Abstract
Designing agents capable of explaining complex
sequential decisions remains a significant open
problem in human-AI interaction. 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 explanation as modelreconciliation. The framework hypothesizes that
one of the common reasons for a user’s confusion
could be the mismatch between the user’s model
of the agent’s task model and the model used by
the agent to generate the decisions. While this is a
general framework, most works that have been explicitly built on this explanatory philosophy have
focused on classical planning 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 a more general
planning paradigm and discuss how such methods
could be used when user models are no longer explicitly available. Specifically, 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 with in the context of planning with
MDPs