Building concepts for AI agents using information theoretic co-clustering

Abstract

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.