Abstract
This paper reviews the main approaches for extending arc consistency propagation in constraint optimization frameworks and discusses full and partial arc consistency propagation based on Larrosa’s W-NC* and W-AC*2001 algorithms [Larrosa 2002]. We implement these full/partial propagation algorithms in branch and bound search and compare their performance on MaxCSP models. We empirically demonstrate that maintaining arc consistency is more efficient than other partial propagation. We also demonstrate that the end result of constraint propagation can be used as an effective heuristic for guiding search in constraint optimization problems.
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Zheng, J., Horsch, M.C. (2003). A Comparison of Consistency Propagation Algorithms in Constraint Optimization. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_14
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DOI: https://doi.org/10.1007/3-540-44886-1_14
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