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

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 XCS
Ⅲ Advanced Architectures
A Study on the Evolution of Learning Classifier Systems
Learning Classifier Systems Meet Multiagent Environments
Ⅳ The Bibliography
A Bigger Learning Classifier Systems Bibliography
Ⅴ Appendix
An Algorithmic Description of XCS
Author Index