Conditional Independence for Iterated Belief Revision

Gabriele Kern-Isberner*, J.L.A. Heyninck, Christoph Beierle

*Corresponding author for this work

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


Conditional independence is a crucial concept for efficient probabilistic reasoning. For symbolic and qualitative reasoning, however, it has played only a minor role. Recently, Lynn, Delgrande, and Peppas have considered conditional independence in terms of syntactic multivalued dependencies. In this paper, we define conditional independence as a semantic property of epistemic states and present axioms for iterated belief revision operators to obey conditional independence in general. We show that c-revisions for ranking functions satisfy these axioms, and exploit the relevance of these results for iterated belief revision in general.
Original languageEnglish
Title of host publicationProceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22)
EditorsLuc De Raedt
PublisherInternational Joint Conferences on Artificial Intelligence
Number of pages7
ISBN (Electronic)978-1-956792-00-3
Publication statusPublished - 23 Jul 2022
Event31st International Joint Conference on Artificial Intelligence - Messe WIen, Vienna, Austria
Duration: 23 Jul 202229 Jul 2022
Conference number: 31


Conference31st International Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI-ECAI 2022
Internet address


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