A model-based approach for merging prioritized knowledge bases in possibilistic logic
Guilin Qi · 2007
This paper presents a new approach for merging prior-itized knowledge bases in possibilistic logic. Our ap-proach is semantically defined by a model-based merg-ing operator in propositional logic and the merged result of our approach is a normal possibility distribution. We also give an algorithm to obtain the syntactical counter-part of the semantic approach. The logical properties of our approach are considered. Finally, we analyze the computational complexity of our merging approach.