Unifying Justifications and Debugging for Answer-Set Programs

Recently, (Viegas Damásio et al. 2013) introduced a way to construct propositional formulae encoding provenance information for logic programs. From these formulae, justifications for a given interpretation are extracted but it does not explain why such interpretation is not an answer-set (debugging). Resorting to a meta-programming transformation for debugging logic programs, (Gebser et al. 2008) does the converse. Here we unify these complementary approaches using meta-programming transformations. First, an answer-set program is constructed in order to generate every provenance propositional models for a program, both for well-founded and answer-set semantics, suggesting alternative repairs to bring about (or not) a given interpretation. In particular, we identify what changes must be made to a program in order for an interpretation to be an answer-set, thus providing the basis to relate provenance with debugging. With this meta-programming method, one does not have the need to generate the provenance propositional formulas and thus obtain debugging and justification models directly from the transformed program. This enables computing provenance answer-sets in an easy way by using AS solvers. We show that the provenance approach generalizes the debugging one, since any error has a counterpart provenance but not the other way around. Because the method we propose is based on meta-programming, we extended an existing tool and developed a proof-of-concept built to help computing our models.