Monte-Carlo Tree Search: A New Framework for Game AI 论文
2008Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment引用 305
Artificial Intelligence in GamesDigital Games and MediaReinforcement Learning in Robotics
摘要
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.