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).
Current answer set solvers work on variable-free programs. Hence, a grounder is needed that, given an input program with first-order variables, computes an equivalent ground (variable-free) program. Gringo is such a grounder. Its output can be processed further with clasp, claspD, claspar, or claspfolio.
Clingo stands for clasp on Gringo and combines both systems in a monolithic way. Its input language is that of Gringo, and its output corresponds to that of clasp.
iClingo is an incremental ASP system implemented on top of Clingo. It is based on the idea that the grounder as well as the solver are implemented in a stateful way. Thus, both keep their previous states while increasing an incremental parameter. As regards grounding, at each incremental step, the goal is to produce only ground rules stemming from the current program slice, without re-producing previous ground rules. The ground program slices are then gradually passed to the solver that accumulates ground rules and computes answer sets for them.
Clingcon is an answer set solver for constraint logic programs, building upon the answer set solver Clingo and the CSP solver Gecode. It extends the high-level modeling language of ASP with constraint solving capacities. Constraints over finite domain integer variables can be used in logic programs. Clingcon adopts state-of-the-art techniques from the area of SMT, like conflict-driven learning and theory propagation.
claspfolio is a portfolio solver for ASP that makes use of machine-learning techniques for performing algorithm selection, choosing among different configurations of clasp.
claspar is a parallelized version of clasp using MPI to distribute search.
claspD is an extension of clasp that allows for solving disjunctive logic programs.
Coala is a versatile compiler from action languages to answer set programs. It supports different encodings, variables and LTL style queries.
The Potassco Labs suite comprises programs related to Answer Set Programming. These are either small utilities or projects in an early development phase.