Explainable Goal Recognition: A Framework Based on Weight of Evidence

Abeer Alshehri, Tim Miller, Mor Vered

Abstract: We introduce and evaluate an eXplainable goal recognition (XGR) model that uses the Weight of Evidence (WoE) framework to explain goal recognition problems. Our model provides human-centered explanations that answer `why?' and `why not?' questions. We computationally evaluate the performance of our system over eight different goal recognition domains showing it does not significantly increase the underlying recognition run time. Using a human behavioral study to obtain the ground truth from human annotators, we further show that the XGR model can successfully generate human-like explanations. We then report on a study with 40 participants who observe agents playing a Sokoban game and then receive explanations of the goal recognition output. We investigated participants’ understanding obtained by explanations through task prediction, explanation satisfaction, and trust.