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...

In possibility theory, there are two kinds of possibilistic causal networks depending if possibilistic conditioning is based on the minimum or on the product operator. Similarly there are also two kinds of possibilistic logic: standard (min-based) possibilistic logic and quantitative (product-based) possibilistic logic. Recently, several equivalent transformations between standard possibilistic...

General automata are considered with respect to normal-ization over semirings. Possibilistic automata are deened as normal pessimistic fuzzy automata. Possibilistic automata are analogous to stochas-tic automata where stochastic (+=) semirings are replaced by possi-bilistic (_=^) semirings; but where stochastic automata must be normal, fuzzy automata may be (resulting in possibilistic automata)...

The graded possibilistic clustering paradigm includes as the two extreme cases the “probabilistic” assumption and the “possibilistic” assumption adopted by many clustering algorithms. We propose an implementation of a graded possibilistic clustering algorithm based on an interval equality constraint enforcing both the normality condition and the required graded possibilistic condition. Experime...

— In this paper we study the optimal saving problem in the framework of possibility theory. The notion of possibilistic precautionary saving is introduced as a measure of the way the presence of possibilistic risk (represented by a fuzzy number) influences a consumer in establishing the level of optimal saving. The notion of prudence of an agent in the face of possibilistic risk is defined and ...

Automatically recognizing the e-learning activities is an important task for improving the online learning process. Probabilistic graphical models such as Hidden Markov Models and Conditional Random Fields have been successfully used in order to identify a web user activity. For such models, the sequences of observation are crucial for training and inference processes. Despite the efficiency of...