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外文资料--Machine learning and games.pdf

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外文资料--Machine learning and games.pdf

MACHLEARN200663211–215DOI101007/S109940068919XGUESTEDITORIALMACHINELEARNINGANDGAMESMICHAELBOWLINGJOHANNESFURNKRANZTHOREGRAEPELRONMUSICKPUBLISHEDONLINE10MAY2006SPRINGERSCIENCEBUSINESSMEDIA,LLC2006THEHISTORYOFTHEINTERACTIONOFMACHINELEARNINGANDCOMPUTERGAMEPLAYINGGOESBACKTOTHEEARLIESTDAYSOFARTIFICIALINTELLIGENCE,WHENARTHURSAMUELWORKEDONHISFAMOUSCHECKERPLAYINGPROGRAM,PIONEERINGMANYMACHINELEARNINGANDGAMEPLAYINGTECHNIQUESSAMUEL,1959,1967SINCETHEN,BOTHFIELDSHAVEADVANCEDCONSIDERABLY,ANDRESEARCHINTHEINTERSECTIONOFTHETWOCANBEFOUNDREGULARLYINCONFERENCESINTHEIRRESPECTIVEFIELDSANDINGENERALAICONFERENCESFORSURVEYSOFTHEFIELDWEREFERTOGINSBERG1998,SCHAEFFER2000,FURNKRANZ2001;EDITEDVOLUMESHAVEBEENCOMPILEDBYSCHAEFFERANDVANDENHERIK2002ANDBYFURNKRANZANDKUBAT2001INRECENTYEARS,THECOMPUTERGAMESINDUSTRYHASDISCOVEREDAIASANECESSARYINGREDIENTTOMAKEGAMESMOREENTERTAININGANDCHALLENGINGAND,VICEVERSA,AIHASDISCOVEREDCOMPUTERGAMESASANINTERESTINGANDREWARDINGAPPLICATIONAREATHEINDUSTRY’SPERSPECTIVEISWITNESSEDBYAPLETHORAOFRECENTBOOKSONGENTLEINTRODUCTIONSTOAITECHNIQUESFORGAMEPROGRAMMERSCOLLINS,2002;CHAMPANARD,2003;BOURGSEEMANN,2004;SCHWAB,2004ORASERIESOFEDITEDCOLLECTIONSOFARTICLESRABIN,2002,2003,2006AIRESEARCHONCOMPUTERGAMESBEGANTOFOLLOWDEVELOPMENTSINTHEGAMESINDUSTRYEARLYON,BUTSINCEJOHNLAIRD’SKEYNOTEADDRESSATTHEAAAI2000CONFERENCE,INWHICHHEADVOCATEDINTERACTIVECOMPUTERGAMESASACHALLENGINGANDREWARDINGAPPLICATIONAREAFORAILAIRDVANLENT,2001,NUMEROUSWORKSHOPSFUORKIN,2004;AHAETAL,2005,CONFERENCES,ANDSPECIALISSUESOFJOURNALSFORBUSLAIRD,2002DEMONSTRATETHEGROWINGIMPORTANCEOFGAMEPLAYINGAPPLICATIONSFORARTIFICIALINTELLIGENCEMBOWLINGENVELOPEBACKEMAILBOWLINGCSUALBERTACAJFURNKRANZEMAILFUERNKRANZINFORMATIKTUDARMSTADTDETGRAEPELEMAILTHOREGMICROSOFTCOMRMUSICKEMAILMUSICKIKUNICOMSPRINGER212MACHLEARN200663211–215GAMES,WHETHERCREATEDFORENTERTAINMENT,SIMULATION,OREDUCATION,PROVIDEGREATOPPORTUNITIESFORMACHINELEARNINGTHEVARIETYOFPOSSIBLEVIRTUALWORLDSANDTHESUBSEQUENTMLRELEVANTPROBLEMSPOSEDFORTHEAGENTSINTHOSEWORLDSISLIMITEDONLYBYTHEIMAGINATIONFURTHERMORE,NOTONLYISTHEGAMESINDUSTRYLARGEANDGROWINGHAVINGSURPASSEDTHEMOVIEINDUSTRYINREVENUEAFEWYEARSBACK,BUTITISFACEDWITHATREMENDOUSDEMANDFORNOVELTYTHATITSTRUGGLESTOPROVIDEAGAINSTTHISBACKDROP,MACHINELEARNINGDRIVENSUCCESSESWOULDDRAWHIGHPROFILEATTENTIONTOTHEFIELDSURPRISINGLYHOWEVER,THEMORECOMMERCIALTHEGAMETODATE,THELESSIMPACTLEARNINGHASMADETHISISQUITEUNLIKEOTHERGREATMATCHESBETWEENAPPLICATIONANDDATADRIVENANALYTICSSUCHASDATAMININGANDOLAPTOPICSOFPARTICULARIMPORTANCEFORSUCCESSFULGAMEAPPLICATIONSINCLUDELEARNINGHOWTOPLAYTHEGAMEWELL,PLAYERMODELING,ADAPTIVITY,MODELINTERPRETATIONANDOFCOURSEPERFORMANCETHESENEEDSCANBERECASTASACALLFORNEWPRACTICALANDTHEORETICALTOOLSTOHELPWITHLEARNINGTOPLAYTHEGAMEGAMEWORLDSPROVIDEEXCELLENTTESTBEDSFORINVESTIGATINGTHEPOTENTIALTOIMPROVEAGENTS’CAPABILITIESVIALEARNINGTHEENVIRONMENTCANBECONSTRUCTEDWITHVARYINGCHARACTERISTICS,FROMDETERMINISTICANDDISCRETEASINCLASSICALBOARDANDCARDGAMESTONONDETERMINISTICANDCONTINUOUSASINACTIONCOMPUTERGAMESLEARNINGALGORITHMSFORSUCHTASKSHAVEBEENSTUDIEDQUITETHOROUGHLYPROBABLYTHEBESTKNOWNINSTANCEOFALEARNINGGAMEPLAYINGAGENTISTHEBACKGAMMONPLAYINGPROGRAMTDGAMMONTESAURO,1995LEARNINGABOUTPLAYERSOPPONENTMODELING,PARTNERMODELING,TEAMMODELING,ANDMULTIPLETEAMMODELINGAREFASCINATING,INTERDEPENDENTANDLARGELYUNSOLVEDCHALLENGESTHATAIMATIMPROVINGPLAYBYTRYINGTODISCOVERANDEXPLOITTHEPLANS,STRENGTHS,ANDWEAKNESSESOFAPLAYER’SOPPONENTSAND/ORPARTNERSONEOFTHEGRANDCHALLENGESINTHISLINEOFWORKAREGAMESLIKEPOKER,WHEREOPPONENTMODELINGISCRUCIALTOIMPROVEOVERGAMETHEORETICALLYOPTIMALPLAYBILLINGSETAL,2002BEHAVIORCAPTUREOFPLAYERSCREATINGACONVINCINGAVATARBASEDONAPLAYER’SINGAMEBEHAVIORISANINTERESTINGANDCHALLENGINGSUPERVISEDLEARNINGTASKFOREXAMPLE,INMASSIVEMULTIPLAYERONLINEROLEPLAYINGGAMESMMORGSANAVATARTHATISTRAINEDTOSIMULATEAUSER’SGAMEPLAYINGBEHAVIORCOULDTAKEHISCREATOR’SPLACEATTIMESWHENTHEHUMANPLAYERCANNOTATTENDTOHISGAMECHARACTERFIRSTSTEPSINTHISAREAHAVEBEENMADEINCOMMERCIALVIDEOGAMESSUCHASFORZAMOTORSPORTXBOXWHERETHEPLAYERCANTRAINA“DRIVATAR”THATLEARNSTOGOAROUNDTHETRACKINTHESTYLEOFTHEPLAYERBYOBSERVINGANDLEARNINGFROMTHEDRIVINGSTYLEOFTHATPLAYERANDGENERALIZINGTONEWTRACKSANDCARSMODELSELECTIONANDSTABILITYONLINESETTINGSLEADTOWHATISEFFECTIVELYTHEUNSUPERVISEDCONSTRUCTIONOFMODELSBYSUPERVISEDALGORITHMSMETHODSFORBIASINGTHEPROPOSEDMODELSPACEWITHOUTSIGNIFICANTLOSSOFPREDICTIVEPOWERARECRITICALNOTJUSTFORLEARNINGEFFICIENCY,BUTINTERPRETIVEABILITYANDENDUSERCONFIDENCEOPTIMIZINGFORADAPTIVITYBUILDINGOPPONENTSTHATCANJUSTBARELYLOSEININTERESTINGWAYSISJUSTASIMPORTANTFORTHEGAMEWORLDASCREATINGWORLDCLASSOPPONENTSTHISREQUIRESBUILDINGHIGHLYADAPTIVEMODELSTHATCANSUBSTANTIVELYPERSONALIZETOADVERSARIESORPARTNERSWITHAWIDERANGEOFCOMPETENCEANDRAPIDSHIFTSINPLAYSTYLEBYINTRODUCINGAVERYDIFFERENTSETOFUPDATEANDOPTIMIZATIONCRITERIAFORLEARNERS,AWEALTHOFNEWRESEARCHTARGETSARECREATEDMODELINTERPRETATION“WHAT’SMYNEXTMOVE”ISNOTTHEONLYQUERYDESIREDOFMODELSINAGAME,BUTITISCERTAINLYTHEONEWHICHGETSTHEMOSTATTENTIONCREATINGTHEILLUSIONOFINTELLIGENCEREQUIRES“PAINTINGAPICTURE”OFANAGENT’STHINKINGPROCESSTHEABIL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