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The Springer International Engineering and Computer Science

Reasoning with Limited Knowledge

By: Chilukuri K. Mohan

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Hardcover | 31 May 2000

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Hardcover
320 Pages

Dimensions(cm)
23.39 x 15.6 x 1.91

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$249.00

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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.

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Expert Systems & Knowledge-Based Systems

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Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Expert Systems & Knowledge-Based Systems

Preface	p. xi
Knowledge-Based Systems	p. 1
Early Expert Systems	p. 2
Roles, Tasks, Applications	p. 4
Structure of an Expert System	p. 5
Knowledge Representation	p. 6
To use, or not to use?	p. 7
Verification and Validation	p. 10
The rest of this book	p. 12
Bibliographic Notes	p. 15
Bibliography	p. 17
Practical Reasoning	p. 19
Formal Inference	p. 20
Syntax	p. 20
Inference Rules	p. 21
Semantics	p. 23
Temporal Logic	p. 25
Non-Monotonic Reasoning	p. 28
Truth Maintenance	p. 31
Model Based Reasoning	p. 33
Bibliographic Notes	p. 35
Bibliography	p. 37
Exercises	p. 39
Uncertainty	p. 41
Probability	p. 42
Likelihoods of Sufficiency and Necessity	p. 45
Probabilistic Inference Networks	p. 48
Interpolating Conditional Probabilities	p. 51
Combining Evidence	p. 55
Logical Inferences in Probabilistic Networks	p. 60
Cycles and Multiple Dependencies	p. 63
Reasoning in Acyclic Networks	p. 66
Decision Theory and Utilities	p. 75
Expected-Value Decision-Making	p. 75
Utility Theory	p. 78
Dempster-Shafer Calculus	p. 82
Belief Mass	p. 82
Combining Evidence	p. 83
Fuzzy Systems	p. 85
Certainty Factors	p. 87
Bibliographic Notes	p. 89
Bibliography	p. 91
Exercises	p. 93
Rule Based Programming	p. 99
Grammar Rules	p. 100
Rewrite Rules	p. 101
Petri Nets	p. 102
Ordering the rules	p. 103
Backward ho!	p. 105
Production Rules	p. 107
Inference Engine	p. 113
Matching	p. 114
Conflict Resolution	p. 118
Specifying and Verifying Rules	p. 120
Reasoning about actions	p. 122
Nondeterminism	p. 122
Variables	p. 124
Priorities	p. 124
Bibliographic Notes	p. 125
Bibliography	p. 127
Exercises	p. 129
Evolving Classifiers	p. 133
Learning Classifier Systems	p. 134
Representation	p. 136
Rule Firing	p. 138
Credit Allocation	p. 140
Rule Discovery	p. 143
Grouping Rules	p. 145
Examples of Classifier Systems	p. 147
Bibliographic Notes	p. 150
Bibliography	p. 153
Connectionist Systems	p. 157
Neural Networks	p. 158
Node Functions	p. 159
Network Architecture	p. 160
Neural Learning	p. 161
Connectionism and Expert Systems	p. 163
KBCNN	p. 163
MACIE	p. 166
Bibliographic Notes	p. 171
Bibliography	p. 173
Exercises	p. 175
Case Based Reasoning Systems	p. 177
Overview	p. 178
Retrieval	p. 180
Adaptation	p. 183
Derivational Adaptation	p. 184
Structural Adaptation	p. 185
Case Library	p. 186
Constructing the Case Library	p. 187
Hierarchical Organization of Cases	p. 188
Interfaces and Feedback	p. 189
Case Based Learning	p. 190
Examples	p. 191
Analogical Reasoning	p. 193
Bibliographic Notes	p. 195
Bibliography	p. 197
Exercises	p. 199
Knowledge Acquisition	p. 201
Key Concerns	p. 202
Hurdles	p. 202
Language problems	p. 202
Difficulty	p. 203
Fallibility	p. 203
Resistance	p. 204
Interpretation	p. 204
Procedure	p. 205
Interacting with Experts	p. 208
Unstructured Meetings	p. 209
Nominal-Group Technique	p. 209
Delphi Method	p. 210
Blackboarding	p. 210
Structured Interviews	p. 210
Case Studies	p. 211
Retrospective Case Study	p. 211
Observational Case Study	p. 212
Combining Different Techniques	p. 212
Personal Construct Technology	p. 212
Grid Analysis	p. 213
Logic of Confirmation	p. 217
Rule Generation Procedure	p. 218
Induction of Knowledge	p. 219
Splitting	p. 221
Multi-class problems	p. 227
Termination condition	p. 227
Multivalued attributes	p. 228
Computational Cost	p. 229
Guidelines for Inductive Learning	p. 229
Bibliographic Notes	p. 231
Bibliography	p. 233
Exercises	p. 235
Data Mining	p. 237
Preprocessing	p. 238
Overall Statistics	p. 238
Noise	p. 239
Missing Values	p. 240
Transforming Representations	p. 241
Normalization	p. 241
Dimensionality Reduction	p. 241
Data Reduction	p. 242
Knowledge Discovery	p. 244
Classification Trees	p. 245
Clustering	p. 245
Association Rules	p. 247
Prediction	p. 252
Bibliographic Notes	p. 254
Bibliography	p. 255
Distributed Experts	p. 259
Distributed Artificial Intelligence	p. 260
Blackboard Systems	p. 263
Hearsay	p. 265
HASP	p. 270
GBB	p. 271
DVMT	p. 272
BB1	p. 273
Multiagent Systems	p. 274
MAS Architectures	p. 275
Agent Interactions	p. 278
KQML	p. 278
Agent coordination protocols	p. 283
Cooperation protocols	p. 285
Negotiation	p. 287
Example Applications	p. 288
Sensor Net	p. 288
Emergency Management	p. 289
Traffic Management	p. 289
Bibliographic Notes	p. 290
Bibliography	p. 291
Exercises	p. 294
Index	p. 297
Table of Contents provided by Syndetics. All Rights Reserved.

The Springer International Engineering and Computer Science

Reasoning with Limited Knowledge

At a Glance

Hardcover

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