DocumentCode :
2662625
Title :
To Create Adaptive Game Opponent by Using UCT
Author :
He, Suoju ; Xie, Fan ; Wang, Yi ; Luo, Sai ; Fu, Yiwen ; Yang, Jiajian ; Liu, Zhiqing ; Zhu, Qiliang
Author_Institution :
Sch. of Software Eng., Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2008
fDate :
10-12 Dec. 2008
Firstpage :
67
Lastpage :
70
Abstract :
Adaptive game AI improves adaptability of opponent AI as well as the challenge level of the gameplay; as a result the entertainment of game is augmented. Opponent game AI is usually implemented by scripted rules in video games, but the most updated algorithm of UCT (upper confidence bound for trees) which perform excellent in computer go can also be used to achieve excellent result to control non-player characters (NPCs) in video games. In this paper, the prey and predator game genre of Dead End is used as a test-bed, the basic principle of UCT is presented, and the effectiveness of its application to game AI development is demonstrated. The experiment compares the performance of different NPCs control approaches: given a 300 milliseconds for each simulation step, the approach of UCT with recognized playerpsilas strategy pattern is better than the one of UCT without recognized playerpsilas strategy pattern, the worst one is Monte-Carlo approach.
Keywords :
adaptive systems; artificial intelligence; computer games; trees (mathematics); Dead End; adaptive game AI; adaptive game opponent; nonplayer characters; opponent game AI; prey and predator game genre; upper confidence bound for trees; video games; Application software; Artificial intelligence; Discrete event simulation; Dogs; Games; Helium; Machine learning algorithms; Pattern recognition; Software engineering; Testing; Adaptive Game AI; Dead End; UCT;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence for Modelling Control & Automation, 2008 International Conference on
Conference_Location :
Vienna
Print_ISBN :
978-0-7695-3514-2
Type :
conf
DOI :
10.1109/CIMCA.2008.81
Filename :
5172601
Link To Document :
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