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Constraint programming is a successful technology for solving a wide range of problems in business and industry which require satisfying a set of constraints. Central to solving constraint satisfaction problems is enforcing a level of local consistency. In this thesis, we propose efficient filtering algorithms for enforcing strong local consistencies. In addition, since such filtering algorithms can be too expensive to enforce all the time, we propose some automated heuristics that can dynamically select the most appropriate filtering algorithm. Published by AI Access, a not-for-profit publisher of open access texts with a highly respected scientific board. We publish monographs and collected works. Our texts are available electronically for free and in hard copy at close to cost.