关键词: 需求生成, 多智能体, 生成式人工智能, 大模型, 多模态

Abstract: Military requirement generation in joint operation involves many participants and a heavy workload.The process relies on individual experience and multiple sources of documents,which leads to problems such as low efficiency in requirement generation and difficulty supporting the design of joint operation system.With the development of large language models (LLMs),LLMs-driven agents have shown excellent performance in various fields,and multi-agent system can efficiently handle complex tasks by leveraging group intelligence through distributed decision-making.To address the low efficiency in military requirement generation,a framework for military requirement generation with LLMs-driven multi-agent system is proposed.The framework includes a multi-modal information acquisition agent,military expert agents,a moderator and other components.The multi-modal information acquisition agent can rapidly process multi-modal information,extract military requirements and provide the user with a question-and-answer function.Military expert agents simulate human experts discussing the generation of requirements through natural language dialogues.Driven by LLMs,these agents can perceive the environment and autonomously use tools such as Ar-xiv,search engines and other resources to support the dialogues.The moderator receives instructions from the human user,refines the content of the instructions using LLMs and generates dialogue prompts and problem background descriptions.Using the Russia-Ukraine conflict as an experimental case,military requirements are generated from relevant multi-modal information.The experimental results show that when the multi-modal information capacity is within the maximum processing capacity of LLMs,the framework significantly reduces the time consumption for military requirement generation,with time savings of 80% to 85% for video resources and 90% to 95% for audio resources.

Key words: Requirement generation, Multi-agent, Generative AI, LLMs, Multi-modal