GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
On Interpolation and Automatization for Frege Systems
SIAM Journal on Computing
A machine program for theorem-proving
Communications of the ACM
An exponential separation between regular and general resolution
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
The Complexity of Resolution Refinements
LICS '03 Proceedings of the 18th Annual IEEE Symposium on Logic in Computer Science
Resolution is Not Automatizable Unless W[P] is Tractable
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Towards understanding and harnessing the potential of clause learning
Journal of Artificial Intelligence Research
Formalizing dangerous SAT encodings
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Regular and general resolution: an improved separation
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Pool resolution and its relation to regular resolution and DPLL with clause learning
LPAR'05 Proceedings of the 12th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
Input distance and lower bounds for propositional resolution proof length
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Clause-Learning Algorithms with Many Restarts and Bounded-Width Resolution
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
An Exponential Lower Bound for Width-Restricted Clause Learning
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Solving #SAT and Bayesian inference with backtracking search
Journal of Artificial Intelligence Research
On Modern Clause-Learning Satisfiability Solvers
Journal of Automated Reasoning
On the power of clause-learning SAT solvers with restarts
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Artificial Intelligence
On the power of clause-learning SAT solvers as resolution engines
Artificial Intelligence
Clause-learning algorithms with many restarts and bounded-width resolution
Journal of Artificial Intelligence Research
Lower bounds for width-restricted clause learning on small width formulas
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Lower bounds for width-restricted clause learning on formulas of small width
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
An overview of parallel SAT solving
Constraints
An improved separation of regular resolution from pool resolution and clause learning
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Exponential separations in a hierarchy of clause learning proof systems
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Currently, the most effective complete SAT solvers are based on the DPLL algorithm augmented by clause learning. These solvers can handle many real-world problems from application areas like verification, diagnosis, planning, and design. Without clause learning, however, DPLL loses most of its effectiveness on real world problems. Recently there has been some work on obtaining a deeper understanding of the technique of clause learning. In this paper we utilize the idea of effective p-simulation, which is a new way of comparing clause learning with general resolution and other proof systems. We then show that pool proofs, a previously used characterization of clause learning, can effectively p-simulate general resolution. Furthermore, this result holds even for the more restrictive class of greedy, unit propagating, pool proofs, which more accurately characterize clause learning as it is used in practice. This result is surprising and indicates that clause learning is significantly more powerful than was previously known.