Inductive learning of relational productions
Steven A. Vere · ACM SIGART Bulletin · 1977
Relational productions provide a mathematically tractable formal model for operators in discrete systems. Two paradigms for the inductive learning of such operators are considered: before-and-after situation pairs and situation sequences. An operational theory for the generalization of productions for these paradigms is then developed, based on previous work in concept induction. This theory has been computer implemented. In examples three "blocks world" operators are learned from six before-and-after pairs and also from a sequence of fifteen blocks world situations. A transformational grammar learning example of Hayes-Roth is repeated with an improvement in speed of two orders of magnitude.