Browsing by Author "Steinmetz, Erik"

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    Computer Go and Monte Carlo Tree Search: Opening Book and Parallel Solutions
    (2016-05) Steinmetz, Erik
    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.
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    Improving Play of Monte-Carlo Engines in the Game of Go
    (2014-08-12) Steinmetz, Erik; Gini, Maria
    We explore the effects of using a system similar to an opening book to improve the capabilities of computer Go software based on Monte Carlo Tree Search methods. This system operates by matching the board against clusters of board configurations from games played by experts. It does not require an exact match of the current board to be present in the expert games. Experimentation included results from over 120,000 games in tournaments using the open source Go engines Fuego, Orego, Pachi, and Gnugo. The parameters of operating our matching system were explored in over thirty different combinations to find the best results. We find that this system through its filtering or biasing the choice of a next move to a small subset of possible moves can improve play even though this can only be applied effectively to the initial moves of a game.