GAI Networks for Decision Making under Certainty

Christophe Gonzales 1 Patrice Perny 1
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : This paper deals with preference elicitation and preference-based optimization in the context of multiattribute utility theory under certainty. We focus on the generalized additive decomposable utility model which allows interactions between attributes while preserving some decomposability. We first present a systematic elicitation procedure for such utility functions. This procedure relies on a graphical model called a GAI-network which is used to represent and manage independences between attributes, just as junction graphs model independences between random variables in Bayesian networks. Then, we propose an optimization procedure relying on this network to compute efficiently the solution of optimization problems over a product set.
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Conference papers
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Submitted on : Monday, March 20, 2017 - 11:47:49 AM
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  • HAL Id : hal-01492604, version 1


Christophe Gonzales, Patrice Perny. GAI Networks for Decision Making under Certainty. 19th International Joint Conference on Artificial Intelligence -- workshop on advances in preference handling, Jul 2005, Edinburgh, United Kingdom. pp.100-105. ⟨hal-01492604⟩



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