert system recom- mendation, it will be better received than if it simply provides an answer

and even gives a statistical value of the reliability of that answer. Ask

the guestion, "What would you do? You said you would consider these

parameters, bam, bam, bam. Have you considered X, Y, and Z? What is your

decision? Your decision is somewhat inconsistent with what your preprogrammed answer was. It is also inconsistent with the answer we provided."

"Is that your final decision?" Yes? Then the system learns from that and

considers it subsequently in the life of an emergency when the same type

of decision is called for again. In that situation, you deviated from

your standard answer, you deviated frcm the past solution, and you deviated

from the expert system's answer.

208
"Do you still want to do this?" Yes! Situations are going to change suddenly . Decision factors that are built into the system; because of fatigue, stress,

or public pressure will cause the expert, the decision-maker, to learn during

the process. Therefore, you have to continue to ask questions because he's

under stress and you have to make sure that he or she doesn't forget. The

system becomes an automated tickler system that then compares and reminds

the decision-maker that he has varied somewhat, reminds him to consider the

justification. Why? One of the biggest single issues in public safety today

is justification after the fact. Why did you make that decision? Where are

your records? What is your liability? Did you follow your plan, San Jose,

California? No. Why? Because the information I had (which I plugged into

the expert system which gave me a record of the decision parameters and

subsequently the decision) varied somewhat from what I expected in my plan.

The decision tool, your expert system and the software and hardware that

makes up that system, serves a multiple purpose to the local government

users

.

Remember who the user is. Expert systems are critically needed in emergency

management and they're needed now. But if you forget who the ultimate user

is, we will repeat past failures. A project in Florida involving NOAA, FEMA,

and the State of Florida and over 30 local governments, the "Probablistic

Decision Tool for Hurricane Evacuation" , left out several critical success

parameters

.

1) the expert system was essentially based on the decisions

of one expert;

2) the only interactivity of the expert system cross- referenced to that one expert.

3) no local parameters for the risk was acceptable, no

local definition of what actions they thought should

take place under certain conditions.

Make these mistakes nationally, and it will take five to ten years to develop

a meaningful expert system.

209
One final comment on the interconnect ivity standpoint. When I preach distributive systems, I don't want to come across as saying that it is not critical

that one local government be able to learn frcm another local government. So

if we are to go with a micro- or PC-based expert system, we must rapidly move

to a situation that a flag is placed in the decision process of City A, based

on the recent experiences of City B. We can no longer afford to spend years

tranferring experience frcm one local government to another.

210
The SMOKEY Project:

An Intelligent Sensor-Based

Fireman's Assistant

Jaime G. Carbonell

Carnegie-Mellon University

Pittsburgh PA, 15213

28 April 1986

Abstract

This paper reports on the SMOKEY project, an artificial intelligence (Al) approach to automating the

diagnosis, assessment and suppression of fires in large man-made structures such as aircraft carriers or building complexes. The requirements for automation and its desirability in certain classes of

emergency situations are discussed. Based on these requirements, the design and implementation of

SMOKEY, a successful Al prototype, emerged. Certain computational mechanisms were employed

and extended, including agenda-based control structures for dynamic task prioritization in timecritical situations, and evidentiary combination rules for reasoning under uncertainty.

The task-domain research was supported by the Naval Air Systems Command under contract number

N60530-83-M-487A, and the research on the underlying Al technology was supported in part by the

Office of Naval Research under contract number N00014-84-K-0345.

211
1 . Background: The Need for Automated Fire Assessment

The detection, assessment and management of accidental fires on man-made structures, such as

ships and buildings, requires rapid data-gathering and decision making. Present sensor-based

equipment, such as smoke detectors connected to fire alarms, provide adequate technology for the

detection phase. But, assessing the severity and nature of the fire requires the presence of skilled

personnel. Moreover, formulating an effective containment or suppression strategy requires an

accurate and timely assessment, prediction of expected spread or growth of the fire, knowledge of

available fire-fighting equipment, and extrapolation of expected sideffects of the fire (e.g., structural

weakening or release of toxic fumes). Typically, skilled human intervention is required to make

real-time decisions beyond the initial detection stage.

There are many situations, however, where automating the assessment of the severity and possible

spread of the fire, as well as automating the selection of an appropriate fire-fighting strategy could

prove far more effective than present practice requiring human intervention. Rudimentary systems,

such as sprinklers and the release of heavy inert oxygen-suppressant gases, tied directly to smoke or

heat sensors are in existence and undergoing continued improvement. However, here we focus on

much more sophisticated systems providing flexible functionality comparable to that of skilled firefighting personnel.

Automating the fire assessment and strategy selection is most appropriate in situations where too

much information overwhelms human judgement, either because human reaction time is too slow, or

because the requisite expertise is unavailable or too far removed from the site. We consider each

case in turn:

• In some situations accurate decision making requires processing vast amounts of data at a rate significantly higher than human capacity permits. Such a situation exists, for

instance, aboard aircraft carriers where the information from thousands of sensors converges upon a complex panel, and the officer in charge must make decisions in seconds based not only upon the dynamically changing sensor readings, but also upon

topological knowledge of the ship (which rooms are connected to which others; what

212
combustible materials are stored where; which passages can be chosen as escape routes

for anyone trapped by the smoke; what fire fighting equipment is stored where; etc.) The

complexity of an aircraft carrier coupled with the real time nature of the task makes it

difficult for a human to make accurate decisions in a timely manner, especially for large

fires or multiple simultaneous emergencies.

• There are certain situations, where it is not the volume of relevant data that overwhelms

human capacity, but the need for split-second reaction time prevents human

consideration, let alone accurate judgement. For instance, a fire rapidly spreading

towards a fuel tank or towards the magazine, requires immediate action (e.g., flooding the

fire with water or inert gas, or closing fuel line valves). The cost of inaction or delayed

action might be far greater than the benefits derived from more considered deliberation.

On the other hand, flooding the library or the computer room with water, is likely to cause more damage than the fire. Therefore, intelligent assessment is required, and if the

danger is immediate action should be taken by the automated system. If not, available

human expertise should be consulted, or at least provided the option of overruling the

recommendations of an automated system.

• There are situations where assessment of a fire situation requires on-site expertise, but

where that expertise simply cannot be present in human form just for the unlikely

eventuality of a fire. Most large commercial buildings, hotels, apartment houses,

factories, etc. are wired with sophisticated smoke and heat sensors, but cannot have an

everpresent on-site fireman in the eventuality that a fire may potentially occur. In such

cases, automating the expertise in a central system connected to the sensors can result

in timely assessments and calls to the fire stations with information as to the location,

spread direction, combustion materials (and therefore required fire-fighting equipment),

occupancy and smoke-clear escape routes. Such information can result in more timely

and appropriate actions by the human firefighters (or by the automated system lighting up

the clear escape routes, informing the arriving firefighting personnel of the state of the

combustion, and perhaps controlling the appropriate sprinkler or inert gas system).

213
2. SMOKEY: The Task Definition

The task domain for the SMOKEY project is fire assessment and fire control aboard modern aircraft

carriers such as the USS Carl Vinson, and thus the research focused primarily on the first of the three

scenarios above: real-time processing of large volumes of time-variant information. Although we

believe the basic technology is extensible to the other two situations as well, the rest of this paper

focuses only on the SMOKEY analysis and its prototype implementation.

In order to test the feasibility of automating the sensor based fire assessment tasks for an aircraft

carrier, we started by interviewing several naval officers with operational experience in fire

assessment and suppression processes. We recreated some scenarios where different classes of

fires might be manifest, and recorded their step-by-step reasoning processes. In order to get more

accurate assessments and more accurate reporting, we slowed down the simulated development of

the fire by a factor of 10 in several cases. Although the details of the knowledge engineering process

are beyond the scope of this paper, we mention that our technique combined classical protocol

analysis, with post-hoc interrogation to re-analyze and justify past decisions. Afterwards, we reduced

these problem-solving traces to rules and augmented those rules with algorithmic processes for

graph traversal tasks, as discussed below, and with evidentiary combination rules to replace the

ad- hoc methods we observed for reasoning under uncertainty. Finally, we hypothesized the

existence of additional sensors (mostly differential air pressure sensors) that could easily be installed. We then constructed a computational prototype to investigate the feasibility of a fully automated fire

diagnosis, assessment and advice generator, based on simulated sensor readings.

3. Design Requirements

SMOKEY was specifically designed to address problems of real-time multi-sensor fusion using Al

technology. There are few Al systems that perform real-time analysis of external sensor reading, one

such being SUX [6] for analysis of acoustic signatures from submarines. However most Al systems,

even interactive ones such as XSEL [5] or POLITICS [3, 1] do not address serious real-time demands.

As such, the development of SMOKEY dictated an analysis of its requirements and a significant effort

214
in the extension and integration of Al-based computational techniques. The first step was to build a

prototype to demonstrate the feasibility of real-time automation of decision making as a fire develops.

Our design criteria were to develop a system to perform the following tasks:

• Take readings from large numbers of sensors putting some 3,000 sensors of different

types including smoke detectors, heat detectors, air-flow meters (to determine air

circulation) position sensors (to determine whether hatches or doors are open or closed),

and more complex sensors, such as smoke analyzers (to determine whether the smoke

contains ozone, hydrocarbons, metallic oxides, etc.) •- with the assumption that the bulk

of the sensors are simple smoke detectors, and the other, more sophisticated sensors

could be assembled and installed from commercially available components.

• Localize and assess the extent and nature of the fire. The old adage "where there's

smoke there's probably fire" is too simplistic. The air circulation system aboard ship, or

in a modern building, quickly distributes the smoke well beyond the immediate

combustion area.

• Assess the criticality of the fire. Criticality depends on many factors, including:

o The nature of the combustion by-products. Certain plastics, for instance, produce

highly toxic fumes, whereas paper fires do not.

o The nature of the combustion materials. Oil fires are harder to put out than paper

fires. And, metallic combustion (such as magnesium) is harder yet to control.

o The location and direction of spread of the fire. A fire spreading in the direction of

the magazine or the jet fuel tanks is obviously more serious than one of similar size

in a remote galley or lavatory.

o The present extent of a fire, including differentiation between previously affected

areas and the combustion front

o The amount of combustible material in the immediate area that can feed the fire.

o The accessibility of the affected region to firemen and fire suppressant materials.

215
o The extent to which the fire blocks normal exit routes or otherwise hampers

escape.

o The degree to which external factors impact fire suppression (low water pressure,

untrained crews, etc.)

• Take direct on-line action), such as lighting up signs to signal exit passages for endangered personnel that avoid the smoke afflicted areas (as opposed to lighting up the

shortest escape routes which might lead to impassible regions), sounding appropriate

alarms, notifying appropriate personnel, etc....

• Recommend off line actions to the fire fighting crew, including what fire suppressant

equipment they should use (e.g. water or oxygen suppressants such as foam or C02

), and what additional equipment is necessary (e.g. gas masks if toxic fumes are present).

• Perform all of the above in real time with partial information •• and be able to reconsider

past decisions in light of new information. In most real-time decision-making tasks,

conclusions must be reached using available data, rather than waiting until all relevant

information is gathered and analyzed. A perfect analysis of what should have been done

to combat a fire hours earlier is of much less use than timely if not always perfect

recommendations.

• Allow for errorful sensor inputs caused by random sensor failures, and possibly by the fire

itself burning out sensors or melting the wires leading from sensors inside the

combustion front.

Given the magnitude of the task in trying to achieve all of the objectives set forth above, we opted

for a comprehensive design and the implementation of a small pilot system to investigate the

feasibility of the endeavor. Our first step was to analyze computational mechanisms required to cope

with all the complexities in real time. SMOKEY was the resultant successful prototype converged

upon, after two earlier less successful attempts were abandoned in the design stage. SMOKEY runs

on a dedicated LISP workstation (presently the microvax II), but to place it in operational use would

require its translation to a conventional language like C (for faster operation and for direct interfacing

216
with all the external sensors).

4. The SMOKEY Architecture

SMOKEY requires a static knowledge base and a dynamic working memory. The former is quite

large and consists of:

• A graph structure encoding the topology of the 3D structure (a hypothetical part of an

aircraft carrier in our test domain •- but could be replaced by a building complex or any

other structure containing rooms, passages, doors, and air circulation systems). The

topological graph represents rooms as structured nodes (with which it associates the

placement of sensors, and the potentially flammable materials contained therein), and

represents passages and air vent connections as labeled arcs connecting the nodes.

This graph was implemented in FrameKit [2], a simple and fast frame-based language for

storing static information economically by inheriting general facts shared by multiple

instances.

• Information about flammable substances and their combustion characteristics, including

gaseous byproducts, flash points, etc.

• Information about fire suppressant substances and their means of delivery. Side-effects

of these substances are also recorded, such as the damage water can produce to books

or computer equipment.

The dynamic knowledge base consists primarily of a time-stamped sensor-poll buffer indicating the

last 3 readings of each sensor and the time at which it was read (thus trends can be detected -- it may

be important, for instance, to determine if a room is getting hotter or cooler after the sprinkler system

goes off to infer the effectiveness of the fire suppression method). The second component of the

dynamic memory is a set of pending hypotheses -- the system's current beliefs about the nature of the

fire prior to recommending action -- rank ordered by their criticality (thus decisions about whether the

fire is about to spread towards the magazine are given immediate attention, whereas hypotheses

about water damage to documents is given less priority).

217
5. The Agenda Control Structure

In order to control the multiple processes (sensing, generating hypotheses, testing hypotheses,

suggesting actions, updating working memory from new information gathered, etc.) and provide realtime response, a computational discipline is required in which one can assign priorities to each task

dynamically depending upon what problems are judged most critical at that time. These priorities

determine the focus of attention of the SMOKEY interpreter -- with low priority tasks being attended if

and only if there are no higher priority ones awaiting action. We selected an extended agenda-control

mechanism implemented in RuleKit [9], a rule-based language we developed precisely for real-time

control tasks.

The agenda mechanism consists of an ordered set of priority levels (called "buckets"), each of

which groups a list of tasks of roughly equivalent priority. A task can be:

• A conditional sensing action, e.g., polling external sensors and thus updating the sensorpoll buffer.

• A status update, posting and propagating values of sensor readings on the topological

graph.

• A hypothesis formation, e.g., if a certain configuration of readings is detected, a

hypothesis about the nature or spread of the fire is posted in the "pending hypotheses"

list

• A hypothesis test, as more information is gathered past hypotheses are confirmed, ruled

out, or modified.

• An immediate action generator, e.g., lighting up signs once a safe smoke-free exit

passage is calculated.

• A recommendation generator, e.g. advising the fire-control team as to the best firesuppressant substance to use given the assesed nature and extent of the fire.

• An internal attention focus shift, where reprioritization of tasks occurs as new information

218
is discovered (e.g. a new combustion front breaks out), or a past hypothesis that was

guiding action is later invalidated, again in light of new information.

Priority-1: Taskj , Taskj

2

, ... Task

1 n Priority-2: Task 21 , Task

2

'

2

, ... Taskj

m

Priority-k: Task

k j. Task k 2

, ... Task

k

The agenda mechanism operates by attempting to perform each task at the first level of priority. If

any task is carried out, all tasks at that level are again considered. However, if none are carried out

(either because all are awaiting more information, or are awaiting external action such as the firemen

arriving on the scene), then the tasks at the next priority level are considered, and if possible carried

out. If any task at any level was carried out, control returns to the first priority level (new information

gathered, or new hypotheses posted, or simply time having passed, may now enable a higher priority

task to execute). However, if at any priority level no task is executable, control passes to the next

lower priority level. In this fashion, high-priority tasks are always tried and if possible carried out, and

low-priority ones are carried out only when awaiting for the high priority ones to be executable.

Each task in the agenda consists of five components:

TEST: (check sensors, hypotheses, state, time, ...)

ACTION: (update hypothesis, state OR external action)

TASK-i ADD: (insert new tasks in agenda at new priority)

DEL: (remove or demote other tasks in agenda)

KTEST: (check whether to remove this task from agenda)

The test field is a conjunction of predicates on the sensor poll buffer, the pending hypotheses, the

clock, and any other internal working memory elements. If all these predicates are satisfied, the task

is executed, which means that the set of internal and actions specified in the action field are carried

out sequentially. Also, as a task is executed, new tasks may be inserted into the agenda at specified

priority levels (specified in the add field), or old tasks may be removed (specified in the del field). For

instance, if a task confirms the hypothesis that an electrical fire is present, its action can select the fire

suppressant (e.g., C02

), the add field inserts a task to stop the electrical current through the afflicted

219
wires, and the del field removes the tasks looking for combustion byproducts of other types of fires.

If the test field of a task is not satisfied, the ktest field is evaluated to determine if the task should

remove itself from the agenda. Thus, tasks can have their own expiration conditions. Real time

processing requires that only the potentially relevant tasks be considered at any one time • a small

fragment of the total set of tasks that may apply in different circumstances. The dynamic addition and

deletion of tasks in the agenda, together with the prioritized task consideration provides the

necessary software architecture to support real-time sensor analysis and assessment of time-variant

phenomena.

6. Evidentiary Combination Methods

Any complex network of sensors cannot be expected to function perfectly all the time. Sensor

malfunction or wiring problems will yield occasional false positives (spurious readings) and persistent

false negatives (non-functional sensors). The artificial intelligence program performing diagnosis and

situation assessment from these sensors cannot ignore such problems, and must combine with

statistical techniques to provide robust behavior in the presence of uncertainty. We borrowed the

evidentiary combination rules below from the POLITICS system [1] used to simulate human decision

making in the face of possible uncertainty of data and possible uncertainty of the inference rules

themselves. Other approaches such as those of MYCIN [10] or PROSPECTOR [4] were less well

suited to the current task.

In essence, the evidentiary combination rules are derived from standard probability theory,

assuming independence of possible observation errors (e.g. flawed sensors), and independence

among the reliability measures on the inference rules. Such independence assumptions provide the

most accurate simplification of otherwise intractably complex mathematics.

Sufficiency conditions are considered separately from necessity conditions for arriving at a given

conclusion given a set of observations. Thus, the problem of calculating the certainty of a conclusion

factors into a disjunctive component (only one sufficiency condition need be present with high

220
reliability), and a conjunctive one (all necessity conditions need be present with high reliability).

Looking at the disjunctive case first, we identify two sources of uncertainty, the a-priori uncertainty

associated with the reliability of each observation (the P(D.)'s in the formula below), and the degree to

which an observable is a sufficient condition to assert by itself the conclusion (the l(D,,Rj)'s in the

formula below). For instance, detecting partially oxidized hydrocarbons is a weak indicator of oil fires

(internal combustion motors may produce high ambient levels of such byproducts, and some plastic

fires may also produce them), whereas detecting high concentrations of ozone is a much stronger

indicator of electrical-arc fires. The I's in the formula measure the strength of each indicator. Taking

together the strength of an indicator with the reliability of the observation produces a measure of

confidence in the conclusion. However, multiple measures must be combined, as multiple sources of

corroboratory information can produce a much higher confidence in the conclusion, even if each

source is individually somewhat suspect. The disjunctive combination formula below integrates

multiple observation certainties and indicator strengths to yield a composite confidence measure on

the conclusion.

Por

(C.) = 1 -n,

kBl (1 - P(D

i )l(D.,R

j))

P

4

(C.) = II k

=1 [1 - l(Dj) Rj)(1 -P(Dj))]

The corresponding conjunctive formula for necessity conditions is given in the second equation. In

both cases we assume that all P(D

j )'s are true probabilities, and that indicator strengths are

normalized in the [0,1] interval, with 0 meaning no correlation and 1 meaning absolute correlation.

The output P(C.)'s are then true probabilities (whose accuracy depends on the validity of the

independence assumptions mentioned earlier). Inference rules may thus be chained and their

certainty values propagated by applying the formulae above. We found these rules to be quite

workable in SMOKEY, and much faster to compute than DempsterShafer [8], fuzzy logic [11] or other

complex methods less suitable for real time performance. (The reader interested in probabilistic logic

in Al is referred to [7] as a starting point.)

221
7. Concluding Remark

The SMOKEY project produced a successful pilot implementation of a fully-automated fire

assessment and suppression system, but thus far has remained within the laboratory. It may now be

time to build a scaled- up version and place it in a real operational setting as a field trial with complete

manual overrides by human operators. The primary contributions of SMOKEY have the

computational mechanisms developed for real-time artificial intelligence systems, which are already

finding applications outside their initial context. RuleKit and FrameKit, for instance, have seen

multiple uses at many different sites, and the agenda-driven discipline is gaining popularity. Finally,

the computationally tractable evidentiary combination methods proved to be just what the fireman

ordered for efficient real-time probabilistic reasoning. With all these tools in place, we are now ready

to start engineering larger scale real-time decision making systems.

8. References

1. Carbonell, J. G., "The POLITICS Project: Subjective Reasoning in a Multi- Actor Planning

Domain," in Computer Science Research Review, Carnegie-Mellon University, 1979-1980.

2. Carbonell, J. G. and Joseph, R., "The FrameKit + Reference Manual", CMU Computer Science

Department internal paper.

3. Carbonell, J. G., Subjective Understanding: Computer Models of Belief Systems, Ann Arbor, Ml: UMI research press, 1981.

4. Duda. R. O., Hart, P. E., Konolige, K. and Reboh, R., "A Computer-Based Consultant for Mineral

Exploration," Tech. report 641 5, SRI, 1979.

5. McDermott, J., "XSEL: A Computer Salesperson's Assistant," in Machine Intelligence 10, Hayes,

J., Michie, D. and Pao, Y-H., eds., Chichester UK: Ellis Horwood Ltd., 1982", pp. 325-337.

6. Nii, P. H. and Figenbaum, E. A., "Rule-Based Understanding of Signals," in Pattern-Directed

Inference Systems, Waterman and Hayes- Roth, 1978, eds., New York: Academic Press, 1978.

7. Nilsson, N. J., "Probabilistic Logic," Artificial Intelligence, Vol. 28, No. 1, February 1986, pp. 71-87.

8. Shafer, G. A., A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ,

1979.

9. Shell, P. and Carbonell, J. G., "The RuleKit Reference Manual", CMU Computer Science

Department internal paper.

10. Shortliffe, E., Computer Based Medical Consultations: MYCIN, New York: Elsevier, 1976.

11. Zadeh, L. A., "Fuzzy Logic and Approximate Thinking," Synthese, Vol. 30, 1975, pp. 407-428.

222
FIRST SYMPOSIUM ON THE THEORY AND APPLICATION

OF EXPERT SYSTEMS IN EMERGENCY MANAGEMENT OPERATIONS

Sponsored by

The Federal Emergency Management Agency

and

The National Bureau of Standards

Wednesday, April 24 and Thursday, April 25, 1985

Department of Commerce Main Auditorium

14 ch Street and Pennsylvania Ave., N.W.

Washington, D.C.

PROGRAM

Wednesday, April 24, 1985

9:00 A.M. - 9:10 A.M.

Opening Remarks Saul I. Gass

Symposium Chairman

University of Maryland

National Bureau of Standards

9:10 A.M. - 9:20 A.M.

Welcome Gen. Louis 0. Giuffrida

Director

Federal Emergency Management

Agency

9:20 A.M. - 9:30 A.M.

Introductory Remarks Raymond G. Karamer

Deputy Director

National Bureau of Standards

223
9:30 A.M. - 10:30 A.M.

A Theory of Expert Systems A. B. Whinston, Purdue

University

10:30 A.M. - 11:30 A.M.

The Management of Uncertainty in Expert Systems L. A. Zadeh, University of

California, Berkeley

LUNCH 11:30 A.M. - 1:00 P.M.

1:00 P.M. - 2:00 P.M.

New AI Tools for Building Management Aid Systems D. L. Waltz, Thinking

Machines Corporation

2:00 P.M. - 3:00 P.M.

TIMM: A Tool for Building Expert Systems J. N. Craig, General

Research Corporation

3:00 P.M. - 4:00 P.M.

Emergency Management Decisions at the Local Level.... R. S. Wilkerson, Federal

Emergency Management

Agency

4:00 P.M. - 5:00 P.M.

Decision Support Systems in Emergency Management

Operations: A Microcomputer Perspective F. De Balogh, University

of Southern California

Thursday, April 25, 1985

9:00 A.M. - 10:00 A.M.

Information Technology for Emergency Management R. L. Chartrand, Library

of Congress

10:00 A.M. - 11:00 A.M.

Emergency Management for Chemical Spills C. Johnson, Oak Ridge

National Laboratory

11:00 A.M. - 12:00 Noon

Roles of Simulation in the Application of Expert

Systems in Emergency Management Operations A. B. Clymer,

Clymer Technology

LUNCH 12:00 Noon - 1:30 P.M.

224
1:30 P.M. - 2:15 P.M.

Expert System for Fire Emergency Management... G. N. Berlin,

Modeling Systems

Incorporated

2:15 P.M. - 3:00 P.M.

Sensor-Based Diagnosis in the SMOKEY System J. Carbonell,

Carnegie-Mellon

University

3:00 P.M. - 3:45 P.M.

The VENTILATION EXPERT for Underground Coal Mines J. L. Kohler,

The Pennsylvania

State University

3:45 P.M. - 5:00 P.M.

Panel Discussion

225
FIRST SYMPOSIUM ON THE THEORY AND APPLICATION OF

EXPERT SYSTEMS IN EMERGENCY MANAGEMENT OPERATIONS

Speakers' Names and Addresses

Dr. Carroll K. Johnson

Oak Ridge National Laboratory

Building 4500 North

Mail Stop CI

Oak Ridge, TN 37830

(615) 574-4975

Dr. David L. Waltz

Thinking Machines Corp,

245 First Street

Cambridge, MA 02142

(617) 876-1111

Mr. Geoffrey N. Berlin

Modeling Systems, Inc.

1718 Peach Tree St. N.W.

Atlanta, GA 30309

(404) 876-9977

Mr. Ben Clymer

Clymer Technology

32 Willow Drive, Apt. IB

Ocean, NJ 07712

(201) 493-4364

Dr. Lotfi A. Zadeh

Department of Electrical Engineering

University of California

Berkeley, CA 94704

(415) 642-4959

Dr. Frank De Balogh

Decision Support Laboratory

Institute of Safety and Systems Management

Room 109

University of Southern California

Los Angeles, CA 90089-0021

(213) 743-4048

Dr. Andrew Whinston

Krannet School of Management

Purdue University

West Lafayette, IN 47907

(317) 494-4468

Dr. Jaime Carbonell

Computer Science Department

Carnegie-Mellon University

Pittsburgh, PA 15213

(412) 268-3064

Mr. Robert Lee Chartrand

Congressional Research Service

Library of Congress

Washington, D.C. 20540

Dr. Jeffery L. Kohler

Department of Mineral Engineering

The Pennsylvania State University

University Park, PA 16802

(814) 865-4491

Mr. Joseph N. Craig

General Research Corp.

7655 Old Springhouse Rd,

McLean, VA 22102

(202) 287-7056

(703) 893-5900

Mr. Robert S. Wilkerson

State and Local Programs and Support

Federal Emergency Management Agency

500 C Street, S.W.

Washington, D.C. 20472

(202) 646-2861

226
APPENDIX I: EXPERT SYSTEMS AND EMERGENCY MANAGEMENT: BACKGROUND PAPER

Saul I. Gass

College of Business and Management

University of Maryland

College Park, MD 20742

and

Robert E. Chapman

Mathematical Analysis Division

Center for Applied Mathematics

National Bureau of Standards

Gaithersburg, MD 20899

This background paper was prepared for the speakers at the Symposium.

It served to introduce the disparate communities of expert systems and

emergency management to the basic problems and procedures of the two

fields. We reproduce it here as background material for those readers

who are external to either or both of these fields.

227
EXPERT SYSTEMS AND EMERGENCY MANAGEMENT: BACKGROUND PAPER

The National Bureau of Standards (NBS) is conducting a research effort under

the sponsorship of the Federal Emergency Management Agency (FEMA) to identify

specific areas of emergency management operations where the use of expert

systems would be of greatest benefit to FEMA. A symposium, held on April

24-25, 1985, is a key component of the NBS research effort, as it provided a forum through which researchers in the area of artificial intelligence can meet and interact with emergency management personnel. The symposium was of

immediate assistance to FEMA because it provided guidance on how FEMA should

structure and budget its expert systems development and training programs.

The following material was prepared as background information for the

Symposium's speakers.

I. An Introduction to Expert Systems

Although there are numerous texts and articles available on the subject of

expert systems, one which is particularly useful as a reference document for

the upcoming symposium is Building Expert Systems .! This text is recommended

because chapter 10 contains a discussion of the Oak Ridge National Laboratory

(ORNL) hazardous chemical spill program, an expert system which addresses an emergency management problem. The following brief description of an ideal

expert system has been excerpted from the text; the system is illustrated

schematically in Diagram 1.

At the most basic level, an expert system may be defined as a computer-based

model which is able to replicate the decision of one or more human experts in

a well-defined subject area. Furthermore, the types of problems addressed by

expert systems are sufficiently difficult to require significant human

expertise for their solution. An expert system is therefore a "model" of the

expertise which the human "expert" brings to bear on the problem. The major

components which 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 current

problem that may be useful in formulating a solution. The inference procedure

provides a control structure for bringing to bear the information contained in

the knowledge base (i.e., facts and rules) on the problem.

Notice that it is the distinct nature (i.e., separation) of knowledge base and

the inference procedure which taken together captures the human decision

maker's expertise. This separation and its associated flexibility is absent

in traditional models which use a rigidly defined control structure to

manipulate the information contained in a database. Knowledge is therefore

the key and to a great degree the performance of an expert system is a function of the size and quality of the knowledge base that it possesses.

Some authors thus choose to use the term knowledge-based expert systems to

emphasize the importance of knowledge in modeling human expertise.

*F. Hayes-Roth, D. A. Waterman and D. B. Lenat (editors), Building Expert

Systems , Reading, MA: Addi son-Wesley , 1983.

228
r

An ideal expert system contains: (1) a language processor for problemoriented 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,

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

language processor mediates information exchanges between the expert system

and the user. Typically, the language processor interprets questions posed by

the user, commands given by the user, and volunteered information. Conversely

the language processor formats information generated by the system, including

answers to questions, explanations and justifications for its behavior, and

requests for data.

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

"facts" constitute a body of information that is widely shared, publically

available, and generally agreed upon by experts in the field. The "rules" are often based upon less well-known heuristics (rules of plausible reasoning,

rules of good guessing) that characterize expert-level decision making in the

subject area.

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

decisions that the expert system manipulates. Every expert system uses some form of intermediate decision representation. However, only a few explicitly

employ all of the components shown in Diagram 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 record the potential actions awaiting execution, which

generally correspond to knowledge base rules that seem relevant to some decision place in the global data base previously. 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.

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 rules.

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

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the potential rules. The consistency enforcer attempts to maintain a consistent representation of the merging 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.

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 hypothese 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 in Diagram 1. The

key distinctions are the inclusion of the knowledge base and a justifier in an expert system; both components are contained within the dashed rectangular

area.

It was noted earlier that in an expert system the knowledge base and the

control structure were kept separate. In a conventional model, the

programmer's and domain specialist's expertise are embedded in the code.

Thus, if one were to extract a dozen lines of code from a conventional model,

it would be difficult to say what domain specific knowledge is being applied

to the problem. In an expert system, a representative section of code may

include a rule or provide information on the inferences being made. By

maintaining a separation of the knowledge base and the control strategy, the

knowledge in an expert system becomes more easily identified, more explicit,

and more accessible. This implies that an expert system is both flexible and

can be kept highly modular. Therefore, an expert system should be easier to

change or augment than a conventional model. Since the domain knowledge is

intermixed with the control structure (i.e, algorithms) in a conventional

model, it is not as clear how the knowledge embedded in the code should change

in order to improve the model.

The second key distinction between an expert system and a conventional model

is the inclusion of a justifier. The justifier enables the user to make

inquiries so that an "audit trail" from the problem's statement of its

solution can be established. Expert systems are therefore more likely to be

"transparent" to the user than conventional models. Furthermore, since the

explanations provided by the expert system are in terms of its goals and its

rules, they may be quite illuminating. The explanation capability may

therefore facilitate the development of the system since it promotes a classification of previously unformalized knowledge in the problem domain.

251
Two other items which serve to distinguish an expert system from a conventional model are the elements marked with asterisks in Diagram 1. They

are: (1) an agenda in the global data base; and (2) a scheduler in the

control structure. In a conventional model, the plan for generating a solution is governed by one or more algorithms. The global data base thus

consists of inputs, partial solutions and one or more actual solutions.

Consequently, the agenda is predetermined by the plan (i.e., the algorithms

employed); the plan thus becomes a part of the control structure. Because the

rules are not explicit in a conventional model, the scheduler is also subsumed

under the plan.

One of the primary reasons for the increased importance of expert systems

within the modeling community 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.

If one were to build an expert system from scratch, 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.

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 artificial intelligence language, such as LISP in order to

exploit its symbolic manipulation capabilities, or a common language such as FORTRAN. The main distinction between building an expert system based on an artificial intelligence or common language and a shell is that the analyst

is relieved of the painstaking task of LISP or FORTRAN programming.

Consequently, the shell enables the analyst (e.g., 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.

232
The relationship between the idealized expert system and the various aspects

of emergency management which follow will be illustrated through reference to

the ORNL hazardous chemical spill program. This program has proved extremely

useful to ORNL both for responding to emergency situations and as an educational tool.

Clearly, an accidental spill of oil or chemicals at ORNL may produce a serious

emergency, depending on the properties and quantity of the substance released,

the location, and whether or not the material enters a body of water.

Emergency countermeasures must be applied immediately, particularly if the

situation presents a health, safety, or environmental hazard. Appropriate

countermeasures for various types of spill situations are governed by

extensive reasoning. The U.S. National Oil and Hazardous Substances Pollution

Contingency plan dictates the handling of many situations. Regional, Federal,

State, city, and installation contingency plans also exist. In addition, at

the individual installation level, a spill prevention, control, and counter- measures plan (SPCC plan) is required by Federal law for nontransportati on facilities that may release oil spills to navigable waters.

As may be the case in other emergency response situations, the evaluation of

the magnitude and severity of the spill are often carried out under hectic

conditions before the scope of the spill emergency is understood. Mistakes in

early containment, notifications, and hazard appraisal can be costly, and the more organized those early steps can be made, the better. Knowledge

engineering may be viewed simply as a technique for formalizing common sense heuristic solutions into an understandable and computationally practicable

form.

The program is potentially most useful during off -shift hours, when decisions

must be made by personnel lacking formal training in the physical sciences.

In such situations the program acts in a consulting mode while gathering

information concerning a spill. Meanwhile, in the background, the program

uses its various knowledge sources plus the accumulating facts to model and

simulate the spill, to monitor for actions that should be taken or additional

information that should be gathered, and to issue any appropriate messages and

warnings.

As we shall see shortly, the ORNL hazardous chemical spill program includes

some of the most important aspects of an emergency management problem. First,

the system must integrate diverse sources of knowledge (e.g., SPCC plans,

characteristics of each chemical, geographical features). Second, it must be

capable of reasoning heuristically with incomplete and errorful data. Third,

it must be able to accept data and advice continuously as they become

available. Finally, it must be able to allocate limited resources to various

tasks in a reasonable order.

II. The Nature of Emergency Management

The ability of public officials to respond correctly to an emergency situation

is conditioned by their being able to recognize that a particular type of

emergency has occurred and to initiate predetermined and ad hoc actions

designed to alleviate the situation. Some localities have established

contingency plans in the area of public safety. For example, New York City

233
has a computer-based system that directs the repositioning of fire trucks

based on the location and severity of ongoing fires. Many metropolitan areas and local councils of governments have centralized the process by which an emergency is declared and resources positioned for action and response, e.g.,

during a hurricane or blizzard. Some localities have functioning command

centers that enable them to pool, coordinate and allocate emergency resources.

In general , however, the management of the response to an emergency tends to

be reactive instead of proactive. Other aspects of emergency response are:

(1) the need to determine appropriate guidelines for response actions and

allocations of resources under changing conditions, (2) the training of

emergency managers, and (3) the development of computer-based decision aids

for use by emergency managers.

From a national perspective, FEMA has an important and direct role in ensuring

that our citizens and their property are given the best protection under all

emergencies. FEMA's major activity areas prior to an emergency are mitigation

and preparedness. Given an emergency, FEMA is then concerned with response

and recovery (see Chart 1).

For a major emergency, large quantities of information must be assembled from

disparate sources, quickly processed and analyzed, and translated into a form

useful for decisionmaking at many levels, up to and including the President.

The collection and dissemination of such information is the function of the

National Emergency Management System (NEMS). This system includes

telecommunications, computers, and information on networks that link Federal

and regional command centers. In terms of FEMA's coordinating and operational

functions, our discussion is focused on the question "How can FEMA gather,

analyze, and transmit information to all levels of emergency managers to

assist them in determining the best response(s) to an emergency situation?"

And more specifically, we are concerned with how expert systems can aid in

answering this question.

III. The Framework for Emergency Mobilization!

A. Background

1. National Security Decision Directive 47 sets forth principles and policies

for emergency mobilization preparedness. It states that it is the policy

of the United States to have an emergency mobilization capability that

will ensure that government, at all levels, in partnership with the

private sector and the American people, can respond decisively and

effectively to any major national emergency.

^-Portions of Sections III and IV are abstracted from Federal Preparedness

C i rcu 1 a r 2 , The Conceptual Framework for Emergen cy Mobilization Preparednes s.

It is important to point out that the discussion which follows is intended to

be illustrative; it should not be viewed as a definitive statement of the

activities involved in emergency mobilization preparedness.

234
Chart 1 FOUR PHASES OF COMPREHENSIVE EMERGENCY MANAGEMENT

11

MITIGATION

(LONG-TERM)

DEFINITION: Any activities which ac- (uallv eliminate or reduce the probability of occurrence of a disaster It also includes long-term activities which re- duce the effects of unavoidable disasters

PREPAREDNESS

(TO RESPOND)

DEFINITION: Preparedness activities are necessarv to the extent thai mitigation measures have not. or cannot, prevent

disasters. In the preparedness phase, gov- ernments. organizations, and individuals develop plans to save lives and mini- mize disaster damage Preparedness

measures also seek to enhance disaster response operations.

RESPONSE ITOFMFRCFNO)

DF.FINrnON Response activities .r

cur immediately befnra. luring and directly after an emergency or di- saster. Generally. they are te- ylgned to provide varnin«. population protection, and mergenrv in- sistence. They also sees tu tracer

the probebllltv of aeconderv Jemsge

and to speed reccrvtrv operations.

RECOVERY

(SHORT AND LONG TERM)

! DEFINITION: Recovery continues until

all systems return to normal or Better Shori term recovery returns vital life- support systems to minimum operating standards Long-term recovery may continue (or a number of years after a disaster Their purpose is 10 return life to normal, or improved levels.

2

GENERAL MEASURES: Building codes Hazards analyse!

Tax Incentives/disincentives

Zoning and Land use management

Buldg. uac regulatlone/ssfetv codaa Compliance and enforcement Resource alloc. /lnceratac* snaring Preventive haalth care Public education

Emergency Operaclona Plans

Emergency exercises/ training

Warning systems installation

Emergency communications sv stems

Evacuation plana and training

•.(source lnwntoriee/contect lists Emergency Operation* Casters

Mutual aid agreements

Public information/education

Hazards analvais updates

Activate plana, warnings, broadcast Notify public authorities Mobilize emergency t ease /equipment Provide shelter, mass care Lav 4 ordar, traffic control Baargmcy medical assistance Mas emergency operations centers Delcar* disaster/evacuate Search and rescue

Damage Insurance/ loans and grants Temporsry housing Long-term medical care Disaster unemployment insurance

Public Information

Health and saferv education

Recons true t Ion Counseling programs Economic Impact studies

Disaster Assistance Centers 3

HAZARD SPECIFIC MEASURES

FLOOD: Dam construction- insccct.jn Scream channe 1/ levees / f loodvai 1

s

Flood slain management

Coastal zone management

Flood Insurance Contour farming/reforestation

Temporary levee construction

Stream flow monitoring

Ice and debris removal Temporary flood proofing

Flood warning svs terns

Helicopter search Boat rescue Sandbagging Evacuation

Damage assessment Mass shelter and care

Decontamination of water sources Clear debris and replant crops

Pump out flooded baaemente

Remove temporary f loodproof log Monitor disease Repair and rebuild Vector control EPIDEMIC: School innoculatiotu Rodent/insect eradication Water punficanon

Sanitary waste disposal Health codev laws/ inspections Public health education

Stockpiling drugs

Physician preparedness plana

Public notification

Quarantine regulations and plans

Emergency medical authorities

Innoculatlon programs, general

Quarantine

- Disinfect property Secure bodies Isolate carriers Public Information Innoculatlon programs, specif lc

Continuing research into causes Long-term restorative therapy

Case studies 4 evaluation Community health programs

Inf rastructural Improvements

FIRE: Fire codes

No- smoking lava Fire toning

Fir* safety education Sprinkler ordinances

Smoke detector lavs

Fire drills/exit signs Call boxes/ smoke detectors

Police crowd control training

Fire department aid agreements

pirvilohlM riicngiiics1 irainina [fauning Automatic sprinkler installation

Fire suppression Containment

Crowd control Emergency medical services Mass shelter 4 csre Invest lgst ion/assessment

Rebuilding

lazing burned-out buildings Reforestation

Incident evaluation

LI 1 1 gat Ion

CosBBunlty reorganization HAZARDOUS MATERIALS SPILL:

Transport speed limits Container structure codes Corporate licensing Restricted routing

Materials identification codes

Containment and scrubbing equipment

Stockpile neutralizing materials

Emergency training for transporters

Special apparatus for emergencies

Re spender training Chemical data information systems

Identify material Notlfv National Response Center/ CHEMTREC

Containment, cordon area

Pliaae tracking Alr/wacer/soll contamination control

Saasaess existing regulations

Decootamlnate environment Cleanup and disposal

Public awareness Litigation

Environmental impact statements LANDSLIDE: Forest management

Preserve ground cover Maintain natural runoff Zoning 4 Land codes Real estate disclosure lavs

Reinforce threatened structures

Landahift monitors

Stream rechannellzatlon

Erosion controls/ground covers

Assess stability of new formation Reinforce against secondary slip Evacuation

Dlgout and debris removal

Reseed denuded areas Sew land—use planning Stablize slopes Evaluate runoff, erosion Community avaxeneae programs WIND: Roof anchors uinoov size and thickness codes Mobile home tiedovns windbreaks

Storm shelter construction

Property protection measures

Store watch and warning guides

Tui LttdO" Spot CCaT occvox^c — —

Activate warning svstem Reinforce damaged property

Broadcast all-clear Damage assssemant

Reconstruction

Debris clearance 4 removal Decontamination

Water purification HURRICANE STORM SURGE:

Sarrler islands zoning Coastal wetlands protection

Replace coastal sand dunes Construct breaxvaters/levees Coastal zone management

Public information programs

Vertical evacuation plan*

Storm tracking

Shutter windows

Seek shelter

Evacuate planes and boats

Tiedovns

Enforce evacuation Traffic routing 4 control Search & rescue Reinforce weakened structures

Rebuild destroyed sand dunes iulkhead eroding areas Seed/etabllze eroding coast

Evaluate/ address secondary ef feces

GASOLINE SHORTAGE:

Alternatives research Allocations/international sharing Mass transit systems/car pooling Design energy efficient engines Reduce speed limns Energy conservation program

Stockpile reserve* Reallocate to shortage area*

Rationing plans Resource management plans

Odd-even purchase program Minimum purchase requirements

Increase refinery production Deregulate oil Increase gasoline prices- Activate alternatives Administer set-salde program*

Excess profits tax on companies

Reaaaess allocation plan Two-way truck hauling Rebuild stockpiles

Share resources

NUCLEAR PLANT ACCIDENT:

Site zoning Waste management/containment

research Plant safety codes/ inspections Plant operator training Environmental impact research/ statements

Radiological emergency response plan Contamination monitoring

Identify vulnerable populations

Shelter preparation

Designate Governor's technician

Emergency procedures rehearsal

Plant personnel creinlng

Community education program

Contain radioactivity Radiological mooltorlng/mapplng

Radiological exposure control Aomlnleter rsdlo protective drugs Flume cracking

Activate near-site Emerg Ope. Cer.

Reassess siting requirements Monitor deterioration of contaminant Decontamination Recovery and reentry On-site cleanup Investigation snd reporting

ATTACK

Continuity of government funding Coord of defense/all-rlsk plans Maintain National Guard /Reserves

Host area/hardened shelter dev't Diplomacy

Arms control agreements t_

Defense mobilization plans

Protection of vital records

Continuity of government plana

Shiclear Civil Protection plans

Emergency resource mgc plana

Shelter management training

War-related exercises

Disperse succaaaloo teems Deploy critical resources Activate rationing

Activate vital workers plan Acclvate emergency broadcaacs

RADET monitoring

>

Redevelop/organize communities

Reconstitute governments

Provide securltv Allocate scare resources

' Often recoverv includes mitigation measures againsi repeal ol the disaster Thus recoverv and mmgation are relaieo —showing circular nature of mitigation.

i preparedness, response and recovery

EACH COLUMN SHOWS'. This chart was developed for use by comprehensive emergency management review

1. A definition of the phase: leaders. Many of the measures could be debated. We encourage clarification, editing

2. Examples of general measures which apply to all hazards: and. and suggestions for making it more useful

3. Examples of specific measures which apply to particular hazards. Contact: Hilarv Whittaker. Director. Emergency Management Project NATIONAL GOVERNORS' ASSOCIATION t:o:i 6:4-5365 N'GA : 12 'Si 235
2. Executive Order 11490, Assignment of Emergency Preparedness Functions to

Federal Departments and Agencies, requires departments and agencies to

prepare national plans and programs and to attain an appropriate level of

readiness with regard to the functions assigned. It also requires the

Director of FEMA to establish Federal policies for and coordinate all

emergency preparedness activities and functions of the Federal Government

and be responsible for the preparation of guidance to Federal departments

and agencies to assist them in performing their assigned emergency

functions.

B. Def i nition sl

1. Emergency. A sudden, generally unexpected event which does or could do

harm to people, the environment, resources, property, or institutions.

Emergencies range from relatively local events to regional and national

events and may be caused by natural or technological factors, human

actions, or national security-related events. The two types of

emergencies are:

a. (1) National Security Emergency . Any natural, man-caused, or technological occurrence which, because of its size or intent, seriously

degrades or threatens the national security of the United States.

b. (2) Dome stic Emergency . A natural disaster or other emergency that

does not seriously endanger national security.

2. Mobilization . The process of marshalling appropriate resources to manage

emergencies. Mobilization as used here is defined broadly and applied to

both civil and military resources.

3. Preparedness . A state of readiness to respond to and manage any type of

emergency.

C . Dimensions of Emergency Mobilization

Three dimensions should be considered in emergency mobilization preparedness:

the spectrum of emergencies, mobilization categories, and time phases.

1. Spectrum of Emergencies . Emergencies may be classified according to type

and size. Consistent with the concept of integrated emergency management,

plans for the full spectrum of emergencies, irrespective of type, should,

to the extent possible, reflect common policies and compatible procedures

so as to assure that efficient and effective use is made of all available

capabi 1 ities

a. Type of Emergency . Figure 1 shows examples of different emergencies,

grouped according to type. Each type is then described.

*A1 1 definitions are taken from Federal Preparedness Circular 2.

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(1) Natural disasters occur as a result of nature and occasionally can be

predicted; but, there is usually little that can be done to prevent them.

Actions are needed to evacuate the affected population, take shelter,

and/or mitigate the disaster's effects either before or after the event.

(2) Technological emergencies result from events which are primarily

unintentional acts of man. Actions are needed to minimize occurrence and

limit damage from those which do occur.

(3) Resource shortages occur when there is an insufficient supply of a resource on which society has become dependent. Actions are necessary to

provide additional resources or substitute, when possible, and conserve existing supplies. Resource management programs should include standby

plans and procedures for governmental intervention, as necessary, into the

market system to ensure the enhancement of supply and the allocation of resources to military and essential civilian needs.

(4) Disorders are intentional acts of violence by one or more persons.

They can sometimes be predicted and prevented. Proper action here is to

emphasize prevention, be prepared to take immediate action to limit

damage, and provide for recovery and restoration.

(5) War is an armed conflict between nation-states or between political

factions within a nation-state. War is caused by man and can be

prevented. Prevention (deterrence) is most desirable; but, if deterrence

fails, U.S. objectives will be pursued by military or other means and will

be guided by a plan for terminating the war in a manner that ensures achievement of those objectives.

b. Size of Emergency . The various types of emergencies described above

can be ranked by magnitude into local, regional, national, and

international emergencies. It should be recognized, however,, that there

are variations in individual emergencies and that local emergencies may be

of national or international interest and/or impact (see Figure 2).

c. Role of Government . Within the spectrum of emergencies, different

levels of government play different roles in varied situations.

(1) In a domestic emergency, which occurs within a single geographic area,

the situation can generally be managed by local and State resources.

Federal assistance is provided only where authorized or required by law

and when local and State governments need supplemental help.

(2) In a regional emergency which occurs across several States or within a single State, but with impact over several States, the Federal Government

may coordinate the Federal response among several States and apply

Federal resources across the entire region. Federal assistance is

provided where authorized or required by law and to supplement, as necessary, local and State resources.

238
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(3) In a domestic emergency, which impacts on the entire Nation, the

Federal Government has the responsibility to coordinate the Federal

response, provide assistance where authorized or required by law, and

supplement local and State resources.

(4) In a conventional war, responsibility for management of national

security emergencies rests primarily with the Federal Government.

(5) In a nuclear war situation, preparedness is a joint Federal, State,

and local responsibility. Responsibility for national response and

recovery is primarily a Federal responsibility dependent upon State and

local coordinated support.

2. Mobilization Categories .

Seven mobilization categories are established according to the type of resource required to manage emergencies.

a. 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.

b. Industrial mobilization is the process of marshalling the industrial

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

support military operations and the needs of the civil sector during

domestic or national security emergencies.

c. Economic mobilization is the process of marshalling the money, credit,

and taxes needed to: (1) finance the management of the emergency;

(2) maintain a stable economy; and (3) stimulate key sectors of the

economy.

d. Infrastructure mobilization is the process of marshalling the output

of infrastructure systems to support the entire mobilization.

Infrastructure systems include transportation, energy, communications,

automated information processing, water, and agriculture.

e. Human resources mobilization is the process of marshalling people to

provide needed labor. Human resources mobilization involves identifying

and allocating human resources among competing demands.

f. Government mobilization is the process of marshalling 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; courts;

and supporting communications, facilities, procedures, and authorities to

manage the emergency. Government mobilization activates and controls

other aspects of mobilization.

240
g. Civil preparedness mobilization is the process of marshalling

resources to provide protection for the people, industry, and institutions

of the United States against the effects 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

reconstitution following the emergency.

3. Time Phases .

The final consideration in emergency mobilization preparedness is the timing

of management actions. Four phases have been established for planning

purposes. In a real emergency, the phases may not exist in clearly

discernible stages, but they do provide a useful framework for planning for

the full range of management actions. During each of these phases, the

following types of actions are taken: mitigation, preparedness, response, and

recovery.

Figure 3 shows the four mobilization phases.

a. Phase 1, Normal Operations Phase, is the 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.

b. Phase 2, Preparation 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.

c. 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, protect the public, provide

critical assistance, limit damage, and reduce the probability of secondary

effects.

d. 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 recovery actions may continue for many years.

D . Relat i onship Amon g the Spect rum of Emergencies, Mobilization Categories , and Time Phases.

Figures 4 and 5 show the relationship of the spectrum of emergencies,

mobilization categories, and time phases.

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IV. The Scope of Emergency Management Problems

In this section we describe the diversity of activities and related problems

that FEMA must address in carrying out its mission. FEMA's activities include

day-to-day data gathering, responding to the immediate needs of an emergency,

and strategic planning and analysis. It is not our purpose to be complete

here, but instead to highlight areas that may be addressed by advanced

technological procedures, especially those of expert systems and artificial

intelligence. Our aim is to emphasize the decisionmaking requirements faced

by FEMA and related organizations and individuals.

Central to all of FEMA's activities is a viable and current information

support system that is directed toward the needs of emergency managers. Such

a system is defined to include not only data and descriptive information but

also analysis and interpretive programs. The establishment and maintenance of

this support system, although a problem in itself, cannot be accomplished

without recognition of the decision problems and other related problems of

emergency managers. These include the development and implementation of

training procedures for emergency managers at all levels, the requirement of

emergency managers at all levels to be able to find and interpret current

emergency response rules and regulations, the need of FEMA to be able to

evaluate proposed emergency guidelines and legislation, and the basic

management procedural requirements for coordinating mul ti -regi onal responses

and allocation of resources. Thus, to be effective, an information support

system must be designed to reflect the needs of these varied problems. In particular, we must be cognizant of the many confounding and limiting aspects

inherent in the broad spectrum of emergency operations. These include the

varied nature of emergencies and the extremely wide range of possible

responses requiring formal evaluation procedures that, as of today, do not

exist. At best, such procedures are now based on past experiences, which are

not necessarily general izable, and current, untested doctrines. The

operational experiences of field emergency managers are not extensive, and, as there is much turnover in these positions, the "hands-on" knowledge that

exists in the field fluctuates and is never at a high level of expertise.

Compounding an emergency operation decision process is the lack of complete

information, and whatever information is available is often conflicting. But,

decisions must be made. By its very nature, an emergency is a complex

situation that often requires a nonstandard response: a response that has

been evaluated in terms of the available information. The capability of

emergency managers to assimilate and evaluate changing situations is a function of their ability for analyzing available information and their past

experiences. And, as we have noted, these capabilities are rather limited.

The time taken to respond to an emergency can be critical. Public safety

organizations, e.g., police, fire, ambulance service, are always on duty and

take pride in their short response times. But for broader emergency

situations, like toxic and explosive spills or hurricanes that require

decision structures and response systems that are basically dormant, the

ability to mount a proper and timely response is difficult. Here, emergency

managers must be able to couple their static personnel and resource base with

sporadic incoming information to make a dynamic set of responses. This can only be effective if a full range of contingency plans has been developed and

tested by the emergency management team. Such contingency planning is not a

245
priority item at most levels of emergency management. It is costly; it

requires experienced personnel; but, it is a tactical activity that must be

placed on a solid base.

The analysis of emergencies and emergency responses is backed up with some theory on the physics of particular emergencies, but little theory on the

behavioral aspects of most emergencies. There are fire spread models, nuclear

blast and fallout models, network flow models, epidemiological models, some of

which can be of assistance in contingency planning, as well as in an operational situation. A key question is how available theory can be utilized

in an emergency information support system, especially at the local level.

One is quick to recognize that the major concerns of emergency management

operations, like all complex decision situations, have both technological and

human components. And, as the problem is of national importance and something

must be done to understand better all aspects of emergency management, there

is a danger of rushing to apply technical methodologies. An integrated

emergency information-communications system will be ineffective if we ignore

the personnel and behavorial aspects of who is to use it, how will the users

be trained, and what the decision and resource needs of the users are. To

propose an expert system is one thing, but to develop one that can be shown to

be of value to emergency managers is quite another matter. Cost-benefit

analyses of such proposals are difficult; but performing such an analysis for

a proposed expert system will highlight the strong and weak aspects and, thus,

make for better choices. The full range of emergency management operations

must be laid out and subjected to analysis in terms of needs and priorities.

V. The Managers of Emergencies

From a general emergency perspective, the individuals and groups that concern themselves with an emergency, i.e., the stakeholders and actors, include

persons at the Federal, State and local levels, as well as persons in private

sector organizations. Thus, emergency management operations and supporting

systems must be responsive to a wide diversity of needs and priorities. A

listing of Federal groups includes FEMA officials, emergency managers and

emergency response systems; The White House; Congress; Departments of

Transportation, Energy, Commerce, and Interior; Environmental Protection

Agency; Nuclear Regulatory Commission; and the National Oceanographi c and

Atmospheric Administration, among others. Of course, the interactions and

needs of these groups change by type of emergency. Depending upon the stage of an emergency, most information is used for status reports and not for

decisionmaking. However, as an emergency is not static, the needs of the

Federal stakeholders and actors can change rapidly from a passive to an active

mode. Thus, the support requirements are quite complex, and, at this point in

time, not well understood.

At the State level, the activities of emergency management are usually

centralized within the governor's office and supervised by the State Director

of Emergency Management. As is often the case, emergencies know no State

lines or regional boundaries. Thus, coordination efforts must be developed

among multi-state and multi -regional agencies, as well as between individual

State agencies. Here, resources such as the National Guard and the full range

of State safety personnel have to be allocated and coordinated based upon the

changing demands of an emergency. Officials, public safety officers, and

246
volunteers on the firing line at the local level have similar requirements.

Coordination is a must for the impacted city and county agencies and for local

councils of government. Public safety at this lower level requires fire,

police, and other emergency services to be responsive to the decisions of the

respective chiefs or directors of public safety. They must be ready to act on decisions made at the scene and on the decisions of those evaluating the

overall needs of the threatened area. Decisions on when and how to evacuate

an area must not be made in an ad hoc manner, but be based on tested

contingency plans and the most current evaluation of the emergency conditions.

The integration of private sector groups such as the Red Cross, the National

Voluntary Organizations Active in Disasters (NVOAD), and the Civil Defense

National Radio System must be coordinated at all levels. How best to manage

these diverse groups and how best to manage the data, information, and

decision needs of the stakeholders and actors is the major research task

facing FEMA; and, it, is the area in which we see expert systems being able to

make a major contribution.

VI. The Range of Emergencies

In this section, we offer without comment a listing of emergencies that are of concern to FEMA and/or State and local agencies:

- Nuclear plant: meltdown, radiation fallout

- Earthquake: dams, spills, fire, landslides

- Hurricane: winds, flooding, tornadoes

- Floods

- Volcano eruption

- Mobilization

- Enemy attack

- Civil disorder

- Terrorist attack

- Epidemic

- Power failure

- Resource shortage

- Economic emergency

- Agricultural emergency

- Boating, ships, ferry accidents

- Airplane crash

- Fire: city, forest, chemical, gas, oil

- Explosion: chemical, gas, oil

- Mine disaster

- Hazardous spi 1 1

s

- Toxic airborne release

- Subway /Transportati on disaster

- Dam Failure

- Water shortage/pollution

- Ai r pol lution

Fortunately, we in the United States encounter a small subset of such

emergencies and those that we do encounter are usually at a low level of

intensity. However, our Federal emergency disaster costs are quite high. For

example, as reported by the United Press International, the United States spent

$552 million on disaster relief in 1984. During 1984 the President directed

FEMA to allocate supplemental Federal aid 38 times, the greatest number of

247
allocations since 1979 and 11 more than the annual average over the past

decade. Twenty-six of the 38 disasters were due to floods that affected 33,800

families. A total of 41,000 families sought Federal aid. The most costly

disaster ($164 million) occurred in New York and New Jersey and was due to a severe storm and torrential rains. Other disasters included tornadoes, citrus

freeze, and fires.

VIII. The Management Problem

The full range of the management problem is described in the terms of Chart 1: Four Phases of Comprehensive Emergency Management. We briefly describe

aspects of these phases and associated activities.

A. Mitigation

The basic activities of mitigation, which are carried out at all levels of

government are legislation, codes, prevention, and education. Decisionmaking

here tends to be concentrated in setting priorities and the allocation of

funds and other resources. Computer-based aids include information retrieval

systems for analyzing current codes and legislation and econometric models for

analyzing the cost-benefit aspects of proposed regulations.

B. Preparedness

The activities in this phase involve many of the stakeholders and actors across a wide range of activities. Here we are concerned with training, resource positioning, status reporting and communications, shelter and medical

facilities, evacuation procedures, contingency planning, and education.

Decisions need to be made that relate to the succeeding phases of response and

recovery. Computer-based aids include location models, man-machine simulation

exercises, routing procedures, and automated detection systems (e.g., for

earthquakes). Expert systems could have an important role in training and

contingency planning.

C. Response

The full range of stakeholders and actors are involved in this phase. The

main activities are information gathering and analysis, allocation of

resources, coordination of responses and resources, evaluation of proposed

responses, evacuation, and protection. The main technological aids are communications and information processing. It is this phase that requires a wide range of expertise, e.g., transportation, traffic control, search,

handling of toxic materials, nuclear plant operations, radiation and plume

dispersion, weather, medical, and public safety. It is during this phase of an emergency that expert systems have the greatest potential.

248
D. Recovery

This last phase of an emergency includes the activities of insurance and

loans, cleanup and reconstruction, assessment, and post audit of the emergency

response system. Federal and local authorities can be assisted in this phase

by expert systems and other data processing aids that enable them to determine

if a request for assistance can be given within the restrictions of the

applicable legislation and regulations. Post-audit of an emergency is

important so as to be able to feed back to the response system information on what worked and what did not. Such feed back is important to an expert system

and represents the true nature of artificial intelligence.

249
APPENDIX II: EXPERT SYSTEMS AND EMERGENCY MANAGEMENT: RESEARCH DIRECTIONS

Saul I. Gass

College of Business and Management

University of Maryland

College Park, MD 20742

and

Robert E. Chapman

Mathematical Analysis Division

Center for Applied Mathematics

National Bureau of Standards

Gaithersburg, MD 20899

This appendix reproduces material that describes possible research

directions in expert systems and emergency management for FEMA. It is based on the presentations at the Symposium and a follow-up

Workshop on Expert Systems Applications and FEMA Research Activities

held on April 26, 1985 at FEMA Headquarters.

250
EXPERT SYSTEMS AND EMERGENCY MANAGEMENT: RESEARCH DIRECTIONS

This report is based on presentations and discussions of the First

Symposium on the Theory and Application of Expert Systems in Emergency

Management Operations (April 24-25, 1985) and the related Workshop on Expert

Systems Applications and FEMA Research Activities (April 26, 1985). The

purpose of this report is to describe research directions in expert systems

for consideration by FEMA management.

A general conclusion is that the new and emerging field of expert systems

could be of value to Federal and local emergency managers. Based on the

materials presented at the Symposium and Workshop, we have evidence that

expert systems (ES) have already been integrated with emergency management

(EM) systems and have proven to be worthwhile decision-aids. However, as the

full realization and benefits of expert systems, as well as its parent field

of artificial intelligence (AI), are yet to be established, there is a need

for FEMA to proceed with caution in its commitment of funds and reliance on a future payoff.

FEMA's role in an ES/EM program can take on many directions:

- projects and research activities that support FEMA

Headquarters (operational activity)

- prototype projects designed to support State and Local

projects (demonstration activity)

- establishing a central source for dissemination of

ES/EM publications (clearinghouse activity)

- holding of symposia and workshops on state-of-the-art

ES/EM developments (knowledge transfer activity)

- setting standards for and the dissemination of ES/EM

software and related computer-based training aids that

support the operations of local emergency centers

(technology transfer activity)

- developing ES/EM training exercise to be used at the

National Emergency Training Center and at the local level

(education activity)

To date, in association with the National Bureau of Standards, and the

Department of Transportation, FEMA is supporting ES/EM applications in data error analysis (NBS), message management systems (NBS), and transportation

network analysis (DOT). These projects are designed to support elements of

FEMA Headquarters. FEMA has also purchased the software package TIMM (The

Intelligent Machine Model), an aid for the development of expert systems, that

251
will be used in support of the message management and transportation projects.

The question to be addressed now is "What other ES/EM areas should FEMA

support?" Our response to this question is in terms of possible

demonstrations and research activities. First, we give an unordered list of

emergencies and corresponding areas that appear to be within the domain of

ES/EM (see Figure 1). The items shown in Figure 1 are directed at the

response phase of emergencies. It is in this phase that expert systems appear

to have the greatest potential. Note that most of the response-oriented

expert systems would be operated by State and local agencies. At the National

level, FEMA's response-oriented expert systems could be applied to issues such

as mobilization and enemy attack.

Prototypical FEMA Expert System Activities

The areas of expert systems applications shown in Figure 1 are the

f ol 1 owi ng:

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)

5. The problems associated with the 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 FEMA

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, weather, time of day and other

critical factors. The development of a prototype expert system for evacuation

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routing, designed and tested for a specific community, should be investigated

by FEMA. The current DOT transportation expert system, plus 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 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 FEMA to

investigate. 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.

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 prepardness 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 eligilibity for loans

and government reconstruction programs.

With the expected heavy use of personal computers by field personnel,

FEMA should investigate which field-based expert system activities can be

generalized and made available.

FEMA Expert Systems Projects

This section describes expert system applications in areas that stem from

FEMA's operational and educational activities; these areas are of broader

scope and/or are more technically oriented than the prototypical 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.

254
1. Model 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

systems. 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 further 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.

This project should be able to use the expert system data analysis system being

developed by NBS for FEMA; also, the construction of the expert system model

output reviewer should be facilitated by the use of TIMM. 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.

2. 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 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 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 validated. Personnel at

the Institute of Safety and Systems Management, University of Southern

California have undertaken some research in this area.

3. 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

255
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 (e.g., the message management system being developed by NBS).

4. Information/Knowledge 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.

5. Related Research Proposals

As noted, the expert system/artificial intelligence 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. We cite them here, although we feel that, in

general, due to resource constraints and uncertainty in the ES/AI field, FEMA

should not be in the forefront of advanced research, but FEMA's expert system

activities should be directed towards utilization of tested expert system

theory and practice.

a. Natural Language Dialogue Interface

This proposal is 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.

b. Decision Rules in Expert Systems

There are a number of basic areas of interest here such as:

(1) The manner in which an ES 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 ifetime.

(5) The use of decision models such as the Analytic Hierarchy

Process within an expert system.

256
c. Validation of ES

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 ES,

FEMA must establish a process with which the ES 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

importance to FEMA.

d. Expert Systems Shells and PC 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/ES

based on field requirements, as well as FEMA Headquarters' applications.

6. General FEMA Acti vities

a. Standards and guidelines for the development of expert systems by

FEMA and field personnel.

b. Expert system technology clearinghouse for distribution of emergency

management related systems and information.

c. Develop expert system curriculum and materials for training of

emergency management personnel.

d. Investigate ES/EM activities being done by other countries.

e. Tracking of other fields use of ES/AI and microcomputers and

relationship to emergency management.

f. Setting FEMA expert system priorities.

U.S. GOVERNMENT PRINTING OFFICE: 1986- 18 1- 07 6/ »t 0 1 9 7

257
NBS-1 14A (REV. 2-8C)

U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET (See instructions)

1. PUBLICATION OR

" REPORT NO.

NBS/SP-7I7

2, Performing Organ. Report No. 3. Publication Date November 1986

4. TITLE AND SUBTITLE

Theory and Application of Expert Systems in Emergency Management Operations

(Proceedings of a Symposium)

5. AUTHOR(S)

Saul I. Gass, Robert E. Chapman, editors

6. PERFORMING ORGANIZATION (If joint or other than NBS, see instructions)

national bureau of standards Federal Emergency Management

DEPARTMENT OF COMMERCE Agency,

Washington, DC 20472

Gaithersburg, MP 20899

7. Contract/Grant No.

8. Type of Report & Period Covered

Final

9. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS (Street. City, State, ZIP)

National Bureau of Standards Federal Emergency Management Agency

Department of Commerce Washington, DC 20472

Gaithersburg, MD 20899

10. SUPPLEMENTARY NOTES

Library of Congress Catalog Card Number 86-600546

~J Document describes a computer program; SF-185, FIPS Software Summary, is attached.

11. ABSTRACT (A 200-word or less factual summary of most significant information. If document includes a si gnificant

bibliography or literature survey, mention it here) The First Symposium on The Theory and Application of Expert Systems in Emergency

Management, held at the Department of Commerce, Washington, D.C. (April 24 and 25, 1985) was funded by the Federal Emergency Management Agency and organized by the National

Bureau of Standards' Operations Research Division. The purpose of the symposium was to

bring together researchers in expert systems, artificial intelligence, and emergency

operations in a forum to review the concepts of expert systems and the problems of

emergency management, with the objective of determining how expert systems can be used

to augment the experience of local, State and Federal emergency managers faced with the

difficult tasks of determining the best response to an emergency situation. Speakers

addressed the following areas: The theory and uncertainty aspects of expert systems,

artificial intelligence's future role in emergency management, technology for building

and using expert systems, emergency management decisions and information needs and uses,

applications of expert systems in the management of chemical spills and shipboard and

coal mine fires, and the role and use of simulation in emergency management expert

systems

12. KEY WORDS (Six to twelve entries; alphabetical order; capitalize only proper names; and separate key words by semicolon s) artificial intelligence; chemical spills; coal mine fires; decision support systems;

emergency management; expert systems; fire management; shipboard fires; simulation;

uncertainty

13. AVAILABILITY

[Xl Unlimited For Official Distribution. Do Not Release to NTIS

[V | Order From Superintendent of Documents, U.S. Government Printing Office, Washington, D.C.

20402.

| Order From National Technical Information Service (NTIS), Springfield, VA. 22161

14. NO. OF

PRINTED PAGES

264

15. Price

USCOMM-DC 6043-P80
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