12 Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from 13 argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at the object-lan14 guage level. In spite of its expressive power, an important limitation in P-DeLP is that imprecise, fuzzy information cannot 15 be expressed in the object la...

Classical ontologies are not suitable to represent imprecise nor uncertain pieces of information. As a solution we will combine fuzzy Description Logics with a possibilistic layer. Then, we will show how to perform reasoning by relying on classical existing reasoners. Description Logics (DLs for short) are a family of logics for representing structured knowledge which have proved to be very use...

This paper discuss mainly issues related for modeling decision making under uncertain, vagueness, risky and imprecise information. There will be presented a description of five ordinal methods for modeling decision making under uncertainty in the context of linguistic data: Possibilistic Decisisonmaking, Revised Possibilistic Decisisonmaking, Commensurate L-Fuzzy Risk Minimization, Fuzzy relati...

The study of fuzzy intervals is of particular interest in temporal database research. In order to optimize the storage of fuzzy temporal intervals, some transformations have been proposed. In this paper we analyze the possibilistic evaluation of the ill-known temporal intervals. We propose a framework to deal with the evaluation of ill-known temporal intervals. It is shown how the reasoning beh...

Possibilistic Defeasible Logic Programming (\Pdelp) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at object-language level. The aim of this paper is twofold: first to present an approach towards extending \Pdelp in order to incorporate fuzzy constants and fuzzy unification, and af...

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...

A semantics is given to possibilistic logic, a logic that handles weighted classical logic formulae, and where weights are interpreted as lower bounds on degrees of certainty or possibility, in the sense of Zadeh's possibility theory. The proposed semantics is based on fuzzy sets of interpretations. It is tolerant to partial inconsistency. Satisfiability is extended from interpretations to fuzz...

Portfolio selection is an important issue for researchers and practitioners. Compared with the conventional probabilistic mean-variance method, fuzzy number can better describe an uncertain environment with vagueness and ambiguity. In this paper, the portfolio selection model with transaction costs and lending is proposed by means of possibilistic mean and possibilistic variance under the assum...

Possibilistic Defeasible Logic Programming (P-DeLP) is a logic programming language which combines features from argumentation theory and logic programming, incorporating the treatment of possibilistic uncertainty at object-language level. This paper presents a first approach towards extending P-DeLP to incorporate fuzzy constants and fuzzy propositional variables. We focus on how to characteri...