Learning Policies for Resolving Demand-Capacity Imbalances During Pre-tactical Air Traffic Management

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 trajector...

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Bibliographic Details
Published inMultiagent System Technologies Vol. 10413; pp. 238 - 255
Main Authors Kravaris, Theocharis, Vouros, George A., Spatharis, Christos, Blekas, Konstantinos, Chalkiadakis, Georgios, Garcia, Jose Manuel Cordero
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2017
Springer International Publishing
SeriesLecture Notes in Computer Science
Online AccessGet full text
ISBN3319647970
9783319647975
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-64798-2_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.
ISBN:3319647970
9783319647975
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-64798-2_15