Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Alevizos Bastas"'
Autor:
Alevizos Bastas, George A. Vouros
Publikováno v:
Aerospace, Vol 10, Iss 6, p 557 (2023)
With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic control
Externí odkaz:
https://doaj.org/article/49387d7accff480fbb8d1fe9c9029433
Autor:
Alevizos Bastas, George Vouros
Publikováno v:
Information Sciences. 613:763-785
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::171db390dfd290a105aa5a0e0e46ebaa
https://doi.org/10.1016/j.ins.2022.09.015
https://doi.org/10.1016/j.ins.2022.09.015
Autor:
Vouros, Alevizos Bastas, George A.
Publikováno v:
Aerospace; Volume 10; Issue 6; Pages: 557
With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic control
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::23a39fd58787e2be01b05eb0dc96fc78
Autor:
George Vouros, George Papadopoulos, Alevizos Bastas, Jose Manuel Cordero, Rubén Rodrigez Rodrigez
Dense and complex air traffic scenarios require higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCO) use today. However, the air traffic control (ATC) domain,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::40d756ad88fa5373a9510959a7763c18
https://doi.org/10.3233/faia220066
https://doi.org/10.3233/faia220066
Autor:
Theocharis Kravaris, Jose Manuel Cordero, George A. Vouros, Alevizos Bastas, Christos Spatharis, Konstantinos Blekas
Publikováno v:
Neural Computing and Applications. 35:147-159
In this work we investigate the use of hierarchical multiagent reinforcement learning methods for the computation of policies to resolve congestion problems in the air traffic management domain. To address cases where the demand of airspace use excee
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4e1f1f4c3dc8b0bdb2054c651f6f3551
https://doi.org/10.1007/s00521-021-05748-7
https://doi.org/10.1007/s00521-021-05748-7
Publikováno v:
arXiv e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=r3c4b2081b22::47eb621555c7a07c907b88ea8a81116d
Autor:
Theocharis Kravaris, Alevizos Bastas, Konstantinos Blekas, George A. Vouros, Chistos Spatharis
Publikováno v:
IISA
In this work we investigate the use of hierarchical collaborative reinforcement learning methods (H-CMARL) for the computation of joint policies to resolve congestion problems in the Air Traffic Management (ATM) domain. In particular, to address case
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82864084f9dc0950e9572399898b6afe
https://doi.org/10.1109/iisa.2019.8900719
https://doi.org/10.1109/iisa.2019.8900719