Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/78284
Title: Personality-based adaptation for teamwork in game agents
Authors: Tan, C.T. 
Cheng, H.-L. 
Issue Date: 2007
Citation: Tan, C.T.,Cheng, H.-L. (2007). Personality-based adaptation for teamwork in game agents. Proceedings of the 3rd Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2007 : 37-42. ScholarBank@NUS Repository.
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. Copyright © 2007, Association for the Advancement of Artificial Intelligence.
Source Title: Proceedings of the 3rd Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2007
URI: http://scholarbank.nus.edu.sg/handle/10635/78284
ISBN: 9781577353256
Appears in Collections:Staff Publications

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