We developed a system able to automatically solve logical puzzles in natural language. Our solution is composed by a parser and an inference module. The parser translates the text into first order logic (FOL), while the MACE4 model finder is used to compute the models of the given FOL theory. We also empower our software agent with the capability to provide Yes/No answers to natural language questions related to each puzzle. Moreover, in line with Explainalbe Artificial Intelligence (XAI), the agent can back its answer, providing a graphical representation of the proof. The advantage of using reasoning for Natural Language Understanding (NLU) instead of Machine learning is that the user can obtain an explanation of the reasoning chain. We illustrate how the system performs on various types of natural language puzzles, including 382 knights and knaves puzzles. These features together with the overall performance rate of 80.89\% makes the proposed solution an improvement upon similar solvers for natural language understanding in the puzzles domain.

翻译：我们开发了一个能够自动解答自然语言中逻辑谜题的系统。 我们的解答是由一个解析器和一个推理模块组成的。 解析器将文字翻译为一阶逻辑( FOL), 而MACE4 模型查找器被用来计算给定的 FOL 理论的模型模型。 我们还赋予我们的软件代理器能力, 使其能够对与每个解谜有关的自然语言问题提供是/ 否答案。 此外, 根据 Exploralfical Intelligence (XAI), 代理器可以支持其答案, 提供证据的图形表达。 使用自然语言理解( NLU) 而不是机器学习的推理的优点是用户可以获得对逻辑链的解释。 我们演示了系统如何在各种自然语言谜题上进行演化, 包括382 骑士和 knaves 拼图。 这些特性加上80. 89\\\\\ 的总体性能, 使得拟议解决方案改进了类似解算器在谜题域中了解自然语言的解算法。