One-sentence summary: This work demonstrates the potential of LLM-driven agents to achieve zero-shot autonomous planning for multi-robot collaboration tasks and analyzes the impact of the agent number, agent personality, and network topology on consensus-seeking processes.
Background: In recent months, multi-agent systems driven by large language models (LLMs) have received rapidly increasing attention. It is reported that the problem-solving ability of LLMs can be significantly enhanced through collaboration between multiple agents. The works in MetaGPT, CAMEL, and ChatDev break down complex tasks into simpler sub-tasks, which are then handled by different agents separately. These collaboration strategies, to some extent, can reduce hallucinations and enhance the ability to solve complex tasks.
Topic addressed: Our work considers a fundamental problem in multi-agent systems: consensus seeking. When multiple LLMs are used to solve the same task, they may have different solutions initially, but they can eventually reach the same solution through continuous negotiation. This is essentially a consensus-seeking process. Consensus seeking also widely exists in collective decision-making systems such as animal groups and human societies. It is also a core research problem in the fields of multi-robot systems and federated learning.
Research gap: Consensus seeking via LLMs has not been specifically studied so far. There are many important questions that need to be answered. For instance, if we use multiple LLMs to assist us in negotiations or problem-solving, it is important for us to know whether they can eventually reach a consensus amongst themselves. If they can, how long would it take and what factors can influence the final consensus outcome? If they cannot, what factors may lead to this failure? The answers to these questions play a pivotal role in our proper utilization of LLMs. For example, it would be beneficial if we could predict the final negotiation outcome even before deploying LLMs or we know how to obtain desired negotiation outcomes by adjusting some prompts.
Problem setup: In this work, we study a specific consensus-seeking task. Specifically, in an LLM-driven multi-agent system, each agent starts with an initial state represented by a numerical value. The objective for them is to continuously adjust their states to achieve the same final state. Throughout this process, each agent can perceive the states of the other agents, and based on this information, formulate strategies to adjust their own states.
Significance: This consensus-seeking task is an abstraction of more complex tasks. Understanding this simple task can lay the necessary foundations for understanding more complex ones. Specifically, in this task, the state of each agent corresponds to a point in the set of real numbers. In more complex tasks, the state of each agent may correspond to a point within a more complex set (e.g., a set of solutions).
Findings:
Application to multi-robot aggregation: The LLM-driven consensus seeking framework is further applied as a cooperative planner to a multi-robot aggregation task. In this task, multiple robots starting from different initial positions plan and move to a common position in the plane. It is a consensus seeking problem in Euclidean space. This application is important since it shows the potential of LLM-driven agents to achieve zero-shot autonomous task planning based on simple verbal commands.
@misc{chen2023multiagent,
title={Multi-Agent Consensus Seeking via Large Language Models},
author={Huaben Chen and Wenkang Ji and Lufeng Xu and Shiyu Zhao},
year={2023},
eprint={2310.20151},
archivePrefix={arXiv},
primaryClass={cs.CL}
}