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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13703))

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Abstract

The field of machine learning focuses on computationally efficient, yet approximate algorithms. On the contrary, the field of formal methods focuses on mathematical rigor and provable correctness. Despite their superficial differences, both fields offer mutual benefit. Formal methods offer methods to verify and explain machine learning systems, aiding their adoption in safety critical domains. Machine learning offers approximate, computationally efficient approaches that let formal methods scale to larger problems. This paper gives an introduction to the track “Formal Methods Meets Machine Learning” (F3ML) and shortly presents its scientific contributions, structured into two thematic subthemes: One, concerning formal methods based approaches for the explanation and verification of machine learning systems, and one concerning the employment of machine learning approaches to scale formal methods.

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References

  1. Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319–342 (1988)

    Article  MathSciNet  Google Scholar 

  2. Athalye, A., Engstrom, L., Ilyas, A., Kwok, K.: Synthesizing robust adversarial examples. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 80, pp. 284–293. PMLR (2018). https://proceedings.mlr.press/v80/athalye18b.html

  3. Bahar, R.I., et al.: Algebric decision diagrams and their applications. Formal Methods Syst. Des. 10(2), 171–206 (1997)

    Article  Google Scholar 

  4. Barrett, C., Tinelli, C.: Satisfiability modulo theories. In: Clarke, E., Henzinger, T., Veith, H., Bloem, R. (eds.) Handbook of Model Checking, pp. 305–343. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-10575-8_11

    Chapter  Google Scholar 

  5. Boyer, R.S., Elspas, B., Levitt, K.N.: Select-a formal system for testing and debugging programs by symbolic execution. ACM SigPlan Not. 10(6), 234–245 (1975)

    Article  Google Scholar 

  6. Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)

    Google Scholar 

  7. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)

  8. Clarke, E.M.: Model checking. In: Ramesh, S., Sivakumar, G. (eds.) FSTTCS 1997. LNCS, vol. 1346, pp. 54–56. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0058022

    Chapter  Google Scholar 

  9. Cordy, M., et al.: A decade of featured transition systems. In: ter Beek, M.H., Fantechi, A., Semini, L. (eds.) From Software Engineering to Formal Methods and Tools, and Back. LNCS, vol. 11865, pp. 285–312. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30985-5_18

    Chapter  Google Scholar 

  10. Doran, D., Schulz, S., Besold, T.R.: What does explainable AI really mean? A new conceptualization of perspectives. arXiv preprint arXiv:1710.00794 (2017)

  11. Došilović, F.K., Brčić, M., Hlupić, N.: Explainable artificial intelligence: a survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215. IEEE (2018)

    Google Scholar 

  12. Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  13. Everett, G.D., McLeod Jr., R.: Software testing. Testing Across the Entire (2007)

    Google Scholar 

  14. Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)

  15. Gossen, F., Steffen, B.: Algebraic aggregation of random forests: towards explainability and rapid evaluation. Int. J. Softw. Tools Technol. Transf. (2021). https://doi.org/10.1007/s10009-021-00635-x

    Article  Google Scholar 

  16. Gros, T.P., Hermanns, H., Hoffmann, J., Klauck, M., Steinmetz, M.: Deep statistical model checking. In: Gotsman, A., Sokolova, A. (eds.) FORTE 2020. LNCS, vol. 12136, pp. 96–114. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50086-3_6

    Chapter  Google Scholar 

  17. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5) (2018). https://doi.org/10.1145/3236009

  18. Hartmanns, A., Klauck, M.: The modest state of learning, sampling, and verifying strategies. In: Margaria, T., Steffen, B. (eds.) ISoLA 2022. LNCS, vol. 13703, pp. 406–432. Springer, Cham (2022)

    Google Scholar 

  19. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  20. Jegourel, C., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B., Sedwards, S.: Importance sampling for stochastic timed automata. In: Fränzle, M., Kapur, D., Zhan, N. (eds.) SETTA 2016. LNCS, vol. 9984, pp. 163–178. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47677-3_11

    Chapter  Google Scholar 

  21. Jegourel, C., Legay, A., Sedwards, S.: Importance splitting for statistical model checking rare properties. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 576–591. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39799-8_38

    Chapter  Google Scholar 

  22. Jüngermann, F., Kretínský, J., Weininger, M.: Algebraically explainable controllers: decision trees and support vector machines join forces. Int. J. Softw. Tools Technol. Transf. (2022, to appear)

    Google Scholar 

  23. Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

  24. Khmelnitsky, I., et al.: Analysis of recurrent neural networks via property-directed verification of surrogate models. Int. J. Softw. Tools Technol. Transf. (2022, to appear)

    Google Scholar 

  25. King, J.C.: Symbolic execution and program testing. Commun. ACM 19(7), 385–394 (1976)

    Article  MathSciNet  Google Scholar 

  26. Kohli, P., Chadha, A.: Enabling pedestrian safety using computer vision techniques: a case study of the 2018 Uber Inc. self-driving car crash. In: Arai, K., Bhatia, R. (eds.) FICC 2019. LNNS, vol. 69, pp. 261–279. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-12388-8_19

    Chapter  Google Scholar 

  27. Larsen, K.G., Legay, A., Mikučionis, M., Poulse, D.B.: Importance splitting in uppaal. In: Margaria, T., Steffen, B. (eds.) ISoLA 2022. LNCS, vol. 13703, pp. 433–447. Springer, Cham (2022)

    Google Scholar 

  28. Lazreg, S., Cordy, M., Legay, A.: Verification of variability-intensive stochastic systems with statistical model checking. In: Margaria, T., Steffen, B. (eds.) ISoLA 2022. LNCS, vol. 13703, pp. 448–471. Springer, Cham (2022)

    Google Scholar 

  29. Legay, A., Delahaye, B., Bensalem, S.: Statistical model checking: an overview. In: Barringer, H., et al. (eds.) RV 2010. LNCS, vol. 6418, pp. 122–135. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16612-9_11

    Chapter  Google Scholar 

  30. Legay, A., Lukina, A., Traonouez, L.M., Yang, J., Smolka, S.A., Grosu, R.: Statistical model checking. In: Steffen, B., Woeginger, G. (eds.) Computing and Software Science. LNCS, vol. 10000, pp. 478–504. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91908-9_23

    Chapter  Google Scholar 

  31. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. (CSUR) 54(6), 1–35 (2021)

    Article  Google Scholar 

  32. Müller-Olm, M., Schmidt, D., Steffen, B.: Model-checking. In: Cortesi, A., Filé, G. (eds.) SAS 1999. LNCS, vol. 1694, pp. 330–354. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48294-6_22

    Chapter  Google Scholar 

  33. Murthy, S.K.: Automatic construction of decision trees from data: a multi-disciplinary survey. Data Min. Knowl. Discov. 2(4), 345–389 (1998)

    Google Scholar 

  34. Murtovi, A., Bainczyk, A., Nolte, G., Schlüter, M., Bernhard, S.: Forest gump: a tool for veriification and explanation. Int. J. Softw. Tools Technol. Transf. (2022, to appear)

    Google Scholar 

  35. Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24(12), 1565–1567 (2006)

    Article  Google Scholar 

  36. Rao, Q., Frtunikj, J.: Deep learning for self-driving cars: chances and challenges. In: Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, pp. 35–38 (2018)

    Google Scholar 

  37. Rodrigues, G.N., et al.: Modeling and verification for probabilistic properties in software product lines. In: 16th IEEE International Symposium on High Assurance Systems Engineering, HASE 2015, Daytona Beach, FL, USA, 8–10, January 2015, pp. 173–180. IEEE Computer Society (2015). https://doi.org/10.1109/HASE.2015.34

  38. Schlüter, M., Nolte, G., Steffen, B.: Towards rigorous understanding of neural networks via semantics preserving transformation. Int. J. Softw. Tools Technol. Transf. (2022, to appear)

    Google Scholar 

  39. Silver, D., et al.: Mastering the game of go without human knowledge. Nature 550(7676), 354–359 (2017)

    Google Scholar 

  40. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  41. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)

    Google Scholar 

  42. Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)

  43. Usman, M., Sun, Y., Gopinath, D., Dange, R., Manolache, L., Pasareanu, C.: An overview of structural coverage metrics for testing neural networks. Int. J. Softw. Tools Technol. Transf. (2022, to appear)

    Google Scholar 

  44. Vardi, M.Y., Wolper, P.: An automata-theoretic approach to automatic program verification. In: 1st Symposium in Logic in Computer Science (LICS). IEEE Computer Society (1986)

    Google Scholar 

  45. Woodcock, J., Larsen, P.G., Bicarregui, J., Fitzgerald, J.: Formal methods: practice and experience. ACM Comput. Surv. (CSUR) 41(4), 1–36 (2009)

    Article  Google Scholar 

  46. Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., Zhu, J.: Explainable AI: a brief survey on history, research areas, approaches and challenges. In: Tang, J., Kan, M.-Y., Zhao, D., Li, S., Zan, H. (eds.) NLPCC 2019. LNCS (LNAI), vol. 11839, pp. 563–574. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32236-6_51

    Chapter  Google Scholar 

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Acknowledgements

As organisers of the track, we would like to thank all authors for their contributions. We would also like to thank all reviewers for their insights and helpful comments and all participants of the track for asking interesting questions, giving constructive comments and partaking in lively discussions.

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Correspondence to Maximilian Schlüter .

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Larsen, K., Legay, A., Nolte, G., Schlüter, M., Stoelinga, M., Steffen, B. (2022). Formal Methods Meet Machine Learning (F3ML). In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_24

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