  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 domains. 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
60 participants who observe agents playing Sokoban game and then receive
explanations of the goal recognition output. We investigate participants'
understanding obtained by explanations through task prediction, explanation
satisfaction, and trust.
