Efficient sampling method for monte carlo tree search problem

Kazuki Teraoka, Kohei Hatano, Eiji Takimoto

研究成果: Contribution to journalArticle

5 引用 (Scopus)

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We consider Monte Carlo tree search problem, a variant of Min-Max tree search problem where the score of each leaf is the expectation of some Bernoulli variables and not explicitly given but can be estimated through (random) playouts. The goal of this problem is, given a game tree and an oracle that returns an outcome of a playout, to find a child node of the root which attains an approximate min-max score. This problem arises in two player games such as computer Go. We propose a simple and efficient algorithm for Monte Carlo tree search problem.

元の言語英語
ページ(範囲)392-398
ページ数7
ジャーナルIEICE Transactions on Information and Systems
E97-D
発行部数3
DOI
出版物ステータス出版済み - 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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