To create intelligent adaptive neuro-controller of game opponent from UCT-created data

Authors:
Yiwen Fu;Si Yang;Suoju He;Jiajian Yang;Xiao Liu;Yang Chen;Donglin Ji
Affiliations:
International School, Beijing University of Posts and Telecommunications, Beijing, China;International School, Beijing University of Posts and Telecommunications, Beijing, China;International School, Beijing University of Posts and Telecommunications, Beijing, China;International School, Beijing University of Posts and Telecommunications, Beijing, China;International School, Beijing University of Posts and Telecommunications, Beijing, China;International School, Beijing University of Posts and Telecommunications, Beijing, China;International School, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 2
Year:
2009

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Abstract

Game AI controlled by UCT which achieves excellent performance in computer go can be applied to control non-player characters (NPCs) in video games. While, it is computation intensive algorithm, so applying it to on-line game is not suitable. But data collected from NPC controlled by UCT is able to be utilized to train Neuro-Controler. Furthermore, Neuro-Controler is an efficient algorithm due to its capability of extracting knowledge from training data which is generated from UCT. In order to obtain outstanding peiformance of Neurol-Controler, training data is a key factor but the structure of Neurol-Controler is also important. In this paper, the prey and predator game genre of Dead- End is utilized as a test-bed, the basic principle of UCT and Neurol-Controller is drawn, and the effectiveness of their application to game AI development is demonstrated.