Many works in the past showed that human judgments of uncertainty do not conform very well to Probability Theory. The present paper reports four experiments that were conducted in order to evaluate if human judgments of uncertainty conform better to Possibility Theory. At first, two experiments investigate the descriptive properties of some basic possibilistic measures. Then a new measurement a...

In search of semantic richer and more flexible database modelling and database querying techniques, different approaches based on fuzzy set theory have been developed. Among the most successful approaches are the possibilistic and similarity based models. More recently, extended possibilistic logic and various extensions to fuzzy sets have been applied to further enrich flexible database models...

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...

Possibilistic logic is a weighted logic introduced and developed since the mid-1980s, in the setting of arti(cial intelligence, with a view to develop a simple and rigorous approach to automated reasoning from uncertain or prioritized incomplete information. Standard possibilistic logic expressions are classical logic formulas associated with weights, interpreted in the framework of possibility...

CP-nets (Conditional preference networks) are a well-known compact graphical representation of preferences in Artificial Intelligence, that can be viewed as a qualitative counterpart to Bayesian nets. In case of binary attributes it captures specific partial orderings over Boolean interpretations where strict preference statements are defined between interpretations which differ by a single fli...

We explore an approach to possibilistic fuzzy c-means clustering that avoids a severe drawback of the conventional approach, namely that the objective function is truly minimized only if all cluster centers are identical. Our approach is based on the idea that this undesired property can be avoided if we introduce a mutual repulsion of the clusters, so that they are forced away from each other....

In this paper, we propose a new approach for iterated revision in possibilistic logic by applying a one-step revision operator. We first argue that the set of KM postulates for revision is too strong to define a practical one-step revision operator and some of them should be weakened. We then present a semantic approach for iterated revision in possibilistic logic using a one-step revision oper...

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...