Abstract
Besides the representation of knowledge, the handling of its dynamics in the light of new information, generally termed belief change, is one of the fundamental problems in Artificial Intelligence. The great variety of approaches that has been set forth up to now, usually each method coming along with a descriptive axiom scheme (for a survey, cf. [Gärdenfors and Rott, 1994]), corresponds to the many different interpretations and names the term change has been given. Gärdenfors [1988] identified three fundamental types of belief change, revision,expansion and update. Katsuno and Mendelzon [1991] argued that the axioms of Gärdenfors for revision are only adequate to describe a change in knowledge about a static world, but not for recording changes in an evolving world. They called this latter type of change update,with erasure being its inverse operation (cf. [Katsuno and Mendelzon, 1991]). Conditioning has been regarded as an adequate method for revising probabilistic beliefs (cf. e.g. [Paris, 1994; Gärdenfors, 1988]), but Dubois and Prade [1997] emphasize that actually, conditioning does not correspond to revision but rather to focusing.
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Kern-Isberner, G. (2001). Revising and Updating Probabilistic Beliefs. In: Williams, MA., Rott, H. (eds) Frontiers in Belief Revision. Applied Logic Series, vol 22. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9817-0_20
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DOI: https://doi.org/10.1007/978-94-015-9817-0_20
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