A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Extending and implementing the stable model semantics
Artificial Intelligence
Logic programs with stable model semantics as a constraint programming paradigm
Annals of Mathematics and Artificial Intelligence
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
ASSAT: computing answer sets of a logic program by SAT solvers
Eighteenth national conference on Artificial intelligence
Bounded LTL model checking with stable models
Theory and Practice of Logic Programming
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Conflict-driven answer set enumeration
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
An ASP based method for subassembly identification
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Scalable formula decomposition for propositional satisfiability
Proceedings of the Third C* Conference on Computer Science and Software Engineering
Abstract answer set solvers with backjumping and learning
Theory and Practice of Logic Programming
Conflict-driven answer set solving: From theory to practice
Artificial Intelligence
Tableau Calculi for Logic Programs under Answer Set Semantics
ACM Transactions on Computational Logic (TOCL)
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Recent research shows that SAT (propositional satisfiability) techniques can be employed to build efficient systems to compute answer sets for logic programs. ASSAT and CMODELS are two well-known such systems. They find an answer set from the full models for the completion of the input program, which is (iteratively) augmented with loop formulas. Making use of the fact that, for non-tight programs, during the model generation, a partial assignment may be extensible to a full model but may not grow into any answer set, we propose to add answer set extensibility checking on partial assignments. Furthermore, given a partial assignment, we identify a class of loop formulas that are "active" on the assignment. These "active" formulas can be used to prune the search space. We also provide an efficient method to generate these formulas. These ideas can be implemented with a moderate modification on SAT solvers. We have developed a new answer set solver SAG on top of the SAT solver MCHAFF. Empirical studies on well-known benchmarks show that in most cases it is faster than the state-of-the-art answer set solvers, often by an order of magnitude. In the few cases when it is not the winner, it is close to the top performer, which shows its robustness.