Advances In Learning Classifier Systems学习分类器系统进展

2001-10  

1 edition (2001年10月1日)  

Pier L. Lanzi  

272  

This subseries of Lecture Notes in Computer Science reports new devel-opments in artificial intelligence research and teaching,quickly,informally,and at a high level,The timeliness of a manuscript is more important than its form,which may be unfinished or tentative.The type of material considered for publication includes. -drafts of original papers or monographs. -technical reports of high quality and broad interest, -advanced-level lectures, -reports of meetings,provided they are of exceptional interest and focused on a single topic. Publication of Lecture Notes is inteded as service to the computer science community in that the publisher Springer-Verlag offers global distribution of documents which would otherwise have a restricted readership.One pub-lished and copyrighted they can be cited in the scientific literature.

Ⅰ Theory An Artificial Economy of Post Production Systems Simple Markov Models of the Genetic Algorithm in Classifier Systems：Accuracy-Based Fitness Simple Markov Models of the Genetic Algorithm in Classifier Systems：Multi-step Tasks Probability-Enhanced Predictions in the Anticipatory Classifier System YACS：Combining Dynamic Programming with Generalization in Classifier Systems A Self-Adaptive Classifier System What Makes a Problem Hard for XCS?Ⅱ Applications Applying a Learning Classifier System to Mining Explanatory and Predictive Models from a Large Clinical Database Strength and Money：An LC S Approach to Increasing Returns Using Classifier Systems as Adaptive Expert Systems for Control Mining Oblique Data with XCSⅢ Advanced Architectures A Study on the Evolution of Learning Classifier Systems Learning Classifier Systems Meet Multiagent EnvironmentsⅣ The Bibliography A Bigger Learning Classifier Systems BibliographyⅤ Appendix An Algorithmic Description of XCSAuthor Index

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