Possibilistic logic, an extension of first-order logic, deals with uncertainty that can be es­ timated in terms of possibility and necessity measures. Syntactically, this means that a first-order formula is equipped with a possi­ bility degree or a necessity degree that ex­ presses to what extent the formula is pos­ sibly or necessarily true. Possibilistic reso­ lution yields a calculus for pos...

Possibilistic networks are important and efficient tools for reasoning under uncertainty. This paper proposes a new graphical model for decision making under uncertainty based on possibilistic networks. In possibilistic decision problems under uncertainty, available knowledge is expressed by means of possibility distribution and preferences are encoded by means another possibility distribution ...

In this paper, we introduce a possibilistic argumentation-based decision making framework which is able to capture uncertain information and exceptions/defaults. In particular, we define the concept of a possibilistic decision making framework which is based on a possibilistic default theory, a set of decisions and a set of prioritized goals. This set of goals captures user preferences related ...

The paper deals with a possibilistic imprecise second-order probability model. It is argued that such models appear naturally in a number of situations. They lead to the introduction of a new type of previsions, called possibilistic previsions, which formally generalise coherent upper and lower previsions. The converse problem is also looked at: given a possibilistic prevision, under what condi...

The current models and methods for PLP are usually restricted on some special types and usually the same type of possibilistic distribution. This paper focuses on linear programming problems with general possibilistic resources (GRPLP) and linear programming problems with general possibilistic objective coe cients (GOPLP). By introducing some new concepts of the largest most possible point, the...

Possibility theory offers either a qualitative, or a numerical framework for representing uncertainty, in terms of dual measures of pos­ sibility and necessity. This leads to the ex­ istence of two kinds of possibilistic causal graphs where the conditioning is either based on the minimum, or on the product opera­ tor. Benferhat et al. [3] have investigated the connections between min-based grap...

The analysis and processing of large data are a challenge for researchers. Several approaches have been used to model these complex data, and they are based on some mathematical theories: fuzzy, probabilistic, possibilistic, and evidence theories. In this work, we propose a new unsupervised classification approach that combines the fuzzy and possibilistic theories; our purpose is to overcome th...

Ranking functions are qualitative degrees of uncertainty ascribed to events charged by uncertainty and taking as their values non-negative integers in the sense of ordinal numbers. Introduced are ranking functions induced by real-valued possibilistic measures and it is shown that different possibilistic measures with identical ranking functions yield the same results when applied in decision pr...