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...

When the information about uncertainty cannot be quantified in a simple, probabilistic way, the topic of possibilistic decision theory is often a natural one to consider. The development of possibilistic decision theory has lead to a series of possibilistic criteria, e.g pessimistic possibilistic qualitative utility, possibilistic likely dominance , binary possibilistic utility and possibilisti...

Vast pools of historical financial information are available on economies, industry, and individual companies that affect investors’ selection of appropriate portfolios. Fuzzy data provides a good tool to reflect investors’ opinions based on this information. A possibilistic mean variance safety-first portfolio selection model is developed to support investors’ decision making, to take into con...

We prove that possibilistic linear programming problems (introduced by Buckley in [2]) are well-posed, i.e. small changes of the membership function of the parameters may cause only a small deviation in the possibilistic distribution of the objective function.

Uncertain information is present in many real applications e.g., medical domain, weather forecast, etc. The most common approaches for leading with this information are based on probability however some times; it is difficult to find suitable probabilities about some events. In this paper, we present a possibilistic logic programming approach which is based on possibilistic logic and PStable se...

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...

Recently, a good set of logic programming semantics has been defined for capturing possibilistic logic program. Practically all of them follow a credulous reasoning approach. This means that given a possibilistic logic program one can infer a set of possibilistic models. However, sometimes it is desirable to associate just one possibilistic model to a given possibilistic logic program. One of t...

The goal of this research is to develop a novel second-generation-based biogas supply chain network design (BG-SCND) model that takes into account the triple bottom line approach. Biogas promising renewable energy source can be obtained from variety easily accessible second-generation wastes, including animal manure, municipal waste, and agricultural leftovers. Integrated optimization generatio...

Possibilistic answer set programming (PASP) extends answer set programming (ASP) by attaching to each rule a degree of certainty. While such an extension is important from an application point of view, existing semantics are not well-motivated, and do not always yield intuitive results. To develop a more suitable semantics, we first introduce a characterization of answer sets of classical ASP p...

This paper discusses a class of uncertain optimization problems, in which unknown parameters are modeled by fuzzy intervals. The membership functions the intervals interpreted as possibility distributions for values parameters. It is shown how known concepts robustness and light robustness, traditional interval uncertainty representation parameters, can be generalized to choose solutions that o...