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dc.contributor.authorJunker, Ulrich
dc.contributor.authorTsoukiàs, Alexis
dc.date.accessioned2014-04-01T12:17:29Z
dc.date.available2014-04-01T12:17:29Z
dc.date.issued2008
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/13015
dc.language.isoenen
dc.subjectPareto-Optimizationen
dc.subjectDecision Rulesen
dc.subject.ddc003en
dc.titleDecision Policy Design as Pareto-Minimization of Infeasible Lower Boundsen
dc.typeCommunication / Conférence
dc.description.abstractenA decision policy chooses an outcome dependent on given input parameters. Policies can adequately be represented by production rules, which are at the heart of modern business rule management systems. Classic ways of policy design are rule authoring by experts or learning from data. In this paper, we show that policies can also be derived from a model consisting of constraints and preferences. We can design a policy that respects the given preferences by solving a particular combinatorial Pareto-optimization problem. We consider a combined parameter and decision space and introduce a rule for each Pareto-minimal infeasible lower bound in this space. The approach gives interesting insights in the relationships between combinatorial optimization under preferences and rule-based decision making.en
dc.identifier.citationpages14en
dc.relation.ispartofseriestitleTechnical Reporten
dc.relation.ispartofseriesnumberWS-08-09en
dc.relation.ispartoftitleAdvances in Preference Handling. Papers from the AAAI Workshopen
dc.relation.ispartofeditorChomicki, Jan
dc.relation.ispartofeditorConitzer, Vincent
dc.relation.ispartofeditorJunker, Ulrich
dc.relation.ispartofeditorPerny, Patrice
dc.relation.ispartofpublnameAAAI Pressen
dc.relation.ispartofdate2008
dc.relation.ispartofpages156en
dc.subject.ddclabelRecherche opérationnelleen
dc.relation.ispartofisbn978-1-57735-377-5en
dc.relation.conftitle4th Multidisciplinary Workshop on Advances in Preference Handlingen
dc.relation.confdate2008-07
dc.relation.confcityChicagoen
dc.relation.confcountryÉtats-Unisen
dc.relation.forthcomingnonen


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