AI for AOC Targeting, Air Tasking Orders, and Battle Rhythm Planning

á The Department of the Air Force (DAF), through judge advocates (JAGs), has a national defense-related mission requirement that deploys JAGs to advise Commanders, Air Operations Center (AOC) divisions and support Combatant Commands regarding targeting decisions. This requirement involves synthesizing large amounts of data in a short amount of time to provide legal advice on the legality of decision making during combat operations. The pace of warfare is accelerating, driven largely by the adoption of sensors, big data and emerging technologies to include Artificial Intelligence. JAGs advising commanders and personnel supporting the divisions within the AOC heavily rely on manual processes to track changes to the rules of engagement (ROE) and authorities.á The specific problem/opportunity addressed in the recently completed 'Phase I-like feasibility study' is how new AOC weapon systems will accelerate many, but not all, AOC processes. This will greatly inhibit the ability to accelerate the pace of decision making to match the future speed of conflict. Technical requirements have been gathered to address the solutions required for mission sensitive JAG decision making workflows to keep pace with other AOC components. As such, the proposed SBIR technology being developed through the AI in the AOC project will accelerate this process by combining natural language processing (NLP) and cognitive search powered by Machine Learning (ML), to enable operators to extract advanced meaning from mission-relevant ROE, execute orders, operations orders and documented authorities. Additionally, this technology will learn from usersĺ searches in order to provide relevant and complete results presenting all critical information to AOC divisions and combatant commanders via an intuitive advanced user interface.á