Hierarchical simulation for complex domains

Abstract

A key element in the continuing growth of air traffic is the increased use of automation. The Next Generation (NextGen) 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 themselves. 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.