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- Title
An Air Force Pilot Training Recommendation System Using Advanced Analytical Methods.
- Authors
Forrest, Nicholas C.; Hill, Raymond R.; Jenkins, Phillip R.
- Abstract
The planning of individualized pilot training programs is an intensive process. Over 120 maneuvers are introduced into the training program over time while ensuring maneuver competencies. This work introduces a novel, deep-learning based approach for automatically generating training plans for pilot trainees to significantly reduce instructor pilot planning requirements. 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.
- Subjects
UNITED States. Air Force; FLIGHT training; RECOMMENDER systems; FLIGHT simulators; DEEP learning; EXERCISE therapy; AIR forces
- Publication
INFORMS Journal on Applied Analytics, 2022, Vol 52, Issue 2, p198
- ISSN
2644-0865
- Publication type
Article
- DOI
10.1287/inte.2021.1099