PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
- URL: http://arxiv.org/abs/2404.17662v2
- Date: Mon, 17 Jun 2024 12:39:47 GMT
- Title: PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
- Authors: Qinglin Zhu, Runcong Zhao, Jinhua Du, Lin Gui, Yulan He,
- Abstract summary: 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.
- Score: 18.383262467079078
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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