用大模型让多个智能体自主分配目标，效率接近最优。
Decentralized Multi-Agent Goal Assignment for Path Planning using Large Language Models
- 智能体基于环境信息自动生成目标偏好，通过固定规则分配任务。
- 大模型提示设计得当可实现接近最优的完成时间，优于传统启发式方法。
- 适合研究多智能体协作、路径规划与大模型应用的读者。
在完全可观测的网格世界中，针对去中心化多智能体路径规划中的目标分配问题，本文比较了贪心启发式、最优分配与基于大语言模型（LLM）的智能体方案。智能体基于结构化环境表示（如栅格可视化与场景数据）独立生成目标优先级，随后交换排名，通过固定冲突解决规则（如按智能体编号顺序）确定分配，无需协商或迭代协调。实验表明，在合理提示设计与量化信息支持下，LLM驱动的智能体能实现近似最优的完成时间（makespan），显著优于传统启发式方法。结果凸显了语言模型在去中心化目标分配中的潜力，并强调了信息结构对系统性能的关键影响。
原文摘要 · Abstract (English)
Coordinating multiple autonomous agents in shared environments under decentralized conditions is a long-standing challenge in robotics and artificial intelligence. This work addresses the problem of decentralized goal assignment for multi-agent path planning, where agents independently generate ranked preferences over goals based on structured representations of the environment, including grid visualizations and scenario data. After this reasoning phase, agents exchange their goal rankings, and assignments are determined by a fixed, deterministic conflict-resolution rule (e.g., agent index ordering), without negotiation or iterative coordination. We systematically compare greedy heuristics, optimal assignment, and large language model (LLM)-based agents in fully observable grid-world settings. Our results show that LLM-based agents, when provided with well-designed prompts and relevant quantitative information, can achieve near-optimal makespans and consistently outperform traditional heuristics. These findings underscore the potential of language models for decentralized goal assignment in multi-agent path planning and highlight the importance of information structure in such systems.
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