Learning Classifier Systems : From Foundations to Applications / edited by Pier L. Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.

Format:

Book

Contributor:

Lanzi, Pier L., editor.

Stolzmann, Wolfgang, 1966- editor.

Wilson, Stewart W., 1937- editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1813.

Lecture Notes in Artificial Intelligence ; 1813

Language:

English

Subjects (All):

Artificial intelligence.

Logic, Symbolic and mathematical.

Computers.

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Computation by Abstract Devices.

Local Subjects:

Artificial Intelligence.

Mathematical Logic and Formal Languages.

Computation by Abstract Devices.

Physical Description:

1 online resource (X, 354 pages).

Edition:

First edition 2000.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.

System Details:

text file PDF

Summary:

Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.

Contents:

Basics

What Is a Learning Classifier System?

A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999)

State of XCS Classifier System Research

An Introduction to Learning Fuzzy Classifier Systems

Advanced Topics

Fuzzy and Crisp Representations of Real-Valued Input for Learning Classifier Systems

Do We Really Need to Estimate Rule Utilities in Classifier Systems?

Strength or Accuracy? Fitness Calculation in Learning Classifier Systems

Non-homogeneous Classifier Systems in a Macro-evolution Process

An Introduction to Anticipatory Classifier Systems

A Corporate XCS

Get Real! XCS with Continuous-Valued Inputs

Applications

XCS and the Monk's Problems

Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases

An Adaptive Agent Based Economic Model

The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques

Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems

The Bibliography

A Learning Classifier Systems Bibliography.

Other Format:

Printed edition:

ISBN:

978-3-540-45027-6

9783540450276

Access Restriction:

Restricted for use by site license.