Partitioning agents and shaping their evaluation functions in air traffic problems with hard constraints

  • Authors:

  • William Curran;Adrian Agogino;Kagan Tumer

  • Affiliations:

  • Oregon State University, Corvallis, OR, USA;NASA Ames Research Center, Moffet Field, CA, USA;Oregon State University, Corvallis, OR, USA

  • Venue:
  • Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
  • Year:
  • 2013

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Abstract

Hundreds of thousands of hours of delay, costing millions of dollars annually, are reported by US airports. The task of managing delay may be modeled as a multiagent congestion problem with agents who collectively impact the system. In this domain, agents are tightly coupled, and the environment can quickly change, making it difficult for agents to assess how they impact the system. We combine the noise reduction of fitness function shaping, the robustness of cooperative coevolutionary algorithms, and agent partitioning to perform hard constraint optimization on the congestion and reduce the delay throughout the National Air Space (NAS). Our results show that an autonomous partitioning of the agents using system features leads to up to 540x speed over simple hard constraint enforcement, as well as up to a 21% improvement in performance over a greedy scheduling solution corresponding to hundreds of hours of delay saved in a single day.