{"type":"ScholarlyArticle","title":"Lexicographic Entailment, Syntax Splitting and the Drowning Problem","authors":[{"name":"Jesse Heyninck","slug":"jesse-heyninck","given":"Jesse","family":"Heyninck","affiliation":"Open Universiteit Heerlen , the Netherlands; University of Cape Town and CAIR , South-Africa; Vrije Universiteit Brussel , Belgium; Technische Universität Dortmund , Germany"},{"name":"Gabriele Kern-Isberner","slug":"gabriele-kern-isberner","given":"Gabriele","family":"Kern-Isberner","affiliation":"Technische Universität Dortmund , Germany"},{"name":"Thomas Meyer","slug":"thomas-meyer","given":"Thomas","family":"Meyer","affiliation":"University of Cape Town and CAIR , South-Africa"}],"abstract":"Lexicographic inference is a well-known and popular approach to reasoning with non-monotonic conditionals. It is a logic of very high-quality, as it extends rational closure and avoids the so-called drowning problem. It seems, however, this high quality comes at a cost, as reasoning on the basis of lexicographic inference is of high computational complexity. In this paper, we show that lexicographic inference satisfies syntax splitting, which means that we can restrict our attention to parts of the belief base that share atoms with a given query, thus seriously restricting the computational costs for many concrete queries. Furthermore, we make some observations on the relationship between c-representations and lexicographic inference, and reflect on the relation between syntax splitting and the drowning problem.","doi":"10.24963/ijcai.2022/369","url":"https://ijcai.org/proceedings/2022/369","pdf":"https://ijcai.org/proceedings/2022/0369.pdf","markdown":"https://ijcai.org/proceedings/2022/369.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/369","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":"Knowledge Representation and Reasoning","track":"Main Track","number":369,"pages":{"first":2662,"last":2668},"keywords":["Knowledge Representation and Reasoning: Non-monotonic Reasoning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986866","length_sec":90,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985009","length_sec":336,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Aravanis et al., 2019] Theofanis Aravanis, Pavlos Peppas, and Mary-Anne Williams. 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