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Abstract: The paper discusses a class of Markov Regenerative Stochastic Petri Nets (MRSPN) characterized by the fact that the stochastic process subordinated to two consecutive regeneration time points is a semi-Markov reward process. This class of SPN's can accommodate transitions with generally distributed firing time and associated memory policy of both enabling and age type, thus generalizing and encompassing all the previous definitions of MRSPN. An unified analytical procedure is developed for the derivation of closed form expressions for the transient and steady state probabilities.