An automated model-based adaptive architecture in modern games

Tan, CT; Cheng, HL

An automated model-based adaptive architecture in modern games

Tan, CT Cheng, HL

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010, 2010, pp. 186 - 191
Issue Date:: 2010-12-01

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Field	Value	Language
dc.contributor.author	Tan, CT	en_US
dc.contributor.author	Cheng, HL	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Proceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010, 2010, pp. 186 - 191	en_US
dc.identifier.isbn	9781577354789	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16162
dc.description.abstract	This paper proposes an automatic model-based approach that enables adaptive decision making in modern virtual games. It builds upon the Integrated MDP and POMDP Learning AgeNT (IMPLANT) architecture (Tan and Cheng 2009) which has shown to provide plausible adaptive decision making in modern games. However, it suffers from highly timeconsuming manual model specification problems. By incorporating an automated priority sweeping based model builder for the MDP, as well as using the Tactical Agent Personality (Tan and Cheng 2007) for the POMDP, the work in this paper aims to resolve these problems. Empirical proof of concept is shown based on an implementation in a modern game scenario, whereby the enhanced IMPLANT agent is shown to exhibit superior adaptation performance over the old IMPLANT agent whilst eliminating manual model specifications and at the same time still maintaining plausible speeds. Copyright © 2010, Association for the Advancement of Artificial.	en_US
dc.relation.ispartof	Proceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2010	en_US
dc.title	An automated model-based adaptive architecture in modern games	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Stanford, USA	en_US
pubs.embargo.period	Not known	en_US
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 - HCTD - Human Centred Technology Design
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper proposes an automatic model-based approach that enables adaptive decision making in modern virtual games. It builds upon the Integrated MDP and POMDP Learning AgeNT (IMPLANT) architecture (Tan and Cheng 2009) which has shown to provide plausible adaptive decision making in modern games. However, it suffers from highly timeconsuming manual model specification problems. By incorporating an automated priority sweeping based model builder for the MDP, as well as using the Tactical Agent Personality (Tan and Cheng 2007) for the POMDP, the work in this paper aims to resolve these problems. Empirical proof of concept is shown based on an implementation in a modern game scenario, whereby the enhanced IMPLANT agent is shown to exhibit superior adaptation performance over the old IMPLANT agent whilst eliminating manual model specifications and at the same time still maintaining plausible speeds. Copyright © 2010, Association for the Advancement of Artificial.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/16162