Monte Carlo search, and specifically the UCT (Upper Confidence Bounds applied to Trees) algorithm, has contributed to a significant improvement in the game of Go and has received considerable attention in other applications. This article investigates two enhancements to the UCT algorithm. First, we consider the possible adjustments to UCT when the search tree is treated as a graph (and information amongst transpositions are shared). The second modification introduces move groupings, which may reduce the effective branching factor. Experiments with both enhancements were performed using artificial trees and in the game of Go. From the experimental results we conclude that both exploiting the graph structure and grouping moves may contribute to an increase in the playing strength of game programs using UCT.
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机译：Monte Carlo Search，特别是UCT（应用于树木的上部置信度）算法，有助于Go游戏的显着改进，并在其他应用中得到了相当大的关注。本文调查了对UCT算法的两个增强功能。首先，我们考虑当搜索树被视为图形时对UCT的可能调整（以及共享转换中的信息）。第二种修改引入了移动分组，其可以减少有效的分支因子。使用人造树和去的比赛进行了增强功能的实验。从实验结果来看，我们得出结论，利用图形结构和分组移动都可能有助于使用UCT的游戏程序的竞争力。
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