Conditional Syntax Splitting for Non-monotonic Inference Operators

Jesse Heyninck*, Gabriele Kern-Isberner, Thomas Meyer, Jonas Philipp Haldimann, Christoph Beierle

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review


Syntax splitting is a property of inductive inference operators that ensures we can restrict our attention to parts of the conditional belief base that share atoms with a given query. To apply syntax splitting, a conditional belief base needs to consist of syntactically disjoint conditionals. This requirement is often too strong in practice, as conditionals might share atoms. In this paper we introduce the concept of conditional syntax splitting, inspired by the notion of conditional independence as known from probability theory. We show that lexicographic inference and system W satisfy conditional syntax splitting, and connect conditional syntax splitting to several known properties from the literature on non-monotonic reasoning, including the drowning effect.

Original languageEnglish
Title of host publicationProceedings of the 37th AAAI Conference on Artificial Intelligence
EditorsBrian Williams, Yiling Chen, Jennifer Neville
PublisherAAAI Press
Number of pages9
ISBN (Electronic)9781577358800
Publication statusPublished - 27 Jun 2023
Event37th AAAI Conference on Artificial Intelligence - Washington, United States
Duration: 7 Feb 202314 Feb 2023
Conference number: 37

Publication series

SeriesAAAI Conference on Artificial Intelligence. Conference Proceedings


Conference37th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI 2023
Country/TerritoryUnited States


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