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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 10/2020

17-04-2020 | Original Article

Bipolar fuzzy Petri nets for knowledge representation and acquisition considering non-cooperative behaviors

Authors: Xue-Guo Xu, Yun Xiong, Dong-Hui Xu, Hu-Chen Liu

Published in: International Journal of Machine Learning and Cybernetics | Issue 10/2020

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Abstract

Fuzzy Petri nets (FPNs) are a promising modeling tool for knowledge representation and reasoning. As a new type of FPNs, bipolar fuzzy Petri nets (BFPNs) are developed in this article to overcome the shortcomings and improve the performance of traditional FPNs. In order to depict expert knowledge more accurately, the BFPN model adopts bipolar fuzzy sets (BFSs), which are characterized by the satisfaction degree to property and the satisfaction degree to its counter property, to represent knowledge parameters. Because of the increasing scale of expert systems, a concurrent hierarchical reasoning algorithm is introduced to simplify the structure of BFPNs and reduce the computation complexity of knowledge reasoning algorithm. In addition, a large group expert weighting method is proposed for knowledge acquisition by taking experts’ non-cooperative behaviors into account. A realistic case of risk index evaluation system is presented to show the effectiveness and practicality of the proposed BFPNs. The result shows that the new BFPN model is feasible and efficient for knowledge representation and acquisition.
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Metadata
Title
Bipolar fuzzy Petri nets for knowledge representation and acquisition considering non-cooperative behaviors
Authors
Xue-Guo Xu
Yun Xiong
Dong-Hui Xu
Hu-Chen Liu
Publication date
17-04-2020
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 10/2020
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01118-2

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