Pythagorean Fuzzy Petri Nets for Knowledge Representation and Reasoning in Large Group Context

Abstract

Fuzzy Petri nets (FPNs) are a graphical-based knowledge representation and reasoning tool widely used in many fields for intelligent decision making. However, the traditional FPNs could not accurately represent domain experts’ uncertain and ambiguous knowledge with the increasing complexity of knowledge base systems. Additionally, the task of assessing the truth degrees of input places is limited to small-scale expert groups in current practices. In response to these problems, we develop a new type of FPN model, called Pythagorean FPNs (PFPNs), for knowledge representation and reasoning in the large group context. Pythagorean fuzzy sets are introduced to capture imprecise and ambiguous knowledge and a large group truth determination method is proposed to get the truth degrees of input places with massive data. Finally, an application example regarding security risk assessment is given to show the effectiveness and advantages of our proposed PFPNs.

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