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
Maintaining Arc-Consistency within Dynamic Backtracking
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Modelling and solving English Peg Solitaire
Computers and Operations Research
Generalised arc consistency for the AllDifferent constraint: An empirical survey
Artificial Intelligence
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
SAT Modulo Theories: Enhancing SAT with Special-Purpose Algorithms
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
Data structures for generalised arc consistency for extensional constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Tailoring solver-independent constraint models: a case study with ESSENCE' and MINION
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Propagation = lazy clause generation
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Efficient reasoning for nogoods in constraint solvers with BDDs
PADL'08 Proceedings of the 10th international conference on Practical aspects of declarative languages
Nogood processing in csps
Identifying and exploiting problem structures using explanation-based constraint programming
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Computing explanations for the unary resource constraint
CPAIOR'05 Proceedings of the Second international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Finite domain bounds consistency revisited
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Learning When to Use Lazy Learning in Constraint Solving
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Ensemble classification for constraint solver configuration
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
An empirical study of learning and forgetting constraints
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
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Explanations are a technique for reasoning about constraint propagation, which have been applied in many learning, backjumping and user-interaction algorithms for constraint programming. To date explanations for constraints have usually been recorded “eagerly” when constraint propagation happens, which leads to inefficient use of time and space, because many will never be used. In this paper we show that it is possible and highly effective to calculate explanations retrospectively when they are needed. To this end, we implement “lazy” explanations in a state of the art learning framework. Experimental results confirm the effectiveness of the technique: we achieve reduction in the number of explanations calculated up to a factor of 200 and reductions in overall solve time up to a factor of 5.