Abstract
Petri nets are considered as an effective tool to model and analyze the behavior of complex concurrent systems. There is growing interest in the research community to capture the dynamic behavior of real world systems with adaptive features. Their Petri net models are developed to have advanced learning and reasoning capabilities. This paper examines the progress in this field highlighting the efforts focusing on methodologies framed in the adaptive context.