Possibilistic Stable Model Computing

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Possibilistic Stable Model Computing
Possibilistic Stable model Semantics is an extension of Stable Model Semantics that allows to merge uncertain and non monotonic reasoning into a unique framework. To achieve this aim, knowledge is represented by a normal logic program where each rule is given with its own degree of certainty. By this way, it formally defines a distribution of possibility over atom sets that, on its turn, induces for each atom a possibility and a necessity measures. The latter underpins the definition of a possibilistic stable model in which every consequence of the program is given with a level of certainty. In this work we explain how we can compute the possibilistic stable models of a possibilistic normal logic program by using available softwares for Answer Set Programming and we describe the main lines of the system that we have developed.
Pascal Nicolas, Claire Lefèvre
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where ASP
Authors Pascal Nicolas, Claire Lefèvre
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