Advances in Learning Classifier Systems: 4th International Workshop, IWLCS 2001, San Francisco, CA, USA, July 7-8, 2001. Revised Papers: 2321 - Brossura

Stolzmann, Wolfgang; Wilson, Stewart W.; Lanzi, Pier L.

Brossura

ISBN 10: 3540437932 ISBN 13: 9783540437932

Casa editrice: Springer, 2008

Riassunto

UniversityofPennsylvania,USA TimKovacs UniversityofBirmingham,UK PierLucaLanzi PolitecnicodiMilano,Italy RickL. Riolo UniversityofMichigan,USA OlivierSigaud AnimatLab-LIP6,France RobertE. Smith TheUniversityofTheWestofEngland,UK WolfgangStolzmann DaimlerChryslerAG,Germany KeikiTakadama ATRInternational,Japan StewartW. Wilson TheUniversityofIllinoisatUrbana-Champaign,USA PredictionDynamics,USA TableofContents ITheory BiasingExplorationinanAnticipatoryLearningClassi?erSystem . . . . . . . 3 MartinV. Butz An Incremental Multiplexer Problem and Its Uses in Classi?er System Research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 LawrenceDavis,ChunshengFu,StewartW. Wilson AMinimalModelofCommunicationforaMulti-agentClassi?erSystem. . 32 ´ GillesEn´ee,CathyEscazut A Representation for Accuracy-Based Assessment of Classi?er System PredictionPerformance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 JohnH. Holmes ASelf-AdaptiveXCS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 JacobHurst,LarryBull TwoViewsofClassi?erSystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 TimKovacs SocialSimulationUsingaMulti-agentModelBasedonClassi?erSystems: TheEmergenceofVacillatingBehaviourinthe ElFarol BarProblem. . . 88 LuisMiramontesHercog,TerenceC. Fogarty II Applications XCSandGALE:AComparativeStudyofTwoLearningClassi?erSystems onDataMining. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 EsterBernad´o,XavierLlor`a,JosepM. Garrell APreliminaryInvestigationofModi?edXCSasaGenericDataMining Tool. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 PhillipWilliamDixon,DavidW. Corne,MartinJohnOates ExplorationsinLCSModelsofStockTrading . . . . . . . . . . . . . . . . . . . . . . . . . 151 SoniaSchulenburg,PeterRoss On-LineApproachforLossReductioninElectricPowerDistribution NetworksUsingLearningClassi?erSystems. . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Patr´?ciaAm ancioVargas,ChristianoLyraFilho, FernandoJ. VonZuben VIII TableofContents CompactRulesetsfromXCSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 StewartW. Wilson III Appendix AnAlgorithmicDescriptionofACS2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 MartinV. Butz,WolfgangStolzmann AuthorIndex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231 BiasingExplorationinan AnticipatoryLearningClassi?erSystem MartinV. Butz DepartmentofCognitivePsychology,UniversityofWurz ¨ burg R¨ ontgenring11,97070Wurz ¨ burg,Germany butz@psychologie. uni-wuerzburg. de Abstract. Thechapterinvestigateshowmodelandbehaviorallearning can be improved in an anticipatory learning classi?er system by bi- ing exploration. First, theappliedsystemACS2isexplained. Next,an overviewoverthepossibilitiesofapplyingexplorationbiasesinanant- ipatory learning classi?er systemand speci?cally ACS2 is provided.

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Contenuti

Theory.- Biasing Exploration in an Anticipatory Learning Classifier System.- An Incremental Multiplexer Problem and Its Uses in Classifier System Research.- A Minimal Model of Communication for a Multi-agent Classifier System.- A Representation for Accuracy-Based Assessment of Classifier System Prediction Performance.- A Self-Adaptive XCS.- Two Views of Classifier Systems.- Social Simulation Using a Multi-agent Model Based on Classifier Systems: The Emergence of Vacillating Behaviour in the “El Farol” Bar Problem.- Applications.- XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining.- A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool.- Explorations in LCS Models of Stock Trading.- On-Line Approach for Loss Reduction in Electric Power Distribution Networks Using Learning Classifier Systems.- Compact Rulesets from XCSI.- An Algorithmic Description of ACS2.

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 3540437932
• ISBN 13: 9783540437932
• Rilegatura: Copertina flessibile
• Numero di pagine: 240
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania