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Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Sixth Edition

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Artificial Intelligence: Structures and Strategies for Complex Problem Solving, Sixth Edition

by George F Luger

Publisher: Addison-Wesley

Copyright Year: 2009

Publishing Date: 2008/02/26

eText ISBN-10: 0-321-55787-5

eText ISBN-13: 978-0-321-55787-2

Print ISBN-10: 0-321-54589-3

Print ISBN-13: 978-0-321-54589-3

Course: Computer Science

Pages: 784

eTextbook $60.00

(180 day subscription)

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Table of Contents | Description

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ABOUT THIS TITLE - TABLE OF CONTENTS

Publisher’s Acknowledgements, xvi

Part I. Artificial Intelligence: Its Roots and Scope, 1-a

1. AI: Early History and Applications, 3

1.1. From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice, 3

1.2. Overview of AI Application Areas, 20

1.3. Artificial Intelligence—A Summary, 30

1.4. Epilogue and References, 31

1.5. Exercises, 33

Part II. Artificial Intelligence as Representation and Search, 34

2. The Predicate Calculus, 45

2.0. Introduction, 45

2.1. The Propositional Calculus, 45

2.2. The Predicate Calculus, 50

2.3. Using Inference Rules to Produce Predicate Calculus Expressions, 62

2.4. Application: A Logic-Based Financial Advisor, 73

2.5. Epilogue and References, 77

2.6. Exercises, 77

3. Structures and Strategies for State Space Search, 79

3.0. Introduction, 79

3.1. Graph Theory, 82

3.2. Strategies for State Space Search, 93

3.3. Using the State Space to Represent Reasoning with the Predicate Calculus, 107

3.4. Epilogue and References, 121

3.5. Exercises, 121

4. Heuristic Search, 123

4.0. Introduction, 123

4.1. Hill Climbing and Dynamic Programming, 127

4.2. The Best-First Search Algorithm, 133

4.3. Admissibility, Monotonicity, and Informedness, 145

4.4. Using Heuristics in Games, 150

4.5. Complexity Issues, 157

4.6. Epilogue and References, 161

4.7. Exercises, 162

5. Stochastic Methods, 165

5.0. Introduction, 165

5.1. The Elements of Counting, 167

5.2. Elements of Probability Theory, 170

5.3. Applications of the Stochastic Methodology, 182

5.4. Bayes’ Theorem, 184

5.5. Epilogue and References, 190

5.6. Exercises, 191

6. Building Control Algorithms for State Space Search, 193

6.0. Introduction, 193

6.1. Recursion-Based Search, 194

6.2. Production Systems, 200

6.3. The Blackboard Architecture for Problem Solving, 217

6.4. Epilogue and References, 219

6.5. Exercises, 220

Part III. Capturing Intelligence: The AI Challenge, 222

7. Knowledge Representation, 227

7.0. Issues in Knowledge Representation, 227

7.1. A Brief History of AI Representational Systems, 228

7.2. Conceptual Graphs: A Network Language, 248

7.3. Alternative Representations and Ontologies, 258

7.4. Agent Based and Distributed Problem Solving, 265

7.5. Epilogue and References, 270

7.6. Exercises, 273

8. Strong Method Problem Solving, 277

8.0. Introduction, 277

8.1. Overview of Expert System Technology, 279

8.2. Rule-Based Expert Systems, 286

8.3. Model-Based, Case Based, and Hybrid Systems, 298

8.4. Planning, 314

8.5. Epilogue and References, 329

8.6. Exercises, 331

9. Reasoning in Uncertain Situations, 333

9.0. Introduction, 333

9.1. Logic-Based Abductive Inference, 335

9.2. Abduction: Alternatives to Logic, 350

9.3. The Stochastic Approach to Uncertainty, 363

9.4. Epilogue and References, 378

9.5. Exercises, 380

Part IV. Machine Learning, 384

10. Machine Learning: Symbol-Based, 387

10.0. Introduction, 387

10.1. A Framework for Symbol-based Learning, 390

10.2. Version Space Search, 396

10.3. The ID3 Decision Tree Induction Algorithm, 408

10.4. Inductive Bias and Learnability, 417

10.5. Knowledge and Learning, 422

10.6. Unsupervised Learning, 433

10.7. Reinforcement Learning, 442

10.8. Epilogue and References, 449

10.9. Exercises, 450

11. Machine Learning: Connectionist, 453

11.0. Introduction, 453

11.1. Foundations for Connectionist Networks, 455

11.2. Perceptron Learning, 458

11.3. Backpropagation Learning, 467

11.4. Competitive Learning, 474

11.5. Hebbian Coincidence Learning, 484

11.6. Attractor Networks or “Memories”, 495

11.7. Epilogue and References, 505

11.8. Exercises, 506

12. Machine Learning: Genetic and Emergent, 507

12.0. Social and Emergent Models of Learning, 507

12.1. The Genetic Algorithm, 509

12.2. Classifier Systems and Genetic Programming, 519

12.3. Artificial Life and Society-Based Learning, 530

12.4. Epilogue and References, 541

12.5. Exercises, 542

13. Machine Learning: Probabilistic, 543

13.0. Stochastic and Dynamic Models of Learning, 543

13.1. Hidden Markov Models (HMMs), 544

13.2. Dynamic Bayesian Networks and Learning, 554

13.3. Stochastic Extensions to Reinforcement Learning, 564

13.4. Epilogue and References, 568

13.5. Exercises, 570

Part V. Advanced Topics for AI Problem Solving, 572

14. Automated Reasoning, 575

14.0. Introduction to Weak Methods in Theorem Proving, 575

14.1. The General Problem Solver and Difference Tables, 576

14.2. Resolution Theorem Proving, 582

14.3. Prolog and Automated Reasoning, 603

14.4. Further Issues in Automated Reasoning, 609

14.5. Epilogue and References, 617

14.6. Exercises, 617

15. Understanding Natural Language, 619

15.0. The Natural Language Understanding Problem, 619

15.1. Deconstructing Language: An Analysis, 622

15.2. Syntax, 625

15.3. Transition Network Parsers and Semantics, 633

15.4. Stochastic Tools for Language Understanding, 649

15.5. Natural Language Applications, 658

15.6. Epilogue and References, 666

15.7. Exercises, 667

Part VI. Epilogue, 670

16. Artificial Intelligence as Empirical Enquiry, 673

16.0. Introduction, 673

16.1. Artificial Intelligence: A Revised Definition, 675

16.2. The Science of Intelligent Systems, 688

16.3. AI: Current Challenges and Future Directions, 698

16.4. Epilogue and References, 703

Bibliography, 705

Author Index, 735

Subject Index, 743

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ABOUT THIS TITLE - DESCRIPTION

In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence—solving the complex problems that arise wherever computer technology is applied. Ideal for an undergraduate course in AI, the Sixth Edition presents the fundamental concepts of the discipline first then goes into detail with the practical information necessary to implement the algorithms and strategies discussed. Students learn how to use a number of different software tools and techniques to address the many challenges faced by today’s computer scientists.

Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one- or two-semester undergraduate course on AI.

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