Journal of the ACM (JACM)
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
On the approximability of trade-offs and optimal access of Web sources
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Multicriteria Optimization
Near Admissible Algorithms for Multiobjective Search
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Multi-objective Russian Doll search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A practical efficient fptas for the 0-1 multi-objective knapsack problem
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Choquet Optimization Using GAI Networks for Multiagent/Multicriteria Decision-Making
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Interactive cost configuration over decision diagrams
Journal of Artificial Intelligence Research
Efficient approximation algorithms for multi-objective constraint optimization
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Solving limited memory influence diagrams
Journal of Artificial Intelligence Research
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This paper deals with multiobjective optimization in the context of multiattribute utility theory. The alternatives (feasible solutions) are seen as elements of a product set of attributes and preferences over solutions are represented by generalized additive decomposable (GAI) utility functions modeling individual preferences or criteria. Due to decomposability, utility vectors attached to solutions can be compiled into a graphical structure closely related to junction trees, the so-called GAI net. We first show how the structure of the GAI net can be used to determine efficiently the exact set of Pareto-optimal solutions in a product set and provide numerical tests on random instances. Since the exact determination of the Pareto set is intractable in worst case, we propose a near admissible algorithm with performance guarantee, exploiting the GAI structure to approximate the set of Pareto optimal solutions. We present numerical experimentations, showing that both utility decomposition and approximation significantly improve resolution times in multiobjective search problems.