大型语言模型在解决多智能体集群问题中面临的挑战
Challenges Faced by Large Language Models in Solving Multi-Agent Flocking
AI总结：
本文探讨了大型语言模型在多智能体集群任务中面临的挑战，发现它们难以理解空间形状和距离保持，并提出了未来改进方向。
摘要：
集群是一种行为，其中系统中的多个智能体试图保持彼此靠近，同时避免碰撞并维持期望的队形。这种行为在自然界中可观察到，并在机器人技术中有应用，包括自然灾害搜救、野生动物追踪以及周边监视和巡逻。最近，大型语言模型（LLMs）作为个体决策者展现出了解决各种协作任务的惊人能力。用LLMs解决多智能体集群问题将证明它们在需要空间和分散决策的情境中的实用性。然而，当由LLM驱动的智能体被赋予实现多智能体集群的任务时，它们未能达到期望的行为。经过大量测试，我们发现，以LLM作为个体决策者的智能体通常倾向于收敛到其初始位置的平均值，或彼此发散。在分解问题后，我们发现LLM无法以有意义的方式理解维持形状或保持距离。用LLMs解决多智能体集群问题将增强它们理解协作空间推理的能力，并为解决更复杂的多智能体任务奠定基础。本文讨论了LLMs在多智能体集群中面临的挑战，并提出了未来改进和研究的领域。
英文摘要：
Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation. This is observed in the natural world and has applications in robotics, including natural disaster search and rescue, wild animal tracking, and perimeter surveillance and patrol. Recently, large language models (LLMs) have displayed an impressive ability to solve various collaboration tasks as individual decision-makers. Solving multi-agent flocking with LLMs would demonstrate their usefulness in situations requiring spatial and decentralized decision-making. Yet, when LLM-powered agents are tasked with implementing multi-agent flocking, they fall short of the desired behavior. After extensive testing, we find that agents with LLMs as individual decision-makers typically opt to converge on the average of their initial positions or diverge from each other. After breaking the problem down, we discover that LLMs cannot understand maintaining a shape or keeping a distance in a meaningful way. Solving multi-agent flocking with LLMs would enhance their ability to understand collaborative spatial reasoning and lay a foundation for addressing more complex multi-agent tasks. This paper discusses the challenges LLMs face in multi-agent flocking and suggests areas for future improvement and research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
