HCC: Medium: Agent-Facilitated, Video-Mediated Multiparty Interactions in Support Groups

支持团体帮助人们向有类似经历的人学习;众所周知，它们能有效减轻负面生活事件造成的压力。随着因特网的普及，支助小组也扩大到视频会议形式。面对面和远程支持小组由具有广泛背景和资格的主持人领导。 不幸的是，这样的辅导员往往遭受倦怠，导致支持小组关闭。因此，自动化的主持人提供了一种方法，用于维护支持小组时，人类主持人不可用。该项目的主要目的是：（一）确定和评估一个有效的自主组调解人的特点;（二）研究和开发计算方法，用于测量个人的参与度和群体凝聚力在视频为媒介的多方互动;和（三）开发和评估一个自主组调解人，可以通过计算手段最大限度地提高群体凝聚力。为了实现这些目标，该项目建立和研究了一个自主代理服务器的形式，社会辅助机器人远程支持组通过缩放或类似的平台。团体中的人际联系和联盟使支持团体更有效。因此，该项目将使机器人促进者能够选择促进策略，增加小组成员的参与和连通性。这项研究推动了人工智能技术的发展，以了解人机交互，并有助于开发可以扩大获得心理健康支持的技术。该项目的活动将包括为当地市中心的K-12学生举办年度外展会议，展示自动化主持人并讨论压力管理，以教育STEM和心理健康。该项目还将通过K-12外展活动以及每年培训和指导来自系统性服务不足群体的五名本科生研究人员来扩大对计算的参与。该项目推进了社会互动代理和机器人的最新技术，这些机器人能够与多个用户进行视频互动。本研究结合了表达机器人和代理的具体化，自主会话促进算法的研究，和新的促进策略的用户参与。 为此，该项目将首先使用人类驱动的代理，通过绿野仙踪（WoZ）策略，设计代理的行动空间（包括语言和非语言行为），以调节支持小组。 WoZ的研究还将测试一个假设，即一个具体的代理促进者在吸引用户和向小组参与者投射能力方面与人类促进者一样有效。在对WoZ研究期间记录的数据进行编码后，将对多模态机器学习模型进行训练，以自动识别参与和会话阶段和行为。将通过网络分析，根据二元参与和个人反应评估群体凝聚力。研究团队最终将建立一个自主的促进者，利用强化学习模型来优化提高团队凝聚力。自主的主持人将进行评估，对第二个代理，优化平等获得对话发言权，在个人参与和小组凝聚力评估会后问卷。这项工作将建立自动化小组促进技术，可以帮助弥合差距，提供支持小组时，人类辅导员缺席或短缺。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Support groups help people to learn from others who share similar experiences; they are known to be effective in reducing stress caused by negative life events. With broader access to the Internet, support groups have also expanded into video conferencing format. In-person and remote support groups are led by facilitators with wide ranging backgrounds and qualifications. Unfortunately, such facilitators often suffer from burnout, leading to support group closure. Hence, automated facilitators offer a way for maintaining support groups when human facilitators are unavailable. The main aims of this project are (i) to identify and evaluate the characteristics of an effective autonomous group facilitator; (ii) to study and develop computational methods for measuring individual engagement and group cohesion in video-mediated multiparty interaction; and (iii) to develop and evaluate an autonomous group facilitator that can maximize group cohesion through computational means. To achieve these aims, this project builds and studies an autonomous agent facilitator in the form of a socially assistive robot for remote support groups via Zoom or a similar platform. The interpersonal connectedness and alliances in a group make a support group more effective. Therefore, the project will enable the robot facilitator to choose the facilitation strategy that increases group members’ participation and connectedness. This research advances AI technologies for understanding human-robot interaction and contributes to the development of technologies that can broaden access to mental health support. The project activities will include annual outreach sessions for local inner-city K-12 students demonstrating the automated facilitator and discussing stress management, to educate about STEM and mental health. This project will also broaden participation in computing through the K-12 outreach activities and through training and mentoring five undergraduate researchers per year from systematically underserved groups.This project advances the state-of-the-art in socially interactive agents and robots capable of interacting with multiple users, in video-mediated interaction. The research incorporates the study of expressive robot and agent embodiment, algorithm for autonomous conversation facilitation, and user engagement for novel facilitation strategies. To this end, the project will first use a human-driven agent, through a Wizard-of-Oz (WoZ) strategy, to design the agent’s action space (both verbal and nonverbal behaviors) necessary for moderating a support group. The WoZ study will also test the hypothesis that an embodied agent facilitator is as effective as a human facilitator in engaging users and projecting competence to group participants. After coding the data recorded during the WoZ study, multimodal machine learning models will be trained for automatic recognition of engagement and conversational stages and acts. Group cohesion will be assessed based on dyadic engagement and individual responses, through network analysis. The research team will finally build an autonomous facilitator leveraging a reinforcement learning model that optimizes for increasing group cohesion. The autonomous facilitator will be evaluated against a second agent that optimizes for equal access to the conversational floor, in terms of individual engagement and group cohesion assessed by post-session questionnaires. This work will build technologies for automated group facilitation that can assist to bridge the gaps in delivering support groups when human facilitators are absent or in short supply.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.