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Learning policies for resolving demand-capacity imbalances during pre-tactical air traffic management

Kravaris Theocharis, Vouros, George A, Spatharis Christos, Blekas, Konstantinos D, Chalkiadakis Georgios, Garcia Jose Manuel Cordero

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URI: http://purl.tuc.gr/dl/dias/3F8984A1-1E9D-4EBC-BA1D-B282A8434E95
Year 2017
Type of Item Conference Full Paper
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Bibliographic Citation T. Kravaris, G. A. Vouros, C. Spatharis, K. Blekas, G. Chalkiadakis and J. M. C. Garcia, "Learning policies for resolving demand-capacity imbalances during pre-tactical air traffic management, " in 15th German Conference on Multiagent System Technologies, 2017, pp. 238-255. doi: 10.1007/978-3-319-64798-2_15 https://doi.org/10.1007/978-3-319-64798-2_15
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Summary

In this work we propose and investigate the use of collaborative reinforcement learning methods for resolving demand-capacity imbalances during pre-tactical Air Traffic Management. By so doing, we also initiate the study of data-driven techniques for predicting multiple correlated aircraft trajectories; and, as such, respond to a need identified in contemporary research and practice in air-traffic management. Our simulations, designed based on real-world data, confirm the effectiveness of our methods in resolving the demand-capacity problem, even in extremely hard scenarios.

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