Learning personal agents with adaptive player modeling in virtual worlds

There has been growing interest in creating intelligent agents in virtual worlds that do not follow fixed scripts predefined by the developers, but react accordingly based on actions performed by human players during their interaction. In order to achieve this objective, previous approaches have att...

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Main Authors: KANG, Yilin, TAN, Ah-hwee
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Language:English
Published: Institutional Knowledge at Singapore Management University 2010
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Online Access:https://ink.library.smu.edu.sg/sis_research/6665
https://ink.library.smu.edu.sg/context/sis_research/article/7668/viewcontent/Player_Model_IAT_2010.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-76682022-01-13T09:34:40Z Learning personal agents with adaptive player modeling in virtual worlds KANG, Yilin TAN, Ah-hwee There has been growing interest in creating intelligent agents in virtual worlds that do not follow fixed scripts predefined by the developers, but react accordingly based on actions performed by human players during their interaction. In order to achieve this objective, previous approaches have attempted to model the environment and the user’s context directly. However, a critical component for enabling personalized virtual world experience is missing, namely the capability to adapt over time to the habits and eccentricity of a particular player. To address the above issue, this paper presents a cognitive agent with learning player model capability for personalized recommendations. Specifically, a self-organizing neural model, named FALCON (Fusion Architecture for Learning and Cognition), is deployed, which enables an autonomous agent to adapt and function during the players’ interaction. We have developed personal agents with adaptive player models as tour guides in a virtual world environment. Our experimental results show that we are able to learn user models that evolve and adapt with players in real time. Furthermore, the virtual tour guides with player models outperform those without adaptive player modeling in terms of recommendation accuracy. 2010-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6665 info:doi/10.1109/WI-IAT.2010.201 https://ink.library.smu.edu.sg/context/sis_research/article/7668/viewcontent/Player_Model_IAT_2010.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Player modeling Virtual world Learning agent Databases and Information Systems Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Player modeling
Virtual world
Learning agent
Databases and Information Systems
Graphics and Human Computer Interfaces
spellingShingle Player modeling
Virtual world
Learning agent
Databases and Information Systems
Graphics and Human Computer Interfaces
KANG, Yilin
TAN, Ah-hwee
Learning personal agents with adaptive player modeling in virtual worlds
description There has been growing interest in creating intelligent agents in virtual worlds that do not follow fixed scripts predefined by the developers, but react accordingly based on actions performed by human players during their interaction. In order to achieve this objective, previous approaches have attempted to model the environment and the user’s context directly. However, a critical component for enabling personalized virtual world experience is missing, namely the capability to adapt over time to the habits and eccentricity of a particular player. To address the above issue, this paper presents a cognitive agent with learning player model capability for personalized recommendations. Specifically, a self-organizing neural model, named FALCON (Fusion Architecture for Learning and Cognition), is deployed, which enables an autonomous agent to adapt and function during the players’ interaction. We have developed personal agents with adaptive player models as tour guides in a virtual world environment. Our experimental results show that we are able to learn user models that evolve and adapt with players in real time. Furthermore, the virtual tour guides with player models outperform those without adaptive player modeling in terms of recommendation accuracy.
format text
author KANG, Yilin
TAN, Ah-hwee
author_facet KANG, Yilin
TAN, Ah-hwee
author_sort KANG, Yilin
title Learning personal agents with adaptive player modeling in virtual worlds
title_short Learning personal agents with adaptive player modeling in virtual worlds
title_full Learning personal agents with adaptive player modeling in virtual worlds
title_fullStr Learning personal agents with adaptive player modeling in virtual worlds
title_full_unstemmed Learning personal agents with adaptive player modeling in virtual worlds
title_sort learning personal agents with adaptive player modeling in virtual worlds
publisher Institutional Knowledge at Singapore Management University
publishDate 2010
url https://ink.library.smu.edu.sg/sis_research/6665
https://ink.library.smu.edu.sg/context/sis_research/article/7668/viewcontent/Player_Model_IAT_2010.pdf
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