Evolving Multi-Layer Neural Networks for Othello

被引:1
作者
Makris, Vassilis [1 ]
Kalles, Dimitris [1 ]
机构
[1] Hellen Open Univ, Sch Sci & Technol, GR-26335 Patras, Greece
来源
9TH HELLENIC CONFERENCE ON ARTIFICIAL INTELLIGENCE (SETN 2016) | 2016年
关键词
Artificial Neural Networks; evolution strategies; board games; Othello; PROGRAM; EVOLUTION; CHECKERS; GAME; PLAY;
D O I
10.1145/2903220.2903231
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Othello has long been a favorite AI subject due to its very simple rules, its very low branching factor, its well defined strategic concepts and its dramatic changes in board topology as a game unfolds. In this paper, we investigate several neural network architectures using co-evolutionary learning techniques, with the objective to learn to play Othello strongly. Our resulting neural networks were able to learn to play the game at an expert-master level and to discover advanced strategies, within a few thousand generations, without any prior knowledge, beyond the game rules.
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页数:6
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