Extending the Smodels system with cardinality and weight constraints
Logic-based artificial intelligence
ASSAT: computing answer sets of a logic program by SAT solvers
Eighteenth national conference on Artificial intelligence
Enhancing disjunctive logic programming systems by SAT checkers
Artificial Intelligence
Weight constraints as nested expressions
Theory and Practice of Logic Programming
SAT-based answer set programming
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
On look-ahead heuristics in disjunctive logic programming
Annals of Mathematics and Artificial Intelligence
Enhancing DLV instantiator by backjumping techniques
Annals of Mathematics and Artificial Intelligence
JELIA '08 Proceedings of the 11th European conference on Logics in Artificial Intelligence
Detecting Inconsistencies in Large Biological Networks with Answer Set Programming
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
Achieving compositionality of the stable model semantics for smodels programs1
Theory and Practice of Logic Programming
Computing Stable Models via Reductions to Difference Logic
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Characterising equilibrium logic and nested logic programs: Reductions and complexity1,2
Theory and Practice of Logic Programming
Head-elementary-set-free logic programs
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
ICLP'07 Proceedings of the 23rd international conference on Logic programming
Integrating inductive definitions in SAT
LPAR'07 Proceedings of the 14th international conference on Logic for programming, artificial intelligence and reasoning
SAT(ID): satisfiability of propositional logic extended with inductive definitions
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
A 25-year perspective on logic programming
Dynamic Magic Sets and super-coherent answer set programs
AI Communications - Answer Set Programming
Compact translations of non-disjunctive answer set programs to propositional clauses
Logic programming, knowledge representation, and nonmonotonic reasoning
Dynamic magic sets for programs with monotone recursive aggregates
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
ASPIDE: integrated development environment for answer set programming
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
The third answer set programming competition: preliminary report of the system competition track
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Application of answer set programming for public health data integration and analysis
ARES'11 Proceedings of the IFIP WG 8.4/8.9 international cross domain conference on Availability, reliability and security for business, enterprise and health information systems
Data integration and answer set programming
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
On elementary loops of logic programs
Theory and Practice of Logic Programming
Magic Sets for disjunctive Datalog programs
Artificial Intelligence
RW'13 Proceedings of the 9th international conference on Reasoning Web: semantic technologies for intelligent data access
Advanced conflict-driven disjunctive answer set solving
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Complexity-sensitive decision procedures for abstract argumentation
Artificial Intelligence
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Disjunctive logic programming under the stable model semantics [GL91] is a new methodology called answer set programming (ASP) for solving combinatorial search problems. This programming method uses answer set solvers, such as dlv [Lea05], gnt[Jea05], smodels [SS05], assat [LZ02], cmodels[Lie05a]. Systems dlv and gnt are more general as they work with the class of disjunctive logic programs, while other systems cover only normal programs. DLV is uniquely designed to find the answer sets for disjunctive logic programs. On the other hand, gnt first generates possible stable model candidates and then tests the candidate on the minimality using system SMODELS as an inference engine for both tasks. Systems CMODELS and ASSAT use SAT solvers as search engines. They are based on the relationship between the completion semantics [Cla78], loop formulas [LZ02] and answer set semantics for logic programs. Here we present the implementation of a SAT-based algorithm for finding answer sets for disjunctive logic programs within cmodels. The work is based on the definition of completion for disjunctive programs [LL03] and the generalisation of loop formulas [LZ02] to the case of disjunctive programs [LL03]. We propose the necessary modifications to the SAT based ASSAT algorithm [LZ02] as well as to the generate and test algorithm from [GLM04] in order to adapt them to the case of disjunctive programs. We implement the algorithms in cmodels and demonstrate the experimental results.