COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
CoRR(2024)
摘要
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.
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