Artificial Intelligence

Name: Artificial Intelligence
Price: 6.75 USD
Availability: InStock
ISBN: 9780070522619

ISBN-10: 0070522618

ISBN-13: 9780070522619

Edition: N/A

Authors: Elaine Rich

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Book details

Publisher: McGraw-Hill Higher Education
Binding: Hardcover
Pages: 448
Language: English

Preface	p. XV
Problems and Search	p. 1
What Is Artificial Intelligence?	p. 3
The AI Problems	p. 3
The Underlying Assumption	p. 6
What Is an AI Technique?	p. 8
The Level of the Model	p. 22
Criteria for Success	p. 24
Some General References	p. 26
One Final Word	p. 27
Exercises	p. 28
Problems, Problem Spaces, and Search	p. 29
Defining the Problem as a State Space Search	p. 29
Production Systems	p. 36
Problem Characteristics	p. 44
Production System Characteristics	p. 55
Issues in the Design of Search Programs	p. 57
Additional Problems	p. 60
Summary	p. 61
Exercises	p. 61
Heuristic Search Techniques	p. 63
Generate-and-Test	p. 64
Hill Climbing	p. 65
Best-First Search	p. 73
Problem Reduction	p. 82
Constraint Satisfaction	p. 88
Means-Ends Analysis	p. 94
Summary	p. 97
Exercises	p. 98
Knowledge Representation	p. 103
Knowledge Representation Issues	p. 105
Representations and Mappings	p. 105
Approaches to Knowledge Representation	p. 109
Issues in Knowledge Representation	p. 115
The Frame Problem	p. 126
Summary	p. 129
Using Predicate Logic	p. 131
Representing Simple Facts in Logic	p. 131
Representing Instance and Isa Relationships	p. 137
Computable Functions and Predicates	p. 139
Resolution	p. 143
Natural Deduction	p. 164
Summary	p. 165
Exercises	p. 166
Representing Knowledge Using Rules	p. 171
Procedural versus Declarative Knowledge	p. 171
Logic Programming	p. 173
Forward versus Backward Reasoning	p. 177
Matching	p. 182
Control Knowledge	p. 188
Summary	p. 192
Exercises	p. 192
Symbolic Reasoning under Uncertainty	p. 195
Introduction to Nonmonotonic Reasoning	p. 195
Logics for Nonmonotonic Reasoning	p. 199
Implementation Issues	p. 208
Augmenting a Problem Solver	p. 209
Implementation: Depth-First Search	p. 211
Implementation: Breadth-First Search	p. 222
Summary	p. 226
Exercises	p. 227
Statistical Reasoning	p. 231
Probability and Bayes' Theorem	p. 231
Certainty Factors and Rule-Based Systems	p. 233
Bayesian Networks	p. 239
Dempster-Shafer Theory	p. 242
Fuzzy Logic	p. 246
Summary	p. 247
Exercises	p. 248
Weak Slot-and-Filler Structures	p. 251
Semantic Nets	p. 251
Frames	p. 257
Exercises	p. 275
Strong Slot-and-Filler Structures	p. 277
Conceptual Dependency	p. 277
Scripts	p. 284
CYC	p. 288
Exercises	p. 294
Knowledge Representation Summary	p. 297
Syntactic-Semantic Spectrum of Representation	p. 297
Logic and Slot-and-Filler Structures	p. 299
Other Representational Techniques	p. 301
Summary of the Role of Knowledge	p. 302
Exercises	p. 303
Advanced Topics	p. 305
Game Playing	p. 307
Overview	p. 307
The Minimax Search Procedure	p. 310
Adding Alpha-Beta Cutoffs	p. 314
Additional Refinements	p. 319
Iterative Deepening	p. 322
References on Specific Games	p. 324
Exercises	p. 326
Planning	p. 329
Overview	p. 329
An Example Domain: The Blocks World	p. 332
Components of a Planning System	p. 333
Goal Stack Planning	p. 339
Nonlinear Planning Using Constraint Posting	p. 347
Hierarchical Planning	p. 354
Reactive systems	p. 356
Other Planning Techniques	p. 357
Exercises	p. 357
Understanding	p. 359
What Is Understanding?	p. 359
What Makes Understanding Hard?	p. 360
Understanding as Constraint Satisfaction	p. 367
Summary	p. 375
Exercises	p. 375
Natural Language Processing	p. 377
Introduction	p. 379
Syntactic Processing	p. 385
Semantic Analysis	p. 397
Discourse and Pragmatic Processing	p. 415
Summary	p. 424
Exercises	p. 426
Parallel and Distributed AI	p. 429
Psychological Modeling	p. 429
Parallelism in Reasoning Systems	p. 430
Distributed Reasoning Systems	p. 433
Summary	p. 445
Exercises	p. 445
Learning	p. 447
What Is Learning?	p. 447
Rote Learning	p. 448
Learning by Taking Advice	p. 450
Learning in Problem Solving	p. 452
Learning from Examples: Induction	p. 457
Explanation-Based Learning	p. 471
Discovery	p. 475
Analogy	p. 479
Formal Learning Theory	p. 482
Neural Net Learning and Genetic Learning	p. 483
Summary	p. 483
Exercises	p. 484
Connectionist Models	p. 487
Introduction: Hopfield Networks	p. 488
Learning in Neural Networks	p. 492
Applications of Neural Networks	p. 514
Recurrent Networks	p. 517
Distributed Representations	p. 520
Connectionist AI and Symbolic AI	p. 522
Exercises	p. 525
Common Sense	p. 529
Qualitative Physics	p. 530
Commonsense Ontologies	p. 533
Memory Organization	p. 540
Case-Based Reasoning	p. 543
Exercises	p. 545
Expert Systems	p. 547
Representing and Using Domain Knowledge	p. 547
Expert System Shells	p. 549
Explanation	p. 550
Knowledge Acquisition	p. 553
Summary	p. 556
Exercises	p. 557
Perception and Action	p. 559
Real-Time Search	p. 561
Perception	p. 563
Action	p. 569
Robot Architectures	p. 573
Summary	p. 576
Exercises	p. 577
Conclusion	p. 579
Components of an AI Program	p. 579
Exercises	p. 580
References	p. 583
Acknowledgements	p. 605
Author Index	p. 607
Subject Index	p. 613
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