Modeling Group Dynamics for Personalized Robot-Mediated Interactions

TL;DR

This paper reviews models of group dynamics in human-human-robot interaction, emphasizing their importance for personalization and proposing future directions including relational affect models for better understanding.

Contribution

It categorizes existing group dynamic models, evaluates their features, and advocates for relational affect models to improve personalization in HHRI.

Findings

Models of social dominance, affect, cohesion, and conflict are categorized.

Existing models' features and interpersonal capturing capabilities are evaluated.

Relational affect models are proposed as promising for future personalization.

Abstract

The field of human-human-robot interaction (HHRI) uses social robots to positively influence how humans interact with each other. This objective requires models of human understanding that consider multiple humans in an interaction as a collective entity and represent the group dynamics that exist within it. Understanding group dynamics is important because these can influence the behaviors, attitudes, and opinions of each individual within the group, as well as the group as a whole. Such an understanding is also useful when personalizing an interaction between a robot and the humans in its environment, where a group-level model can facilitate the design of robot behaviors that are tailored to a given group, the dynamics that exist within it, and the specific needs and preferences of the individual interactants. In this paper, we highlight the need for group-level models of human understanding in human-human-robot interaction research and how these can be useful in developing personalization techniques. We survey existing models of group dynamics and categorize them into models of social dominance, affect, social cohesion, and conflict resolution. We highlight the important features these models utilize, evaluate their potential to capture interpersonal aspects of a social interaction, and highlight their value for personalization techniques. Finally, we identify directions for future work, and make a case for models of relational affect as an approach that can better capture group-level understanding of human-human interactions and be useful in personalizing human-human-robot interactions.