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Book
Air Dominance Through Machine Learning: A Preliminary Exploration of Artificial Intelligence–Assisted Mission Planning
Authors: --- --- --- --- --- et al.
Year: 2020 Publisher: Santa Monica, Calif. RAND Corporation

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Abstract

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.

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Book
Fighter Basing Options to Improve Access to Advanced Training Ranges
Authors: --- --- --- --- --- et al.
Year: 2021 Publisher: Santa Monica, Calif. RAND Corporation

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The U.S. Air Force (USAF) has determined that its fighter pilots do not currently have sufficient access to training ranges with airspace, threat emitters, targets, and electronic support measures capable of representing advanced potential adversaries. The USAF is developing a plan to upgrade certain ranges with these capabilities. In addition, the USAF may consider potential fighter squadron restationing options that would improve access to the upgraded training ranges. The authors developed an optimization model to determine the combinations of range upgrades and squadron restationing options that provide the highest levels of effectiveness given different policy constraints. They developed one-time move costs associated with squadron restationing and compared those with preliminary range upgrade cost estimates. Finally, the authors collected data on the risks from natural hazards and power outages for the set of bases and ranges under consideration. The authors found that range upgrades alone might not ensure sufficient access to advanced ranges and that restationing fighter squadrons can provide additional access, but the amount depends on institutional freedom to make restationing decisions. The one-time costs for restationing a fighter squadron and range modernization are on the same order of magnitude, but range upgrades may be substantially more expensive over the long term. The authors recommend that the USAF assess the effectiveness, costs, and risks of restationing presented in this report against other potential solutions for providing access to advanced ranges.

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