An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods

Nicholas C. Forrest (), Raymond R. Hill () and Phillip R. Jenkins ()
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Nicholas C. Forrest: Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio 45433
Raymond R. Hill: Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio 45433
Phillip R. Jenkins: Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio 45433

Interfaces, 2022, vol. 52, issue 2, 198-209

Abstract: The U.S. Air Force has a severe shortage of pilots. The Air Force’s Pilot Training Next (PTN) program seeks a more efficient pilot-training environment emphasizing the use of virtual reality flight simulators alongside periodic real aircraft experience. The objective of the PTN program is to accelerate the training pace and progress in undergraduate pilot training. Currently, instructor pilots spend excessive time planning and scheduling flights. This research focuses on methods to autogenerate the planning of in-flight events using hybrid filtering and deep learning techniques. The resulting approach captures temporal trends of user-specific and program-wide student performance to recommend a feasible set of graded flight events for evaluation in students’ next training exercise to improve their progress toward fully qualified status.

Keywords: recommender system; deep learning; neural networks (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:52:y:2022:i:2:p:198-209

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