Autonomous learning via nested clustering

Autonomous learning in the architectures of intelligent control requires special procedures performed upon acquired knowledge. This affects the structure of world representation and it is intimately linked with mechanisms of behavior generation. This paper illuminates algorithms of unsupervised learning performed via nested clustering which is goal driven and exercises simulation of decision making process. The recursion experience→rule→conceptual entity is shown to create a multiresolutional control system capable of representing the environment and creating control rules that allow it to achieve the assigned goal