让大模型自主分配任务，提升多智能体系统效率
Self-Resource Allocation in Multi-Agent LLM Systems
- 用大模型做规划者而非调度器，更好协调多个智能体并行工作
- 规划者方法使任务执行效率更高，资源利用率显著提升
- 明确告知智能体能力可优化策略，尤其适合处理弱能力节点
随着大语言模型作为智能体的发展，将多个智能体连接成多智能体系统以并发解决任务成为研究热点，重点聚焦于任务分配与协调。本文探讨大模型如何在考虑成本、效率和性能的前提下，有效分配计算任务。实验表明，大模型作为规划者在资源分配任务中表现出高准确性和有效性。相比调度器方法，规划者在处理并发动作时表现更优，提升了整体效率并更好利用智能体资源。此外，向规划者提供工作者能力的显式信息，可显著改进其分配策略，尤其在应对表现不佳的工作者时效果明显。
原文摘要 · Abstract (English)
With the development of LLMs as agents, there is a growing interest in connecting multiple agents into multi-agent systems to solve tasks concurrently, focusing on their role in task assignment and coordination. This paper explores how LLMs can effectively allocate computational tasks among multiple agents, considering factors such as cost, efficiency, and performance. In this work, we address key questions, including the effectiveness of LLMs as orchestrators and planners, comparing their effectiveness in task assignment and coordination. Our experiments demonstrate that LLMs can achieve high validity and accuracy in resource allocation tasks. We find that the planner method outperforms the orchestrator method in handling concurrent actions, resulting in improved efficiency and better utilization of agents. Additionally, we show that providing explicit information about worker capabilities enhances the allocation strategies of planners, particularly when dealing with suboptimal workers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。