The Mahjong game has widely been acknowledged to be a difficult problem in the field of imperfect information games. Because of its unique characteristics of asymmetric, serialized and multi-player game information, conventional methods of dealing with perfect information games are difficult to beapplied directly on the Mahjong game. Therefore, AI (artificial intelligence)-based studies to handle the Mahjong game become challenging. In this study, an efficient AI-based method to play the Mahjong game is proposed based on the knowledge and game-tree searching strategy. Technically, we simplify the Mahjong game framework from multi-player to single-player. Based on the above intuition, an improved search algorithm is proposed to explore the path of winning. Meanwhile, three node extension strategies are proposed based on heuristic information to improve the search efficiency. Then, an evaluation function is designed to calculate the optimal solution by combining the winning rate, score and risk value assessment. In addition, we combine knowledge and Monte Carlo simulation to construct an opponent model to predict hidden information and translate it into available relative probabilities. Finally, dozens of experiments are designed to prove the effectiveness of each algorithm module. It is also worthy to mention that, the first version of the proposed method, which is named as KF-TREE, has won the silver medal in the Mahjong tournament of 2019 Computer Olympiad.

中文翻译：

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一种有效的基于AI的知识和游戏树搜索策略来玩麻将游戏的方法

在不完善的信息游戏领域，麻将游戏已被广泛认为是一个难题。由于其不对称，序列化和多玩家游戏信息的独特特征，处理完美信息游戏的常规方法很难直接应用于麻将游戏。因此，用于处理麻将游戏的基于AI（人工智能）的研究变得充满挑战。在这项研究中，基于知识和游戏树搜索策略，提出了一种有效的基于AI的麻将游戏玩法。从技术上讲，我们将麻将游戏框架从多人游戏简化为单人游戏。基于上述直觉，提出了一种改进的搜索算法来探索获胜的途径。同时，提出了基于启发式信息的三种节点扩展策略，以提高搜索效率。然后，设计一个评估函数，通过组合获胜率，得分和风险值评估来计算最佳解决方案。此外，我们将知识与蒙特卡洛模拟相结合，构造了一个对手模型来预测隐藏信息并将其转化为可用的相对概率。最后，设计了数十个实验来证明每个算法模块的有效性。还值得一提的是，该方法的第一个版本称为KF-TREE，已在2019年计算机奥林匹克麻将锦标赛中获得银牌。得分和风险价值评估。此外，我们将知识与蒙特卡洛模拟相结合，构造了一个对手模型来预测隐藏信息并将其转化为可用的相对概率。最后，设计了数十个实验来证明每个算法模块的有效性。还值得一提的是，该方法的第一个版本称为KF-TREE，已在2019年计算机奥林匹克麻将锦标赛中获得银牌。得分和风险价值评估。此外，我们将知识与蒙特卡洛模拟相结合，构造了一个对手模型来预测隐藏信息并将其转化为可用的相对概率。最后，设计了数十个实验来证明每个算法模块的有效性。值得一提的是，该方法的第一个版本称为KF-TREE，已在2019年计算机奥林匹克麻将锦标赛中获得银牌。