Coordination Framework for Collaborative Disposal of Multi-intelligent Agents Based on Large Language Models
Addressing the decision-making conundrum faced by commanders in response to major sudden in-cidents,this paper proposes a coordination framework for collaborative disposal of multi-intelligent agents based on large language models.The framework optimizes collective decision-making efficiency and action planning through strategies such as agent role generation,multi-level Monte-Carlo tree and interactive prompt learning.It introduces hierarchical mechanisms and workflow management concepts,enhancing col-laboration efficiency through the reward function shared among agents.A transparent and implicit communi-cation model ensures node status consistency.Experimental results demonstrate that the framework per-forms well under various scenarios,significantly improving reaction speed and response efficiency com-pared to traditional task allocation methods.
large language models(LLMs)collaborative disposalmulti-intelligence agent(MIA)disposal planning
万方数据
维普
