Uploaded October 2021 | Updated September 2026, 2 weeks ago
Siddharth Srivastava
Assistant Professor
School of Computing, Informatics, & Decision Systems Engineering, Arizona State University
October 29, 2021
https://www.ri.cmu.edu/event/ri-seminar-siddharth-srivastava-arizona-state-university-assistant-professor-2021-10-22/
The Unusual Effectiveness of Abstractions for Assistive AI
Abstract: Can we balance efficiency and reliability while designing assistive AI systems? What would such AI systems need to provide? In this talk I will present some of our recent work addressing these questions. In particular, I will show that a few fundamental principles of abstraction are surprisingly effective in designing efficient and reliable AI systems that can plan and act over multiple timesteps. Our results show that abstraction mechanisms are invaluable not only in improving the efficiency of sequential decision making, but also in developing AI systems that can explain their own behavior to non-experts, and in computing user-interpretable assessments of the limits and capabilities of Black-Box AI systems. I will also present some of our work on learning the requisite abstractions in a bottom-up fashion. Throughout the talk I will highlight the theoretical guarantees that our methods provide along with results from empirical evaluations featuring decision-support/digital AI systems and physical robots.
Brief Bio: Siddharth Srivastava is an Assistant Professor of Computer Science in the School of Computing and Augmented Intelligence at Arizona State University. Prof. Srivastava was a Staff Scientist at the United Technologies Research Center in Berkeley. Prior to that, he was a postdoctoral researcher in the RUGS group at the University of California Berkeley. He received his PhD in Computer Science from the University of Massachusetts Amherst. His research interests include robotics and AI, with a focus on reasoning, planning, and acting under uncertainty. His work on integrated task and motion planning for household robotics has received coverage from international news media. He is a recipient of the NSF CAREER award, a Best Paper award at the International Conference on Automated Planning and Scheduling (ICAPS) and an Outstanding Dissertation award from the Department of Computer Science at UMass Amherst. He served as conference chair for ICAPS 2019 and currently serves as an Associate Editor for the Journal of AI Research.
Siddharth Srivastava
Assistant Professor
School of Computing, Informatics, & Decision Systems Engineering, Arizona State University
October 29, 2021
https://www.ri.cmu.edu/event/ri-seminar-siddharth-srivastava-arizona-state-university-assistant-professor-2021-10-22/
The Unusual Effectiveness of Abstractions for Assistive AI
Abstract: Can we balance efficiency and reliability while designing assistive AI systems? What would such AI systems need to provide? In this talk I will present some of our recent work addressing these questions. In particular, I will show that a few fundamental principles of abstraction are surprisingly effective in designing efficient and reliable AI systems that can plan and act over multiple timesteps. Our results show that abstraction mechanisms are invaluable not only in improving the efficiency of sequential decision making, but also in developing AI systems that can explain their own behavior to non-experts, and in computing user-interpretable assessments of the limits and capabilities of Black-Box AI systems. I will also present some of our work on learning the requisite abstractions in a bottom-up fashion. Throughout the talk I will highlight the theoretical guarantees that our methods provide along with results from empirical evaluations featuring decision-support/digital AI systems and physical robots.
Brief Bio: Siddharth Srivastava is an Assistant Professor of Computer Science in the School of Computing and Augmented Intelligence at Arizona State University. Prof. Srivastava was a Staff Scientist at the United Technologies Research Center in Berkeley. Prior to that, he was a postdoctoral researcher in the RUGS group at the University of California Berkeley. He received his PhD in Computer Science from the University of Massachusetts Amherst. His research interests include robotics and AI, with a focus on reasoning, planning, and acting under uncertainty. His work on integrated task and motion planning for household robotics has received coverage from international news media. He is a recipient of the NSF CAREER award, a Best Paper award at the International Conference on Automated Planning and Scheduling (ICAPS) and an Outstanding Dissertation award from the Department of Computer Science at UMass Amherst. He served as conference chair for ICAPS 2019 and currently serves as an Associate Editor for the Journal of AI Research.
