  For autonomous agents to act as trustworthy partners to human users, they
must be able to reliably communicate their competency for the tasks they are
asked to perform. Towards this objective, we develop probabilistic world models
based on deep generative modelling that allow for the simulation of agent
trajectories and accurate calculation of tasking outcome probabilities. By
combining the strengths of conditional variational autoencoders with recurrent
neural networks, the deep generative world model can probabilistically forecast
trajectories over long horizons to task completion. We show how these
forecasted trajectories can be used to calculate outcome probability
distributions, which enable the precise assessment of agent competency for
specific tasks and initial settings.
