  The Next Generation Air Transportation System will introduce new, advanced
sensor technologies into the cockpit. With the introduction of such systems,
the responsibilities of the pilot are expected to dramatically increase. In the
ALARMS (ALerting And Reasoning Management System) project for NASA, we focus on
a key challenge of this environment, the quick and efficient handling of
aircraft sensor alerts. It is infeasible to alert the pilot on the state of all
subsystems at all times. Furthermore, there is uncertainty as to the true
hazard state despite the evidence of the alerts, and there is uncertainty as to
the effect and duration of actions taken to address these alerts. This paper
reports on the first steps in the construction of an application designed to
handle Next Generation alerts. In ALARMS, we have identified 60 different
aircraft subsystems and 20 different underlying hazards. In this paper, we show
how a Bayesian network can be used to derive the state of the underlying
hazards, based on the sensor input. Then, we propose a framework whereby an
automated system can plan to address these hazards in cooperation with the
pilot, using a Time-Dependent Markov Process (TMDP). Different hazards and
pilot states will call for different alerting automation plans. We demonstrate
this emerging application of Bayesian networks and TMDPs to cockpit automation,
for a use case where a small number of hazards are present, and analyze the
resulting alerting automation policies.
