Probabilistic and possibilistic models of sequential decision problems are known to possess good behavioral algorithmic properties. In this paper, the range under uncertainty that dynamically consistent, consequentialist allow for tree reduction is enlarged by considering a representation both probabilistic possibilistic. The corresponding utility functional expected highly likely states, an op...

Possibilistic DL-Lite is an extension of the DL-Lite framework to deal with uncertain pieces of information. In this paper, we deal with inconsistency in DL-Lite in case where the assertions are uncertain and encoded in a possibility theory framework. We investigate three inconsistency-tolerant reasoning methods for possibilistic DL-Lite knowledge bases which are based on the selection of one c...

This paper deals with a portfolio selection problem with independently estimated possibilistic return rates. Under such a circumstance, a distributive investment has been regarded as a good solution in the traditional portfolio theory. However, the conventional possibilistic approach yields a concentrated investment solution. Considering the reason why a distributive investment is advocated, a ...

The Kalman filter (KF) is a commonly used algorithm for predicting the state variables of system. It based on model system and some measurements (observed over time) that are characterized by their own uncertainty. This article defines possibilistic KF whose main feature to predict values associated uncertainty when contributions nonrandom nature present. defined in mathematical framework possi...

In the last decade, several approaches were introduced in literature to merge multiple and potentially conflicting pieces of information. Within the growing field of application favourable to distributed information, data fusion strategies aim at providing a global and consistent point of view over a set of sources which can contradict each other. Moreover, in many situations, the pieces of inf...

Risk aversion is one of the main themes in risk theory. Risk theory is treated usually by probability theory. The aim of this paper is to propose an approach of the risk aversion by possibility theory initiated by Zadeh in 1978 as an alternative of probability theory in the modeling of uncertain situations. The main notions studied in this paper are the possibilistic risk premium and the possib...

In this paper, we investigate how linguistic information can be incorporated into classical propositional logic. First, we show that Zadeh’s extension principle can be justified and at the same time generalized by considerations about transformation of possibility measures. Using these results, we show how linguistic uncertainty about the truth value of a proposition leads to the introduction o...

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzz...

We prove that possibilistic linear programming problems (introduced by Buckley in [2]) are well-posed, i.e. small changes of the membership function of the parameters may cause only a small deviation in the possibilistic distribution of the objective function.

Based on our earlier work on partial logics and extended logic programs [Wag91, Wag94, HJW96], and on the possibilistic logic of [DLP94], we de ne a compositional possibilistic rst-order logic with two kinds of negation.