Paper Info
Title
Interval-Based Possibilistic Networks.
Abstract
In this paper, we study foundations of interval-based possibilistic networks where possibility degrees associated with nodes are no longer singletons but sub-intervals of [0,1]. This extension allows to compactly encode and reason with epistemic uncertainty and imprecise beliefs as well as with multiple expert knowledge. We propose a natural semantics based on compatible possibilistic networks. The last part of the paper shows that computing the uncertainty bounds of any event can be computed in interval-based networks without extra computational cost with respect to standard possibilistic networks.
Year
DOI
Venue
2014
10.1007/978-3-319-11508-5_4
SUM
Field
DocType
Citations 
ENCODE,Data mining,Uncertainty quantification,Natural semantics,Computer science,Chain rule,Possibility theory,Artificial intelligence,Possibilistic logic,Possibility distribution,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Salem Benferhat12585216.23
Sylvain Lagrue29715.11
Karim Tabia310725.11