Knowledge representation and reasoning based on generalised fuzzy Petri nets

The aim of this paper is to present a new methodology for knowledge representation and reasoning based on generalised fuzzy Petri nets. Recently, this net model has been proposed as a new class of fuzzy Petri nets. The new class extends the existing fuzzy Petri nets by introducing two operators: t-norms and s-norms, which are supposed to function as substitute for the min and max operators. This model is more flexible than the traditional one as in the former class the user has the chance to define the input/output operators. The choice of suitable operators for a given reasoning process and the speed of reasoning process are very important, especially in real-time decision support systems. The advantages of the proposed methodology are shown in an application in train traffic control decision support.