In a context where the increasing complexity of discrete event systems (DES) makes flexible modeling tools essential, interoperability between different formalisms has become a central challenge. This work addresses this issue by exploring the transformation between three widely used formalisms: automata, Petri Nets, and DEVS models (Discrete Event System Specification). The proposed approach involves two main steps. First, a systematic review of existing transformation rules reported in the literature is conducted. Second, generative artificial intelligence is employed to automatically generate transformation rules. The rules derived from the literature and those generated by the AI are then compared. Based on this comparison, a refined set of transformation rules was manually defined. These rules were applied to a practical case study: modeling the behavior of an autonomous cleaning robot. The robot’s behavior was initially modeled as an automaton and then transformed into a Labelled Petri Net using the defined rules. This transformation was implemented in Python, enabling automated validation of the correspondence between the two formalisms while preserving the system’s behavioral logic.