Monte-carlo tree search: A new framework for game ai

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.