Abstract
Constraint satisfaction problems have wide application in artificial intelligence. They involve finding values for problem variables where the values must be consistent in that they satisfy restrictions on which combinations of values are allowed. Recent research on finite domain constraint satisfaction problems suggest that Maintaining Arc Consistency (MAC) is the most efficient general CSP algorithm for solving large and hard problems. In the first part of this paper we explain why maintaining full, as opposed to limited, arc consistency during search can greatly reduce the search effort. Based on this explanation, in the second part of the paper we show how to modify MAC in order to make it even more efficient. Experimental results prove that the gain in efficiency can be quite important.
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References
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© 1997 Springer-Verlag Berlin Heidelberg
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Sabin, D., Ereuder, E.C. (1997). Understanding and improving the MAC algorithm. In: Smolka, G. (eds) Principles and Practice of Constraint Programming-CP97. CP 1997. Lecture Notes in Computer Science, vol 1330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017438
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DOI: https://doi.org/10.1007/BFb0017438
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