Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
A filtering algorithm for constraints of difference in CSPs
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
BerkMin: A fast and robust Sat-solver
Discrete Applied Mathematics
Propagation via lazy clause generation
Constraints
MINION: A Fast, Scalable, Constraint Solver
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
An Exponential Lower Bound for Width-Restricted Clause Learning
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Journal of Artificial Intelligence Research
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Domain filtering can degrade intelligent backtracking search
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Lazy clause generation reengineered
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Learning When to Use Lazy Learning in Constraint Solving
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Nogood processing in csps
On freezing and reactivating learnt clauses
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Lazy explanations for constraint propagators
PADL'10 Proceedings of the 12th international conference on Practical Aspects of Declarative Languages
Lower bounds for width-restricted clause learning on small width formulas
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
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Conflict-driven constraint learning provides big gains on many CSP and SAT problems. However, time and space costs to propagate the learned constraints can grow very quickly, so constraints are often discarded (forgotten) to reduce overhead. We conduct a major empirical investigation into the overheads introduced by unbounded constraint learning in CSP. To the best of our knowledge, this is the first published study in either CSP or SAT. We obtain three significant results. The first is that a small percentage of learnt constraints do most propagation. While this is conventional wisdom, it has not previously been the subject of empirical study. Second, we show that even constraints that do no effective propagation can incur significant time overheads. Finally, by implementing forgetting, we confirm that it can significantly improve the performance of modern learning CSP solvers, contradicting some previous research.