Title: Advances in learning classifier systems
First published: 2002
Description: The Fourth International Workshop on Learning Classi?er Systems (IWLCS 2001)washeldJuly7-8,2001,inSanFrancisco,California,duringtheGenetic andEvolutionaryComputationConference(GECCO2001). Wehaveincluded inthisvolumerevisedandextendedversionsofelevenofthepaperspresented attheworkshop. Thevolumeisorganizedintotwomainparts. The?rstisdedicatedtoimportant theoreticalissuesoflearningclassi?ersystemsresearchincludingthein?uence ofexplorationstrategy,amodelofself-adaptiveclassi?ersystems,andtheuse ofclassi?ersystemsforsocialsimulation. Thesecondpartcontainspapersd- cussing applications of learning classi?er systems such as data mining, stock trading,andpowerdistributionnetworks. AnappendixcontainsapaperpresentingaformaldescriptionofACS,arapidly emerginglearningclassi?ersystemmodel. Thisbookistheidealcontinuationofthetwovolumesfromthepreviouswo- shops,publishedbySpringer-VerlagasLNAI1813andLNAI1996.^ Wehopeit willbeausefulsupportforresearchersinterestedinlearningclassi?ersystems andwillprovideinsightsintothemostrelevanttopicsandthemostinteresting openissues. April2002 PierLucaLanzi WolfgangStolzmann StewartW. Wilson Organization The Fourth International Workshop on Learning Classi?er Systems (IWLCS 2001)washeldJuly7-8,2001inSanFrancisco(CA),USA,duringtheGenetic andEvolutionaryConference(GECCO2001). OrganizingCommittee PierLucaLanzi PolitecnicodiMilano,Italy WolfgangStolzmann DaimlerChryslerAG,Germany StewartW. Wilson TheUniversityofIllinoisatUrbana-Champaign,USA PredictionDynamics,USA ProgramCommittee ErikBaum NECResearchInstitute,USA AndreaBonarini PolitecnicodiMilano,Italy LashonB. Booker TheMITRECorporation,USA MartinV. Butz UniversityofWur ¨ zburg,Germany LawrenceDavis NuTechSolutions,USA TerryFogarty SouthbankUniversity,UK JohnH. Holmes 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´?ciaAmˆancioVargas,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.
Subjects: Congresses, Machine learning, Computer science, Artificial intelligence
LC classification: Q325.5 .I87 2001, Q325.5 .I87 2001