Genetic Algorithms and Evolutionary Computation

By: Martin V. Butz

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Hardcover | 31 January 2002

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204 Pages

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23.39 x 15.6 x 1.27

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Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior.

Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning.

Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Reference, Information & Interdisciplinary Subjects Research & Information Information theory Cybernetics & Systems Theory Medicine Medicine in General

Machine Learning Education Organisation & Management of Education Examinations & Assessment Mathematical Theory of Computation

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Paperback

Published: 5th November 2012

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List of Figures	p. ix
List of Tables	p. xvi
Foreword	p. xvii
Preface	p. xix
Complex Systems Approach	p. xx
Towards ACS2	p. xxiii
ACS2	p. xxiv
Road Map	p. xxv
Acknowledgments	p. xxvii
Background	p. 1
Anticipations	p. 2
Psychology Discovers Anticipations	p. 2
Theory of Anticipatory Behavioral Control	p. 3
Importance of Anticipations	p. 4
Genetic Algorithms	p. 6
Evolutionary Principles	p. 6
GA Framework	p. 8
An Illustrative Example	p. 10
Learning Classifier Systems	p. 11
Holland's Cognitive System	p. 13
LCS framework	p. 14
Problems in Traditional LCSs	p. 15
XCS Classifier System	p. 16
ACS2	p. 23
Framework	p. 25
Environmental Interaction	p. 25
Knowledge Representation	p. 26
A Behavioral Act	p. 27
Reinforcement Learning	p. 29
The Anticipatory Learning Process	p. 30
The Process in Detail	p. 30
The ALP in Action: A Simple Gripper Problem	p. 33
Causes for Over-Specialization	p. 35
Genetic Generalization in ACS2	p. 37
Accurate, Maximally General Classifiers in ACS2	p. 38
The GA Idea	p. 39
How the GA Works	p. 41
Interaction of ALP, GA, RL, and Behavior	p. 43
Subsumption	p. 44
Evolutionary Pressures of ALP and GA	p. 45
All Interactions	p. 47
Experiments with ACS2	p. 51
Gripper Problem Revisited	p. 52
Population without GA	p. 52
Population with GA	p. 54
Multiplexer Problem	p. 55
Environmental Setting	p. 56
Evolution of a Multiplexer Model	p. 57
ACS2 as a Classifier	p. 63
Maze Environment	p. 64
Environmental Setting	p. 65
Maze6	p. 66
Woods14	p. 68
Blocks World	p. 69
Environmental Setting	p. 71
Model Learning	p. 73
Hand-Eye Coordination Task	p. 76
Environmental Setting	p. 76
Model Learning	p. 78
Result Summary	p. 79
Limits	p. 81
GA Challenges	p. 81
Overlapping Classifiers	p. 82
Interfering Specificities	p. 85
Non-determinism and a First Approach	p. 87
ACS2 in a Non-determinism Task	p. 88
Probability-Enhanced Effects	p. 89
Model Aliasing	p. 93
Model Exploitation	p. 99
Improving Model Learning	p. 99
Increasing Exploration	p. 100
Combining Exploration with Action Planning	p. 104
Enhancing Reinforcement Learning	p. 107
Response-Effect Learning Task	p. 107
Mental Acting	p. 108
Lookahead Action Selection	p. 110
ACS2 in the Response-Effect Task	p. 111
Stimulus-Response-Effect Task	p. 112
Model Exploitation Recapitulation	p. 113
Related Systems	p. 115
Estimated Learning Algorithm	p. 115
Dyna	p. 117
Schema Mechanism	p. 118
Expectancy Model SRS/E	p. 119
Summary, Conclusions, and Future Work	p. 121
Summary	p. 121
Model Representation Enhancements	p. 123
Classifier Structure	p. 123
ACS2 Structure	p. 126
Model Learning Modifications	p. 127
Observations in Nature	p. 127
Relevance and Influence	p. 130
Attentional Mechanisms	p. 131
Additional Memory	p. 133
Adaptive Behavior	p. 134
Reinforcement Learning Processes	p. 135
Behavioral Module	p. 136
ACS2 in the Future	p. 137
Appendices	p. 139
Parameters in ACS2	p. 139
Algorithmic Description of ACS2	p. 141
Initialization	p. 141
The Main Execution Loop	p. 142
Formation of the Match Set	p. 143
Choosing an Action	p. 143
Formation of the Action Set	p. 144
Application of the ALP	p. 144
Reinforcement Learning	p. 149
GA Application	p. 149
Subsumption	p. 152
ACS2 C++ Code Documentation	p. 153
Getting Started	p. 153
Structure of the Code	p. 154
The Controller - ACSConstants.h	p. 154
The Executer - acs2++.cc	p. 156
Environments	p. 157
ACS2 modules	p. 159
Performance Output	p. 160
Glossary	p. 161
References	p. 165
Index	p. 171
Table of Contents provided by Syndetics. All Rights Reserved.

Genetic Algorithms and Evolutionary Computation

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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