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Induction of logic programs: FOIL and related systems

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Abstract

FOIL is a first-order learning system that uses information in a collection of relations to construct theories expressed in a dialect of Prolog. This paper provides an overview of the principal ideas and methods used in the current version of the system, including two recent additions. We present examples of tasks tackled by FOIL and of systems that adapt and extend its approach.

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Authors and Affiliations

  1. University of Sydney, 2006, Sydney, Australia

    J. R. Quinlan

  2. University of Tasmania, 7250, Launceston, Australia

    R. M. Cameron-Jones

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  1. J. R. Quinlan
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  2. R. M. Cameron-Jones
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Additional information

John Ross Quinlan: He is Professor of Computer Science at the University of Sydney. He received the BSc degree in Physics and Computing from the University of Sydney in 1965 and the PhD degree in Computer Science from the University of Washington in 1968. His research focuses on aspects of machine learning and theory simplification in both attribute-value and relational formalisms.

Richard Michael Cameron-Jones: He received his BSc(Eng) degree in electrical engineering in 1980 from Imperial College of Science and Technology, University of London, and his PhD in Artificial Intelligence in 1991 from the University of Edinburgh. He worked in the UK avionics industry before starting his PhD which was in the arca of computer vision. After the PhD he worked as a researcher in machine learning, with Professor Quinlan, before taking up his current position as a lecturer at the University of Tasmania, Launceston. His research interests in machine learning include inductive logic programming, instance based learning and minimum encoding length techniques.

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Quinlan, J.R., Cameron-Jones, R.M. Induction of logic programs: FOIL and related systems. NGCO 13, 287–312 (1995). https://doi.org/10.1007/BF03037228

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