[
Blankenbeckler, P.N., Graves, T.R., & Wampler, R.L. (2014). Designing interactive multimedia instruction to address soldiers’ learning needs. Alexandria, VA, ARI Research Report #1979.
]Search in Google Scholar
[
Bostrom, N., & Sandberg, A. (2009). Cognitive Enhancement: Methods, Ethics, Regulatory Challenges. Science and Engineering Ethics, Vol. 15, Issue 3, 311-41. DOI:10.1007/s11948-009-9142-5.
]Search in Google Scholar
[
Brunyé, T.T., et al. (2020). Retrieval practice enhances near but not far transfer of spatial memory. Journal of Experimental Psychology: Learning Memory and Cognition, Vol. 46, 24-45. Available at: https://doi.org/10.1016/j.bandc.2018.09.008.
]Search in Google Scholar
[
Brunyé, T.T., Smith, A.M., Horner, C.B., & Thomas, A.K. (2018). Verbal long-term memory is enhanced by retrieval practice but impaired by prefrontal direct current stimulation. Brain and Cognition, Vol. 128, 80-88.
]Search in Google Scholar
[
Campbell, C., Cantrell, G., Generalao, T., Sawyer, A., & Takitch, J. (2006). Interactive multimedia instruction for US Army training. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, 1105-1110. Waynesville, NC: Association for the Advancement of computing in education (AACE).
]Search in Google Scholar
[
Chase, W.G., & Simon, H.A. (1973). The MIND’S eye in chess. Visual Information Processing, Proceedings of the Eighth Annual Carnegie Symposium on Cognition, 215-281. Available at: https://doi.org/10.1016/B978-0-12-170150-5.50011-1.
]Search in Google Scholar
[
Chuang, H.M., & Cheng, D.W. (2022). Conversational AI over military scenarios using intent detection and response generation. Applied Sciences, Vol. 12, Issue 5, 2494. Available at: https://doi.org/10.3390/app12052494.
]Search in Google Scholar
[
Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations & Trends in Signal Processing, Vol. 7, Issue 3-4, 197-387.
]Search in Google Scholar
[
Gao, L., Chen, Y., Zhang, B., & Gao, Y. (2019). A real-time target detection and recognition system for UAVs based on improved YOLOv3 and ST-C3D. IEEE Access, Vol. 7, 35028-35036.
]Search in Google Scholar
[
Lackey, S.J., Salcedo, J.N., Matthews, G., & Maxwell, D.B. (2014). Virtual world room clearing: A study in training effectiveness. In Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). Orlando, FL.
]Search in Google Scholar
[
Li, E., Zhou, Z., & Chen, X. (2018). Edge intelligence: On-demand deep learning model co-inference with device-edge synergy. In Proceedings Workshop Mobile Edge Commun/MECOMM, 31-36.
]Search in Google Scholar
[
Li, L., Ota, K., & Dong, M. (2018). Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics, Vol. 14, Issue 10, 4665-4673. DOI: 10.1109/TII.2018.2842821.
]Search in Google Scholar
[
McDaniel, M.A., & Einstein, G.O. (2006). Material appropriate difficulty: A framework for determining when difficulty is desirable for improving learning. In Healy, A.F. (Ed.), Decades of behavior. Experimental cognitive psychology and its applications, 73-85. Washington, D.C.: American Psychological Association. Available at: https://doi.org/10.1037/10895-006.
]Search in Google Scholar
[
NATO. (2020). Science & Technology Trends 2020-2040, Exploring the S&T Edge, NATO Science & Technology Organization. Available at: https://www.nato.int/nato_static_fl2014/assets/pdf/2020/4/pdf/190422-ST_Tech_Trends_Report_2020-2040.pdf.
]Search in Google Scholar
[
O’Hanlon, M. (2019a). Forecasting change in military technology, 2020-2040. Foreign Policy at Brookings.
]Search in Google Scholar
[
O’Hanlon, M. (2019b). The Senkaku Paradox: Risking Great Power War Over Small Stakes. Washington, DC: Brookings Institution Press.
]Search in Google Scholar
[
Paisner, M., Cox, M.T., Maynord, M., & Perlis, D. (2014). Goal-driven autonomy for cognitive systems. Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 36. Available at: https://escholarship.org/uc/item/5vq1h9jc.
]Search in Google Scholar
[
Porkoláb, I., & Négyesi, I. (2019). A mesterséges intelligencia alkalmazási lehetőségeinek kutatása a haderőben. Honvédségi Szemle. Available at: https://honvedelem.hu/images/media/5f2bd1646eeb8298912683.pdf.
]Search in Google Scholar
[
Prelipcean, G., Boscoianu, M., & Moisescu, F. (2010). New Ideas on the Artificial Intelligence Support in Military Applications. AIKED’10: Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases, 34-39. Available at: https://dl.acm.org/doi/10.5555/1808036.1808044.
]Search in Google Scholar
[
Sha, J., Chen, Y., Gao, Y., & Li, X. (2021). Deep neural networks-based target detection and recognition for UAV. IEEE Access, Vol. 9, 92960-92968.
]Search in Google Scholar
[
Spain, R.D., Priest, H.A., & Murphy, J.S. (2012). Current trends in adaptive training with military applications: An introduction. Military Psychology, Vol. 24, Issue 2, 87-95.
]Search in Google Scholar
[
Swets, J.A., & Bjork, R.A. (1990). Enhancing human performance: An evaluation of “new age” techniques considered by the U.S. Army. Psychological Science, Vol. 1, Issue 2, 85-96.
]Search in Google Scholar
[
Zhang, T., et al. (2017). Current trends in the development of intelligent unmanned autonomous systems. Frontiers of Information Technology & Electronic Engineer, Vol. 18, Issue 1, 68-85.
]Search in Google Scholar