In this paper, we investigate belief revision in possibilistic logic, which is a weighted logic proposed to deal with incomplete and uncertain information. Existing revision operators in possibilistic logic are restricted in the sense that the input information can only be a formula instead of a possibilistic knowledge base which is a set of weighted formulas. To break this restriction, we cons...

Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory framework. Despite their importance, only the junction tree adaptation has been proposed for exact reasoning with such networks. This paper explores alternative algorithms using compilation techniques. We first propose possibilistic...

The paper deals with the problem of the fuzzy data clustering. In other words, objects attributes can be represented by fuzzy numbers or fuzzy intervals. A direct algorithm of possibilistic clustering is the basis of an approach to the fuzzy data clustering. The paper provides the basic ideas of the method of clustering and a plan of the direct possibilistic clustering algorithm. Definitions of...

This paper presents an approach for specifying user preferences related to services by means of a preference meta-model, which is mapped to logic programs with possibilistic ordered disjunction following a Model-Driven Methodology (MDM). MDM allows to specify problem domains by means of meta-models which can be converted to instance models or other meta-models through transformation functions. ...

In possibilistic clustering the objects are assigned to clusters according to the so-called membership degrees taking values in the unit interval. Differently from fuzzy clustering, it is not required that the sum of the membership degrees of an object in all the clusters is equal to one. This is very helpful in the presence of outliers, which are usually assigned to the clusters with membershi...

Possibilistic networks are useful tools for reasoning under uncertainty. Uncertain pieces of information can be described by different measures: possibility measures, necessity measures and more recently, guaranteed possibility measures, denoted by Δ. This paper first proposes the use of guaranteed possibility measures to define a so-called Δ-based possibilistic network. This graphical represen...

†University of Sfax, National Engineering School of Sfax, Control and Energy Management Laboratory, Sfax, BPW 30308, Tunisia Summary The human iris is attracting a lot of attention leading to an increased demand for a robust and reliable iris-based recognition system. However, typical systems have random performances which can be efficient for some contexts and not for others. Generally, in rea...

A method of instance-based learning is introduced which makes use of possibility theory and fuzzy sets. Particularly, a possibilistic version of the similarity-guided extrapolation principle underlying the instancebased learning paradigm is proposed. This version is compared to the commonly used probabilistic approach from a methodological point of view. Moreover, aspects of knowledge represent...

This work deals with the problem of identification of Discrete Possibilistic Dynamic System (DPDS), using the technologies of Genetic Algorithmms (GA). Applying the results from [5-9,11-13,15,16,18-20], the fuzzy recurrent process with possibilistic uncertainty, the source of which is expert knowledge reflections on the states of evolutionary complex extremal system, is constructed. The dynamic...