Intuitionistic Fuzzy Petri Nets for Knowledge Representation and Reasoning
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. Subject Categories and Descriptors I.2.4 [Artificial Intelligence] Knowledge Representation Formalisms and MethodsRepresentations(procedural and rule based) General Terms: Algorithm, Performance