Although the development of AI in games is remarkable, intelligent machines still lag behind humans in games that require the ability of language understanding. In this paper, we focus on the crossword puzzle resolution task. Solving crossword puzzles is a challenging task since it requires the ability to answer natural language questions with knowledge and the ability to execute a search over possible answers to find an optimal set of solutions for the grid. Previous solutions are devoted to exploiting heuristic strategies in search to find solutions while having limited ability to explore the search space. We build a comprehensive system for crossword puzzle resolution based on Monte Carlo Tree Search (MCTS). As far as we know, we are the first to model the crossword puzzle resolution problem as a Markov Decision Process and apply the MCTS to solve it. We construct a dataset for crossword puzzle resolution based on daily puzzles from The New York Times with detailed specifications of both the puzzle and clue database selection. Our method achieves state-of-the-art performance on the dataset. The code of the system and experiments in this paper is publicly available: .

中文翻译：

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基于蒙特卡罗树搜索的填字游戏解决系统


尽管人工智能在游戏中的发展令人瞩目，但在需要语言理解能力的游戏中，智能机器仍然落后于人类。在本文中，我们重点关注填字游戏解决任务。解决填字游戏是一项具有挑战性的任务，因为它需要利用知识回答自然语言问题的能力，以及对可能答案进行搜索以找到网格的最佳解决方案集的能力。以前的解决方案致力于利用搜索中的启发式策略来寻找解决方案，但探索搜索空间的能力有限。我们构建了一个基于蒙特卡罗树搜索（MCTS）的纵横字谜解决方案的综合系统。据我们所知，我们是第一个将填字游戏解决问题建模为马尔可夫决策过程并应用 MCTS 来解决它的。我们根据《纽约时报》的每日谜题构建了一个用于填字游戏解决的数据集，其中包含谜题和线索数据库选择的详细规范。我们的方法在数据集上实现了最先进的性能。本文的系统和实验的代码已公开：。