Laurent Garcia

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In this work, we introduce a new framework able to deal with a reasoning that is at the same time non monotonic and uncertain. In order to take into account a certainty level associated to each piece of knowledge, we use possibility theory to extend the non monotonic semantics of stable models for logic programs with default negation. By means of a(More)
Possibilistic logic bases and possibilistic graphs are two different frameworks of interest for representing knowledge. The former ranks the pieces of knowledge (expressed by logical formulas) according to their level of certainty, while the latter exhibits relationships between variables. The two types of representation are semantically equivalent when(More)
Possibilistic logic bases and possibilistic graphs are two different frameworks of interest for representing knowledge. The former stratifies the pieces of knowledge (expressed by logical formulas) accor?i � g to their level of certainty, while the latter exhibits relationships between variables. The two types of representations are semantically equivalent(More)
In this article we present the framework of Pos-sibilistic Influence Diagrams (PID), which allow to model in a compact form problems of sequential decision making under uncertainty, when only ordinal data on transitions likelihood or preferences are available. The graphical part of a PID is exactly the same as that of usual influence diagrams, however the(More)
Linguistic negation processing is a challenging problem studied by a large number of researchers from different communities, i.e. logic, linguistics, etc. We are interested in finding the positive interpretations of a negative sentence represented as " x is not A ". In this paper, we do not focus on the single set of translations but on two approximation(More)