Abstract
In recent years, AI models based on GPT have advanced rapidly. These models are capable of generating text, translating between different languages, and answering questions with high accuracy. However, the process behind their outputs remains a black box, making it difficult to ascertain the data influencing their responses. These AI models do not always produce accurate outputs and are known for generating incorrect information, known as hallucinations, whose causes are hard to pinpoint. Moreover, they still face challenges in solving complex problems that require step-by-step reasoning, despite various improvements like the Chain-of-Thought approach. There’s no guarantee that these models can independently perform logical reasoning from scratch, raising doubts about the reliability and accuracy of their inferences. To address these concerns, this study proposes the incorporation of an explicit logical structure into the AI’s text generation process. As a validation experiment, a text-based agent capable of playing the Werewolf game, which requires deductive reasoning, was developed using GPT-4. By comparing the model combined with an external explicit logical structure and a baseline that lacks such a structure, the proposed method demonstrated superior reasoning capabilities in subjective evaluations, suggesting the effectiveness of adding an explicit logical framework to the conventional AI models.