Learning to Fly Simple and Robust

We report on new experiments with machine learning in the reconstruction of human sub-cognitive skill. The particular problem considered is to generate a clone of a human pilot performing a flying task on a simulated aircraft. The work presented here uses the human behaviour to create constraints for a search process that results in a controller – pilot’s clone. Experiments in this paper indicate that this approach, called “indirect controllers”, results in pilot clones that are, in comparison with those obtained with traditional “direct controllers”, simpler, more robust and easier to understand. An important feature of indirect controllers in this paper is the use of qualitative constraints.