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.
Produkteigenschaften
- Artikelnummer: 9783540437932
- Medium: Buch
- ISBN: 978-3-540-43793-2
- Verlag: Springer Berlin Heidelberg
- Erscheinungstermin: 12.06.2002
- Sprache(n): Englisch
- Auflage: 2002
- Serie: Lecture Notes in Artificial Intelligence
- Produktform: Kartoniert
- Gewicht: 376 g
- Seiten: 236
- Format (B x H x T): 155 x 235 x 14 mm
- Ausgabetyp: Kein, Unbekannt