Artificial intelligence/
Elaine Rich, Kevin Knight, Shivashankar B Nair
- 3rd ed.
- New Delhi: Tata McGraw-Hill, 2009.
- xviii, 568 p. : ill. ; 26 cm.
PART I: PROBLEMS AND SEARCH 1. What is Artificial Intelligence? 1.1 The AI Problems 4 1.2 The Underlying Assumption 6 1.3 What is an AI Technique? 7 1.4 The Level of the Model 18 1.5 Criteria for Success 20 1.6 Some General References 21 1.7 One Final Word and Beyond 22 2. Problems, Problem Spaces, and Search 2.1 Defining the Problem as a State Space Search 25 2.2 Production Systems 30 2.3 Problem Characteristics 36 2.4 Production System Characteristics 43 2.5 Issues in the Design of Search Programs 45 2.6 Additional Problems 47 3. Heuristic Search Techniques 3.1 Generate-and-Test 50 3.2 Hill Climbing 52 3.3 Best-first Search 57 3.4 Problem Reduction 64 3.5 Constraint Satisfaction 68 3.6 Means-ends Analysis 72
PART II: KNOWLEDGE REPRESENTATION 4. Knowledge Representation Issues 4.1 Representations and Mappings 79 4.2 Approaches to Knowledge Representation 82 4.3 Issues in Knowledge Representation 86 4.4 The Frame Problem 96 5. Using Predicate Logic 5.1 Representing Simple Facts in Logic 99 5.2 Representing Instance and ISA Relationships 103 5.3 Computable Functions and Predicates 105 5.4 Resolution 208 5.5 Natural Deduction 124 6. Representing Knowledge Using Rules 6.1 Procedural Versus Declarative Knowledge 129 -' 6.2 Logic Programming 132 6.3 Forward Versus Backward Reasoning 134 6.4 Matching 138 6.5 Control Knowledge 142 7. Symbolic Reasoning Under Uncertainty 7.1 Introduction to Nonmonotonic Reasoning 147 7.2 Logics for Nonmonotonic Reasoning 150 7.3 Implementation Issues 157 7.4 Augmenting a Problem-solver 158 7.5 Implementation: Depth-first Search 160 7.6 Implementation: Breadth-first Search 166 8. Statistical Reasoning 3.1 Probability and Bayes' Theorem 172 8.2 Certainty Factors and Rule-based Systems 174 8.3 Bayesian Networks 179 8.4 Dempster-Shafer Theory 181 8.5 Fuzzy Logic 184 9. Weak Slot-and-Filler Structures 9.1 Semantic Nets 188 9.2 Frames 193 10. Strong Slot-and-Filler Structures 10.1 Conceptual Dependency 207 10.2 Scripts 212 10.3 CYC 216 11. Knowledge Representation Summary 11.1 Syntactic-semantic Spectrum of Representation 222 11.2 Logic and Slot-and-filler Structures 224 11.3 Other Representational Techniques 225 11.4 Summary of the Role of Knowledge 227
PART III: ADVANCED TOPICS 12. Game Playing 12.1 Overview 231. 12.2 The Minimax Search Procedure 233 12.3 Adding Alpha-beta Cutoffs 236 12.4 Additional Refinements 240 12.5 Iterative Deepening 242 12.6 References on Specific Games 244 13. Planning 13.1 Overview 247 13.2 An Example Domain: The Blocks World 250. 13.3 Components of a Planning System 250 13.4 Goal Stack Planning 255 13.5 Nonlinear Planning Using Constraint Posting 262 13.6 Hierarchical Planning 268 13.7 Reactive Systems 269 13.8 Other Planning Techniques 269 14. Understanding. 14.1 What is Understanding? 272 14.2 What Makes Understanding Hard? 273 14.3 Understanding as Constraint Satisfaction 278 15. Natural Language Processing 15.1 Introduction 286 15.2 Syntactic Processing 291 15.3 Semantic Analysis 300 15.4 Discourse and Pragmatic Processing 313 15.5 Statistical Natural Language Processing 321 15.6 Spell Checking 325 16. Parallel and Distributed AI 16.1 Psychological Modeling 333 16.2 Parallelism in Reasoning Systems 334 16.3 Distributed Reasoning Systems 336 17. Learning 17.1 What is Learning? 347 17.2 Rote Learning 348 17.3 Learning by Taking Advice 349 17.4 Learning in Problem-solving 351 17.5 Learning from Examples: Induction 355 17.6 Explanation-based Learning 364 17.7 Discovery 367 17.8 Analogy 371 17.9 Formal Learning Theory 372 17.10 Neural Net Learning and Genetic Learning 373 18. Connectionist Models 18.1 Introduction: Hopfield Networks 377 18.2 Learning in Neural Networks 379 18.3 Applications of Neural Networks 396 18.4 Recurrent Networks 399 18.5 Distributed Representations 400 18.6 Connectionist AI and Symbolic AI 403 19. Common Sense 19.1 Qualitative Physics 409 19.2 Common Sense Ontologies 411 19;d Memory Organization 417 19.4 Case-based Reasoning 419 20. Expert Systems 20.1 Representing and Using Domain Knowledge 422 20.2 Expert System Shells 424 20.3 Explanation 425 20.4 Knowledge Acquisition 427 21. Perception and Action 21.1 Real-time Search 433 21.2 Perception 434 21.3 Action 438 21.4 Robot Architectures 441 22. Fuzzy Logic Systems 22.1 Introduction 445 22.2 Crisp Sets 445 22.3 Fuzzy Sets 446 22.4 Some Fuzzy Terminology 446 22.5 Fuzzy Logic Control 447 22.6 Sugeno Style of Fuzzy Inference Processing 453 22.7 Fuzzy Hedges 454 22.8 a Cut Threshold 454 22.9 Neuro Fuzzy Systems 455 22.10 Points to Note 455 23. Genetic Algorithms: Copying Nature's Approaches 23.1 A Peek into the Biological World 457 23.2 Genetic Algorithms (OAs) 458 23.3 Significance of the Genetic Operators 470 23.4 Termination Parameters 471 23.5 Niching and Speciation 471 23.6 Evolving Neural Networks 472 23.7 Theoretical Grounding 474 23.8 Ant Algorithms 476 23.9 Points to'Ponder 477 24. Artificial Immune Systems 24.1 Introduction 479 24.2 The Phenomenon of Immunity 479 24.3 Immunity and Infection 480 24.4 The Innate Immune System—The First Line of Defence 24.5 The Adaptive Immune System—The Second Line of Defence 481 24.6 Recognition 483 24.7 Clonal Selection 484 24.7 Learning 485 24.8 Immune Network Theory 485 24.9 Mapping Immune Systems to Practical Applications 486 24.10 Other Applications 493 24.11 Points to Ponder 493 25. Prolog—The Natural Language of Artificial Intelligence 25.1 Introduction 496 25.2 Converting English to Prolog Facts and Rules 496 25.3 Goals 497 25.4 Prolog Terminology 499 25.5 Variables 499 25.6 Control Structures 500 25.7 Arithmetic Operators 500 25.8 Matching in Prolog 502 25.9 Backtracking 503 25.10 Cuts 505 25.11 Recursion 506 25.12 Lists 508 25.13 Dynamic Databases 512 25.14 Input/Output and Streams 515 25.15 Some Aspects Specific to LPA Prolog 516 26. Conclusion 26.1 Components of an AI Program 529 26.2 AI Skeptics—An Open Argument 529