Stroke based rendering methods have recently become a popular solution for the generation of stylized paintings. However, the current research in this direction is focused mainly on the improvement of final canvas quality, and thus often fails to consider the intelligibility of the generated painting sequences to actual human users. In this work, we motivate the need to learn more human-intelligible painting sequences in order to facilitate the use of autonomous painting systems in a more interactive context (e.g. as a painting assistant tool for human users or for robotic painting applications). To this end, we propose a novel painting approach which learns to generate output canvases while exhibiting a painting style which is more relatable to human users. The proposed painting pipeline Intelli-Paint consists of 1) a progressive layering strategy which allows the agent to first paint a natural background scene before adding in each of the foreground objects in a progressive fashion. 2) We also introduce a novel sequential brushstroke guidance strategy which helps the painting agent to shift its attention between different image regions in a semantic-aware manner. 3) Finally, we propose a brushstroke regularization strategy which allows for „60-80% reduction in the total number of required brushstrokes without any perceivable differences in the quality of generated canvases. Through both quantitative and qualitative results, we show that the resulting agents not only show enhanced efficiency in output canvas generation but also exhibit a more natural-looking painting style which would better assist human users express their ideas through digital artwork.