AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING)

网络群体协作援助和响应式互动人工智能研究所 (AI-CARING)

• 批准号: 2112633
• 负责人: Sonia Chernova
• 金额: $ 1999.58万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112633&HistoricalAwards=false
• 关键词: AI; Institute; Collaborative; Assistance; Responsive

People collaborate with one another in work, home, and social settings, and these interactions change over time based on the capabilities, roles, responsibilities, norms, and interpersonal relationships of those in the group. Human-AI Interaction (HAI) systems can provide assistance in managing group collaborations by providing timely information about the status, context, and needs of group members, and by interacting on their behalf with other such AI systems. The area of home care for aging adults is a prime example of a complex assistive setting inspiring this research. Older adults, family caregivers, medical professionals, friends and neighbors often collaborate to respond to changing needs. To assist in such settings, HAI systems need to: (a) model the physical, mental, and social capabilities and needs of people by integrating data across many sensory modalities; (b) detect physical, cognitive, social and psychological changes in user capabilities and needs; (c) understand the dynamic relationships and capabilities across the support network; and (d) adapt interactive behaviors in order to assist the user most effectively. This project will develop approaches in human-AI interaction that learn personalized models of human behavior and how they change over time, and use that knowledge to better collaborate, communicate, and assist the user. To drive these innovations, the Institute will serve as a nexus point for collaborative efforts across academia and industry. In addition to advanced research, these collaborations will actively build the next generation of talent for a diverse, well-trained workforce through a wide range of workforce development, education, outreach, broadening participation, and knowledge transfer programs designed to disseminate knowledge about, and enthusiasm for, the development of interactive AI systems.The AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING) will develop a discipline focused on personalized, longitudinal, collaborative AI -- characterized by the design, development, and deployment of interactive, intelligent HAI systems embedded within communities of users over extended periods of time (months and years). Envisioned HAI systems will take the form of virtual assistants embedded in common consumer devices (e.g., cell phones, smart speakers) that will interact with users via speech, gesture, visual, auditory, and mixed reality interfaces. HAI systems will establish personalized longitudinal models of user abilities, goals, values, and interpersonal relationships based on aggregated sensor observations and the history of past interactions. Building on such models, networked teams of agents will provide coordinated assistance through personalized and value-driven interactions that operate in accordance with users’ personal and social norms. Researchers in computing, social sciences, and healthcare will collaborate to design, develop, and deploy HAI systems that include sample-efficient techniques for user modeling and personalization, robust methods for longitudinal human-AI teaming, socially-conscious and dignity-preserving AI methodologies, explainable systems, novel guidelines for experimental design, and novel benchmarks and metrics for these areas. Co-design approaches, research demonstrations and long-term field evaluations will involve households (instrumented with different types of sensors) that include older adults with cognitive and physical impairments, their family, informal caregivers, professional health providers and community partners. AI-CARING systems will reinforce daily routines, recognize changes in behavior, provide team support for caregivers, scaffold planning for interactions with professionals, and provide ethical encouragement and feedback regarding an individual's varying abilities. These fundamental capabilities will scaffold responsive and personalized Human-AI Interaction that will transform our day-to-day experiences with AI systems. The long-term impact of this work will go beyond caregiving, extending to any application that includes long-term Human-AI Interaction through speech, gesture, visual and mixed reality interfaces.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.

人们在工作、家庭和社交环境中相互协作，这些互动随着时间的推移而变化，这些互动基于团队中成员的能力、角色、责任、规范和人际关系。人机交互（HAI）系统可以通过提供有关组成员的状态、上下文和需求的及时信息，以及代表他们与其他此类AI系统进行交互，来帮助管理组协作。老年人的家庭护理领域是激发这项研究的复杂辅助环境的一个主要例子。老年人、家庭照顾者、医疗专业人员、朋友和邻居经常合作，以应对不断变化的需求。为了在这种情况下提供帮助，人工智能系统需要：（a）通过整合多种感官模式的数据，模拟人的身体、心理和社会能力和需求;（B）检测用户能力和需求中的身体、认知、社会和心理变化;（c）了解整个支持网络的动态关系和能力;以及（d）调整交互行为以便最有效地帮助用户。 该项目将开发人类与人工智能交互的方法，学习人类行为的个性化模型以及它们如何随时间变化，并利用这些知识更好地合作，沟通和帮助用户。为了推动这些创新，该研究所将成为学术界和工业界合作努力的联系点。除了先进的研究，这些合作将积极建立下一代人才的多元化，训练有素的劳动力，通过广泛的劳动力发展，教育，推广，扩大参与和知识转移计划，旨在传播知识，和热情，交互式人工智能系统的发展。人工智能研究所的协作援助和响应互动的网络群体（AI-CARING）将开发一门专注于个性化，纵向，协作AI的学科-其特征是设计，开发和部署嵌入用户社区的交互式智能HAI系统，时间跨度很长（数月或数年）。 设想中的HAI系统将采取嵌入普通消费者设备中的虚拟助理的形式（例如，蜂窝电话、智能扬声器），其将经由语音、手势、视觉、听觉和混合现实接口与用户交互。 HAI系统将根据聚合的传感器观察和过去交互的历史，建立用户能力、目标、价值观和人际关系的个性化纵向模型。在这种模式的基础上，联网的代理人团队将根据用户的个人和社会规范，通过个性化和价值驱动的互动提供协调一致的援助。计算，社会科学和医疗保健领域的研究人员将合作设计，开发和部署HAI系统，其中包括用于用户建模和个性化的样本效率技术，纵向人类-AI团队的强大方法，具有社会意识和尊严保护的AI方法，可解释的系统，实验设计的新指南以及这些领域的新基准和指标。 共同设计方法、研究示范和长期实地评价将涉及家庭（装有不同类型的传感器），其中包括有认知和身体障碍的老年人、他们的家人、非正规护理人员、专业保健提供者和社区伙伴。人工智能护理系统将加强日常工作，识别行为变化，为护理人员提供团队支持，为与专业人员的互动制定计划，并提供有关个人不同能力的道德鼓励和反馈。这些基本功能将支撑响应式和个性化的人机交互，这将改变我们与人工智能系统的日常体验。 这项工作的长期影响将超越奖励，扩展到任何包括通过语音、手势、视觉和混合现实界面进行长期人机交互的应用。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

Fitness shaping for multiple teams

• DOI: 10.1145/3512290.3528829
• 发表时间: 2022-07
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: J. Cook;Kagan Tumer