Author Description

U.S. air superiority, a cornerstone of U.S. deterrence efforts, is being challenged by competitors-most 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 Modeling-a U.S. Department of Defense-standard 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.