Bull L. (ed.) Applications of Learning Classifier Systems

Springer, 2004. — 309 p.The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments? In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together? John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older "standard AI' . One focus was what Holland called "classifier systems": sets of competing rule-like "classifiers", each a hypothesis as to how best to react to some aspect of the environment – or to another rule. The system embracing such a rule "population" would explore its available actions and responses, rewarding and rating the active rules accordingly. Then "good" classifiers would be selected and reproduced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.Learning Classifier Systems: A Brief Introduction
Data Mining
Data Mining using Learning Classifier Systems
NXCS Experts for Financial Time Series Forecasting
Encouraging Compact Rulesets from XCS for Enhanced Data Mining
Modelling and Optimization
The Fighter Aircraft LCS: A Real-World, Machine Innovation Application
Traffic Balance using Learning Classifier Systems in an Agent-based Simulation
A Multi-Agent Model of the UK Market in Electricity Generation
Exploring Organizational-Learning Oriented Classifier Systems in Real-World Problems
Control
Distributed Routing in Communication Networks using the Temporal Fuzzy Classifier System - a Study on Evolutionary Multi-Agent Control
The Development of an Industrial Learning Classifier System for Data-Mining in a Steel Hop Strip Mill
Application of Learning Classifier Systems to the On-Line Reconfiguration of Electric Power Distribution Networks
Towards Distributed Adaptive Control for Road Traffic Junction Signals using Learning Classifier Systems
Bibliography of Real-W orId Classifier Systems Applications