Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making.

In multi-objective decision planning and learning, much attention is paid toproducing optimal solution sets that contain an optimal policy for everypossible user preference profile. We argue that the step that follows, i.e,determining which policy to execute by maximising the user's intrinsic utilityfunction over this (possibly infinite) set, is under-studied. This paper aimsto fill this gap. We build on previous work on Gaussian processes and pairwisecomparisons for preference modelling, extend it to the multi-objective decisionsupport scenario, and propose new ordered preference elicitation strategiesbased on ranking and clustering. Our main contribution is an in-depthevaluation of these strategies using computer and human-based experiments. Weshow that our proposed elicitation strategies outperform the currently usedpairwise methods, and found that users prefer ranking most. Our experimentsfurther show that utilising monotonicity information in GPs by using a linearprior mean at the start and virtual comparisons to the nadir and ideal points,increases performance. We demonstrate our decision support framework in areal-world study on traffic regulation, conducted with the city of Amsterdam.