Abstract
Abductive reasoning plays an essential part in day-to-day problem-solving. It has been considered a powerful mechanism for hypothetical reasoning in the presence of incomplete knowledge; a form of “common sense” reasoning. In machine learning, abduction is viewed as a conceptual method in which data and the bond that jointly brings the different types of inference. The traditional Mode-Directed Inverse Entailment (MDIE) based systems such as Progol and Aleph for the abduction were not data-efficient since their execution time with the large dataset was too long. We present a new abductive learning procedure using Meta Inverse Entailment (MIE). MIE is similar to Mode-Directed Inverse Entailment (MDIE) but does not require user-defined mode declarations. In this paper, we use an implementation of MIE in Python called PyGol. We evaluate and compare this approach to reveal the microbial interactions in the ecosystem with state-of-art-of methods for abduction, such as Progol and Aleph. Our results show that PyGol has comparable predictive accuracies but is significantly faster than Progol and Aleph.
ILP 2022, 31st International Conference on Inductive Logic Programming, Cumberland Lodge, Windsor, UK.
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Notes
- 1.
Available from https://github.com/PyGol.
- 2.
Available from https://github.com/danyvarghese/IJCLR22-Abduction.
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Varghese, D., Barroso-Bergada, D., Bohan, D.A., Tamaddoni-Nezhad, A. (2024). Efficient Abductive Learning of Microbial Interactions Using Meta Inverse Entailment. In: Muggleton, S.H., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2022. Lecture Notes in Computer Science(), vol 13779. Springer, Cham. https://doi.org/10.1007/978-3-031-55630-2_10
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