Learning by Composition

Abstract

An approach to learning based on structural composition provides the basis for much of my current research work. The focus is on how existing structural elements can be composed to form larger structural elements. The importance of uniformity of representation under composition is stressed. A general learning framework is discussed which includes mechanisms for automatically proposing new compositions. Filters are used to select only the best of the proposed compositions. This framework is currently being explored in the domain of puzzle solving, where operators are the elements being composed to form macro-operators. This work should have important implications for automated mathematical discovery, automatic programming, and concept learning.