  There is a growing interest in designing autonomous agents that can work
alongside humans. Such agents will undoubtedly be expected to explain their
behavior and decisions. While generating explanations is an actively researched
topic, most works tend to focus on methods that generate explanations that are
one size fits all. As in the specifics of the user-model are completely
ignored. The handful of works that look at tailoring their explanation to the
user's background rely on having specific models of the users (either analytic
models or learned labeling models). The goal of this work is thus to propose an
end-to-end adaptive explanation generation system that begins by learning the
different types of users that the agent could interact with. Then during the
interaction with the target user, it is tasked with identifying the type on the
fly and adjust its explanations accordingly. The former is achieved by a
data-driven clustering approach while for the latter, we compile our
explanation generation problem into a POMDP. We demonstrate the usefulness of
our system on two domains using state-of-the-art POMDP solvers. We also report
the results of a user study that investigates the benefits of providing
personalized explanations in a human-robot interaction setting.
