Understanding and Improving the MAC Algorithm

@inproceedings{Sabin1997UnderstandingAI,
  title={Understanding and Improving the MAC Algorithm},
  author={Daniel Sabin and Eugene C. Freuder},
  booktitle={CP},
  year={1997}
}
Constraint satisfaction problems have wide application in artiicial intelligence. They involve nding 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 nite domain constraint satisfaction problems suggest that Maintaining Arc Consistency (MAC) is the most eecient general CSP algorithm for solving large and hard problems. In the rst part of this paper we explain why maintaining full… CONTINUE READING

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