Toward Introspective Human Versus Machine Learning of Simulated Airplane Flight Dynamics.
Dan Tappan, Matt Hempleman · MAICS · 2014
This paper presents the preliminary results of an extensible Java architecture for modeling, simulating, visualizing, and analyzing modularized, plug-and-play machine-learning strategies applied to instrument-based airplane flight control. A set of basic flight maneuvers challenged the machine to learn how to fly unsupervised by trial and error, from which the learning module attempted to introspectively determine interdependencies among the many inputs and outputs. For baseline comparison, this work also included a pilot study on human subjects who conducted the same experiments. The overarching goal was to determine how, and how well, both groups learned to solve the same flight-related problems on their own, which could be useful to refine and expand the learning strategies.