PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games

We present PLAYER*, a novel framework for Large Language Model (LLM)-based agents in Murder Mystery Games (MMGs). MMGs pose unique challenges, including undefined state spaces, absent intermediate rewards, and the need for strategic interaction in a continuous language domain. PLAYER* addresses these complexities through a sensor-based representation of agent states, a question-targeting mechanism guided by information gain, and a pruning strategy to refine suspect lists and enhance decision-making efficiency. To enable systematic evaluation, we propose WellPlay, a dataset comprising 1,482 inferential questions across 12 games, categorized into objectives, reasoning, and relationships. Experiments demonstrate PLAYER*'s capacity to achieve superior performance in reasoning accuracy and efficiency compared to existing approaches, while also significantly improving the quality of agent-human interactions in MMGs. This study advances the development of reasoning agents for complex social and interactive scenarios.