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Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback
Han Shao · Lee Cohen · Avrim Blum · Yishay Mansour · Aadirupa Saha · Matthew Walter

Tue Dec 12 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #1901

In this work, we propose a multi-objective decision making framework that accommodates different user preferences over objectives, where preferences are learned via policy comparisons. Our model consists of a known Markov decision process with a vector-valued reward function, with each user having an unknown preference vector that expresses the relative importance of each objective. The goal is to efficiently compute a near-optimal policy for a given user. We consider two user feedback models. We first address the case where a user is provided with two policies and returns their preferred policy as feedback. We then move to a different user feedback model, where a user is instead provided with two small weighted sets of representative trajectories and selects the preferred one. In both cases, we suggest an algorithm that finds a nearly optimal policy for the user using a number of comparison queries that scales quasilinearly in the number of objectives.

Author Information

Han Shao (Toyota Technological Institute at Chicago)
Lee Cohen (Toyota Technological Institute at Chicago)
Avrim Blum (Toyota Technological Institute at Chicago)
Yishay Mansour (Tel Aviv University / Google)
Aadirupa Saha (Apple)

Aadirupa Saha is a PhD student at the department of Computer Science and Automation (CSA), Indian Institute of Science (IISc), Bangalore and was a research intern at Google, Mountain View, CA (June-Sept, 2019). Her research interests broadly lie in the areas of Machine Learning, Statistical Learning Theory and Optimization. Her current research specifically focuses on decision making under uncertainty from sequential data, reinforcement learning, and preference based rank aggregation problems.

Matthew Walter (TTI-Chicago)

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