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...

Increasing energy demand and the detrimental environmental impacts of fossil fuels have led to development renewable sources. Rapid growth for wood pellets over last decade has established as a potential source in globally competitive market. Integrated decision making including all stakeholders pellet supply chain (WPSC) is essential smooth transition commercially viable production. In this as...

Classical information retrieval (IR) methods often lose valuable information when aggregating weights, which may diminish the discriminating power between documents. To cope with this problem, the paper presents an approach for ranking documents in IR, based on a vector-based ordering technique already considered in fuzzy logic for multiple criteria analysis purpose. Moreover, the proposed appr...

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...

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.

Multi-objective De Novo linear programming (MODNLP) is problem for designing optimal system by reshaping the feasible set (Fiala [3] ). This paper deals with MODNLP having possibilistic objective functions coefficients. The problem is considered by inserting possibilistic data in the objective functions coefficients. The solution of the problem is defined and established under the using of effi...

This paper first proposes a new graphical model for decision making under uncertainty based on min-based possibilistic networks. A decision problem under uncertainty is described by means of two distinct min-based possibilistic Computing Optimistic Decisions 1039 networks: the first one expresses agent’s knowledge while the second one encodes agent’s preferences representing a qualitative utili...