Generative AI in Mafia-like Game Simulation
This work addresses the incremental improvement of AI in game simulations for developers and researchers, highlighting specific gains but noting limitations.
The study tackled the problem of applying Generative AI to role-playing simulations like Spyfall, finding that GPT-4 showed enhanced adaptability over GPT-3.5-turbo with significant improvements in posing relevant questions and forming human-like responses, though it faced challenges in bluffing and predicting opponent moves.
In this research, we explore the efficacy and potential of Generative AI models, specifically focusing on their application in role-playing simulations exemplified through Spyfall, a renowned mafia-style game. By leveraging GPT-4's advanced capabilities, the study aimed to showcase the model's potential in understanding, decision-making, and interaction during game scenarios. Comparative analyses between GPT-4 and its predecessor, GPT-3.5-turbo, demonstrated GPT-4's enhanced adaptability to the game environment, with significant improvements in posing relevant questions and forming human-like responses. However, challenges such as the model;s limitations in bluffing and predicting opponent moves emerged. Reflections on game development, financial constraints, and non-verbal limitations of the study were also discussed. The findings suggest that while GPT-4 exhibits promising advancements over earlier models, there remains potential for further development, especially in instilling more human-like attributes in AI.