Enrichment of Qualitative Beliefs for Reasoning under Uncertainty
  This paper deals with enriched qualitative belief functions for reasoning
under uncertainty and for combining information expressed in natural language
through linguistic labels. In this work, two possible enrichments (quantitative
and/or qualitative) of linguistic labels are considered and operators
(addition, multiplication, division, etc) for dealing with them are proposed
and explained. We denote them $qe$-operators, $qe$ standing for
"qualitative-enriched" operators. These operators can be seen as a direct
extension of the classical qualitative operators ($q$-operators) proposed
recently in the Dezert-Smarandache Theory of plausible and paradoxist reasoning
(DSmT). $q$-operators are also justified in details in this paper. The
quantitative enrichment of linguistic label is a numerical supporting degree in
$[0,\infty)$, while the qualitative enrichment takes its values in a finite
ordered set of linguistic values. Quantitative enrichment is less precise than
qualitative enrichment, but it is expected more close with what human experts
can easily provide when expressing linguistic labels with supporting degrees.
Two simple examples are given to show how the fusion of qualitative-enriched
belief assignments can be done.
