Choquet Optimization Using GAI Networks for Multiagent/Multicriteria Decision-Making

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 GAIutility 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.