Multi-agent systems driven by large language models (LLMs) have shown promising abilities for solving complex tasks in a collaborative manner. This work considers a fundamental problem in multi-agent collaboration: consensus seeking. When multiple agents work together, we are interested in how they can reach a consensus through inter-agent negotiation. To that end, this work studies a consensus-seeking task where the state of each agent is a numerical value and they negotiate with each other to reach a consensus value. It is revealed that when not explicitly directed on which strategy should be adopted, the LLM-driven agents primarily use the average strategy for consensus seeking although they may occasionally use some other strategies. Moreover, this work analyzes the impact of the agent number, agent personality, and network topology on the negotiation process. The findings reported in this work can potentially lay the foundations for understanding the behaviors of LLM-driven multi-agent systems for solving more complex tasks. Furthermore, LLM-driven consensus seeking is applied to a multi-robot aggregation task. This application demonstrates the potential of LLM-driven agents to achieve zero-shot autonomous planning for multi-robot collaboration tasks. Project website: windylab.github.io/ConsensusLLM/.

翻译：由大语言模型（LLMs）驱动的多智能体系统在协作解决复杂任务方面展现出巨大潜力。本研究探讨了多智能体协作中的一个基本问题：共识寻求。当多个智能体共同工作时，我们关注它们如何通过智能体间的协商达成共识。为此，本研究考察了一项共识寻求任务，其中每个智能体的状态是一个数值，它们通过相互协商以达成一个共识值。研究发现，当没有明确指示应采用何种策略时，LLM驱动的智能体主要使用平均策略来寻求共识，尽管它们偶尔也会使用其他一些策略。此外，本研究分析了智能体数量、智能体个性和网络拓扑对协商过程的影响。本工作报告的发现可能为理解LLM驱动的多智能体系统解决更复杂任务的行为奠定基础。进一步地，我们将LLM驱动的共识寻求应用于一个多机器人聚集任务。该应用展示了LLM驱动的智能体在多机器人协作任务中实现零样本自主规划的潜力。项目网站：windylab.github.io/ConsensusLLM/。