Probabilistic belief revision via similarity of worlds modulo evidence

Gavin Rens*, Thomas Meyer, Gabriele Kern-Isberner, Abhaya Nayak

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

Similarity among worlds plays a pivotal role in providing the semantics for different kinds of belief change. Although similarity is, intuitively, a context-sensitive concept, the accounts of similarity presently proposed are, by and large, context blind. We propose an account of similarity that is context sensitive, and when belief change is concerned, we take it that the epistemic input provides the required context. We accordingly develop and examine two accounts of probabilistic belief change that are based on such evidence-sensitive similarity. The first switches between two extreme behaviors depending on whether or not the evidence in question is consistent with the current knowledge. The second gracefully changes its behavior depending on the degree to which the evidence is consistent with current knowledge. Finally, we analyze these two belief change operators with respect to a select set of plausible postulates.

Original languageEnglish
Title of host publicationKI 2018
Subtitle of host publicationAdvances in Artificial Intelligence - 41st German Conference on AI, 2018, Proceedings
EditorsFrank Trollmann, Anni-Yasmin Turhan
Place of PublicationCham, Switzerland
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Pages343-356
Number of pages14
ISBN (Electronic)9783030001117
ISBN (Print)9783030001100
DOIs
Publication statusPublished - 1 Jan 2018
Event41st German Conference on Artificial Intelligence, KI 2018 - Berlin, Germany
Duration: 24 Sep 201828 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11117 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference41st German Conference on Artificial Intelligence, KI 2018
CountryGermany
CityBerlin
Period24/09/1828/09/18

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Keywords

  • Bayesian conditioning
  • Belief revision
  • Lewis imaging
  • Probability
  • Similarity

Cite this

Rens, G., Meyer, T., Kern-Isberner, G., & Nayak, A. (2018). Probabilistic belief revision via similarity of worlds modulo evidence. In F. Trollmann, & A-Y. Turhan (Eds.), KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, 2018, Proceedings (pp. 343-356). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11117 LNAI). Cham, Switzerland: Springer-VDI-Verlag GmbH & Co. KG. https://doi.org/10.1007/978-3-030-00111-7_29