Constraint-directed negotiation of resource reallocations
Distributed Artificial Intelligence (Vol. 2)
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
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
On integrating constraint propagation and linear programming for combinatorial optimization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Representation Selection for Constraint Satisfaction: A Case Study Using n-Queens
IEEE Expert: Intelligent Systems and Their Applications
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
CSPLIB: A Benchmark Library for Constraints
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Dual Models of Permutation Problems
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Semiring-Based CSPs and Valued CSPs: Basic Properties and Comparison
Over-Constrained Systems
Node and arc consistency in weighted CSP
Eighteenth national conference on Artificial intelligence
Model induction: a new source of CSP model redundancy
Eighteenth national conference on Artificial intelligence
A Constraint-Based Nurse Rostering System Using a Redundant Modeling Approach
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Constraint-directed search: a case study of job-shop scheduling
Constraint-directed search: a case study of job-shop scheduling
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Nondeterministic Control for Hybrid Search
Constraints
Stronger Consistencies in WCSPs with Set Variables
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Weighted constraint satisfaction with set variables
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Dual modelling of permutation and injection problems
Journal of Artificial Intelligence Research
Combining knowledge- and corpus-based word-sense-disambiguation methods
Journal of Artificial Intelligence Research
Enhancing cooperative search with concurrent interactions
Journal of Artificial Intelligence Research
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Existential arc consistency: getting closer to full arc consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Hybrid algorithms in constraint programming
CSCLP'06 Proceedings of the constraint solving and contraint logic programming 11th annual ERCIM international conference on Recent advances in constraints
A parameterized local consistency for redundant modeling in weighted CSPs
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Nondeterministic control for hybrid search
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Speeding up weighted constraint satisfaction using redundant modeling
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
A nurse rostering system using constraint programming and redundant modeling
IEEE Transactions on Information Technology in Biomedicine
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In classical constraint satisfaction, redundant modeling has been shown effective in increasing constraint propagation and reducing search space for many problem instances. In this paper, we investigate, for the first time, how to benefit the same from redundant modeling in weighted constraint satisfaction problems (WCSPs), a common soft constraint framework for modeling optimization and over-constrained problems. Our work focuses on a popular and special class of problems, namely, permutation problems. First, we show how to automatically generate a redundant permutation WCSP model from an existing permutation WCSP using generalized model induction. We then uncover why naively combining mutually redundant permutation WCSPs by posting channeling constraints as hard constraints and relying on the standard node consistency (NC*) and arc consistency (AC*) algorithms would miss pruning opportunities, which are available even in a single model. Based on these observations, we suggest two approaches to handle the combined WCSP models. In our first approach, we propose $m\text {-NC}_{\text c}^*$ and $m\text {-AC}_{\text c}^*$ and their associated algorithms for effectively enforcing node and arc consistencies in a combined model with m sub-models. The two notions are strictly stronger than NC* and AC* respectively. While the first approach specifically refines NC* and AC* so as to apply to combined models, in our second approach, we propose a parameterized local consistency LB(m,驴). The consistency can be instantiated with any local consistency 驴 for single models and applied to a combined model with m sub-models. We also provide a simple algorithm to enforce LB(m,驴). With the two suggested approaches, we demonstrate their applicabilities on several permutation problems in the experiments. Prototype implementations of our proposed algorithms confirm that applying $2\text {-NC}_{\text c}^*,\;2\text {-AC}_{\text c}^*$ , and LB(2,驴) on combined models allow far more constraint propagation than applying the state-of-the-art AC*, FDAC*, and EDAC* algorithms on single models of hard benchmark problems.