Unsupervised Grammar Induction in a Framework of Information Compression
  by Multiple Alignment, Unification and Search
  This paper describes a novel approach to grammar induction that has been
developed within a framework designed to integrate learning with other aspects
of computing, AI, mathematics and logic. This framework, called "information
compression by multiple alignment, unification and search" (ICMAUS), is founded
on principles of Minimum Length Encoding pioneered by Solomonoff and others.
Most of the paper describes SP70, a computer model of the ICMAUS framework that
incorporates processes for unsupervised learning of grammars. An example is
presented to show how the model can infer a plausible grammar from appropriate
input. Limitations of the current model and how they may be overcome are
briefly discussed.
