In the last decade, several approaches were introduced in literature to merge multiple and potentially conflicting pieces of information. Within the growing field of application favourable to distributed information, data fusion strategies aim at providing a global and consistent point of view over a set of sources which can contradict each other. Moreover, in many situations, the pieces of inf...

Risk aversion is one of the main themes in risk theory. Risk theory is treated usually by probability theory. The aim of this paper is to propose an approach of the risk aversion by possibility theory initiated by Zadeh in 1978 as an alternative of probability theory in the modeling of uncertain situations. The main notions studied in this paper are the possibilistic risk premium and the possib...

In this paper, we investigate how linguistic information can be incorporated into classical propositional logic. First, we show that Zadeh’s extension principle can be justified and at the same time generalized by considerations about transformation of possibility measures. Using these results, we show how linguistic uncertainty about the truth value of a proposition leads to the introduction o...

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzz...

We prove that possibilistic linear programming problems (introduced by Buckley in [2]) are well-posed, i.e. small changes of the membership function of the parameters may cause only a small deviation in the possibilistic distribution of the objective function.

Based on our earlier work on partial logics and extended logic programs [Wag91, Wag94, HJW96], and on the possibilistic logic of [DLP94], we de ne a compositional possibilistic rst-order logic with two kinds of negation.

Multi-objective De Novo linear programming (MODNLP) is problem for designing optimal system by reshaping the feasible set (Fiala [3] ). This paper deals with MODNLP having possibilistic objective functions coefficients. The problem is considered by inserting possibilistic data in the objective functions coefficients. The solution of the problem is defined and established under the using of effi...

Approximate reasoning with words is one of the remarkable human capability that manipulates perceptions in a wide variety of physical and mental tasks whether in fuzzy or uncertain surroundings. To model this remarkable human capability, L.A. Zadeh (1999) proposed a new concept of "computing with words", which is a methodology in which the objects of computation are words and propositions drawn...

This paper first proposes a new graphical model for decision making under uncertainty based on min-based possibilistic networks. A decision problem under uncertainty is described by means of two distinct min-based possibilistic Computing Optimistic Decisions 1039 networks: the first one expresses agent’s knowledge while the second one encodes agent’s preferences representing a qualitative utili...

In this paper, a new method for merging multiple inconsistent knowledge bases in the framework of possibilistic logic is presented. We divide the fusion process into two steps: one is called the splitting step and the other is called the combination step. Given several inconsistent possibilistic knowledge bases (i.e. the union of these possibilistic bases is inconsistent), we split each of them...