Mutual understanding of artificial agents' decisions is key to ensuring a trustworthy and successful human-robot interaction. Hence, robots are expected to make reasonable decisions and communicate them to humans when needed. In this article, the focus is on an approach to modeling and reasoning about the comparison of two competing plans, so that robots can later explain the divergent result. First, a novel ontological model is proposed to formalize and reason about the differences between competing plans, enabling the classification of the most appropriate one (e.g., the shortest, the safest, the closest to human preferences, etc.). This work also investigates the limitations of a baseline algorithm for ontology-based explanatory narration. To address these limitations, a novel algorithm is presented, leveraging divergent knowledge between plans and facilitating the construction of contrastive narratives. Through empirical evaluation, it is observed that the explanations excel beyond the baseline method.

翻译：人工智能体决策的相互理解是确保可信赖且成功的人机交互的关键。因此，机器人需要做出合理决策，并在必要时向人类传达这些决策。本文重点研究一种对两个竞争性计划进行比较进行建模与推理的方法，以便机器人后续能够解释其差异结果。首先，本文提出了一种新颖的本体模型，用于形式化并推理竞争性计划之间的差异，从而能够对最合适的计划（例如最短、最安全、最符合人类偏好等）进行分类。本研究还探讨了一种基于本体的解释叙事基线算法的局限性。为应对这些局限，本文提出了一种新算法，该算法利用计划间的差异知识，并促进了对比性叙事的构建。通过实证评估，观察到该方法生成的解释效果显著优于基线方法。