Incremental Elicitation of Rank-Dependent Aggregation Functions Based on Bayesian Linear Regression
Abstract
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.
Cite
Text
Bourdache et al. "Incremental Elicitation of Rank-Dependent Aggregation Functions Based on Bayesian Linear Regression." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/280Markdown
[Bourdache et al. "Incremental Elicitation of Rank-Dependent Aggregation Functions Based on Bayesian Linear Regression." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/bourdache2019ijcai-incremental/) doi:10.24963/IJCAI.2019/280BibTeX
@inproceedings{bourdache2019ijcai-incremental,
title = {{Incremental Elicitation of Rank-Dependent Aggregation Functions Based on Bayesian Linear Regression}},
author = {Bourdache, Nadjet and Perny, Patrice and Spanjaard, Olivier},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {2023-2029},
doi = {10.24963/IJCAI.2019/280},
url = {https://mlanthology.org/ijcai/2019/bourdache2019ijcai-incremental/}
}