Abstract
Inductive programming can liberate users from performing tedious and repetitive tasks.
- Bengio, Y., Courville, A. and Vincent, P. Representation learning: A review and new perspectives. Pattern Analy. Machine Intell. 35, 8 (2013), 1798--1828. Google ScholarDigital Library
- 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/Google Scholar
- 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. In AAAI, 2010.Google ScholarDigital Library
- Chandola, V., Banerjee, A. and V. Kumar, V. Anomaly detection: A survey. ACM Computing Surveys 41, 3 (2009), 15. Google ScholarDigital Library
- Cypher, A. (Ed). Watch What I Do: Programming by Demonstration. MIT Press, Cambridge, MA, 1993. Google ScholarDigital Library
- Ferri-Ramírez, C., Hernández-Orallo, J. and Ramírez-Quintana, M.J. Incremental learning of functional logic programs. In Proceedings of FLOPS, 2001, 233--247. Google ScholarDigital Library
- Flener, P. and Schmid, U. An introduction to inductive programming. AI Review 29, 1 (2009), 45--62. Google ScholarDigital Library
- Gulwani, S. Dimensions in program synthesis. In Proceedings of PPDP, 2010. Google ScholarDigital Library
- Gulwani, S. Automating string processing in spreadsheets using input-output examples. In Proceedings of POPL, 2011; http://research.microsoft.com/users/sumitg/flashfill.html. Google ScholarDigital Library
- Gulwani, S. Example-based learning in computer-aided STEM education. Commun. ACM 57, 8 (Aug 2014), 70--80. Google ScholarDigital Library
- Gulwani, S., Harris, W. and Singh, R. Spreadsheet data manipulation using examples. Commun. ACM 55, 8 (Aug. 2012), 97--105. Google ScholarDigital Library
- Henderson, R.J. and Muggleton, S.H. Automatic invention of functional abstractions. Latest Advances in Inductive Logic Programming, 2012.Google Scholar
- Hernández-Orallo, J. Deep knowledge: Inductive programming as an answer, Dagstuhl TR 13502, 2013.Google Scholar
- Hofmann, M. and Kitzelmann, E. I/O guided detection of list catamorphisms---towards problem specific use of program templates in IP. In ACM SIGPLAN PEPM, 2010. Google ScholarDigital Library
- Jha, J., Gulwani, S., Seshia, S. and Tiwari, A. Oracle-guided component-based program synthesis. In Proceedings of the ICSE, 2010. Google ScholarDigital Library
- Katayama, S. Efficient exhaustive generation of functional programs using Monte-Carlo search with iterative deepening. In Proceedings of PRICAI, 2008. Google ScholarDigital Library
- Kitzelmann, E. Analytical inductive functional programming. LOPSTR 2008, LNCS 5438. Springer, 2009, 87--102. Google ScholarDigital Library
- Kitzelmann, E. Inductive programming: A survey of program synthesis techniques. In AAIP, Springer, 2010, 50--73.Google Scholar
- Kitzelmann, E. and Schmid, U. Inductive synthesis of functional programs: An explanation based generalization approach. J. Machine Learning Research 7, (Feb. 2006), 429--454. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Lau, T.A. Why programming-by-demonstration systems fail: Lessons learned for usable AI. AI Mag. 30, 4, (2009), 65--67.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- Le, V. and Gulwani, S. FlashExtract: A framework for data extraction by examples. In Proceedings of PLDI, 2014. Google ScholarDigital Library
- Lieberman, H. (Ed). Your Wish is My Command: Programming by Example. Morgan Kaufmann, 2001.Google ScholarDigital Library
- Lin, D., Dechter, E., Ellis, K., Tenenbaum, J.B. and Muggleton, S.H. Bias reformulation for one-shot function induction. In Proceedings of ECAI, 2014.Google Scholar
- Marcus, G.F. The Algebraic Mind. Integrating Connectionism and Cognitive Science. Bradford, Cambridge, MA, 2001.Google ScholarCross Ref
- 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.Google Scholar
- Menon, A., Tamuz, O., Gulwani, S., Lampson, B. and Kalai, A. A machine learning framework for programming by example. In Proceedings of the ICML, 2013.Google Scholar
- Miller, R.C. and Myers, B.A. Multiple selections in smart text editing. In Proceedings of IUI, 2002, 103--110. Google ScholarDigital Library
- Muggleton, S.H. Inductive Logic Programming. New Generation Computing 8, 4 (1991), 295--318. Google ScholarDigital Library
- Muggleton, S.H. and Lin, D. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited. IJCAI 2013, 1551--1557. Google ScholarDigital Library
- Muggleton, S.H., Lin, D., Pahlavi, N. and Tamaddoni-Nezhad, A. Meta-interpretive learning: application to grammatical inference. Machine Learning 94 (2014), 25--49. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Olsson, R. Inductive functional programming using incremental program transformation. Artificial Intelligence 74, 1 (1995), 55--83. Google ScholarDigital Library
- Perelman, D., Gulwani, S., Grossman, D. and Provost, P. Test-driven synthesis. PLDI, 2014. Google ScholarDigital Library
- Raza, M., Gulwani, S. and Milic-Frayling, N. Programming by example using least general generalizations. AAAI, 2014.Google ScholarCross Ref
- Schmid, U. and Kitzelmann, E. Inductive rule learning on the knowledge level. Cognitive Systems Research 12, 3 (2011), 237--248. Google ScholarDigital Library
- Schmid, U. and Wysotzki, F. Induction of recursive program schemes. ECML 1398 LNAI (1998), 214--225. Google ScholarDigital Library
- Shapiro, E.Y. An algorithm that infers theories from facts. IJCAI (1981), 446--451. Google ScholarDigital Library
- Solar-Lezama, A. Program Synthesis by Sketching. Ph.D thesis, UC Berkeley, 2008. Google ScholarDigital Library
- Summers, P.D. A methodology for LISP program construction from examples. JACM 24, 1 (1977), 162--175. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Young, S. Cognitive user interfaces. IEEE Signal Processing 27, 3 (2010), 128--140.Google ScholarCross Ref
Index Terms
- Inductive programming meets the real world
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