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17-04-2020 | Original Article | Issue 10/2020

International Journal of Machine Learning and Cybernetics 10/2020

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

Journal:
International Journal of Machine Learning and Cybernetics > Issue 10/2020
Authors:
Xue-Guo Xu, Yun Xiong, Dong-Hui Xu, Hu-Chen Liu
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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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