The development of Courses of Action (COAs) in military operations is traditionally a time-consuming and intricate process. Addressing this challenge, this study introduces COA-GPT, a novel algorithm employing Large Language Models (LLMs) for rapid and efficient generation of valid COAs. COA-GPT incorporates military doctrine and domain expertise to LLMs through in-context learning, allowing commanders to input mission information - in both text and image formats - and receive strategically aligned COAs for review and approval. Uniquely, COA-GPT not only accelerates COA development, producing initial COAs within seconds, but also facilitates real-time refinement based on commander feedback. This work evaluates COA-GPT in a military-relevant scenario within a militarized version of the StarCraft II game, comparing its performance against state-of-the-art reinforcement learning algorithms. Our results demonstrate COA-GPT's superiority in generating strategically sound COAs more swiftly, with added benefits of enhanced adaptability and alignment with commander intentions. COA-GPT's capability to rapidly adapt and update COAs during missions presents a transformative potential for military planning, particularly in addressing planning discrepancies and capitalizing on emergent windows of opportunities.

翻译：军事行动中行动方案（COAs）的制定历来是一个耗时且复杂的流程。针对这一挑战，本研究提出COA-GPT算法，这是一种利用大语言模型（LLMs）快速高效生成有效行动方案的新颖算法。COA-GPT通过上下文学习将军事条令和领域专业知识融入LLMs，使得指挥官能够输入任务信息（包括文本和图像格式），并接收符合战略要求的行动方案以供审查和批准。独特之处在于，COA-GPT不仅能在数秒内生成初始行动方案以加速制定过程，还能根据指挥官的反馈进行实时优化。本研究在军事化版本的《星际争霸II》游戏中，于军事相关场景下评估了COA-GPT，并将其性能与最先进的强化学习算法进行比较。结果表明，COA-GPT在更快速地生成战略合理的行动方案方面具有优越性，并具备更强的适应性和与指挥官意图的一致性。COA-GPT在任务中快速调整和更新行动方案的能力，为军事规划带来了变革性潜力，特别是在应对计划偏差和把握突发机遇窗口方面。