Modeling belief in dynamic systems Part II: revision and update

Nir Friedman, Joseph Y. Halpern

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


The study of belief change has been an active area in philosophy and AI. In recent years two special cases of belief change, belief revision and belief update, have been studied in detail. In a companion paper (Friedman & Halpern, 1997), we introduce a new framework to model belief change. This framework combines temporal and epistemic modalities with a notion of plausibility, allowing us to examine the change of beliefs over time. In this paper, we show how belief revision and belief update can be captured in our framework. This allows us to compare the assumptions made by each method, and to better understand the principles underlying them. In particular, it shows that Katsuno and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on several strong assumptions that may limit its applicability in artificial intelligence. Finally, our analysis allow us to identify a notion of minimal change that underlies a broad range of belief change operations including revision and update.

Original languageAmerican English
Pages (from-to)117-167
Number of pages51
JournalJournal of Artificial Intelligence Research
StatePublished - 1999

Bibliographical note

Funding Information:
This work has been supported by grants LM05708 and LM06245 from the National Library of Medicine and IRI-9528444 from the National Science Foundation. Computing resources were provided by the CAM IS project, funded under grant No. LM05305 from the National Library of Medicine.


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