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...

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...