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Theory of Mind for Multi-Agent Collaboration via Large Language Models

arxiv.org · 8,692 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. HTML conversions sometimes display errors due to content that did not convert correctly from the source. This paper uses the following packages that are not yet supported by the HTML conversion tool. Feedback on these issues are not necessary; they are known and are being worked on. Authors: achieve the best HTML results from your LaTeX submissions by following these best practices. While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored. This study evaluates LLM-based agents in a multi-agent cooperative text game with Theory of Mind (ToM) inference tasks, comparing their performance with Multi-Agent Reinforcement Learning (MAR

Theory of Mind for Multi-Agent Collaboration via Large Language Models Huao Li 1 , Yu Quan Chong 2 , Simon Stepputtis 2 , Joseph Campbell 2 , Dana Hughes 2 , Michael Lewis 1 , Katia Sycara 2 1 University of Pittsburgh, Pittsburgh, PA hul52,cmlewis@pitt.edu 2 Carnegie Mellon University, Pittsburgh, PA yuquanc,sstepput,jacampbe,danahugh,sycara@andrew.cmu.edu Abstract While Large Language Models (LLMs) have demonstrated impressive accomplishments in both reasoning and planning, their abilities in multi-agent collaborations remains largely unexplored. This study evaluates LLM-based agents in a mul

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