Potassco, the Potsdam Answer Set Solving Collection, bundles tools for Answer Set Programming developed at the University of Potsdam: Clingo combines both gringo and clasp into a monolithic system. This way it offers more control over the grounding and solving process than gringo and clasp can offer individually - e.g., incremental grounding and solving. Clingo comes with its own version of claspD-2 and hence now supports parallel- and disjunctive solving. Attention! The languages of Gringo 3 and 4 are not fully compatible because Gringo 4 adheres to the recent ASP language standard. For processing legacy encodings, we recommend downloading the latest version of Gringo 3 in addition to Gringo 4.

References in zbMATH (referenced in 102 articles , 2 standard articles )

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  1. Hecher, Markus: Treewidth-aware reductions of normal \textscASPto \textscSAT- is normal \textscASPHarder than \textscSATafter all? (2022)
  2. Linsbichler, Thomas; Maratea, Marco; Niskanen, Andreas; Wallner, Johannes P.; Woltran, Stefan: Advanced algorithms for abstract dialectical frameworks based on complexity analysis of subclasses and SAT solving (2022)
  3. Abels, Dirk; Jordi, Julian; Ostrowski, Max; Schaub, Torsten; Toletti, Ambra; Wanko, Philipp: Train scheduling with hybrid answer set programming (2021)
  4. Alviano, Mario; Batsakis, Sotiris; Baryannis, George: Modal logic S5 satisfiability in answer set programming (2021)
  5. Amendola, Giovanni; Dodaro, Carmine; Faber, Wolfgang; Ricca, Francesco: Paracoherent answer set computation (2021)
  6. Bertolucci, Riccardo; Capitanelli, Alessio; Dodaro, Carmine; Leone, Nicola; Maratea, Marco; Mastrogiovanni, Fulvio; Vallati, Mauro: Manipulation of articulated objects using dual-arm robots via answer set programming (2021)
  7. Calimeri, Francesco; Cauteruccio, Francesco; Cinelli, Luca; Marzullo, Aldo; Stamile, Claudio; Terracina, Giorgio; Durand-Dubief, Françoise; Sappey-Marinier, Dominique: A logic-based framework leveraging neural networks for studying the evolution of neurological disorders (2021)
  8. Calimeri, Francesco; Manna, Marco; Mastria, Elena; Morelli, Maria Concetta; Perri, Simona; Zangari, Jessica: I-DLV-sr: a stream reasoning system based on I-DLV (2021)
  9. Cropper, Andrew; Morel, Rolf: Learning programs by learning from failures (2021)
  10. Eiter, Thomas; Kaminski, Tobias: Pruning external minimality checking for answer set programs using semantic dependencies (2021)
  11. Evans, Richard; Bošnjak, Matko; Buesing, Lars; Ellis, Kevin; Pfau, David; Kohli, Pushmeet; Sergot, Marek: Making sense of raw input (2021)
  12. Evans, Richard; Hernández-Orallo, José; Welbl, Johannes; Kohli, Pushmeet; Sergot, Marek: Making sense of sensory input (2021)
  13. Lierler, Yuliya; Robbins, Justin: DualGrounder: lazy instantiation via clingo multi-shot framework (2021)
  14. 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)
  15. Suchan, Jakob; Bhatt, Mehul; Varadarajan, Srikrishna: Commonsense visual sensemaking for autonomous driving -- on generalised neurosymbolic online abduction integrating vision and semantics (2021)
  16. Arias, Joaquín; Carro, Manuel; Chen, Zhuo; Gupta, Gopal: Justifications for goal-directed constraint answer set programming (2020)
  17. Baryannis, George; Tachmazidis, Ilias; Batsakis, Sotiris; Antoniou, Grigoris; Alviano, Mario; Papadakis, Emmanuel: A generalised approach for encoding and reasoning with qualitative theories in answer set programming (2020)
  18. Bichler, Manuel; Morak, Michael; Woltran, Stefan: selp: a single-shot epistemic logic program solver (2020)
  19. Bichler, Manuel; Morak, Michael; Woltran, Stefan: lpopt: a rule optimization tool for answer set programming (2020)
  20. Bomanson, Jori; Janhunen, Tomi: Boosting answer set optimization with weighted comparator networks (2020)

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