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).