Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression

Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression

Nadjet Bourdache, Patrice Perny, Olivier Spanjaard

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 2023-2029. https://doi.org/10.24963/ijcai.2019/280

We introduce a new model-based incremental choice procedure for multicriteria decision support, that interleaves the analysis of the set of alternatives and the elicitation of weighting coefficients that specify the role of criteria in rank-dependent models such as ordered weighted averages (OWA) and Choquet integrals.  Starting from a prior distribution on the set of weighting parameters, we propose an adaptive elicitation approach based on the minimization of the expected regret to iteratively generate preference queries. The answers of the Decision Maker are used to revise the current distribution until a solution can be recommended with sufficient confidence. We present numerical tests showing the interest of the proposed approach.
Keywords:
Machine Learning: Learning Preferences or Rankings
Knowledge Representation and Reasoning: Knowledge Representation and Decision ; Utility Theory
Multidisciplinary Topics and Applications: Recommender Systems