Grammatically Biased Learning: Learning Horn Theories Using an Explicit Antecedent 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 xed. 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 the antecedent of each clause can be generated by a special antecedent description language; it is shown that this technique can be used to make use of many diierent types of background knowledge, including constraints on how predicates can be used, programming clich es, overgeneral theories, incomplete theories, and theories syntactically close to the target theory. The approach thus uniies many of the problems previously studied in the eld of knowledge-based learning.