Intuitionistic Fuzzy Petri Nets for Knowledge Representation and Reasoning
Meng Fei-xiang, Lei Ying-jie, Zhang Bo, Shen Xiao-yong, Zhao Jing-yu
Journal of Digital Information Management, Vol. 14, No. 2, 2016.
DOI: 
Link: 
Abstract:
Fuzzy Petri nets (FPNs) are an ideal modeling
tool for knowledge-based systems, which are based
on fuzzy production rules. FPNs are widely used in knowledge
representation and reasoning, assessment, fault
diagnosis, exception handling, and other fields, but they
have the defects of single membership degree. To solve
this problem, intuitionistic fuzzy Petri nets (IFPNs) were
presented for knowledge representation and reasoning.
First, the IFPN model was constructed for knowledge representation
by combining intuitionistic fuzzy sets theory
with Petri nets theory. Second, an algorithm based on
IFPN was proposed, and the matrix operation was introduced
into the reasoning process to make full use of the
parallel computing capability of Petri nets. Finally, an example
was illustrated to prove the feasibility and advantages
of the proposed IFPN model and reasoning algorithm.
Moreover, the reasoning result was analyzed and
discussed. Compared with FPN, IFPN can describe three
states, namely, the support state, the opposite state, and
the neutral state. Thus, IFPN can overcome the single
membership degree of FPN and describe the reasoning
result more comprehensively and precisely than FPN.
Moreover, IFPN is an effective extension and development
of FPN and will become a promising method for
knowledge representation and reasoning.