Inductive programming: Difference between revisions

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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|journalseries=Advances in Computers|volume=37|pages=1–57|year=1993|url=http://www.merl.com/publications/docs/TR92-04.pdf|doi=10.1016/S0065-2458(08)60402-7|isbn=9780120121373}}</ref> usually opposed to 'deductive' program synthesis,<ref>{{cite book|editor1-first=M.L.|editor1-last=Lowry|editor2-first=R.D.|editor2-last=McCarthy|title=Automatic software design|year=1991}}</ref><ref>{{cite journal|first1=Z.|last1=Manna|first2=R.|last2=Waldinger|title=Fundamentals of deductive program synthesis|journal=IEEE Trans Softw Eng|volume=18 | issue = 8|pages=674–704|year=1992|doi=10.1109/32.153379|citeseerx=10.1.1.51.817}}</ref><ref>{{Cite book|first1=P.|last1=Flener|chapter=Achievements and Prospects of Program Synthesis |title=Computational Logic: Logic Programming and Beyond; Essays in Honour of Robert A. Kowalski|editor1-first=A.|editor1-last=Kakas|editor2-first=F.|editor2-last=Sadri|journal=Computational Logic: Logic Programming and Beyond; Essays in Honour of Robert A. Kowalski|volume=LNAI 2407|pages=310–346|year=2002|doi=10.1007/3-540-45628-7_13|series=Lecture Notes in Computer Science|isbn=978-3-540-43959-2}}</ref> where the specification is usually complete.

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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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.

{{Programming paradigms navbox}}

[[Category:Programming paradigms]]