Application of Monte-Carlo Tree Search in a Fighting Game AI

被引：0
|
作者：
Yoshida, Shubu [1 ]
Ishihara, Makoto [2 ]
Miyazaki, Taichi [1 ]
Nakagawa, Yuto [2 ]
Harada, Tomohiro [1 ]
Thawonmas, Ruck [1 ]
机构：
[1] Ritsumeikan Univ, Coll Informat Sci & Engn, Intelligent Comp Entertainment Lab, Kyoto, Shiga, Japan
[2] Ritsumeikan Univ, Grad Sch Informat Sci & Engn, Kyoto, Shiga, Japan
来源：
2016 IEEE 5TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS | 2016年
关键词：
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper describes an application of Monte-Carlo Tree Search (MCTS) in a lighting game AI. MCTS is a best first search technique that uses stochastic simulations. In this paper, we evaluate its effectiveness on FightingICE, a game AI competition platform at Computational Intelligence and Games Conferences. Our results confirm that MCTS is an effective search for controlling a game AI in the aforementioned platform.
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页数：2
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