Belief merging is an important but difficult problem in Artificial Intelligence, especially when sources of information are pervaded with uncertainty. Many merging operators have been proposed to deal with this problem in possibilistic logic, a weighted logic which is powerful for handling inconsistency and dealing with uncertainty. They often result in a possibilistic knowledge base which is a...

We have already formalized several models of the possibilistic linear regression analysis, where it is assumed that possibilistic parameters are non-interactive, i.e., the joint possibilistic distribution of parameters is defined by minimum operators. In this paper, we will deal with the interactive case in which quadratic membership functions defined by A. Celmins are considered. With the same...

Fuzzy clustering is an approach using the fuzzy set theory as a tool for data grouping, which has advantages over traditional clustering in many applications. Many fuzzy clustering algorithms have been developed in the literature including fuzzy c-means and possibilistic clustering algorithms, which are all objective-function based methods. Different from the existing fuzzy clustering approache...

This paper is a first step in the direction of extending possibilistic planning to take advantage of the expressive power and reasoning capabilities of fuzzy description logics. Fuzzy description logics are used to describe knowledge about the world and about actions. Fundamental definitions are given and the possibilistic planning problem is recast in this new setting.

Controlled Query Evaluation (CQE) is a logical framework for the protection of secrets in databases. In this article, we extend the CQE framework to possibilistic logic: knowledge base, a priori knowledge and privacy policy are expressed with necessity-valued formulas that represent several degrees of certainty. We present a formal security de nition and analyze an appropriate controlled evalua...

In 2001 Carlsson and Fullér introduced the possibilistic mean value, variance and covariance of fuzzy numbers. In 2002 Fullér and Majlender introduced the notations of crisp weighted possibilistic mean value, variance and covariance of fuzzy numbers, which are consistent with the extension principle. In this paper we will show some (normative) properties of possibility distributions.

Possibility theory is applied to the updating problem in a knowledge base that describes the state of an evolving system. The system evolution is described by a possibilistic Markov chain whose agreement with the axioms of updating is examined. Then it is explained how to recover a possibilistic Markov chain from a set of transition constraints, on the basis of a specificity ordering.

With this paper we present a brief overview of selected promi­ nent approaches to rule frameworks and formal rule languages for the representation of and reasoning with uncertain or imprecise knowledge. This work covers selected probabilistic and possibilistic logics, as well as implementations of uncertainty and possibilistic reasoning in rule engine

Fuzzy clustering is a useful tool for capturing intrinsic structure of data sets. This paper proposes several formulations for soft transition of fuzzy memberships from probabilistic partition to possibilistic one. In the proposed techniques, the free memberships are given by introducing additional penalty term used in Possibilistic c-Means. The new features of the proposed techniques are demon...