Rent 9780321263186 | Artificial Intelligence : Structures and Strategies for Complex Problem Solving

Summary Table of Contents Author Biography

Much has changed since the early editions of Artificial Intelligence were published. To reflect this the introductory material of this fifth edition has been substantially revised and rewritten to capture the excitement of the latest developments in AI work. Artificial intelligence is a diverse field. To ask the question "what is intelligence?" is to invite as many answers as there are approaches to the subject of artificial intelligence. These could be intelligent agents, logical reasoning, neural networks, expert systems, evolutionary computi... MOREng and so on. This fifth edition covers all the main strategies used for creating computer systems that will behave in "intelligent" ways. It combines the broadest approach of any text in the marketplace with the practical information necessary to implement the strategies discussed, showing how to do this through Prolog or LISP programming.

[Shelving Category: Artificial Intelligence] "One of the few books on the market that covers all the topics I have included in my course for the past 10 years." Bruce Maxim, University of Michigan Dearborn "The book is a perfect complement to an AI course. It gives the reader both an historical point of view and a practical guide to all the techniques. It is THE book I recommEND as an introduction to this field." Pascal RebreyEND, Dalarna University "Excellent additions and improvements. I will use the 5th EDITION in my introduction and advanced AI courses." Peter Funk, Mtrade;lardalen 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 Can machines think like people? This question is the driving force behind Artificial Intelligence, but it is only the starting point of this ever-evolving, exciting discipline. AI uses different strategies to solve the complex problems that arise wherever computer technology is applied, from those areas pertaining to perception and adaptation (neural networks, genetic algorithms) to the fields of intelligent agents, natural language understanding and sTOChastic models. George Luger examines complex problem solving techniques while demonstrating his enthusiasm and excitement for the study of intelligence itself. He shows how to use a number of different software tools and techniques to address the many challenges faced by today's computer scientists. New to this EDITION Brand new chapter which introduces the sTOChastic methodology. ExtENDed material in many sections addresses the continuing importance of agent-based problem solving and embodiment in AI technology. Presentation of issues in natural language understanding, including sections on sTOChastic methods for language comprehension; Markov models; CART trees; mutual information clustering; and statistic based parsing. Further discussion of the AI ENDeavor from the perspectives of philosophy, psychology, and neuro-psychology. Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one or two semester university course on AI, as well as an invaluable reference for researchers in the field or practitioners wishing to employ the power of current AI techniques in their work. After receiving his PhD from the University of Pennsylvania,George Lugerspent five years researching and teaching at the Department of Artificial Intelligence of the University of Edinburgh. He is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico.

Preface

vii

Publisher's Acknowledgements

xvi

PART I ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE

(34)

... MORE

AI: Early History and Applications

(32)

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

(17)

Overview of AI Application Areas

(10)

Artificial Intelligence---A Summary

(1)

Epilogue and References

(2)

Exercises

(2)

PART II ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH

(188)

The Predicate Calculus

(34)

Introduction

(1)

The Propositional Calculus

(5)

The Predicate Calculus

(12)

Using Inference Rules to Produce Predicate Calculus Expressions

(11)

Application: A Logic-Based Financial Advisor

(4)

Epilogue and References

(1)

Exercises

(2)

Structures and Strategies for State Space Search

(44)

Introduction

(3)

Graph Theory

(11)

Strategies for State Space Search

(14)

Using the State Space to Represent Reasoning with the Predicate Calculus

107

(14)

Epilogue and References

121

(1)

Exercises

121

(2)

Heuristic Search

123

(42)

Introduction

123

(4)

Hill-Climbing and Dynamic Programming

127

(6)

The Best-First Search Algorithm

133

(12)

Admissibility, Monotonicity, and Informedness

145

(5)

Using Heuristics in Games

150

(7)

Complexity Issues

157

(4)

Epilogue and References

161

(1)

Exercises

162

(3)

Stochastic Methods

165

(28)

Introduction

165

(2)

The Elements of Counting

167

(3)

Elements of Probability Theory

170

(12)

Applications of the Stochastic Methodology

182

(2)

Bayes' Theorem

184

(6)

Epilogue and References

190

(1)

Exercises

191

(2)

Building Control Algorithms for State Space Search

193

(30)

Introduction

193

(1)

Recursion-Based Search

194

(6)

Production Systems

200

(17)

The Blackboard Architecture for Problem Solving

217

(2)

Epilogue and References

219

(1)

Exercises

220

(3)

PART III REPRESENTATION AND INTELLIGENCE: THE AI CHALLENGE

223

(162)

Knowledge Representation

227

(50)

Issues in Knowledge Representation

227

(1)

A Brief History of AI Representational Schemes

228

(20)

Conceptual Graphs: A Network Language

248

(10)

Alternatives to Explicit Representation

258

(7)

Agent-Based and Distributed Problem Solving

265

(5)

Epilogue and References

270

(3)

Exercises

273

(4)

Strong Method Problem Solving

277

(56)

Introduction

277

(2)

Overview of Expert System Technology

279

(7)

Rule-Based Expert Systems

286

(12)

Model-Based, Case-Based, and Hybrid Systems

298

(16)

Planning

314

(15)

Epilogue and References

329

(2)

Exercises

331

(2)

Reasoning in Uncertain Situations

333

(52)

Introduction

333

(2)

Logic-Based Abductive Inference

335

(15)

Abduction: Alternatives to Logic

350

(13)

The Stochastic Approach to Uncertainty

363

(16)

Epilogue and References

379

(2)

Exercises

381

(4)

PART IV MACHINE LEARNING

385

(160)

Machine Learning: Symbol-Based

387

(66)

Introduction

387

(3)

A Framework for Symbol-based Learning

390

(6)

Version Space Search

396

(12)

The ID3 Decision Tree Induction Algorithm

408

(9)

Inductive Bias and Learnability

417

(5)

Knowledge and Learning

422

(11)

Unsupervised Learning

433

(9)

Reinforcement Learning

442

(7)

Epilogue and References

449

(1)

Exercises

450

(3)

Machine Learning: Connectionist

453

(54)

Introduction

453

(2)

Foundations for Connectionist Networks

455

(3)

Perceptron Learning

458

(9)

Backpropagation Learning

467

(7)

Competitive Learning

474

(10)

Hebbian Coincidence Learning

484

(11)

Attractor Networks or ``Memories''

495

(10)

Epilogue and References

505

(1)

Exercises

506

(1)

Machine Learning: Social and Emergent

507

(38)

Social and Emergent Models of Learning

507

(2)

The Genetic Algorithm

509

(10)

Classifier Systems and Genetic Programming

519

(11)

Artificial Life and Society-Based Learning

530

(11)

Epilogue and References

541

(1)

Exercises

542

(3)

PART V ADVANCED TOPICS FOR AI PROBLEM SOLVING

545

(90)

Automated Reasoning

547

(44)

Introduction to Weak Methods in Theorem Proving

547

(1)

The General Problem Solver and Difference Tables

548

(6)

Resolution Theorem Proving

554

(21)

Prolog and Automated Reasoning

575

(6)

Further Issues in Automated Reasoning

581

(7)

Epilogue and References

588

(1)

Exercises

589

(2)

Understanding Natural Language

591

(44)

The Natural Language Understanding Problem

591

(3)

Deconstructing Language: A Symbolic Analysis

594

(3)

Syntax

597

(9)

Syntax and Knowledge with ATN Parsers

606

(10)

Stochastic Tools for Language Analysis

616

(7)

Natural Language Applications

623

(7)

Epilogue and References

630

(2)

Exercises

632

(3)

PART VI LANGUAGES AND PROGRAMMING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE

635

(186)

An Introduction to Prolog

641

(82)

Introduction

641

(1)

Syntax for Predicate Calculus Programming

642

(12)

Abstract Data Types (ADTs) in Prolog

654

(4)

A Production System Example in Prolog

658

(5)

Designing Alternative Search Strategies

663

(5)

A Prolog Planner

668

(3)

Prolog: Meta-Predicates, Types, and Unification

671

(8)

Meta-Interpreters in Prolog

679

(15)

Learning Algorithms in Prolog

694

(10)

Natural Language Processing in Prolog

704

(12)

Epilogue and References

716

(3)

Exercises

719

(4)

An Introduction to Lisp

723

(98)

Introduction

723

(1)

LISP: A Brief Overview

724

(22)

Search in LISP: A Functional Approach to the Farmer, Wolf, Goat, and Cabbage Problem

746

(5)

Higher-Order Functions and Abstraction

751

(4)

Search Strategies in LISP

755

(4)

Pattern Matching in LISP

759

(2)

A Recursive Unification Function

761

(4)

Interpreters and Embedded Languages

765

(2)

Logic Programming in LISP

767

(9)

Streams and Delayed Evaluation

776

(4)

An Expert System Shell in LISP

780

(7)

Semantic Networks and Inheritance in LISP

787

(4)

Object-Oriented Programming Using CLOS

791

(12)

Learning in LISP: The ID3 Algorithm

803

(12)

Epilogue and References

815

(1)

Exercises

816

(5)

PART VII EPILOGUE

821

(34)

Artificial Intelligence as Empirical Enquiry

823

(32)

Introduction

823

(2)

Artificial Intelligence: A Revised Definition

825

(13)

The Science of Intelligent Systems

838

(10)

AI: Current Challenges and Future Directions

848

(5)

Epilogue and References

853

(2)

Bibliography

855

(28)

Author Index

883

(8)

Subject Index

891

George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico.

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