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.
