Towards meta-interpretive learning of programming language semantics

We introduce a new application for inductive logic programming: learning the semantics of programming languages from example evaluations. In this short paper, we explored a simplified task in this domain using the Metagol meta-interpretive learning system. We highlighted the challenging aspects of this scenario, including abstracting over function symbols, nonterminating examples, and learning non-observed predicates, and proposed extensions to Metagol helpful for overcoming these challenges, which may prove useful in other domains.Comment: ILP 2019, to appea

Sketched Answer Set Programming

Answer Set Programming (ASP) is a powerful modeling formalism for combinatorial problems. However, writing ASP models is not trivial. We propose a novel method, called Sketched Answer Set Programming (SkASP), aiming at supporting the user in resolving this issue. The user writes an ASP program while marking uncertain parts open with question marks. In addition, the user provides a number of positive and negative examples of the desired program behaviour. The sketched model is rewritten into another ASP program, which is solved by traditional methods. As a result, the user obtains a functional and reusable ASP program modelling her problem. We evaluate our approach on 21 well known puzzles and combinatorial problems inspired by Karp's 21 NP-complete problems and demonstrate a use-case for a database application based on ASP.Comment: 15 pages, 11 figures; to appear in ICTAI 201

Inductive logic programming at 30

Inductive logic programming (ILP) is a form of logic-based machine learning. The goal of ILP is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we survey recent work in the field. In this survey, we focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs that generalise from few examples, (iii) new approaches for predicate invention, and (iv) the use of different technologies, notably answer set programming and neural networks. We conclude by discussing some of the current limitations of ILP and discuss directions for future research.Comment: Extension of IJCAI20 survey paper. arXiv admin note: substantial text overlap with arXiv:2002.11002, arXiv:2008.0791

Inductive programming meets the real world

© 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

Inductive logic programming at 30: a new introduction

Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.Comment: Paper under revie