Artificial Intelligence for Computer Games

214 Pages·2011·2.85 MB·English

by  Vadim Bulitko

Artificial Intelligence for Computer Games

Pedro Antonio González-Calero
Marco Antonio Gómez-Martín
Editors
Artificial Intelligence
for Computer Games
ABC
Editors
PedroAntonioGonzález-Calero MarcoAntonioGómez-Martín
UniversidadComplutensedeMadrid UniversidadComplutensedeMadrid
Dpto.deIngenieríadel Dpto.deIngenieríadel
SoftwareeInteligenciaArtificial SoftwareeInteligenciaArtificial
28040,Madrid 28040,Madrid
Spain Spain
[email protected] [email protected]
ISBN978-1-4419-8187-5 e-ISBN978-1-4419-8188-2
DOI10.1007/978-1-4419-8188-2
SpringerNewYorkDordrechtHeidelbergLondon
LibraryofCongressControlNumber:2011922495
(cid:2)c SpringerScience+BusinessMedia,LLC2011
Allrightsreserved.Thisworkmaynotbetranslatedorcopiedinwholeorinpartwithoutthewritten
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Preface
Gameshasbeenaninterestingdomainforartificialintelligence(AI)researchsince
theoriginsofthedisciplinebackinthe 1950s,althoughmainlyboardgamessuch
as chess, checkers, or backgammon. Nevertheless, it is between 1999 and 2000
when a number of researches on AI identify interactive video games as an inter-
estingresearchdomainforAI(see,forexample,JohnE.Laird,MichaelvanLent:
“Human-LevelAI’sKillerApplication:InteractiveComputerGames”.AAAI/IAAI
2000: 1171–1178). The long-term dream of AI research of building intelligent
robots with human-like abilities for interacting in the real world is still far from
beingfulfilled due, amongothercauses, to the complexitiesof sensing and acting
in the real world. Video games provide synthetic worlds where complex behav-
ior can be shown but where perception and actuation are perfectly under control
and, therefore, became a perfect platform for experimentingwith software robots
(i.e.,agents).
TechniquesusedforAIincommercialvideogamesarestillfarfromstate-of-the
art in academia, but with graphics in video games coming close to photo realis-
tic quality, and multi-processor architectures getting common in console and PC
gameplatforms,sophisticatedartificial intelligenceisgetting intothe focusofthe
videogameindustryasthenextbigthingforenhancingtheplayerexperience,while
profitingfromthenumberofspareCPU cyclesavailableinmodernhardware.For
thatreason,industryisgettinginterestedinacademicresearchinAItoproviderich,
robust, and scalable techniques for controlling non-player characters and provide
richernarrativeschemesingames.
Thisbookcollectssome ofthe mostrelevantresultsfromacademiain thearea
of artificial intelligence for games. The selection of contributionshas been biased
toward rigorous and theoretically grounded work that is also supported with de-
velopedprototypes,whichshouldpavethewayfortheintegrationofacademicAI
techniquesintostate-of-the-artelectronicentertainmentgames.Thechaptersinthe
bookcoverdifferentareasrelevanttoAIincommercialgames:pathfinding,decision
making,learning,authoring,andstorytelling.
Regardingpathfinding,thebookdescribesrecentreal-timeheuristicsearchalgo-
rithms that alleviate the scalability problem of A* techniquesused in commercial
games, which at the same time exhibit a visually appealing behavior. Techniques
are also presented that, based on the semantic annotation of 3D virtual worlds,
vii
viii Preface
can learn pathfindingbehavior by analyzingtraces from actual players. Tools and
techniquesaredescribedforincorporatingsemanticinformationintothegamede-
signanddevelopmentprocess,thusimprovingtheembeddedinformationcontained
inimmersivegameworlds,andleadingtonewpossibilitiesforNPCconstructions
suchasmeaningfulin-gamelearningandagentportability.
Regarding decision making, the book describes new techniques for authoring
toolsthatfacilitatetheconstructionbygamedesigners(typicallynon-programmers)
of behavior controlling software, by reusing patterns or actual cases of past be-
havior, represented as behavior trees. Using domination games as a test bed, the
book describes different approaches for building cooperative agents, with differ-
entrequirementsforknowledgeengineering,frompurelyhand-codedto inductive
approaches.Techniquesfor automaticallyor semi-automaticallylearningcomplex
behavior from recorded traces of human players using different combinations of
reinforcementlearningandcase-basedreasoningarealsodescribed.
Much research on artificial intelligence in games has been devoted to creating
opponentsthat play competentlyagainst human players, while an alternative goal
is to try to deliver the best possible experience within the context of the game.
This novel goal is much more attainable by approaching AI reasoning for games
as “storytelling reasoning.” Several technologicalapproaches are presented in the
contextofsuchaperspective,includingtheuseofplanningtechniquesforcamera
placementandsequencingofplotpointsinagame,andconstraintoptimizationfor
automaticallyadaptinglightingqualitiesofascenetotheplayerpreferences.
KeyresultsfromappliedresearchonAIwithinthelast10yearshavebeencol-
lectedheretoprovideareferenceworkforbothacademiaandindustrythatwillhelp
toclosethegapbetweenbothworlds.
Madrid PedroAntonioGonzález-Calero
October2010 MarcoAntonioGómez-Martín
Contents
Real-TimeHeuristicSearchforPathfindinginVideoGames................. 1
Vadim Bulitko, Yngvi Björnsson, Nathan R. Sturtevant,
andRamonLawrence
Embedding Informationinto Game Worlds to Improve
InteractiveIntelligence ............................................................ 31
G.MichaelYoungblood,FrederickW.P.Heckel,D.HunterHale,
andPriyeshN.Dixit
Empowering Designers with Libraries of Self-Validated
Query-EnabledBehaviourTrees ................................................. 55
Gonzalo Flórez-Puga, David Llansó, Marco Antonio
Gómez-Martín,PedroP. Gómez-Martín,Belén Díaz-Agudo,
andPedroAntonioGonzález-Calero
GameAIforDominationGames................................................. 83
Chad Hogg, Stephen Lee-Urban, Héctor Muñoz-Avila,
BryanAuslander,andMeganSmith
Case-Based Reasoning and User-Generated Artificial
IntelligenceforReal-TimeStrategyGames.....................................103
SantiagoOntañónandAshwinRam
GameAIasStorytelling...........................................................125
MarkRiedl,DavidThue,andVadimBulitko
IntelligentMachinimaGenerationforVisualStorytelling ....................151
ArnavJhalaandR.MichaelYoung
IntelligentAdaptiveLighting.....................................................171
MagySeifEl-Nasr,JosephZupko,andChinmayRao
Index.................................................................................197
ix