Information Processing Letters
The well-founded semantics for general logic programs
Journal of the ACM (JACM)
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Proceedings of the eleventh international conference on Logic programming
Mixed integer programming methods for computing nonmonotonic deductive databases
Journal of the ACM (JACM)
Disjunctive stable models: unfounded sets, fixpoint semantics, and computation
Information and Computation
ACM Transactions on Database Systems (TODS)
Artificial Intelligence
Proceedings of the 1999 international conference on Logic programming
Declarative problem-solving in DLV
Logic-based artificial intelligence
Logic programming and knowledge representation-the A-prolog perspective
Artificial Intelligence
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
WFS + Branch and Bound = Stable Models
IEEE Transactions on Knowledge and Data Engineering
Computation of Stable Models and Its Integration with Logical Query Processing
IEEE Transactions on Knowledge and Data Engineering
Computing Well-founded Semantics Faster
LPNMR '95 Proceedings of the Third International Conference on Logic Programming and Nonmonotonic Reasoning
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
XSB: A System for Effciently Computing WFS
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Solving Advanced Reasoning Tasks Using Quantified Boolean Formulas
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Propositional Satisfiability in Answer-Set Programming
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Transformation-based bottom-up computation of the well-founded model
Theory and Practice of Logic Programming
ASSAT: computing answer sets of a logic program by SAT solvers
Artificial Intelligence - Special issue on nonmonotonic reasoning
Unfolding partiality and disjunctions in stable model semantics
ACM Transactions on Computational Logic (TOCL)
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
DisLoP: a research project on Disjunctive Logic Programming
AI Communications
Experimenting with heuristics for answer set programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
On look-ahead heuristics in disjunctive logic programming
Annals of Mathematics and Artificial Intelligence
Discarte: a disjunctive internet cartographer
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
The DLV Project: A Tour from Theory and Research to Applications and Market
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
On the relation among answer set solvers
Annals of Mathematics and Artificial Intelligence
A 25-year perspective on logic programming
The disjunctive datalog system DLV
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
Conflict-driven answer set solving: From theory to practice
Artificial Intelligence
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Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning. The language of DLP is very expressive and supports the representation of problems of high computational complexity (specifically, all problems in the complexity class Σ$^p_2$=NP$^{NP}$). The DLP encoding of a large variety of problems is often very concise, simple, and elegant. In this paper, we explain the computational process commonly performed by DLP systems, with a focus on search space pruning, which is crucial for the efficiency of such systems. We present two suitable operators for pruning (Fitting's and Well-founded), discuss their peculiarities and differences with respect to efficiency and effectiveness. We design an intelligent strategy for combining the two operators, exploiting the advantages of both. We implement our approach in DLV - the state-of-the-art DLP system - and perform some experiments. These experiments show interesting results, and evidence how the choice of the pruning operator affects the performance of DLP systems.