Symbol statistics for concept formation in AI agents

High level conceptual thought seems to be at the basis of the impressive human cognitive ability. Classical top-down (Logic based) and bottom-up (Connectionist) approaches to the problem have had limited success to date. We identify a small body of work that represents a different approach to AI. We call this work the Bottom Up Symbolic (BUS) approach and present a new BUS method to concept construction. The main novelty of our work is that we apply statistical methods in the concept construction process. Our findings here suggest that such methods are necessary since a symbolic description of the true agent-environment interaction dynamics is often hidden among a background of non-representative descriptions, especially if data from unconstrained real-world experiments is considered. We consider such data (from a mobile robot randomly roaming an office environment) and show how our method can correctly grow a set of true concepts from the data.