Hybrid Abductive Inductive Learning

Abstract

This thesis introduces a new Machine Learning technique called Hybrid Abductive Inductive Learning (HAIL) that integrates Abductive Logic Programming (ALP) and Inductive Logic Programming (ILP) in order to automate the learning of first-order theories from examples and prior knowledge. A semantics is proposed called Kernel Set Subsumption (KSS) that generalises the well-known inference method of Bottom Generalisation by deriving hypotheses with more than one clause. A corresponding proof procedure is presented, called HAIL, which extends the ALP procedure of Kakas and Mancarella and integrates it within a generalisation of Muggleton’s widely-used ILP system Progol5. HAIL is shown to overcome some of the limitations of Progol5 — including a previously unsuspected incompleteness — and to enlarge the class of learning problems soluble in practice.