Collaborative Research: Human-Machine Collaboration for Design Space Exploration

本项目研究人工智能（AI）代理与人类在设计空间探索（DSE）中的协作。这项研究的核心是设计复杂系统的新视角，即机器对人类的补充，而不是取代人类。本项目探讨了设计空间探索背景下人机协同设计的两个研究问题。首先，工程师如何从与独立的专家人工智能代理团队合作中受益，每个人在人机对话中扮演不同的角色？第二，人工智能代理如何在明确的行为之外推断出工程师的潜在设计意图？这些问题是在考虑用户体验因素和工程师的认知风格的同时解决的。第一个问题是通过开发具有不同角色（例如，评论家，分析师，历史学家）和不同主动性水平（主动，被动）的设计助理来解决的，并通过人类参与者研究测量它们对设计质量，多样性，学习，代理感知和系统信任的影响。第二个问题是通过使用概率图模型来解决的，包括动态贝叶斯网络和条件随机场，考虑到显式和隐式的人类行为，然后使用马尔可夫决策过程来估计最佳行动。通过对用户体验和认知风格影响的研究，我们将发现不同偏好信息处理模式的设计者通过这些因素和利益的机制。这个项目的第一个智力价值是对人工智能工具的探索和评估，这些工具大大超出了工程设计空间探索（DSE）的技术水平。该项目将推进人类多智能体DSE、DSE空间中的概率意图推理模型以及人机协作设计的具体化框架方面的知识。第二个智力上的优点是提供了协作DSE的新数据集，以阐明设计师如何使用他们的明确行为和与人工智能助手的非语言交流。第三个智力价值是研究具有不同认知风格的人如何在设计任务中从人工智能助手中受益。这个项目的研究问题适用于大量的设计问题，因此可以对许多从事设计的行业产生影响，包括建筑、医学、城市规划、工业设计和商业管理。通过与人工智能合作的新模式来增强人类的能力，可以显著影响设计在上述领域的执行方式。此外，这里的研究项目在执行过程中提供了教育和推广的机会。学生将直接参与这一研究议程，研究将与人工智能、机器人、用户体验和设计课程相结合。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project researches the collaboration between artificial intelligence (AI) agents and humans for Design Space Exploration (DSE). At the core of the research is a new perspective on designing complex systems, one in which machines complement humans instead of replacing them. The project addresses two research questions for human-machine collaborative design in the context of Design Space Exploration. First, how can engineers benefit from working with a team of separate expert AI agents, each taking a different role in the human-machine dialog? Second, how can an AI agent infer the engineer's underlying design intentions beyond explicit actions? These questions are addressed while taking into account user experience considerations and an engineer's cognitive style. The first question is approached by developing design assistants with various roles (e.g., Critic, Analyst, Historian) and different levels of initiative (proactive, reactive) and measuring their effect on design quality, diversity, learning, agent perception, and trust in the system through human-participant studies. The second question is approached by using probabilistic graphical models, including Dynamic Bayes Nets and Conditional Random Fields, taking into account explicit and implicit human behaviors, and then using Markov Decision Processes to estimate the best action. Research on user experience and the effects of cognitive style will identify the mechanisms through which these agents and benefit designers with different preferred modes of processing information.The first intellectual merit of this project is the exploration and evaluation of AI tools that significantly go beyond the state of the art in Engineering Design Space Exploration (DSE). This project will advance knowledge towards human-multi-agent DSE, towards models of probabilistic intention inference in the DSE space, and towards embodied frameworks for human-machine collaborative design. The second intellectual merit is the provision of new data sets of collaborative DSE to shed light on how designers use their explicit actions and nonverbal communication with AI assistants. The third intellectual merit is investigating how people with different cognitive styles can benefit from AI assistants in design tasks. The research questions in this project apply to a large number of design problems, and can thus have impact on many industries which engage in design, including architecture, medicine, urban planning, industrial design, and business management. Enhancing the capabilities of humans through new modes of collaboration with artificial intelligence can significantly impact how design is performed across the above areas. Additionally, the research project here provides opportunities for education and outreach during its execution. Students will be directly involved in this research agenda, and the research will be integrated with AI, robotics, user experience, and design courses.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.