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

支持小组帮助人们从有相似经历的人那里学习；众所周知，他们在减少负面生活事件造成的压力方面是有效的。随着更广泛的互联网接入，支持小组也扩展到视频会议形式。面对面和远程支持小组由具有广泛背景和资质的主持人领导。不幸的是，这样的促进者经常感到精疲力竭，导致支持小组关闭。因此，自动化促进器提供了一种在人工促进器不可用时维护支持组的方法。本项目的主要目的是(I)识别和评估有效的自主群体促进者的特征；(Ii)研究和开发用于测量视频媒体多方互动中的个体参与度和群体凝聚力的计算方法；以及(Iii)开发和评估一个能够通过计算手段使群体凝聚力最大化的自主群体促进者。为了实现这些目标，该项目通过Zoom或类似的平台，以社交辅助机器人的形式为远程支持团体构建和研究了一个自主代理调解器。团队中的人际联系和联盟会使支持团队更有效率。因此，该项目将使机器人促进者能够选择增加小组成员参与度和连接性的促进策略。这项研究推动了人工智能技术的发展，以了解人与机器人的互动，并有助于开发能够扩大获得心理健康支持的技术。项目活动将包括为当地市中心的K-12学生举行的年度外联会议，展示自动辅导器并讨论压力管理，以教育STEM和心理健康。该项目还将通过K-12外联活动以及每年培训和指导来自系统服务不足群体的五名本科生研究人员，扩大对计算的参与。该项目促进了能够与多个用户互动的社会互动代理和机器人的最先进水平，以及视频媒介互动。研究内容包括表现型机器人和智能体的实现、自主会话促进算法和用户参与新的促进策略。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)，以主持支持小组。WOZ的研究还将检验这样一个假设，即在吸引用户并向小组参与者展示能力方面，具体化代理促进者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，将训练多模式机器学习模型，以自动识别参与和对话阶段和行为。团队凝聚力将通过网络分析，基于二元参与和个人反应进行评估。研究团队最终将建立一个自主促进者，利用强化学习模型来优化提高团队凝聚力。根据会后问卷所评估的个人参与度和群体凝聚力，将对照另一名优化平等发言机会的代理人对自主调解人进行评估。这项工作将建立自动化小组促进技术，帮助弥合在人类协调员缺席或短缺时提供支持小组方面的差距。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.