Order Matters: Generating Progressive Explanations for Planning Tasks in
  Human-Robot Teaming
  Prior work on generating explanations in a planning and decision-making
context has focused on providing the rationale behind an AI agent's decision
making. While these methods provide the right explanations from the explainer's
perspective, they fail to heed the cognitive requirement of understanding an
explanation from the explainee's (the human's) perspective. In this work, we
set out to address this issue by first considering the influence of information
order in an explanation, or the progressiveness of explanations. Intuitively,
progression builds later concepts on previous ones and is known to contribute
to better learning. In this work, we aim to investigate similar effects during
explanation generation when an explanation is broken into multiple parts that
are communicated sequentially. The challenge here lies in modeling the humans'
preferences for information order in receiving such explanations to assist
understanding. Given this sequential process, a formulation based on goal-based
MDP for generating progressive explanations is presented. The reward function
of this MDP is learned via inverse reinforcement learning based on explanations
that are retrieved via human subject studies. We first evaluated our approach
on a scavenger-hunt domain to demonstrate its effectively in capturing the
humans' preferences. Upon analyzing the results, it revealed something more
fundamental: the preferences arise strongly from both domain dependent and
independence features. The correlation with domain independent features pushed
us to verify this result further in an escape room domain. Results confirmed
our hypothesis that the process of understanding an explanation was a dynamic
process. The human preference that reflected this aspect corresponded exactly
to the progression for knowledge assimilation hidden deeper in our cognitive
process.
