Artificial Intelligence (Handbook Of Perception And Cognition)

395 Pages·1996·20.36 MB·English

by  Margaret A. Boden

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Artificial
Intelligence
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Handbook of Perception and Cognition
2nd Edition
Series Editors
Edward Carterette
and Morton Friedman
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Artificial
Intelligence
Edited by
Margaret A. Boden
School of Cognitive and Computing Sciences
University of Sussex
Brighton, England
Academic Press
San Diego New York Boston
London Sydney Tokyo Toronto
@
This book is printed on acid-free paper.
Copyright 0 1996 by ACADEMIC PRESS, INC.
All Rights Reserved.
No part of this publication may be reproduced or transmitted in any form or by any
means, electronic or mechanical, including photocopy, recording, or any information
storage and retrieval system, without permission in writing from the publisher.
Academic Press, Inc.
A Division of Harcourt Brace & Company
525 B Street, Suite 1900, San Diego, California 92101-4495
United Kingdom Edition published by
Academic Press Limited
24-28 Oval Road, London NWl 7DX
Library of Congress Cataloging-in-Publication Data
Artificial intelligence / edited by Margaret A. Boden.
P. cm. -- (Handbook of perception and cognition, 2nd ed.
series)
Includes bibliographical references and index.
ISBN 0-12-161964-8 (case : alk paper)
1. Artificial intelligence. I. Boden, Margaret A. I. Series:
Handbook of perception and cognition (2nd ed).
Q335.A7857 1996
006.3-dc20 95-44625
CIP
PRINTED IN THE UNITED STATES OF AMERICA
96 97 9899 00 01BC 9 8 7 6 5 4 3 2 1
Cont en
ts
Contributors xi
Foreword xiii
Preface xv
1 Philosophical Foundations
Andy Clark
I. An Evolving Engagement 1
11. Symbol-System A1 3
111. Connectionism 7
IV. Artificial Life 14
V. The Wasteland 19
References 20
2 Planfzing and Problem Solving
Robert Inder
I. Some Basic Ideas 24
11. The General Problem Solver 28
A. GPS in Action: The Tower of Hanoi 30
111. STRIPS 33
IV. Handling Interference 36
V. Nonlinear Planning 38
VI. More Sophisticated Planning 43
V
vi Contents
VII. Planning and Cognitive Science 48
References 53
3 Representation of Knowledge
Derek Partridge
I. Representation and Knowledge 55
A. Representation as Structure and Function 56
B. Representational Choice 58
C. What Is Knowledge? 64
D. A Pair of Paradigms 66
11. Classical Approaches 68
A. Semantic Networks 68
B. Frames, Scripts, and Schemata 74
C. Logic-Based Representations 78
D. Procedural Representations 81
E. Analogical and Iconic Representation 84
References 85
Machine Learning
Stuart Russell
I. Introduction 89
A. A General Model of Learning 90
B. Types of Learning Systems 92
11. Knowledge-Free Inductive Learning Systems 93
A. Learning Attribute-Based Representations 96
B. Learning General Logical Representations 102
C. Learning Neural Networks 107
D. Learning Probabilistic Representations 108
111. Learning in Situated Agents 110
A. Learning and Using Models of Uncertain
Environments 112
B. Learning Utilities 115
C. Learning the Value of Actions 116
D. Generalization in Reinforcement Learning 116
IV. Theoretical Models of Learning 118
A. Identification of Functions in the Limit 118
B. Simplicity and Kolmogorov Complexity 119
C. Computational Learning Theory 120
V. Learning from Single Examples 123
A. Analogical and Case-Based Reasoning 123
B. Learning by Explaining Observations 125
Contents vii
VI. Forming New Concepts 127
A. Forming New Concepts in Inductive Learning 128
B. Concept Formation Systems 128
VII. Summary 129
References 130
5
Connectionism and Neural Networks
Harry Barrow
I. Introduction 135
11. Understanding and Modeling Cognition and Perception 135
111. Reference Sources 137
IV. Biological Origins 137
V. Early Developments: Logical Models 140
VI. Adaptive Networks 141
VII. The Dartmouth Conference 141
VIII. Perceptrons 142
IX. Adalines and the LMS Algorithm 145
X. Minsky and Papert’s Perceptrons 145
XI. Back Propagation 146
XII. NETtalk 150
XIII. The Future 152
References 154
Expert Systems and Theories of Knowledge
John Fox
I. Introduction 157
11. Knowledge Engineering-The First Decade 158
A. The Nature of Expert Systems 158
B. Making Knowledge Explicit 159
C. Semantic Networks, Frames, and Objects 163
D. Assessment of the First Generation of Expert
Systems 165
111. Second-Generation Expert Systems-From Decision
Making to Expertise 165
A. Task-Oriented Analysis of Knowledge 166
€3. Reasoning with Uncertainty 170
C. Assessment and Critique 172
IV. The Third Decade-Systematic Engineering of
Knowledge 173
A. Ontological Engineering 175
B. Developing Formal Theories of Knowledge 178
viii Contents
V. Conclusions 180
References 180
7
Machine Vision
David C. Hogg
I. Introduction 183
A. Aims of Machine Vision 183
B. A Hierarchy of Models 185
C. Computational Models of Biological Vision Systems 187
11. Image Formation 187
A. Projection 187
B. Digital Images 188
C. Steerable Cameras 190
111. Feature Detection 191
IV. Building Descriptions Using General Properties
of Objects 195
A. Image Segmentation 198
B. Stereo Vision 20 1
V. Using Object Models 210
A. Feature Space Methods 210
B. “Model-Based” Methods 21 1
C. Dealing with Generic Objects and Nonrigid Objects 22 1
VI. Conclusion 224
References 225
8 Natural Language Processing
Mark Steedman
I. Introduction 229
A. Scope of the Study 229
B. The Anatomy of a Processor 230
C. The Relevance of Computation 23 1
11. Computational Theories of Processing 232
A. The Grammar 232
B. The Algorithm 245
C. The Oracle 255
111. Conclusion 262
IV. Further Reading 263
References 264
9
Creativity
Margaret A. Boden
I. The Definition of Creativity 267
Contents ix
A. Psychological Studies of Creativity 267
B. Defining Creativity 268
11. Impossibilist Creativity 269
A. Mapping Conceptual Spaces 269
B. Exploring Conceptual Spaces 270
C. Transforming Conceptual Spaces 271
111. Improbabilist Creativity 272
A. A1 Models of Association 272
B. A1 Models of Analogy 273
C. A1 Models of Induction 276
IV. A1 Models of the Arts 277
A. Music 277
B. Visual Arts 279
C. Verbal Tcxts 28 1
V. A1 Models of Science 282
A. Meta-DENDRAL 282
B. The BACON Family 283
C. An Integrated Discovery System 284
D. Scientific Revolutions 284
VI. Self-Transforming Programs 285
A. AM and EURISKO 285
B. Genetic Algorithms 286
VII. Conclusion 289
References 289
10
Human-Computer Interaction
Mike Sharples
I. Interacting with Computers 293
A. Pioneers of HCI 294
B. Interfaces 296
C. Interaction Devices 296
D. Communicating with a Computer 297
E. Styles of Communication 298
F. Communicating through the Computer 30 1
11. The Psychology of Computer Use 30 1
A. Applying Psychology to Design 302
B. Using Psychology to Create New Models 305
C. Applying Psychology to Evaluation 306
D. Applying Studies of Human-Computer Interaction
to Psychology 306
111. Modeling Human-Computer Interaction 307
A. The Designer’s Model of the Computer 307