Exploiting Partial Assignments for Efficient Evaluation of Answer Set Programs with External Source Access

Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs and efficient solvers. HEX-programs extend ASP with external atoms for access to arbitrary external information. In this work, we extend the evaluation principles of external atoms to partial assignments, lift nogood learning to this setting, and introduce a variant of nogood minimization. This enables external sources to guide the search for answer sets akin to theory propagation. Our benchmark experiments demonstrate a clear improvement in efficiency over the state-of-the-art HEX-solver.