Advances in Learning Classifier Systems

1. Front Matter

   Pages I-VIII

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2. Theory

   1. An Artificial Economy of Post Production Systems

      • Eric B. Baum, Igor Durdanovic

      Pages 3-20

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

      • Larry Bull

      Pages 21-28

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

      • Larry Bull

      Pages 29-36

   4. Probability-Enhanced Predictions in the Anticipatory Classifier System

      • Martin V. Butz, David E. Goldberg, Wolfgang Stolzmann

      Pages 37-51

   5. YACS: Combining Dynamic Programming with Generalization in Classifier Systems

      • Pierre Gérard, Olivier Sigaud

      Pages 52-69

   6. A Self-Adaptive Classifier System

      • Jacob Hurst, Larry Bull

      Pages 70-79

   7. What Makes a Problem Hard for XCS?

      • Tim Kovacs, Manfred Kerber

      Pages 80-99

3. Applications

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

      • John H. Holmes

      Pages 103-113

   2. Strength and Money: An LCS Approach to Increasing Returns

      • Sonia Schulenburg, Peter Ross

      Pages 114-137

   3. Using Classifier Systems as Adaptive Expert Systems for Control

      • Olivier Sigaud, Pierre Gérard

      Pages 138-157

   4. Mining Oblique Data with XCS

      • Stewart W. Wilson

      Pages 158-174

4. Advanced Architectures

   1. A Study on the Evolution of Learning Classifier Systems

      • Tiago Sepúlveda, Mário Rui Gomes

      Pages 177-191

   2. Learning Classifier Systems Meet Multiagent Environments

      • Keiki Takadama, Takao Terano, Katsunori Shimohara

      Pages 192-210

5. The Bibliography

   1. A Bigger Learning Classifier Systems Bibliography

      • Tim Kovacs, Pier Luca Lanzi

      Pages 213-249

6. Appendix

   1. An Algorithmic Description of XCS

      • Martin V. Butz, Stewart W. Wilson

      Pages 253-272

7. Back Matter

   Pages 273-273

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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.

• Adaptation
• Algorithmic Learning
• Classification
• Classifier Systems
• Clustering
• Data Analysis
• Data Mining
• Evolutionary Computing
• Learning Classifier Systems
• Multiagent Learning
• Rule-Based Systems
• algorithms
• expert system
• genetic algorithms
• learning

• Politecnico di Milano Dipartimento di Elettronica e Informazione, Artificial Intelligence and Robotics Laboratory, Milan, Italy

  Pier Luca Lanzi

• DaimlerChrysler AG Research and Technology,Cognition and Robotics, Berlin, Germany

  Wolfgang Stolzmann

• Prediction Dynamics, Concord, USA

  Stewart W. Wilson

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