This work examines two aspects of Monte Carlo Tree Search (MCTS), a recent invention in the field of artificial intelligence. We propose a method to guide a Monte Carlo Tree Search in the initial moves of the game of Go. Our method matches the current state of a Go board against clusters of board configurations that are derived from a large number of games played by experts. The main advantage of this method is that it does not require an exact match of the current board, and hence is effective for a longer sequence of moves compared to traditional opening books. We apply this method to two different open-source Go-playing programs. Our experiments show that this method, through its filtering or biasing the choice of a next move to a small subset of possible moves, improves play effectively in the initial moves of a game. We also conduct a study of the effectiveness of various kinds of parallelization of MCTS, and add our own parallel MCTS variant. This variant introduces the notion of using multiple algorithms in the root version of parallelization. The study is conducted across two different domains: Go and Hex. Our study uses a consistent measure of performance gains in terms of winning rates against a fixed opponent and uses enough trials to provide statistically significant results.
University of Minnesota Ph.D. dissertation. May 2016. Major: Computer Science. Advisor: Maria Gini. 1 computer file (PDF); ix, 97 pages.
Computer Go and Monte Carlo Tree Search: Opening Book and Parallel Solutions.
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