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Applications of belief revision

Authors: Mary-Anne Williams;

Applications of belief revision

Abstract

Belief revision provides support for intelligent information processing and deci sion making The objective of this paper is to illustrate how theoretical ideas in belief revision have laid the foundation for practical implementations and real world applications Belief revision operators are often based on one of the following constructive de nitions an epistemic entrenchment ordering of sentences or a system of spheres which is essentially a total preorder over possible world states We outline each of these constructions and show how they can be implemented and used for real world applications To illustrate the epistemic entrenchment construction we use an application in software engineering and for the system of spheres construction an application in marketing research In section we introduce the AGM paradigm for belief revision In section we describe the epistemic entrenchment construction for revision functions and show how it can be implemented using a standard theorem prover An application in requirements engineering is used to illustrate its applicability In section we describe the systems of spheres construction and illustrate how it can be implemented using relational database technology The construction is then used to model changes in consumer preferences in a marketing research application

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Average
Top 10%
Top 10%
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