Questioning the Unknown: Optimising Multi-Agent Collaboration in Narrative-Driven Games

We present Questum, 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. Questum 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, categorised into objectives, reasoning, and relationships. Experiments demonstrate Questum'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.