Game Theoretic Modeling of Pilot Behavior During Mid-Air Encounters

Abstract

We show how to combine Bayes nets and game theory to predict the behavior of hybrid systems involving both humans and automated components. We call this novel framework Semi Network-Form Games, and illustrate it by predicting aircraft pilot behavior in potential near mid-air collisions. At present, at the beginning of such potential collisions, a collision avoidance system in the aircraft cockpit advises the pilots what to do to avoid the collision. However studies of mid-air encounters have found wide variability in pilot responses to avoidance system advisories. In particular, pilots rarely perfectly execute the recommended maneuvers, despite the fact that the collision avoidance system's effectiveness relies on their doing so. Rather pilots decide their actions based on all information available to them (advisory, instrument readings, visual observations). We show how to build this aspect into a semi network-form game model of the encounter and then present computational simulations of the resultant model.