An Approximate Algorithm for Min-Based Possibilistic Networks

Min‐based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min‐based networks, which consists of propagating information through the network structure to answer queries. Exact inferenc...

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Bibliographic Details
Published in:International journal of intelligent systems Vol. 29; no. 7; pp. 615 - 633
Main Authors: Ajroud, Amen, Benferhat, Salem
Format: Journal Article
Language:English
Published: Hoboken, NJ Blackwell Publishing Ltd 01.07.2014
Wiley
John Wiley & Sons, Inc
Subjects:
Algorithms
Applied sciences
Approximation
Computer science; control theory; systems
Convergence
Exact sciences and technology
Inference
Information retrieval. Graph
Intelligent systems
Networks
Propagation
Queries
Tasks
Theoretical computing
Belief
Network structure
Experimental result
Possibility theory
Probabilistic approach
Query
Information network
Imperfect information
Probabilistic net
Inference
Graph method
Information structure
ISSN:0884-8173, 1098-111X
Online Access:Get full text
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Summary:Min‐based (or qualitative) possibilistic networks are important tools to efficiently and compactly represent and analyze uncertain information. Inference is a crucial task in min‐based networks, which consists of propagating information through the network structure to answer queries. Exact inference computes posteriori possibility distributions, given some observed evidence, in a time proportional to the number of nodes of the network when it is simply connected (without loops). On multiply connected networks (with loops), exact inference is known as a hard problem. This paper proposes an approximate algorithm for inference in min‐based possibilistic networks. More precisely, we adapt the well‐known approximate algorithm Loopy Belief Propagation (LBP) on qualitative possibilistic networks. We provide different experimental results that analyze the convergence of possibilistic LBP.
Bibliography:ark:/67375/WNG-MQ0JVD6K-W
istex:C3D2F407D1E3C3923AD75E9CBB85CF2B30E60F38
ArticleID:INT21649
benferhat@cril.fr
e‐mail
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ISSN:0884-8173
1098-111X
DOI:10.1002/int.21649