AAAI Publications, Twenty-Fourth International Joint Conference on Artificial Intelligence

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Mining Expert Play to Guide Monte Carlo Search in the Opening Moves of Go
Erik S. Steinmetz, Maria Gini

Last modified: 2015-06-23

Abstract


We propose a method to guide a Monte Carlo 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.

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