A Model-based Approach for Merging Prioritized Knowledge Bases in Possibilistic Logic

摘要

This paper presents a new approach for merging prioritized knowledge bases in possibilistic logic. Our approach is semantically denned by a model-based merging 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 counterpart of the semantic approach. The logical properties of our approach are considered. Finally, we analyze the computational complexity of our merging approach.