  For safe and reliable deployment in the real world, autonomous agents must
elicit appropriate levels of trust from human users. One method to build trust
is to have agents assess and communicate their own competencies for performing
given tasks. Competency depends on the uncertainties affecting the agent,
making accurate uncertainty quantification vital for competency assessment. In
this work, we show how ensembles of deep generative models can be used to
quantify the agent's aleatoric and epistemic uncertainties when forecasting
task outcomes as part of competency assessment.
