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| Preface | |
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| The Study of Intelligence--Foundations and Issues | |
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| The Study of Intelligence | |
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| Characterizing Intelligence | |
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| Studying Intelligence: The Synthetic Approach | |
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| Foundations of Classical Artificial Intelligence and Cognitive Science | |
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| Cognitive Science: Preliminaries | |
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| The Cognitivistic Paradigm | |
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| An Architecture for an Intelligent Agent | |
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| The Fundamental Problems of Classical Al and Cognitive Science | |
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| Real Worlds versus Virtual Worlds | |
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| Some Well-Known Problems with Classical Systems | |
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| The Fundamental Problems of Classical Al | |
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| Remedies and Alternatives | |
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| A Framework for Embodied Cognitive Science | |
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| Embodied Cognitive Science: Basic Concepts | |
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| Complete Autonomous Agents | |
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| Biological and Artificial Agents | |
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| Designing for Emergence--Logic-Based and Embodied Systems | |
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| Explaining Behavior | |
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| Neural Networks for Adaptive Behavior | |
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| From Biological to Artificial Neural Networks | |
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| The Four or Five Basics | |
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| Distributed Adaptive Control | |
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| Types of Neural Networks | |
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| Beyond Information Processing: A Polemic Digression | |
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| Approaches and Agent Examples | |
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| Braitenberg Vehicles | |
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| Motivation | |
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| The Fourteen Vehicles | |
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| Segmentation of Behavior and the Extended Braitenberg Architecture | |
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| The Subsumption Architecture | |
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| Behavior-Based Robotics | |
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| Designing a Subsumption-Based Robot | |
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| Examples of Subsumption-Based Architectures | |
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| Conclusions: The Subsumption Approach to Designing Intelligent Systems | |
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| Artificial Evolution and Artificial Life | |
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| Basic Principles | |
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| An Introduction to Genetic Algorithms: Evolving a Neural Controller for an Autonomous Agent | |
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| Examples of Artificially Evolved Agents | |
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| Toward Biological Plausibility: Cell Growth from Genome-Based Cell-to-Cell Communication | |
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| Real Robots, Evolution of Hardware, and Simulation | |
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| Artificial Life: Additional Examples | |
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| Methodological Issues and Conclusions | |
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| Other Approaches | |
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| The Dynamical Systems Approach | |
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| Behavioral Economics | |
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| Schema-Based Approaches | |
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| Principles of Intelligent Systems | |
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| Design Principles of Autonomous Agents | |
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| The Nature of the Design Principles | |
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| Design Principles for Autonomous Agents | |
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| Design Principles in Context | |
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| The Principle of Parallel, Loosely Coupled Processes | |
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| Control Architectures for Autonomous Agents | |
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| Traditional Views on Control Architectures | |
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| Parallel, Decentralized Approaches | |
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| Case Study: A Self-Sufficient Garbage Collector | |
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| The Principle of Sensory-Motor Coordination | |
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| Categorization: Traditional Approaches | |
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| The Sensory-Motor Coordination Approach | |
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| Case Study: The SMC Agents | |
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| Application: Active Vision | |
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| The Principles of Cheap Design, Redundancy, and Ecological Balance | |
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| The Principle of Cheap Design | |
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| The Redundancy Principle | |
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| The Principle of Ecological Balance | |
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| The Value Principle | |
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| Value Systems | |
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| Self-Organization | |
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| Learning in Autonomous Agents | |
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| Human Memory: A Case Study | |
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| Memory Defined | |
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| Problems of Classical Notions of Memory | |
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| The Frame-of-Reference Problem in Memory Research | |
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| The Alternatives | |
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| Implications for Memory Research | |
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| Design and Evaluation | |
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| Agent Design Considerations | |
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| Preliminary Design Considerations | |
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| Agent Design | |
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| Putting It All Together: Control Architectures | |
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| Summary and a Fundamental Issue | |
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| Evaluation | |
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| General Introduction | |
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| Performing Agent Experiments | |
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| Measuring Behavior | |
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| Future Directions | |
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| Theory, Technology, and Applications | |
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| Hard Problems | |
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| Theory and Technology | |
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| Applications | |
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| Intelligence Revisited | |
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| Elements of a Theory of Intelligence | |
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| Implications for Society | |
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| Glossary | |
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| References | |
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| Author Index | |
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| Subject Index | |