Understanding Intelligence / Edition 1

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By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior — thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI," and "behavior-based AI."

This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life,self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems,or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.

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Editorial Reviews

Science - Arthur B. Markman
Understanding Intelligence is comprehensive and highly readable introduction to embodied cognitive science. It will be particularly helpful for people interested in getting involved in the construction of intelligent agents.
Computing Reviews - H. Van Dyke
People trained in classical AI will find this book an articulate and thought-provoking challenge to much that they have taken for granted. People new to cognitive science will find it a stimulating introduction to one of the field's most productive controversies. Pfeifer and Scheier deserve our thanks for a thorough,accessible, and courteous contribution in the best tradition of scholarly debate.
From the Publisher
" Understanding Intelligence is comprehensive and highly readable introduction to embodied cognitive science. It will be particularly helpful for people interested in getting involved in the construction of intelligent agents." Arthur B. Markman Science

"People trained in classical AI will find this book an articulate and thought-provoking challenge to much that they have taken for granted. People new to cognitive science will find it a stimulating introduction to one of the field's most productive controversies.

Pfeifer and Scheier deserve our thanks for a thorough, accessible, and courteous contribution in the best tradition of scholarly debate." H. Van Dyke ComputingReviews

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Product Details

  • ISBN-13: 9780262661256
  • Publisher: MIT Press
  • Publication date: 9/1/2001
  • Series: Bradford Books Series
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 700
  • Sales rank: 1,190,764
  • Product dimensions: 7.00 (w) x 10.00 (h) x 1.25 (d)

Meet the Author

Rolf Pfeifer is Professor of Computer Science and Director of the Artificial Intelligence Laboratory in the Department of Informatics at the University of Zurich. He is the author of Understanding Intelligence (MIT Press, 1999).

Christian Scheier is a Postdoctoral Fellow at the California Institute of Technology, Pasadena, California.

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Table of Contents

Pt. I The Study of Intelligence - Foundations and Issues 1
1 The Study of Intelligence 3
1.1 Characterizing Intelligence 6
1.2 Studying Intelligence: The Synthetic Approach 21
2 Foundations of Classical Artificial Intelligence and Cognitive Science 35
2.1 Cognitive Science: Preliminaries 35
2.2 The Cognitivistic Paradigm 39
2.3 An Architecture for an Intelligent Agent 47
3 The Fundamental Problems of Classical AI and Cognitive Science 59
3.1 Real Worlds versus Virtual Worlds 59
3.2 Some Well-Known Problems with Classical Systems 63
3.3 The Fundamental Problems of Classical AI 64
3.4 Remedies and Alternatives 74
Pt. II A Framework for Embodied Cognitive Science 79
4 Embodied Cognitive Science: Basic Concepts 81
4.1 Complete Autonomous Agents 82
4.2 Biological and Artificial Agents 99
4.3 Designing for Emergence - Logic-Based and Embodied Systems 111
4.4 Explaining Behavior 127
5 Neural Networks for Adaptive Behavior 139
5.1 From Biological to Artificial Neural Networks 140
5.2 The Four or Five Basics 143
5.3 Distributed Adaptive Control 152
5.4 Types of Neural Networks 167
5.5 Beyond Information Processing: A Polemic Digression 172
Pt. III Approaches and Agent Examples 179
6 Braitenberg Vehicles 181
6.1 Motivation 181
6.2 The Fourteen Vehicles 182
6.3 Segmentation of Behavior and the Extended Braitenberg Architecture 195
7 The Subsumption Architecture 199
7.1 Behavior-Based Robotics 201
7.2 Designing a Subsumption-Based Robot 202
7.3 Examples of Subsumption-Based Architectures 206
7.4 Conclusions: The Subsumption Approach to Designing Intelligent Systems 219
8 Artificial Evolution and Artificial Life 227
8.1 Basic Principles 230
8.2 An Introduction to Genetic Algorithms: Evolving a Neural Controller for an Autonomous Agent 234
8.3 Examples of Artificially Evolved Agents 240
8.4 Toward Biological Plausibility: Cell Growth from Genome-Based Cell-to-Cell Communication 250
8.5 Real Robots, Evolution of Hardware, and Simulation 255
8.6 Artificial Life: Additional Examples 260
8.7 Methodological Issues and Conclusions 270
9 Other Approaches 277
9.1 The Dynamical Systems Approach 277
9.2 Behavioral Economics 283
9.3 Schema-Based Approaches 292
Pt. IV Principles of Intelligent Systems 297
10 Design Principles of Autonomous Agents 299
10.1 The Nature of the Design Principles 299
10.2 Design Principles for Autonomous Agents 302
10.3 Design Principles in Context 318
11 The Principle of Parallel, Loosely Coupled Processes 327
11.1 Control Architectures for Autonomous Agents 330
11.2 Traditional Views on Control Architectures 337
11.3 Parallel, Decentralized Approaches 345
11.4 Case Study: A Self-Sufficient Garbage Collector 357
12 The Principle of Sensory-Motor Coordination 377
12.1 Categorization: Traditional Approaches 378
12.2 The Sensory-Motor Coordination Approach 392
12.3 Case Study: The SMC Agents 407
12.4 Application: Active Vision 431
13 The Principles of Cheap Design, Redundancy, and Ecological Balance 435
13.1 The Principle of Cheap Design 435
13.2 The Redundancy Principle 446
13.3 The Principle of Ecological Balance 455
14 The Value Principle 467
14.1 Value Systems 469
14.2 Self-Organization 475
14.3 Learning in Autonomous Agents 485
15 Human Memory: A Case Study 503
15.1 Memory Defined 503
15.2 Problems of Classical Notions of Memory 506
15.3 The Frame-of-Reference Problem in Memory Research 511
15.4 The Alternatives 516
15.5 Implications for Memory Research 530
Pt. V Design and Evaluation 535
16 Agent Design Considerations 537
16.1 Preliminary Design Considerations 539
16.2 Agent Design 542
16.3 Putting It All Together: Control Architectures 562
16.4 Summary and a Fundamental Issue 569
17 Evaluation 577
17.1 General Introduction 578
17.2 Performing Agent Experiments 588
17.3 Measuring Behavior 593
Pt. VI Future Directions 605
18 Theory, Technology, and Applications 607
18.1 Hard Problems 607
18.2 Theory and Technology 612
18.3 Applications 618
19 Intelligence Revisited 631
19.1 Elements of a Theory of Intelligence 631
19.2 Implications for Society 638
Glossary 645
References 659
Author Index 677
Subject Index 681
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