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@article{Muggleton:1994:10.1145/181668.181671,
author = {Muggleton, S},
doi = {10.1145/181668.181671},
journal = {ACM SIGART Bulletin},
pages = {5--11},
title = {Inductive logic programming},
url = {http://dx.doi.org/10.1145/181668.181671},
volume = {5},
year = {1994}
}

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TY - JOUR
AB - <jats:p> Inductive Logic Programming (ILP) is a research area which investigates the construction of first-order definite clause theories from examples and background knowledge. ILP systems have been applied successfully in a number of real-world domains. These include the learning of structure-activity rules for drug design, finite-element mesh design rules, rules for primary-secondary prediction of protein structure and fault diagnosis rules for satellites. There is a well established tradition of learning-in-the-limit results in ILP. Recently some results within Valiant's PAC-learning framework have also been demonstrated for ILP systems. In this paper it is argued that algorithms can be directly <jats:italic>derived</jats:italic> from the formal specifications of ILP. This provides a common basis for Inverse Resolution, Explanation-Based Learning, Abduction and Relative Least General Generalisation. A new general-purpose, efficient approach to predicate invention is demonstrated. ILP is underconstrained by its logical specification. Therefore a brief overview of extra-logical constraints used in ILP systems is given. Some present limitations and research directions for the field are identified. </jats:p>
AU - Muggleton,S
DO - 10.1145/181668.181671
EP - 11
PY - 1994///
SN - 0163-5719
SP - 5
TI - Inductive logic programming
T2 - ACM SIGART Bulletin
UR - http://dx.doi.org/10.1145/181668.181671
VL - 5
ER -

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