Recognition of tractable satisfiability problems through balanced polynomial representations
Proceedings of the 5th Twente workshop on on Graphs and combinatorial optimization
Dynamic Constraint Weighting for Over-Constrained Problems
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
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
Equivalent literal propagation in the DLL procedure
Discrete Applied Mathematics - The renesse issue on satisfiability
Building structure into local search for SAT
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Advances in local search for satisfiability
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Aligning CNF- and equivalence-reasoning
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
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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We propose a new local search platform that splits a CNF formula into three sub-components: i) a minimal dependency lattice (representing the core connections between logic gates), ii) a conjunction of equivalence clauses, and iii) the remaining clauses. We also adopt a new hierarchical cost function that focuses on solving the core components of the problem first. We then show experimentally that our platform not only significantly outperforms existing local search approaches but is also competitive with modern systematic solvers on highly structured problems.