skip to main content
research-article
Open access

Languages with Decidable Learning: A Meta-theorem

Published: 06 April 2023 Publication History

Abstract

We study expression learning problems with syntactic restrictions and introduce the class of finite-aspect checkable languages to characterize symbolic languages that admit decidable learning. The semantics of such languages can be defined using a bounded amount of auxiliary information that is independent of expression size but depends on a fixed structure over which evaluation occurs. We introduce a generic programming language for writing programs that evaluate expression syntax trees, and we give a meta-theorem that connects such programs for finite-aspect checkable languages to finite tree automata, which allows us to derive new decidable learning results and decision procedures for several expression learning problems by writing programs in the programming language.

References

[1]
Rajeev Alur, Rastislav Bodík, Eric Dallal, Dana Fisman, Pranav Garg, Garvit Juniwal, Hadas Kress-Gazit, P. Madhusudan, Milo M. K. Martin, Mukund Raghothaman, Shambwaditya Saha, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, and Abhishek Udupa. 2015. Syntax-Guided Synthesis. In Dependable Software Systems Engineering (NATO Science for Peace and Security Series, D: Information and Communication Security, Vol. 40). IOS Press, 1–25.
[2]
Angello Astorga, P. Madhusudan, Shambwaditya Saha, Shiyu Wang, and Tao Xie. 2019. Learning Stateful Preconditions modulo a Test Generator. In Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2019). Association for Computing Machinery, New York, NY, USA. 775–787. isbn:9781450367127 https://doi.org/10.1145/3314221.3314641
[3]
Angello Astorga, Shambwaditya Saha, Ahmad Dinkins, Felicia Wang, P. Madhusudan, and Tao Xie. 2021. Synthesizing Contracts Correct modulo a Test Generator. Proc. ACM Program. Lang., 5, OOPSLA (2021), Article 104, oct, 27 pages. https://doi.org/10.1145/3485481
[4]
Patrick Blackburn, Maarten de Rijke, and Yde Venema. 2001. Modal Logic. Cambridge University Press. https://doi.org/10.1017/CBO9781107050884
[5]
Manuel Bodirsky. 2021. Complexity of Infinite-Domain Constraint Satisfaction. Cambridge University Press. https://doi.org/10.1017/9781107337534
[6]
J. Richard Büchi. 1990. On a Decision Method in Restricted Second Order Arithmetic. Springer New York, New York, NY. 425–435. isbn:978-1-4613-8928-6 https://doi.org/10.1007/978-1-4613-8928-6_23
[7]
J. Richard Buchi and Lawrence H. Landweber. 1969. Solving Sequential Conditions by Finite-State Strategies. Trans. Amer. Math. Soc., 138 (1969), 295–311. issn:00029947 http://www.jstor.org/stable/1994916
[8]
J. Richard Büchi. 1960. Weak Second-Order Arithmetic and Finite Automata. Mathematical Logic Quarterly, 6, 1-6 (1960), 66–92. https://doi.org/10.1002/malq.19600060105 arxiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/malq.19600060105.
[9]
Thierry Cachat. 2002. Two-Way Tree Automata Solving Pushdown Games. Springer-Verlag, Berlin, Heidelberg. 303–317. isbn:3540003886
[10]
José Cambronero, Sumit Gulwani, Vu Le, Daniel Perelman, Arjun Radhakrishna, Clint Simon, and Ashish Tiwari. 2023. FlashFill++: Scaling Programming by Example by Cutting to the Chase. In Principles of Programming Languages. ACM. https://www.microsoft.com/en-us/research/publication/flashfill-scaling-programming-by-example-by-cutting-to-the-chase/
[11]
Alonzo Church. 1963. Application of Recursive Arithmetic to the Problem of Circuit Synthesis. Journal of Symbolic Logic, 28, 4 (1963), 289–290. https://doi.org/10.2307/2271310
[12]
H. Comon, M. Dauchet, R. Gilleron, C. Löding, F. Jacquemard, D. Lugiez, S. Tison, and M. Tommasi. 2007. Tree Automata Techniques and Applications. Available on: http://www.grappa.univ-lille3.fr/tata. release October, 12th 2007.
[13]
Bruno Courcelle. 1990. The monadic second-order logic of graphs. I. Recognizable sets of finite graphs. Information and Computation, 85, 1 (1990), 12–75. issn:0890-5401 https://doi.org/10.1016/0890-5401(90)90043-H
[14]
Professor Bruno Courcelle and Dr Joost Engelfriet. 2012. Graph Structure and Monadic Second-Order Logic: A Language-Theoretic Approach (1st ed.). Cambridge University Press, New York, NY, USA. isbn:0521898331, 9780521898331
[15]
John Doner. 1970. Tree acceptors and some of their applications. J. Comput. System Sci., 4, 5 (1970), 406–451. issn:0022-0000 https://doi.org/10.1016/S0022-0000(70)80041-1
[16]
Calvin C. Elgot. 1961. Decision Problems of Finite Automata Design and Related Arithmetics. Trans. Amer. Math. Soc., 98, 1 (1961), 21–51. issn:00029947 http://www.jstor.org/stable/1993511
[17]
Javier Esparza, Orna Kupferman, and Moshe Y. Vardi. 2021. Verification. In Handbook of Automata Theory, Jean-Éric Pin (Ed.). European Mathematical Society Publishing House, Zürich, Switzerland, 1415–1456.
[18]
Richard Evans and Edward Grefenstette. 2018. Learning Explanatory Rules from Noisy Data. J. Artif. Int. Res., 61, 1 (2018), Jan., 1–64. issn:1076-9757
[19]
Azadeh Farzan, Danya Lette, and Victor Nicolet. 2022. Recursion Synthesis with Unrealizability Witnesses. In Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI 2022). Association for Computing Machinery, New York, NY, USA. 244–259. isbn:9781450392655 https://doi.org/10.1145/3519939.3523726
[20]
Henning Fernau. 2009. Algorithms for learning regular expressions from positive data. Information and Computation, 207, 4 (2009), 521–541. issn:0890-5401 https://doi.org/10.1016/j.ic.2008.12.008
[21]
J. Flum and M. Grohe. 2006. Parameterized Complexity Theory (Texts in Theoretical Computer Science. An EATCS Series). Springer-Verlag, Berlin, Heidelberg. isbn:3540299521 https://doi.org/10.1007/3-540-29953-X
[22]
Maurice Funk, Jean Christoph Jung, Carsten Lutz, Hadrien Pulcini, and Frank Wolter. 2019. Learning Description Logic Concepts: When can Positive and Negative Examples be Separated? In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, 1682–1688. https://doi.org/10.24963/ijcai.2019/233
[23]
Pranav Garg, Christof Löding, P. Madhusudan, and Daniel Neider. 2014. ICE: A Robust Framework for Learning Invariants. In Computer Aided Verification, Armin Biere and Roderick Bloem (Eds.). Springer International Publishing, Cham. 69–87. isbn:978-3-319-08867-9
[24]
Pranav Garg, Christof Löding, P. Madhusudan, and Daniel Neider. 2015. Quantified data automata for linear data structures: a register automaton model with applications to learning invariants of programs manipulating arrays and lists. Formal Methods in System Design, 47, 1 (2015), 01 Aug, 120–157. issn:1572-8102 https://doi.org/10.1007/s10703-015-0231-6
[25]
2002. Automata Logics, and Infinite Games: A Guide to Current Research, Erich Grädel, Wolfgang Thomas, and Thomas Wilke (Eds.). Springer-Verlag, Berlin, Heidelberg. isbn:3540003886
[26]
Sumit Gulwani. 2011. Automating String Processing in Spreadsheets Using Input-Output Examples. In Proceedings of the 38th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL ’11). Association for Computing Machinery, New York, NY, USA. 317–330. isbn:9781450304900 https://doi.org/10.1145/1926385.1926423
[27]
Annegret Habel. 1992. Graph-theoretic aspects of HRL’s. Springer Berlin Heidelberg, Berlin, Heidelberg. 117–144. isbn:978-3-540-47340-4 https://doi.org/10.1007/BFb0013882
[28]
Travis Hance, Marijn Heule, Ruben Martins, and Bryan Parno. 2021. Finding Invariants of Distributed Systems: It’ s a Small (Enough) World After All. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21). USENIX Association, 115–131. isbn:978-1-939133-21-2 https://www.usenix.org/conference/nsdi21/presentation/hance
[29]
Shivam Handa and Martin C. Rinard. 2020. Inductive Program Synthesis over Noisy Data. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020). Association for Computing Machinery, New York, NY, USA. 87–98. isbn:9781450370431 https://doi.org/10.1145/3368089.3409732
[30]
Wilfrid Hodges. 1993. The countable case. Cambridge University Press, 323–359. https://doi.org/10.1017/CBO9780511551574.009
[31]
Qinheping Hu, John Cyphert, Loris D’Antoni, and Thomas Reps. 2020. Exact and Approximate Methods for Proving Unrealizability of Syntax-Guided Synthesis Problems. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020). Association for Computing Machinery, New York, NY, USA. 1128–1142. isbn:9781450376136 https://doi.org/10.1145/3385412.3385979
[32]
Radoslav Ivanov, Kishor Jothimurugan, Steve Hsu, Shaan Vaidya, Rajeev Alur, and Osbert Bastani. 2021. Compositional Learning and Verification of Neural Network Controllers. ACM Trans. Embed. Comput. Syst., 20, 5s (2021), Article 92, sep, 26 pages. issn:1539-9087 https://doi.org/10.1145/3477023
[33]
Michael J. Kearns and Umesh Vazirani. 1994. An Introduction to Computational Learning Theory. The MIT Press. isbn:9780262276863 https://doi.org/10.7551/mitpress/3897.001.0001
[34]
Jason R. Koenig, Oded Padon, Neil Immerman, and Alex Aiken. 2020. First-Order Quantified Separators. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020). Association for Computing Machinery, New York, NY, USA. 703–717. isbn:9781450376136 https://doi.org/10.1145/3385412.3386018
[35]
Jason R. Koenig, Oded Padon, Sharon Shoham, and Alex Aiken. 2022. Inferring Invariants with Quantifier Alternations: Taming the Search Space Explosion. In Tools and Algorithms for the Construction and Analysis of Systems, Dana Fisman and Grigore Rosu (Eds.). Springer International Publishing, Cham. 338–356. isbn:978-3-030-99524-9
[36]
James Koppel. 2021. Version Space Algebras are Acyclic Tree Automata. https://doi.org/10.48550/ARXIV.2107.12568
[37]
James Koppel, Zheng Guo, Edsko de Vries, Armando Solar-Lezama, and Nadia Polikarpova. 2022. Searching Entangled Program Spaces. Proc. ACM Program. Lang., 6, ICFP (2022), Article 91, aug, 29 pages. https://doi.org/10.1145/3547622
[38]
Paul Krogmeier and P. Madhusudan. 2022. Learning Formulas in Finite Variable Logics. Proc. ACM Program. Lang., 6, POPL (2022), Article 10, jan, 28 pages. https://doi.org/10.1145/3498671
[39]
Paul Krogmeier and P. Madhusudan. 2023. Languages With Decidable Learning: A Meta-Theorem. https://doi.org/10.48550/ARXIV.2302.05741
[40]
Paul Krogmeier, Umang Mathur, Adithya Murali, P. Madhusudan, and Mahesh Viswanathan. 2020. Decidable Synthesis of Programs with Uninterpreted Functions. In Computer Aided Verification, Shuvendu K. Lahiri and Chao Wang (Eds.). Springer International Publishing, Cham. 634–657. isbn:978-3-030-53291-8
[41]
Orna Kupferman, P. Madhusudan, P. S. Thiagarajan, and Moshe Y. Vardi. 2000. Open Systems in Reactive Environments: Control and Synthesis. In CONCUR (Lecture Notes in Computer Science, Vol. 1877). Springer, 92–107.
[42]
Orna Kupferman, Nir Piterman, and Moshe Y. Vardi. 2010. An Automata-Theoretic Approach to Infinite-State Systems. Springer Berlin Heidelberg, Berlin, Heidelberg. 202–259. isbn:978-3-642-13754-9 https://doi.org/10.1007/978-3-642-13754-9_11
[43]
Pat Langley and Sean Stromsten. 2000. Learning Context-Free Grammars with a Simplicity Bias. In Machine Learning: ECML 2000, Ramon López de Mántaras and Enric Plaza (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg. 220–228. isbn:978-3-540-45164-8
[44]
Yunyao Li, Rajasekar Krishnamurthy, Sriram Raghavan, Shivakumar Vaithyanathan, and H. V. Jagadish. 2008. Regular Expression Learning for Information Extraction. In EMNLP.
[45]
P. Madhusudan. 2011. Synthesizing Reactive Programs. In Computer Science Logic (CSL’11) - 25th International Workshop/20th Annual Conference of the EACSL, Marc Bezem (Ed.) (Leibniz International Proceedings in Informatics (LIPIcs), Vol. 12). Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany. 428–442. isbn:978-3-939897-32-3 issn:1868-8969 https://doi.org/10.4230/LIPIcs.CSL.2011.428
[46]
Kenneth L. McMillan. 1992. Symbolic Model Checking: an approach to the state explosion problem. Ph.D. Dissertation. Carnegie Mellon. thesis.pdf CMU Tech Rpt. CMU-CS-92-131.
[47]
Anders Miltner, Adrian Trejo Nuñez, Ana Brendel, Swarat Chaudhuri, and Isil Dillig. 2022. Bottom-up Synthesis of Recursive Functional Programs Using Angelic Execution. Proc. ACM Program. Lang., 6, POPL (2022), Article 21, jan, 29 pages. https://doi.org/10.1145/3498682
[48]
Anders Miltner, Saswat Padhi, Todd Millstein, and David Walker. 2020. Data-Driven Inference of Representation Invariants. In Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2020). Association for Computing Machinery, New York, NY, USA. 1–15. isbn:9781450376136 https://doi.org/10.1145/3385412.3385967
[49]
Tom M. Mitchell. 1982. Generalization as search. Artificial Intelligence, 18, 2 (1982), 203–226. issn:0004-3702 https://doi.org/10.1016/0004-3702(82)90040-6
[50]
Thomas M. Mitchell. 1997. Machine Learning (1 ed.). McGraw-Hill, Inc., USA. isbn:0070428077
[51]
Ulrich Möncke and Reinhard Wilhelm. 1991. Grammar flow analysis. In Attribute Grammars, Applications and Systems, Henk Alblas and Bořivoj Melichar (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg. 151–186. isbn:978-3-540-38490-8
[52]
Stephen H. Muggleton, Dianhuan Lin, Niels Pahlavi, and Alireza Tamaddoni-Nezhad. 2014. Meta-interpretive learning: application to grammatical inference. Machine Learning, 94, 1 (2014), 01 Jan, 25–49. issn:1573-0565 https://doi.org/10.1007/s10994-013-5358-3
[53]
Daniel Neider and Ivan Gavran. 2018. Learning Linear Temporal Properties. In 2018 Formal Methods in Computer Aided Design (FMCAD). 1–10. https://doi.org/10.23919/FMCAD.2018.8603016
[54]
Daniel Neider, P. Madhusudan, Shambwaditya Saha, Pranav Garg, and Daejun Park. 2020. A Learning-Based Approach to Synthesizing Invariants for Incomplete Verification Engines. Journal of Automated Reasoning, 64, 7 (2020), 01 Oct, 1523–1552. issn:1573-0670 https://doi.org/10.1007/s10817-020-09570-z
[55]
Peter-Michael Osera and Steve Zdancewic. 2015. Type-and-Example-Directed Program Synthesis. In Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’15). Association for Computing Machinery, New York, NY, USA. 619–630. isbn:9781450334686 https://doi.org/10.1145/2737924.2738007
[56]
Amir Pnueli. 1977. The temporal logic of programs. In 18th Annual Symposium on Foundations of Computer Science (sfcs 1977). 46–57. https://doi.org/10.1109/SFCS.1977.32
[57]
Amir Pnueli and Roni Rosner. 1989. On the Synthesis of a Reactive Module. In POPL. ACM Press, 179–190.
[58]
Amir Pnueli and Roni Rosner. 1990. Distributed Reactive Systems Are Hard to Synthesize. In FOCS. IEEE Computer Society, 746–757.
[59]
Nadia Polikarpova, Ivan Kuraj, and Armando Solar-Lezama. 2016. Program Synthesis from Polymorphic Refinement Types. In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’16). Association for Computing Machinery, New York, NY, USA. 522–538. isbn:9781450342612 https://doi.org/10.1145/2908080.2908093
[60]
Oleksandr Polozov and Sumit Gulwani. 2015. FlashMeta: A Framework for Inductive Program Synthesis. In Proceedings of the 2015 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2015). Association for Computing Machinery, New York, NY, USA. 107–126. isbn:9781450336895 https://doi.org/10.1145/2814270.2814310
[61]
Michael O. Rabin. 1969. Decidability of Second-Order Theories and Automata on Infinite Trees. Trans. Amer. Math. Soc., 141 (1969), 1–35. issn:00029947 http://www.jstor.org/stable/1995086
[62]
Michael Oser Rabin. 1972. Automata on Infinite Objects and Church’s Problem. American Mathematical Society, Boston, MA, USA. isbn:0821816632
[63]
Yasubumi Sakakibara. 2005. Learning context-free grammars using tabular representations. Pattern Recognition, 38, 9 (2005), 1372–1383. issn:0031-3203 https://doi.org/10.1016/j.patcog.2004.03.021 Grammatical Inference.
[64]
Armando Solar-Lezama, Liviu Tancau, Rastislav Bodik, Sanjit Seshia, and Vijay Saraswat. 2006. Combinatorial Sketching for Finite Programs. In Proceedings of the 12th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS XII). Association for Computing Machinery, New York, NY, USA. 404–415. isbn:1595934510 https://doi.org/10.1145/1168857.1168907
[65]
James W. Thatcher and Jesse B. Wright. 1968. Generalized finite automata theory with an application to a decision problem of second-order logic. Mathematical systems theory, 2 (1968), 57–81. https://doi.org/10.1007/BF01691346
[66]
Boris A. Trakhtenbrot. 1961. Finite automata and logic of monadic predicates. Doklady Akademii Nauk SSSR, 140, 326-329 (1961), 122–123.
[67]
Steffen van Bergerem. 2019. Learning Concepts Definable in First-Order Logic with Counting. In 2019 34th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS). 1–13. https://doi.org/10.1109/LICS.2019.8785811
[68]
Steffen van Bergerem, Martin Grohe, and Martin Ritzert. 2022. On the Parameterized Complexity of Learning First-Order Logic. In Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS ’22). Association for Computing Machinery, New York, NY, USA. 337–346. isbn:9781450392600 https://doi.org/10.1145/3517804.3524151
[69]
Kurt Vanlehn and William Ball. 1987. A Version Space Approach to Learning Context-free Grammars. Machine Learning, 2, 1 (1987), 01 Mar, 39–74. issn:1573-0565 https://doi.org/10.1023/A:1022812926936
[70]
Moshe Y. Vardi. 1998. Reasoning about the past with two-way automata. In Automata, Languages and Programming, Kim G. Larsen, Sven Skyum, and Glynn Winskel (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg. 628–641. isbn:978-3-540-68681-1
[71]
Xinyu Wang, Isil Dillig, and Rishabh Singh. 2017. Program Synthesis Using Abstraction Refinement. Proc. ACM Program. Lang., 2, POPL (2017), Article 63, Dec., 30 pages. issn:2475-1421 https://doi.org/10.1145/3158151
[72]
Xinyu Wang, Isil Dillig, and Rishabh Singh. 2017. Synthesis of Data Completion Scripts Using Finite Tree Automata. Proc. ACM Program. Lang., 1, OOPSLA (2017), Article 62, Oct., 26 pages. https://doi.org/10.1145/3133886
[73]
Yuepeng Wang, Xinyu Wang, and Isil Dillig. 2018. Relational Program Synthesis. Proc. ACM Program. Lang., 2, OOPSLA (2018), Article 155, Oct., 27 pages. issn:2475-1421 https://doi.org/10.1145/3276525
[74]
Jianan Yao, Runzhou Tao, Ronghui Gu, Jason Nieh, Suman Jana, and Gabriel Ryan. 2021. DistAI: Data-Driven Automated Invariant Learning for Distributed Protocols. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). USENIX Association, 405–421. isbn:978-1-939133-22-9 https://www.usenix.org/conference/osdi21/presentation/yao
[75]
He Zhu, Stephen Magill, and Suresh Jagannathan. 2018. A Data-Driven CHC Solver. In Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2018). Association for Computing Machinery, New York, NY, USA. 707–721. isbn:9781450356985 https://doi.org/10.1145/3192366.3192416

Cited By

View all

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Programming Languages
Proceedings of the ACM on Programming Languages  Volume 7, Issue OOPSLA1
April 2023
901 pages
EISSN:2475-1421
DOI:10.1145/3554309
Issue’s Table of Contents
This work is licensed under a Creative Commons Attribution 4.0 International License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 April 2023
Published in PACMPL Volume 7, Issue OOPSLA1

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. exact learning
  2. interpretable learning
  3. learning symbolic languages
  4. program synthesis
  5. tree automata
  6. version space algebra

Qualifiers

  • Research-article

Funding Sources

  • Amazon Inc.
  • Discovery Partners Institute

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)234
  • Downloads (Last 6 weeks)37
Reflects downloads up to 06 Jan 2025

Other Metrics

Citations

Cited By

View all

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Full Access

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media