We advocate exploring complex models by combining data miners (to find a small set of most critical examples) and of multiobjective optimizers (that focus on those critical examples). An example of such a combination is the GALE optimizer that intelligently explores thousands of scenarios by examining just a few dozen of the most informative examples. GALE-style reasoning enables a very fast, very wide ranging exploration of behaviors, as well as the effects of those behaviors’ limitations. This paper applies GALE to the continuous descent approach (CDA) model within the Georgia Tech Work Models that Compute framework. CDA is a model of pilot interactions: with each other and also with the navigation systems critical to safe flight. We show that, using CDA+GALE, it is possible to identify and mitigate factors that make pilots unable to complete all their required tasks in the context of different 1) function allocation strategies, 2) pilot cognitive control strategies, and 3) operational contexts that impact and safe aircraft operation. We also show that other optimization methods can be so slow to run that, without GALE, it might be impractical to find those mitigations.
['Joseph Krall', 'Tim Menzies', 'Misty Davies']
Learning Mitigations for Pilot Issues When Landing Aircraft (via Multiobjective Optimization and Multiagent Simulations)