  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.
