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合作，这些互动会根据小组中人员的能力，角色，责任，规​​范和人际关系而随着时间的流逝而变化。人类互动（HAI）系统可以通过及时提供有关小组成员的状态，上下文和需求的信息，并代表他们与其他此类AI系统进行互动，从而为管理小组协作提供帮助。老龄化成年人的家庭护理领域是激发这项研究的复杂辅助设置的一个典型例子。老年人，家庭护理人员，医疗专业人员，朋友和邻居经常合作满足不断变化的需求。为了协助这种情况，HAI系统需要：（a）通过整合许多感官方式的数据来对人们的身体，心理和社交能力和需求进行建模； （b）检测用户能力和需求的身体，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； （d）适应交互式行为，以最有效地为用户提供帮助。该项目将开发人类互动中的方法，以学习人类行为的个性化模型以及它们如何随着时间的变化，并利用这些知识更好地协作，交流和协助用户。为了推动这些创新，该研究所将成为整个学术界和行业合作努力的联系点。除了高级研究外，这些合作还将通过广泛的劳动力发展，教育，外展，扩大参与和知识转移计划来积极建立多样性，训练有素的劳动力的下一代人才，旨在传播互动式AI系统的知识和热情的知识。纵向，协作AI - 以互动性的智能HAI系统的设计，开发和部署为特征，这些系统嵌入了长时间（几个月和几年）的用户社区中。设想的HAI系统将采用嵌入在通用消费者设备（例如，手机，智能扬声器）中的虚拟助手的形式，该助手将通过语音，手势，视觉，听觉和混合现实接口与用户进行交互。 HAI系统将基于汇总的传感器观察和过去互动的历史建立个性化用户能力，目标，价值观和人际关系的个性化纵向模型。在此类模型的基础上，网络代理团队将通过按照用户的个人和社会规范运作的个性化和价值驱动的互动来提供协调的援助。 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 methods, explainable systems, novel guidelines for experimental design, and novel benchmarks and metrics for these areas.共同设计的方法，研究演示和长期现场评估将涉及家庭（具有不同类型的传感器的仪器），其中包括具有认知和身体障碍的老年人，其家人，非正式护理人员，专业健康提供者和社区伙伴。 AI-Caring系统将加强日常工作，认识行为的变化，为护理人员提供团队支持，脚手架计划与专业人员的互动，并提供有关个人不同能力的道德鼓励和反馈。这些基本功能将脚手架响应式和个性化的人类互动，这将改变我们与AI系统的日常体验。这项工作的长期影响将超越护理，扩展到包括通过语音，手势，视觉和混合现实接口的长期人类互动的任何应用。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估标准通过评估来评估的。

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