Multi-Agent Consensus Seeking via Large Language Models
It addresses consensus seeking for multi-agent collaboration, but is incremental as it analyzes existing strategies and applies them to a specific domain.
This work tackles the problem of consensus seeking in multi-agent systems driven by large language models (LLMs), revealing that agents primarily use an average strategy during negotiation, and applies this to a multi-robot aggregation task to demonstrate zero-shot autonomous planning.
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/.