Building concepts for AI agents using information theoretic co-clustering

High level conceptual thought seems to be at the basis of the impressive human cognitive ability, and AI researchers aim to replicate this ability in artificial agents. Classical top-down (Logic based) and bottom-up (Connectionist) approaches to the problem have had limited success to date. We review 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. While valid concepts have been constructed using previous methods under this approach, we show in this paper that the one-sided clustering methods generally used there may fail to uncover valid concepts even when they clearly exist. We show that by using a Co-clustering algorithm that searches for an optimal partitioning based on the Mutual Information between the category and consequent components of a concept, the concept formation outcome is improved. We test our approach on data from experiments using a real mobile robot operating in the real world, and show that our Co-clustering based approach leads to significant performance improvement compared to previous approaches.