Cognitive meta-learning of syntactically inferred concepts

Abstract

This paper outlines a proposal for a two-level cognitive architecture reproducing the process of abstract thinking in human beings. The key idea is the use of a level devoted to the extraction of compact representation for basic concepts, with additional syntactic inference carried on at a meta-level, in order to provide generalization. Higher-level concepts are inferred according to a principle of simplicity, consistent with Kolmogorov complexity, and merged back into the lower level in order to widen the underlying knowledge base.