clasp

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 90 articles )

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  1. Amendola, Giovanni; Ricca, Francesco; Truszczynski, Miroslaw: New models for generating hard random Boolean formulas and disjunctive logic programs (2020)
  2. Semenov, Alexander; Otpuschennikov, Ilya; Gribanova, Irina; Zaikin, Oleg; Kochemazov, Stepan: Translation of algorithmic descriptions of discrete functions to SAT with applications to cryptanalysis problems (2020)
  3. Amendola, Giovanni; Dodaro, Carmine; Ricca, Francesco: Better paracoherent answer sets with less resources (2019)
  4. Amendola, Giovanni; Ricca, Francesco; Truszczynski, Miroslaw: Beyond NP: quantifying over answer sets (2019)
  5. Banbara, Mutsunori; Inoue, Katsumi; Kaufmann, Benjamin; Okimoto, Tenda; Schaub, Torsten; Soh, Takehide; Tamura, Naoyuki; Wanko, Philipp: \textitteaspoon: solving the curriculum-based course timetabling problems with answer set programming (2019)
  6. Calimeri, Francesco; Ianni, Giovambattista; Pacenza, Francesco; Perri, Simona; Zangari, Jessica: Incremental answer set programming with overgrounding (2019)
  7. Cuteri, Bernardo; Dodaro, Carmine; Ricca, Francesco; Schüller, Peter: Partial compilation of ASP programs (2019)
  8. Alviano, Mario; Dodaro, Carmine; Maratea, Marco: Shared aggregate sets in answer set programming (2018)
  9. Bliem, Bernhard: Treewidth in non-ground answer set solving and alliance problems in graphs (2018)
  10. Brewka, Gerhard; Ellmauthaler, Stefan; Gonçalves, Ricardo; Knorr, Matthias; Leite, João; Pührer, Jörg: Reactive multi-context systems: heterogeneous reasoning in dynamic environments (2018)
  11. Dahlem, Marc; Bhagyanath, Anoop; Schneider, Klaus: Optimal scheduling for exposed datapath architectures with buffered processing units by ASP (2018)
  12. Frioux, Clémence; Schaub, Torsten; Schellhorn, Sebastian; Siegel, Anne; Wanko, Philipp: Hybrid metabolic network completion (2018)
  13. Alviano, Mario: Model enumeration in propositional circumscription via unsatisfiable core analysis (2017)
  14. Banbara, Mutsunori; Kaufmann, Benjamin; Ostrowski, Max; Schaub, Torsten: \textitclingcon: the next generation (2017)
  15. Dahlem, Marc; Jain, Tripti; Schneider, Klaus; Gillmann, Michael: Automatic synthesis of optimal-size concentrators by answer set programming (2017)
  16. Lierler, Yuliya: What is answer set programming to propositional satisfiability (2017)
  17. Lindauer, Marius; Hoos, Holger; Leyton-Brown, Kevin; Schaub, Torsten: Automatic construction of parallel portfolios via algorithm configuration (2017)
  18. Merhej, Elie; Schockaert, Steven; De Cock, Martine: Repairing inconsistent answer set programs using rules of thumb: a gene regulatory networks case study (2017)
  19. Polash, Md Masbaul Alam; Newton, M. A. Hakim; Sattar, Abdul: Constraint-directed search for all-interval series (2017)
  20. Toda, Takahisa: Dualization of Boolean functions using ternary decision diagrams (2017)

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