Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Computer Go: an AI oriented survey
Artificial Intelligence
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Proceedings of the 24th international conference on Machine learning
A Fast Indexing Method for Monte-Carlo Go
CG '08 Proceedings of the 6th international conference on Computers and Games
Mimicking Go Experts with Convolutional Neural Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Learning consensus opinion: mining data from a labeling game
Proceedings of the 18th international conference on World wide web
The Development of Human Expertise in a Complex Environment
Minds and Machines
A methodology for learning players| styles from game records
International Journal of Artificial Intelligence and Soft Computing
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We investigate the problem of learning to predict moves in the board game of Go from game records of expert players. In particular, we obtain a probability distribution over legal moves for professional play in a given position. This distribution has numerous applications in computer Go, including serving as an efficient stand-alone Go player. It would also be effective as a move selector and move sorter for game tree search and as a training tool for Go players. Our method has two major components: a) a pattern extraction scheme for efficiently harvesting patterns of given size and shape from expert game records and b) a Bayesian learning algorithm (in two variants) that learns a distribution over the values of a move given a board position based on the local pattern context. The system is trained on 181,000 expert games and shows excellent prediction performance as indicated by its ability to perfectly predict the moves made by professional Go players in 34% of test positions.