PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
<|reference_start|>PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games: We propose PLAYER*, a novel framework that addresses the limitations of existing agent-based approaches built on Large Language Models (LLMs) in handling complex questions and understanding interpersonal relationships in dynamic environments. PLAYER* enhances path planning in Murder Mystery Games (MMGs) using an anytime sampling-based planner and a questioning-driven search framework. By equipping agents with a set of sensors, PLAYER* eliminates the need for pre-defined questions and enables agents to navigate complex social interactions. We additionally make a contribution by introducing a quantifiable evaluation method using multiple-choice questions and present WellPlay, a dataset containing 1,482 question-answer pairs. Experimental results demonstrate PLAYER*'s superiority over existing multi-agent methods, enhancing the generalisability and adaptability of agents in MMGs and paving the way for more effective multi-agent interactions.<|reference_end|>
@article{zhu2024player*:,
    title={PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction
  in Murder Mystery Games},
    author={Qinglin Zhu, Runcong Zhao, Jinhua Du, Lin Gui, Yulan He},
    journal={arXiv preprint arXiv:2404.17662},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17662},
    primaryClass={cs.CL}
}