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    Inductive programming meets the real world

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    © Gulwani, S. et al. | ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Communications of the ACM, http://dx.doi.org/10.1145/2736282[EN] Since most end users lack programming skills they often spend considerable time and effort performing tedious and repetitive tasks such as capitalizing a column of names manually. Inductive Programming has a long research tradition and recent developments demonstrate it can liberate users from many tasks of this kind.Gulwani, S.; Hernández-Orallo, J.; Kitzelmann, E.; Muggleton, SH.; Schmid, U.; Zorn, B. (2015). Inductive programming meets the real world. Communications of the ACM. 58(11):90-99. doi:10.1145/2736282S90995811Bengio, Y., Courville, A. and Vincent, P. Representation learning: A review and new perspectives.Pattern Analy. Machine Intell. 35, 8 (2013), 1798--1828.Bielawski, B. Using the convertfrom-string cmdlet to parse structured text.PowerShell Magazine, (Sept. 9, 2004); http://www.powershellmagazine.com/2014/09/09/using-the-convertfrom-string-cmdlet-to-parse-structured-text/Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka-Jr, E.R. and T.M. Mitchell, T.M. Toward an architecture for never-ending language learning. InAAAI, 2010.Chandola, V., Banerjee, A. and V. Kumar, V. Anomaly detection: A survey.ACM Computing Surveys 41, 3 (2009), 15.Cypher, A. (Ed).Watch What I Do: Programming by Demonstration.MIT Press, Cambridge, MA, 1993.Ferri-Ramírez, C., Hernández-Orallo, J. and Ramírez-Quintana, M.J. Incremental learning of functional logic programs. InProceedings of FLOPS, 2001, 233--247.Flener, P. and Schmid, U. An introduction to inductive programming.AI Review 29, 1 (2009), 45--62.Gulwani, S. Dimensions in program synthesis. InProceedings of PPDP, 2010.Gulwani, S. Automating string processing in spreadsheets using input-output examples. InProceedings of POPL, 2011; http://research.microsoft.com/users/sumitg/flashfill.html.Gulwani, S. Example-based learning in computer-aided STEM education.Commun. ACM 57, 8 (Aug 2014), 70--80.Gulwani, S., Harris, W. and Singh, R. Spreadsheet data manipulation using examples.Commun. ACM 55, 8 (Aug. 2012), 97--105.Henderson, R.J. and Muggleton, S.H. Automatic invention of functional abstractions.Latest Advances in Inductive Logic Programming, 2012.Hernández-Orallo, J. Deep knowledge: Inductive programming as an answer, Dagstuhl TR 13502, 2013.Hofmann, M. and Kitzelmann, E. I/O guided detection of list catamorphisms---towards problem specific use of program templates in IP. InACM SIGPLAN PEPM, 2010.Jha, J., Gulwani, S., Seshia, S. and Tiwari, A. Oracle-guided component-based program synthesis. InProceedings of the ICSE, 2010.Katayama, S. Efficient exhaustive generation of functional programs using Monte-Carlo search with iterative deepening. InProceedings of PRICAI, 2008.Kitzelmann, E. Analytical inductive functional programming.LOPSTR 2008, LNCS 5438.Springer, 2009, 87--102.Kitzelmann, E. Inductive programming: A survey of program synthesis techniques. InAAIP, Springer, 2010, 50--73.Kitzelmann, E. and Schmid, U. Inductive synthesis of functional programs: An explanation based generalization approach.J. Machine Learning Research 7, (Feb. 2006), 429--454.Kotovsky, K., Hayes, J.R. and Simon, H.A. Why are some problems hard? Evidence from Tower of Hanoi.Cognitive Psychology 17, 2 (1985), 248--294.Lau, T.A. Why programming-by-demonstration systems fail: Lessons learned for usable AI.AI Mag. 30, 4, (2009), 65--67.Lau, T.A., Wolfman, S.A., Domingos, P. and Weld, D.S. Programming by demonstration using version space algebra.Machine Learning 53, 1-2 (2003), 111--156.Le, V. and Gulwani, S. FlashExtract: A framework for data extraction by examples. InProceedings of PLDI, 2014.Lieberman, H. (Ed).Your Wish is My Command: Programming by Example.Morgan Kaufmann, 2001.Lin, D., Dechter, E., Ellis, K., Tenenbaum, J.B. and Muggleton, S.H. Bias reformulation for one-shot function induction. InProceedings of ECAI, 2014.Marcus, G.F. The Algebraic Mind.Integrating Connectionism and Cognitive Science.Bradford, Cambridge, MA, 2001.Martìnez-Plumed, C. Ferri, Hernández-Orallo, J. and M.J. Ramírez-Quintana. On the definition of a general learning system with user-defined operators.arXiv preprint arXiv:1311.4235, 2013.Menon, A., Tamuz, O., Gulwani, S., Lampson, B. and Kalai, A. A machine learning framework for programming by example. InProceedings of the ICML, 2013.Miller, R.C. and Myers, B.A. Multiple selections in smart text editing. InProceedings of IUI, 2002, 103--110.Muggleton, S.H. Inductive Logic Programming.New Generation Computing 8, 4 (1991), 295--318.Muggleton, S.H. and Lin, D. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited.IJCAI 2013, 1551--1557.Muggleton, S.H., Lin, D., Pahlavi, N. and Tamaddoni-Nezhad, A. Meta-interpretive learning: application to grammatical inference.Machine Learning 94(2014), 25--49.Muggleton, S.H., De Raedt, L., Poole, D., Bratko, I., Flach, P. and Inoue, P. ILP turns 20: Biography and future challenges.Machine Learning 86, 1 (2011), 3--23.Olsson, R. Inductive functional programming using incremental program transformation.Artificial Intelligence 74, 1 (1995), 55--83.Perelman, D., Gulwani, S., Grossman, D. and Provost, P. Test-driven synthesis.PLDI, 2014.Raza, M., Gulwani, S. and Milic-Frayling, N. Programming by example using least general generalizations.AAAI, 2014.Schmid, U. and Kitzelmann, E. Inductive rule learning on the knowledge level.Cognitive Systems Research 12, 3 (2011), 237--248.Schmid, U. and Wysotzki, F. Induction of recursive program schemes.ECML 1398 LNAI(1998), 214--225.Shapiro, E.Y. An algorithm that infers theories from facts.IJCAI(1981), 446--451.Solar-Lezama, A.Program Synthesis by Sketching.Ph.D thesis, UC Berkeley, 2008.Summers, P.D. A methodology for LISP program construction from examples.JACM 24, 1 (1977), 162--175.Tenenbaum, J.B., Griffiths, T.L. and Kemp, C. Theory-based Bayesian models of inductive learning and reasoning.Trends in Cognitive Sciences 10, 7 (2006), 309--318.Young, S. Cognitive user interfaces.IEEE Signal Processing 27, 3 (2010), 128--140

    The State of the Art of Automatic Programming

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    Automaatprogrammeerimine või koodi genereerimine on teatud tüüpi arvutiprogrammide loomisviis, kus kood genereeritakse mõne tööriista abil, mis võimaldab arendajatel koodi kirjutada kõrgemal abstraktsioonitasemel. Selliste programmide rakendamine tarkvaraarenduse protsessis on hea viis programmeerijate produktiivsuse tõstmiseks, võimaldades neil keskenduda pigem käesolevale ülesandele kui implementatsiooni detailidele. Senises teaduskirjanduses on vaadeldud konkreetseid lähenemisi või meetodeid eraldi. Väga vähesed uurimustööd vaatlevad aga kogu valdkonna viimast taset. Käesolevas töös käsitletakse automaatprogrammeerimist olemasoleva kirjanduse süstemaatilise kirjandusülevaate meetodi abil. Töö teeb ülevaate teemaga seonduvatest algoritmidest, probleemidest ning uurmisvaldkonna avatud uurimisküsimustest ning võrdleb valdkonna hetketaset praktika hetketasemega. Vaaldeldud 37 asjakohasest uuringust tegelesid 19 automaatprogrammeerimise üldise määratlemise ja alateemadega. Kolmkümmend uuringut pakkusid välja konkreetse algoritmi või lähenemisviisi. Esitatud tehnikatest rakendati 2 praktikas. Viimasel ajal on automaatprogrammerimise fookus nihkunud programmide sünteesilt induktiivsele programmeerimisele, mille on põhjustanud läbimurded tehisintellekti valdkonnas. Mõistete ja alateemade määratlus on teadlaste vahel ühtne. Õigete spetsifikatsioonide sõnastamine ja piisava teabe andmine automatiseerimiseks on endiselt lahtine uurimisküsimus.Automatic programming or code generation is a type of computer programming where the code is generated using some tools allowing developers to write code at the higher level of abstraction. Implementing these types of programs into the software development process is a good way to boost programmers’ performance by focusing on the task at hand rather than implementation details. Current literature on the subject reviews single approach or method. Very few of them are reviewing state of the art in general. This paper reviews the state of the art of automatic programming by overviewing the existing literature on the topic using systematic literature review method. The paper overviews approaches and algorithms of the topic, examines issues and open questions in the field and compares the state of the art to the state of the practice. Of 37 relevant studies, 19 addressed general definitions and subtopics of automatic programming. 30 presented specific algorithms or approaches. 2 of proposed techniques were implemented in practice. Currently, the focus of automatic programming shifted from program synthesis to inductive programming, caused by a breakthrough in artificial intelligence. Definition of the term and subtopics is consistent between scholars. However, formulating correct specification and providing sufficient information for automation is still an open research question

    Knowledge transformers : a link between learning and creativity

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    The purpose of this paper is to investigate whether knowledge transformers which are featured in the learning process, are also present in the creative process. This is achieved by reviewing models and theories of creativity and identifying the existence of the knowledge transformers. The investigation shows that there is some evidence to show that the creative process can be explained through knowledge transformers. Hence, it is suggested that one of links between learning and creativity is through the knowledge transformers

    Knowledge transformers : a link between learning and creativity

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    The purpose of this paper is to investigate whether knowledge transformers that are featured in the learning process are also present in the creative process. First, this was achieved by reviewing accounts of inventions and discoveries with the view of explaining them in terms of knowledge transformers. Second, this was achieved by reviewing models and theories of creativity and identifying the existence of the knowledge transformers. The investigation shows that there is some evidence to show that the creative process can be explained through knowledge transformers. Hence, it is suggested that one of links between learning and creativity is through the knowledge transformers
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