Possibilistic uncertainty handling for answer set programming

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 possibility distribution we define a clear semantics of such programs by introducing what is a possibilistic stable model. We also propose a syntactic process based on a fix-point operator to compute these particular models representing the deductions of the program and their certainty. Then, we show how

• Type: Journal
• Year: 2006
• Where: AMAI
• Authors: Pascal Nicolas, Laurent Garcia, Igor Stéphan, Claire Lefèvre
• Keywords: AMAI 2006, Artificial Intelligence, Non Monotonic Semantics, Possibilistic Stable Model, Stable Model