Hybrid Abductive Inductive Learning

Research output: Other contributionPhD thesis (not Bristol)

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.
Translated title of the contributionHybrid Abductive Inductive Learning
Original languageEnglish
Publication statusPublished - 2005

Bibliographical note

Other identifier: 2000718

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