Personality-based Adaptation for Teamwork in Game Agents

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dc.contributor.author Tan, C
dc.contributor.author Cheng, H
dc.contributor.editor Schaeffer, J
dc.contributor.editor Mateas, M
dc.date.accessioned 2012-02-02T11:07:40Z
dc.date.issued 2007-01
dc.identifier.citation Proceedings of The Artificial Intelligence and Interactive Digital Entertainment Conference, 2007, pp. 37 - 42
dc.identifier.other E1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/16210
dc.description.abstract This paper presents a novel learning framework to provide computer game agents the ability to adapt to the player as well as other game agents. Our technique generally involves a personality adaptation module encapsulated in a reinforcement learning framework. Unlike previous work in which adaptation normally involves a decision process on every single action the agent takes, we introduce a two-level process whereby adaptation only takes place on an abstracted actions set which we coin as agent personality. With the personality defined, each agent will then take actions according to the restrictions imposed in its personality. In doing so, adaptation takes place in appropriately defined intervals in the game, without disrupting or slowing down the game constantly with intensive decision-making computations, hence improving enjoyment for the player. Moreover, by decoupling adaptation from action selection, we have a modular adaptive system that can be used with existing action planning methods. With an actual typical game scenario that we have created, it is shown that a team of agents using our framework to adapt towards the player are able to perform better than a team with scripted behavior. Consequently, we also show the team performs even better when adapted towards each other
dc.publisher AAI
dc.title Personality-based Adaptation for Teamwork in Game Agents
dc.type Conference Proceeding
dc.description.version Published
dc.parent Proceedings of The Artificial Intelligence and Interactive Digital Entertainment Conference
dc.journal.number en_US
dc.publocation USA en_US
dc.publocation USA
dc.publocation USA
dc.publocation USA
dc.publocation USA
dc.identifier.startpage 37 en_US
dc.identifier.endpage 42 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference Artificial Intelligence and Interactive Digital Entertainment Conference
dc.conference Artificial Intelligence and Interactive Digital Entertainment Conference
dc.conference Artificial Intelligence and Interactive Digital Entertainment Conference
dc.conference Artificial Intelligence and Interactive Digital Entertainment Conference
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 111813
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom Artificial Intelligence and Interactive Digital Entertainment Conference en_US
dc.date.activity 20070606 en_US
dc.date.activity 2007-06-06
dc.date.activity 2007-06-06
dc.date.activity 2007-06-06
dc.date.activity 2007-06-06
dc.location.activity Stanford, USA en_US
dc.location.activity Stanford, USA
dc.location.activity Stanford, USA
dc.location.activity Stanford, USA
dc.location.activity Stanford, USA
dc.description.keywords NA en_US
dc.description.keywords Social Sciences
dc.description.keywords Science & Technology
dc.description.keywords Physical Sciences
dc.description.keywords Economics
dc.description.keywords Mathematics, Interdisciplinary Applications
dc.description.keywords Social Sciences, Mathematical Methods
dc.description.keywords Business & Economics
dc.description.keywords Mathematics
dc.description.keywords Mathematical Methods In Social Sciences
dc.description.keywords ECONOMICS
dc.description.keywords MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
dc.description.keywords SOCIAL SCIENCES, MATHEMATICAL METHODS
dc.description.keywords Forecasting
dc.description.keywords Log scoring
dc.description.keywords Model combination
dc.description.keywords S&P 500 returns
dc.description.keywords PROBABILISTIC FORECASTS
dc.description.keywords INTERVAL FORECASTS
dc.description.keywords COMBINING DENSITY
dc.description.keywords SCORING RULES
dc.description.keywords MODELS
dc.description.keywords NA
dc.description.keywords NA
dc.description.keywords NA
dc.description.keywords NA
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group /University of Technology Sydney/Strength - Human Centred Technology Design
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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