Monte-Carlo Tree Search: A New Framework for Game AI

Guillaume Chaslot, Sander Bakkes, Istvan Szita, Pieter Spronck

Classic approaches to game AI require either a high quality of domain knowledge, or a long time to generate effective AI behaviour. These two characteristics hamper the goal of establishing challenging game AI. In this paper, we put forward Monte-Carlo Tree Search as a novel, unified framework to game AI. 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.


Subjects: 1.8 Game Playing; 3. Automated Reasoning

Submitted: Aug 13, 2008

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