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MachLearn200663211–215DOI10.1007/s109940068919xGUESTEDITORIALMachinelearningandgamesMichaelBowlingJohannesF¨urnkranzThoreGraepelRonMusickPublishedonline10May2006SpringerScienceBusinessMedia,LLC2006ThehistoryoftheinteractionofmachinelearningandcomputergameplayinggoesbacktotheearliestdaysofArtificialIntelligence,whenArthurSamuelworkedonhisfamouscheckerplayingprogram,pioneeringmanymachinelearningandgameplayingtechniquesSamuel,1959,1967.Sincethen,bothfieldshaveadvancedconsiderably,andresearchintheintersectionofthetwocanbefoundregularlyinconferencesintheirrespectivefieldsandingeneralAIconferences.ForsurveysofthefieldwerefertoGinsberg1998,Schaeffer2000,F¨urnkranz2001editedvolumeshavebeencompiledbySchaefferandvandenHerik2002andbyF¨urnkranzandKubat2001.Inrecentyears,thecomputergamesindustryhasdiscoveredAIasanecessaryingredienttomakegamesmoreentertainingandchallengingand,viceversa,AIhasdiscoveredcomputergamesasaninterestingandrewardingapplicationarea.TheindustrysperspectiveiswitnessedbyaplethoraofrecentbooksongentleintroductionstoAItechniquesforgameprogrammersCollins,2002Champanard,2003BourgSeemann,2004Schwab,2004oraseriesofeditedcollectionsofarticlesRabin,2002,2003,2006.AIresearchoncomputergamesbegantofollowdevelopmentsinthegamesindustryearlyon,butsinceJohnLairdskeynoteaddressattheAAAI2000conference,inwhichheadvocatedInteractiveComputerGamesasachallengingandrewardingapplicationareaforAILairdvanLent,2001,numerousworkshopsFuOrkin,2004Ahaetal.,2005,conferences,andspecialissuesofjournalsForbusLaird,2002demonstratethegrowingimportanceofgameplayingapplicationsforArtificialIntelligence.M.Bowlingenvelopebackemailbowlingcs.ualberta.caJ.F¨urnkranzemailfuernkranzinformatik.tudarmstadt.deT.Graepelemailthoregmicrosoft.comR.Musickemailmusickikuni.comSpringer212MachLearn200663211–215Games,whethercreatedforentertainment,simulation,oreducation,providegreatopportunitiesformachinelearning.ThevarietyofpossiblevirtualworldsandthesubsequentMLrelevantproblemsposedfortheagentsinthoseworldsislimitedonlybytheimagination.Furthermore,notonlyisthegamesindustrylargeandgrowinghavingsurpassedthemovieindustryinrevenueafewyearsback,butitisfacedwithatremendousdemandfornoveltythatitstrugglestoprovide.Againstthisbackdrop,machinelearningdrivensuccesseswoulddrawhighprofileattentiontothefield.Surprisinglyhowever,themorecommercialthegametodate,thelessimpactlearninghasmade.ThisisquiteunlikeothergreatmatchesbetweenapplicationanddatadrivenanalyticssuchasdataminingandOLAP.Topicsofparticularimportanceforsuccessfulgameapplicationsincludelearninghowtoplaythegamewell,playermodeling,adaptivity,modelinterpretationandofcourseperformance.TheseneedscanberecastasacallfornewpracticalandtheoreticaltoolstohelpwithlearningtoplaythegameGameworldsprovideexcellenttestbedsforinvestigatingthepotentialtoimproveagentscapabilitiesvialearning.Theenvironmentcanbeconstructedwithvaryingcharacteristics,fromdeterministicanddiscreteasinclassicalboardandcardgamestonondeterministicandcontinuousasinactioncomputergames.Learningalgorithmsforsuchtaskshavebeenstudiedquitethoroughly.ProbablythebestknowninstanceofalearninggameplayingagentistheBackgammonplayingprogramTDGammonTesauro,1995.learningaboutplayersOpponentmodeling,partnermodeling,teammodeling,andmultipleteammodelingarefascinating,interdependentandlargelyunsolvedchallengesthataimatimprovingplaybytryingtodiscoverandexploittheplans,strengths,andweaknessesofaplayersopponentsand/orpartners.OneofthegrandchallengesinthislineofworkaregameslikePoker,whereopponentmodelingiscrucialtoimproveovergametheoreticallyoptimalplayBillingsetal.,2002.behaviorcaptureofplayersCreatingaconvincingavatarbasedonaplayersingamebehaviorisaninterestingandchallengingsupervisedlearningtask.Forexample,inMassiveMultiplayerOnlineRoleplayingGamesMMORGsanavatarthatistrainedtosimulateausersgameplayingbehaviorcouldtakehiscreatorsplaceattimeswhenthehumanplayercannotattendtohisgamecharacter.FirststepsinthisareahavebeenmadeincommercialvideogamessuchasForzaMotorsportXboxwheretheplayercantrainaDrivatarthatlearnstogoaroundthetrackinthestyleoftheplayerbyobservingandlearningfromthedrivingstyleofthatplayerandgeneralizingtonewtracksandcars.modelselectionandstabilityOnlinesettingsleadtowhatiseffectivelytheunsupervisedconstructionofmodelsbysupervisedalgorithms.Methodsforbiasingtheproposedmodelspacewithoutsignificantlossofpredictivepowerarecriticalnotjustforlearningefficiency,butinterpretiveabilityandenduserconfidence.optimizingforadaptivityBuildingopponentsthatcanjustbarelyloseininterestingwaysisjustasimportantforthegameworldascreatingworldclassopponents.Thisrequiresbuildinghighlyadaptivemodelsthatcansubstantivelypersonalizetoadversariesorpartnerswithawiderangeofcompetenceandrapidshiftsinplaystyle.Byintroducingaverydifferentsetofupdateandoptimizationcriteriaforlearners,awealthofnewresearchtargetsarecreated.modelinterpretationWhatsmynextmoveisnottheonlyquerydesiredofmodelsinagame,butitiscertainlytheonewhichgetsthemostattention.Creatingtheillusionofintelligencerequirespaintingapictureofanagentsthinkingprocess.TheabilitytodescribethecurrentstateofamodelandtheprocessofinferenceinthatmodelfromSpringerMachLearn200663211–215213decisiontodecisionenablesqueriesthatprovidethefoundationforahostofsocialactionsinagamesuchaspredictions,contracts,counterfactualassertions,advice,justification,negotiation,anddemagoguery.Thesecanhaveasmuchormoreinfluenceonoutcomesasactualingameactions.performanceResourcerequirementsforupdateandinferencewillalwaysbeofgreatimportance.TheAIdoesnotgetthebulkoftheCPUormemory,andthemachinesdrivingthemarketwillalwaysbeunderpoweredcomparedtotypicaldesktopsatanypointintime.Thisspecialissuecontainsthreearticlesandoneresearchnotethatspanthewiderangeofresearchintheintersectionofgameplayingandmachinelearning.Inthefirstcontribution,AdaptiveGameAIwithDynamicScripting,Sproncketal.tackletheproblemofadaptivitybydynamicallymodifyingtheruleswhichgoverncharacterbehavioringame.Thispaperistargetedatthecommercialgamesindustry,andprovidessomegoodinsightintoproblemsfacedbythecreatorsoftodaysroleplayinggames.Theauthorsproposefourfunctionalandfourcomputationalrequirementsforonlinelearningingames.Theythenproceedtoshowhowdynamicscriptingfitsintothoserequirements,andprovideexperimentalevidenceofthepotentialpromiseofthisapproach.Dynamicscriptingcanbecharacterizedasstochasticoptimization.Theauthorsevaluatedynamicscriptingonboththetaskofprovidingthetoughestopponentpossible,andonthetaskofdifficultyscaling.Gooddifficultyscalingunderpinswhatmakesmostgamesfun,andsolvingthisproblemisoftenverychallengingandthesolutionsarealmostalwaysadhoc.TheauthorspresentexperimentaldatathatcomparesdynamicscriptingtostaticopponentsandthosecontrolledbyQLearningandMonteCarlo.ThetestenvironmentsincludebothsimulatedgamesandanactualcommercialgameNeverwinterNights,andhelptopresentaveryinterestingstudywhichissuretoblazeapathforfurtherinterestingresearch.Thesecondpaper,UniversalParameterOptimizationinGamesBasedonSPSAbySzepesvariandKocsis,considerstheproblemofoptimizingparameterstoimprovetheperformanceofparameterizedpoliciesforgameplay.TheyconsidertheSimultaneousPerturbationStochasticApproximationSPSAmethodintroducedbySpall1992whichisageneralgradientfreeoptimizationmethodthatisapplicabletoawiderangeofoptimizationproblems.TheauthorsdemonstratethatSPSAisapplicabletoawiderangeoftypicaloptimizationproblemsingamesandproposeseveralmethodstoenhancetheperformanceofSPSA.Theseenhancementsincludetheuseofcommonrandomnumbersandantitheticvariables,acombinationwithRPROPandthereuseofsamples.TheapplicationtogamesconsidersthedomainoflearningtoplayOmahaHiLoPokerwiththeirpokerprogramMcRaise.SPSAcombinedwiththeirproposedenhancementsleadstopokerperformancecompetitivewithTDlearning,themethodsosuccessfullyusedbyTesauro1995,forlearningaworldclassevaluationfunctionforBackgammonandstillusedintodaysworldclassbackgammonprogramssuchasJellyFishandSnowie.Thethirdcontribution,LearningtoBidinBridgebyMarkovitchandAmit,addressestheproblemofbiddinginthegameofBridge.WhileresearchinBridgeplayinghaspioneeredMonteCarlosearchalgorithmsfortheplayingphaseofcardgamesandresultedinprogramsofconsiderablestrengthGinsberg,1999,thebiddingphase,inwhichthegoalthesocalledcontractofthesubsequentplayingphaseisdetermined,isstillamajorweaknessofexistingBridgeprograms.ThispaperisaboutanapproachthatsupportsthedifficultbiddingphaseinthegameBridgewithtechniquesfrommachinelearning,inparticularopponentmodelingviathelearningofdecisionnetsandviamodelbasedMonteCarlosamplingtoaddresstheproblemofhiddeninformation.Theevaluationclearlyestablishesthatthesystemimproveswithlearning,anditseemsthatthelevelofplayachievedbythisprogramsurpassesthelevelSpringer214MachLearn200663211–215ofthebiddingmoduleofcurrentstateoftheartprogramsandapproachesthatofanexpertplayer.Finally,SadikovandBratkopresentaresearchnoteonLearningLongtermChessStrategiesfromDatabases.Theyaddresstheproblemofknowledgediscoveryingamedatabases.Formanygamesorsubgamessuchaschessendgames,therearegamedatabasesavailable,whichcontainperfectinformationaboutthegameinthesensethatforeverypossibleposition,thegametheoreticoutcomeisstoredinadatabase.However,althoughthesedatabasescontainallinformationtoallowperfectplay,theyarenotamenabletohumananalysis,andaretypicallynotverywellunderstood.Forexample,chessGrandmasterJohnNunnanalyzedseveralsimplechessendgamedatabasesresultinginaseriesofwidelyacknowledgedendgamebooksNunn,1992,1994b,1995,butreadilyadmittedthathedoesnotyetunderstandallaspectsofthedatabasesheanalyzedNunn,1994a.Thispaperreportsonanattempttomakeheadwaybyautomaticallyconstructingplayingstrategiesfromchessendgamedatabases.Itdescribesamethodforbreakinguptheproblemintodifferentgamephases.Foreachphase,itisthenproposedtolearnaseparateevaluationfunctionvialinearregression.Experimentsinthethekingandrookvs.king,orkingandqueenvs.kingandrookendgamesshowencouragingresults,butalsoillustratethedifficultyoftheproblem.MachinelearninghasbeeninstrumentaltodateinbuildingsomeoftheworldsbestplayersinBackgammonandhasleadtointerestingresultsingameslikeChessandGo.Tomoveintomainstreamcommercialgames,machinelearningresearchhastofacewhatinmanywaysaretheharderproblemsoflosingininterestingways,creatingmoreusefulillusionsofintelligence,hyperfastadaptation,andtakingonpersona.Thearticlesinthisspecialissueprovideaglimpseintodifferentfacetsofalloftheseproblems.ReferencesAha,D.W.,Mu˜nozAvilaH.M.,vanLent,M.Eds.,2005.Reasoning,representation,andlearningincomputergamesProceedingsoftheIJCAIworkshop.Edinburgh,ScotlandNavalResearchLaboratory,NavyCenterforAppliedResearchinArtificialIntelligence.TechnicalReportAIC05127.Billings,D.,Pe˜na,L.,Schaeffer,J.,Szafron,D.2002.Thechallengeofpoker.ArtificialIntelligence,1341–2,201–240,SpecialIssueonGames,ComputersandArtificialIntelligence.Bourg,D.M.,SeemannG.2004.AIforgamedevelopersCreatingintelligentbehavioringames.OReilly.Champanard,A.2003.AIgamedevelopment.NewRidersPublishing.Collins,M.2002.AdvancedAIgamedevelopment.WordwarePublishingInc.Forbus,K.D.,LairdJ.E.2002.GuesteditorsintroductionAIandtheentertainmentindustry.IEEEIntelligentSystems,174,15–16.Fu,D.OrkinJ.Eds.,2004.ChallengesofgameAIProceedingsoftheAAAI04workshop.AAAIPress.TechnicalReportWS0404.F¨urnkranz,J.2001.MachinelearningingamesAsurvey.InF¨urnkranzandKubat,2001,pp.11–59.F¨urnkranz,J.,KubatM.Eds.,2001.Machinesthatlearntoplaygames.Huntington,NYNovaSciencePublishers.Ginsberg,M.L.1998.Computers,gamesandtherealworld.ScientificAmericanPresents94.SpecialIssueonExploringIntelligence.Ginsberg,M.L.1999.GIBStepstowardanexpertlevelbridgeplayingprogram.InProceedingsoftheInternationalJointConferenceonArtificialIntelligenceIJCAI99,pp.584–589,Stockholm,Sweden.Laird,J.E.,vanLent,M.2001.HumanlevelAIskillerapplicationInteractivecomputergames.AIMagazine,222,15–26.Nunn,J.1992.Secretsofrookendings.Batsford.Nunn,J.1994a.Extractinginformationfromendgamedatabases.InH.J.vandenHerik,I.S.Herschberg,andJ.W.H.M.UiterwijkEds.,Advancesincomputerchess7,pp.19–34.Maastricht,TheNetherlandsUniversityofLimburg.Nunn,J.1994b.Secretsofpawnlessendings.Batsford.Springer
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