"These characteristics are shared by many other problems in robotics and other domains that require some kind of planning," added Baldi. "Imagine a robot tasked with cleaning up your kitchen: there is an astronomical number of sequences of moves, but only very few lead to a clean kitchen. And randomly moving dirty dishes around is not going to do it." "More broadly, this work is part of a general effort to bridge machine learning AI and symbolic AI to address complex problems that humans solve through planning and reasoning," added Baldi. In the study, researchers wanted to understand how and why the AI made its moves and how long it took to perfect its method.