{"type":"ScholarlyArticle","title":"Overcoming the Grounding Bottleneck Due to Constraints in ASP Solving: Constraints Become Propagators","authors":[{"name":"Bernardo Cuteri","slug":"bernardo-cuteri","given":"Bernardo","family":"Cuteri","affiliation":"University of Calabria , Italy"},{"name":"Carmine Dodaro","slug":"carmine-dodaro","given":"Carmine","family":"Dodaro","affiliation":"University of Calabria , Italy"},{"name":"Francesco Ricca","slug":"francesco-ricca","given":"Francesco","family":"Ricca","affiliation":"University of Calabria , Italy"},{"name":"Peter Schüller","slug":"peter-schuller","given":"Peter","family":"Schüller","affiliation":"TU Wien , Austria"}],"abstract":"Answer Set Programming (ASP) is a well-known formalism for Knowledge Representation and Reasoning, successfully employed to solve many AI problems, also thanks to the availability of efficient implementations. Traditionally, ASP systems are based on the ground&solve approach, where the grounding transforms a general input program into its propositional counterpart, whose stable models are then computed by the solver using the CDCL algorithm. This approach suffers an intrinsic limitation: the grounding of one or few constraints may be unaffordable from a computational point of view; a problem known as grounding bottleneck. In this paper, we develop an innovative approach for evaluating ASP programs, where some of the constraints of the input program are not grounded but automatically translated into propagators of the CDCL algorithm that work on partial interpretations. We implemented the new approach on top of the solver WASP and carried out an experimental analysis on different benchmarks. Results show that our approach consistently outperforms state-of-the-art ASP systems by overcoming the grounding bottleneck.","doi":"10.24963/ijcai.2020/234","url":"https://ijcai.org/proceedings/2020/234","pdf":"https://ijcai.org/proceedings/2020/0234.pdf","markdown":"https://ijcai.org/proceedings/2020/234.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/234","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":234,"pages":{"first":1688,"last":1694},"keywords":["Knowledge Representation and Reasoning: Non-monotonic Reasoning, Common-Sense Reasoning","Knowledge Representation and Reasoning: Other","Knowledge Representation and Reasoning: Knowledge Representation Languages"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25107/file_25107.m3u8","length_sec":298,"provider":"hls"}],"references":[{"position":1,"raw":"[Alviano and Dodaro, 2016] Mario Alviano and Carmine Dodaro. 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