Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Possibilistic constraint satisfaction problems or “how to handle soft constraints?”
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Artificial Intelligence
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Systematic design of program analysis frameworks
POPL '79 Proceedings of the 6th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Compiling Semiring-Based Constraints with clp (FD, S)
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Semiring-Based CSPs and Valued CSPs: Basic Properties and Comparison
Over-Constrained Systems
AI Communications
Constraint solving over semirings
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Quantitative Observables and Averages in Probabilistic Constraint Programming
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
Learning and Solving Soft Temporal Constraints: An Experimental Study
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
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We propose an abstraction scheme for soft constraint problems and we study its main properties. Processing the abstracted version of a soft constraint problem can help us in many ways: for example, to find good approximations of the optimal solutions, or also to provide us with information that can make the subsequent search for the best solution easier. The results of this paper show that the proposed scheme is promising; thus they can be used as a stable formal base for any experimental work specific to a particular class of soft constraint problems.