{"type":"ScholarlyArticle","title":"Bypassing the ASP Bottleneck: Hybrid Grounding by Splitting and Rewriting","authors":[{"name":"Alexander Beiser","slug":"alexander-beiser","given":"Alexander","family":"Beiser","affiliation":"TU Wien , Vienna , Austria"},{"name":"Markus Hecher","slug":"markus-hecher","given":"Markus","family":"Hecher","affiliation":"Massachusetts Institute of Technology , Cambridge , MA , United States"},{"name":"Kaan Unalan","slug":"kaan-unalan","given":"Kaan","family":"Unalan","affiliation":"TU Wien , Vienna , Austria"},{"name":"Stefan Woltran","slug":"stefan-woltran","given":"Stefan","family":"Woltran","affiliation":"TU Wien , Vienna , Austria"}],"abstract":"Answer Set Programming (ASP) is a key paradigm for problems in artificial intelligence and industrial contexts. In ASP, problems are modeled via a set of rules. Over the time this paradigm grew into a rich language, enabling complex rule types like aggregate expressions. Most practical ASP systems follow a ground-and-solve pattern, where rule schemes are grounded and resulting rules are solved. There, the so-called grounding bottleneck may prevent from solving, due to sheer grounding sizes. Recently body-decoupled grounding (BDG) demonstrated how to reduce grounding sizes by delegating effort to solving. However, BDG provides limited interoperability with traditional grounders and only covers simple rule types. \r\nIn this work, we establish hybrid grounding — based on a novel splitting theorem that allows us to freely combine BDG with traditional grounders. To mitigate huge groundings in practice, we define rewriting procedures for efficiently deferring grounding effort of aggregates to solving. Our experimental results indicate that this approach is competitive, especially for instances, where traditional grounding fails.","doi":"10.24963/ijcai.2024/360","url":"https://ijcai.org/proceedings/2024/360","pdf":"https://ijcai.org/proceedings/2024/0360.pdf","markdown":"https://ijcai.org/proceedings/2024/360.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/360","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":360,"pages":{"first":3250,"last":3258},"keywords":["Knowledge Representation and Reasoning: KRR: Computational complexity of reasoning","Knowledge Representation and Reasoning: KRR: Non-monotonic reasoning","Knowledge Representation and Reasoning: KRR: Applications","Knowledge Representation and Reasoning: KRR: Logic programming"],"videos":[],"references":[{"position":1,"raw":"[Alviano and Faber, 2018] Mario Alviano and Wolfgang Faber. 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