Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001, San Francisco, CA, USA, July 7-8, 2001. Revised Papers

by Pier L. Lanzi (Editor), Wolfgang Stolzmann (Editor), Stewart W. Wilson (Editor)

Overview

This book constitutes the thoroughly refereed post-proceedings of the 4th International Workshop on Learning Classifier Systems, IWLCS 2001, held in San Francisco, CA, USA, in July 2001.
The 12 revised full papers presented together with a special paper on a formal description of ACS have gone through two rounds of reviewing and improvement. The first part of the book is devoted to theoretical issues of learning classifier systems including the influence of exploration strategy, self-adaptive classifier systems, and the use of classifier systems for social simulation. The second part is devoted to applications in various fields such as data mining, sk trading, and power distributionn networks.

Product Details

ISBN-13:	9783540437932
Publisher:	Springer Berlin Heidelberg
Publication date:	08/05/2002
Series:	Lecture Notes in Computer Science , #2321
Edition description:	2002
Pages:	236
Product dimensions:	6.10(w) x 9.17(h) x 0.36(d)

Table of Contents

Theory.- Biasing Exploration in an Anticipatory Learning Classifier System.- An Incremental Multiplexer Problem and Its Uses in Classifier System Research.- A Minimal Model of Communication for a Multi-agent Classifier System.- A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance.- A Self-Adaptive XCS.- Two Views of Classifier Systems.- Social Simulation Using a Multi-agent Model Based on Classifier Systems: The Emergence of Vacillating Behaviour in the “El Farol” Bar Problem.- Applications.- XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining.- A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool.- Explorations in LCS Models of Sk Trading.- On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems.- Compact Rulesets from XCSI.- An Algorithmic Description of ACS2.