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Possibilistic Merging and Distance-Based Fusion of Propositional Information

Possibilistic Merging and Distance-Based Fusion of Propositional Information The problem of merging multiple sources information is central in many information processing areas such as databases integrating problems, multiple criteria decision making, expert opinion pooling, etc. Recently, several approaches have been proposed to merge propositional bases, or sets of (non-prioritized) goals. These approaches are in general semantically defined. Like in belief revision, they use implicit priorities, generally based on Dalal's distance, for merging the propositional bases and return a new propositional base as a result. An immediate consequence of the generation of a propositional base is the impossibility of decomposing and iterating the fusion process in a coherent way with respect to priorities since the underlying ordering is lost. This paper presents a general approach for fusing prioritized bases, both semantically and syntactically, when priorities are represented in the possibilistic logic framework. Different classes of merging operators are considered depending on whether the sources are consistent, conflicting, redundant or independent. We show that the approaches which have been recently proposed for merging propositional bases can be embedded in this setting. The result is then a prioritized base, and hence the process can be coherently decomposed and iterated. Moreover, this encoding provides a syntactic counterpart for the fusion of propositional bases. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

Possibilistic Merging and Distance-Based Fusion of Propositional Information

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References (26)

Publisher
Springer Journals
Copyright
Copyright © 2002 by Kluwer Academic Publishers
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mathematics, general; Computer Science, general; Complex Systems
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1023/A:1014446411602
Publisher site
See Article on Publisher Site

Abstract

The problem of merging multiple sources information is central in many information processing areas such as databases integrating problems, multiple criteria decision making, expert opinion pooling, etc. Recently, several approaches have been proposed to merge propositional bases, or sets of (non-prioritized) goals. These approaches are in general semantically defined. Like in belief revision, they use implicit priorities, generally based on Dalal's distance, for merging the propositional bases and return a new propositional base as a result. An immediate consequence of the generation of a propositional base is the impossibility of decomposing and iterating the fusion process in a coherent way with respect to priorities since the underlying ordering is lost. This paper presents a general approach for fusing prioritized bases, both semantically and syntactically, when priorities are represented in the possibilistic logic framework. Different classes of merging operators are considered depending on whether the sources are consistent, conflicting, redundant or independent. We show that the approaches which have been recently proposed for merging propositional bases can be embedded in this setting. The result is then a prioritized base, and hence the process can be coherently decomposed and iterated. Moreover, this encoding provides a syntactic counterpart for the fusion of propositional bases.

Journal

Annals of Mathematics and Artificial Intelligence – Springer Journals

Published: Oct 10, 2004

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