Cascaded directed arc consistency and no-good learning for the maximal constraint satisfaction problem
This paper describes new branch and bound methods for overconstrained CSPs. The first method is an extension of directed arc consistency preprocessing, used in conjunction with forward checking. After computing directed arc consistency counts, inferred counts are derived for each value, based on the counts of supporting values in future variables. This inference process can be ‘cascaded’ from the end to the beginning of the search order, to augment the initial counts. The second method is a form of wipeout-driven nogood learning: the method for finding nogoods is described and conditions for the validity of the nogood are established. In tests with random problems, significant improvements in efficiency were found with cascaded DACCs; in contrast, no-good learning did not enhance performance when used alone or in any combination of strategies.
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