Artificial Intelligence
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Compiling propositional weighted bases
Artificial Intelligence - Special issue on nonmonotonic reasoning
Propositional independence: formula-variable independence and forgetting
Journal of Artificial Intelligence Research
Compiling knowledge into decomposable negation normal form
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On the compilation of stratified belief bases under linear and possibilistic logic policies
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Interval-based possibilistic logic
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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Possibilistic knowledge bases gather propositional formulas associated with degrees belonging to a linearly ordered scale. These degrees reflect certainty or priority, depending if the formulas encode pieces of beliefs or goals to be pursued. Possibilistic logic provides a simple format that turns to be useful for handling qualitative uncertainty, exceptions or preferences. The main result of the paper provides a way for compiling a possibilistic knowledge base in order to be able to process inference from it in polynomial time. The procedure is based on a symbolic treatment of the degrees under the form of sorted literals and on the idea of forgetting variables. The number of sorted literals that are added corresponds exactly to the number of priority levels existing in the base, and the number of binary clauses added in the compilation is also equal to this number of levels. The resulting extra compilation cost is very low.