In this paper we consider the extensions of description logics that were proposed to represent uncertain or vague knowledge, focusing on the fuzzy and possibilistic formalisms. We compare these two approaches and comment on their differences concentrating on the consistency issue of knowledge bases represented in these extended frameworks. We present a classification of existing algorithms and ...

In this paper a general bottleneck combinatorial optimization problem with uncertain element weights modeled by fuzzy intervals is considered. A rigorous possibilistic formalization of the problem and solution concepts in this setting that lead to finding robust solutions under fuzzy weights are given. Some algorithms for finding a solution according to the introduced concepts and evaluating op...

In this paper a wide class of sequencing problems with imprecise parameters is discussed. The imprecision is modeled by using closed intervals and fuzzy intervals, whose membership functions are regarded as possibility distributions for the values of unknown parameters. A possibilistic interpretation of fuzzy problems is provided, some solution concepts are proposed and some algorithms for comp...

Fuzzy rule bases are built of linguistic, qualitative knowledge. By using fuzzy rules we are able to specify simple models of complex systems. But, we have to pay a price for this simpliication. In general, fuzzy knowledge is gradually incomplete and gradually inconsistent. This paper deals with the detection of such partial gaps of knowledge or local contradictions. In order to do so we introd...

A possibilistic framework for instance-based prediction is presented which formalizes the generalization beyond experience by means of fuzzy rules. In comparison with related instance-based approaches such as the well-known Nearest Neighbor classifier, this method distinguishes itself by the following: First, by suggesting (guaranteed) degrees of possibility for competing outcomes rather than m...

Conventional Fuzzy regression using possibilistic concepts allows the identification of models from uncertain data sets. However, some limitations still exist. This paper deals with a revisited approach for possibilistic fuzzy regression methods. Indeed, a new modified fuzzy linear model form is introduced where the identified model output can envelop all the observed data and ensure a total in...

Most of the real life classification problems have ill defined, imprecise or fuzzy class boundaries. Feedforward neural networks with conventional backpropagat ion learning algorithm are not tailored to these kinds of classafication problems. Hence, an this paper, feedforward neural networks, that use fuzzy objective functions in the backpropagation learning algorithm, are investigated. A learn...

This paper presents a possibilistic (fuzzy) model in optimal siting and sizing of Distributed Generation (DG) for loss reduction and improve voltage profile in power distribution system. Multi-objective problem is developed in two phases. In the first one, the set of non-dominated planning solutions is obtained (with respect to the objective functions of fuzzy economic cost, and exposure) using...

In this paper we propose a propositional temporal language based on fuzzy temporal constraints which turns out to be expressive enough for domains –like many coming from medicine– where knowledge is of propositional nature and an explicit handling of time, imprecision and uncertainty are required. The language is provided with a natural possibilistic semantics to account for the uncertainty iss...