Inductive logic programming
 Stephen Muggleton
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A new research area, Inductive Logic Programming, is presently emerging. While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears. The background to present developments within this area is discussed and various goals and aspirations for the increasing body of researchers are identified. Inductive Logic Programming needs to be based on sound principles from both Logic and Statistics. On the side of statistical justification of hypotheses we discuss the possible relationship between Algorithmic Complexity theory and ProbablyApproximatelyCorrect (PAC) Learning. In terms of logic we provide a unifying framework for Muggleton and Buntine’s Inverse Resolution (IR) and Plotkin’s Relative Least General Generalisation (RLGG) by rederiving RLGG in terms of IR. This leads to a discussion of the feasibility of extending the RLGG framework to allow for the invention of new predicates, previously discussed only within the context of IR.
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 Title
 Inductive logic programming
 Journal

New Generation Computing
Volume 8, Issue 4 , pp 295318
 Cover Date
 19910201
 DOI
 10.1007/BF03037089
 Print ISSN
 02883635
 Online ISSN
 18827055
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Learning
 logic programming
 induction
 predicate invention
 inverse resolution
 information compression
 Industry Sectors
 Authors

 Stephen Muggleton ^{(1)}
 Author Affiliations

 1. The Turing Institute, 36 North Hanover St., G1 2AD, Glasgow, UK