In multi-objective decision planning and learning, much attention
is paid to producing optimal solution sets that contain an optimal
policy for every possible user preference profile. We argue that
the step that follows, i.e, determining which policy to execute by
maximising the user’s intrinsic utility function over this (possibly
infinite) set, is under-studied. This paper aims to fill this gap. We
build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective
decision support scenario, and propose new ordered preference
elicitation strategies based on ranking and clustering. Our main
contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed
elicitation strategies outperform the currently used pairwise meth-
ods, and found that users prefer ranking most. Our experiments
further show that utilising monotonicity information in GPs by
using a linear prior mean at the start and virtual comparisons to
the nadir and ideal points, increases performance. We demonstrate
our decision support framework in a real-world study on traffic
regulation, conducted with the city of Amsterdam
Original languageEnglish
Title of host publicationAAMAS 2018: Proceedings of the Seventeenth International Joint Conference on Autonomous Agents and Multi-Agent Systems
Number of pages9
Publication statusPublished - Jul 2018
Event17th International Conference on Autonomous Agents and Multiagent Systems: AAMAS 2018 - Stockholm, Sweden
Duration: 10 Jul 201815 Jul 2018
Conference number: 17


Conference17th International Conference on Autonomous Agents and Multiagent Systems: AAMAS 2018
Abbreviated titleAAMAS
Internet address

ID: 36362723