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Several inductive logic programming systems that proved influential appeared in the early 1990s. [[First-order inductive learner|FOIL]], introduced by [[Ross Quinlan]] in 1990<ref>{{Cite journal |last=Quinlan |first=J. R. |date=August 1990 |title=Learning logical definitions from relations |journal=Machine Learning |volume=5 |issue=3 |pages=239–266 |doi=10.1007/bf00117105 |issn=0885-6125|doi-access=free }}</ref> was based on upgrading [[Propositional calculus|propositional]] learning algorithms [[AQ (machine learning)|AQ]] and [[ID3 algorithm|ID3]].<ref name=":12">{{Cite book |last1=Nienhuys-Cheng |first1=Shan-hwei |title=Foundations of inductive logic programming |last2=Wolf |first2=Ronald de |date=1997 |publisher=Spinger |isbn=978-3-540-62927-6 |series=Lecture notes in computer science Lecture notes in artificial intelligence |___location=Berlin Heidelberg |pages=354–358}}</ref> [[Golem (ILP)|Golem]], introduced by Muggleton and Feng in 1990, went back to a restricted form of Plotkin's least generalisation algorithm.<ref name=":12"/><ref name="Springer/Ohmsha">{{Cite journal |last1=Muggleton |first1=Stephen H. |last2=Feng |first2=Cao |date=1990 |editor-last=Arikawa |editor-first=Setsuo |editor2-last=Goto |editor2-first=Shigeki |editor3-last=Ohsuga |editor3-first=Setsuo |editor4-last=Yokomori |editor4-first=Takashi |title=Efficient Induction of Logic Programs |url=https://dblp.org/rec/conf/alt/MuggletonF90.bib |journal=Algorithmic Learning Theory, First International Workshop, ALT '90, Tokyo, Japan, October 8–10, 1990, Proceedings |publisher=Springer/Ohmsha |pages=368–381}}</ref> The [[Progol]] system, introduced by Muggleton in 1995, first implemented inverse entailment, and inspired many later systems.<ref name=":12"/><ref name=":3">{{Cite journal |last1=Cropper |first1=Andrew |last2=Dumančić |first2=Sebastijan |date=2022-06-15 |title=Inductive Logic Programming At 30: A New Introduction |journal=Journal of Artificial Intelligence Research |volume=74 |page=808 |doi=10.1613/jair.1.13507 |issn=1076-9757|doi-access=free |arxiv=2008.07912 }}</ref><ref name=":2">{{cite journal |last1=Muggleton |first1=S.H. |year=1995 |title=Inverting entailment and Progol |journal=New Generation Computing |volume=13 |issue=3–4 |pages=245–286 |citeseerx=10.1.1.31.1630 |doi=10.1007/bf03037227 |s2cid=12643399}}</ref> [[Aleph (ILP)|Aleph]], a descendant of Progol introduced by Ashwin Srinivasan in 2001, is still one of the most widely used systems {{As of|2022|lc=y|bare=}}.<ref name=":3" />
At around the same time, the first practical applications emerged, particularly in [[bioinformatics]], where by 2000 inductive logic programming had been successfully applied to drug design, carcinogenicity and mutagenicity prediction, and elucidation of the structure and function of proteins.<ref>{{Citation |last=Džeroski |first=Sašo |title=Relational Data Mining Applications: An Overview |date=2001 |url=http://link.springer.com/10.1007/978-3-662-04599-2_14 |work=Relational Data Mining |pages=339–364 |editor-last=Džeroski |editor-first=Sašo |access-date=2023-11-27 |place=Berlin, Heidelberg |publisher=Springer Berlin Heidelberg |language=en |doi=10.1007/978-3-662-04599-2_14 |isbn=978-3-642-07604-6 |editor2-last=Lavrač |editor2-first=Nada|editor2-link=Nada Lavrač}}</ref> Unlike the focus on [[automatic programming]] inherent in the early work, these fields used inductive logic programming techniques from a viewpoint of [[relational data mining]]. The success of those initial applications and the lack of progress in recovering larger traditional logic programs shaped the focus of the field.<ref>{{Citation |last=De Raedt |first=Luc |editor-first1=<!-- Deny Citation Bot--> |editor-last1=<!-- Deny Citation Bot--> |url=http://dx.doi.org/10.1007/978-3-540-68856-3 |title=Logical and Relational Learning |series=Cognitive Technologies |page=14|place=Berlin, Heidelberg |publisher=Springer |year=2008 |doi=10.1007/978-3-540-68856-3 |bibcode=2008lrl..book.....D |isbn=978-3-540-20040-6}}</ref>
Recently, classical tasks from automated programming have moved back into focus, as the introduction of meta-interpretative learning makes predicate invention and learning recursive programs more feasible. This technique was pioneered with the [[Metagol]] system introduced by Muggleton, Dianhuan Lin, Niels Pahlavi and Alireza Tamaddoni-Nezhad in 2014.<ref>{{Cite journal |last1=Muggleton |first1=Stephen H. |last2=Lin |first2=Dianhuan |last3=Pahlavi |first3=Niels |last4=Tamaddoni-Nezhad |first4=Alireza |date=2013-05-01 |title=Meta-interpretive learning: application to grammatical inference |url=http://dx.doi.org/10.1007/s10994-013-5358-3 |journal=Machine Learning |volume=94 |issue=1 |pages=25–49 |doi=10.1007/s10994-013-5358-3 |s2cid=254738603 |issn=0885-6125}}</ref> This allows ILP systems to work with fewer examples, and brought successes in learning string transformation programs, answer set grammars and general algorithms.<ref>{{Cite journal |last1=Cropper |first1=Andrew |last2=Dumančić |first2=Sebastijan |last3=Evans |first3=Richard |last4=Muggleton |first4=Stephen |date=2022 |title=Inductive logic programming at 30 |journal=Machine Learning |language=en |volume=111 |issue=1 |pages=147–172 |doi=10.1007/s10994-021-06089-1 |issn=0885-6125|doi-access=free }}</ref>
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