A Structure-preserving Clause Form Translation
Journal of Symbolic Computation
ACM Transactions on Database Systems (TODS)
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Nested expressions in logic programs
Annals of Mathematics and Artificial Intelligence
ICLP '02 Proceedings of the 18th International Conference on Logic Programming
Consistent query answering in databases
ACM SIGMOD Record
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
CMODELS: SAT-based disjunctive answer set solver
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
A Revised Concept of Safety for General Answer Set Programs
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
nfn2dlp and nfnsolve: Normal Form Nested Programs Compiler and Solver
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Interpolable formulas in equilibrium logic and answer set programming
Journal of Artificial Intelligence Research
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Disjunctive logic programming under the answer set semantics (DLP, ASP) has been acknowledged as a versatile formalism for knowledge representation and reasoning during the last decade. Lifschitz, Tang, and Turner have introduced an extended language of DLP, called Nested Logic Programming (NLP), in 1999 [1]. It often allows for more concise representations by permitting a richer syntax in rule heads and bodies. However, that language is propositional and thus does not allow for variables, one of the strengths of DLP.In this paper, we introduce a language similar to NLP, called Normal Form Nested (NPN) programs, which does allow for variables, and present the syntax and semantics. With the presence of variables, domain independence is no longer guaranteed. We study this issue in depth and define the class of safe NPNprograms, which are guaranteed to be domain independent. Moreover, we show that for NPNprograms which are also NLPs, our semantics coincides with the one of [1]; while keeping the standard meaning of answer sets on DLP programs with variables. Finally, we provide an algorithm which translates NPNprograms into DLPprograms, and does so in an efficient way, allowing for the effective implementation of the NPNlanguage on top of existing DLP systems.