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Rule-Based Evolutionary Online Learning Systems [electronic resource] :A Principled Approach to LCS Analysis and Design / by Martin V. Butz.

by Butz, Martin V [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Fuzziness and Soft Computing: 191Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: XXI, 266 p. online resource.ISBN: 9783540312314.Subject(s): Engineering | Neurosciences | Artificial intelligence | Mathematics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Neurosciences | Applications of MathematicsDDC classification: 519 Online resources: Click here to access online
Contents:
Prerequisites -- Simple Learning Classifier Systems -- The XCS Classifier System -- How XCS Works: Ensuring Effective Evolutionary Pressures -- When XCS Works: Towards Computational Complexity -- Effective XCS Search: Building Block Processing -- XCS in Binary Classification Problems -- XCS in Multi-Valued Problems -- XCS in Reinforcement Learning Problems -- Facetwise LCS Design -- Towards Cognitive Learning Classifier Systems -- Summary and Conclusions.
In: Springer eBooksSummary: This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.
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Prerequisites -- Simple Learning Classifier Systems -- The XCS Classifier System -- How XCS Works: Ensuring Effective Evolutionary Pressures -- When XCS Works: Towards Computational Complexity -- Effective XCS Search: Building Block Processing -- XCS in Binary Classification Problems -- XCS in Multi-Valued Problems -- XCS in Reinforcement Learning Problems -- Facetwise LCS Design -- Towards Cognitive Learning Classifier Systems -- Summary and Conclusions.

This book offers a comprehensive introduction to learning classifier systems (LCS) – or more generally, rule-based evolutionary online learning systems. LCSs learn interactively – much like a neural network – but with an increased adaptivity and flexibility. This book provides the necessary background knowledge on problem types, genetic algorithms, and reinforcement learning as well as a principled, modular analysis approach to understand, analyze, and design LCSs. The analysis is exemplarily carried through on the XCS classifier system – the currently most prominent system in LCS research. Several enhancements are introduced to XCS and evaluated. An application suite is provided including classification, reinforcement learning and data-mining problems. Reconsidering John Holland’s original vision, the book finally discusses the current potentials of LCSs for successful applications in cognitive science and related areas.

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