Advances in Learning Classifier Systems Third International Workshop, IWLCS 2000, Paris, France, September 15-16, 2000. Revised Papers

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.

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 r...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Lanzi, Pier L. (Editor), Stolzmann, Wolfgang (Editor), Wilson, Stewart W. (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.
Edition:1st ed. 2001.
Series:Lecture Notes in Artificial Intelligence ; 1996
Springer eBook Collection.
Subjects:
Artificial intelligence.
Computers.
Mathematical logic.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of 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.

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