{"type":"ScholarlyArticle","title":"Abstraction for Non-Ground Answer Set Programs (Extended Abstract)","authors":[{"name":"Zeynep G. Saribatur","slug":"zeynep-g-saribatur","given":"Zeynep G.","family":"Saribatur","affiliation":"Institute of Logic and Computation , TU Wien"},{"name":"Thomas Eiter","slug":"thomas-eiter","given":"Thomas","family":"Eiter","affiliation":"Institute of Logic and Computation , TU Wien"},{"name":"Peter Schüller","slug":"peter-schuller","given":"Peter","family":"Schüller","affiliation":"Institute of Logic and Computation , TU Wien"}],"abstract":"Abstraction is a powerful technique that has not been considered much for nonmonotonic reasoning formalisms including Answer Set Programming (ASP), apart from related simplification methods. We introduce a notion for abstracting from the domain of an ASP program that shrinks the domain size and over-approximates the set of answer sets, as well as an abstraction-&-refinement methodology that, starting from an initial abstraction, automatically yields an abstraction with an associated answer set matching an answer set of the original program if one exists. Experiments reveal the potential of the approach, by its ability to focus on the program parts that cause unsatisfiability and by achieving concrete abstract answer sets that merely reflect relevant details.","doi":"10.24963/ijcai.2022/807","url":"https://ijcai.org/proceedings/2022/807","pdf":"https://ijcai.org/proceedings/2022/0807.pdf","markdown":"https://ijcai.org/proceedings/2022/807.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/807","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Journal Track","track":"Journal Track","number":807,"pages":{"first":5767,"last":5771},"keywords":["Knowledge Representation and Reasoning: Non-monotonic Reasoning","Knowledge Representation and Reasoning: Logic Programming"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987239","length_sec":81,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985382","length_sec":709,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Brain et al., 2007] Martin Brain, Martin Gebser, Jörg Pührer, Torsten Schaub, Hans Tompits, and Stefan Woltran. 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Saribatur, Thomas Eiter, and Peter Schüller. Abstraction for non-ground answer set programs. Artif. Intell., 300:103563, 2021.","title":"Abstraction for non-ground answer set programs","authors":"G Zeynep, Thomas Saribatur, Peter Eiter, Schüller","year":"2021","venue":"Artif. Intell","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}