@phdthesis{2000718,
author={Oliver Ray},
title={Hybrid Abductive Inductive Learning},
school={Department of Computing, Imperial College London},
bpages={170},
rpages={8},
month={December},
year={2005},
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.},
abstract-url={http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2000718},
url={http://www.cs.bris.ac.uk/Publications/Papers/2000718.pdf},
keyword={Artificial Intelligence,Logic Programming,Machine Learning},
pubtype={116}
}