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dc.contributor.authorChevaleyre, Yann
dc.contributor.authorKoriche, Frédéric
dc.contributor.authorLang, Jérôme
dc.contributor.authorMengin, Jérôme
HAL ID: 184956
dc.contributor.authorZanuttini, Bruno
dc.date.accessioned2010-09-14T13:24:48Z
dc.date.available2010-09-14T13:24:48Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/4771
dc.language.isoenen
dc.subjectPreference relations
dc.subjectCP-Nets
dc.subject.ddc006.3en
dc.titleLearning Ordinal Preferences on Multiattribute Domains: the Case of CP-Nets
dc.typeChapitre d'ouvrage
dc.description.abstractenA recurrent issue in automated decision making is to extract a preference structure from a set of examples. In this paper, we investigate the problem of learningordinal preference orderings over discrete multattribute, or combinatorial,domains. Specifically, we concentrate on the learnability issue of conditional preference networks, or CP-nets, that have recently emerged as a popular graphicallanguage for representing ordinal preferences in a concise and intuitive manner.This paper provides results in both passive and active learning. In passive learning, the learner aims at finding a CP-net compatible with a given set of examples,while in active learning the learner searches for the cheapest interaction policy with the user for acquiring the target CP-net.
dc.identifier.citationpages454
dc.relation.ispartoftitlePreference Learning
dc.relation.ispartofeditorHüllermeier, Eyke
dc.relation.ispartofpublnameSpringer
dc.relation.ispartofpublcityBerlin Heidelberg
dc.relation.ispartofdate2010
dc.description.sponsorshipprivateouien
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-3-642-14124-9
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-09-29T16:41:34Z


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