PhD graduated
Departure date : 08/31/2017

Supervision : Patrice PERNY

Procédures de décision par élicitation incrémentale de préférences en optimisation multicritère, multi-agents et dans l'incertain

This thesis work falls within the area of algorithmic decision theory which is at the junction of decision theory, operations research and artificial intelligence. Our aim is to produce algorithms allowing the fast resolution of decision problems in complex environments (multiple criteria, multi-agents, uncertainty). This work focuses on decision-theoretic elicitation and uses preferences to efficiently determine the best solutions among a set of alternatives explicitly or implicitly defined (combinatorial optimization). For combinatorial optimization problems, we propose and study a new approach consisting in interleaving incremental preference elicitation and preference-based search. The idea is to use the exploration to identify informative preference queries while exploiting answers to better focus the search on the preferred solutions. This approach leads us to propose incremental elicitation procedures for multi-objective state-space search problems, multicriteria shortest path problems, multicriteria minimum spanning tree problems, multi-agents knapsack problems and sequential decision problems under uncertainty. We provide theoretical guarantees on the correctness of the proposed algorithms and we present numerical tests showing their practical efficiency.

Defence : 05/05/2017

Jury members :

M. Jérôme LANG, Directeur de Recherche (CNRS - l'Université Paris-Dauphine) [Rapporteur]
M. Marc PIRLOT, Professeur(Faculté Polytechnique de Mons) [Rapporteur]
M. Patrice PERNY, Professeur (l'Université Pierre et Marie Curie)
Mme Clarisse DHAENENS, Professeur (Université Lille 1)
M. Nicolas MAUDET, Professeur (Université Pierre et Marie Curie)
M. Vincent MOUSSEAU, Professeur à l'École Centrale de Paris
M. Daniel VANDERPOOTEN, Professeur à l'Université Paris-Dauphine

Associate Professor

1 PhD student (Supervision / Co-supervision)

1 PhD graduated 2022

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