Date and Time: July 2, 2024, 3.00 to 4.30 PM
Venue: NPTEL CRC 301 Studio, IIT Madras
Abstract: AI based sequential decision-making research have either focused on learning from interactions with the environment (i.e., purely data driven) or require the human to provide the entire domain description. While some research has used human domain knowledge as shaping functions, one can argue that human knowledge can be more effectively exploited when learning to act. Specifically, Dr. Natarajan will present his recent research that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithms combine this advice with data. Next, he will discuss methods that can actively solicit these advice instead of requiring them up front. To demonstrate the versatality of the formalism, he will present these advice-taking methods in the context of inverse reinforcement learning, imitation learning, planning and reinforcement learning.
Dr. Sriraam Natarajan is a Professor and the Director for Center for ML at the Department of Computer Science at University of Texas Dallas, a hessian.AI fellow at TU Darmstadt and a RBDSCAII Distinguished Faculty Fellow at IIT Madras. His research interests lie in the field of Artificial Intelligence, with emphasis on Machine Learning, Statistical Relational Learning and AI, Reinforcement Learning, Graphical Models and Biomedical Applications. He is a AAAI senior member and has received the Young Investigator award from US Army Research Office, Amazon Faculty Research Award, Intel Faculty Award, XEROX Faculty Award, Verisk Faculty Award, ECSS Graduate teaching award from UTD and the IU trustees Teaching Award from Indiana University. He is the program chair of AAAI 2024, the general chair of CoDS-COMAD 2024, AI and society track chair of AAAI 2023 and 2022, senior member track chair of AAAI 2023, demo chair of IJCAI 2022, program co-chair of SDM 2020 and ACM CoDS-COMAD 2020 conferences. He was the specialty chief editor of Frontiers in ML and AI journal, and is an associate editor of JAIR, DAMI and Big Data journals.