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- W2122853437 abstract "Go is an ancient oriental game whose complexity has defeated attempts to automate it. We suggest using probability in a Bayesian sense to model the uncertainty arising from the vast complexity of the game tree. We present a simple conditional Markov random field model for predicting the pointwise territory outcome of a game. The topology of the model reflects the spatial structure of the Go board. We describe a version of the Swendsen-Wang process for sampling from the model during learning and apply loopy belief propagation for rapid inference and prediction. The model is trained on several hundred records of professional games. Our experimental results indicate that the model successfully learns to predict territory despite its simplicity." @default.
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- W2122853437 date "2004-12-01" @default.
- W2122853437 modified "2023-09-25" @default.
- W2122853437 title "Modelling Uncertainty in the Game of Go" @default.
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