The complexity of logic-based abduction
Journal of the ACM (JACM)
Improvements to propositional satisfiability search algorithms
Improvements to propositional satisfiability search algorithms
Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Disjunctive stable models: unfounded sets, fixpoint semantics, and computation
Information and Computation
ACM Transactions on Database Systems (TODS)
Beyond NP: the QSAT phase transition
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Declarative problem-solving in DLV
Logic-based artificial intelligence
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Extending and implementing the stable model semantics
Artificial Intelligence
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Default Logic as a Query Language
IEEE Transactions on Knowledge and Data Engineering
Improvements to the Evaluation of Quantified Boolean Formulae
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Pruning Operators for Disjunctive Logic Programming Systems
Fundamenta Informaticae
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Conflict-driven answer set solving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A backbone-search heuristic for efficient solving of hard 3-SAT formulae
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Experimenting with heuristics for answer set programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Experimenting with look-back heuristics for hard ASP programs
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Solving hard ASP programs efficiently
LPNMR'05 Proceedings of the 8th 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
A 25-year perspective on logic programming
The disjunctive datalog system DLV
Datalog'10 Proceedings of the First international conference on Datalog Reloaded
Look-back Techniques for ASP Programs with Aggregates
Fundamenta Informaticae
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Disjunctive logic programming (DLP), also called answer set programming (ASP), is a convenient programming paradigm which allows for solving problems in a simple and highly declarative way. The language of DLP is very expressive and able to represent even problems of high complexity (every problem in the complexity class ${{\Sigma}_{2}^{P}} = {\rm NP}^{{\rm NP}}$ ). During the last decade, efficient systems supporting DLP have become available. Virtually all of these systems internally rely on variants of the Davis---Putnam procedure (for deciding propositional satisfiability [SAT]), combined with a suitable model checker. The heuristic for the selection of the branching literal (i.e., the criterion determining the literal to be assumed true at a given stage of the computation) dramatically affects the performance of a DLP system. While heuristics for SAT have received a fair deal of research, only little work on heuristics for DLP has been done so far. In this paper, we design, implement, optimize, and experiment with a number of heuristics for DLP. We focus on different look-ahead heuristics, also called "dynamic heuristics" (the DLP equivalent of unit propagation [UP] heuristics for SAT). These are branching rules where the heuristic value of a literal Q depends on the result of taking Q true and computing its consequences. We motivate and formally define a number of look-ahead heuristics for DLP programs. Furthermore, since look-ahead heuristics are computationally expensive, we design two techniques for optimizing the burden of their computation. We implement all the proposed heuristics and optimization techniques in DLV--the state-of-the-art implementation of disjunctive logic programming, and we carry out experiments, thoroughly comparing the heuristics and optimization techniques on a large number of instances of well-known benchmark problems. The results of these experiments are very interesting, showing that the proposed techniques significantly improve the performance of the DLV system.