Grammatically Biased Learning: Learning Horn Theories Using an Explicit Clause Description Language.

Every concept learning system produces hypotheses that are written in some sort of constrained language called the concept description language, and for most learning systems, the concept description language is fixed. This paper describes a learning system that makes a large part of the concept description language an explicit input, and discusses some of the possible applications of providing this additional input. In particular, we discuss a technique for learning a set of Horn clauses such that each clause can be generated by a special clause description language; it is shown that this technique can be used to make use of many different types of background knowledge, including constraints on how predicates can be used, programming cliches, overgeneral theories, incomplete theories, and theories syntactically close to the target theory. The approach thus unifies many of the problems previously studied in the field of knowledge-based learning.