Resolution proofs of generalized pigeonhole principles. (Note)
Theoretical Computer Science
The Complexity of Resolution Refinements
LICS '03 Proceedings of the 18th Annual IEEE Symposium on Logic in Computer Science
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Ten challenges in propositional reasoning and search
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Vivifying Propositional Clausal Formulae
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
New Encodings of Pseudo-Boolean Constraints into CNF
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Clause learning can effectively P-simulate general propositional resolution
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
In this paper we prove an exponential separation between two very similar and natural SAT encodings for the same problem, thereby showing that researchers must be careful when designing encodings, lest they accidentally introduce complexity into the problem being studied. This result provides a formal explanation for empirical results showing that the encoding of a problem can dramatically affect its practical solvability. We also introduce a domain-independent framework for reasoning about the complexity added to SAT instances by their encodings. This includes the observation that while some encodings may add complexity, other encodings can actually make problems easier to solve by adding clauses which would otherwise be difficult to derive within a Resolution-based SAT-solver. Such encodings can be used as polytime preprocessing to speed up SAT algorithms.