基于大模型的联动处置多智能代理协同框架  

Coordination Framework for Collaborative Disposal of Multi-intelligent Agents Based on Large Language

作　　者：吴晓宁 李瑞欣 王浪 刘文杰 王宏伟 朱新立 宋江帆 袁梦 WU Xiaoning;LI Ruixin;WANG Lang;LIU Wenjie;WANG Hongwei;ZHU Xinli;SONG Jiangfan;YUAN Meng(North Automatic Control Technology Institute,Taiyuan 030006,China;Key Laboratory of Counter-Terrorism Command&Information Engineering of Ministry of Education(Approval),Engineering University of PAP,Xi’an 710086,China)

机构地区：[1]北方自动控制技术研究所,太原030006 [2]武警工程大学反恐指挥信息工程教育部重点实验室(立项),西安710086

出　　处：《数据采集与处理》2024年第3期559-576,共18页Journal of Data Acquisition and Processing

基　　金：山西省重点研发计划(202102150401013)。

摘　　要：针对指挥员应对重大突发情况时的处置决策难题,提出一种基于大模型的联动处置多智能代理协同框架。该框架通过智能代理角色生成、多层级蒙特卡洛树与交互式提示学习等策略,优化群体决策效率与动作规划,同时引入分层机制与工作流管理理念,通过强化学习奖励函数共享提升协同效率,设计显式与隐式通信模式确保节点状态一致。实验表明,该框架在多种场景下表现优异,与传统任务分配手段相比,大大提高了面对突发事件时的反应速度和处置效率。Addressing the decision-making conundrum faced by commanders in response to major sudden incidents,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 collaboration efficiency through the reward function shared among agents.A transparent and implicit communication model ensures node status consistency.Experimental results demonstrate that the framework performs well under various scenarios,significantly improving reaction speed and response efficiency compared to traditional task allocation methods.

关 键 词：大模型 联动处置 多智能代理 处置规划 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]