Node and arc consistency in weighted CSP
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Reduction operations in fuzzy or valued constraint satisfaction
Fuzzy Sets and Systems - Optimisation and decision
Constraint Processing
The complexity of checking consistency of pedigree information and related problems
Journal of Computer Science and Technology - Special issue on bioinformatics
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Memory intensive branch-and-bound search for graphical models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Exploiting tree decomposition and soft local consistency in weighted CSP
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Efficient haplotype inference with boolean satisfiability
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Mendelian error detection in complex pedigrees using weighted constraint satisfaction techniques
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Bounds arc consistency for weighted CSPs
Journal of Artificial Intelligence Research
Set branching in constraint optimization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Towards efficient consistency enforcement for global constraints in weighted constraint satisfaction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Russian Doll search with tree decomposition
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Soft arc consistency revisited
Artificial Intelligence
Exploiting problem structure for solution counting
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Efficient and accurate haplotype inference by combining parsimony and pedigree information
ANB'10 Proceedings of the 4th international conference on Algebraic and Numeric Biology
DR.FILL: crosswords and an implemented solver for singly weighted CSPs
Journal of Artificial Intelligence Research
An Abstract Interpretation framework for genotype elimination algorithms
Theoretical Computer Science
Pairwise decomposition for combinatorial optimization in graphical models
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Journal of Artificial Intelligence Research
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With the arrival of high throughput genotyping techniques, the detection of likely genotyping errors is becoming an increasingly important problem. In this paper we are interested in errors that violate Mendelian laws. The problem of deciding if a Mendelian error exists in a pedigree is NP-complete (Aceto et al., J Comp Sci Technol 19(1):42---59, 2004). Existing tools dedicated to this problem may offer different level of services: detect simple inconsistencies using local reasoning, prove inconsistency, detect the source of error, propose an optimal correction for the error. All assume that there is at most one error. In this paper we show that the problem of error detection, of determining the minimum number of errors needed to explain the data (with a possible error detection) and error correction can all be modeled using soft constraint networks. Therefore, these problems provide attractive benchmarks for weighted constraint network (WCN) solvers. Because of their sheer size, these problems drove us into the development of a new WCN solver toulbar2 which solves very large pedigree problems with thousands of animals, including many loops and several errors.