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Chapitre D'ouvrage Année : 2010

Learning Ordinal Preferences on Multiattribute Domains: the Case of CP-nets

Résumé

A recurrent issue in decision making is to extract a preference structure by observing the user's behavior in different situations. In this paper, we investigate the problem of learning ordinal preference orderings over discrete multi-attribute, or combinatorial, domains. Specifically, we focus on the learnability issue of conditional preference networks, or CP- nets, that have recently emerged as a popular graphical language for representing ordinal preferences in a concise and intuitive manner. This paper provides results in both passive and active learning. In the passive setting, the learner aims at finding a CP-net compatible with a supplied set of examples, while in the active setting the learner searches for the cheapest interaction policy with the user for acquiring the target CP-net.
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Dates et versions

hal-00944354 , version 1 (13-02-2014)

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Yann Chevaleyre, Frédéric Koriche, Jérôme Lang, Jérôme Mengin, Bruno Zanuttini. Learning Ordinal Preferences on Multiattribute Domains: the Case of CP-nets. Johannes Fürnkranz; Eyke Hüllermeier. Preference Learning, Springer, pp.273-296, 2010, ⟨10.1007/978-3-642-14125-6_13⟩. ⟨hal-00944354⟩
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