  Human-supervision in multi-agent teams is a critical requirement to ensure
that the decision-maker's risk preferences are utilized to assign tasks to
robots. In stressful complex missions that pose risk to human health and life,
such as humanitarian-assistance and disaster-relief missions, human mistakes or
delays in tasking robots can adversely affect the mission. To assist human
decision making in such missions, we present an alert-generation framework
capable of detecting various modes of potential failure or performance
degradation. We demonstrate that our framework, based on state machine
simulation and formal methods, offers probabilistic modeling to estimate the
likelihood of unfavorable events. We introduce smart simulation that offers a
computationally-efficient way of detecting low-probability situations compared
to standard Monte-Carlo simulations. Moreover, for certain class of problems,
our inference-based method can provide guarantees on correctly detecting task
failures.
