Handbook of logic in artificial intelligence and logic programming (vol. 3)
Engineering an Incremental ASP Solver
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Achieving compositionality of the stable model semantics for smodels programs1
Theory and Practice of Logic Programming
A reductive semantics for counting and choice in answer set programming
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A new perspective on stable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Modularity aspects of disjunctive stable models
Journal of Artificial Intelligence Research
Symmetric splitting in the general theory of stable models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Stable models and circumscription
Artificial Intelligence
Reactive answer set programming
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Journal of Artificial Intelligence Research
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The module theorem by Janhunen et al. demonstrates how to provide a modular structure in answer set programming, where each module has a well-defined input/output interface which can be used to establish the compositionality of answer sets. The theorem is useful in the analysis of answer set programs, and is a basis of incremental grounding and reactive answer set programming. We extend the module theorem to the general theory of stable models by Ferraris et al. The generalization applies to non-ground logic programs allowing useful constructs in answer set programming, such as choice rules, the count aggregate, and nested expressions. Our extension is based on relating the module theorem to the symmetric splitting theorem by Ferraris et al. Based on this result, we reformulate and extend the theory of incremental answer set computation to a more general class of programs.