  As artificial intelligence (AI) algorithms are increasingly used in
mission-critical applications, promoting user-trust of these systems will be
essential to their success. Ensuring users understand the models over which
algorithms reason promotes user trust. This work seeks to reconcile differences
between the reward model that an algorithm uses for online partially observable
Markov decision (POMDP) planning and the implicit reward model assumed by a
human user. Action discrepancies, differences in decisions made by an algorithm
and user, are leveraged to estimate a user's objectives as expressed in
weightings of a reward function.
