Communication robots have the potential to contribute to effective human-XAI interaction as an interface that goes beyond textual or graphical explanations. One of their strengths is that they can use physical and vocal expressions to add detailed nuances to explanations. However, it is not clear how a robot can apply such expressions, or in particular, how we can develop a strategy to adaptively use such expressions depending on the task and user in dynamic interactions. To address this question, this paper proposes DynEmph, a method for a communication robot to decide where to emphasize XAI-generated explanations with physical expressions. It predicts the effect of emphasizing certain points on a user and aims to minimize the expected difference between predicted user decisions and AI-suggested ones. DynEmph features a strategy for deciding where to emphasize in a data-driven manner, relieving engineers from the need to manually design a strategy. We further conducted experiments to investigate how emphasis selection strategies affect the performance of user decisions. The results suggest that, while a naive strategy (emphasizing explanations for an AI's most probable class) does not necessarily work better, DynEmph effectively guides users to better decisions under the condition that the performance of the AI suggestion is high.

翻译：通信机器人作为超越文本或图形解释的接口，有望促进有效的人机交互解释（Human-XAI Interaction）。其优势之一在于能够通过肢体动作和语音表达为解释添加细微的语意层次。然而，机器人如何运用这些表达方式，特别是在动态交互中如何根据任务和用户自适应地制定运用策略，目前尚不明确。针对这一问题，本文提出DynEmph方法——一种让通信机器人通过物理表达决定XAI生成解释中重点强调位置的方法。该方法预测对用户强调特定要点的影响，并旨在最小化预测用户决策与AI建议决策之间的期望差异。DynEmph采用数据驱动策略决定强调位置，使工程师无需手动设计策略。我们进一步开展实验，探究不同强调选择策略如何影响用户决策表现。结果表明：虽然朴素策略（强调AI最可能类别的解释）未必更优，但在AI建议性能较高的情况下，DynEmph能有效引导用户做出更优决策。