Probabilistic description logics for subjective uncertainty

Gutierrez Basulto, Victor ORCID: https://orcid.org/0000-0002-6117-5459, Jung, Jean Christoph, Lutz, Carsten and Schröder, Lutz 2017. Probabilistic description logics for subjective uncertainty. Journal of Artificial Intelligence Research 58 , pp. 1-66. 10.1613/jair.5222

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Abstract

We propose a family of probabilistic description logics (DLs) that are derived in a principled way from Halpern's probabilistic first-order logic. The resulting probabilistic DLs have a two-dimensional semantics similar to temporal DLs and are well-suited for representing subjective probabilities. We carry out a detailed study of reasoning in the new family of logics, concentrating on probabilistic extensions of the DLs ALC and EL, and showing that the complexity ranges from PTime via ExpTime and 2ExpTime to undecidable.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	AI Access Foundation
ISSN:	1076-9757
Date of First Compliant Deposit:	4 June 2018
Date of Acceptance:	3 January 2017
Last Modified:	06 May 2023 04:32
URI:	https://orca.cardiff.ac.uk/id/eprint/111926

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