Learning policies for resolving demand-capacity imbalances during pre-tactical air traffic managementLearning policies for resolving demand-capacity imbalances during pre-tactical air traffic management Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2018-07-032017enIn 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.http://creativecommons.org/licenses/by/4.0/238-25515th German Conference on Multiagent System TechnologiesMultiagent System Technologies Kravaris Theocharis Vouros, George A Spatharis Christos Blekas, Konstantinos D Chalkiadakis Georgios Χαλκιαδακης Γεωργιος Garcia Jose Manuel Cordero Springer Verlag Air traffic management