AI Seminar Series 2024: Explainable Planning and Decision Making, Dr. William Yeoh
The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.
Abstract:
With increasing proliferation and integration of planning and decision-making systems in our daily life, there is a surge of interest in systems that can explain the reasons for their decisions. In this talk, I will describe such explainable systems through the lens of model reconciliation, which is the problem of reconciling the mental models of the AI system and the human user receiving the explanation. I will also provide an overview of our work in this space, including how logic- and argumentation-based approaches can be used.
Presenter Bio:
William Yeoh is an associate professor in the Department of Computer Science and Engineering and the director of the Division of Computational and Data Sciences at Washington University in St. Louis. His research focus is on AI with the goal of enabling effective human-AI collaboration. He builds on his primary expertise in distributed constraint optimization as well as automated planning and scheduling to develop optimization algorithms for human-aware agent-based systems. He is an NSF CAREER recipient and was named in IEEE's AI's 10-to-Watch list in 2015. He currently serves on the editorial board of the Artificial Intelligence journal, on the IFAAMAS Board of Directors, on the ICAPS Council, and on the SoCS Council.
The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.
Abstract:
With increasing proliferation and integration of planning and decision-making systems in our daily life, there is a surge of interest in systems that can explain the reasons for their decisions. In this talk, I will describe such explainable systems through the lens of model reconciliation, which is the problem of reconciling the mental models of the AI system and the human user receiving the explanation. I will also provide an overview of our work in this space, including how logic- and argumentation-based approaches can be used.
Presenter Bio:
William Yeoh is an associate professor in the Department of Computer Science and Engineering and the director of the Division of Computational and Data Sciences at Washington University in St. Louis. His research focus is on AI with the goal of enabling effective human-AI collaboration. He builds on his primary expertise in distributed constraint optimization as well as automated planning and scheduling to develop optimization algorithms for human-aware agent-based systems. He is an NSF CAREER recipient and was named in IEEE’s AI’s 10-to-Watch list in 2015. He currently serves on the editorial board of the Artificial Intelligence journal, on the IFAAMAS Board of Directors, on the ICAPS Council, and on the SoCS Council.
