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