In this article, we introduce a general concept of fuzzy operators. These operators are then used to generalize the possibilistic conditioning formulation proposed by Nguyen [1]. This generalization depends on the relation which exists between this conditioning and the probabilistic t-norm. By using other t-norms, other conditionings are obtained, and their properties are studied. One applicati...

Developing efficient approaches for reasoning under inconsistency is an important issue in many applications. Several methods have been proposed to compile, possibly inconsistent, weighted or stratified bases. This paper focuses on the well-known linear order and possibilistic logic strategies. It provides a way for compiling a stratified belief base in order to be able to process inference fro...

This paper studies the extension of possibilistic logic to the case when weights attached to formulas are symbolic and stand for variables that lie in a totally ordered scale, and only partial knowledge is available on the relative strength of these weights. A proof of the soundness and the completeness of this logic according to the relative certainty semantics in the sense of necessity measur...

Possibilistic logic is a weighted logic introduced and developed since the mid-1980s, in the setting of arti(cial intelligence, with a view to develop a simple and rigorous approach to automated reasoning from uncertain or prioritized incomplete information. Standard possibilistic logic expressions are classical logic formulas associated with weights, interpreted in the framework of possibility...

Variables' annotations in over-constrained problems are proposed and described in order to express preferences for optimal solution selection using preferences on variables. The basic interpretation of variables' annotations is presented and correspondence with hierarchical CSP and possibilistic CSP is described. An algorithm for solving systems of constraints with variables' annotations using ...

Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at object-language level. This paper presents a first approach towards extending P-DeLP to incorporate fuzzy constants and fuzzy propositional variables. We focus on how to characteri...

ABSTRICT This paper uses the possibility distribution approach to estimate the insurance loss amount. A special class of parametric possibility distributions is used to model insurance loss variables. The parameters of the possibility distribution are estimated by combining statistical analysis of sample data and domain knowledge provided by actuarial experts. Insurance premiums are calculated ...

Variables’ annotations in over-constrained problems are proposed and described in order to express preferences for optimal solution selection using preferences on variables. The basic interpretation of variables’ annotations is presented and correspondence with hierarchical CSP and possibilistic CSP is described. An algorithm for solving systems of constraints with variables’ annotations using ...

In this work we address the data reduction problem for fuzzy data. In particular, following a possibilistic approach, several component models for handling twoand three-way fuzzy data sets are introduced. The two-way models are based on classical Principal Component Analysis (PCA), whereas the three-way ones on threeway generalizations of PCA, as Tucker3 and CANDECOMP/PARAFAC. The hereproposed ...

We consider discrete possibilistic systems for which the available information is given by one-step transition possibilities and initial possibilities. These systems can be represented by a collection of variables satisfying a possibilistic counterpart of the Markov condition. This means that, given the values assumed by a selection of variables, the possibility that a subsequent variable assum...