Learning Concepts by Interaction

摘要

This paper presents a theory of how robots may learn concepts by interacting with their environment in an unsupervised way. First, categories of activities are learned, then abstractions over those categories result in concepts. The meanings of concepts are discussed. Robotic systems that learn categories of activities and concepts are presented. Introduction If machines could acquire conceptual knowledge with the same facility as humans, then AI would be much better off. There's no denying the dream of a machine that knows roughly what we know, organized roughly as we organize it, with roughly the same values and motives as we have. It makes sense, then, to ask how this knowledge is acquired by humans and how might it be acquired by machines. I focus on the origins of conceptual knowledge, the earliest distinctions and classes, the first efforts to carve the world at its joints. One reason is just the desire to get to the bottom of, or in this case the beginning of, anything. ...