Li Ang Zhang

This report characterizes the nature of the impact that artificial intelligence and machine learning tools could bring to the U.S. Space Force's space domain awareness mission, with a focus on the process to quantify the risk of collision in space.

Mission planning involves the assignment of discrete assets to prioritized targets, including the dynamic routing of those assets to their destinations under complex environmental conditions. This report presents a description of how artificial intelligence (AI) can be used to conduct mission planning and how AI methods compare with more-traditional operations research approaches.

U.S. air superiority is being challenged by competitors. The authors of this report demonstrate a prototype of a proof-of-concept artificial intelligence system to help develop and evaluate new concepts of operations for the air domain. The initial findings highlight both the potential of reinforcement learning to tackle complex, collaborative air mission planning problems, and some significant challenges facing this approach.