基于证明数的蒙特卡洛树搜索
Proof Number Based Monte-Carlo Tree Search
AI总结：
提出结合蒙特卡洛树搜索与证明数搜索的PN-MCTS算法，在多个博弈中超越MCTS，最高胜率达96.2%。
摘要：
本文提出了一种新的博弈搜索算法PN-MCTS，该算法结合了蒙特卡洛树搜索（MCTS）和证明数搜索（PNS）。这两种算法已成功应用于多个领域的决策制定。我们定义了三个区域，在这些区域中，MCTS树中收集的证明数和反证数所提供的额外知识可能被利用：最终走子选择、子树求解以及UCB1选择机制。我们在不同的时间设置下测试了所有可能的组合，在多个博弈中与原始UCT进行对弈：连线行动（7×7和8×8棋盘大小）、MiniShogi、Knightthrough和Awari。此外，我们将这一新算法进行扩展，通过将PNS作为额外一层叠加在MCTS树之上，以妥善处理像Awari这样存在平局的博弈。实验表明，PN-MCTS在所有测试的博弈领域中均能超越MCTS，在连线行动中实现了高达96.2%的胜率。
英文摘要：
This paper proposes a new game-search algorithm, PN-MCTS, which combines Monte-Carlo Tree Search (MCTS) and Proof-Number Search (PNS). These two algorithms have been successfully applied for decision making in a range of domains. We define three areas where the additional knowledge provided by the proof and disproof numbers gathered in MCTS trees might be used: final move selection, solving subtrees, and the UCB1 selection mechanism. We test all possible combinations on different time settings, playing against vanilla UCT on several games: Lines of Action ($7$$\times$$7$ and $8$$\times$$8$ board sizes), MiniShogi, Knightthrough, and Awari. Furthermore, we extend this new algorithm to properly address games with draws, like Awari, by adding an additional layer of PNS on top of the MCTS tree. The experiments show that PN-MCTS is able to outperform MCTS in all tested game domains, achieving win rates up to 96.2% for Lines of Action.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
