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Learning Belief Distributions for Game Moves
Learning Belief Distributions for Game Moves
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机译：学习游戏动作的信念分布
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摘要
We describe an apparatus for learning to predict moves in games such as chess, Go and the like, from historical game records. We obtain a probability distribution over legal moves in a given board configuration. This enables us to provide an automated game playing system, a training tool for players and a move selector/sorter for input to a game tree search system. We use a pattern extraction system to select patterns from historical game records. Our learning algorithm learns a distribution over the values of a move given a board position based on local pattern context. In another embodiment we use an Independent Bernoulli model whereby we assume each moved is played independently of other available moves.
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