Choquet Optimization using {GAI} Networks for Multiagent/Multicriteria Decision-Making

Jean-Philippe Dubus 1 Christophe Gonzales 1 Patrice Perny 1
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This paper is devoted to preference-based recommendation or configuration in the context of multiagent (or multicriteria) decision making. More precisely, we study the use of decomposable utility functions in the search for Choquet-optimal solutions on combinatorial domains. We consider problems where the alternatives (feasible solutions) are represented as elements of a product set of finite domains and evaluated according to different points of view (agents or criteria) leading to different objectives. Assuming that objectives take the form of GAI-utility functions over attributes, we investigate the use of GAI networks to determine efficiently an element maximizing an overall utility function defined by a Choquet integral.
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Submitted on : Wednesday, March 30, 2016 - 5:40:22 PM
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Jean-Philippe Dubus, Christophe Gonzales, Patrice Perny. Choquet Optimization using {GAI} Networks for Multiagent/Multicriteria Decision-Making. Algorithmic Decision Theory, Oct 2009, Venice, Italy. pp.377-389, ⟨10.1007/978-3-642-04428-1_33⟩. ⟨hal-01295342⟩



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