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: westlakeintelligentrobotics.github.io/ConsensusLLM/.

翻译：由大语言模型驱动的多智能体系统在协作解决复杂任务方面展现出良好潜力。本研究关注多智能体协作中的根本性问题：共识达成。当多个智能体协同工作时，我们关注它们如何通过智能体间的协商达成共识。为此，本研究探讨了一项共识达成任务：每个智能体的状态为数值，它们通过相互协商达成共识值。研究表明，当未明确指定应采用何种策略时，大语言模型驱动的智能体主要采用平均策略来达成共识，尽管偶尔也会使用其他策略。此外，本研究分析了智能体数量、智能体个性及网络拓扑结构对协商过程的影响。本文的研究发现可为理解大语言模型驱动的多智能体系统在解决复杂任务中的行为奠定基础。进一步地，我们将基于大语言模型的共识达成方法应用于多机器人聚合任务。该应用案例展示了基于大语言模型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：westlakeintelligentrobotics.github.io/ConsensusLLM/。