Plan Explanations as Model Reconciliation -- An Empirical Study
  Recent work in explanation generation for decision making agents has looked
at how unexplained behavior of autonomous systems can be understood in terms of
differences in the model of the system and the human's understanding of the
same, and how the explanation process as a result of this mismatch can be then
seen as a process of reconciliation of these models. Existing algorithms in
such settings, while having been built on contrastive, selective and social
properties of explanations as studied extensively in the psychology literature,
have not, to the best of our knowledge, been evaluated in settings with actual
humans in the loop. As such, the applicability of such explanations to human-AI
and human-robot interactions remains suspect. In this paper, we set out to
evaluate these explanation generation algorithms in a series of studies in a
mock search and rescue scenario with an internal semi-autonomous robot and an
external human commander. We demonstrate to what extent the properties of these
algorithms hold as they are evaluated by humans, and how the dynamics of trust
between the human and the robot evolve during the process of these
interactions.
