Focusing on empowering the command and control(C2)procedure of generative AI,we analyze the challenges of course of action(COA)generation in C2 and the prospects of pre-trained large language models(LLMs).Then,a COA generation me-thod based on pre-trained LLMs,COA-Gen,is proposed.Firstly,a multi-round generation framework is designed to align the generated plans with objectives.Secondly,a multi-factor prompt templates is constructed to integrate vast amounts of multi-source information.Lastly,knowledge-augmented generation technology is introduced to improve the generation quality of the few-shot military domain.To validate the effectiveness of the generated plans,an emulation environment based on the StarCraft Ⅱ engine and the"Tiger Claw"scenario is established.The results show the robustness of the method and its alignment with the commander's intention.The feasibility of using LLMs for COA generation has been verified.Additionally,different pre-trained models exhibit varying performances in the same task,indicating that the choice of model in real-world applications can lead to ac-tion plans with different styles,thereby affect the ultimate outcomes.
关键词
大模型/生成式人工智能/智能决策/指挥与控制/作战行动方案
Key words
Large language model/Generative AI/Intelligent decision-making/Command and control/Course of action
维普
万方数据