Multi-Agent Consensus Seeking via Large Language Models
Huaben Chen Wenkang Ji Lufeng Xu Shiyu Zhao
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/.