Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2010

Abstract

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.

Keywords

Player modeling, Virtual world, Learning agent

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2010), Aug 31- Sep 3

Volume

2

First Page

173

Last Page

180

ISBN

9780769541914

Identifier

10.1109/WI-IAT.2010.201

Publisher

IEEE Computer Society

City or Country

USA

Additional URL

https://doi.org/10.1109/WI-IAT.2010.201

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