This paper proposes a new decision making approch based on quantitative possibilistic influence diagrams which are extension of standard influence diagrams in the possibilistic framework. We will in particular treat the case where several expert opinions relative to value nodes are available. An initial expert assigns confidence degrees to other experts and fixes a similarity threshold that pro...

In this paper we explain in a tutorial manner the technique of reasoning in probabilistic and possibilistic network structures, which is based on the idea to decompose a multi-dimensional probability or possibility distribution and to draw inferences using only the parts of the decomposition. Since constructing probabilistic and possibilistic networks by hand can be tedious and time-consuming, ...

The notion of continuity from above (upper continuity) for lattice-valued possibilistic measures as investigated in [7] has been proved to be a rather strong condition when imposed as demand on such a measure. Hence, our aim will be to introduce some versions of this upper continuity weakened in the sense that the conditions imposed in [7] to the whole definition domain of the possibilistic mea...

Possibilistic networks are important tools for modelling and reasoning, especially in the presence of imprecise and/or uncertain information. These graphical models have been successfully used in several real applications. Since their construction by experts is complex and time consuming, several researchers have tried to learn them from data. In this paper, we try to present and discuss releva...

Article history: Received 11 September 2009 Accepted 20 September 2009 Available online 17 November 2009 Causality and belief change play an important role in many applications. This paper focuses on the main issues of causality and interventions in possibilistic graphical models. We show that interventions, which are very useful for representing causal relations between events, can be naturall...

The solution of the prediction problem is presented for the finite possibilistic modelling [3,4,6,7]. A recurrent variant of finite possibilistic models is considered. In this variant, we define the regularization condition for constructing a quasi-optimal estimator of fuzzy transition operator (FTO). We construct the discrete recurrent extremal fuzzy process with possibilistic uncertainty, the...

Possibilistic logic is an important uncertainty reasoning mech anism based on Zadeh s possibility theory and classical logic The deduc tion rules of possibilistic logic have been obtained from classical resolu tion rule by attaching possibility or necessity weight to ordinary clauses However since not all possibility valued formulae can be converted into equivalent possibilistic clauses the res...

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...