Learning Classifier Systems

1. Front Matter

   Pages I-X

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2. Basics

   1. What Is a Learning Classifier System?

      • John H. Holland, Lashon B. Booker, Marco Colombetti, Marco Dorigo, David E. Goldberg, Stephanie Forrest et al.

      Pages 3-32

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

      • Pier Luca Lanzi, Rick L. Riolo

      Pages 33-61

   3. State of XCS Classifier System Research

      • Stewart W. Wilson

      Pages 63-81

   4. An Introduction to Learning Fuzzy Classifier Systems

      • Andrea Bonarini

      Pages 83-104

3. Advanced Topics

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

      • Andrea Bonarini, Claudio Bonacina, Matteo Matteucci

      Pages 107-124

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

      • Lashon B. Booker

      Pages 125-141

   3. Strength or Accuracy? Fitness Calculation in Learning Classifier Systems

      • Tim Kovacs

      Pages 143-160

   4. Non-homogeneous Classifier Systems in a Macro-evolution Process

      • Claude Lattaud

      Pages 161-174

   5. An Introduction to Anticipatory Classifier Systems

      • Wolfgang Stolzmann

      Pages 175-194

   6. A Corporate XCS

      • Andy Tomlinson, Larry Bull

      Pages 195-208

   7. Get Real! XCS with Continuous-Valued Inputs

      • Stewart W. Wilson

      Pages 209-219

4. Applications

   1. XCS and the Monk’s Problems

      • Shaun Saxon, Alwyn Barry

      Pages 223-242

   2. Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases

      • John H. Holmes

      Pages 243-261

   3. An Adaptive Agent Based Economic Model

      • Sonia Schulenburg, Peter Ross

      Pages 263-282

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

      • Robert E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, R. K. Mehra

      Pages 283-300

   5. Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems

      • Wolfgang Stolzmann, Martin Butz

      Pages 301-317

5. The Bibliography

   1. A Learning Classifier Systems Bibliography

      • Tim Kovacs, Pier Luca Lanzi

      Pages 321-347

6. Back Matter

   Pages 349-349

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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.

• Extension
• agents
• algorithmic learning
• algorithms
• autonomous robot
• data mining
• evolution
• fuzzy
• genetic algorithms
• knowledge
• knowledge discovery
• learning
• machine learning
• robot
• robotics

• Dipartimento di Elettronica ed Informatzione, Politecnico di Milano, Milano, Italy

  Pier Luca Lanzi

• Institut für Psychologie III, Universität Würzburg, Würzburg, Germany

  Wolfgang Stolzmann

• Prediction Dynamics, Concord, USA

  Stewart W. Wilson

• Department of General Engineering, University of Illinois at Urbana-Champaign, USA

  Stewart W. Wilson

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