Mining multi-modal communication patterns in interaction with explainable and non-explainable robots
This research addresses the problem of designing adaptable robots for better human understanding, though it is incremental in analyzing communication patterns.
The study investigated human interaction patterns with explainable and non-explainable robots during a board game, finding statistically significant correlations such as men preferring non-explainable robots and women preferring explainable robots, and humans mirroring the robot's modality.
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.