Determining Truth Degrees of Input Places in Fuzzy Petri Nets

Fuzzy Petri net (FPN), as one type of high-level Petri nets, has attracted a lot of attention over the recent decade due to its adequacy for knowledge representation and logic reasoning. However, in the FPN literature, the truth degrees of input places are usually given directly or supposed by researchers. No or little research has been performed on the determination of initial marking vector for a specific FPN. In this correspondence paper, we introduce a group decision-making model using hesitant 2-tuple linguistic term sets to obtain the initial truth values of FPNs based on domain experts' knowledge and gathered data. As is illustrated by the numerical example, the proposed framework can well capture domain experts' diversity judgements and derive initial truth degrees for an FPN under different types of uncertainties.