OAC Core: Small: Collaborative Research: Conversational Agents for Supporting Sustainable Implementation and Systemic Diffusion of Cyberinfrastructure and Science Gateways

通过设计一个聊天机器人，利用机器学习的力量，将科学家/教育工作者与现有的工具、数据集和其他专家联系起来，这个社会技术项目旨在消除美国STEM社区中重要的用户支持障碍。将开发一个名为Vidura的聊天机器人，以对话代理的形式向新手和专家用户提供个性化用户支持，以促进跨学科研究和教育合作。该项目中的聊天机器人调查使NSF和其他联邦机构20多年来在科学网关(SG)和网络基础设施(CI)方面的投资受益。Vidura聊天机器人日以继夜地工作，当人类代理(即领域专家或CI支持人员)提供的用户支持不可用和/或成本太高时，Vidura聊天机器人会产生最大的影响，因为科学门户的用户接收激增。Vidura聊天机器人最初将在神经科学SG CyNeuro中进行原型制作，但将在多个领域的SGS中进行修改，以造福于更广泛的研究/教育社区。该项目以一种协同的方式整合了人类通信科学、对话代理设计、推荐算法、机器学习技术、领域主题建模，从而推动了社会科学、计算机科学和神经科学的发展。此外，这个社会技术项目提供了研究机会，使社会科学和计算机科学的本科生和研究生受益，并创建了新的跨学科课程。该项目活动将惠及不同背景的学生，因为它将在两所大型公立大学进行，其中一所是西班牙裔服务机构。项目目标和活动将集中回答三个主要研究问题：(I)如何设计一个聊天机器人来收集用户需求并熟练地创建用户配置文件，以便提供个性化的专家服务支持和保持采用？(2)如何在支持行动期间使用科学领域内/跨科学领域的无监督机器学习的基本推荐算法来装备聊天机器人通信和定制对话流？(3)如何在数据密集型/计算密集型应用社区(如神经科学界，以及在SGCI、XSEDE、NSG、CyVerse和Jetstream等CI提供商社区)的研究和教育工作流中实施聊天机器人框架，以评估跨域效用并确定持续相关性的最佳做法？该项目的发现最终将促进关于如何使对话代理实现为聊天机器人接口的相关底层推荐算法的知识，以便以更低的成本和更高的便利性向科学领域用户提供专家服务。该奖项反映了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。

By designing a chatbot that harnesses the power of machine learning to connect scientists/educators with existing tools, datasets, and other experts, this sociotechnical project aims to remove significant user support barriers in the US STEM community. A chatbot called Vidura will be developed to provide personalized user support to novice and expert users in the form of a conversational agent to facilitate interdisciplinary research and education collaborations. The chatbot investigations in the project benefit the investments in science gateways (SG) and cyberinfrastructure (CI) made by the NSF and other federal agencies for over two decades. Vidura chatbot works around the clock and makes the greatest impacts when user support by human agents (i.e., domain specialists or CI support persons) is not available and/or is too costly as a science gateway surges in user uptake. The Vidura chatbot will initially be prototyped in CyNeuro, a neuroscience SG, but will be made accessible to be adapted in SGs across multiple domains to benefit the broader research/education communities.This project integrates human communication science, conversational agent design, recommender algorithms, machine learning techniques, domain topic modeling in a synergistic way that advances social science, computer science, and neuroscience. In addition, this sociotechnical project provides research opportunities to benefit undergraduate and graduate students in both social and computer sciences and creates new interdisciplinary courses. The project activities will benefit students with diverse backgrounds as it will be carried out at two large public universities, one of which is a Hispanic serving institution. The project objectives and activities will focus on answering three main research questions: (i) How to design a chatbot for gathering user requirements and creating user profiles with proficiency in order to provide personalized expert service support and maintain adoption? (ii) How to equip the chatbot communication and custom dialogue flows during support actions with underlying recommender algorithms using un-supervised machine learning within/across science domains? (iii) How to implement a chatbot framework in research and education workflows of data-intensive/computation-intensive application communities (such as the neuroscience community, as well as in CI provider communities of SGCI, XSEDE, NSG, CyVerse, and JetStream) to evaluate utility across domains and identify best practices for ongoing relevance? The project findings will ultimately advance knowledge on how to enable conversational agents implemented as chatbot interfaces with pertinent underlying recommender algorithms in order to provide expert services to scientific domain users with reduced cost and increased convenience.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.