conflict-driven nogood learning answer set solver

 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 LPARSE
 output format), as a SAT solver (on simplified DIMACS/CNF format), or
 as a PB solver (on OPB format).