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.

• Type: Conference
• Year: 2005
• Where: ASP
• Authors: Pascal Nicolas, Claire Lefèvre
• Keywords: Artificial Intelligence, ASP 2005, Normal Logic Program, Possibilistic Stable Models, Stable Model Semantics