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20092023
most citedAn Axiomatic Approach to Robustness in Search Problems with Multiple Scenarios

16 citations · 22 across the 10 of their papers we have counts for

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5 papers · 1 filter

cs.AI2023

Robust Ordinal Regression for Subsets Comparisons with Interactions

Hugo Gilbert, Mohamed Ouaguenouni, Meltem Ozturk +1

This paper is dedicated to a robust ordinal method for learning the preferences of a decision maker between subsets. The decision model, derived from Fishburn and LaValle (1996) an…

cs.AI2022★ 1 cited

Cautious Learning of Multiattribute Preferences

Hugo Gilbert, Mohamed Ouaguenouni, Meltem Ozturk +1

This paper is dedicated to a cautious learning methodology for predicting preferences between alternatives characterized by binary attributes (formally, each alternative is seen as…

cs.AI2020

Bayesian preference elicitation for multiobjective combinatorial optimization

Nadjet Bourdache, Patrice Perny, Olivier Spanjaard

We introduce a new incremental preference elicitation procedure able to deal with noisy responses of a Decision Maker (DM). The originality of the contribution is to propose a Baye…

cs.AI2019★ 5 cited

Beyond Pairwise Comparisons in Social Choice: A Setwise Kemeny Aggregation Problem

Hugo Gilbert, Tom Portoleau, Olivier Spanjaard

In this paper, we advocate the use of setwise contests for aggregating a set of input rankings into an output ranking. We propose a generalization of the Kemeny rule where one mini…

cs.AI2012★ 16 cited

An Axiomatic Approach to Robustness in Search Problems with Multiple Scenarios

Patrice Perny, Olivier Spanjaard

This paper is devoted to the search of robust solutions in state space graphs when costs depend on scenarios. We first present axiomatic requirements for preference compatibility w…