Understanding Intelligence

ISBN-10: 026266125X
ISBN-13: 9780262661256
Edition: Reprint 
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Book details

List price: $60.00
Publisher: MIT Press
Publication date: 7/27/2001
Binding: Paperback
Pages: 700
Size: 6.50" wide x 9.50" long x 1.75" tall
Weight: 0.990
Language: English

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.

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

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