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Generating explanation to explain its behavior is an essential capability fora robotic teammate. Explanations help human partners better understand thesituation and maintain trust of their teammates. Prior work on robot generatingexplanations focuses on providing the reasoning behind its decision making.These approaches, however, fail to heed the cognitive requirement ofunderstanding an explanation. In other words, while they provide the rightexplanations from the explainer's perspective, the explainee part of theequation is ignored. In this work, we address an important aspect along thisdirection that contributes to a better understanding of a given explanation,which we refer to as the progressiveness of explanations. A progressiveexplanation improves understanding by limiting the cognitive effort required ateach step of making the explanation. As a result, such explanations areexpected to be smoother and hence easier to understand. A general formulationof progressive explanation is presented. Algorithms are provided based onseveral alternative quantifications of cognitive effort as an explanation isbeing made, which are evaluated in a standard planning competition domain.

Progressive Explanation Generation for Human-robot Teaming