Learning Classifier Systems

10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007, Revised Selected Papers

AvJaume Bacardit,Ester Bernadó-Mansilla

Häftad, Engelska, 2008

Del i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.

Produktinformation

Utgivningsdatum:2008-10-23
Mått:155 x 235 x 18 mm
Vikt:487 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:307
Upplaga:2008
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540881377

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Programmeringsböcker inom Data och IT

Innehållsförteckning

Learning Classifier Systems: Looking Back and Glimpsing Ahead.- Knowledge Representations.- Analysis of Population Evolution in Classifier Systems Using Symbolic Representations.- Investigating Scaling of an Abstracted LCS Utilising Ternary and S-Expression Alphabets.- Evolving Fuzzy Rules with UCS: Preliminary Results.- Analysis of the System.- A Principled Foundation for LCS.- Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS.- Mechanisms.- Analysis and Improvements of the Classifier Error Estimate in XCSF.- A Learning Classifier System with Mutual-Information-Based Fitness.- On Lookahead and Latent Learning in Simple LCS.- A Learning Classifier System Approach to Relational Reinforcement Learning.- Linkage Learning, Rule Representation, and the ?-Ary Extended Compact Classifier System.- New Directions.- Classifier Conditions Using Gene Expression Programming.- Evolving Classifiers Ensembles with Heterogeneous Predictors.- Substructural Surrogates for Learning Decomposable Classification Problems.- Empirical Evaluation of Ensemble Techniques for a Pittsburgh Learning Classifier System.- Applications.- Technology Extraction of Expert Operator Skills from Process Time Series Data.- Analysing Learning Classifier Systems in Reactive and Non-reactive Robotic Tasks.