Unpublished draft, Ĭoulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. 47, 235–256 (2002)īerlekamp, E., Wolfe, D.: Mathematical go: chilling gets the last point. This process is experimental and the keywords may be updated as the learning algorithm improves.Īuer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multi-armed bandit problem. These keywords were added by machine and not by the authors. The resulting algorithm, Monte Carlo tree search (MCTS), was not only a major breakthrough for computer Go but also an important invention for many other domains. The solution to the difficulty of Go was a combination of random sampling and search. ![]() However, despite the simple rules which had changed only slightly in these 2,000 years, Go is arguably the last two-player zero-sum game in which human beings are still superior to computers. Since Go is also one of such games, intuitively minimax search should also work for Go. ![]() ![]() The minimax search-based approach is known to be effective for most games in this category. As widely known, computers are now superior to human beings in most of the popular two-player zero-sum perfect information games including checkers, chess, shogi, and Go.
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