Order Matters: Generating Progressive Explanations for Planning Tasks in Human-Robot Teaming. (arXiv:2004.07822v1 [cs.AI])

Prior work on generating explanations has been focused on providing the
rationale behind the robot’s decision making. While these approaches provide
the right explanations from the explainer’s perspective, they fail to heed the
cognitive requirement of understanding an explanation from the explainee’s
perspective. In this work, we set out to address this issue from a planning
context by considering the order of information provided in an explanation,
which is referred to as the progressiveness of explanations. Progressive
explanations contribute to a better understanding by minimizing the cumulative
cognitive effort required for understanding all the information in an
explanation. As a result, such explanations are easier to understand. Given the
sequential nature of communicating information, a general formulation based on
goal-based Markov Decision Processes for generating progressive explanation is
presented. The reward function of this MDP is learned via inverse reinforcement
learning based on explanations that are provided by human subjects. Our method
is evaluated in an escape-room domain. The results show that our progressive
explanation generation method reduces the cognitive load over two baselines.

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