Author Description

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. Because of the value of quick turnaround and the relative simplicity of the simulated operational environment, there has been considerable interest in improving the mission planning process with the addition of reinforcement learning techniques for artificial intelligence (AI), which could produce better, faster, or simply unique solutions for human consideration. This report provides a description of how AI can be used to conduct mission planning and how AI methods compare with more traditional operations research (OR) approaches. One important aspect of mission planning is proper route planning, which can minimize risk to pilots and systems, reduce enemy information about U.S. assets, and increase the likelihood of successful mission execution. Although only a subset of all route planning, planning for an individual package to penetrate enemy airspace is a scenario that is frequently encountered by the Department of the Air Force (DAF). Using in-house modeling software, researchers explored the feasibility of applying AI to this task, comparing AI performance against an optimization approach, and assessing the limitations of this approach. This report is the fifth in a five-volume series addressing how AI could be employed to assist warfighters in four distinct areas: cybersecurity, predictive maintenance, wargames, and mission planning. This report is aimed primarily at those with an interest in mission planning, operations research, and AI applications more generally.