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 103 articles )

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  1. Amendola, Giovanni; Dodaro, Carmine; Faber, Wolfgang; Ricca, Francesco: Paracoherent answer set computation (2021)
  2. Bogaerts, Bart; Gamba, Emilio; Guns, Tias: A framework for step-wise explaining how to solve constraint satisfaction problems (2021)
  3. Doherty, Patrick; Szalas, Andrzej: Rough set reasoning using answer set programs (2021)
  4. Meli, Daniele; Sridharan, Mohan; Fiorini, Paolo: Inductive learning of answer set programs for autonomous surgical task planning. Application to a training task for surgeons (2021)
  5. Alviano, Mario; Dodaro, Carmine: Unsatisfiable core analysis and aggregates for optimum stable model search (2020)
  6. Amendola, Giovanni; Ricca, Francesco; Truszczynski, Miroslaw: New models for generating hard random Boolean formulas and disjunctive logic programs (2020)
  7. Bichler, Manuel; Morak, Michael; Woltran, Stefan: lpopt: a rule optimization tool for answer set programming (2020)
  8. Bomanson, Jori; Janhunen, Tomi: Boosting answer set optimization with weighted comparator networks (2020)
  9. Calimeri, Francesco; Dodaro, Carmine; Fuscà, Davide; Perri, Simona; Zangari, Jessica: Technical note. Efficiently coupling the (\mathscrI)-DLV grounder with ASP solvers (2020)
  10. Dodaro, Carmine; Ricca, Francesco: The external interface for extending WASP (2020)
  11. Gebser, Martin; Maratea, Marco; Ricca, Francesco: The Seventh Answer Set Programming Competition: design and results (2020)
  12. 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)
  13. Amendola, Giovanni; Dodaro, Carmine; Ricca, Francesco: Better paracoherent answer sets with less resources (2019)
  14. Amendola, Giovanni; Ricca, Francesco; Truszczynski, Miroslaw: Beyond NP: quantifying over answer sets (2019)
  15. 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)
  16. Calimeri, Francesco; Ianni, Giovambattista; Pacenza, Francesco; Perri, Simona; Zangari, Jessica: Incremental answer set programming with overgrounding (2019)
  17. Calimeri, Francesco; Perri, Simona; Zangari, Jessica: Optimizing answer set computation via heuristic-based decomposition (2019)
  18. Cuteri, Bernardo; Dodaro, Carmine; Ricca, Francesco; Schüller, Peter: Partial compilation of ASP programs (2019)
  19. Paramonov, Sergey; Stepanova, Daria; Miettinen, Pauli: Hybrid ASP-based approach to pattern mining (2019)
  20. Alviano, Mario; Dodaro, Carmine; Maratea, Marco: Shared aggregate sets in answer set programming (2018)

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