We investigate interaction patterns for humans interacting with explainable and non-explainable robots. Non-explainable robots are here robots that do not explain their actions or non-actions, neither do they give any other feedback during interaction, in contrast to explainable robots. We video recorded and analyzed human behavior during a board game, where 20 humans verbally instructed either an explainable or non-explainable Pepper robot to move objects on the board. The transcriptions and annotations of the videos were transformed into transactions for association rule mining. Association rules discovered communication patterns in the interaction between the robots and the humans, and the most interesting rules were also tested with regular chi-square tests. Some statistically significant results are that there is a strong correlation between men and non-explainable robots and women and explainable robots, and that humans mirror some of the robot's modality. Our results also show that it is important to contextualize human interaction patterns, and that this can be easily done using association rules as an investigative tool. The presented results are important when designing robots that should adapt their behavior to become understandable for the interacting humans.

翻译：我们研究了人类与可解释和不可解释机器人交互时的互动模式。这里的不可解释机器人不会解释自身行为或不作为，也不会在交互过程中提供任何反馈，这与可解释机器人形成对比。我们通过视频记录并分析了一款棋盘游戏中的人类行为，其中20名人类分别用语言指令可解释或不可解释的Pepper机器人移动棋盘上的物体。我们将视频的转写文本与标注转化为用于关联规则挖掘的事务数据。关联规则揭示了机器人与人类交互中的通信模式，并通过常规卡方检验验证了最有趣的规则。具有统计显著性的结果包括：男性与不可解释机器人、女性与可解释机器人之间存在强相关性，且人类会部分镜像机器人的模态特征。我们的结果还表明，必须对人类交互模式进行情境化理解，而关联规则作为研究工具可轻松实现这一目标。这些发现对于设计能自适应调整行为以使交互者易于理解的机器人具有重要意义。