Natural language understanding for logical games
This work addresses the need for explainable AI in natural language understanding for logical puzzles, offering an improvement over similar solvers in this domain.
The paper tackled the problem of automatically solving logical puzzles in natural language by developing a system that parses text into first-order logic and uses a model finder for inference, achieving an overall performance rate of 80.89% on tasks like knights and knaves puzzles.
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.