Frontiers of Expert Systems: Reasoning With Limited Knowledge

Front Cover
Springer Science & Business Media, 2000 M05 31 - 303 pages
The development of modern knowledge-based systems, for applications ranging from medicine to finance, necessitates going well beyond traditional rule-based programming. Frontiers of Expert Systems: Reasoning with Limited Knowledge attempts to satisfy such a need, introducing exciting and recent advances at the frontiers of the field of expert systems.
Beginning with the central topics of logic, uncertainty and rule-based reasoning, each chapter in the book presents a different perspective on how we may solve problems that arise due to limitations in the knowledge of an expert system's reasoner.
Successive chapters address (i) the fundamentals of knowledge-based systems, (ii) formal inference, and reasoning about models of a changing and partially known world, (iii) uncertainty and probabilistic methods, (iv) the expression of knowledge in rule-based systems, (v) evolving representations of knowledge as a system interacts with the environment, (vi) applying connectionist learning algorithms to improve on knowledge acquired from experts, (vii) reasoning with cases organized in indexed hierarchies, (viii) the process of acquiring and inductively learning knowledge, (ix) extraction of knowledge nuggets from very large data sets, and (x) interactions between multiple specialized reasoners with specialized knowledge bases.
Each chapter takes the reader on a journey from elementary concepts to topics of active research, providing a concise description of several topics within and related to the field of expert systems, with pointers to practical applications and other relevant literature.
Frontiers of Expert Systems: Reasoning with Limited Knowledge is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.
 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

KnowledgeBased Systems
1
11 Early Expert Systems
2
12 Roles Tasks Applications
4
13 Structure of an Expert System
5
14 Knowledge Representation
6
15 To use or not to use?
7
16 Verification and Validation
10
17 The rest of this book
12
Bibliography
173
65 Exercises
175
Case Based Reasoning Systems
177
71 Overview
178
72 Retrieval
180
73 Adaptation
183
731 Derivational Adaptation
184
732 Structural Adaptation
185

18 Bibliographic Notes
15
Bibliography
17
Practical Reasoning
19
21 Formal Inference
20
212 Inference Rules
21
213 Semantics
23
22 Temporal Logic
25
23 NonMonotonic Reasoning
28
24 Truth Maintenance
31
25 Model Based Reasoning
33
26 Bibliographic Notes
35
Bibliography
37
27 Exercises
39
Uncertainty
41
31 Probability
42
32 Likelihoods of Sufficiency and Necessity
45
33 Probabilistic Inference Networks
48
34 Interpolating Conditional Probabilities
51
35 Combining Evidence
55
36 Logical Inferences in Probabilistic Networks
60
37 Cycles and Multiple Dependencies
63
38 Reasoning in Acyclic Networks
66
39 Decision Theory and Utilities
75
392 Utility Theory
78
310 DempsterShafer Calculus
82
3102 Combining Evidence
83
311 Fuzzy Systems
85
312 Certainty Factors
87
313 Bibliographic Notes
89
Bibliography
91
314 Exercises
93
Rule Based Programming
99
41 Grammar Rules
100
42 Rewrite Rules
101
421 Petri Nets
102
43 Ordering the rules
103
44 Backward ho
105
45 Production Rules
107
46 Inference Engine
113
47 Matching
114
48 Conflict Resolution
118
49 Specifying and Verifying Rules
120
491 Reasoning about actions
122
493 Variables
124
410 Bibliographic Notes
125
Bibliography
127
Evolving Classifiers
133
51 Learning Classifier Systems
134
52 Representation
136
53 Rule Firing
138
54 Credit Allocation
140
55 Rule Discovery
143
56 Grouping Rules
145
57 Examples of Classifier Systems
147
58 Bibliographic Notes
150
Bibliography
153
Connectionist Systems
157
61 Neural Networks
158
611 Node Functions
159
612 Network Architecture
160
613 Neural Learning
161
614 Connectionism and Expert Systems
163
63 MACIE
166
64 Bibliographic Notes
171
74 Case Library
186
741 Constructing the Case Library
187
742 Hierarchical Organization of Cases
188
75 Interfaces and Feedback
189
76 Case Based Learning
190
77 Examples
191
78 Analogical Reasoning
193
79 Bibliographic Notes
195
Bibliography
197
710 Exercises
199
Knowledge Acquisition
201
81 Key Concerns
202
8112 Difficulty
203
8114 Resistance
204
812 Procedure
205
82 Interacting with Experts
208
821 Unstructured Meetings
209
823 Delphi Method
210
826 Case Studies
211
8262 Observational Case Study
212
831 Grid Analysis
213
832 Logic of Confirmation
217
833 Rule Generation Procedure
218
84 Induction of Knowledge
219
841 Splitting
221
842 Multiclass problems
227
844 Multivalued attributes
228
845 Computational Cost
229
85 Bibliographic Notes
231
Bibliography
233
86 Exercises
235
Data Mining
237
91 Preprocessing
238
912 Noise
239
913 Missing Values
240
92 Transforming Representations
241
923 Data Reduction
242
93 Knowledge Discovery
244
931 Classification Trees
245
933 Association Rules
247
94 Prediction
252
95 Bibliographic Notes
254
Bibliography
255
Distributed Experts
259
101 Distributed Artificial Intelligence
260
102 Blackboard Systems
263
1021 Hearsay
265
1022 HASP
270
1023 GBB
271
1024 DVMT
272
1025 BB1
273
103 Multiagent Systems
274
1031 MAS Architectures
275
104 Agent Interactions
278
1042 Agent coordination protocols
283
1043 Cooperation protocols
285
1044 Negotiation
287
105 Example Applications
288
1052 Emergency Management
289
106 Bibliographic Notes
290
Bibliography
291
107 Exercises
294
Index
297
Copyright

Other editions - View all

Common terms and phrases

Bibliographic information