Extending and implementing the stable model semantics
Artificial Intelligence
On Logical Constraints in Logic Programming
LPNMR '95 Proceedings of the Third International Conference on Logic Programming and Nonmonotonic Reasoning
Theory and Practice of Logic Programming
ASSAT: computing answer sets of a logic program by SAT solvers
Artificial Intelligence - Special issue on nonmonotonic reasoning
A Constructive semantic characterization of aggregates in answer set programming
Theory and Practice of Logic Programming
Well-founded and stable semantics of logic programs with aggregates
Theory and Practice of Logic Programming
Logic programs with monotone abstract constraint atoms*
Theory and Practice of Logic Programming
Loop formulas for logic programs with arbitrary constraint atoms
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Properties and applications of programs with monotone and convex constraints
Journal of Artificial Intelligence Research
Answer sets for logic programs with arbitrary abstract constraint atoms
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Answer sets for propositional theories
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
Level Mapping Induced Loop Formulas for Weight Constraint and Aggregate Logic Programs
Fundamenta Informaticae
Level Mapping Induced Loop Formulas for Weight Constraint and Aggregate Logic Programs
Fundamenta Informaticae
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We improve the formulations of loop formulas for weight constraint and aggregate programs by investigating the level mapping characterization of the semantics for these programs. First, we formulate a level mapping characterization of the stable model semantics for weight constraint programs, based on which we define loop formulas for these programs. This approach makes it possible to build loop formulas for programs with arbitrary weight constraints without introducing new atoms. Secondly, we further use level mapping to characterize the semantics and propose loop formulas for aggregate programs. The main result is that for aggregate programs not involving the inequality comparison operator, the dependency graphs can be built in polynomial time. This compares to the previously known exponential time method.