Personality-based Adaptation for Teamwork in Game Agents

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Show simple item record Tan, Chek Tien en_US Cheng, Ho-Lun en_US
dc.contributor.editor Jonathan Schaeffer and Michael Mateas en_US 2012-02-02T11:07:40Z 2012-02-02T11:07:40Z 2007 en_US
dc.identifier 2010006407 en_US
dc.identifier.citation Tan Chek Tien and Cheng Ho-Lun 2007, 'Personality-based Adaptation for Teamwork in Game Agents', , AAI, USA, , pp. 37-42. en_US
dc.identifier.issn - en_US
dc.identifier.other E1UNSUBMIT en_US
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 en_US
dc.language en_US
dc.publisher AAI en_US
dc.relation.isbasedon NA en_US
dc.title Personality-based Adaptation for Teamwork in Game Agents en_US
dc.parent Proceedings of The Artificial Intelligence and Interactive Digital Entertainment Conference en_US
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 37 en_US
dc.identifier.endpage 42 en_US FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 111813 en_US
dc.personcode 0000071587 en_US
dc.percentage 100 en_US 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 20070606 en_US
dc.location.activity Stanford, USA en_US
dc.description.keywords NA en_US

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