Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

By Luisa M. Zintgraf, Diederik Marijn Roijers, Sjoerd Linders, C. Jonker, Ann Nowé. 2018

54 citations recorded by the source corpus.

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 methods, 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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Patrick Mannion
2. Ann Nowé
3. Diederik Marijn Roijers
4. Roxana Rădulescu
5. Julian Reiss

In Computational Theory and Mathematics

1. Patrick Mannion
2. Ann Nowé
3. Diederik Marijn Roijers

Citations in the corpus

• Multi-objective multi-agent decision making: a utility-based analysis and survey (2019, methodology): “ dierent values for all objectives is a typical setting for negotiation [46]. Note that in multi-objective settings, agents and/or users are often incapable of specifying their utilities numerically [133]. However, recently there has been research in automated negotiation focusing on preference unce”