COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations
<|reference_start|>COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations: 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.<|reference_end|>
@article{goecks2024coa-gpt:,
    title={COA-GPT: Generative Pre-trained Transformers for Accelerated Course of
  Action Development in Military Operations},
    author={Vinicius G. Goecks, Nicholas Waytowich},
    journal={arXiv preprint arXiv:2402.01786},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01786},
    primaryClass={cs.AI cs.CL cs.HC cs.LG}
}