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

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 mo...

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Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 11; no. 10; pp. 2297 - 2311
Main Authors Xu, Xue-Guo, Xiong, Yun, Xu, Dong-Hui, Liu, Hu-Chen
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020
Springer Nature B.V
Subjects
Accuracy
Algorithms
Artificial Intelligence
Complex Systems
Computational Intelligence
Control
Engineering
Expert systems
Fuzzy logic
Fuzzy sets
Knowledge acquisition
Knowledge representation
Mechatronics
Original Article
Pattern Recognition
Petri nets
Reasoning
Robotics
Subject specialists
Systems Biology
Weighting methods
Fuzzy Petri net (FPN)
Knowledge acquisition
Expert system
Bipolar fuzzy set (BFS)
Knowledge representation
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Summary: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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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-020-01118-2