Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

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

Nadjet Bourdache
Patrice Perny
Olivier Spanjaard

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.
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Dates and versions

hal-02202468 , version 1 (31-07-2019)

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  • HAL Id : hal-02202468 , version 1

Cite

Nadjet Bourdache, Patrice Perny, Olivier Spanjaard. Incremental Elicitation of Rank-Dependent Aggregation Functions based on Bayesian Linear Regression. IJCAI-19 - Twenty-Eighth International Joint Conference on Artificial Intelligence, Aug 2019, Macao, China. pp.2023-2029. ⟨hal-02202468⟩
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