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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

Great Hall & Hall B1+B2 (level 1) #1901
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Tue 12 Dec 8:45 a.m. PST — 10:45 a.m. PST


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.

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