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Preference Modeling with Possibilistic Networks and Symbolic Weights: A Theoretical Study

Ben Amor, Nahla and Dubois, Didier and Gouider, Héla and Prade, Henri Preference Modeling with Possibilistic Networks and Symbolic Weights: A Theoretical Study. (2016) In: European Conference on Artificial Intelligence (ECAI 2016), 29 August 2016 - 2 September 2016 (The Hague, Netherlands).

(Document in English)

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Official URL: http://dx.doi.org/10.3233/978-1-61499-672-9-1203


The use of possibilistic networks for representing conditional preference statements on discrete variables has been proposed only recently. The approach uses non-instantiated possibility weights to define conditional preference tables. Moreover, additional information about the relative strengths of these symbolic weights can be taken into account. The fact that at best we have some information about the relative values of these weights acknowledges the qualitative nature of preference specification. These conditional preference tables give birth to vectors of symbolic weights that reflect the preferences that are satisfied and those that are violated in a considered situation. The comparison of such vectors may rely on different orderings: the ones induced by the product-based, or the minimum-based chain rule underlying the possibilistic network, the discrimin, or leximin refinements of the minimum-based ordering, as well as Pareto ordering, and the symmetric Pareto ordering that refines it. A thorough study of the relations between these orderings in presence of vector components that are symbolic rather numerical is presented. In particular, we establish that the product-based ordering and the symmetric Pareto ordering coincide in presence of constraints comparing pairs of symbolic weights. This ordering agrees in the Boolean case with the inclusion between the sets of preference statements that are violated. The symmetric Pareto ordering may be itself refined by the leximin ordering. The paper highlights the merits of product-based possibilistic networks for representing preferences and provides a comparative discussion with CP-nets and OCF-networks.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to IOS editor. The definitive version is available at http://ebooks.iospress.nl This papers appears in Volume 285 Frontiers in Artificial Intelligence and Applications ISSN : 0922-6389 The original PDF is vailable at: http://ebooks.iospress.nl/publication/44874
HAL Id:hal-01436166
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Tunis (TUNISIA)
Laboratory name:
Deposited On:03 Jan 2017 16:05

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