游戏要好玩需发展良好的AI.ppt

1,EvolvingReactiveNPCsfortheReal-TimeSimulationGame,IEEESymposiumonComputationalIntelligenceandGames,2,Outline,MotivationObjectiveIntroductionThegame:Buildhowever,themovementofacharacterisapttobeunrealistic.thereisatrendtowardsfuzzystatemachine(FuSM).,10,Adaptationandlearning:NNs,EAs,andArtificiallife,Theadaptationandlearningingameswillbeoneofthemostmajorissuesmakinggamesmoreinterestingandrealistic.Neuralnetwork,andevolutionaryalgorithms(e.g.geneticalgorithm)arepromisingartificialintelligencetechniquesforlearningincomputergames.NN-isbadlytrainedGE-requiredtoomanycomputationsandweretooslowtoproduceusefulresults.,11,Co-evolution,Bysimultaneouslyevolvingtwoormorespecieswithcoupledfitness.Superiorstrategiesforanenvironmenthavebeendiscoveredbyco-evolutionaryapproaches.,12,Reactivebehavior,Reactivemodelperformseffectivelysinceitconsidersthecurrentsituationonly.Neuralnetworksandbehavior-basedapproachesarerecentlyusedforthereactivebehaviorofNPCskeepingtherealityofbehaviors.,13,Thegame:Build&Build,Build&Builddevelopedinthisresearchisareal-timestrategicsimulationgame,inwhichtwonationsexpandtheirownterritory.Eachnationhassoldierswhoindividuallybuildtownsandfightagainsttheenemies,whileatowncontinuallyproducessoldiersforagivenperiod.,14,Thegame:Build&Build,15,Designingthegameenvironment,Thegamestartstwocompetitiveunitsinarestrictedlandwithaninitialfund.Theunitsareabletotakesomeactionsatthenormallandbutnotattherockland.Aunitcanbuildatownwhenthenationhasenoughmoney,whiletownsproduceunitsusingsomemoney.,16,Designingthegameenvironment(cont.),17,DesigningNPCs,NPCcanmoveby4directionsaswellasbuildtowns,attackunitsortowns,andmergewithotherNPCs.TheattackactionsareautomaticallyexecutedwhenanopponentlocatesbesidetheNPC.,18,DesigningNPCs(cont.),19,DesigningNPCs(cont.),20,Basicbehaviormodel(cont.),Twodifferentgridscalesareusedfortheinputoftheneuralnetworksuchas55and1111.,21,Basicbehaviormodel(cont.),Inordertoactivelyseekadynamicsituation,themodelselectsarandomactionwithaprobability(inthispaper,a=0.2)inadvance.,fiveneuralnetworksareusedtodecidewhethertheassociatingactionexecutesornot.,22,Co-evolutionarybehaviorgeneration,Weusethegeneticalgorithmtogeneratebehaviorsystemsthatareaccommodatedtoseveralenvironments.Twopair-wisecompetitionpatternsareadoptedtoeffectivelycalculatethefitnessofanindividual.,23,Co-evolutionarybehaviorgeneration(cont.),ThefitnessofanindividualismeasuredbythescoresagainstrandomlyselectedMopponents.,24,ExperimentandResults,Fourdifferentbattlemaps=demonstratetheproposedmethodingeneratingstrategiesadaptivetoeachenvironment.,25,ExperimentandResults(cont.),Thecasewith1111showsmorediversebehaviorsthanthatwith55,sinceitobservesinformationonamorelargearea.55obtainslowerwinningaveragesforcomplexenvironment,whileitperformsbetterwhentheenvironmentisrathersimple.,26,ExperimentandResults(cont.),Fig.8.Winningratebetween55behaviorand1111behaviorateachgenerationonmaptype3.,The1111showsthebetterperformancethanthe55,sinceitconsidersmorevariousinputconditionssoastogeneratediverseactions.,27,ExperimentandResults(cont.),Fortheplainmap,55behaviorsystemshowsasimplestrategythattriestobuildatownasmuchaspossible.BuildingatownleadstogeneratemanyNPCssoastoslowlyencroachonthebattlemapasshownsinFig.9.,28,Discussion,Thereactivesystemshowsgoodperformanceonsimpleenvironmentsliketheplainmap,butitdoesnotworkwellforcomplexenvironments.Also,theamountofinputinformationisimportantforthereactivesystemwhentheenvironmentisnotsimple.,29,Conclusion,AreactivebehaviorsystemwaspresentedfortheflexibleandreactivebehavioroftheNPC.Co-evolutionaryapproacheshaveshownthepotentialitiesoftheautomaticgenerationofexcellentstrategiesco