Understanding IntelligenceThe 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. 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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Spis treści
The Study of Intelligence | 3 |
11 Characterizing Intelligence | 6 |
The Synthetic Approach | 21 |
Foundations of Classical Artificial Intelligence and Cognitive Science | 35 |
22 The Cognitivistic Paradigm | 39 |
23 An Architecture for an Intelligent Agent | 47 |
The Fundamental Problems of Classical Artificial Intelligence and Cognitive Science | 59 |
32 Some WellKnown Problems with Classical Systems | 63 |
103 Design Principles in Context | 318 |
The Principle of Parallel Loosely Coupled Processes | 327 |
111 Control Architectures for Autonomous Agents | 330 |
112 Traditional Views on Control Architectures | 337 |
113 Parallel Decentralized Approaches | 345 |
A SelfSufficient Garbage Collector | 357 |
The Principle of SensoryMotor Coordination | 377 |
Traditional Approaches | 378 |
33 The Fundamental Problems | 64 |
34 Remedies and Alternatives | 74 |
A Framework for Embodied Cognitive Science | 79 |
Embodied Cognitive Science Basic Concepts | 81 |
41 Complete Autonomous Agents | 82 |
42 Biological and Artificial Agents | 99 |
43 Designing for EmergenceLogicBased and Embodied Systems | 111 |
44 Explaining Behavior | 127 |
Neural Networks for Adaptive Behavior | 139 |
51 From Biological to Artificial Neural Networks | 140 |
52 The Four or Five Basics | 143 |
53 Distributed Adaptive Control | 152 |
54 Types of Neural Networks | 167 |
A Polemic Digression | 172 |
Approaches and Agent Examples | 179 |
Braitenberg Vehicles | 181 |
62 The Fourteen Vehicles | 182 |
63 Segmentation of Behavior and the Extended Braitenberg Architecture | 195 |
The Subsumption Architecture | 199 |
71 BehaviorBased Robotics | 201 |
72 Designing a SubsumptionBased Robot | 202 |
73 Examples of SubsumptionBased Architectures | 206 |
The Subsumption Approach to Designing Intelligent Systems | 219 |
Artificial Evolution and Artificial Life | 227 |
81 Basic Principles | 230 |
Evolving a Neural Controller for an Autonomous Agent | 234 |
83 Examples of Artificially Evolved Agents | 240 |
Cell Growth form GenomeBased CelltoCell Communication | 250 |
85 Real Robots Evolution of Hardware and Simulation | 255 |
Additional Examples | 260 |
87 Methodological Issues and Conclusions | 270 |
Other Approaches | 277 |
92 Behavioral Economics | 283 |
93 SchemaBased Approaches | 292 |
Principles of Intelligent Systems | 297 |
Design Principles of Autonomous Agents | 299 |
102 Design Principles for Autonomous Agents | 302 |
122 The SensoryMotor Coordination Approach | 392 |
The SMC Agents | 407 |
Active Vision | 431 |
The Principles of Cheap Design Redundancy and Ecological Balance | 435 |
132 The Redundancy Principle | 446 |
133 The Principle of Ecological Balance | 455 |
The Value Principle | 467 |
141 Value Systems | 469 |
142 SelfOrganization | 475 |
143 Learning in Autonomous Agents | 485 |
A Case Study | 503 |
152 Problems of Classical Notions of Memory | 506 |
153 The FrameofReference Problem in Memory Research | 511 |
154 Alternatives | 516 |
155 Implications for Memory Research | 530 |
Design and Evaluation | 535 |
Agent Design Considerations | 537 |
161 Preliminary Design Considerations | 539 |
162 Agent Design | 542 |
Control Architectures | 562 |
164 Summary and a Fundamental Issue | 569 |
Evaluation | 577 |
171 The Basics of Agent Evaluation | 578 |
172 Performing Agent Experiments | 588 |
173 Measuring Behavior | 593 |
Future Directions | 605 |
Theory Technology and Applications | 607 |
182 Theory and Technology | 612 |
183 Applications | 618 |
Intelligence Revisited | 631 |
192 Implications for Society | 638 |
Glossary | 645 |
659 | |
Author Index | 677 |
681 | |