Optimizing general chain programs

Abstract

The goal of knowledge compilation is to transform programs in order to speed up their evaluation. In Inductive Logic Programming, two major approaches to speed-up learning exist: Approaches that intertwine the learning and the optimization process and approaches that separate these two processes. We follow the latter approach and present a new equivalence-preserving transformation method for programs with ordered clauses. It eliminates redundancies that make forward inference procedures slow. We introduce general chain rules, a specific class of ordered clauses, whose syntactical features are exploited in a new forward inference method. The comparison of the time needed by this method to evaluate the transformed program with the time needed by a standard forward inference procedure for the original program confirms the expected speed-up.