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.

翻译：我们提出并评估了一种基于证据权重（WoE）框架的可解释目标识别（XGR）模型，用于解释目标识别问题。该模型提供以人为中心的解释，可回答“为什么？”和“为什么不？”的问题。我们通过计算评估了该系统在八个不同领域上的性能。利用人类行为研究从标注者处获取真实数据，我们进一步证明XGR模型能够成功生成类人解释。随后，我们报告了一项包含60名参与者的研究，这些参与者观察推箱子游戏中的智能体行为，并接收目标识别输出的解释。我们通过任务预测、解释满意度与信任度指标，考察了参与者通过解释获得的理解程度。