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Khaled Belahcene

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3 papers

1 author row

IJCAI Conference 2019 Conference Paper

Comparing Options with Argument Schemes Powered by Cancellation

Khaled Belahcene
Christophe Labreuche
Nicolas Maudet
Vincent Mousseau
Wassila Ouerdane

We introduce a way of reasoning about preferences represented as pairwise comparative statements, based on a very simple yet appealing principle: cancelling out common values across statements. We formalize and streamline this procedure with argument schemes. As a result, any conclusion drawn by means of this approach comes along with a justification. It turns out that the statements which can be inferred through this process form a proper preference relation. More precisely, it corresponds to a necessary preference relation under the assumption of additive utilities. We show the inference task can be performed in polynomial time in this setting, but that finding a minimal length explanation is NP-complete.

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IJCAI Conference 2018 Conference Paper

Accountable Approval Sorting

Khaled Belahcene
Yann Chevaleyre
Christophe Labreuche
Nicolas Maudet
Vincent Mousseau
Wassila Ouerdane

We consider decision situations in which a set of points of view (voters, criteria) are to sort a set of candidates to ordered categories (Good/Bad). Candidates are judged good, when approved by a sufficient set of points of view; this corresponds to NonCompensatory Sorting. To be accountable, such approval sorting should provide guarantees about the decision process and decisions concerning specific candidates. We formalize accountability using a feasibility problem expressed as a boolean satisfiability formulation. We illustrate different forms of accountability when a committee decides with approval sorting and study the information that should be disclosed by the committee.

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IJCAI Conference 2017 Conference Paper

A Model for Accountable Ordinal Sorting

Khaled Belahcene
Christophe Labreuche
Nicolas Maudet
Vincent Mousseau
Wassila Ouerdane

We address the problem of multicriteria ordinalsorting through the lens of accountability, i. e. theability of a human decision-maker to own a recommendationmade by the system. We put forward anumber of model features that would favor the capabilityto support the recommendation with a convincingexplanation. To account for that, we designa recommender system implementing and formalizingsuch features. This system outputs explanationsdefined under the form of specific argumentschemes tailored to represent the specific rules ofthe model. At the end, we discuss possible andpromising argumentative perspectives.

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