ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Regular resolution versus unrestricted resolution
SIAM Journal on Computing
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Act, and the rest will follow: exploiting determinism in planning as satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Symbolic model checking using SAT procedures instead of BDDs
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Dependent and Independent Variables in Propositional Satisfiability
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Tuning SAT Checkers for Bounded Model Checking
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Unrestricted vs restricted cut in a tableau method for Boolean circuits
Annals of Mathematics and Artificial Intelligence
Towards understanding and harnessing the potential of clause learning
Journal of Artificial Intelligence Research
On the power of top-down branching heuristics
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
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The techniques for making decisions, i.e., branching, play a central role in complete methods for solving structured CSP instances. In practice, there are cases when SAT solvers benefit from limiting the set of variables the solver is allowed to branch on to so called input variables. Theoretically, however, restricting branching to input variables implies a super-polynomial increase in the length of the optimal proofs for DPLL (without clause learning), and thus input-restricted DPLL cannot polynomially simulate DPLL. In this paper we settle the case of DPLL with clause learning. Surprisingly, even with unlimited restarts, input-restricted clause learning DPLL cannot simulate DPLL (even without clause learning). The opposite also holds, and hence DPLL and input-restricted clause learning DPLL are polynomially incomparable. Additionally, we analyse the effect of input-restricted branching on clause learning solvers in practice with various structural real-world benchmarks.