Abstracting soft constraints: framework, properties, examples
Artificial Intelligence
Soft Concurrent Constraint Programming
ESOP '02 Proceedings of the 11th European Symposium on Programming Languages and Systems
Labeling and Partial Local Consistency for Soft Constraint Programming
PADL '00 Proceedings of the Second International Workshop on Practical Aspects of Declarative Languages
Compiling Semiring-Based Constraints with clp (FD, S)
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
The Rough Guide to Constraint Propagation
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Constraint Propagation for Soft Constraints: Generalization and Termination Conditions
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Semirings for Soft Constraint Solving and Programming (LECTURE NOTES IN COMPUTER SCIENCE)
Semirings for Soft Constraint Solving and Programming (LECTURE NOTES IN COMPUTER SCIENCE)
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Rewriting Logic Framework for Soft Constraints
Electronic Notes in Theoretical Computer Science (ENTCS)
Semiring-Based Soft Constraints
Concurrency, Graphs and Models
Branch and Bound Algorithms to Solve Semiring Constraint Satisfaction Problems
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Which Soft Constraints do you Prefer?
Electronic Notes in Theoretical Computer Science (ENTCS)
Implementing an abstraction framework for soft constraints
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
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Although Constraint Programming (CP) is considered a useful tool for tackling combinatorial problems, its lack of flexibility when dealing with uncertainties and preferences is still a matter for research. Several formal frameworks for soft constraints have been proposed within the CP community: all of them seem to be theoretically solid, but few practical implementations exist. In this paper we present an implementation for Mozart of one of these frameworks, which is based on a semiring structure. We explain how the soft constraints constructs were adapted to the propagation process that Mozart performs, and show how they can be transparently integrated with current Mozart hard propagators. Additionally, we show how over-constrained problems can be successfully relaxed and solved, and how preferences can be added to a problem, while keeping the formal model as a direct reference.