Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Towards a theory of declarative knowledge
Foundations of deductive databases and logic programming
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Extending and implementing the stable model semantics
Artificial Intelligence
Default Logic as a Query Language
IEEE Transactions on Knowledge and Data Engineering
A Deductive System for Non-Monotonic Reasoning
LPNMR '97 Proceedings of the 4th 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
On Indefinite Databases and the Closed World Assumption
Proceedings of the 6th Conference on Automated Deduction
ASSAT: computing answer sets of a logic program by SAT solvers
Eighteenth national conference on Artificial intelligence
Optimal implementation of conjunctive queries in relational data bases
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
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)
Conflict-directed backjumping revisited
Journal of Artificial Intelligence Research
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
GrinGo: a new grounder for answer set programming
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
CMODELS: SAT-based disjunctive answer set solver
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
The nomore++ approach to answer set solving
LPAR'05 Proceedings of the 12th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
Parameterized Complexity
The DLV Project: A Tour from Theory and Research to Applications and Market
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
A Module-Based Framework for Multi-language Constraint Modeling
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
A 25-year perspective on logic programming
Grounding FO and FO(ID) with bounds
Journal of Artificial Intelligence Research
Towards computing revised models for FO theories
INAP'09 Proceedings of the 18th international conference on Applications of declarative programming and knowledge management
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
The intelligent grounder of DLV
Correct Reasoning
Constraint Propagation for First-Order Logic and Inductive Definitions
ACM Transactions on Computational Logic (TOCL)
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Disjunctive logic programming (DLP) is a powerful formalism for knowledge representation and reasoning. The high expressiveness of DLP language, together with the recent availability of some efficient DLP system, has favoured the application of DLP in emerging areas like Knowledge Management and Information Integration. These applications have often to deal with huge input data, and have evidenced the need to improve the efficiency of DLP instantiators. Program instantiation is the first phase of a DLP computation; in this phase, variables are replaced by constants to generate a ground program which is then evaluated by propositional algorithms in the second phase of the computation. The instantiation process may be computationally expensive, and in fact its efficiency has been recognized to be a key issue for solving real-world problems by using disjunctive logic programming. Given a program P, a good instantiation for P is a ground program P驴 having precisely the same answer sets as P and such that: (1) P驴 can be computed efficiently from P, and (2) P驴 does not contain "useless" rules, (P驴 is as small as possible) and can thus be evaluated efficiently. In this paper, we present a structure-based backjumping algorithm for the instantiation of disjunctive logic programs, that meets the above requirements. In particular, given a rule r to be grounded, our algorithm exploits both the semantical and the structural information about r for computing efficiently the ground instances of r, avoiding the generation of "useless" rules. That is, from each general rule r, we compute only a relevant subset of its ground instances, avoiding the generation of "useless" instances, while fully preserving the semantic of the program. We have implemented this algorithm in DLV--the state-of-the-art implementation of DLP--and we have carried out an experimentation activity on an ample collection of benchmark problems. The experimental results are very positive: the new technique improves sensibly the efficiency of the DLV system on many program classes.