让机器人用对比方式解释为何选这个方案而非另一个。
Ontological foundations for contrastive explanatory narration of robot plans
- 用本体模型形式化比较不同行动方案的差异。
- 新算法生成的解释比基线方法更优，能突出关键区别。
- 适合需要透明决策解释的协作机器人场景。
人工代理决策的相互理解是确保人机信任与成功交互的关键。因此，机器人需做出合理决策，并在必要时向人类解释。本文提出一种建模与推理两种竞争性计划差异的方法，使机器人能够后续解释结果分歧。首先，构建了一种新颖的本体模型，用于形式化和推理计划间的差异，从而分类出最合适的方案（如最短路径、最安全、最符合人类偏好等）。同时，分析了基于本体解释叙述的基线算法的局限性。为克服这些不足，提出一种新算法，利用计划间的差异知识，促进对比性叙事的构建。实证评估显示，该方法生成的解释显著优于基线方法。
原文摘要 · Abstract (English)
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.
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