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Design of simulation-based pilot training systems using machine learning agents
Linköping University, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
Linköping University, Sweden.
2022 (English)In: Aeronautical Journal, ISSN 0001-9240, Vol. 126, no 1300, p. 907-Article in journal (Refereed) Published
Abstract [en]

The high operational cost of aircraft, limited availability of air space, and strict safety regulations make training of fighter pilots increasingly challenging. By integrating Live, Virtual, and Constructive simulation resources, efficiency and effectiveness can be improved. In particular, if constructive simulations, which provide synthetic agents operating synthetic vehicles, were used to a higher degree, complex training scenarios could be realised at low cost, the need for support personnel could be reduced, and training availability could be improved. In this work, inspired by the recent improvements of techniques for artificial intelligence, we take a user perspective and investigate how intelligent, learning agents could help build future training systems. Through a domain analysis, a user study, and practical experiments, we identify important agent capabilities and characteristics, and then discuss design approaches and solution concepts for training systems to utilise learning agents for improved training value. © The Author(s).

Place, publisher, year, edition, pages
Cambridge University Press , 2022. Vol. 126, no 1300, p. 907-
Keywords [en]
Air combat training, Flight simulation, LVC simulation, Machine learning, Reinforcement learning, Flight simulators, Intelligent agents, Personnel training, Training aircraft, Air combat, Combat training, Design of simulations, Learning agents, Machine learning agents, Pilot training systems, Training Systems
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-58897DOI: 10.1017/aer.2022.8Scopus ID: 2-s2.0-85125567666OAI: oai:DiVA.org:ri-58897DiVA, id: diva2:1647229
Note

Funding details: VINNOVA, NFFP7/2017-04885; Funding details: Knut och Alice Wallenbergs Stiftelse; Funding details: Vetenskapsrådet, VR, 2020/5-230; Funding text 1: This work was partially supported by the Swedish Governmental Agency for Innovation Systems (grant NFFP7/2017-04885), and the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. This work was supported by computation resources provided by the Swedish National Infrastructure for Computing (SNIC) at Tetralith/NSC partially funded by the Swedish Research Council through grant agreement no. 2020/5-230.

Available from: 2022-03-25 Created: 2022-03-25 Last updated: 2023-07-07Bibliographically approved

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