The objective of this paper is to introduce the hybrid logic methodology into possibilistic reasoning. It has been well-known that possibilistic logic has some strong modal logic flavor. However, modal logic lacks the capability of referring to states or possible worlds though states are crucial to its semantics. Hybrid logic circumvents the problem by blending the classical logic mechanism int...

Possibilistic Stable model Semantics is an extension of Stable Model Semantics that allows to merge uncertain and non monotonic reasoning into a unique framework. To achieve this aim, knowledge is represented by a normal logic program where each rule is given with its own degree of certainty. By this way, it formally defines a distribution of possibility over atom sets that, on its turn, induce...

Students on Business School courses will require a certain level of numerical ability; therefore, Mathematics and Statistics are important elements the curriculum (Cottee et. al., 2014). often struggle with these quantitative parts their course this is sometimes seen as part general "Mathematics Problem" that impacts many disciplines including biology, economics, nursing psychology (Mac an Bhai...

In this paper, we propose a modal logic for reasoning about possibilistic belief fusion. This is a combination of multiagent epistemic logic and possibilistic logic. We use graded epistemic operators to represent agents’ uncertain beliefs, and the operators are interpreted in accordance with possibilistic semantics. We employ ordered fusion based on a level skipping strategy to resolve the inco...

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 contains two important contributions for the development of possibilistic causal networks. The first one concerns the representation of interventions in possibilistic networks. We provide the counterpart of the ”DO” operator, recently introduced by Pearl, in possibility theory framework. We then show that interventions can equivalently be represented in different ways in possibilisti...

In this paper, possibilistic linear programming problems are investigated. After reviewing relations among conjunction and implication functions, necessity fractile optimization models with various implication functions are applied to the possibilistic linear problems. We show that the necessity fractile optimization models are reduced to semi-infinite linear programming problems. A simple nume...

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Because of the lack of decision power of possibilistic decision theory, several refinements have then been proposed. Unfortunately, these refinements do not allow to circumvent the difficulty when the decision problem is sequential. In this article, we propose to extend lexicographic refi...

We deal with DNA combinatorial code constructions, as found in the literature, taking the point of view of possibilistic information theory and possibilistic coding theory. The possibilistic framework allows one to tackle an intriguing information-theoretic question: what is channel noise in molecular computation? We examine in detail two representative DNA string distances used for DNA code co...

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming pr...