Wed, May 03, 2017 @ 02:00 PM - 04:00 PM
PhD Candidate: Elaine Short
Title: Managing Multi-Party Social Dynamics for Socially Assistive Robotics
Location: RTH 406
Maja Matarić (Chair)
Gisele Ragusa (External)
This dissertation presents a domain-independent computational model of moderation of multi-party human-machine interactions that enables a robot or virtual agent to act as a moderator in a group interaction.
A moderator is defined in this work as an agent that regulates social and task outcomes in a goal-oriented social interaction. This model has multiple applications in human-machine interaction: groups of people often require some management or facilitation to ensure smooth and productive interaction, especially when the context is emotionally fraught or the participants do not know each other well. A particularly relevant application domain for moderation is in Socially Assistive Robotics (SAR), where systems are frequently deployed without complex speech understanding or dialogue management, but where group interactions can benefit from a moderator's participation. The evaluation of the model focuses on intergenerational interactions, but the model is applicable to various other SAR domains as well, including group therapy, informal teaching between peers, and social skills therapy.
Moderation is formalized as a decision-making problem, where measures of task performance and positive social interaction in a group are maximized through the behavior of a social moderator. This framework provides a basis for the development of a series of control algorithms for robot moderators to assist groups of people in improving task performance and managing the social dynamics of interactions in diverse domains. Based on reliably-sensed features of the interaction such as task state and voice activity, the moderator takes social actions that can predictably alter task performance and the social dynamics of the interaction. Thus the moderator is able to support human-human interaction in unpredictable, open-ended, real-world contexts.
The model of moderation provides a framework for developing algorithms that enable robots to moderate group interactions without the need for speech recognition; it complements dialogue systems and human-computer interaction, providing conversational agents with additional strategies for managing dynamics of group interaction. Four algorithms are developed based on the model: a basic moderation algorithm, a task-goal-based moderation algorithm, a social-feature-based moderation algorithm, and a combined algorithm that takes into account both task goals and social features. These algorithms are validated in both peer-group interactions and inter-generational family interactions where the moderator supports interactions including members of multiple generations within the same family. The work is intended for short- and long-term deployments of socially assistive robots and virtual agents, and can be applied across assistive domains to facilitate social interactions and improve task performance.
Audiences: Everyone Is Invited
Contact: Lizsl De Leon