clasp: A conflict-driven answer set solver. clasp is part of the Potassco project hosted at SourceForge. Source code and pre-compiled binaries are available on the Potassco download page. clasp is an answer set solver for (extended) normal logic programs. It combines the high-level modeling capacities of answer set programming (ASP) with state-of-the-art techniques from the area of Boolean constraint solving. The primary clasp algorithm relies on conflict-driven nogood learning, a technique that proved very successful for satisfiability checking (SAT). Unlike other learning ASP solvers, clasp does not rely on legacy software, such as a SAT solver or any other existing ASP solver. Rather, clasp has been genuinely developed for answer set solving based on conflict-driven nogood learning. clasp can be applied as an ASP solver (on SMODELS format, as output by Gringo), as a SAT solver (on a simplified version of DIMACS/CNF format), or as a PB solver (on OPB format).

References in zbMATH (referenced in 59 articles )

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  1. Lierler, Yuliya: What is answer set programming to propositional satisfiability (2017)
  2. Merhej, Elie; Schockaert, Steven; De Cock, Martine: Repairing inconsistent answer set programs using rules of thumb: a gene regulatory networks case study (2017)
  3. Polash, Md Masbaul Alam; Newton, M. A. Hakim; Sattar, Abdul: Constraint-directed search for all-interval series (2017)
  4. Toda, Takahisa: Dualization of Boolean functions using ternary decision diagrams (2017)
  5. Doherty, Patrick; Kvarnström, Jonas; Szałas, Andrzej: Iteratively-supported formulas and strongly supported models for Kleene answer set programs (extended abstract) (2016)
  6. Wen, Lian; Wang, Kewen; Shen, Yi-Dong; Lin, Fangzhen: A model for phase transition of random answer-set programs (2016)
  7. 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)
  8. Calautti, Marco; Greco, Sergio; Spezzano, Francesca; Trubitsyna, Irina: Checking termination of bottom-up evaluation of logic programs with function symbols (2015)
  9. Erdem, Esra; Oztok, Umut: Generating explanations for biomedical queries (2015)
  10. Erdem, Esra; Patoglu, Volkan; Saribatur, Zeynep Gozen: Diagnostic reasoning for robotics using action languages (2015)
  11. Heule, Marijn J. H.; Szeider, Stefan: A SAT approach to clique-width (2015)
  12. Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; Nicolas, Jacques; Saez-Rodriguez, Julio; Schaub, Torsten; Siegel, Anne: Learning Boolean logic models of signaling networks with ASP (2015)
  13. Hoos, Holger; Lindauer, Marius; Schaub, Torsten: claspfolio 2: advances in algorithm selection for answer set programming (2014)
  14. Hurley, Barry; Kotthoff, Lars; Malitsky, Yuri; O’Sullivan, Barry: Proteus: a hierarchical portfolio of solvers and transformations (2014)
  15. Muggleton, Stephen H.; Lin, Dianhuan; Pahlavi, Niels; Tamaddoni-Nezhad, Alireza: Meta-interpretive learning: application to grammatical inference (2014)
  16. Andres, Benjamin; Gebser, Martin; Schaub, Torsten; Haubelt, Christian; Reimann, Felix; Glaß, Michael: Symbolic system synthesis using answer set programming (2013)
  17. Andres, Benjamin; Sauer, Matthias; Gebser, Martin; Schubert, Tobias; Becker, Bernd; Schaub, Torsten: Accurate computation of sensitizable paths using answer set programming (2013)
  18. Benhamou, Belaïd: Dynamic and static symmetry breaking in answer set programming (2013)
  19. Collet, Guillaume; Eveillard, Damien; Gebser, Martin; Prigent, Sylvain; Schaub, Torsten; Siegel, Anne; Thiele, Sven: Extending the metabolic network of Ectocarpus siliculosus using answer set programming (2013)
  20. Gebser, Martin; Jost, Holger; Kaminski, Roland; Obermeier, Philipp; Sabuncu, Orkunt; Schaub, Torsten; Schneider, Marius: Ricochet robots: a transverse ASP benchmark (2013)

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