Design of Simulation-Based Pilot Training Systems using Machine Learning Agents
2021 (English)In: Proceedings of the 32nd Congress of the International Council of Aeronautical Sciences (ICAS), Bonn: The International Council of the Aeronautical Sciences , 2021, Vol. 32, article id ICAS_2020_0130Conference paper, Published paper (Refereed)
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 realized 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 utilize learning agents for improved training value.
Place, publisher, year, edition, pages
Bonn: The International Council of the Aeronautical Sciences , 2021. Vol. 32, article id ICAS_2020_0130
Keywords [en]
Air Combat Training, Flight Simulation, LVC Simulation, Machine Learning, Reinforcement Learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-180916Scopus ID: 2-s2.0-85083942868ISBN: 9783932182914 (electronic)OAI: oai:DiVA.org:liu-180916DiVA, id: diva2:1609445
Conference
32nd Congress of the International Council of Aeronautical Sciences (ICAS), September 6-10, 2021, Shanghai, China
Funder
Vinnova, 2017-04885Wallenberg AI, Autonomous Systems and Software Program (WASP)2021-11-082021-11-082025-11-17Bibliographically approved