COA Generation Based on Pre-trained Large Language Models
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.
Large language modelGenerative AIIntelligent decision-makingCommand and controlCourse of action
万方数据
维普
