Social mediator robots facilitate human-human interactions by producing behavior strategies that positively influence how humans interact with each other in social settings. As robots for social mediation gain traction in the field of human-human-robot interaction, their ability to "understand" the humans in their environments becomes crucial. 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 among its members. Group dynamics are defined as the influential actions, processes, and changes that occur within and between group interactants. Since an individual's behavior may be deeply influenced by their interactions with other group members, the social dynamics existing within a group can influence the behaviors, attitudes, and opinions of each individual and the group as a whole. Therefore, models of group dynamics are critical for a social mediator robot to be effective in its role. In this paper, we survey existing models of group dynamics and categorize them into models of social dominance, affect, social cohesion, conflict resolution, and engagement. We highlight the multimodal features these models utilize, and emphasize the importance of capturing the interpersonal aspects of a social interaction. Finally, we make a case for models of relational affect as an approach that may be able to capture a representation of human-human interactions that can be useful for social mediation.

翻译：社会调解机器人通过产生行为策略来促进人与人之间的互动，这些策略能够积极影响人们在社交环境中相互交流的方式。随着社会调解机器人逐渐在人-人-机器人交互领域受到关注，它们“理解”环境中人类的能力变得至关重要。这一目标需要建立对人类理解模型，该模型将交互中的多个个体视为一个整体，并表征其成员之间存在的群体动力学。群体动力学被定义为群体成员之间及群体内部发生的影响性行动、过程和变化。由于个体的行为可能深受与其他群体成员互动的影响，群体内部存在的社会动力学能够影响每个个体以及整个群体的行为、态度和观点。因此，群体动力学模型对于社会调解机器人有效履行其角色至关重要。本文综述了现有的群体动力学模型，并将其归类为社会支配模型、情感模型、社会凝聚力模型、冲突解决模型和参与度模型。我们强调了这些模型所利用的多模态特征，并突出了捕捉社交互动中人际方面的重要性。最后，我们论证了关系情感模型作为一种方法，能够捕捉对社交调解有用的人际互动表征。