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.

翻译：我们提出了PLAYER*，这是一个新颖的框架，旨在解决现有基于大语言模型（LLMs）的智能体方法在处理动态环境中复杂问题及理解人际关系方面的局限性。PLAYER*通过采用一种基于任意时间采样的规划器和一个问题驱动的搜索框架，增强了在谋杀之谜游戏（MMGs）中的路径规划能力。通过为智能体配备一组传感器，PLAYER*消除了对预定义问题的依赖，并使智能体能够驾驭复杂的社会交互。此外，我们引入了一种使用多项选择题的可量化评估方法，并提出了WellPlay数据集，该数据集包含1,482个问答对，作为一项额外贡献。实验结果表明，PLAYER*优于现有的多智能体方法，增强了智能体在MMGs中的泛化能力和适应性，并为实现更有效的多智能体交互铺平了道路。