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Explaining Robust Additive Decision Models: Generation of Mixed Preference-Swaps by Using MILP

Abstract : In this paper, we are interested in the question of explanation in Multicriteria Decision Aiding (MCDA) in general, and the explanation of the robust additive model in particular. To this end, a previous work has laid the foundations for explaining the necessary preference relation through a sequence of preference swaps. We propose to extend this work by introducing the concept of "mixed explanation" where the computation of its components is done through the resolution of a Mixed-Integer Linear Program. With the help of several examples, we motivate the interest of such an extension and open a discussion toward several promising further questions.
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https://hal.archives-ouvertes.fr/hal-03230519
Contributor : Wassila Ouerdane <>
Submitted on : Thursday, May 20, 2021 - 8:45:01 AM
Last modification on : Tuesday, July 20, 2021 - 3:06:58 AM
Long-term archiving on: : Saturday, August 21, 2021 - 6:10:53 PM

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  • HAL Id : hal-03230519, version 1

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Manuel Amoussou, Khaled Belahcene, Christophe Labreuche, Nicolas Maudet, Vincent Mousseau, et al.. Explaining Robust Additive Decision Models: Generation of Mixed Preference-Swaps by Using MILP. From Multiple Criteria Decision Aid to Preference Learning (DA2PL 2020), Nov 2020, Trento (virtual), Italy. ⟨hal-03230519⟩

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