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Table of Contents


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


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