Generalized Enrichment of Pilot Training Data Through Automated Classification of Pilot training Objectives, Scenarios, and Performance

OUSD (R&E) MODERNIZATION PRIORITY: Artificial Intelligence/Machine Learning; General Warfighting Requirements (GWR) TECHNOLOGY AREA(S): Air Platform The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws. Please direct questions to the Air Force SBIR/STTR HelpDesk: usaf.team@afsbirsttr.us. OBJECTIVE: Adapt and apply classification algorithms to pilot training data to identify training scenarios with similar objectives and similar levels of pilot proficiency in achieving those objectives DESCRIPTION: USAF pilot training produces volumes of system-based and observer-based human performance data. It is difficult to meaningfully organize these data sets, and this effort to applies machine learning and classification techniques to enrich the data sets and to enhance rapid retrieval of relevant data. This effort explores and demonstrates: 1. Classification of data sets exhibiting similar training objectives preferably mapped to mission types, Mission Essential CompetenciesTM (MECs), and/or Ready Aircrew Program (RAP) requirements 2. Automated observation of training providing scores of scenario applicability to different objectives and constructs 3. Classification of individual pilot, team, and team-of-team proficiency exhibited during training 4. Classification of scenario complexity; perceived versus actual difficulty PHASE I: This topic is intended for technology proven ready to move directly into a Phase II. Therefore, a Phase I award is not required. The offeror is required to provide detail and documentation in the Direct to Phase II proposal which demonstrates accomplishment of a Phase I-like effort, including a feasibility study. This includes determining, insofar as possible, the scientific and technical merit and feasibility of ideas appearing to have commercial potential. PHASE II: Eligibility for D2P2 is predicated on the offeror having performed a Phase I-like effort predominantly separate from the SBIR Programs. The goal of phase II is to prototype, demonstrate, and evaluate classification techniques applied to at least three of the areas identified in the problem description with representative data sets. Document lessons learned, needs for further research, and strengths/limitations of the considered approaches. PHASE III DUAL USE APPLICATIONS: The contractor will pursue commercialization of the various technologies developed in Phase II for transitioning expanded mission capability to a broad range of potential government and civilian users and alternate mission applications. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program. REFERENCES: Kabudi, T., Pappas, I., Olsen D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, vol 2. (2021).; Monllao Olive, D. (2019). Automatic classification of students in online courses using machine learning technqiues. [Master's Thesis, University of Western Australia].; Watz, E., Neubauer, P., Kegley, J., Bennett, W. (2018). Managing Learning and Tracking Performance across Multiple Mission Sets. Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). KEYWORDS: pilot training; proficiency; classification; data management; scenario complexity; scenario applicability; competencies; machine learning