SARA (Socially-Aware Robot Assistant) is an embodied intelligent personal assistant that analyses the user's visual (head and face movement), vocal (acoustic features) and verbal (conversational strategies) behaviours to estimate its rapport level with the user, and uses its own appropriate visual, vocal and verbal behaviors to achieve task and social goals. The presented agent aids conference attendees by eliciting their preferences through building rapport, and then making informed personalized recommendations about sessions to attend and people to meet.

A critical aspect of any recommendation process is explaining the reasoning behind each recommendation. These explanations can not only improve users' experiences, but also change their perception of the recommendation quality. This work describes our human-centered design for our conversational movie recommendation agent, which explains its decisions as humans would. After exploring and analyzing a corpus of dyadic interactions, we developed a computational model of explanations. We then incorporated this model in the architecture of a conversational agent and evaluated the resulting system via a user experiment. Our results show that social explanations can improve the perceived quality of both the system and the interaction, regardless of the intrinsic quality of the recommendations. CCS CONCEPTS • Human-centered computing → Natural language interfaces.

In this paper, we present a framework for facilitation robots that regulate imbalanced engagement density in a four-participant conversation as the forth participant with proper procedures for obtaining initiatives. Four is the special number in multiparty conversations. In three-participant conversations, the minimum unit for multiparty conversations, social imbalance, in which a participant is left behind in the current conversation, sometimes occurs. In such scenarios, a conversational robot has the potential to objectively observe and control situations as the fourth participant. Consequently, we present model procedures for obtaining conversational initiatives in incremental steps to harmonize such four-participant conversations. During the procedures, a facilitator must be aware of both the presence of dominant participants leading the current conversation and the status of any participant that is left behind. We model and optimize these situations and procedures as a partially observable Markov decision process (POMDP), which is suitable for real-world sequential decision processes. The results of experiments conducted to evaluate the proposed procedures show evidence of their acceptability and feeling of groupness.

In this article of the “Interdisciplinary Insights Into Group and Team Dynamics” special issue, we provide guidance for computer scientists and social scientists who seek an interdisciplinary approach to group research. We include how-to guidelines for researchers interested in initiating and maintaining collaborations, and discuss opportunities and pitfalls of interdisciplinary group research. Last, we include a brief case study that portrays some of the complications of creating shared understanding.

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