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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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URIhttp://purl.tuc.gr/dl/dias/3F8984A1-1E9D-4EBC-BA1D-B282A8434E95-
Identifierhttps://link.springer.com/chapter/10.1007%2F978-3-319-64798-2_15-
Identifierhttps://doi.org/10.1007/978-3-319-64798-2_15-
Languageen-
Extent18 pagesen
TitleLearning policies for resolving demand-capacity imbalances during pre-tactical air traffic managementen
CreatorKravaris Theocharisen
CreatorVouros, George Aen
CreatorSpatharis Christosen
CreatorBlekas, Konstantinos Den
CreatorChalkiadakis Georgiosen
CreatorΧαλκιαδακης Γεωργιοςel
CreatorGarcia Jose Manuel Corderoen
PublisherSpringer Verlagen
Content SummaryIn 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.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2018-07-03-
Date of Publication2017-
SubjectAir traffic managementen
Bibliographic CitationT. 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_15en

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