Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Aggregating partially ordered preferences: impossibility and possibility results
TARK '05 Proceedings of the 10th conference on Theoretical aspects of rationality and knowledge
Generalized value decomposition and structured multiattribute auctions
Proceedings of the 8th ACM conference on Electronic commerce
mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A dichotomy theorem on the existence of efficient or neutral sequential voting correspondences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Choquet Optimization Using GAI Networks for Multiagent/Multicriteria Decision-Making
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Graphical representation of ordinal preferences: languages and applications
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Preferences in AI: An overview
Artificial Intelligence
Decision making with multiple objectives using GAI networks
Artificial Intelligence
Multi-agent soft constraint aggregation via sequential voting
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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This paper deals with preference representation and aggregation in the context of multiattribute utility theory. We consider a set of alternatives having a combinatorial structure. We assume that preferences are compactly represented by graphical utility models derived from generalized additive decomposable (GAI) utility functions. Such functions enable to model interactions between attributes while preserving some decomposability property. We address the problem of finding a compromise solution from several GAI utilities representing different points of view on the alternatives. This scheme can be applied both to multicriteria decision problems and to collective decision making problems over combinatorial domains. We propose a procedure using graphical models for the fast determination of a Pareto-optimal solution achieving a good compromise between the conflicting utilities. The procedure relies on a ranking algorithm enumerating solutions according to the sum of all the GAI utilities until a boundary condition is reached. Numerical experiments are provided to highlight the practical efficiency of our procedure.