Abstract
Real world data are often noisy and fuzzy. Most traditional logical machine learning methods require the data to be first discretized or pre-processed before being able to produce useful output. Such short-coming often limits their application to real world data. On the other hand, neural networks are generally known to be robust against noisy data. However, a fully trained neural network does not provide easily understandable rules that can be used to understand the underlying model. In this paper, we propose a Differentiable Learning from Interpretation Transition (\(\delta \)-LFIT) algorithm, that can simultaneously output logic programs fully explaining the state transitions, and also learn from data containing noise and error.
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References
Abadi, M., Agarwal, A., Barham, P., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/, software available from tensorflow.org
Evans, R., Grefenstette, E.: Learning explanatory rules from noisy data. CoRR abs/1711.04574 (2017). http://arxiv.org/abs/1711.04574
d’Avila Garcez, A.S., Broda, K., Gabbay, D.M.: Symbolic knowledge extraction from trained neural networks: a sound approach. Artif. Intell. 125(1), 155–207 (2001)
d’Avila Garcez, A.S., Zaverucha, G.: The connectionist inductive learning and logic programming system. Appl. Intell. 11(1), 59–77 (1999)
Gentet, E., Tourret, S., Inoue, K.: Learning from interpretation transition using feed-forward neural network. In: Proceedings of ILP 2016, CEUR Proceedings 1865, pp. 27–33 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Inoue, K., Ribeiro, T., Sakama, C.: Learning from interpretation transition. Mach. Learn. 94(1), 51–79 (2013). https://doi.org/10.1007/s10994-013-5353-8
Martínez, D., Alenyà, G., Ribeiro, T., Inoue, K., Torras, C.: Relational reinforcement learning for planning with exogenous effects. J. Mach. Learn. Res. 18(78), 1–44 (2017). http://jmlr.org/papers/v18/16-326.html
Ribeiro, T., Magnin, M., Inoue, K., Sakama, C.: Learning multi-valued biological models with delayed influence from time-series observations. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 25–31, December 2015. https://doi.org/10.1109/ICMLA.2015.19.
Ribeiro, T., Inoue, K.: Learning prime implicant conditions from interpretation transition. In: Davis, J., Ramon, J. (eds.) ILP 2014. LNCS (LNAI), vol. 9046, pp. 108–125. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23708-4_8
Rolnick, D., Veit, A., Belongie, S., Shavit, N.: Deep learning is robust to massive label noise (2018). https://openreview.net/forum?id=B1p461b0W
Seltzer, M.L., Yu, D., Wang, Y.: An investigation of deep neural networks for noise robust speech recognition. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7398–7402. IEEE (2013)
Streck, A., Siebert, H., Klarner, H.: PyBoolNet: a python package for the generation, analysis and visualization of boolean networks. Bioinformatics 33(5), 770–772 (2016). https://doi.org/10.1093/bioinformatics/btw682
Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. CoRR abs/1707.02968 (2017). http://arxiv.org/abs/1707.02968
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Phua, Y.J., Inoue, K. (2020). Learning Logic Programs from Noisy State Transition Data. In: Kazakov, D., Erten, C. (eds) Inductive Logic Programming. ILP 2019. Lecture Notes in Computer Science(), vol 11770. Springer, Cham. https://doi.org/10.1007/978-3-030-49210-6_7
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