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"Incorporating Prosocial Constraints and Exploiting Problem Structure in Sequential Decision-Making" - Christine Herlihy, University of Maryland, College Park
Date & Time: April 06, 2023 from 03:30 PM - 04:30 PM
Location: Virtual via Zoom
This event is part of the Young Achievers Symposium series hosted by Penn State's Center for Socially Responsible Artificial Intelligence.
About the Talk
Sequential decision-making tasks canonically feature an agent who must explore the environment and exploit the knowledge it gains to maximize total expected reward over some time horizon. However, when algorithms are used to make decisions and induce behaviors over time in high-stakes domains, it is often necessary to trade-off reward maximization against competing objectives, such as individual or group fairness, cooperation, or risk mitigation. Additionally, when the decisions to be made are combinatorial, careful use of the structural information which characterizes or connects our decision points may facilitate our search for efficient solutions.
In this talk, Herlihy considers a constrained resource allocation task characterized by: (1) the presence of multiple objectives; and (2) the need to exploit different types of structure contained within the problem instances in order to ensure tractability and exploit externalities. She specifically considers the restless bandit setting, where a decision-maker is tasked with determining which subset of individuals (referred to as arms) should receive a beneficial intervention at each timestep, subject to the satisfaction of a budget constraint. Each restless arm is formalized as a Markov decision process (MDP), and receipt of the intervention results in an increased probability of a favorable state transition at the next timestep, relative to lack of receipt. Her group’s core contributions include the introduction of novel algorithms to address two limitations of Whittle index-based policies, including (1) the lack of distributive fairness guarantees; and (2) the inability to exploit externalities when resources are allocated within a community.
About the Speaker
Christine Herlihy is a Ph.D. student in computer science at the University of Maryland, College Park, where she is advised by John P. Dickerson. Her research interests include sequential decision-making under uncertainty (i.e., multi-armed bandits; reinforcement learning), algorithmic fairness, knowledge representation and reasoning, and health care. During her Ph.D., she has interned at Amazon Robotics, Microsoft Research, and Google Research. She earned her M.S. from Georgia Tech and completed her undergraduate studies at Georgetown University.
About the Series
The CSRAI Young Achievers Symposium series highlights early career researchers in diverse fields of AI for social impact. The series seeks to focus on emerging research, stimulate discussions, and initiate collaborations that can advance research in artificial intelligence for societal benefit.
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