  Designing robots capable of generating interpretable behavior is a
prerequisite for achieving effective human-robot collaboration. This means that
the robots need to be capable of generating behavior that aligns with human
expectations and, when required, provide explanations to the humans in the
loop. However, exhibiting such behavior in arbitrary environments could be
quite expensive for robots, and in some cases, the robot may not even be able
to exhibit the expected behavior. Given structured environments (like
warehouses and restaurants), it may be possible to design the environment so as
to boost the interpretability of the robot's behavior or to shape the human's
expectations of the robot's behavior. In this paper, we investigate the
opportunities and limitations of environment design as a tool to promote a type
of interpretable behavior -- known in the literature as explicable behavior. We
formulate a novel environment design framework that considers design over
multiple tasks and over a time horizon. In addition, we explore the
longitudinal aspect of explicable behavior and the trade-off that arises
between the cost of design and the cost of generating explicable behavior over
a time horizon.
