- -

Inductive programming meets the real world

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Inductive programming meets the real world

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Paper.pdf
Tamaño: 4.799Mb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: p90-gulwani.pdf
Tamaño: 2.801Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Gulwani, Sumit es_ES
dc.contributor.author Hernández-Orallo, José es_ES
dc.contributor.author Kitzelmann, Emanuel es_ES
dc.contributor.author Muggleton, Stephen H. es_ES
dc.contributor.author Schmid, Ute es_ES
dc.contributor.author Zorn, Benjamin es_ES
dc.date.accessioned 2016-05-31T09:51:57Z
dc.date.available 2016-05-31T09:51:57Z
dc.date.issued 2015-11
dc.identifier.issn 0001-0782
dc.identifier.uri http://hdl.handle.net/10251/64984
dc.description © 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 es_ES
dc.description.abstract [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. es_ES
dc.language Inglés es_ES
dc.publisher Association for Computing Machinery (ACM) es_ES
dc.relation.ispartof Communications of the ACM es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Inductive programming es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Inductive programming meets the real world es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1145/2736282
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation 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:10.1145/2736282 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1145/2736282 es_ES
dc.description.upvformatpinicio 90 es_ES
dc.description.upvformatpfin 99 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 58 es_ES
dc.description.issue 11 es_ES
dc.relation.senia 302241 es_ES
dc.description.references Bengio, Y., Courville, A. and Vincent, P. Representation learning: A review and new perspectives.Pattern Analy. Machine Intell. 35, 8 (2013), 1798--1828. es_ES
dc.description.references 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/ es_ES
dc.description.references 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. es_ES
dc.description.references Chandola, V., Banerjee, A. and V. Kumar, V. Anomaly detection: A survey.ACM Computing Surveys 41, 3 (2009), 15. es_ES
dc.description.references Cypher, A. (Ed).Watch What I Do: Programming by Demonstration.MIT Press, Cambridge, MA, 1993. es_ES
dc.description.references Ferri-Ramírez, C., Hernández-Orallo, J. and Ramírez-Quintana, M.J. Incremental learning of functional logic programs. InProceedings of FLOPS, 2001, 233--247. es_ES
dc.description.references Flener, P. and Schmid, U. An introduction to inductive programming.AI Review 29, 1 (2009), 45--62. es_ES
dc.description.references Gulwani, S. Dimensions in program synthesis. InProceedings of PPDP, 2010. es_ES
dc.description.references Gulwani, S. Automating string processing in spreadsheets using input-output examples. InProceedings of POPL, 2011; http://research.microsoft.com/users/sumitg/flashfill.html. es_ES
dc.description.references Gulwani, S. Example-based learning in computer-aided STEM education.Commun. ACM 57, 8 (Aug 2014), 70--80. es_ES
dc.description.references Gulwani, S., Harris, W. and Singh, R. Spreadsheet data manipulation using examples.Commun. ACM 55, 8 (Aug. 2012), 97--105. es_ES
dc.description.references Henderson, R.J. and Muggleton, S.H. Automatic invention of functional abstractions.Latest Advances in Inductive Logic Programming, 2012. es_ES
dc.description.references Hernández-Orallo, J. Deep knowledge: Inductive programming as an answer, Dagstuhl TR 13502, 2013. es_ES
dc.description.references 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. es_ES
dc.description.references Jha, J., Gulwani, S., Seshia, S. and Tiwari, A. Oracle-guided component-based program synthesis. InProceedings of the ICSE, 2010. es_ES
dc.description.references Katayama, S. Efficient exhaustive generation of functional programs using Monte-Carlo search with iterative deepening. InProceedings of PRICAI, 2008. es_ES
dc.description.references Kitzelmann, E. Analytical inductive functional programming.LOPSTR 2008, LNCS 5438.Springer, 2009, 87--102. es_ES
dc.description.references Kitzelmann, E. Inductive programming: A survey of program synthesis techniques. InAAIP, Springer, 2010, 50--73. es_ES
dc.description.references Kitzelmann, E. and Schmid, U. Inductive synthesis of functional programs: An explanation based generalization approach.J. Machine Learning Research 7, (Feb. 2006), 429--454. es_ES
dc.description.references 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. es_ES
dc.description.references Lau, T.A. Why programming-by-demonstration systems fail: Lessons learned for usable AI.AI Mag. 30, 4, (2009), 65--67. es_ES
dc.description.references 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. es_ES
dc.description.references Le, V. and Gulwani, S. FlashExtract: A framework for data extraction by examples. InProceedings of PLDI, 2014. es_ES
dc.description.references Lieberman, H. (Ed).Your Wish is My Command: Programming by Example.Morgan Kaufmann, 2001. es_ES
dc.description.references Lin, D., Dechter, E., Ellis, K., Tenenbaum, J.B. and Muggleton, S.H. Bias reformulation for one-shot function induction. InProceedings of ECAI, 2014. es_ES
dc.description.references Marcus, G.F. The Algebraic Mind.Integrating Connectionism and Cognitive Science.Bradford, Cambridge, MA, 2001. es_ES
dc.description.references 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. es_ES
dc.description.references Menon, A., Tamuz, O., Gulwani, S., Lampson, B. and Kalai, A. A machine learning framework for programming by example. InProceedings of the ICML, 2013. es_ES
dc.description.references Miller, R.C. and Myers, B.A. Multiple selections in smart text editing. InProceedings of IUI, 2002, 103--110. es_ES
dc.description.references Muggleton, S.H. Inductive Logic Programming.New Generation Computing 8, 4 (1991), 295--318. es_ES
dc.description.references Muggleton, S.H. and Lin, D. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited.IJCAI 2013, 1551--1557. es_ES
dc.description.references Muggleton, S.H., Lin, D., Pahlavi, N. and Tamaddoni-Nezhad, A. Meta-interpretive learning: application to grammatical inference.Machine Learning 94(2014), 25--49. es_ES
dc.description.references 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. es_ES
dc.description.references Olsson, R. Inductive functional programming using incremental program transformation.Artificial Intelligence 74, 1 (1995), 55--83. es_ES
dc.description.references Perelman, D., Gulwani, S., Grossman, D. and Provost, P. Test-driven synthesis.PLDI, 2014. es_ES
dc.description.references Raza, M., Gulwani, S. and Milic-Frayling, N. Programming by example using least general generalizations.AAAI, 2014. es_ES
dc.description.references Schmid, U. and Kitzelmann, E. Inductive rule learning on the knowledge level.Cognitive Systems Research 12, 3 (2011), 237--248. es_ES
dc.description.references Schmid, U. and Wysotzki, F. Induction of recursive program schemes.ECML 1398 LNAI(1998), 214--225. es_ES
dc.description.references Shapiro, E.Y. An algorithm that infers theories from facts.IJCAI(1981), 446--451. es_ES
dc.description.references Solar-Lezama, A.Program Synthesis by Sketching.Ph.D thesis, UC Berkeley, 2008. es_ES
dc.description.references Summers, P.D. A methodology for LISP program construction from examples.JACM 24, 1 (1977), 162--175. es_ES
dc.description.references 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. es_ES
dc.description.references Young, S. Cognitive user interfaces.IEEE Signal Processing 27, 3 (2010), 128--140. es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem