Handbook of logic in artificial intelligence and logic programming (vol. 3)
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
Decomposable negation normal form
Journal of the ACM (JACM)
On Stratified Belief Base Compilation
Annals of Mathematics and Artificial Intelligence
Compiling propositional weighted bases
Artificial Intelligence - Special issue on nonmonotonic reasoning
A survey on knowledge compilation
AI Communications
Compiling possibilistic knowledge bases
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Propositional independence: formula-variable independence and forgetting
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Iterated theory base change: a computational model
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Compiling min-based possibilistic causal networks: a mutilated-based approach
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Hi-index | 0.00 |
Developing efficient approaches for reasoning under inconsistency is an important issue in many applications. Several methods have been proposed to compile, possibly inconsistent, weighted or stratified bases. This paper focuses on the well-known linear order and possibilistic logic strategies. It provides a way for compiling a stratified belief base in order to be able to process inference from it in polynomial time. The resulting extra compilation cost is very low. In particular, the number of additional variables, that are added to original stratified bases, corresponds exactly to the number of priority levels existing in the base. Moreover, our compilation approach allows an efficient computation of weighted possibilistic conclusions and possibilistic conditioning.