Handbook of logic in artificial intelligence and logic programming (vol. 3)
Annotated nonmonotonic rule systems
Selected papers from the international workshop on Uncertainty in databases and deductive systems
Preferred answer sets for extended logic programs
Artificial Intelligence
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Possibilistic Logic: From nonmonotonicity to Logic Programming
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
A Complete Calcultis for Possibilistic Logic Programming with Fuzzy Propositional Variables
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A possibilistic inconsistency handling in answer set programming
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
An introduction to fuzzy answer set programming
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Incomplete knowledge in hybrid probabilistic logic programs
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
A possibilistic inconsistency handling in answer set programming
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A top-k query answering procedure for fuzzy logic programming
Fuzzy Sets and Systems
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In this work, we define a new framework in order to improve the knowledge representation power of Answer Set Programming paradigm. Our proposal is to use notions from possibility theory to extend the stable model semantics by taking into account a certainty level, expressed in terms of necessity measure, on each rule of a normal logic program. First of all, we introduce possibilistic definite logic programs and show how to compute the conclusions of such programs both in syntactic and semantic ways. The syntactic handling is done by help of a fix-point operator, the semantic part relies on a possibility distribution on all sets of atoms and we show that the two approaches are equivalent. In a second part, we define what is a possibilistic stable model for a normal logic program, with default negation. Again, we define a possibility distribution allowing to determine the stable models.