Solving difficult SAT instances in the presence of symmetry
Proceedings of the 39th 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
Solving the Round Robin Problem Using Propositional Logic
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Recovering and Exploiting Structural Knowledge from CNF Formulas
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
Additive versus multiplicative clause weighting for SAT
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
SAT-based versus CSP-based constraint weighting for satisfiability
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Ten challenges in propositional reasoning and search
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
UBCSAT: an implementation and experimentation environment for SLS algorithms for SAT and MAX-SAT
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Effective preprocessing in SAT through variable and clause elimination
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Justification-Based Local Search with Adaptive Noise Strategies
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
Justification-Based Non-Clausal Local Search for SAT
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Efficiently exploiting dependencies in local search for SAT
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Massively parallel evolution of SAT heuristics
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Variable dependency in local search: prevention Is better than cure
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Boosting local search thanks to CDCL
LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
Kangaroo: an efficient constraint-based local search system using lazy propagation
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Improved local search for circuit satisfiability
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Depth-driven circuit-level stochastic local search for SAT
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Constraint propagation as the core of local search
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
On the violation of circuits in decomposable negation normal form
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Local search procedures for solving satisfiability problems have attracted considerable attention since the development of GSAT in 1992. However, recentwork indicates that for many real-world problems, complete search methods have the advantage, because modern heuristics are able to effectively exploit problem structure. Indeed, to develop a local search technique that can effectively deal with variable dependencies has been an open challenge since 1997. In this paper we show that local search techniques can effectively exploit information about problem structure producing significant improvements in performance on structured problem instances. Building on the earlier work of Ostrowski et al. we describe how information about variable dependencies can be built into a local search, so that only independent variables are considered for flipping. The cost effect of a flip is then dynamically calculated using a dependency lattice that models dependent variables using gates (specifically and, or and equivalence gates). The experimental study on hard structured benchmark problems demonstrates that our new approach significantly outperforms the previously reported best local search techniques.