Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Soft Constraint Logic Programming and Generalized Shortest Path Problems
Journal of Heuristics
Proceedings of the 17th International Conference on Data Engineering
Soft concurrent constraint programming
ACM Transactions on Computational Logic (TOCL)
Semantic optimization techniques for preference queries
Information Systems
Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Enhancing constraints manipulation in semiring-based formalisms
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Extending soft arc consistency algorithms to non-invertible semirings
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Decision making with multiple objectives using GAI networks
Artificial Intelligence
Expert Systems with Applications: An International Journal
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Many combinatorial optimization problems require the assignment of a set of variables in such a way that an objective function is optimized. Often, the objective function involves different criteria, and it may happen that the requirements are in conflict: assignments that are good wrt. one objective may behave badly wrt. another. An optimal solution wrt. all criteria may not exist, and either the efficient frontier (the set of best incomparable solutions, all equally relevant in the absence of further information) or an approximation has to be looked after. The paper shows how the soft constraints formalism based on semirings, so far exploited for finding approximations, can embed also the computation of the efficient frontier in multi-objective optimization problems. The main result is the proof that the efficient frontier of a multi-objective problem can be obtained as the best level of consistency distilled from a suitable soft constraint problem.