GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Extending and implementing the stable model semantics
Artificial Intelligence
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Constraint Processing
Weight constraints as nested expressions
Theory and Practice of Logic Programming
Answer Set Programming Based on Propositional Satisfiability
Journal of Automated Reasoning
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The Conflict-Driven Answer Set Solver clasp: Progress Report
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Potassco: The Potsdam Answer Set Solving Collection
AI Communications - Answer Set Programming
Challenges in answer set solving
Logic programming, knowledge representation, and nonmonotonic reasoning
Conflict-driven answer set solving: From theory to practice
Artificial Intelligence
Look-back Techniques for ASP Programs with Aggregates
Fundamenta Informaticae
Tableau Calculi for Logic Programs under Answer Set Semantics
ACM Transactions on Computational Logic (TOCL)
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We present the first comprehensive approach to integrating cardinality and weight rules into conflict-driven ASP solving. We begin with a uniform, constraint-based characterization of answer sets in terms of nogoods. This provides the semantic underpinnings of our approach in fixing all necessary inferences that must be supported by an appropriate implementation. We then provide key algorithms detailing the salient features needed for implementing weight constraint rules. This involves a sophisticated unfounded set checker as well as an extended propagation algorithm along with the underlying data structures. We implemented our techniques within the ASP solver clasp and demonstrate their effectiveness by an experimental evaluation.