Simulating Sets in Answer Set Programming

Simulating Sets in Answer Set Programming

Sarah Alice Gaggl, Philipp Hanisch, Markus Krötzsch

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 2634-2640. https://doi.org/10.24963/ijcai.2022/365

We study the extension of non-monotonic disjunctive logic programs with terms that represent sets of constants, called DLP(S), under the stable model semantics. This strictly increases expressive power, but keeps reasoning decidable, though cautious entailment is coNEXPTIME^NP-complete, even for data complexity. We present two new reasoning methods for DLP(S): a semantics-preserving translation of DLP(S) to logic programming with function symbols, which can take advantage of lazy grounding techniques, and a ground-and-solve approach that uses non-monotonic existential rules in the grounding stage. Our evaluation considers problems of ontological reasoning that are not in scope for traditional ASP (unless EXPTIME =ΠP2 ), and we find that our new existential-rule grounding performs well in comparison with native implementations of set terms in ASP.
Keywords:
Knowledge Representation and Reasoning: Logic Programming
Knowledge Representation and Reasoning: Computational Complexity of Reasoning
Knowledge Representation and Reasoning: Non-monotonic Reasoning