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- W1714211023 abstract "A Monte-Carlo evaluation consists in estimating a position by averaging the outcome of several random continuations. The method can serve as an evaluation function at the leaves of a min-max tree. This paper presents a new framework to combine tree search with Monte-Carlo evaluation, that does not separate between a min-max phase and a Monte-Carlo phase. Instead of backing-up the min-max value close to the root, and the average value at some depth, a more general backup operator is defined that progressively changes from averaging to min-max as the number of simulations grows. This approach provides a fine-grained control of the tree growth, at the level of individual simulations, and allows efficient selectivity. The resulting algorithm was implemented in a 9×9 Go-playing program, Crazy Stone, that won the 10th KGS computer-Go tournament." @default.
- W1714211023 created "2016-06-24" @default.
- W1714211023 creator A5004251207 @default.
- W1714211023 date "2007-01-01" @default.
- W1714211023 modified "2023-10-04" @default.
- W1714211023 title "Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search" @default.
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- W1714211023 doi "https://doi.org/10.1007/978-3-540-75538-8_7" @default.
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