Inconsistent knowledge bases usually are regarded as an epistemic hell that have to be avoided at all costs. However, many times it is di cult or impossible to stay away of managing inconsistent knowledge bases. In this paper, we introduce an argumentation-based approach in order to manage inconsistent possibilistic knowledge bases. This approach will be exible enough for managing inconsistent ...

In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in feature space based on the possibilistic approach to clustering. The proposed algorithms retain the properties of the possibilistic clustering, working as density estimators in feature space and showing high robustne...

Possibilistic ATMS are truth maintenance systems oriented towards hypothetical reasoning where both assumptions and justifications can bear an uncertainty weight. Uncertainty is represented in the framework of possibility theory. In possibilistic logic uncertain clauses are handled as such and then in possibilistic ATMS the management of uncertainty is not separated from the other classical cap...

Upper and lower regression models (dual possibilistic models) are proposed for data analysis with crisp inputs and interval or fuzzy outputs. Based on the given data, the dual possibilistic models can be derived from upper and lower directions, respectively, where the inclusion relationship between these two models holds. Thus, the inherent uncertainty existing in the given phenomenon can be ap...

In any learning process, the learners arrive with a great deal of variables, such as their different learning styles, their affective states and their previous knowledge, among many others. In most cases, their previous knowledge is incomplete or it comes with a certain degree of uncertainty. Possibilistic Logic was developed as an approach to automated reasoning from uncertain or prioritized i...

Possibilistic networks are important tools for dealing with uncertain pieces of information. For multiplyconnected networks, it is well known that the inference process is a hard problem. This paper studies a new representation of possibilistic networks, called hybrid possibilistic networks. The uncertainty is no longer represented by local conditional possibility distributions, but by their co...

Analysis of inventory systems involving market-oriented pricing decisions has recently become an interesting topic in the field of inventory control. Price and marketing expenditure are considered as important elements when selling goods and enhancing revenues by manufacturers. The importance of accounting for uncertainty in such environments spurs an interest to develop appropriate decision ma...

Possibilistic logic in general [7, 8, 9, 10] investigates how possibilistic uncertainty about propositions is propagated when making inferences in a formal logical system. In this paper, we look at a very particular aspect of possibilistic logic: we investigate how, under certain independence assumptions, the introduction of possibilistic uncertainty in classical propositional logic leads to th...

As a critical component of sustainable development, transportation system should be designed such that it has positive impact on the economic, environmental, and social sustainability served region. In response, this study introduces concept passenger dissatisfaction with additional walking waiting as an indicator uses while optimizing transit network for perfectives. Due to lack knowledge abou...

In the traditional treatment, risk situations are modeled by random variables. This paper focuses on risk situations described by fuzzy numbers. The goal of the paper is to define and characterize possibilistic risk aversion and study some of its indicators. Key–Words: Possibilistic risk aversion, possibilistic indicators