Artificial Intelligence : Structures and Strategies for Complex Problem Solving

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  • Edition: 4th
  • Format: Hardcover
  • Copyright: 2002-01-01
  • Publisher: Addison Wesley
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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 computing 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.

Author Biography

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

Table of Contents

Prefacep. vii
Publisher's Acknowledgementsp. xv
Artificial Intelligence: Its Roots and Scopep. 1
Al: History and Applicationsp. 3
From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artificep. 3
Overview of AI Application Areasp. 17
Artificial Intelligence--A Summaryp. 28
Epilogue and Referencesp. 29
Exercisesp. 31
Artificial Intelligence as Representation and Searchp. 33
The Predicate Calculusp. 47
Introductionp. 47
The Propositional Calculusp. 47
The Predicate Calculusp. 52
Using Inference Rules to Produce Predicate Calculus Expressionsp. 64
Application: A Logic-Based Financial Advisorp. 75
Epilogue and Referencesp. 79
Exercisesp. 79
Structures and Strategies for State Space Searchp. 81
Introductionp. 81
Graph Theoryp. 84
Strategies for State Space Searchp. 93
Using the State Space to Represent Reasoning with the Predicate Calculusp. 107
Epilogue and Referencesp. 121
Exercisesp. 121
Heuristic Searchp. 123
Introductionp. 123
An Algorithm for Heuristic Searchp. 127
Admissibility, Monotonicity, and Informednessp. 139
Using Heuristics in Gamesp. 144
Complexity Issuesp. 152
Epilogue and Referencesp. 156
Exercisesp. 156
Control and Implementation of State Space Searchp. 159
Introductionp. 159
Recursion-Based Searchp. 160
Pattern-Directed Searchp. 164
Production Systemsp. 171
The Blackboard Architecture for Problem Solvingp. 187
Epilogue and Referencesp. 189
Exercisesp. 190
Representation and Intelligence: The AI Challengep. 193
Knowledge Representationp. 197
Issues in Knowledge Representationp. 197
A Brief History of AI Representational Systemsp. 198
Conceptual Graphs: A Network Languagep. 218
Alternatives to Explicit Representationp. 228
Agent Based and Distributed Problem Solvingp. 235
Epilogue and Referencesp. 240
Exercisesp. 243
Strong Method Problem Solvingp. 247
Introductionp. 247
Overview of Expert System Technologyp. 249
Rule-Based Expert Systemsp. 256
Model-Based, Case Based, and Hybrid Systemsp. 268
Planningp. 284
Epilogue and Referencesp. 299
Exercisesp. 301
Reasoning in Uncertain Situationsp. 303
Introductionp. 303
Logic-Based Abductive Inferencep. 305
Abduction: Alternatives to Logicp. 320
The Stochastic Approach to Uncertaintyp. 333
Epilogue and Referencesp. 344
Exercisesp. 346
Machine Learningp. 349
Machine Learning: Symbol-basedp. 351
Introductionp. 603
A Framework for Symbol-based Learningp. 354
Version Space Searchp. 360
The ID3 Decision Tree Induction Algorithmp. 372
Inductive Bias and Learnabilityp. 381
Knowledge and Learningp. 386
Unsupervised Learningp. 397
Reinforcement Learningp. 406
Epilogue and Referencesp. 413
Exercisesp. 414
Machine Learning: Connectionistp. 417
Introductionp. 417
Foundations for Connectionist Networksp. 419
Perceptron Learningp. 422
Backpropagation Learningp. 431
Competitive Learningp. 438
Hebbian Coincidence Learningp. 446
Attractor Networks or "Memories"p. 457
Epilogue and Referencesp. 467
Exercisesp. 468
Machine Learning: Social and Emergentp. 469
Social and Emergent Models of Learningp. 469
The Genetic Algorithmp. 471
Classifier Systems and Genetic Programmingp. 481
Artificial Life and Society-Based Learningp. 492
Epilogue and Referencesp. 503
Exercisesp. 504
Advanced Topics for AI Problem Solvingp. 507
Automated Reasoningp. 509
Introduction to Weak Methods in Theorem Provingp. 509
The General Problem Solver and Difference Tablesp. 510
Resolution Theorem Provingp. 516
PROLOG and Automated Reasoningp. 537
Further Issues in Automated Reasoningp. 543
Epilogue and Referencesp. 550
Exercisesp. 551
Understanding Natural Languagep. 553
Role of Knowledge in Language Understandingp. 553
Deconstructing Language: A Symbolic Analysisp. 556
Syntaxp. 559
Syntax and Knowledge with ATN Parsersp. 568
Stochastic Tools for Language Analysisp. 578
Natural Language Applicationsp. 585
Epilogue and Referencesp. 592
Exercisesp. 557
Languages and Programming Techniques for Artificial Intelligencep. 597
An Introduction to Prologp. 603
Introductionp. 603
Syntax for Predicate Calculus Programmingp. 604
Abstract Data Types (ADTs) in PROLOGp. 616
A Production System Example in PROLOGp. 620
Designing Alternative Search Strategiesp. 625
A PROLOG Plannerp. 630
PROLOG: Meta-Predicates, Types, and Unificationp. 633
Meta-Interpreters in PROLOGp. 641
Learning Algorithms in PROLOGp. 656
Natural Language Processing in PROLOGp. 666
Epilogue and Referencesp. 673
Exercisesp. 676
An Introduction to LISPp. 679
Introductionp. 679
LISP: A Brief Overviewp. 680
Search in LISP: A Functional Approach to the Farmer, Wolf, Goat, and Cabbage Problemp. 702
Higher-Order Functions and Procedural Abstractionp. 707
Search Strategies in LISPp. 711
Pattern Matching in LISPp. 715
A Recursive Unification Functionp. 717
Interpreters and Embedded Languagesp. 721
Logic Programming in LISPp. 723
Streams and Delayed Evaluationp. 732
An Expert System Shell in LISPp. 736
Semantic Networks and Inheritance in LISPp. 743
Object-Oriented Programming Using CLOSp. 747
Learning in LISP: The ID3 Algorithmp. 759
Epilogue and Referencesp. 771
Exercisesp. 772
Epiloguep. 777
Artificial Intelligence as Empirical Enquiryp. 779
Introductionp. 779
Artificial Intelligence: A Revised Definitionp. 781
The Science of Intelligent Systemsp. 792
AI: Current Issues and Future Directionsp. 803
Epilogue and Referencesp. 807
Bibliographyp. 809
Author Indexp. 837
Subject Indexp. 843
Table of Contents provided by Syndetics. All Rights Reserved.


What we have to learn to do we learn by doing. . . -- ARISTOTLE,Ethics Welcome to the Fourth Edition! I was very pleased to be asked to produce a fourth edition of our artificial intelligence book. It is a compliment to the earlier editions, started more than a decade ago, that our approach to Al has been widely accepted. It is also exciting that, as new developments in the field emerge, we are able to present much of it in each new edition. We thank our readers, colleagues, and students for keeping our topics relevant and presentation up to date. Many sections of the earlier editions have endured remarkably well, including the presentation of logic, search algorithms, knowledge representation, production systems, machine learning, and the programming techniques developed in LISP and PROLOG. These remain central to the practice of artificial intelligence, and required a relatively small effort to bring them up to date. However, several sections, including those on natural language understanding, reinforcement learning, and reasoning under uncertainty, required, and received, extensive reworking. Other topics, such as emergent computation, case-based reasoning, and model-based problem solving, that were treated cursorily in the first editions, have grown sufficiently in importance to merit a more complete discussion. These changes are evidence of the continued vitality of the field of artificial intelligence. As the scope of the project grew, we were sustained by the support of our publisher, editors, friends, colleagues, and, most of all, by our readers, who have given our work such a long and productive life. We were also sustained by our own excitement at the opportunity afforded: Scientists are rarely encouraged to look up from their own, narrow research interests and chart the larger trajectories of their chosen field. Our publisher and readers have asked us to do just that. We are grateful to them for this opportunity. Although artificial intelligence, like most engineering disciplines, must justify itself to the world of commerce by providing solutions to practical problems, we entered the field of AI for the same reasons as many of our colleagues and students: we want to understand and explore the mechanisms of mind that enable intelligent thought and action. We reject the rather provincial notion that intelligence is an exclusive ability of humans, and believe that we can effectively investigate the space of possible intelligences by designing and evaluating intelligent artifacts. Although the course of our careers has given us no cause to change these commitments, we have arrived at a greater appreciation for the scope, complexity, and audacity of this undertaking. In the preface to our earlier editions, we outlined three assertions that we believed distinguished our approach to teaching artificial intelligence. It is reasonable, in writing a preface to this fourth edition, to return to these themes and see how they have endured as our field has grown. The first of these goals was to "unify the diverse branches of AI through a detailed discussion of its theoretical foundations." At the time we adopted that goal, it seemed that the main problem was reconciling researchers who emphasized the careful statement and analysis of formal theories of intelligence (the neats) with those who believed that intelligence itself was some sort of grand hack that could be best approached in an application-driven,ad hocmanner (the scruffies). That simple dichotomy has proven far too simple. In contemporary AI, debates between neats and scruffies have given way to dozens of other debates between proponents of physical symbol systems and students of neural networks, between logicians and designers of artificial life forms that evolve in a most illogical manner, between architects of expert systems and case-based reasoners, and finall

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