  In this study, we explore the application of Large Language Models (LLMs) in
\textit{Jubensha}, a Chinese detective role-playing game and a novel area in
Artificial Intelligence (AI) driven gaming. We introduce the first dataset
specifically for Jubensha, including character scripts and game rules, to
foster AI agent development in this complex narrative environment. Our work
also presents a unique multi-agent interaction framework using LLMs, allowing
AI agents to autonomously engage in this game. To evaluate the gaming
performance of these AI agents, we developed novel methods measuring their
mastery of case information and reasoning skills. Furthermore, we incorporated
the latest advancements in in-context learning to improve the agents'
performance in information gathering, murderer identification, and logical
reasoning. The experimental results validate the effectiveness of our proposed
methods. This work aims to offer a novel perspective on understanding LLM
capabilities and establish a new benchmark for evaluating large language
model-based agents.
