Advances in Learning Classifier Systems : Third International Workshop, IWLCS 2000, Paris, France, September 15-16, 2000. Revised Papers / edited by Pier L. Lanzi, Wolfgang Stolzmann, Stewart W. Wilson.

Format:

Book

Contributor:

Lanzi, Pier L., editor.

Stolzmann, Wolfgang, 1966- editor.

Wilson, Stewart W., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 1996.

Lecture Notes in Artificial Intelligence ; 1996

Language:

English

Subjects (All):

Artificial intelligence.

Computers.

Logic, Symbolic and mathematical.

Artificial Intelligence.

Theory of Computation.

Mathematical Logic and Formal Languages.

Computation by Abstract Devices.

Local Subjects:

Artificial Intelligence.

Theory of Computation.

Mathematical Logic and Formal Languages.

Computation by Abstract Devices.

Physical Description:

1 online resource (VIII, 280 pages).

Edition:

First edition 2001.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.

System Details:

text file PDF

Summary:

Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.

Contents:

Theory

An Artificial Economy of Post Production Systems

Simple Markov Models of the Genetic Algorithm in Classifier Systems: Accuracy-Based Fitness

Simple Markov Models of the Genetic Algorithm in Classifier Systems: Multi-step Tasks

Probability-Enhanced Predictions in the Anticipatory Classifier System

YACS: Combining Dynamic Programming with Generalization in Classifier Systems

A Self-Adaptive Classifier System

What Makes a Problem Hard for XCS?

Applications

Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Clinical Database

Strength and Money: An LCS Approach to Increasing Returns

Using Classifier Systems as Adaptive Expert Systems for Control

Mining Oblique Data with XCS

Advanced Architectures

A Study on the Evolution of Learning Classifier Systems

Learning Classifier Systems Meet Multiagent Environments

The Bibliography

A Bigger Learning Classifier Systems Bibliography

An Algorithmic Description of XCS.

Other Format:

Printed edition:

ISBN:

978-3-540-44640-8

9783540446408

Access Restriction:

Restricted for use by site license.