Exploiting qualitative knowledge to enhance skill acquisition

Abstract

One of the most interesting problems faced by Artificial Intelligence researchers is to reproduce a capability typical of living beings: that of learning to perform motor tasks, a problem known as skill acquisition. A very difficult purpose because the overwhole behavior of an agent is the result of quite a complex activity, involving sensory, planning and motor processing. In this paper, I present a novel approach for acquiring new skills, named Soft Teaching, that is characterized by a learning by experience process, in which an agent exploits a symbolic, qualitative description of the task to perform, that cannot, however, be used directly for control purposes. A specific Soft Teaching technique, named Symmetries, was implemented and tested against a continuous-domained version of well-known pole-balancing.