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.

References in zbMATH (referenced in 66 articles )

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  1. Gelfond, Michael; Zhang, Yuanlin: Vicious circle principle, aggregates, and formation of sets in ASP based languages (2019)
  2. Alviano, Mario; Dodaro, Carmine; Maratea, Marco: Shared aggregate sets in answer set programming (2018)
  3. Lierler, Yuliya: What is answer set programming to propositional satisfiability (2017)
  4. Zhang, Heng; Zhang, Yan: Expressiveness of logic programs under the general stable model semantics (2017)
  5. Zhou, Yi; Zhang, Yan: A progression semantics for first-order logic programs (2017)
  6. Alviano, Mario; Dodaro, Carmine: Anytime answer set optimization via unsatisfiable core shrinking (2016)
  7. Brochenin, Remi; Maratea, Marco; Lierler, Yuliya: Disjunctive answer set solvers via templates (2016)
  8. Doherty, Patrick; Kvarnström, Jonas; Szałas, Andrzej: Iteratively-supported formulas and strongly supported models for Kleene answer set programs (extended abstract) (2016)
  9. Alviano, Mario; Peñaloza, Rafael: Fuzzy answer set computation via satisfiability modulo theories (2015)
  10. Fichte, Johannes Klaus; Szeider, Stefan: Backdoors to tractable answer set programming (2015)
  11. Fichte, Johannes K.; Szeider, Stefan: Backdoors to normality for disjunctive logic programs (2015)
  12. Dvořák, Wolfgang; Järvisalo, Matti; Wallner, Johannes Peter; Woltran, Stefan: Complexity-sensitive decision procedures for abstract argumentation (2014)
  13. Alviano, Mario; Dodaro, Carmine; Faber, Wolfgang; Leone, Nicola; Ricca, Francesco: WASP: a native ASP solver based on constraint learning (2013) ioport
  14. Asuncion, Vernon; Lin, Fangzhen; Zhang, Yan; Zhou, Yi: Ordered completion for first-order logic programs on finite structures (2012)
  15. Faber, Wolfgang; Leone, Nicola; Perri, Simona: The intelligent grounder of DLV (2012)
  16. Calimeri, Francesco; Ianni, Giovambattista; Ricca, Francesco; Alviano, Mario; Bria, Annamaria; Catalano, Gelsomina; Cozza, Susanna; Faber, Wolfgang; Febbraro, Onofrio; Leone, Nicola; Manna, Marco; Martello, Alessandra; Panetta, Claudio; Perri, Simona; Reale, Kristian; Santoro, Maria Carmela; Sirianni, Marco; Terracina, Giorgio; Veltri, Pierfrancesco: The third answer set programming competition: preliminary report of the system competition track (2011) ioport
  17. Erdem, Esra: Applications of answer set programming in phylogenetic systematics (2011) ioport
  18. Faber, Wolfgang; Pfeifer, Gerald; Leone, Nicola: Semantics and complexity of recursive aggregates in answer set programming (2011)
  19. Janhunen, Tomi; Niemelä, Ilkka: Compact translations of non-disjunctive answer set programs to propositional clauses (2011)
  20. Marek, Victor W.; Niemelä, Ilkka; Truszczyński, Mirosław: Origins of answer-set programming -- some background and two personal accounts (2011)

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