TY - GEN
T1 - MAC-DBT revisited
AU - Zivan, Roie
AU - Shapen, Uri
AU - Zazone, Moshe
AU - Meisels, Amnon
PY - 2011
Y1 - 2011
N2 - Dynamic Backtracking (DBT) is a well known algorithm for solving Constraint Satisfaction Problems. In DBT, variables are allowed to keep their assignment during backjump, if they are compatible with the set of eliminating explanations. A previous study has shown that when DBT is combined with variable ordering heuristics, it performs poorly compared to standard Conflict-directed Backjumping (CBJ) [Bak94]. In later studies, DBT was enhanced with constraint propagation methods. The MAC-DBT algorithm was reported by [JDB00] to be the best performing version, improving on both standard DBT and on FC-DBT by a large factor. The present study evaluates the DBT algorithm from a number of aspects. First we show that the advantage of MAC-DBT over FC-DBT holds only for a static ordering. When dynamic ordering heuristics are used, FC-DBT outperforms MAC-DBT. Second, we show theoretically that a combined version of DBT that uses both FC and MAC performs equal or less computation at each step than MAC-DBT. An empirical result which presents the advantage of the combined version on MAC-DBT is also presented. Third, following the study of [Bak94], we present a version of MAC-DBT and FC-DBT which does not preserve assignments which were jumped over. It uses the Nogood mechanism of DBT only to determine which values should be restored to the domains of variables. These versions of MAC-DBT and FC-DBT outperform all previous versions.
AB - Dynamic Backtracking (DBT) is a well known algorithm for solving Constraint Satisfaction Problems. In DBT, variables are allowed to keep their assignment during backjump, if they are compatible with the set of eliminating explanations. A previous study has shown that when DBT is combined with variable ordering heuristics, it performs poorly compared to standard Conflict-directed Backjumping (CBJ) [Bak94]. In later studies, DBT was enhanced with constraint propagation methods. The MAC-DBT algorithm was reported by [JDB00] to be the best performing version, improving on both standard DBT and on FC-DBT by a large factor. The present study evaluates the DBT algorithm from a number of aspects. First we show that the advantage of MAC-DBT over FC-DBT holds only for a static ordering. When dynamic ordering heuristics are used, FC-DBT outperforms MAC-DBT. Second, we show theoretically that a combined version of DBT that uses both FC and MAC performs equal or less computation at each step than MAC-DBT. An empirical result which presents the advantage of the combined version on MAC-DBT is also presented. Third, following the study of [Bak94], we present a version of MAC-DBT and FC-DBT which does not preserve assignments which were jumped over. It uses the Nogood mechanism of DBT only to determine which values should be restored to the domains of variables. These versions of MAC-DBT and FC-DBT outperform all previous versions.
UR - http://www.scopus.com/inward/record.url?scp=79952968543&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-19486-3_9
DO - 10.1007/978-3-642-19486-3_9
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AN - SCOPUS:79952968543
SN - 9783642194856
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 139
EP - 153
BT - Recent Advances in Constraints - 14th Annual ERCIM International Workshop on Constraint Solving and Constraint Logic Programming, CSCLP 2009, Revised Selected Papers
T2 - 14th Annual ERCIM International Workshop on Constraint Solving and Constraint Logic Programming, CSCLP 2009
Y2 - 15 June 2009 through 17 June 2009
ER -