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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Dubus, JP., Gonzales, C., Perny, P. (2009). Choquet Optimization Using GAI Networks for Multiagent/Multicriteria Decision-Making. In: Rossi, F., Tsoukias, A. (eds) Algorithmic Decision Theory. ADT 2009. Lecture Notes in Computer Science(), vol 5783. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04428-1_33
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DOI: https://doi.org/10.1007/978-3-642-04428-1_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04427-4
Online ISBN: 978-3-642-04428-1
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