Artificial intelligence / Elaine Rich.
By: Rich, Elaine.
Material type: BookSeries: McGraw-Hill series in artificial intelligence.Publisher: New York : McGraw-Hill, 1983Description: xii, 436 p. : ill. ; 24 cm.ISBN: 0070522618 .Subject(s): Artificial intelligenceDDC classification: 006.3Item type | Current library | Call number | Copy number | Status | Date due | Barcode | Item holds |
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General Lending | MTU Bishopstown Library Store Item | 006.3 (Browse shelf(Opens below)) | 1 | Available | 00021851 |
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Enhanced descriptions from Syndetics:
Discusses the characteristics of artificial intelligence techniques & problems. Provides descriptions of algorithms; techniques relevant to the construction of 'expert systems' are illustrated.
Bibliography: p. 412-426. - Includes indexes.
Table of contents provided by Syndetics
- Preface (p. XV)
- I Problems and Search (p. 1)
- 1 What Is Artificial Intelligence? (p. 3)
- 1.1 The AI Problems (p. 3)
- 1.2 The Underlying Assumption (p. 6)
- 1.3 What Is an AI Technique? (p. 8)
- 1.4 The Level of the Model (p. 22)
- 1.5 Criteria for Success (p. 24)
- 1.6 Some General References (p. 26)
- 1.7 One Final Word (p. 27)
- 1.8 Exercises (p. 28)
- 2 Problems, Problem Spaces, and Search (p. 29)
- 2.1 Defining the Problem as a State Space Search (p. 29)
- 2.2 Production Systems (p. 36)
- 2.3 Problem Characteristics (p. 44)
- 2.4 Production System Characteristics (p. 55)
- 2.5 Issues in the Design of Search Programs (p. 57)
- 2.6 Additional Problems (p. 60)
- 2.7 Summary (p. 61)
- 2.8 Exercises (p. 61)
- 3 Heuristic Search Techniques (p. 63)
- 3.1 Generate-and-Test (p. 64)
- 3.2 Hill Climbing (p. 65)
- 3.3 Best-First Search (p. 73)
- 3.4 Problem Reduction (p. 82)
- 3.5 Constraint Satisfaction (p. 88)
- 3.6 Means-Ends Analysis (p. 94)
- 3.7 Summary (p. 97)
- 3.8 Exercises (p. 98)
- II Knowledge Representation (p. 103)
- 4 Knowledge Representation Issues (p. 105)
- 4.1 Representations and Mappings (p. 105)
- 4.2 Approaches to Knowledge Representation (p. 109)
- 4.3 Issues in Knowledge Representation (p. 115)
- 4.4 The Frame Problem (p. 126)
- 4.5 Summary (p. 129)
- 5 Using Predicate Logic (p. 131)
- 5.1 Representing Simple Facts in Logic (p. 131)
- 5.2 Representing Instance and Isa Relationships (p. 137)
- 5.3 Computable Functions and Predicates (p. 139)
- 5.4 Resolution (p. 143)
- 5.5 Natural Deduction (p. 164)
- 5.6 Summary (p. 165)
- 5.7 Exercises (p. 166)
- 6 Representing Knowledge Using Rules (p. 171)
- 6.1 Procedural versus Declarative Knowledge (p. 171)
- 6.2 Logic Programming (p. 173)
- 6.3 Forward versus Backward Reasoning (p. 177)
- 6.4 Matching (p. 182)
- 6.5 Control Knowledge (p. 188)
- 6.6 Summary (p. 192)
- 6.7 Exercises (p. 192)
- 7 Symbolic Reasoning under Uncertainty (p. 195)
- 7.1 Introduction to Nonmonotonic Reasoning (p. 195)
- 7.2 Logics for Nonmonotonic Reasoning (p. 199)
- 7.3 Implementation Issues (p. 208)
- 7.4 Augmenting a Problem Solver (p. 209)
- 7.5 Implementation: Depth-First Search (p. 211)
- 7.6 Implementation: Breadth-First Search (p. 222)
- 7.7 Summary (p. 226)
- 7.8 Exercises (p. 227)
- 8 Statistical Reasoning (p. 231)
- 8.1 Probability and Bayes' Theorem (p. 231)
- 8.2 Certainty Factors and Rule-Based Systems (p. 233)
- 8.3 Bayesian Networks (p. 239)
- 8.4 Dempster-Shafer Theory (p. 242)
- 8.5 Fuzzy Logic (p. 246)
- 8.6 Summary (p. 247)
- 8.7 Exercises (p. 248)
- 9 Weak Slot-and-Filler Structures (p. 251)
- 9.1 Semantic Nets (p. 251)
- 9.2 Frames (p. 257)
- 9.3 Exercises (p. 275)
- 10 Strong Slot-and-Filler Structures (p. 277)
- 10.1 Conceptual Dependency (p. 277)
- 10.2 Scripts (p. 284)
- 10.3 CYC (p. 288)
- 10.4 Exercises (p. 294)
- 11 Knowledge Representation Summary (p. 297)
- 11.1 Syntactic-Semantic Spectrum of Representation (p. 297)
- 11.2 Logic and Slot-and-Filler Structures (p. 299)
- 11.3 Other Representational Techniques (p. 301)
- 11.4 Summary of the Role of Knowledge (p. 302)
- 11.5 Exercises (p. 303)
- III Advanced Topics (p. 305)
- 12 Game Playing (p. 307)
- 12.1 Overview (p. 307)
- 12.2 The Minimax Search Procedure (p. 310)
- 12.3 Adding Alpha-Beta Cutoffs (p. 314)
- 12.4 Additional Refinements (p. 319)
- 12.5 Iterative Deepening (p. 322)
- 12.6 References on Specific Games (p. 324)
- 12.7 Exercises (p. 326)
- 13 Planning (p. 329)
- 13.1 Overview (p. 329)
- 13.2 An Example Domain: The Blocks World (p. 332)
- 13.3 Components of a Planning System (p. 333)
- 13.4 Goal Stack Planning (p. 339)
- 13.5 Nonlinear Planning Using Constraint Posting (p. 347)
- 13.6 Hierarchical Planning (p. 354)
- 13.7 Reactive systems (p. 356)
- 13.8 Other Planning Techniques (p. 357)
- 13.9 Exercises (p. 357)
- 14 Understanding (p. 359)
- 14.1 What Is Understanding? (p. 359)
- 14.2 What Makes Understanding Hard? (p. 360)
- 14.3 Understanding as Constraint Satisfaction (p. 367)
- 14.4 Summary (p. 375)
- 14.5 Exercises (p. 375)
- 15 Natural Language Processing (p. 377)
- 15.1 Introduction (p. 379)
- 15.2 Syntactic Processing (p. 385)
- 15.3 Semantic Analysis (p. 397)
- 15.4 Discourse and Pragmatic Processing (p. 415)
- 15.5 Summary (p. 424)
- 15.6 Exercises (p. 426)
- 16 Parallel and Distributed AI (p. 429)
- 16.1 Psychological Modeling (p. 429)
- 16.2 Parallelism in Reasoning Systems (p. 430)
- 16.3 Distributed Reasoning Systems (p. 433)
- 16.4 Summary (p. 445)
- 16.5 Exercises (p. 445)
- 17 Learning (p. 447)
- 17.1 What Is Learning? (p. 447)
- 17.2 Rote Learning (p. 448)
- 17.3 Learning by Taking Advice (p. 450)
- 17.4 Learning in Problem Solving (p. 452)
- 17.5 Learning from Examples: Induction (p. 457)
- 17.6 Explanation-Based Learning (p. 471)
- 17.7 Discovery (p. 475)
- 17.8 Analogy (p. 479)
- 17.9 Formal Learning Theory (p. 482)
- 17.10 Neural Net Learning and Genetic Learning (p. 483)
- 17.11 Summary (p. 483)
- 17.12 Exercises (p. 484)
- 18 Connectionist Models (p. 487)
- 18.1 Introduction: Hopfield Networks (p. 488)
- 18.2 Learning in Neural Networks (p. 492)
- 18.3 Applications of Neural Networks (p. 514)
- 18.4 Recurrent Networks (p. 517)
- 18.5 Distributed Representations (p. 520)
- 18.6 Connectionist AI and Symbolic AI (p. 522)
- 18.7 Exercises (p. 525)
- 19 Common Sense (p. 529)
- 19.1 Qualitative Physics (p. 530)
- 19.2 Commonsense Ontologies (p. 533)
- 19.3 Memory Organization (p. 540)
- 19.4 Case-Based Reasoning (p. 543)
- 19.5 Exercises (p. 545)
- 20 Expert Systems (p. 547)
- 20.1 Representing and Using Domain Knowledge (p. 547)
- 20.2 Expert System Shells (p. 549)
- 20.3 Explanation (p. 550)
- 20.4 Knowledge Acquisition (p. 553)
- 20.5 Summary (p. 556)
- 20.6 Exercises (p. 557)
- 21 Perception and Action (p. 559)
- 21.1 Real-Time Search (p. 561)
- 21.2 Perception (p. 563)
- 21.3 Action (p. 569)
- 21.4 Robot Architectures (p. 573)
- 21.5 Summary (p. 576)
- 21.6 Exercises (p. 577)
- 22 Conclusion (p. 579)
- 22.1 Components of an AI Program (p. 579)
- 22.2 Exercises (p. 580)
- References (p. 583)
- Acknowledgements (p. 605)
- Author Index (p. 607)
- Subject Index (p. 613)