CONTENTS
Preface
Part I
The Study of Intelligence-Foundations and Issues
1
The Study of Intelligence
1.1 Characterizing Intelligence
1.2 Studying Intelligence: The Synthetic Approach
2
Foundations of Classical Artificial Intelligence and Cognitive Science
2.1 Cognitive Science: Preliminaries
2.2 The Cognitivistic Paradigm
2.3 An Architecture for an Intelligent Agent
3
The Fundamental Problems of Classical AI and Cognitive Science
3.1 Real Worlds versus Virtual Worlds
3.2 Some We/I-Known Problems with Classical Systems
3.3 The Fundamental Problems of Classical AI
3.4 Remedies and Alternatives
Part II
A Framework for Embodied Cognitive Science
4
Embodied Cognitive Science: Basic Concepts
4.1 Complete Autonomous Agents
4.2 Biological and Artificial Agents
4.3 Designing for Emergence—Logic-Based and Embodied Systems
4.4 Explaining Behavior
5
Neural Networks for Adaptive Behavior
5.1 From Biological to Artificial Neural Networks
5.2 The Four or Rye Basics
5.3 Distributed Adaptive Control
5.4 Types of Neural Networks
5.5 Beyond Information Processing: A Polemic Digression
Part III
Approaches and Agent Examples
6
Braitenberg Vehicles
6.1 Motivation
6.2 The Fourteen Vehicles
6.3 Segmentation of Behavior and the Extended Braitenberg Architecture
7
The Subsumption Architecture
7.1 Behavior-Based Robotics
7.2 Designing a Subsumption-Based Robot
7.3 Examples of Subsumption-Based Architectures
7.4 Conclusions: The Subsumption Approach to Designing Intelligent Systems
8
Artificial Evolution and Artificial Life
8.1 Basic Principles
8.2 An Introduction to Genetic Algorithms: Evolving a Neural Controller for an Autonomous Agent
8.3 Examples of Artificially Evolved Agents
8.4 Toward Biological Plausibility: Cell Growth from Genome-Based Cell-to-Cell Communication
8.5 Real Robots, Evolution of Hardware, and Simulation
8.6 Artificial Life: Additional Examples
8.7 Methodological Issues and Conclusions
9
Other Approaches
9.1 The Dynamical Systems Approach
9.2 Behavioral Economics
9.3 Schema-Based Approaches
Part IV
Principles of Intelligent Systems
10
Design Principles of Autonomous Agents
10.1 The Nature of the Design Principles
10.2 Design Principles for Autonomous Agents
10.3 Design Principles in Context
11
The Principle of Parallel, Loosely Coupled Processes
11.1 Control Architectures for Autonomous Agents
11.2 Traditional Views on Control Architectures
11.3 Parallel, Decentralized Approaches
11.4 Case Study: A Self-Sufficient Garbage Collector
12
The Principle of Sensory. Motor Coordination
12.1 Categorization: Traditional Approaches
12.2 The Sensory-Motor Coordination Approach
12.3 Case Study: The SMC Agents
12.4 Application: Active Vision
13
The Principles of Cheap Design, Redundancy, and Ecological Balance
13.1 The Principle of Cheap Design
13.2 The Redundancy Principle
13.3 The Principle of Ecological Balance
14
The Value Principle
14.1 Value Systems
14.2 Self-Organization
14.3 Learning in Autonomous Agents
15
Human Memory: A Case Study
15.1 Memory Defined
15.2 Problems of Classical Notions of Memory
15.3 The Frame-of-Reference Problem in Memory Research
15.4 The Alternatives
15.5 Implications for Memory Research
Part V
Design and Evaluation
16
Agent Design Considerations
16.1 Preliminary Design Considerations
16.2 Agent Design
16.3 Putting It All Together: Control Architectures
16.4 Summary and a Fundamental Issue
17
Evaluation
17.1 General Introduction
17.2 Performing Agent Experiments
17.3 Measuring Behavior
Part VI
Future Directions
18
Theory, Technology, and Applications
18.1 Hard Problems
18.2 Theory and Technology
18.3 Applications
19
Intelligence Revisited
19.1 Elements of a Theory of Intelligence
19.2 Implications for Society
Glossary
References
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