PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
arxiv(2024)
Abstract
Recent advancements in Large Language Models (LLMs) have enhanced the
efficacy of agent communication and social interactions. Despite these
advancements, building LLM-based agents for reasoning in dynamic environments
involving competition and collaboration remains challenging due to the
limitations of informed graph-based search methods. We propose PLAYER*, a novel
framework based on an anytime sampling-based planner, which utilises sensors
and pruners to enable a purely question-driven searching framework for complex
reasoning tasks. We also introduce a quantifiable evaluation method using
multiple-choice questions and construct the WellPlay dataset with 1,482 QA
pairs. Experiments demonstrate PLAYER*'s efficiency and performance
enhancements compared to existing methods in complex, dynamic environments with
quantifiable results.
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