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Belief Merging for Possibilistic Belief Bases

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Book cover Advanced Computational Methods for Knowledge Engineering (ICCSAMA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1121))

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Abstract

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 a new belief merging approach for prioritized belief bases. The main idea of this method is to use two operators, namely connective strong operator and averagely increasing operator to merge possibilistic belief bases. By this way, the proposed method allows to keep more useful beliefs, which may be lost in other methods because of drowning effect. The logical properties of merging result are also analyzed and discussed.

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Author information

Authors and Affiliations

  1. University of Finance and Accountancy, Quangngai, Vietnam

    Thi Thanh Luu Le

  2. VNU - University of Engineering and Technology, Hanoi, Vietnam

    Thi Thanh Luu Le & Trong Hieu Tran

Authors
  1. Thi Thanh Luu Le
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  2. Trong Hieu Tran
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Corresponding author

Correspondence to Trong Hieu Tran .

Editor information

Editors and Affiliations

  1. Computer Science and Applications Department LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai An Le Thi

  2. Computer Science and Applications Department LGIPM, University of Lorraine, Metz Cedex 03, France

    Hoai Minh Le

  3. Laboratory of Mathematics, National Institute for Applied Sciences, Saint-Étienne-du-Rouvray Cedex, France

    Tao Pham Dinh

  4. Department of Information Systems, Wroclaw University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

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Le, T.T.L., Tran, T.H. (2020). Belief Merging for Possibilistic Belief Bases. In: Le Thi, H., Le, H., Pham Dinh, T., Nguyen, N. (eds) Advanced Computational Methods for Knowledge Engineering. ICCSAMA 2019. Advances in Intelligent Systems and Computing, vol 1121. Springer, Cham. https://doi.org/10.1007/978-3-030-38364-0_33

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