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Élicitation incrémentale des préférences pour l’optimisation multi-objectifs : modèles non-linéaires, domaines combinatoires et approches tolérantes aux erreurs

Nadjet Bourdache 1
1 DECISION
LIP6
Abstract : This thesis work falls within the area of algorithmic decision theory, a research domain at the crossroad of decision theory, operations research and artificial intelligence. The aim is to produce interactive optimization methods based on incremental preference elicitation in decision problems involving several criteria, opinions of agents or scenarios. Preferences are represented by general decision models whose parameters must be adapted to each decision problem and each decision maker. Our methods interleave the elicitation of parameters and the exploration of the solution space in order to determine the optimal choice for the decision maker. The idea behind this is to use information provided by the elicitation to guide the exploration of the solution space and vice versa. In this thesis, we introduce new incremental elicitation methods for decision making in different contexts : first for decision making in combinatorial domains when the decision models are non-linear, and then in a setting where one takes into account the possibility of inconsistencies in the answers of te decision maker. All the algorithms that we introduce are general and can be applied to a wide range of multiobjective decision problems.
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Submitted on : Monday, September 20, 2021 - 1:03:11 PM
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  • HAL Id : tel-03349211, version 2

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Nadjet Bourdache. Élicitation incrémentale des préférences pour l’optimisation multi-objectifs : modèles non-linéaires, domaines combinatoires et approches tolérantes aux erreurs. Intelligence artificielle [cs.AI]. Sorbonne Université, 2020. Français. ⟨NNT : 2020SORUS255⟩. ⟨tel-03349211v2⟩

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