Adaptive Elicitation of Preferences under Uncertainty in Sequential Decision Making Problems

Adaptive Elicitation of Preferences under Uncertainty in Sequential Decision Making Problems

Nawal Benabbou, Patrice Perny

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 4566-4572. https://doi.org/10.24963/ijcai.2017/637

This paper aims to introduce an adaptive preference elicitation method for interactive decision support in sequential decision problems. The Decision Maker's preferences are assumed to be representable by an additive utility, initially unknown or imperfectly known. We first study the determination of possibly optimal policies when admissible utilities are imprecisely defined by some linear constraints derived from observed preferences. Then, we introduce a new approach interleaving elicitation of utilities and backward induction to incrementally determine an optimal or near-optimal policy. We propose an interactive algorithm with performance guarantees and describe numerical experiments demonstrating the practical efficiency of our approach.
Keywords:
Uncertainty in AI: Sequential Decision Making
Uncertainty in AI: Decision/Utility Theory
Uncertainty in AI: Uncertainty in AI