Cmodels is a system that computes answer sets for either disjunctive logic programs or logic programs containing choice rules. Answer set solver Cmodels uses SAT solvers as a search engine for enumerating models of the logic program -- possible solutions, in case of disjunctive programs SAT solver zChaff is also used for verifying the minimality of found models. The system Cmodels is based on the relation between two semantics: the answer set and the completion semantics for logic programs. For big class of programs called tight, the answer set semantics is equivalent to the completion semantics, so that the answer sets for such a program can be enumerated by a SAT solver. On the other hand for nontight programs [6], and [7] introduced the concept of the loop formulas, and showed that models of completion extended by all the loop formulas of the program are equivalent to the answer sets of th! e program. Unfortunetly number of loop formulas might be large, therefore computing all of them may become computationally expensive. This led to the adoption of the algorithm that computes loop formulas ”as needed” for finding answer sets of a program.

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  1. Doherty, Patrick; Szalas, Andrzej: Rough set reasoning using answer set programs (2021)
  2. Alviano, Mario; Dodaro, Carmine: Unsatisfiable core analysis and aggregates for optimum stable model search (2020)
  3. Calimeri, Francesco; Dodaro, Carmine; Fuscà, Davide; Perri, Simona; Zangari, Jessica: Technical note. Efficiently coupling the (\mathscrI)-DLV grounder with ASP solvers (2020)
  4. De Wulf, Wolf; Bogaerts, Bart: \textsclp2pb: translating answer set programs into pseudo-Boolean theories (2020)
  5. Yang, Zhun: Extending answer set programs with neural networks (2020)
  6. Gelfond, Michael; Zhang, Yuanlin: Vicious circle principle, aggregates, and formation of sets in ASP based languages (2019)
  7. Alviano, Mario; Dodaro, Carmine; Maratea, Marco: Shared aggregate sets in answer set programming (2018)
  8. Lierler, Yuliya: What is answer set programming to propositional satisfiability (2017)
  9. Zhang, Heng; Zhang, Yan: Expressiveness of logic programs under the general stable model semantics (2017)
  10. Zhou, Yi; Zhang, Yan: A progression semantics for first-order logic programs (2017)
  11. Alviano, Mario; Dodaro, Carmine: Anytime answer set optimization via unsatisfiable core shrinking (2016)
  12. Brochenin, Remi; Maratea, Marco; Lierler, Yuliya: Disjunctive answer set solvers via templates (2016)
  13. Doherty, Patrick; Kvarnström, Jonas; Szałas, Andrzej: Iteratively-supported formulas and strongly supported models for Kleene answer set programs (extended abstract) (2016)
  14. Alviano, Mario; Peñaloza, Rafael: Fuzzy answer set computation via satisfiability modulo theories (2015)
  15. Fichte, Johannes Klaus; Szeider, Stefan: Backdoors to tractable answer set programming (2015)
  16. Fichte, Johannes K.; Szeider, Stefan: Backdoors to normality for disjunctive logic programs (2015)
  17. Dvořák, Wolfgang; Järvisalo, Matti; Wallner, Johannes Peter; Woltran, Stefan: Complexity-sensitive decision procedures for abstract argumentation (2014)
  18. Alviano, Mario; Dodaro, Carmine; Faber, Wolfgang; Leone, Nicola; Ricca, Francesco: WASP: a native ASP solver based on constraint learning (2013) ioport
  19. Asuncion, Vernon; Lin, Fangzhen; Zhang, Yan; Zhou, Yi: Ordered completion for first-order logic programs on finite structures (2012)
  20. Faber, Wolfgang; Leone, Nicola; Perri, Simona: The intelligent grounder of DLV (2012)

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