Air Dominance Through Machine Learning: A Preliminary Exploration of Artificial Intelligence-Assisted Mission Planning
RAND Corporation Santa Monica
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U.S. air superiority, a cornerstone of U.S. deterrence efforts, is being challenged by competitorsmost notably, China. The spread of machine learning ML is only enhancing that threat. One potential approach to combat this challenge is to more effectively use automation to enable new approaches to mission planning. The authors of this report demonstrate a prototype of a proof-of-concept artificial intelligence AI system to help develop and evaluate new concepts of operations for the air domain. The prototype platform integrates open-source deep learning frameworks, contemporary algorithms, and the Advanced Framework for Simulation, Integration, and Modelinga U.S. Department of Defensestandard combat simulation tool. The goal is to exploit AI systems ability to learn through replay at scale, generalize from experience, and improve over repetitions to accelerate and enrich operational concept development. In this report, the authors discuss collaborative behavior orchestrated by AI agents in highly simplified versions of suppression of enemy air defenses missions. The initial findings highlight both the potential of reinforcement learning RL to tackle complex, collaborative air mission planning problems, and some significant challenges facing this approach.
- Military Operations, Strategy and Tactics