Automatic discovery of relational concepts by an incremental graph-based representation

• We designed a method to learn relational concepts from a graph-based representation.
• Our method is designed to discover common/useful concepts of an environment.
• Our method can be used by an autonomous agent like a robot.
• Our method learned common concepts in three domains (polygons/furniture/floors).
• Independent human users validated the common concepts learned by our method.

Automatic discovery of concepts has been an elusive area in machine learning. In this paper, we describe a system, called ADC, that automatically discovers concepts in a robotics domain, performing predicate invention. Unlike traditional approaches of concept discovery, our approach automatically finds and collects instances of potential relational concepts. An agent, using ADC, creates an incremental graph-based representation with the information it gathers while exploring its environment, from which common sub-graphs are identified. The subgraphs discovered are instances of potential relational concepts which are induced with Inductive Logic Programming and predicate invention. Several concepts can be induced concurrently and the learned concepts can form arbitrarily hierarchies. The approach was tested for learning concepts of polygons, furniture, and floors of buildings with a simulated robot and compared with concepts suggested by users.