Belief Merging for Possibilistic Belief Bases.
作者： Thi Thanh Luu Le , Trong Hieu Tran
DOI: 10.1007/978-3-030-38364-0_33
关键词:
摘要: Belief merging has received much attention from the research community with a large range of applications in Computer Science and Artificial Inteligence. In this paper, we represent new belief approach for prioritized bases. The main idea method is to use two operators, namely connective strong operator averagely increasing merge possibilistic By way, proposed allows keep more useful beliefs, which may be lost other methods because drowning effect. logical properties result are also analyzed discussed.
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