In this paper, both the uncertainty and the origin of pieces of information is handled in an extended possibilistic logic framework. Each formula is associated with a set (a fuzzy set more generally) which gathers labels of sources according to which the formula is (more or less) certainly true. In case of a single source of information, possibilistic logic is recovered. Soundness and completen...

We consider optimal portfolio selection problems in a possibilistic setting. Using the possibilistic framework, we can integrate more efficiently the experts’ knowledge and the investors’ subjective opinions into a portfolio selection model. In 2002 Carlsson, Fullér and Majlender considered portfolio selection problems under trapezoidal possibility distributions and presented an algorithm of co...

Kernel based neural networks with probabilistic reasoning are suitable for many practical applications. But in uence of data set sizes let the probabilistic approach fail in case of small data amounts. Possibilistic reasoning avoids this drawback because it is independent of class size. The fundamentals of possibilistic reasoning are derived from a probability/possibility consistency principle ...

There has been an ever-increasing interest in multi-disciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise information. This paper covers the problem of their parameters learning from imprecise datasets, i.e., containing multi-valued data. We propose in the first ...

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP p...

Logic programs with ordered disjunction have shown to be a flexible specification language able to model common user preferences in a natural way. However, in some realistic scenarios the preferences should be linked to the evidence of the information when trying to reach a single preferred solution. In this paper, we extend the syntax and the semantics of logic programs with ordered disjunctio...

In this paper we consider the extensions of description logics that were proposed to represent uncertain or vague knowledge, focusing on the fuzzy and possibilistic formalisms. We compare these two approaches and comment on their differences concentrating on the consistency issue of knowledge bases represented in these extended frameworks. We present a classification of existing algorithms and ...

Possibilistic logic is a logic for reasoning with uncertain and partially inconsistent knowledge bases. Its standard version consists in ranking propositional formulas according to their certainty or priority level, by assigning them lower bounds of necessity values. We give a survey of automated deduction techniques for standard possibilistic logic, together with complexity results. We focus o...

An architecture for the implementation of possibilistic models in an object-oriented programming environment (C++ in particular) is described. Fundamental classes for special and general random sets, their associated fuzzy measures, special and general distributions and fuzzy sets, and possibilistic processes are speciied. Supplementary methods|including the fast MM obius transform, the maximum...