Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Using Randomization and Learning to Solve Hard Real-World Instances of Satisfiability
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Propositional Satisfiability and Constraint Programming: A comparative survey
ACM Computing Surveys (CSUR)
Principles of Constraint Programming
Principles of Constraint Programming
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Nogood recording from restarts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Set branching in constraint optimization
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Lazy clause generation reengineered
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Constraint Networks: Techniques and Algorithms
Constraint Networks: Techniques and Algorithms
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The use of restarts techniques associated with learning nogoods in solving Constraint Satisfaction Problems (CSPs) is starting to be considered of major importance for backtrack search algorithms. Recent developments show how to learn nogoods from restarts and that those nogoods are essential when using restarts. Using a backtracking search algorithm, with 2-way branching, generalized nogoods are learned from the last branch of the search tree, immediately before the restart occurs. In this paper we further generalized the learned nogoods but now using domain-splitting branching and set branching. We believe that the use of restarts and learning of domain-splitting generalized nogoods will improve backtrack search algorithms for certain classes of problems.