learning classifier systems (75 Ergebnisse)

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8° Paperback. Zustand: Sehr gut. 218 S. The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed. B07-02-06D Sprache: Deutsch Gewicht in Gramm: 448.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The 5th International Workshop on Learning Classi er Systems (IWLCS2002) was held September 7-8, 2002, in Granada, Spain, during the 7th International Conference on Parallel Problem Solving from Nature (PPSN VII). We have included in this volume revised and extended versions of the papers presented at the workshop. In the rst paper, Browne introduces a new model of learning classi er system, iLCS, and tests it on the Wisconsin Breast Cancer classi cation problem. Dixon et al. present an algorithm for reducing the solutions evolved by the classi er system XCS, so as to produce a small set of readily understandable rules. Enee and Barbaroux take a close look at Pittsburgh-style classi er systems, focusing on the multi-agent problem known as El-farol. Holmes and Bilker investigate the effect that various types of missing data have on the classi cation performance of learning classi er systems. The two papers by Kovacs deal with an important theoretical issue in learning classi er systems: the use of accuracy-based tness as opposed to the more traditional strength-based tness. In the rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and the new SB-XCS are compared in the second paper, where - vacs discusses the different classes of solutions that XCS and SB-XCS tend to evolve.

Zustand: New. Up-to-date results in learning classifier systemsThis book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Atlanta, GA, USA in July 2008, .

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the thoroughly refereed joint post-conference proceedings of two consecutive International Workshops on Learning Classifier Systems that took place in Seattle, WA, USA in July 2006, and in London, UK, in July 2007 - all hosted by the Genetic and Evolutionary Computation Conference, GECCO. The 14 revised full papers presented were carefully reviewed and selected from the workshop contributions. The papers are organized in topical sections on knowledge representation, analysis of the system, mechanisms, new directions, as well as applications.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 ciaAm ancioVargas,ChristianoLyr.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning classi er systems are rule-based systems that exploit evolutionary c- putation and reinforcement learning to solve di cult problems. They were - troduced in 1978 by John H. Holland, the father of genetic algorithms, and since then they have been applied to domains as diverse as autonomous robotics, trading agents, and data mining. At the Second International Workshop on Learning Classi er Systems (IWLCS 99), held July 13, 1999, in Orlando, Florida, active researchers reported on the then current state of learning classi er system research and highlighted some of the most promising research directions. The most interesting contri- tions to the meeting are included in the book Learning Classi er Systems: From Foundations to Applications, published as LNAI 1813 by Springer-Verlag. The following year, the Third International Workshop on Learning Classi er Systems (IWLCS 2000), held September 15{16 in Paris, gave participants the opportunity to discuss further advances in learning classi er systems. We have included in this volume revised and extended versions of thirteen of the papers presented at the workshop.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005). The Genetic and Evolutionary Computation Conference, the main ACM SIGEvo conference, hosted these three editions. The topics presented in this volume summarize the wide spectrum of interests of the Learning Classi er Systems (LCS) community. The topics range from theoretical analysis of mechanisms to practical cons- eration for successful application of such techniques to everyday data-mining tasks. When we started editing this volume, we faced the choice of organizing the contents in a purely chronologicalfashion or as a sequence of related topics that help walk the reader across the di erent areas. In the end we decided to or- nize the contents by area, breaking the time-line a little. This is not a simple endeavor as we can organize the material using multiple criteria. The tax- omy below is our humble e ort to provide a coherent grouping. Needless to say, some works may fall in more than one category. The four areas are as follows: Knowledge representation. These chapters elaborate on the knowledge r- resentations used in LCS. Knowledge representation is a key issue in any learning system and has implications for what it is possible to learn and what mechanisms shouldbe used. Four chapters analyze di erent knowledge representations and the LCS methods used to manipulate them.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Learning Classifier Systems (LCS) are amachine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.