Planning and Learning for Reliable Autonomy in the Open World
Sandhya Saisubramanian,
Assistant Professor
Electrical Engineering and Computer Science

Oregon State University

Safe and reliable decision-making is critical for long-term deployment of autonomous systems. Despite the recent advances in artificial intelligence and robotics, ensuring safe and reliable operation of human-aligned autonomous systems in open-world environments remains a challenge because these systems often operate based on incomplete information. In this talk, I will present an overview of some of our recent efforts in mitigating the undesirable impacts arising due to model incompleteness. First, I will present techniques to overcome Markovian and non-Markovian negative side effects. Second, I will present an approach for reward alignment using explanations. Finally, I will present a technique to maintain and restore safety of autonomous systems using meta-reasoning.

Sandhya Saisubramanian is an Assistant Professor in EECS at Oregon State University. Her research focus is on reliable decision making in single and multiple agents that operate in fully and partially observable open-world environments. She is a recipient of the Outstanding Program Committee member award at the ICAPS 2022 and a Distinguished Paper award at IJCAI 2020. She received her Phd from the University of Massachusetts Amherst.

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AI Seminar Important Reminders:
-> For graduate students in the AI program, attendance is strongly encouraged.