  Designing agents capable of explaining complex sequential decisions remain a
significant open problem in automated decision-making. Recently, there has been
a lot of interest in developing approaches for generating such explanations for
various decision-making paradigms. One such approach has been the idea of {\em
explanation as model-reconciliation}. The framework hypothesizes that one of
the common reasons for the user's confusion could be the mismatch between the
user's model of the task and the one used by the system to generate the
decisions. While this is a general framework, most works that have been
explicitly built on this explanatory philosophy have focused on settings where
the model of user's knowledge is available in a declarative form. Our goal in
this paper is to adapt the model reconciliation approach to the cases where
such user models are no longer explicitly provided. We present a simple and
easy to learn labeling model that can help an explainer decide what information
could help achieve model reconciliation between the user and the agent.
