Index

A

AACES, see Alternatives Analyzer, Comparer, Editor, and Sourcer

Abduction, 18-10

Academic laboratories, 6-1

Accounting

expert systems, 27-2

knowledge, ambiguity of, 27-10

Accounting and auditing, 27-1

applications and impacts of accounting expert systems, 27-7

background, 27-2

future trends, 27-10

historical account, 27-3

era of research prototypes, 27-3

field is born, 27-6

moving into practice, 27-5

research issues, 27-9

state of art, 27-8

ACL, see Agent communication language

Act phase, 18-11

Actor focus stack (AFS), 19-28

Adaptation

goal of, 6-5

process, 6-5

Adaptive machining control, 22-4

Adaptive maintenance, 25-5

Adaptive optimal control, 15-12

Adaptive optimal controller, 15-13

Adaptive optimal control model constructor, 15-13

Administrative tasks, automation of, 28-23

ADPA, see American Defense Preparedness Association Advertising design, 29-1

ADVISOR, 20-5

AEE, see Application Execution Environment

AESOP, 26-3

AFOSR, 34-2

AFS, see Actor focus stack

Agent, 16-1

communication language (ACL), 10-8, 16-8

model, 1-9

systems, major advantage of, 16-5

Agglomerative Hierarchical Clustering (AHC), 13-7

Aggregate notational model, illustrative, 15-6

Agricultural diagnostic expert system, 35-3

Agriculture, 35-1

future trends, 35-10

historical account, 35-3

methodologies and applications, 35-5

domain application aspects, 35-6

methodological aspects, 35-5

proposed domain specific methodology, 35-6

need for expert sytems in, 35-2

information transfer problems, 35-2

suitability and feasibility of expert systems, 35-3

research issues, 35-8

automatic knowledge acquisition, 35-10

integration of software components with agricultural expert systems, 35-8

intelligent retrieval of agricultural data, 35-10

knowledge sharing and reuse, 35-9

AHC, see Agglomerative Hierarchical Clustering

AI, see Artificial intelligence, 4-3

Airborne imaging spectrometer (AIS), 30-14

AIS, see Airborne imaging spectrometer

Alarm

diagnostics, 20-7

Response Procedure (ARP), 20-13

Algorithms, efficient, 17-9

Alphanumeric data, formatted, 14-14

Alternatives Analyzer, Comparer, Editor, and Sourcer (AACES), 34-5

American Defense Preparedness Association (ADPA), 34-3

AMEX, 26-4

Analysis step, 28-8

Anaphora

problems, 19-29

resolution, 19-27

ANNs, see Artificial neural networks

Antecedent-consequent relations, 18-3

Anxiety, 31-19

AOD, see Audit Opinion Decision

API, see Application Programmer Interface

Application

domain, 19-33, 14-23

Execution Environment (AEE), 16-7

Programmer Interface (API), 16-7

Approximate reasoning, 13-13, 24-6

ARP, see Alarm Response Procedure

Artificial intelligence (AI), 2-1, 3-1, 4-3, 8-1, 11-1

advances in, 21-1

application tasks, 24-4

distributed, 16-11

integration of CAD with, 22-1

program, U.S. military, 34-2

search engines, 28-25

systems, 9-1

use of logic within, 18-2

Artificial intelligence (AI), unification of with optimization, 15-1

integration of rules with constraint satisfaction problem, 15-14

constraint and rule satisfaction problems, 15-14

illustrations, 15-14

unified reasoning for CRSP, 15-14

neural network based adaptive optimal control, 15-10

representation of integer programming, 15-9

illustrative integer programming model for optimal savings plan, 15-9

knowledge-based relaxation, 15-9

representation of linear programming models, 15-2

knowledge assisted model formulation, 15-5

object-oriented representation of linear programming models, 15-2

unification of LP model with rule-based system, 15-6

post-model analysis procedure, 15-6

trade-offs between goals in optimization model and rule-based system, 15-7

Artificial neural networks (ANNs), 26-12

ATI technology, 24-3

ATN, see Augmented Transition Network

Attachment rules, 19-32

Attitude determination, 12-8

Attribute, 5-24

classification trees, 10-21

definition content, 31-13

Audit Opinion Decision (AOD), 27-4

Augmented context-free grammar, 19-18

Augmented Transition Network (ATN), 19-4, 19-25

AutoCell, 24-9

Autoepistemic logic, 3-19

Automated diagnostics, 31-3

Autonomous agents, 16-10

Autonomous Underwater Vehicle (AUV), 34-9

AUV, see Autonomous Underwater Vehicle

Auxiliary rules, 5-31

B

Backtracking

limited, 1-6

process, 21-8

Backward-chaining, 30-32

BADD, see Basic Defuzzification Distribution

Bank ATM, 24-8

Basic Defuzzification Distribution (BADD), 13-17

Basic instance attributes, 1024

Bayesian belief networks, 8-1, 8-6

Bayesian model, 3-11

Bayesian reasoning, 8-10

Bayes Theorem, 6-7

BBAE, see Budget Based Analysis, Europe

Behavioral model, 9-2

Behaviors

additional spurious, 9-7

all possible, 9-7

Belief, 18-9

degree of, 8-2

network

example of, 8-8

process of using, 8-9

Benchmark problems, 17-5

Bidirectional inference, 4-5

Binary encoding, 12-6

Binary operators, 13-14

Binary search, 25-12

Binding structures, 19-41

Black-box validation techniques, 5-20

Black-Scholes marthematical model, 26-3

Blackboard

paradigm, 21-3

structure, 4-6

systems, 3-18

Blueprint, 25-4

Boolean combination, 8-4

Boxes-and-arrows approach, 1-4

Brainstorming, 2-6, 2-8

Brand management, 29-1

Budget Based Analysis, Europe (BBAE), 34-11

Business

application agents, 16-1, 16-4

environment, 27-1

objectives, 28-14

process automation, 16-2

system, 28-3, 28-18

C

Candidate generator, 9-12

CAPC, see Competitive-Activation Pattern Classification

CAPEX, 27-4

Capital budgeting decisions, 27-1

CARES system, 11-8

Case

attribute definitions, 31-12

description, 35-7

libraries, 11-9

management, automated, 28-21

Case-based reasoning (CBR), 3-19, 4-7, 11-1-6

applications, 11-6

CARES system, 11-8

SMART system, 11-6

concepts, 11-2

adaptation, 11-5

indexing cases, 11-4

representing case, 11-4

similarity matching, 11-5

emerging hybrid systems, 11-12

information retrieval, 11-13

model-based reasoning, 11-14

multiple media, 11-13

rule-based reasoning, 11-12

history of, 11-2

program, 28-8

research challenges, 11-9

case adaptation and learning, 11-12

case indexing, 11-12

case representation, 11-10

systems, 8-10

tools, 11-9

Cash flow profiler, 26-9

Catalog

agents, 21-13

function, 21-6

Categorial Unification Grammars (CUG), 19-17

CBR, see Case-based reasoning

CD, see Conceptual Dependency

CD-ROM, see Compact Disc-Read Only Memory

CE, see Control Engineers

Centers of Expertise (COE), 28-26

CERES, 30-40

Certainty

factor (CF), 4-13, 8-3, 9-8

theory, 4-6

Certification guidelines, 5-29

CF, see Certainty factor

CGs, see Conceptual Graphs

Chaining, 4-13

CHAMP, 24-9

Check violation, 35-8

Chemicals

data dictionary in domain of, 10-20

table of constants in domain of, 10-22

Chernobyl, 31-2

Chomskyos Transformational Grammar, 19-20

Circular inference structure, 5-17

Circular rules, 5-11, 5-31

Circumscription, 3-16, 3-17, 18-8

Citizen dissatisfaction, 33-5

CL, see Controlled language

Class attribute name, 10-19

Classical probabilistic model, 3-10

Clemical element, 10-34

Clinical database, 11-8

Clinical decision-making, 32-2

Clinical psychology, 2-5

Closed-loop control systems, 9-11

Closed-world assumption, 18-8

CLP, see Constraint logic programming Clustering, 19-43

Code theory, 3-20

COE, see Centers of Expertise

Cognitive psychology, 3-5

COLOSS, see Columbia River Decision Support System

Columbia River Decision Support System (COLOSS), 30-36

Comet, 10-33

Command processor, 25-12

Commercial expert, 26-6

Common Law, 3-19

Common sense ontologies, 10-7

Communication(s)

barriers, 2-1

language, 15-8, 21-4

medium, 21-14

Compact Disc-Read Only Memory (CD-ROM, 14-2

Company acquisition, style of, 19-13

COMPASS, 20-5, 26-7

Competitive-Activation Pattern Classification (CAPC), 30-18

Component-based approach, 9-6

Compositional Rule of Inference (CRI), 13-16

Computer(s)

-based systems, 26-2

developments in, 12-11

-initated dialog, 6-5

networks, 16-10

science, 19-29

Concept(ual)

classification, 31-15

clustering, 3-20

Dependency (CD), 19-6

design, 28-5, 28-7

dictionary, 31-14, 31-15

formation, 18-10

Graphs (CGs), 19-34

model

document, 10-28

role of, 6-6

nets, 3-8

Conceptualization, 3-6, 31-4, 31-8

Concreteness of indexes, 11-5

Condition

monitoring, 20-3

name, 1-24

Conditional probability table (CPT), 8-7

Confidence factors, 4-6

Configuration

problems, 28-7

selection, 21-1

Configuration design, 21-1

background, 21-2

current methods, 21-11

CSP-based methods, 21-11

distributed agent methods, 21-12

problem definition, 21-5

attributes, parts, and catalogs, 21-6

constraints, 21-7

functions, 21-5

properties of design, 21-9

specifying design problem, 21-10

research issues, 21-14

trends, 21-14

CONKRET, 5-25

Constraint(s)

agents, 17-10, 17-12

functional assignment, 29-8

importance of, 29-6

inconsistent, 17-8

logic programming (CLP), 17-2, 17-3, 17-14

propagation, 17-9, 29-5, 29-10

and Rule Satisfaction Problem (CRSP), 15-14

Satisfaction Problem (CSP), 15-14, 17-4, 21-9, 21-11 store, 17-6

-system shell, 4-9

Constraint programming, 17-1

constraint propagation, 17-9

active constraints, 17-10

building constraint agents, 17-12

map coloring, 17-11

propagating changes, 17-9

control in, 17-13

current developments, 17-15

constraints in computing environment, 17-15

interval reasoning, 17-16

mixed initiative programming, 17-15

stochastic techniques, 17-16

history, 17-2

declarative modeling and efficient enforcement, 17-2

propagation, 17-4

search, 17-5

implementation and applications, 17-13

applications of constraint programming, 17-15

constraints embedded in host programming language, 17-13

programming with constraint store, 17-6

CLP(R), 17-7

primitive constraints, 17-6

Consultation session, 2-6

Consumer sales, 14-18

Context-free grammars, 19-20

Control Engineers (CE), 20-16

Controlled language (CL), 19-50

Controller design, 12-8

CORMIX, 30-35

Cosmos, 10-33

Cost-benefit ratios, 27-11

COVADIS, 5-24

CPT, see Conditional probability table

CREDEX, 26-5

CRI, see Compositional Rule of Inference

Crossover operation, 12-5

Cross-subsumption, implicit, 5-15

CRSP, see Constraint and Rule Satisfaction Problem

CSP, see Constraint Satisfaction Problem

CUBUS, 26-8

Cucumber production management under plastic tunnel (CUPTEX), 35-4

CUG, see Categorial Unification Grammars

CUPTEX, see Cucumber production management under plastic tunnel

Customer

analysis process, 28-12

representation of, 29-7

service, 28-17, 28-20

-support applications, 11-9

types of, 28-13

Cycling rule, 5-24

D

DAG, see Directed acyclic graph

DAI, see Distributed Artificial Intelligence

DARPA, see Defense Advanced Research Projects Agency

Darwinian concept of evolution, 12-1

Data

abstraction, 7-7

dictionary (DD), 10-18

mining agents, 16-7

overflow, 24-3

reduction interface agents, 16-6

Database(s)

of cases, 11-2

containing raw log data, 30-13

-like records, 11-4

management system (DBMS), 35-1, 4-23

object, 30-43

storing constraints in, 17-15

systems, 18-6

technology, success of, 16-2

telecommunications, 24-8

DBE, see Design-Based Events

DBMS, see Database management system

DD, see Data dictionary

Dead-end rules, 5-31

Debugging utilities, 4-14

Decision

making, 8-10

accuracy, 32-13

clinical, 32-2

human, 4-1

process, 30-19

using expert systems for, 32-10

support, 28-18

knowledge-based, 30-26

systems (DSS), 3-19, 14-23

tree, 24-5, 32-2

Declarative problem modeling, 17-2

Decomposable design space, 21-10

Decomposable network, 21-13

Deduction rule-based system, 18-11

Deductive reasoning, 18-5

Default conditions, 31-20

Defense Advanced Research Projects Agency (DARPA), 34-8

Degree of belief, 8-2

Dempster-Shafer (DS), 8-5

Dempster-Shafer representation, 3-12

Design

attribute, 21-4

-Based Events (DBE), 20-11

constraints, 21-4

guidelines, 28-9

ideas, sources of, 28-12

model, 1-9, 28-10

space

consistent, 21-9

decomposable, 21-10

Deterministic parsers, 19-23

Developer interface, 4-2

Development-oriented activities, 10-11

Diagnosis, 31-1-23

diagnostics conceptualization, 31-3-22

components of conceptualization, 31-5-7

construction of static submodel, 31-14-16

creation of dynamic submodel, 31-16-17

identification of strategic, tactical, ad factual knowledge, 31-7-14

knowledge map generation, 31-17-20

meaning of conceptualization, 31-4-5

system decomposition, 31-21-22

verifications, 31-20-21

diagnostic techniques and methods, 31-3

Diagnosis Intelligent Automation Language (D-IAL), 4-9

Diagnostics, 14-18

accuracy, 32-2

automated, 31-3

systems, 9-2

D-IAL, see Diagnosis Intelligent Automation Language

DIPMETER ADVISOR, 30-10

Directed acyclic graph (DAG), 8-7

Discourse

analysis, 1944

Representation Theory (DRT), 1944

Discrepancy detector, 9-12

Disequality constraint, 17-12

Disorder symptom, 35-9

Distributed Artificial Intelligence (DAI), 16-11, 20-15, 24-6

Distributed configuration design, 21-12

Distribution costs, 4-17

Diversity, 30-5

DJI, see Dow Jones industrial average

DLLs, see Dynamic Link Libraries

Domain

constraints, 17-11

expertise, 2-2

experts, 2-4

knowledge, 28-9

-specific tools, 4-8

tailored environment, 1-7

Dow Jones industrial average (DJI), 26-10

DRT, see Discourse Representation Theory

DS, see Dempster-Shafer

DSS, see Decision support systems

Dynamic Link Libraries (DLLs), 23-8

Dynamic Predicate Logic, 19-36

Dynamic work flow modeling, 28-21

E

Earth System Science (ESS), 30-37

EB, see Explanation base

EDGE system, 6-6

EDR, see Electronic dictionary

EI, see Explanation implementation

Eigendirections, 3-20

EIS, see Executive information systems

EL, see Episodic Logic

Electrical discharge machining, 22-6

Electronic commerce agents, 16-1, 16-4

Electronic dictionary (EDR), 19-15

Electronic medical record (EMR), 32-12

Electronic salesman, 28-20

Elite selection, 12-4

Elitist replacement strategy, 23-7

Embedded systems, 14-15

Empirical exploration, 6-9

Employee

appraisal, automated, 28-23

benefits systems, 16-5

EMR, see Electronic medical record

Enterprise Project, 10-33

EOS, see Evolutionary Object System

Episodic Logic (EL), 19-44

Error-correction, 7-10

ES, see Expert system

ESDLC, see Expert Systems Development Life Cycle

ESS, see Earth System Science

Estimation tool, 30-20

ET, see Evapotranspiration

EV, see Explanation validation

Evaluation, 5-4, 10-27, 21-7

Evapotranspiration (ET), 35-5

EvEnt, 26-8

Evolutionary Object System (EOS), 23-6

Evolving prototype, 10-14

Executive information systems (EIS), 14-13

Experience, 11-1

Experimentation, 1-4

Experiments, designing, 1-3

Expert(s)

already-busy, 2-2

control, 9-11

domain, 2-4

mining approach, 9-9

multiple, 2-3

scarcity of, 33-4

single vs. multiple, 2-2

team of, 2-3

eXpert eXplorer (XX), 30-15

Expert system (ES), 4-22, 7-1, 31-2

agricultural diagnostic, 35-3

applications, complexity of, 2-1

design of, 3-5, 5-8

development, 18-1

Life Cycle (ESDLC), 1-4

platforms used in, 4-16

software used in, 4-6

explanations, 7-5

forward chaining, 30-28

future, 6-11

government-related, 33-1

heuristic, 9-8

integrated, 14-20

model-based, 4-10

number of developed, 4-5

predominant type of, 6-11

product applications of, 28-19

research on, 32-1

risky nature of, 1-12

rule-based, 4-11, 22-10

second-generation, 30-27

tool market, 4-22

types of, 4-8, 28-15

for welding, 22-7, 22-8

Expert system (ES) development tools, 4-1

choosing tool, 4-12

buyers, 4-17

cost, 4-17

developer interface, 4-14

explanation facility, 4-14

hardware, 4-15

inference and control process, 4-13

knowledge base, 4-13

support, 4-16

system interface, 4-15

user interface, 4-15

future, 4-22

automated knowledge acquisition, 4-23

domain-specific tools, 4-23

integration, 4-23

knowledge discovery in databases, 4-23

tool market, 4-22

World Wide Web, 4-23

historical overview, 4-3

tool market, 4-18

types of tools, 4-4

cased-based reasoning tools, 4-7

domain-specific tools, 4-8

frame-based tools, 4-6

induction tools, 4-7

languages, 4-4

rule-based tools, 4-5

Expert system (ES) interface, 6-1

acceptance and success of expert system, 6-3

background, 6-3

history, 6-3

special needs for expert system interfaces, 6-4

usability issues with expert systems, 6-4

user modeling and adaptive user interface, 6-4

expert systems, 6-2

future trends, 6-11

research issues, 6-8

acceptance, 6-9

effective means of explanation, 6-9

empirical exploration, 6-9

fitting into user environment, 6-10

role of user interface, 6-3

techniques, practices, methodologies, and applications, 6-5

dialog models, 6-6

direct manipulation, 6-8

intelligent interface, 6-7

role of conceptual model, 6-6

user interface and explanation, 6-5

user interface management systems for expert systems, 6-9

WWW interfaces, 6-8

Expert system (ES) technology, 2-1, 20-3

future trends, 2-10

knowledge acquisition methodology, 2-8

knowledge acquisition techniques, 2-4

knowledge-based, 35-3

limitation of current, 22-13

people issues, 2-2

role of knowledge engineer, end-users, and managers, 2-3

selecting domain experts, 2-2

single vs. multiple experts, 2-2

techniques for collaboratove knowledge acquisition, 2-6

Expert system (ES) verification and validation, foundation and application of, 5-1

application of expert system verification and validation, 5-18

components of verification and validation, 5-18

expert system development methodologies, 5-20

methods and techniques, 5-18

verification and validation systems, 5-23

future trends, 5-30

current state and open probolems, 5-31

hybrid intelligent systems, 5-30

historical overview and terminology, 5-2

industrial requirements, 5-27

establishing quality culture, 5-27

how much V&V, 5-29

independent verification and validation, 5-28

V&V in support of certifiction for critical systems, 5-28

research initiatives, 5-30

theoretical foundation of expert system verification, 5-5

foundation of object-oriented expert systems, 5-8

logical foundation of rule-based anomalies, 5-5

V&V in conventional software, 5-4

Explanation(s)

base (EB), 7-3

definitions of, 7-5

effective means of, 6-9

facility, 4-2, 4-3

image-based, 7-8

implementation (EI), 7-2

strategic, 7-4

types of, 6-10

validation (EV), 7-2

Explanation facilities, design and use of, 7-1

factors influencing design of explanation facilities, 7-6

characteristics of explanations, 7-7

interface design and provision strategies, 7-8

task characteristics, 7-6

use characteristics, 7-9

recent studies of use of expert system explanation, 7-5

research issues and future trends, 7-9

stages in development of, 7-2

types of expert system explanations, 7-4

F

Factual knowledge

determining, 31-12

identification of, 31-7

FALCON, 27-8

Fault

detection, 9-12

diagnosis, 4-10

identification, 9-12

Feature-value pairs, 19-48

Finance and investments, 26-1

applications, 26-3

capital investment, 26-9

commercial loan analysis, 26-5

hybrid systems, 26-11

neural network applications, 26-10

predicting stock market behavior, 26-10

stock options pricing, 26-3

background, 26-2

future trends, 26-12

research issues, 26-11

cognitive issues, 26-11

statistical, model development, and validation issues, 26-11

user acceptance, 26-12

Financial expert, 26-6

Financial marketing, 29-2

Financial portfolio management, 12-2

Firefighting knowledge, 30-25

First- Infer-Then-Aggregate (FITA), 13-16

First-order logic (FOL), 18-6

First-principles description, 9-1

FITA, see First-Infer-Then-Aggregate

Fitness function, 12-3, 12-10

Fixed sequence, 1-5

Flexible manufacturing systems (FMS), 4-9

FMS, see Flexible manufacturing systems

FOCES, see Foster Care Expert System

Focus group interview, 2-7

FOL, see First-order logic

Forestry systems, 30-23

Formal expression, 10-24

Forward-chaining expert system, 30-28

Forward reasoning, 22-9

Foster Care Expert System (FOCES), 33-7

Functional assignment constraints, 29-8

Functional decomposition, 21-5, 31-9

Functionality, high-level, 21-5

Functional requirements, 5-18

Functional schemata, 19-20

Fuzzified neural network, 13-8, 13-9

Fuzzy cluster abalysis method, 13-8

Fuzzy clustering, 13-5

Fuzzy expert system, 12-1

Fuzzy language, 3-13

Fuzzy logic, 4-7, 5-30

Fuzzy model, 13-12, 13-17

Fuzzy-neural system models, 13-19

Fuzzy reasoning formulation, 13-13

Fuzzy representation, 3-13

Fuzzy retrieval, 30-6

Fuzzy rules, 13-2

Fuzzy sets, 9-4, 13-4

Fuzzy system identification, 13-6

Fuzzy theory, crisp connectives of, 13-14

Fuzzy Total Scatter Matrix, 13-18

G

GA, see Genetic algorithm

Garden-path sentences, 19-25

Gas

demand, 20-5

furnace model, 13-18

turbine diagnsotics, 20-6

GCCS, see Global Command and Control System

GEMS, 20-9

Generalized upper model (GUM), 10-31

General Problem Solver (GPS), 18-11

Generate and test procedure, 19-31

Generically Used Expert Scheduling System (GUESS), 23-5

Generic scheduling, 23-10

Generic tasks, 31-1

Generic algorithm (GA), 12-1-6, 30-22

background, 12-2

crossover, 12-5

evaluation, 12-4

initialization, 12-3

mutation, 12-5

search, 12-5

selection, 12-4

termination, 12-6

variations, 12-7

future trends, 12-11

research issues, 12-10

techniques and applications, 12-7

Geographical information systems (GIS), 30-4, 30-34

GIS, see Geographical information systems

Glass-box techniques, 5-20

Global Command and Control System (GCCS), 34-5

Goal

decision block, 31-18

regression, 30-30

setting, 29-4

Government

planning function of, 33-9

shrinking, 33-4

Government services and operations, non-defense, 33-1

applications, 33-6

construction and planning applications, 33-7

environmental applications, 33-6

law and law enforcement applications, 33-7

social services applications, 33-7

taxation, 33-8

background, 33-2

factors inhibiting expert system development, 33-5

factors motivating expert system development, 33-4

stratgey of development, 33-2

future trends, 33-10

research issues, 33-8

forecasting and measurement, 33-10

fraud, waste, and abuse, 33-9

planning, 33-9

technical assistance, 33-9

training and job issues, 33-9

transitioning, 33-9

GPS, see General Problem Solver

Grammar, application of, 19-19

Grammatical hypothesis, 19-12

Graphical-user interface (GUI), 4-15, 32-11

Group

repertory grid analysis, 2-8

support systems (GSS), 2-8

Groupware software, 28-4

GSS, see Group support systems

GUESS, see GeneRically Used Expert Scheduling System

GUI, see Graphical-user interface

H

HCl, see Human-Computer Interaction

Head-Driven Phrase Structure Grammars (HPSG), 19-17

Help desk, 4-17, 14-19

Heuristic match, 7-7

Hidden Markov Model (HMM), 19-11, 19-49

Higher-order logic, 1807

HM, see Hypermedia

HMM, see Hidden Markov Model

Home enetertainment, 14-18

Host programming language, 17-14

HPSG, see Head-Driven Phrase Structure Grammars

HSS, see Hypermedia support systems

HT, see Hypertext

Human-Computer Interaction (HCI), 6-2, 7-9, 28-24

Hybrid

intelligent systems, 5-30

solutions, 9-3

system(s), 11-9

loan assessment, 26-12

model developments, 13-1

tool, 4-6

Hybrid systems, fuzzy neural integration, 13-1

case study, 13-17

gas furnace model, 13-18

industrial process model, 13-18

nonlinear system, 13-17

inference engine,

13-3 knowledge base, 13-3

membership functions, 13-4

fuzzified neural network, 13-8

fuzzy clustering, 13-5

grade of membership, 13-5

input selection, 13-8

meaning of membership, 13-4

modification of FCM algorithm, 13-6

research issues, 13-19

unified fuzzy model, 13-12

approximate reasoning, 13-13

crisp connectives of fuzzy theory, 13-14

implication and aggregation, 13-15

inference with rule set, 13-16

Hyperlinks, extended, 14-23

Hypermedia (HM), 145-2

Hypermedia support systems (HSS), 14-16

Hypertext (HT), 14-4, 35-1

Hysteria, 31-19

I

IAA, see Intelligent Analyst Associate

ICAI, see Intelligent Computer-Assisted Instruction

ICE, see Internal Control Evaluation

Identity, 3-3

IDK, see Intelligent Database Kernel

If-then production rules, 5-2

IIFS, see Intelligent Information Fusion System

IIOP, see Internet Inter-ORB Protocol

Illite Age Analysis, 30-21

IM, see lntellimedia

Implementation documents, 10-28

IMPROVER, 5-24

IN-DEPTH, 5-26

Incomplete rule set, 5-7

Inconsistent rule set, 5-6

Independence, condition of, 3-11

Independent verification and validation (IV&V), 5-28

Indexing techniques, 18-13

Induction tools, 4-7

Industrial process model, 13-18

Infectious blood disease, 4-4

Inference

engine, 4-2, 4-3, 19-7

steps, 5-13, 5-14

structure

description, 31-11

satisfying, 5-10

Information

filtering techniques, 16-11

flow of, 19-47

overload, 14-1

processing, 32-3

retrieval (IR), 11-13, 19-30

services, providing, 33-4

systems, 28-17

technology (IT), 14-5, 28-2, 28-3

transfer problems, 35-2

Infrastructure elements, 28-4, 28-6

Initial prototyping, 25-9

Initial roles, 5-10

Input

candidates, 13-8

knowledge roles, 5-9

Inspections, 5-18

Instance attribute name, 10-18

Instantiation, 19-22

Instrumentation systems, 20-8

Integrated applications, survery of emerging, 14-19

Integrated reasoning, 29-3

Integration

document, example of, 10-26

models, 14-6

orientation, 14-11

synergy, 14-18

Intelligent agent technology, 16-1

future trends, 16-12

historical account/background, 16-2

supporting technologies and research issues, 16-8

agent communication languages, 16-8

autonomous agents, 16-10

cooperation and coordination, 16-11

learning, 16-11

mobile agents, 16-10

techniques, practices, and applications, 16-3

business application agents, 16-4

data mining agents, 16-7

electronc commerce agents, 16-4

interface agents, 16-6

Internet agents, 16-3

Intelligent alarm limits, 9-10

Intelligent Analyst Associate (IAA), 34-10

Intelligent Computer-Assisted Instruction (ICAI), 32-11

Intelligent Database Kernel (IDK), 16-7

Intelligent interface

expert systems as, 6-8

models of, 6-7

Intelligent manufacturing and engineering, 22-1

future trends, 22-13

historical background, 22-2

methodologies, techniques, and practices, 22-4

expert system development tools, 22-11

expert systems for welding, 22-7

failure diagnosis using case-based reasoning, 22-7

genetic algorithms in manufacturing and engineering, 22-6

intelligent process control, 22-5

knowledge representation using high-level Petri nets, 22-11

machine learning using neural networks,m 22-6

neural and fuzzy rule-based expery systems for automated welding, 22-10

process diagnosis using probabilistic inference, 22-7

research issues, 22-12

advanced knowledge representation with learning abilities, 22-13

hierarchical integration of intelligent strategies, 22-12

real-time issues, 22-12

Intelligent visual database management systems (IVDBMS), 14-14

Intelligent Information Fusion System (IIFS), 3-042

Intellimedia (IM), 14-2, 14-4

Intentional representation, 3-16

Interactive presentations, 14-18

Internal Control Evaluation (ICE), 27-3

Internet

agents, 16-1, 16-3

Inter-ORB Protocol (IIOP), 16-9

pervasive nature, 16-2

INTERNIST, 32-6

Interoffice facilities (IOF), 24-8

Interval reasoning, 17-16

Interviewing, 2-4

Investigations, 3-12

IOF, see Interoffice facilities

IR, see Information retrieval

IRE, 27-4

Irrigation schedule, 35-8

IT, see Information technology

IV&V, see Independent verification and validation

IVDBMS, see Intelligent visual database management systems

J

Jargons, 19-50

Job announements, automated, 28-23

Joint Photographic Experts Group (JPEG), 14-3

Joint ventures (JV), 19-9

JPEG, see Joint Photographic Experts Group

Judgmental experts, 26-6

JV, see Joint ventures

K

KA, see Knowledge acquisition

KABAL, 26-8

KACTUS, 10-33

KB, see Knowledge base

KBSs, see Knowledge-based systems

KDD, see Knowledge discovery in databases

KE, see Knowledge engineer

KEE, see Knowledge Engineering Environment

KNOBOS, 20-4

Knowledge

acquisition (KA), 1-7, 3-5, 7-2. 10-16, 31-7

automated, 1-13, 4-23

bottleneck, 11-2

document, 10-28

methodology, 2-9

module, 30-13

planning, 2-8

process, 2-10

techniques, 2-4

additional, 5-8

ambivalent, 5-8

analysis, 2-9

base (KB), 4-2, 4-3, 7-3, 25-1

-based manipulation, user data, 14-8

-based relaxation, 15-9

-based systems (KBSs), 1-1

building, 10-2

interaction of with humans, 6-10

codification, 10-5

discovery in databases (KDD), 4-23

domain, 28-9

engineer (KE), 2-3, 6-5, 7-2, 25-1

Engineering Environment (KEE), 20-12

extraction, 2-8

factual, 31-7, 31-12

firefighting, 30-25

heuristic, 19-23

lack of, 28-2

level, 5-3

medical, 32-2, 32-3

meta-, 30-25

modeling, 1-13

problem-solving, 4-10

Refinement Using Semantic Trees (KRUST), 5-23

representation

formalisms, 10-3

ontologies, 10-7, 10-25

scheme, 30-41

server utility, 10-30

sharing of, 10-4

source(s), 10-6

documented, 14-10

multiple, 30-19

specification, 5-22

surface, 5-2

tactical, 31-10

total, 3-18

validation (KV), 7-2

verification, 2-9

Knowledge based systems, methodologies for building, 1-1

boxes-and-arrows approaches, 1-4

focused approaches, 1-7

full-fledged methodologies, 1-8

meaning of methodology, 1-2

prospects, 1-11

Knowledge representation, 3-1-2, 28-18, 28-24

background, 3-4

development of logic, 3-4

patterns, 3-5

uncertainty, 3-4

fundamental tools, 3-2

future trends, 3-21

putting word into computer, 3-1

research issues, 3-20

techniques, practices, and methodologies, 3-6

conceptual representation, 3-6

one world representation, 3-15

relational representation, 3-7

representation of uncertainty, 3-8

Knowledge sharing and reuse, 10

design criteria, 10-9

how to use ontologies, 10-7

interlinguas, 10-28

KIF, 10-28

PIF, 10-29

methodology to build ontologies, 10-14

conceptualization, 10-17

documentation, 10-27

evaluation, 10-27

formalization, 1-24

implementation, 10-26

integration, 10-25

knowledge acquisition, 10-16

specification, 10-14

ontological commitments, 10-6

ontologies and knowledge bases, 10-4

ontology development process, 10-10

development-oriented actvities, 10

integral activities, 10

project management activities, 10-10

ontology life cycle, 10-12

ontology server, 10-32

problems with reuse software or share knowledge, 10-2

systems using ontologies, 10-33 types of ontologies, 10-6

well-known ontologies, 10-29

Cyc, 10-29

engineering ontologies, 10-31

example of knowledge representation

ontology, 10-30

linguistic ontologies, 10-31

planning ontologies, 10-32

KRUST, see Knowledge Refinement Using Semantic Trees

KV, see Knowledge validation

L

Lagrangian relaxation, 15-10

Language

natural, 3-19

understanding capabilities, 16-12

Large Steam Turbine Expert System (LSTES), 20-9

Learning

algorithm, 13-11

control, 22-3

machine, 3-19

process, stages of, 7-8

schemes, 13-2

unsupervised, 13-2

Legacy systems, 24-4

Lexical analysis, 19-11

Lexicon loader, 19-48

Licensing costs, 4-17

Life cycle, 25-3

Likelihood view, 13-4

Linguistics analysis, 19-5

LITHO, 30-12

LMOS, see Loop Maintenance Operation System Loan Probe, 27-5

Local fusion system, 20-15

Logic, 18-1

autoepistemic, 3-19

development of, 3-4

Dynamic Predicate, 19-36

Episodic, 19-44

future trends, 18-14

fuzzy, 4-7, 5-30

historical background, 18-2

intentional, 3-15

logical reasoning systems, 18-11

case-based systems, 18-13

logic programming systems and theorem provers, 18-12

meta-level reasoning, 18-13

rule-based systems, 18-11

semantic networks and frame systems, 18-12

machine-oriented, 18-2

modal, 304

nonmonotonal, 3-4, 3-16

possibilistic, 3-14

programming, 17-6, 17-7

reasoning and, 18-4

abduction, 18-10

deduction, 18-4

induction, 18-9

nonclassical logics, 18-8

probabilistic reasoning, 18-8

research issues, 18-13

second-order, 10-15

temporal, 3-4

Logistics, 14-17

Loop Maintenance Operation System (LMOS), 24-7

Low-Voltage Network (LVN), 20-16

LSTES, see Large Steam Turbine Expert System

LVN, see Low-Voltage Network

M

Machine

learning, 3-19, 16-11

translation (MT), 19-2

troubleshooting expert system, 22-7

Machining

control, 22-2

industries, 22-4

Macroverification, 25-6

MAGIC, 33-2, 33-8

Magidan Army Medical Center (MAMC), 34-8

Mainframe tools, purchasers of, 4-21

Maintenance Assistance for Knowledge Engineers (MAKE), 5-20

MAKE, see Maintenance Assistance for Knowledge Engineers

Malpractice suits, 25-8

MAMC, see Magidan Army Medical Center

Management

accounting, 27-7

Advisor, 26-9, 27-6

expert, 26-6

Information System (MIS), 28-21, 28-22

Manufacturing

applications, 22-11

system, intelligent, 22-12

Mapping ontology, 10-32

Market share analysis, 29-2

Marketing, expert systems for, 29-1

application areas of marketing expert systems, 29-1

architecture of salesman expert system, 29-3

expert system for customized purchasing support, 29-3

from decision aid to true marketing, 29-3

menswear case, constraints, 29-6

importance of constraints, 29-6

types of constraints, 29-7

variables, 29-5

problem types and AI techniques, 29-2

reasoning procedure in salesman expert system, 29-9

rules in menswear case, 29-8

customer-product rules, 29-8

sales strategic rules for variable and value ordering, 29-8

trends of AI research for marketing, 29-13

Mass production, 3-10 Mathematical induction, 18-7

Mathematical methods, 3-2

MBD, see Model-based diagnosis

MBR, see Model-based reasoning

ME, see Microelectronics

Measurement

data, forecasting trends in, 28-22

interpretation, 9-10

view, 13-4

Media tool, 14-13

Medical diagnosis, 8-5

Medical domain, selecting, 32-8

Medical expert systems, 32-15

Medical knowledge, 32-2, 32-3

Medicine, expert systems in, 32-1

future trends, 32-11

processing temporal knowledge, 32-11

system evaluation, 32-12

system integration, 32-12

historical account, 32-2

methodologies, 32-3

Bayesian statistical approaches, 32-4

case-based reasoning, 32-6

neural network, 32-5

object-oriented programming, 32-6

rule-based reasoning, 32-4

research issues, 32-8

acceptability issues, 32-10

ethical and legal considerations, 32-10

knowledge acquisition and representation, 32-9

selecting medical domain, 32-8

validation and verification, 32-9

status of applications research, 32-6

EEG analysis system, 32-7

INTERNIST/CADUCEUS, 32-6

MYCIN, 32-6

other expert systems developed in 1990s, 32-7

PUFF, 32-7

QMR, 32-7

Meeting schedulers, 28025

Membership, grade of, 13-5

Memory

long-term, 18-12

shared, 21-3

working, 18-11

Mental map, 31-17

Message

passing, 4-6

routing, 19-30

Meta-data extraction, 30-43

Meta-knowledge, 5-23, 30-25

Metalanguages, 3-21

Meta-ontologies, 10-7

Meta-programming, 30-11

Meteorological data, 35-10

Methodological pyramid, 1-3

Methodology(ies)

full-fledged, 1-4, 1-8

KBS specific, 1-8

original meaning of, 1-2

papers discussing, 1-11, 1-12

reengineering, 28-3

Metrication, 25-11

Metric of goodness, 11-11

Microelectronics (ME), 19-9

Microverification, 25-6

Military expert system applications, 34-1

AI program in U.S. military, 34-2

current and recent applications, 34-3

intelligence applications, 34-10

logistics, 34-5

quality of life applications, 34-10

robotics, 34-9

strategic decision-making and planning, 34-3

current and recent applications, training and education, 34-7

future trends, 34-12

research issues, 34-11

knowledge acquisition, 34-11

verification and validation, 34-11

MIS, see Management Information System

Mixed-initiated dialog, 6-5

MM, see Multimedia

Model(s)

agent, 1-9

automatic acquisition of, 9-13

-based diagnosis (MBD), 8-10, 9-10, 9-11

-based systems, 9-3

Bayesian, 3-11

behavioral, 9-2

Black-Scholes mathematical, 26-3

building, 5-30

categories, 9-4

classical probabilistic, 3-10

conceptual, 6-6

design, 1-9

expertise, 1-9

formulation, knowledge assisted, 15-5

fuzzy-neural system, 13-19

gas furnace, 13-18

generalized upper, 10-31

Hidden Markov, 19-11

hybrid system, 13-1

improved tools for, 9-13

industrial process, 13-18

integration, 14-6

knowledge, 1-13

libraries, 9-13

neural network, 15-10

notational, 15-7

optimization, 15-7

organizational, 1-9

overuse of term, 9-3

process, 31-16

programming, 15-3

rule-based, 18-12

structural, 9-2

system integration, 14-6, 14-15

task, 1-9

time series, 26-13

two-level, 19-12

user, 19-2

Model-based reasoning (MBR), 9-1-15, 28-16

applications of MBR, 9-9-13

control, 9-11

diagnosis, 9-11-13

monitoring, 9-10-11

where MBR used, 9-9-10

background and history, 9-2-3

techniques and methodologies, 9-3-9

model-based diagnosis, 9-7-9

model categories, 9-4-5

qualitative models, 9-5

qualitative simulation, 9-6-7

trends and open issues, 9-13-14

MomsExperten, 27-9

Monitoring, 9-9, 9-10

MPC, see Multimedia PC

MT, see Machine translation

Multidirectional programming, constraints for, 17-3

Multifunction part problem, 21-2

Multimedia (MM), 14-2

documents, 14-18

PC (MPC), 14-3

Multimedia (MM) expert systems, 14-1

application domains and emerging integrated applications, 14-17-19

application domains, 14-17

survey of emerging integrated applications, 14-19

background, 14-2

integration advantages, 14-5

media technologies overview, 14-2

future trends, 14-23

integration models, 14-6

evolution of system integration models, 14-14

integration orientations, 14-10

software architectures, 14-6

research issues, 14-19

Multiple attribute uses, 31-19

Multiple media, 11-13

Mutation, 12-5

MYCIN, 7-4, 30-3

N

Natural language (NL), 3-19, 19-1

Generation (NLG), 19-45

interfaces, 18-7

Understanding (NLU), 18-6, 34-10

Natural language processing, associated with expert systems, 19-1

challenges and solutions, 19-9

discource and pragmatic analysis, 19-43

interactive systems and spoken language understanding, 19-46

language generation, 19-45

morphological analysis, 19-11

semantic analysis, 19-29

standard paradigm for NLP, 19-10

syntactic analysis, 19-15

future trends in NLP domain, 19-48

history, 19-2

ALPAC report and first AI programs, 19-3

conceptual approach, 19-6

modern times, 19-7

origins, 19-2

Natural numbers, 10-34

Natural resources, expert systems for management of, 30-1

ecosystem management, 30-37

CERES, 30-40

ESS/IIFS, 30-42

SEA, 30-40

SIRENAS, 30-39

TSDSYS, 30-39

vegetation succession, 30-38

expert systems, 30-3

forestry, 30-23

GYPSEX, 30-26

ISPBEX, 30-26

PHOENIX, 30-24

SEIDAM for forestry, 30-29

UNU-AES, 30-28

geological exploration and mineral analysis, 30-5

Baldwinos SNN for mineral identification from well logs, 30-17

contouring assistant, 30-16

DIPMETER ADVISOR, 30-10

GeoX, 30-19

LITHO, 30-12

maceral analysis, 30-20

MatchMod, 30-21

META/LOG, 30-12

PROSPECTOR, 30-7

scope and historical development, 30-5

SPECTRUM, 30-13

XX/MAGIC, 30-15

management of water resources, 30-32

COLOSS, 30-36

CORMX mixing zone model, 30-34

expert GIS for water resource planning, 30-33

STEWARD, 30-36

scope, 30-4

technological development, 30-3

wildlife taxonomy, 30-31

NCARAI, 34-2

Nearest neighbor

algorithm, 3-20

matching, 11-5

NEOMYCIN, 7-4

Neural network(s), 13-2, 15-2, 20-14

artificial, 6-10

for determination of welding conditions, 22-10

learning schemes using, 22-13

model, 15-10

self-learning mechanisms of, 32-5

typical, 24-5

NGT, see Nominal group technique

Niching methods, 12-7

NL, see Natural language

NLG, see Natural Language Generation

NLU, see Natural Language Understanding

Noisy data interpretation, 12-2

Nominal group technique (NGT), 2-6

Nonclassical logics, 18-8

Non-expert users, 6-1

Nonfunctional requirements, 5-18

Nonlinear programming, 15-2

Nonmonotonic logic, 3-4

Nonmonotonic reasoning, 18-8

Notational model, individual, 15-7

Nuclear power industry, 20-10

O

Object

database, 30-43

Management Group (OMG), 16-9

-oriented expert system (OOES), 5-9

-oriented programming (OOP), 4-11, 5-8, 14-13, 32-6

Request Brokers (ORBs), 16-9

Obsessive behavior, 31-19

ODBC, see Open Database Connectivity

OLAP, see Online analytic processing

OMG, see Object Management Group

On-center-off-surrounding competitive activation, 30-18

Online analytic processing (OLAP),

16-6 On-line help, 4-14

Ontological distinction principle, 10-9

Ontology(ies)

construction of, 102-5

development process, 10-10

life cycle, 10-12

to make computable, 10-11

mapping, 10-32

methodology to build, 10-14

purpose of, 10-15

requirement specification document, 10-16

at run-time, 10-8

task, 10-32

well-known, 10-2

OOES, see Object-oriented expert system

OOP, see Object-oriented programming

Open Database Connectivity (ODBC), 16-12

Open-loop systems, 9-11

Open world representation, 3-15

Operations management, 14-17

Operator support system, 9-11

Optimization model, 15-7

Optimizing control, 22-3

ORBs, see Object Request Brokers

Organizational model, 1-9

Organizational performance, 28-4

Organizational strategy process, 33-2, 33-3

OTTER, 18-12

Output knowledge roles, 5-9

P

PAF, see Potential actor focus

Pairwise synergy, examples of, 14-7

PALOS, see Planning Assistant for Logistical Systems

PARCs, see Potential arcs

Parent genotypes, 12-3

Parse stack, 19-23

Partially Shared Views (PSV), 10-29

Part-function multiplicity, 21-6

PCA, see Principal Components Analysis

PD, see Probability distribution

Perfective maintenance, 25-5

Personal assistants, 28-23

Personalized search profiles, 16-3

Phobia, 31-19

PHOENIX, 30-24

Phrase

breaks, 19-49

-structure grammar, 19-16

PhysSys ontology, 10-32

Planning

Assistant for Logistical Systems (PALOS), 34-7

component, 19-45

Plant Tables (PT), 20-13

Plausiblity, 18-9

Point-and-click operations, 30-36

Police Officer Large Expert System, 33-7

Pollution control, 30-38

Polytrees, 8-9

Possibilistic logic, 3-14

Postcondition names, 10-23

Potential actor focus (PAF), 19-28

Potential arcs (PARCs), 30-34

Power industry, 20-1

applications, 20-3

entire generation process, 20-3

gas turbine diagnostics, 20-6

generators, 20-9

nuclear power industry, 20-10

steam turbine diagnostics, 20-9

history of expert systems within power industry, 20-2

research issues and future trends, 20-14

distributed artificial intelligence, 20-15

hybrid systems, 20-16

neural networks, 20-14

Powerset, 8-6

Pragmatics, 3-21

Precedence constraints, 23-9

Precondition

-evaluation function, 21-9

names, 10-23

Prediction, 9-9

Predictive features, 11-5

Predictive occurrences, 19-39

Preliminary design, 25-9

Prescriptive statements, 1-2

Presentation

graphics, 28-25

systems, 14-3

Pressurised Water Reactors (PWA), 20-11

Principal Components Analysis (PCA), 20-18

Priority implementation, 31-17

Probability

distribution (PD), 8-6

framework of classical, 3-5

theory, 3-3

Problem

-situation description, 11-10

-solving

controlling, 8-10

strategy, 7-1

task, 11-4

techniques, 30-4

termination, 5-25

Procedural semantics, 19-4

Process

-based approach, 9-6

control systems, intelligent, 22-5

model, 31-16

monitoring, 22-2

Production planning, 12-8

Professor Marvel, 25-13

Profit maximization, 26-11

Programming

language, 30-3

methodology, 25-12

model, structure of specific, 15-3

Project management tasks, 10-12

PROJECT, 26-9

Projection, 17-7

PROLOG, 19-35

Propagation, 21-7

constraints, 17-10

definition of, 17-4

process, 3-9

of values, 9-7

Property Theory, 19-36

Propositions, 18-6

Prospect drilling, 30-7

PROSPECTOR, 3-7

Protocol analysis, 2-5

Prototype, 19-47

development, 24-10

initial, 25-9

system, 5-20

Pseudoformalism, 31-19

PSV, see Partially Shared Views

PT, see Plant Tables

PUFF, 32-7

Purchasing agents, 29-14

PWA, see Pressurised Water Reactors

Q

QA, see Quality assurance

QMR, 32-7

QOL, see Quality of Life

Qualiative model predictive control, 9-11

Qualification problem, 18-8

Qualitative reasoning, 3-14, 3-15, 9-5

Qualitative state, 9-5

Quality assurance (QA), 10-10, 28-22

Quality control, 14-17

Quality culture, 5-27

Quality of Life (QOL), 34-10

Quantifier scope, 19-28

Quantity space, 9-5

R

Random sample, 1-3

Random set view, 13-4

Rapid prototyping, 5-22

RBR, see

Rule-based reasoning

RBS, see Rule-based systems

Reactive rule-based system, 18-11

Realization component, 19-45

Real-time processing, 22-11

Reasoning

approximate, 13-13, 24-6

basis for, 3-6

Bayesian, 8-10

case-based, 3-19, 8-10, 24-5, 25-10

deductive, 18-5

forward, 22-9

interval, 17-16

intgrated, 29-3

meta-level, 18-13

methods, 19-5

model-based, 9-1, 9-6, 9-14, 11-14, 25-10

nonmonotonic, 18-8

procedure, in saleman expert system, 29-9

process, 31-5

qualitative, 9-5

rule-based, 11-1215-2, 32-4

techniques, 9-6

Recursive Transition Networks (RTN), 19-24

Redundancy, 25-7

Reengineering

life cycle, 28-5

methodology, 28-3

Reengineering, designing innovative business systems through, 28-128-27

approaches to identifying expert system applications, 28-17

business system components approach, 28-18

ES generic task approach, 28-19

knowledge-intensive organizational functions approach, 28-17

knowledge-intensive work activities approach, 28-17

business applications of expert systems, 28-19

customer service applications of expert systems, 28-20

expertise applications of expert systems, 28-24

management applications of expert system, 28-21

process applications of expert systems, 28-21

product applications of expert systems, 28-19

workforce applications of expert systems, 28-22

business reengineering, 28-2

business system, 28-3

infrastructure elements, 28-4

reengineering life cycle, 28-5

reengineering methodology, 28-3

design concepts, 28-7

constraints, 28-8

design guidelines, 28-9

design process, 28-8

design realization and optimization, 28-9

generic design model, 28-10

requirements, 28-8

expert system concepts, 28-15\

case-based reasoning, 28-15

model-based reasoning, 28-16

expert system concepts, rule-based systems, 28-16

future of expert systems in business, 28-25

automated and support ES applications, 28-26

IPSS and knowledge discovery, 28-26

IS and IT infrastructure, 28-25

knowledge repositories, 28-25

reengineering design, 28-10

customer analysis process, 28-12

reengineering design levels, 28-11

reengineering design process, 28-11

sources of design ideas, 28-12

transforming customer needs into business requirements, 28-13

types of customers, 28-13

Reference, disambiguating, 19-26

Relational representation, 3-7

Relation attribute name, 10-19

Remembering, 3-1

Remote sensing, 30-29

Repertory grid analysis, 2-5

Representation

logic-based, 3-8

problem, 3-20

Requirements

analysis, 5-21, 5-22

specifications, 5-4, 10-5

Research

community, 17-17

issues, 33-9

scientist, 30-28

Reserve forces, 34-4

Resource constraints, 23-8

Retail sales prediction, 27-8

Risk probabilities, 30-20

RMS error, see

Root mean squared error

Role fillers, 19-39

Root mean squared (RMS) error

Roulette wheel selection, 12-4

Routine tasks, automation of, 6-7

RTN, see Recursive Transition Networks

Rule

aggregation function, 13-15

base, 3-13, 18-11

inference chain for, sample, 8-4

counterpart anomaly in, 5-14

-based reasoning (RBR), 11-12, 15-2

-based representations, 5-5

-based systems (RBS) 5-6, 5-30, 28-16

-induction method, 26-10

structure, 19-21

of thumb, 5-2

S

SABRE, 34-4

Sample applications, 7-10

Satisfaction criteria, 23-8

Scanner technology, 35-1

Schankian prototypes, 19-8

Scheduling, 23-1

general background, 23-1

GUESS, 23-5

in NASA environment, 23-2

object-oriented structure of GUESS, 23-5

major scheduling approaches used in GUESS, 23-6

resource modeling in GUESS, 23-7

system(s)

benchmark intelligent, 23-3

generic, 23-10

identifying expert, 23-1

testing and performance of GUESS, 23-9

SDLC, see Systems Development Life Cycle

Seamless modularity, 14-16

Search operators, 12-10

Second-generation expert system, 9-3

Second-order constructions, 19-38

Second-order logic, 10-15

Seed variables, 29-3

SEIDAM, 30-31

Seismic monitoring, 30-41

Self-organized network (SON), 30-17

Semantic analysis, 19-10, 19-29

Semantic conflict, 5-11

Semantic grammar, 19-32, 19-33

Semantic interpretation, 19-4

Semantic networks, 14-12

Semantic problems, 10-3

Semiotics, 3-2

Semiquantitative simulation, 9-10

Sensor readings, 13-5

Sequence of substitutions, 5-10

SGH, see Singapore General Hospital

Shared-memory systems, 21-3

Shell, 25-2

SHOGUN system, 19-9

Similarity view, 13-4

Simulation development software, 4-12

Singapore General Hospital (SGH), 11-8

Single-Input Single-Output (SISO), 13-16

SIRENAS, 33-6

SISO, see

Single-Input Single-Output Situation Semantics, 19-36

SLU, see Spoken Language Understanding

Small platform tool, 4-20

SMART system, 11-6, 11-7

Software

architecture, 14-6, 14-9

design, 25-4

development, 25-4

groupware, 28-4

process, 10-29

quality

achievement, 5-27

assessment, 5-28

assurance (SQA), 5-19, 5-28

control, 5-27

evaluation, 5-27

reusing, 10-2

specification, 5-21

tools, 25-2

used in expert system development, 4-6

Software engineering for expert systems, 25-1

applying expert systems to software engineering, 25-12

PECOS, 25-12

Professor Marvel, 25-13

programmeros apprentice, 25-12

specification-transformation expert system, 25-13

background, 25-2

contributions of software engineering to expert system development, 25-5

evaluation, 25-5

involving clients, 25-11

life cycle models, 25-9

methods of knowledge representation, 25-10

metrics, 25-11

modularization, 25-8

reuse, 25-11

training clients, 25-11

walkthroughs, 25-10

software engineering, 25-3

Solution refinement, 7-7

SON, see Self-organized network

Space, classification for output, 13-17

SPECTRUM 2000 Mindware, 30-12

Speech

acts, 3-21

recognizer, 19-11

understanding systems, 19-8

Sphere quadtrees (SQT), 30-43

Spiral life cycle, 25-4

Spiral modeling, 5-22

Spoken Language Understanding (SLU), 19-10, 19-46, 19-49, 19-51

Spreadsheets, 28-25

SQA, see Software quality assurance

SQT, see Sphere quadtrees

Statistics, 3-3

Steam Turbine Expert System (STES), 20-9

STES, see Steam Turbine Expert System

STEWARD, 30-36

Strategic explanation, 7-4

Structural conflict, 5-11

Structural model, 9-2

Subsumed rule, 5-24

Subsumption, 5-10, 5-12

Support-function

constraints, 21-11

probem, 21-2

Surface knowledge, 5-2

Symbol level, 5-3

Synsets, 19-15

System(s)

agent, 21-13

analysis, 5-21

complexity, 5-29

decomposition, 31-21

developers, 2-10

Development Life Cycle (SDLC), 28-8

engineering

benefits, 10-7

notion of, 9-3

expertise, 7-9

geology and mineralogy, 30-8

integration models, 14-15, 14-6

interface, 4-2

model, 9-12

sample list of, 20-2

theory, fuzziness and, 13-12

transfer, 24-10

T

Table of class attributes, 10-19, 10-22

Table of constants, 10-19

Table of instance attribute, 10-18, 10-21, 10-22

Table of instances, 10-19, 10-23

Tactical knowledge, 31-10

TAG, see Tree Adjunction Grammars

Tagging

problem, 19-14

rules, 19-13

Task(s)

characteristics, 7-6

decision, 31-18

efficiency, 27-2

hierarchy, 31-16

model, 1-9

ontology, 10-7, 10-32

-oriented system, 30-27

real-world, 30-4

Technical documentation, 14-18

Technology

fear of, 28-2

interest curve, 1-12

orientation, 33-5

TED, see Turbine Engine Diagnostics

Telecommunications, 24-1

AI techniques, 24-4

approximate reasoning, 24-6

decision trees and case-based reasoning, 24-5

distributed artificial intelligence, 24-6

hybrid systems, 24-6

model-based reasoning, 24-6

neural networks, 24-5

rule-based systems, 24-4

search, 24-5

applications, 24-7

wireless or satellite communication, 24-9

wireline communication, 24-7

background, 24-2

lessons and research issues, 24-10

Management Network (TMN), 24-2

management tasks, 24-11

telecommunications domains and potential tasks, 24-3

Telephone network, 24-7

Temporal logic, 3-4

Terminal roles, 5-10

Testing, 5-3

Texas Explorer Lisp Machine, 30-26

Texas Water Development Board (TWDB), 30-33

Text planner, 6-10

Thinking machines, 25-2

Tight coupling, 14-9

Time series models, 26-13

TMN, see Telecommunications Management Network

Tool(s)

CBR, 4-22

domain-specific, 4-23

mainframe, 4-21

market, 4-18

small platform, 4-20

sold for workstations, 4-21

vendors, 4-19

Total belief, 18-5

Total disbelief, 18-5

Total knowledge, 3-18

Tournament selection, 12-4

TOVE, 10-33

Tree

Adjunction Grammars (TAG), 19-17

structure, 19-31

Trend lines, 14-13

Triggering rules, 19-42

Troubleshooting dialogue, 14-12

Truth maintenance system, 3-18

TSDSYS, 30-39

Turbine Engine Diagnostics (TED), 34-6

TWDB, see Texas Water Development Board

Two-level model, 19-12

U

UGV, see Unmanned Ground Vehicle

UIMS, see User Interface Management Systems

Unacceptable consequences, 5-28

Uncertainty, 3-3, 3-4

measure, fuzzy-type, 3-9

representation of, 3-8

Uncertainty, expert systems and, 8-1-11

Bayesian belief networks, 8-6-10

belief network defined, 8-6-7

example, 8-9-10

knowledge engineering, 8-7-9

multiply connected belief networks, 8-9

process of using belief network, 8-9

reason for using belief network, 8-7

structure of belief network, 8-7

certainty factors, 8-3

definitions, 8-1-2

Dempster-Shafer approach, 8-5-6

future research, 8-10

MYCIN approach, 8-3-5

where to start, 8-2-3

Unfireable rule, 5-7

Unidirectional processing, 19-22

Unified programming, 15-2

Unknown words, 19-14

Unmanned Ground Vehicle (UGV), 34-9

Unnecessary conditions, 5-11

Unreachable goal, 5-11, 5-16, 5-18

Unreachable rule, 5-24

Unused input, 5-7

Urban Search and Rescue (USAR), 34-9

USAAIC, 34-2

USAR, see Urban Search and Rescue

Usefulness of indexes, 11-5

User

-friendly interface, 29-12

-initiated dialog, 6-5

interface, 4-2

Management Systems (UIMS), 6-8

role of, 6-3

models, 19-2

U.S. military, 34-1

V

V&V, see Validation and verification

Validation, 10-27

terminology, 5-3

and verification (V&V), 32-9

Value

-added products, 28-11

added tax (VAT), 33-8

ordering rules, 29-9

Variable ordering rules, 29-8

VAT, see Value added tax

Vegetation succession, 30-38

Vendor home pages, 29-13

Verbs

conceptualization of, 10-21

diagram, 10-17

dictionary, 10-21, 10-24

Verfication, 5-3, 10-27

VIAD, 2-4

Voice services, 24-1

Voting technique, 2-7

W

Waterfall, 25-3

life cycle, 10-13

model variant, 1-6

Water resources, systems dealing with management of, 30-33

Web browsers, 28-25

Welding engineers, 22-4

Whale Watcher Expert System, 30-32

White-box techniques, 5-20

Witnesses, 3-12

WordNet, 10-31

Working memory, 4-3, 18-11

Workload management, automated, 28-21

Workstations, tools sold for, 4-21

World

knowledge, 19-3

view, 1-2

Wide Web (WWW), 4-23, 6-11

interfaces, 6-8

sites, 16-3

WORM, see Write-once-read-many times

Write-once-read-many times (WORM), 14-2

WWW, see World Wide Web

X

X-ray diffraction (XRD) patterns, 30-21

XRD patterns, see X-ray diffraction patterns, 30-21

XX, see expert explorer