Kagan Tumer's Publications

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Hierarchical Simulation for Complex Domains : Air Traffic Flow Management. W. Curran, A. Agogino, and K. Tumer. In Proceedings of the Genetic and Evolutionary Computation Conference, Vancouver, Canada, July 2014.

Abstract

A key element in the continuing growth of air traffic is the increased use of automation. The Next Generation (Nex-Gen) Air Traffic System will include automated decision support systems and satellite navigation that will let pilots know the precise locations of other aircraft around them. This Next-Gen suggestion system can assist pilots in making good decisions when they have to direct the aircraft them- selves. However, effective automation is critical in achieving the capacity and safety goals of the Next-Gen Air Traffic System. In this paper we show that evolutionary algorithms can be used to achieve this effective automation.

However, it is not feasible to use a standard evolutionary algorithm learning approach in such a detailed simulation. Therefore, we apply a hierarchical simulation approach to an air traffic congestion problem where agents must reach a destination while avoiding separation violations. Due to the dynamic nature of this problem, agents need to learn fast. Therefore, we apply low fidelity simulation for agents learning their destination, and a high fidelity simulation employing the Next-Gen technology for learning separation assurance. The hierarchical simulation approach increases convergence rate, leads to a better performing solution, and lowers computational complexity by up to 50 times.

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BibTeX Entry

@inproceedings{tumer-curran_gecco14,
        author = {W. Curran and A. Agogino   and K. Tumer},
        title = {Hierarchical Simulation for Complex Domains : Air Traffic Flow Management},
        booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference},
	month = {July},
	address = {Vancouver,  Canada},
	abstract={A key element in the continuing growth of air traffic is the increased use of automation. The Next Generation (Nex-Gen) Air Traffic System will include automated decision support systems and satellite navigation that will let pilots know the precise locations of other aircraft around them. This Next-Gen suggestion system can assist pilots in making good decisions when they have to direct the aircraft them- selves. However, effective automation is critical in achieving the capacity and safety goals of the Next-Gen Air Traffic System. In this paper we show that evolutionary algorithms can be used to achieve this effective automation.
	<p>
However, it is not feasible to use a standard evolutionary algorithm learning approach in such a detailed simulation. Therefore, we apply a hierarchical simulation approach to an air traffic congestion problem where agents must reach a destination while avoiding separation violations. Due to the dynamic nature of this problem, agents need to learn fast. Therefore, we apply low fidelity simulation for agents learning their destination, and a high fidelity simulation employing the Next-Gen technology for learning separation assurance. The hierarchical simulation approach increases convergence rate, leads to a better performing solution, and lowers computational complexity by up to 50 times.},
	bib2html_pubtype = {Refereed Conference Papers},
	bib2html_rescat = {Air Traffic Control},
        year = {2014}
        } 

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