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Applications of Learning Classifier Systems: Studies in Fuzziness and Soft Computing, cartea 150

Editat de Larry Bull
en Limba Engleză Hardback – 16 apr 2004
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.
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Specificații

ISBN-13: 9783540211099
ISBN-10: 3540211098
Pagini: 320
Ilustrații: VIII, 305 p.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.63 kg
Ediția:2004
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seria Studies in Fuzziness and Soft Computing

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

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.

Textul de pe ultima copertă

 This carefully edited book brings together a fascinating selection of applications of Learning Classifier Systems (LCS). The book demonstrates the utility of this machine learning technique in recent real-world applications in such domains as data mining, modeling and optimization, and control. It shows how the LCS technique combines and exploits many Soft Computing approaches into a single coherent framework to produce an improved performance over other approaches.

Caracteristici

Brings together recent real-world applications of a machine learning technique whose performance has been greatly improved in recent years and which is experiencing resurgence in interest Includes supplementary material: sn.pub/extras