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
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
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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 Mendelian error exists in a pedigree is NP-complete [1]. 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 error 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 toulbar21 which solves very large pedigree problems with thousands of animals, including many loops and several errors. This paper is a summary of an extended version to appear [17].