Monte-Carlo Tree Search (MCTS) was recently proposed [1, 2, 3] for decision taking in discrete time control problems. It was applied very efficiently to games [4, 5, 6, 7, 8] but also to planning problems and fundamental artificial intelligence tasks [9, 10]. It clearly outperformed alpha-beta techniques when there was no human expertise easy to encode in a value function. In this section, we will describe MCTS and how it allowed great improvements for computer Go. Section II shows the strengths and limitations of MCTS, and in particular, the lack of learning. There are, however, a few known techniques for introducing learning: Rapid-Action Value Estimate (RAVE) and learnt patterns (both well-known now, and discussed below); our focus is on more recent and less widely-known learning techniques introduced in MCTS. The next two sections will show these less standard applications of supervised learning within MCTS: Section III will show how to use past games for improving future games, and section IV will show the inclusion of learning inside a given MCTS run. Section V will be the conclusion.
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