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{{Short description|Area of automatic programming}}
{{Programming paradigms}}▼
'''Inductive programming''' ('''IP''') is a special area of [[automatic programming]], covering research from [[artificial intelligence]] and [[Computer programming|programming]], which addresses [[machine learning|learning]] of typically [[declarative programming|declarative]] ([[logic programming|logic]] or [[functional programming|functional]]) and often [[recursion|recursive]] programs from incomplete specifications, such as input/output examples or constraints.
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Output of an IP system is a program in some arbitrary programming language containing conditionals and loop or recursive control structures, or any other kind of [[Turing completeness|Turing-complete]] [[Knowledge representation and reasoning|representation]] language.
In many applications the output program must be correct with respect to the examples and partial specification, and this leads to the consideration of inductive programming as a special area inside automatic programming or [[program synthesis]],<ref>{{cite journal|first1=A.W.|last1=Biermann|title=Automatic programming|editor1-first=S.C.|editor1-last=Shapiro|journal=Encyclopedia of Artificial Intelligence|pages=18–35|year=1992}}</ref><ref>{{cite book|first1=C.|last1=Rich|first2=R.C.|last2=Waters|title=Approaches to automatic programming|editor1-first=M.C.|editor1-last=Yovits|
In other cases, inductive programming is seen as a more general area where any declarative programming or representation language can be used and we may even have some degree of error in the examples, as in general [[machine learning]], the more specific area of [[structure mining]] or the area of [[symbolic artificial intelligence]]. A distinctive feature is the number of examples or partial specification needed. Typically, inductive programming techniques can learn from just a few examples.
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|journal=Computational Intelligence
|volume=30|issue=3|pages=473–513|year=2014
|doi=10.1111/coin.12004|doi-access=free|hdl=10251/34946|hdl-access=free}}
</ref> abstraction has also been explored as a more powerful approach to [[cumulative learning]] and function invention.<ref>
{{cite journal
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== Application areas ==
The [http://www.cogsys.wiai.uni-bamberg.de/aaip05/objectives.html first workshop on Approaches and Applications of Inductive Programming (AAIP) ] {{Webarchive|url=https://web.archive.org/web/20160303223748/http://www.cogsys.wiai.uni-bamberg.de/aaip05/objectives.html |date=2016-03-03 }} held in conjunction with [[ICML]] 2005 identified all applications where "learning of programs or recursive rules are called for, [...] first in the ___domain of software engineering where structural learning, software assistants and software agents can help to relieve programmers from routine tasks, give programming support for end users, or support of novice programmers and programming tutor systems. Further areas of application are language learning, learning recursive control rules for AI-planning, learning recursive concepts in web-mining or for data-format transformations".
Since then, these and many other areas have shown to be successful application niches for inductive programming, such as [[End-user development|end-user programming]],<ref>
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Other areas where inductive inference has been recently applied are [[knowledge acquisition]],<ref>
{{cite journal
|first1=U.|last1=Schmid|author1-link= Ute Schmid |first2=M.|last2=Hofmann|first3=E.|last3=Kitzelmann
|title=Analytical inductive programming as a cognitive rule acquisition devise
|journal=Proceedings of the Second Conference on Artificial General Intelligence
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</ref> [[artificial general intelligence]],<ref>
{{cite journal
|first1=N.|last1=Crossley|first2=E.|last2=Kitzelmann|first3=M.|last3=Hofmann|first4=U.|last4=Schmid|author4-link= Ute Schmid
|title=Combining analytical and evolutionary inductive programming
|journal=Proceedings of the Second Conference on Artificial General Intelligence
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</ref> and [[cognitive science]] in general.<ref>
{{cite journal
|first1=U.|last1=Schmid|author1-link= Ute Schmid |first2=E.|last2=Kitzelmann
|title=Inductive rule learning on the knowledge level
|journal=Cognitive Systems Research
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== Further reading ==
{{Refbegin}}
* {{cite journal|first1=P.|last1=Flener|first2=U.|last2=Schmid|author2-link= Ute Schmid |title=An introduction to inductive programming|journal=Artificial Intelligence Review|volume=29 | issue = 1|pages=45–62|year=2008|doi=10.1007/s10462-009-9108-7|s2cid=26314997}}
* {{cite book|first1=E.|last1=Kitzelmann|title=Approaches and Applications of Inductive Programming |chapter=Inductive Programming: A Survey of Program Synthesis Techniques |volume=5812|pages=50–73|year=2010|chapter-url=http://emanuel.kitzelmann.org/documents/publications/Kitzelmann2010.pdf|doi=10.1007/978-3-642-11931-6_3|citeseerx=10.1.1.180.1237|series=Lecture Notes in Computer Science|isbn=978-3-642-11930-9}}
* {{cite journal|first1=D.|last1=Partridge|title=The case for inductive programming|journal=Computer|volume=30 | issue = 1|pages=36–41|year=1997|doi=10.1109/2.562924|s2cid=206403583 }}
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* {{cite journal|first1=M.|last1=Hofmann|first2=E.|last2=Kitzelmann|title=A unifying framework for analysis and evaluation of inductive programming systems|journal=Proceedings of the Second Conference on Artificial General Intelligence|pages=55–60|year=2009|url=http://www.atlantis-press.com/php/download_paper.php?id=1839}}
* {{Cite journal | last1 = Muggleton | first1 = S. | last2 = De Raedt | doi = 10.1016/0743-1066(94)90035-3 | first2 = L. | title = Inductive Logic Programming: Theory and methods | journal = The Journal of Logic Programming | volume = 19-20 | pages = 629–679 | year = 1994 | url = https://lirias.kuleuven.be/handle/123456789/125406 | doi-access = free }}
* {{cite book | first1 = N. | last1 = Lavrac |author1-link=Nada Lavrač| first2 = S. | last2 = Dzeroski | title = Inductive Logic Programming: Techniques and Applications | publisher = Ellis Horwood | ___location = New York | year = 1994 | isbn = 978-0-13-457870-5 }} https://web.archive.org/web/20040906084947/http://www-ai.ijs.si/SasoDzeroski/ILPBook/
* {{cite journal|first1=S.|last1=Muggleton|first2=Luc.|last2=De Raedt|first3=D.|last3=Poole|first4=I.|last4=Bratko|first5=P.|last5=Flach|first6=K.|last6=Inoue|first7=A.|last7=Srinivasan|title=ILP turns 20|journal=Machine Learning|volume=86 | issue = 1|pages=3–23|year=2012|doi=10.1007/s10994-011-5259-2|doi-access=free}}
* {{cite journal|first1=S.|last1=Gulwani|first2=J.|last2=Hernandez-Orallo|first3=E.|last3=Kitzelmann|first4=S.H.|last4=Muggleton|first5=U.|last5=Schmid|author5-link= Ute Schmid |first6=B.|last6=Zorn|title=Inductive Programming Meets the Real World|journal=Communications of the ACM|volume=58 | issue = 11|pages=90–99|year=2015|doi=10.1145/2736282|url=http://cacm.acm.org/magazines/2015/11/193326-inductive-programming-meets-the-real-world/abstract|citeseerx=10.1.1.696.3800|hdl=10251/64984|s2cid=425881}}
{{refend}}
== External links ==
* [http://www.inductive-programming.org/ Inductive Programming community page], hosted by the University of Bamberg.
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