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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/5671

Title: Double preference relations for generalised belief change
Authors: Booth, R
Chopra, S
Meyer, T
Ghose, A
Keywords: Belief revision
Belief removal
Belief liberation
Severe withdrawal
Issue Date: Nov-2010
Publisher: Elsevier
Citation: Booth, R, Chopra, S, Meyer, T and Ghose, A. 2010. Double preference relations for generalised belief change. Artificial Intelligence, vol. 174(16-17), pp 1339-1368
Series/Report no.: Workflow;6003
Abstract: Many belief change formalisms employ plausibility orderings over the set of possible worlds to determine how the beliefs of an agent ought to be modified after the receipt of a new epistemic input. While most such possible world semantics rely on a single ordering, we investigate the use of an additional preference ordering—representing, for instance, the epistemic context the agent finds itself in—to guide the process of belief change. We show that the resultant formalism provides a unifying semantics for a wide variety of belief change operators. By varying the conditions placed on the second ordering, different families of known belief change operators can be captured, including AGM belief contraction and revision, Rott and Pagnucco’s severe withdrawal, the systematic withdrawal of Meyer et al., as well as the linear liberation and s-liberation operators of Booth et al. Our approach also identifies novel classes of belief change operators worthy of further investigation.
Description: Copyright: 2010 Elsevier. This is an ABSTRACT ONLY.
URI: http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TYF-50RVNPB-1&_user=958262&_coverDate=11%2F30%2F2010&_rdoc=1&_fmt=high&_orig=gateway&_origin=gateway&_sort=d&_docanchor=&view=c&_searchStrId=1682244470&_rerunOrigin=scholar.google&_acct=C000049363&_version=1&_urlVersion=0&_userid=958262&md5=59de3376870fe87e979971f82c17bc38&searchtype=a
http://ac.els-cdn.com/S0004370210001396/1-s2.0-S0004370210001396-main.pdf?_tid=9ee186fd6939f3b181f746d46a1789df&acdnat=1332488005_9bf652b99d9a36f3cc8d1eb00a555bcb
http://hdl.handle.net/10204/5671
ISSN: 0004-3702
Appears in Collections:Digital intelligence
General science, engineering & technology

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