Learning Classifier Systems: From Foundations to Applications

✍ Scribed by John H. Holland, Lashon B. Booker, Marco Colombetti, Marco Dorigo, David E. Goldberg (auth.), Pier Luca Lanzi, Wolfgang Stolzmann, Stewart W. Wilson (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2000
• Tongue: English
• Leaves: 344
• Series: Lecture Notes in Computer Science 1813 : Lecture Notes in Artificial Intelligence
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

What Is a Learning Classifier System?....Pages 3-32
A Roadmap to the Last Decade of Learning Classifier System Research (From 1989 to 1999)....Pages 33-61
State of XCS Classifier System Research....Pages 63-81
An Introduction to Learning Fuzzy Classifier Systems....Pages 83-104
Fuzzy and Crisp Representations of Real-Valued Input for Learning Classifier Systems....Pages 107-124
Do We Really Need to Estimate Rule Utilities in Classifier Systems?....Pages 125-141
Strength or Accuracy? Fitness Calculation in Learning Classifier Systems....Pages 143-160
Non-homogeneous Classifier Systems in a Macro-evolution Process....Pages 161-174
An Introduction to Anticipatory Classifier Systems....Pages 175-194
A Corporate XCS....Pages 195-208
Get Real! XCS with Continuous-Valued Inputs....Pages 209-219
XCS and the Monk’s Problems....Pages 223-242
Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases....Pages 243-261
An Adaptive Agent Based Economic Model....Pages 263-282
The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques....Pages 283-300
Latent Learning and Action Planning in Robots with Anticipatory Classifier Systems....Pages 301-317
A Learning Classifier Systems Bibliography....Pages 321-347

✦ Subjects

Artificial Intelligence (incl. Robotics); Mathematical Logic and Formal Languages; Computation by Abstract Devices