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Belief functions and default reasoning

delete2000-09-01
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OA
AI
S
Salem Benferhat
A
Alessandro Saffiotti
P
Philippe Smets
DOI:10.1016/S0004-3702(00)00041-2delete
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Abstract

Abstract

En 中文
We present a new approach to deal with default information based on the theory of belief functions. Our semantic structures, inspired by Adams' epsilon semantics, are epsilon-belief assignments, where mass values are either close to 0 or close to 1. In the first part of this paper, we show that these structures can be used to give a uniform semantics to several popular non-monotonic systems, including Kraus, Lehmann and Magidor's system P, Pearl's system Z, Brewka's preferred subtheories, Geffner's conditional entailment, Pinkas' penalty logic, possibilistic logic and the lexicographic approach. In the second part, we use epsilon-belief assignments to build a new system, called LCD, and show that this system correctly addresses the well-known problems of specificity, irrelevance, blocking of inheritance, ambiguity, and redundancy. (C) 2000 Elsevier Science B.V. All rights reserved.
Keywords:
belief functions
default reasoning
infinitesimals
least-commitment principle
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Journal

Artificial Intelligence Review cover
Artificial Intelligence Review
IF:
13.9
Papers:
6.1K
Citations:
1.9W

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