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IEICE Transactions on Information and Systems 2006 E89-D(3):1249-1256; doi:10.1093/ietisy/e89-d.3.1249
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Copyright © 2006 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Biocybernetics, Neurocomputing

Exploiting Intelligence in Fighting Action Games Using Neural Networks

Byeong Heon CHO1,3, Sung Hoon JUNG2, Yeong Rak SEONG1 and Ha Ryoung OH1

1 The authors are with the School of Electrical Engineering, Kookmin University, Seoul 136–702, Korea. E-mail: bhcho{at}etri.re.kr, E-mail: yeong{at}kookmin.ac.kr, E-mail: hroh{at}kookmin.ac.kr., 2 The author is with the Department of Information and Communication Engineering, Hansung University, Seoul 136–792, Korea. E-mail: shjung{at}hansung.ac.kr, 3 Presently, with Electronics and Telecommunications Research Institute, Daejeon 305–700, Korea.

This paper proposes novel methods to provide intelligence for characters in fighting action games by using neural networks. First, how a character learns basic game rules and matches against randomly acting opponents is considered. Since each action takes more than one time unit in general fighting action games, the results of a character's action are exposed not immediately but several time units later. We evaluate the fitness of a decision by using the relative score change caused by the decision. Whenever the scores of fighting characters are changed, the decision causing the score change is identified, and then the neural network is trained by using the score difference and the previous input and output values which induced the decision. Second, how to cope more properly with opponents that act with predefined action patterns is addressed. The opponents' past actions are utilized to find out the optimal counter-actions for the patterns. Lastly, a method in order to learn moving actions is proposed. To evaluate the performance of the proposed algorithm, we implement a simple fighting action game. Then the proposed intelligent character (IC) fights with the opponent characters (OCs) which act randomly or with predefined action patterns. The results show that the IC understands the game rules and finds out the optimal counter-actions for the opponents' action patterns by itself.

Key Words: computer game, artificial intelligence, neural network, reinforcement learning, adaptive system

Manuscript received January 11, 2005. Manuscript revised July 18, 2005.


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