Abstract
AbstractThe 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.
Publisher
Cambridge University Press (CUP)
Reference51 articles.
1. [43] Bacon, P.L. , Harb, J. and Precup, D. The option-critic architecture, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017, pp 1726–1734.
2. Research on Air Combat Maneuver Decision-Making Method Based on Reinforcement Learning
3. [34] Kingma, D.P. and Ba, J. Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, 2014.
4. A framework for describing interaction between human operators and autonomous, automated, and manual control systems;Lundberg;Cognition, Technology and Work,2020
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