## 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 contribution | Hybrid Abductive Inductive Learning |
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Original language | English |

Publication status | Published - 2005 |