DocumentCode :
2669202
Title :
A genetic search method for multi-player game playing
Author :
Hong, Tzung-Pei ; Huang, Ke-Yuan ; Lin, Wen-Yang
Author_Institution :
Dept. of Inf. Manage., I-Shou Univ., Kaohsiung, Taiwan
Volume :
5
fYear :
2000
fDate :
2000
Firstpage :
3858
Abstract :
It is well known that when exploring a game tree, the deeper the depth, the more accurate the move prediction but greater temporal and spatial expansion is required. How to explore the game tree deeper is a great challenge in such research. In T.P. Hong et al (Int. Conf. on Evolutionary Computation, Anchorage, Alaska, USA, p.690-4, 1998), we proposed a genetic algorithm-based search method for two-player games. In this paper, we generalize that method to solve multi-player game-search problems. We propose a genetic algorithm-based approach that can find good next moves in multi-player games without the requirement for great temporal and spatial expansion
Keywords :
game theory; games of skill; genetic algorithms; search problems; evolutionary computation; game tree; game-search problems; genetic algorithm; genetic search method; good next moves; multiplayer game playing; Artificial intelligence; Decision trees; Genetics; Humans; Information management; Machine learning; Machine learning algorithms; Minimax techniques; Performance evaluation; Search methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 2000 IEEE International Conference on
Conference_Location :
Nashville, TN
ISSN :
1062-922X
Print_ISBN :
0-7803-6583-6
Type :
conf
DOI :
10.1109/ICSMC.2000.886612
Filename :
886612
Link To Document :
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