A Psychological Analysis of Preference Semantics in Conditional Logics for Preference Representation

Souhila Kaci 1 Éric Raufaste 2
1 GRAPHIK - Graphs for Inferences on Knowledge
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Qualitative and comparative preference statements of the form “prefer α to β” are useful components of many applications. This statement leads to the comparison of two sets of alternatives: the set of alternatives in which α is true and the set of alternatives in which β is true. Different ways are possible to compare two sets of objects leading to what is commonly known as preference semantics. The choice of the semantics to employ is important as they differently rank-order alternatives. Existing semantics are based on philosophical and non-monotonic reasoning grounds. In the meanwhile, they have been widely and mainly investigated by AI researchers from algorithmic point of view. In this paper, we come to this problem from a new angle and complete existing theoretical investigations of the semantics. In particular, we provide a comparison of the semantics on the basis of their psychological plausibility by evaluating their closeness to human behavior.
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Communication dans un congrès
SUM: Scalable Uncertainty Management, Sep 2014, Oxford, United Kingdom. 8th International Conference on Scalable Uncertainty Management, LNCS (8720), pp.209-222, 2014, Scalable Uncertainty Management. 〈10.1007/978-3-319-11508-5_18〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01082077
Contributeur : Souhila Kaci <>
Soumis le : mercredi 12 novembre 2014 - 15:24:49
Dernière modification le : samedi 27 janvier 2018 - 01:31:51

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Souhila Kaci, Éric Raufaste. A Psychological Analysis of Preference Semantics in Conditional Logics for Preference Representation. SUM: Scalable Uncertainty Management, Sep 2014, Oxford, United Kingdom. 8th International Conference on Scalable Uncertainty Management, LNCS (8720), pp.209-222, 2014, Scalable Uncertainty Management. 〈10.1007/978-3-319-11508-5_18〉. 〈lirmm-01082077〉

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