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...

Probability assessments of events are often linguistic in nature. We model them by means of possibilistic probabilities (a version of Zadeh’s fuzzy probabilities with a behavioural interpretation) with a suitable shape for practical implementation (on a computer). Employing the tools of interval analysis and the theory of imprecise probabilities we argue that the verification of coherence for t...

In this paper, both the uncertainty and the origin of pieces of information is handled in an extended possibilistic logic framework. Each formula is associated with a set (a fuzzy set more generally) which gathers labels of sources according to which the formula is (more or less) certainly true. In case of a single source of information, possibilistic logic is recovered. Soundness and completen...

We consider optimal portfolio selection problems in a possibilistic setting. Using the possibilistic framework, we can integrate more efficiently the experts’ knowledge and the investors’ subjective opinions into a portfolio selection model. In 2002 Carlsson, Fullér and Majlender considered portfolio selection problems under trapezoidal possibility distributions and presented an algorithm of co...

Kernel based neural networks with probabilistic reasoning are suitable for many practical applications. But in uence of data set sizes let the probabilistic approach fail in case of small data amounts. Possibilistic reasoning avoids this drawback because it is independent of class size. The fundamentals of possibilistic reasoning are derived from a probability/possibility consistency principle ...

There has been an ever-increasing interest in multi-disciplinary research on representing and reasoning with imperfect data. Possibilistic networks present one of the powerful frameworks of interest for representing uncertain and imprecise information. This paper covers the problem of their parameters learning from imprecise datasets, i.e., containing multi-valued data. We propose in the first ...

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...

Logic programs with ordered disjunction have shown to be a flexible specification language able to model common user preferences in a natural way. However, in some realistic scenarios the preferences should be linked to the evidence of the information when trying to reach a single preferred solution. In this paper, we extend the syntax and the semantics of logic programs with ordered disjunctio...