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AI Algorithms, Data Structures, and Idioms in PROLOG, LISP, and Java for Artificial Intelligence: Structures and Strategies for Complex Problem Solvin

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Synopses & Reviews

Publisher Comments:

Artificial Intelligence

Structures and Strategies for Complex Problem Solving, Sixth Edition

by George F Luger

 

This accessible, comprehensive book captures the essence of artificial intelligence — solving the complex problems that arise wherever computer technology is applied. With his signature enthusiasm, George Luger demonstrates numerous techniques and strategies for addressing the many challenges facing computer scientists today. Diverse topics on this exciting and ever-evolving field range from perception and adaptation using neural networks and genetic algorithms, intelligent agents with ontologies, automated reasoning, natural language analysis, and stochastic approaches to machine learning.

 

This book is ideal for a one - or two-semester university course on AI.

 

New to this edition:

  • A new chapter on stochastic approaches to machine learning, including first-prder Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation.
  • Presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning.
  • Presentation of agent technology and the use of ontologies.
  • Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi.
  • A new supplemental programming book is available: AI Algorithms in Prolog, Lisp, and Java™. Available online and in print, this book demonstrates these languages as tools for building many of the algorithms presented throughout Luger's AI book.

"There are many ideas in this area that students often find difficult; the clarity and precision of Luger's exposition is informed by sharp, incisive examples with straightforward graphical components."

-- Joseph Lewis, San Diego State University

 

"The book is a perfect complement to an AI course. It gives readers both an historical point of view and a practical guide to all the techniques. It is THE book I would recommend as an introduction to this field."

-- Pascal Rebreyend, Dalarna University 

 

"The style of writing and comprehensive treatment of the subject matter makes this a valuable addition to the AI literature."

-- Malachy Eaton, University of Limerick

 

George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico. He received his Ph.D. from the University of Pennsylvania and spent five years researching and teaching at the Department of Artificial Intelligence at the University of Edinburgh.

Synopsis:

In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence–solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: AI Algorithms in Prolog, Lisp and Java ™. References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence.

About the Author

George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico. He received his PhD from the University of Pennsylvania and spent five years researching and teaching at the Department of Artificial Intelligence at the University of Edinburgh.

Table of Contents

PART I

ARTIFICIAL INTELLIGENCE: ITS ROOTS

AND SCOPE 1

1 AI: 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 35

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 CONTROL AND IMPLEMENTATION OF 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 187

6.4 Epilogue and References 219

6.5 Exercises 220

 

PART III

CAPTURING INTELLIGENCE:

THE AI CHALLENGE 223

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 385

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 Genetic 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 573

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 666

14.6 Exercises 667

 

15 UNDERSTANDING NATURAL LANGUAGE 619

15.0 The Natural Language Understanding Problem 619

15.1 Deconstructing

Product Details

ISBN:
9780136070474
Author:
Luger, George F.
Publisher:
Addison-Wesley
Author:
Stubblefield, William A.
Subject:
Artificial Intelligence
Subject:
Computers-Reference - General
Copyright:
Edition Description:
Trade paper
Publication Date:
August 2008
Binding:
Paperback
Grade Level:
College/higher education:
Language:
English
Pages:
464
Dimensions:
9.7 x 8 x 1.1 in 894 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computer Languages » Lisp
Computers and Internet » Computers Reference » General
Computers and Internet » Software Engineering » Algorithms
Engineering » Mechanical Engineering » General
Reference » Science Reference » Technology
Science and Mathematics » Agriculture » General

AI Algorithms, Data Structures, and Idioms in PROLOG, LISP, and Java for Artificial Intelligence: Structures and Strategies for Complex Problem Solvin New Trade Paper
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Product details 464 pages Addison Wesley Longman - English 9780136070474 Reviews:
"Synopsis" by ,

In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence–solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: AI Algorithms in Prolog, Lisp and Java ™. References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence.

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