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The construction of intelligent machines is the primary goal of research in artificial intelligence and knowledge-based systems. This book is designed to introduce the foundations of mathematical and philosophical approaches to this rapidly expanding research area.
Foundations of Intelligent Knowledge-Based Systems is divided into three parts. Part I uses logic as a guideline and addresses fundamental theoretical and practical issues in developing large scale intelligent knowledge-based systems (IKBS). PartII discusses modal and intentional logic, nonmonotonic logic, induction and reasoning under uncertainty, and also covers advanced concepts such as planning, actions, states, and temporal systems. Part III looks at the architecture and design principles ofIKBSs using a case study, thus illuminating many of the principles and concepts developed in Parts I and II.
This text presents a unified and rigorous study of IKBS theory and application. It is essential reading for advanced undergraduates, graduates, and developers in knowledge-based systems, artificial intelligence, and expert systems.

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CONTENTS
1. INTRODUCTION = 1
 1.1. Characteristics of IKBS = 1
 1.2. Knowledge Representation = 2
 1.3. The Role of Logic = 3
 1.4. Aims and Structure of the Book = 3
2. PROPOSITIONAL LOGIC = 10
 2.1. Introduction = 10
 2.2. The Formal Language of Propositional Logic = 10
 2.3. Semantic Rules = 12
 2.4. Properties of Propositional formulae = 14
 2.5. Computing the Truth Value of Propositional formulae = 15
 2.6. Truth Tables = 16
 2.7. Semantic Trees = 17
 2.8. Semantic Tableaux = 20
 2.9. Proof Theory of Propositional Logic = 23
 2.10. Clauses and Normal Forms = 27
 2.11. Algorithm of Davis and Putnam = 29
 2.12. Resolution Principle = 31
 2.13. Resolution : Applications and Examples = 34
 2.14. Non-clausal Resolution = 35
 2.15. Horn Clauses = 36
 2.16. Compactness Theorem = 38
 2.17. Conclusion = 40
3. FIRST ORDER LOGIC = 41
 3.1. Introduction = 41
 3.2. The Language = 41
 3.3. Prenex Normal Form = 46
 3.4. Skolem Forms, Clausal Forms = 48
 3.5. Herbrand Interpretation = 49
 3.6. Semantic Trees = 54
 3.7. Semantic Tableaux for Predicate Calculus = 57
 3.8. Herbrand's Theorem = 58
 3.9. Implementation of Herbrand's Theorem = 59
 3.10. Validity, Decidability and Completeness Issues = 60
 3.11. The Method of Davis and Putnam = 61
 3.12. Resolution in First Order Logic = 63
 3.13. Ground Resolution = 64
 3.14. General Resolution = 66
 3.15. Unification Algorithm = 67
 3.16. General Resolution Principle for the First Order Logic = 70
 3.17. Completeness of the Resolution Principle = 71
 3.18. Examples Using the Resolution Principle = 74
 3.19. Deletion Strategy = 76
 3.20. Matrix Proof Methods = 80
 3.21. Conclusion = 90
4. AXIOMATIC SYSTEMS = 91
 4.1. Introduction = 91
 4.2. Properties of Axiomatic System = 91
 4.3. Axiomatic Systems for Propositional Logic = 92
 4.4. Soundness and Completeness of propositional Logic = 93
 4.5. Natural Deduction = 95
 4.6. Axioms for Predicate Calculus = 97
 4.7. Natural Deduction for Predicate Calculus = 98
 4.8. Equality in Predicate Calculus = 100
 4.9. First Order Theories, Computability and Logic = 103
 4.10. Proofs, Models, Algorithms and Computability = 107
 4.11. Conclusion = 111
5. KNOWLEDGE REPRESENTATION = 113
 5.1. Introduction = 113
 5.2. Logic Representation = 115
 5.3. Production Systems = 124
 5.4. Network Representation = 133
 5.5. Frames = 139
 5.6. Conceptual Graphs = 145
 5.7. The Object-Oriented Data Model and Object-Oriented Database System = 147
 5.8. Conclusion = 160
6. DEDUCTIVE DATABASES = 161
 6.1. Introduction = 161
 6.2. Deductive Databases = 162
 6.3. Semantics of Deductive Databases = 164
 6.4. Definite Database = 173
 6.5. Negation in Deductive Databases = 200
 6.6. Three-Valued Logic = 209
 6.7. Conclusion = 210
7. REVISABLE BELIEFS = 212
 7.1. Introduction = 212
 7.2. Non-Monotonic Reasoning = 213
 7.3. Default Logic = 214
 7.4. Modal Approach to Revisable Reasoning = 220
 7.5. McDermott's Non-Monotonic Logics = 223
 7.6. Autoepistemic Logic = 225
 7.7. Defeasible Reasoning = 230
 7.8. Circumscription = 234
 7.9. Application of Non-Monotonic Reasoning = 238
 7.10. Conclusion = 243
8. REASONING UNDER UNCERTAINTY = 244
 8.1. Introduction = 244
 8.2. Probability Theory = 245
 8.3. Belief Network = 250
 8.4. The Dempster-Shafer Theory = 264
 8.5. Probabilistic Logic = 270
 8.6. Conclusion = 274
9. MODAL LOGIC = 275
 9.1. Introduction = 275
 9.2. Propositional Modal Logic = 275
 9.3. Valuations and Tautologies = 279
 9.4. Proof Theory = 281
 9.5. Multimodal Languages = 285
 9.6. First Order Modal Logic = 286
 9.7. Proof Methods for Modal Logics = 288
 9.8. Resolution Proof Method for Modal Logics = 289
 9.9. Theory Resolution Method for Modal LOgic = 293
 9.10. Translation into Clausal Form = 295
 9.11. Tableau Proof Methods = 298
 9.12. Matrix-Based Proof Method for Modal Logic = 303
 9.13. Conclusion = 307
10. TEMPORAL LOGIC = 309
 10.1 Introduction =309
 10.2. Requirements of Temporal Reasoning = 309
 10.3. Propositional Temporal Logic (PTL) = 310
 10.4. Branching-Time Propositional Temporal Logic (BPTL) = 315
 10.5. Bounded and Unbounded Time = 318
 10.6. Temporal Logic Based on Dense Time = 318
 10.7. First Order Temporal Modal Logic (FTL) = 319
 10.8. Linear Time = 322
 10.9. Dense Time = 323
 10.10. Proof Theories for Modal Temporal Logic = 323
 10.11. Execution of Temporal Logics = 337
 10.12. Related Work = 359
 10.13. Conclustio = 365
11. MACHINE LEARNING = 367
 11.1. Introduction = 367
 11.2. Rote Learning = 367
 11.3. Induction-Based Learning = 368
 11.4. Learning by Analogy = 377
 11.5. Explanation-Based Learning = 378
 11.6. Learning by Observation and Discovery = 380
 11.7. Learnability Theory = 383
 11.8. Neural Networks = 386
 11.9. Conclusion = 400
12. MULTIAGENT SYSTEMS = 401
 12.1. Introduction = 401
 12.2. Why MAS? = 402
 12.3. Basic Issues and Foundation of MAS = 402
 12.4. The Social Nature of MAS = 403
 12.5. Characterisation of MAS = 403
 12.6. Characteristics of Agency = 408
 12.7. Agent Architecture = 427
 12.8. A Formal Model for Multiagent Systems = 429
 12.9. Conclusion = 436
13. LOGIC PROGRAMMING AND CONSTRAINT SATISFACTION  = 438
 13.1. Introduction = 438
 13.2. Logic Programming = 438
 13.3. Contraint Logic Programming = 455
 13.4. Conclusion = 465
14. META-SYSTEMS = 466
 14.1. Introduction = 466
 14.2. Meta-Programming = 467
 14.3. Typed Representation = 468
 14.4. Ground Representation = 470
 14.5. Meta-Logical Predicates = 471
 14.6. A Meta-Interpreter = 473
 14.7. Conclusion = 475
Referenes = 476
Index = 503