Graphs and algorithms
On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
Journal of the ACM (JACM)
Utility of pathmax in partial order heuristic research
Information Processing Letters
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
Graphs, Dioids and Semirings: New Models and Algorithms (Operations Research/Computer Science Interfaces Series)
Near Admissible Algorithms for Multiobjective Search
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Aggregation Functions (Encyclopedia of Mathematics and its Applications)
Aggregation Functions (Encyclopedia of Mathematics and its Applications)
A new approach to multiobjective A* search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Algebraic Markov decision processes
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Preferences in AI: An overview
Artificial Intelligence
Efficient approximation algorithms for multi-objective constraint optimization
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
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Multiobjective Dynamic Programming (MODP) is a general problem solving method used to determine the set of Pareto-optimal solutions in optimization problems involving discrete decision variables and multiple objectives. It applies to combinatorial problems in which Pareto-optimality of a solution extends to all its sub-solutions (Bellman principle). In this paper we focus on the determination of the preferred tradeoffs in the Pareto set where preference is measured by a Choquet integral. This model provides high descriptive possibilities but the associated preferences generally do not meet the Bellman principle, thus preventing any straightforward adaptation of MODP. To overcome this difficulty, we introduce here a general family of dominance rules enabling an early pruning of some Pareto-optimal sub-solutions that cannot lead to a Choquet optimum. Within this family, we identify the most efficient dominance rules and show how they can be incorporated into a MODP algorithm. Then we report numerical tests showing the actual efficiency of this approach to find Choquet-optimal tradeoffs in multiobjective knapsack problems.