Relevance in the Computation of Non-monotonic Inferences

J.L.A. Heyninck, Thomas Meyer

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


Inductive inference operators generate non-monotonic inference relations on the basis of a set of conditionals. Examples include rational closure, system P and lexicographic inference. For most of these systems, inference has a high worst-case computational complexity. Recently, the notion of syntax splitting has been formulated, which allows restricting attention to subsets of conditionals relevant for a given query. In this paper, we define algorithms for inductive inference that take advantage of syntax splitting in order to obtain more efficient decision procedures. In particular, we show that relevance allows to use the modularity of knowledge base is a parameter that leads to tractable cases of inference for inductive inference operators such as lexicographic inference.
Original languageEnglish
Title of host publicationArtificial Intelligence Research - Third Southern African Conference, SACAIR 2022, Proceedings
Editors Anban Pillay, Edgar Jembere, Aurona Gerber
Place of PublicationCham
PublisherSpringer, Cham
Number of pages13
ISBN (Electronic)978-3-031-22321-1
ISBN (Print)978-3-031-22320-4
Publication statusPublished - 2022
EventThird Southern African Conference: Artificial Intelligence Research - Stellenbosch Institute for Advanced Study, Stellenbosch, South Africa
Duration: 5 Dec 20229 Dec 2022
Conference number: 3

Publication series

SeriesCommunications in Computer and Information Science (CCIS)


ConferenceThird Southern African Conference
Abbreviated titleSACAIR 2022
Country/TerritorySouth Africa
Internet address


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