论文标题
与信息设置蒙特卡洛树搜索一起参加复杂的隐藏角色游戏
Competing in a Complex Hidden Role Game with Information Set Monte Carlo Tree Search
论文作者
论文摘要
智能游戏玩家的进步已在GO和诸如扑克之类的不完美信息游戏（例如不完美的信息游戏）中取得了成功。信息集蒙特卡洛树搜索（ISMCTS）算法家族在不完美的信息游戏中使用蒙特卡洛方法优于先前的算法。在本文中，将单个观察者信息集蒙特卡洛树搜索（SO-ISISCTS）应用于秘密希特勒，这是一种流行的社交推论棋盘游戏，将传统的隐藏角色机制与卡片甲板的随机性结合在一起。与仅隐藏的角色和卡片甲板力学相比，这种组合会导致更复杂的信息模型。它在10108个模拟游戏中显示了So-Imsct的玩法以及更简单的基于规则的代理，并在复杂的信息集域中展示了ISMCTS算法的潜力。
Advances in intelligent game playing agents have led to successes in perfect information games like Go and imperfect information games like Poker. The Information Set Monte Carlo Tree Search (ISMCTS) family of algorithms outperforms previous algorithms using Monte Carlo methods in imperfect information games. In this paper, Single Observer Information Set Monte Carlo Tree Search (SO-ISMCTS) is applied to Secret Hitler, a popular social deduction board game that combines traditional hidden role mechanics with the randomness of a card deck. This combination leads to a more complex information model than the hidden role and card deck mechanics alone. It is shown in 10108 simulated games that SO-ISMCTS plays as well as simpler rule based agents, and demonstrates the potential of ISMCTS algorithms in complicated information set domains.
