Exploiting Justifications for Lazy Grounding of Answer Set Programs

Exploiting Justifications for Lazy Grounding of Answer Set Programs

Bart Bogaerts, Antonius Weinzierl

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 1737-1745. https://doi.org/10.24963/ijcai.2018/240

Answer set programming (ASP) is an established knowledge representation formalism. Lazy grounding avoids the so-called grounding bottleneck of ASP by interleaving grounding and solving; this technique was recently extended to work with conflict-driven clause learning. Unfortunately, it often happens that such a lazy grounding ASP system, at the fixpoint of the evaluation, arrives at an assignment that contains literals that are true but unjustified. The system then is unable to determine the actual causes of the situation and falls back to chronological backtracking, potentially wasting an exponential amount of time. In this paper, we show how top-down query mechanisms can be used to analyze the situation, learn a new clause or nogood, and backjump further in the search tree. Contributions include a rephrasing of lazy grounding in terms of justifications and algorithms to construct relevant justifications without grounding. Initial experiments indicate that the newly developed techniques indeed allow for an exponential speed-up.
Keywords:
Knowledge Representation and Reasoning: Non-monotonic Reasoning
Constraints and SAT: Constraints: Solvers and Tools
Knowledge Representation and Reasoning: Non-classical Logics for Knowledge Representation