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 the object-language level. In spite of its expressive power, an important limitation in P-DeLP is that imprecise, fuzzy information cannot be expressed in the object language. One ...

In this paper, a new Hopfield-model net based on fuzzy possibilistic reasoning is proposed for the classification of multispectral images. The main purpose is to modify the Hopfield network embedded with fuzzy possibilistic -means (FPCM) method to construct a classification system named fuzzy-possibilistic Hopfield net (FPHN). The classification system is a paradigm for the implementation of fu...

Naive Bayes classiiers can be seen as special probabilistic networks with a star-like structure. They can easily be induced from a dataset of sample cases. However, as most probabilistic approaches, they run into problems, if imprecise (i.e, set-valued) information in the data to learn from has to be taken into account. An approach to handle uncertain as well imprecise information, which recent...

Naive Bayes classifiers can be seen as special probabilistic networks with a star-like structure. They can easily be induced from a dataset of sample cases. However, as most probabilistic approaches, they run into problems, if imprecise (i.e, set-valued) information in the data to learn from has to be taken into account. An approach to handle uncertain as well imprecise information, which recen...

Possibilistic networks offer a qualitative approach for modeling epistemic uncertainty. Their practical implementation requires the specification of conditional possibility tables, as in the case of Bayesian networks for probabilities. The elicitation of probability tables by experts is made much easier by means of noisy logical gates that enable multidimensional tables to be constructed from t...

This paper treats risk based on the notions of credibility measure and credibility expected value. Firstly, the paper derives and discusses the credibility expected value. Secondly, the paper presents a new method of analysis of possibilistic portfolios. The new step is a construction by which with a possibilistic portfolio one associates a probabilistic portfolio. The problem solving of possib...

Probability density estimation from data is a widely studied problem. Often, the primary goal is to faithfully mimic the underlying empirical density. Having an interpretable model that allows insight into why certain predictions were made is often of secondary importance. Using logic-based formalisms, such as Markov logic, can help with interpretability, but even in Markov logic it can be diff...

-Possibilistic logic and Bayesian networks have provided advantageous methodologies and techniques for computerbased knowledge representation. This paper proposes a framework that combines these two disciplines to exploit their own advantages in uncertain and imprecise knowledge representation problems. The framework proposed is a possibilistic logic based one in which Bayesian nodes and their ...

We investigate the following extendability problem for systems, for which the available information is given by a monotone set mapping M on the field CT of measurable cylinders of a product ample space (XT ,RT ): given that M is invariant under a RT −RT measurable transformation H of XT , i.e. M(H−1(B)) = M(B) for all B ∈ CT , is it possible to find H-invariant monotone extensions of M to the p...

The conventional wisdom is that the concept of information is closely related to the concept of probability. In Shannon's information theory, information is equated to a reduction in entropy—a probabilistic concept. In this paper, a different view of information is put on the table. Information is equated to restriction. More concretely, a restriction is a limitation on the values which a varia...