  Intelligent robots are redefining a multitude of critical domains but are
still far from being fully capable of assisting human peers in day-to-day
tasks. An important requirement of collaboration is for each teammate to
maintain and respect an understanding of the others' expectations of itself.
Lack of which may lead to serious issues such as loose coordination between
teammates, reduced situation awareness, and ultimately teaming failures. Hence,
it is important for robots to behave explicably by meeting the human's
expectations. One of the challenges here is that the expectations of the human
are often hidden and can change dynamically as the human interacts with the
robot. However, existing approaches to generating explicable plans often assume
that the human's expectations are known and static. In this paper, we propose
the idea of active explicable planning to relax this assumption. We apply a
Bayesian approach to model and predict dynamic human belief and expectations to
make explicable planning more anticipatory. We hypothesize that active
explicable plans can be more efficient and explicable at the same time, when
compared to explicable plans generated by the existing methods. In our
experimental evaluation, we verify that our approach generates more efficient
explicable plans while successfully capturing the dynamic belief change of the
human teammate.
