Efficient sampling method for monte carlo tree search problem

Kazuki Teraoka, Kohei Hatano, Eiji Takimoto

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)392-398
Number of pages7
JournalIEICE Transactions on Information and Systems
Issue number3
Publication statusPublished - 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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