Bull Applications of Learning Classifier Systems

Erscheinungsjahr 2012
ISBN: 978-3-540-39925-4
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 150, 305 Seiten, eBook

Reihe: Studies in Fuzziness and Soft Computing

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 Af' . 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 "popu lation" would explore its available actions and responses, rewarding and rating the active rules accordingly. Then "good" classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.

Zielgruppe

Research

Autoren/Hrsg.

Bull, Larry

Inhaltsverzeichnis

Learning Classifier Systems: A Brief Introduction.- Section 1 — Data Mining.- Data Mining using Learning Classifier Systems.- NXCS Experts for Financial Time Series Forecasting.- Encouraging Compact Rulesets from XCS for Enhanced Data Mining.- Section 2 — 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.- Section 3 — 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-World Classifier Systems Applications.