Abstract
An interesting feature that traditional approaches to inductive logic programming are missing is the ability to treat noisy and non-logical data. Neural-symbolic approaches to inductive logic programming have been recently proposed to combine the advantages of inductive logic programming, in terms of interpretability and generalization capability, with the characteristic capacity of deep learning to treat noisy and non-logical data. This paper concisely surveys and briefly compares three promising neural-symbolic approaches to inductive logic programming that have been proposed in the last five years. The considered approaches use Datalog dialects to represent background knowledge, and they are capable of producing reusable logical rules from noisy and non-logical data. Therefore, they provide an effective means to combine logical reasoning with state-of-the-art machine learning.
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References
Calegari, R., Ciatto, G., Omicini, A.: On the integration of symbolic and sub-symbolic techniques for XAI: a survey. Intelligenza Artificiale 14(1), 7–32 (2020)
Cropper, A., Dumančić, S., Evans, R., Muggleton, S.H.: Inductive logic programming at 30. Mach. Learn. 111, 147–172 (2022). https://doi.org/10.1007/s10994-021-06089-1
Dai, W.Z., Muggleton, S.: Abductive knowledge induction from raw data. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), pp. 1845–1851. International Joint Conferences on Artificial Intelligence Organization (2021)
De Raedt, L., Dumančić, S., Manhaeve, R., Marra, G.: From statistical relational to neural-symbolic artificial intelligence. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020), pp. 4943–4950. International Joint Conferences on Artificial Intelligence Organization (2021)
Evans, R., Grefenstette, E.: Learning explanatory rules from noisy data. J. Artif. Intell. Res. 61, 1–64 (2018)
Muggleton, S.: Inductive logic programming. New Gener. Comput. 8(4), 295–318 (1991)
Muggleton, S.H., Lin, D., Tamaddoni-Nezhad, A.: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Mach. Learn. 100(1), 49–73 (2015)
Payani, A., Fekri, F.: Inductive logic programming via differentiable deep neural logic networks. arXiv preprint arXiv:1906.03523 (2019)
Payani, A., Fekri, F.: Learning algorithms via neural logic networks. arXiv preprint arXiv:1904.01554 (2019)
Sarker, M.K., Zhou, L., Eberhart, A., Hitzler, P.: Neuro-symbolic artificial intelligence: current trends. arXiv preprint arXiv:2105.05330 (2021)
Yu, D., Yang, B., Liu, D., Wang, H.: A survey on neural-symbolic systems. arXiv preprint arXiv:2111.08164 (2021)
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Beretta, D., Monica, S., Bergenti, F. (2022). A Comparative Study of Three Neural-Symbolic Approaches to Inductive Logic Programming. In: Gottlob, G., Inclezan, D., Maratea, M. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2022. Lecture Notes in Computer Science(), vol 13416. Springer, Cham. https://doi.org/10.1007/978-3-031-15707-3_5
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DOI: https://doi.org/10.1007/978-3-031-15707-3_5
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