Sarkozy, Gabor N
Selkow, Stanley M.
This paper implements and analyzes four algorithms for improving computer play of the board game Go. These algorithms use machine pattern learning to find better Monte-Carlo simulation policies for use with Monte-Carlo Tree Search. Two of these algorithms maximize individual move strength, and two minimize overall simulation error. These algorithms are tested using UCT on 9x9 Go with 3x3 patterns.
Worcester Polytechnic Institute
Major Qualifying Project
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