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Computer Science > Artificial Intelligence
Title: COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
(Submitted on 1 Feb 2024 (v1), last revised 28 Mar 2024 (this version, v2))
Abstract: 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.
Submission history
From: Vinicius G. Goecks [view email][v1] Thu, 1 Feb 2024 21:51:09 GMT (13730kb,D)
[v2] Thu, 28 Mar 2024 15:22:42 GMT (12825kb,D)
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