Investigating the Limits of Monte-Carlo Tree Search Methods in Computer Go

被引:2
|
作者
Huang, Shih-Chieh [2 ]
Mueller, Martin [1 ]
机构
[1] Univ Alberta, Dept Comp Sci, Edmonton, AB, Canada
[2] DeepMind Technol, London, England
来源
COMPUTERS AND GAMES, CG 2013 | 2014年 / 8427卷
基金
加拿大自然科学与工程研究理事会;
关键词
D O I
10.1007/978-3-319-09165-5_4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Monte-Carlo Tree Search methods have led to huge progress in computer Go. Still, program performance is uneven - most current Go programs are much stronger in some aspects of the game, such as local fighting and positional evaluation, than in other aspects. Well known weaknesses of many programs include (1) the handling of several simultaneous fights, including the two safe groups problem, and (2) dealing with coexistence in seki. After a brief review of MCTS techniques, three research questions regarding the behavior of MCTS-based Go programs in specific types of Go situations are formulated. Then, an extensive empirical study of ten leading Go programs investigates their performance in two specifically designed test sets containing two safe group and seki situations. The results give a good indication of the state of the art in computer Go as of 2012/2013. They show that while a few of the very top programs can apparently solve most of these evaluation problems in their playouts already, these problems are difficult to solve by global search.
引用
收藏
页码:39 / +
页数:2
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