Fuzzy-Petri Net Reasoning System and Transfering of Knowledge to the Markov Chain

This work presents an overview of a reasoning expert system we have developed and implemented in C++. It is based on fuzzified Petri nets, with rule-based decision-making and appropriate knowledge base (KB). The reasoning algorithm is consisting ofcalculating the degrees of fulfillment (DOFs) for all rules of the KB and their assigning to the places of the Petri net. After this, it follows reasoning process with firing of active transitions and calculating of DOFs for output places (propositions of KB) and determining of fuzzy-distribution for output variables, as well as their defuzzified values. As final step, we are transferring these values to the states of a Markov chain in order to perform different command and control tasks. Markov chains are efficient tool for simulation and modeling of stochastic discrete event processes, especially those in military operations, like command and control activities.