Monte-carlo tree search: A New Framework for Game AI

Guillaume Chaslot*, Sander Bakkes, Istvan Szitai, Pieter Spronck

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review


In this paper, we put forward Monte-Carlo Tree Search as a novel, unified framework to game AI, which doesn't require an evaluation function. In the framework, randomized explorations of the search space are used to predict the most promising game actions. We will demonstrate that Monte-Carlo Tree Search can be applied effectively to (1) classic board-games, (2) modern board-games, and (3) video games.

Original languageEnglish
Pages (from-to)389-390
Number of pages2
JournalBelgian/Netherlands Artificial Intelligence Conference
Publication statusPublished - 2008
Event20th Belgian-Dutch Conference on Artificial Intelligence, BNAIC 2008 - Enschede, Netherlands
Duration: 30 Oct 200831 Oct 2008


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