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Understanding Intelligence Book

Understanding Intelligence
Understanding Intelligence, 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 en, Understanding Intelligence has a rating of 5 stars
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Understanding Intelligence, 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 en, Understanding Intelligence
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  • Understanding Intelligence
  • Written by author Rolf Pfeifer
  • Published by MIT Press, September 2001
  • 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 en
  • 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.
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Preface
Pt. IThe Study of Intelligence - Foundations and Issues1
1The Study of Intelligence3
1.1Characterizing Intelligence6
1.2Studying Intelligence: The Synthetic Approach21
2Foundations of Classical Artificial Intelligence and Cognitive Science35
2.1Cognitive Science: Preliminaries35
2.2The Cognitivistic Paradigm39
2.3An Architecture for an Intelligent Agent47
3The Fundamental Problems of Classical AI and Cognitive Science59
3.1Real Worlds versus Virtual Worlds59
3.2Some Well-Known Problems with Classical Systems63
3.3The Fundamental Problems of Classical AI64
3.4Remedies and Alternatives74
Pt. IIA Framework for Embodied Cognitive Science79
4Embodied Cognitive Science: Basic Concepts81
4.1Complete Autonomous Agents82
4.2Biological and Artificial Agents99
4.3Designing for Emergence - Logic-Based and Embodied Systems111
4.4Explaining Behavior127
5Neural Networks for Adaptive Behavior139
5.1From Biological to Artificial Neural Networks140
5.2The Four or Five Basics143
5.3Distributed Adaptive Control152
5.4Types of Neural Networks167
5.5Beyond Information Processing: A Polemic Digression172
Pt. IIIApproaches and Agent Examples179
6Braitenberg Vehicles181
6.1Motivation181
6.2The Fourteen Vehicles182
6.3Segmentation of Behavior and the Extended Braitenberg Architecture195
7The Subsumption Architecture199
7.1Behavior-Based Robotics201
7.2Designing a Subsumption-Based Robot202
7.3Examples of Subsumption-Based Architectures206
7.4Conclusions: The Subsumption Approach to Designing Intelligent Systems219
8Artificial Evolution and Artificial Life227
8.1Basic Principles230
8.2An Introduction to Genetic Algorithms: Evolving a Neural Controller for an Autonomous Agent234
8.3Examples of Artificially Evolved Agents240
8.4Toward Biological Plausibility: Cell Growth from Genome-Based Cell-to-Cell Communication250
8.5Real Robots, Evolution of Hardware, and Simulation255
8.6Artificial Life: Additional Examples260
8.7Methodological Issues and Conclusions270
9Other Approaches277
9.1The Dynamical Systems Approach277
9.2Behavioral Economics283
9.3Schema-Based Approaches292
Pt. IVPrinciples of Intelligent Systems297
10Design Principles of Autonomous Agents299
10.1The Nature of the Design Principles299
10.2Design Principles for Autonomous Agents302
10.3Design Principles in Context318
11The Principle of Parallel, Loosely Coupled Processes327
11.1Control Architectures for Autonomous Agents330
11.2Traditional Views on Control Architectures337
11.3Parallel, Decentralized Approaches345
11.4Case Study: A Self-Sufficient Garbage Collector357
12The Principle of Sensory-Motor Coordination377
12.1Categorization: Traditional Approaches378
12.2The Sensory-Motor Coordination Approach392
12.3Case Study: The SMC Agents407
12.4Application: Active Vision431
13The Principles of Cheap Design, Redundancy, and Ecological Balance435
13.1The Principle of Cheap Design435
13.2The Redundancy Principle446
13.3The Principle of Ecological Balance455
14The Value Principle467
14.1Value Systems469
14.2Self-Organization475
14.3Learning in Autonomous Agents485
15Human Memory: A Case Study503
15.1Memory Defined503
15.2Problems of Classical Notions of Memory506
15.3The Frame-of-Reference Problem in Memory Research511
15.4The Alternatives516
15.5Implications for Memory Research530
Pt. VDesign and Evaluation535
16Agent Design Considerations537
16.1Preliminary Design Considerations539
16.2Agent Design542
16.3Putting It All Together: Control Architectures562
16.4Summary and a Fundamental Issue569
17Evaluation577
17.1General Introduction578
17.2Performing Agent Experiments588
17.3Measuring Behavior593
Pt. VIFuture Directions605
18Theory, Technology, and Applications607
18.1Hard Problems607
18.2Theory and Technology612
18.3Applications618
19Intelligence Revisited631
19.1Elements of a Theory of Intelligence631
19.2Implications for Society638
Glossary645
References659
Author Index677
Subject Index681


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