The Smodels system is an Answer Set Programming (ASP) implementation based on the stable model semantics of normal logic programs. The basic idea of ASP is to encode the constraints of a problem as a logic program such that the answer sets (stable models) of the program correspond to the solutions of the problem. Then we can solve the problem by letting a logic program engine to find the answer sets of the program.

References in zbMATH (referenced in 238 articles , 1 standard article )

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  1. Bliem, Bernhard; Morak, Michael; Moldovan, Marius; Woltran, Stefan: The impact of treewidth on grounding and solving of answer set programs (2020)
  2. Fandinno, Jorge; Schulz, Claudia: Answering the “why” in answer set programming -- a survey of explanation approaches (2019)
  3. Gelfond, Michael; Zhang, Yuanlin: Vicious circle principle, aggregates, and formation of sets in ASP based languages (2019)
  4. Lifschitz, V., Lühne, P., Schaub, T.: anthem: Transforming gringo Programs into First-Order Theories (Preliminary Report) (2018) arXiv
  5. Balduccini, Marcello; Lierler, Yuliya: Constraint answer set solver \textscezcspand why integration schemas matter (2017)
  6. Lierler, Yuliya: What is answer set programming to propositional satisfiability (2017)
  7. Lierler, Yuliya; Susman, Benjamin: On relation between constraint answer set programming and satisfiability modulo theories (2017)
  8. Cenzer, Douglas; Marek, Victor W.; Remmel, Jeffrey B.: Index sets for finite normal predicate logic programs with function symbols (2016)
  9. Gao, Tiantian; Fodor, Paul; Kifer, Michael: Paraconsistency and word puzzles (2016)
  10. Inclezan, Daniela; Gelfond, Michael: Modular action language (\mathcalALM) (2016)
  11. Lierler, Yuliya; Truszczynski, Miroslaw: On abstract modular inference systems and solvers (2016)
  12. Analyti, Anastasia; Viegas Damásio, Carlos; Antoniou, Grigoris: Extended RDF: computability and complexity issues (2015)
  13. Bruynooghe, Maurice; Blockeel, Hendrik; Bogaerts, Bart; De Cat, Broes; De Pooter, Stef; Jansen, Joachim; Labarre, Anthony; Ramon, Jan; Denecker, Marc; Verwer, Sicco: Predicate logic as a modeling language: modeling and solving some machine learning and data mining problems with IDP3 (2015)
  14. Lifschitz, Vladimir: The dramatic true story of the frame default (2015)
  15. Seki, Hirohisa: On dual programs in co-logic programming (2015)
  16. Analyti, Anastasia; Damásio, Carlos V.; Antoniou, Grigoris; Pachoulakis, Ioannis: Why-provenance information for RDF, rules, and negation (2014)
  17. Bauters, Kim; Schockaert, Steven; De Cock, Martine; Vermeir, Dirk: Semantics for possibilistic answer set programs: uncertain rules versus rules with uncertain conclusions (2014)
  18. Confalonieri, Roberto; Prade, Henri: Using possibilistic logic for modeling qualitative decision: answer set programming algorithms (2014)
  19. Göös, Mika; Lempiäinen, Tuomo; Czeizler, Eugen; Orponen, Pekka: Search methods for tile sets in patterned DNA self-assembly (2014)
  20. Lierler, Yuliya: Relating constraint answer set programming languages and algorithms (2014)

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