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Learning Classifier Systems

From Foundations to Applications

  • Conference proceedings
  • © 2000

Overview

Editors:
  1. Pier Luca Lanzi

    1. Dipartimento di Elettronica ed Informatzione, Politecnico di Milano, Milano, Italy

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  2. Wolfgang Stolzmann

    1. Institut für Psychologie III, Universität Würzburg, Würzburg, Germany

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  3. Stewart W. Wilson

    1. Prediction Dynamics, Concord, USA
      Department of General Engineering, University of Illinois at Urbana-Champaign, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1813)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (17 papers)

  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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Keywords

About this book

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.

Editors and Affiliations

  • 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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