Abstract
Mainstream machine learning methods lack interpretability, explainability, incrementality, and data-economy. We propose using logic programming to rectify these problems. We discuss the FOLD family of rule-based machine learning algorithms that learn models from relational datasets as a set of default rules. These models are competitive with state-of-the-art machine learning systems in terms of accuracy and execution efficiency. We also motivate how logic programming can be useful for theory revision and explanation based learning.
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Acknowledgements
We are grateful to anonymous reviewers and to Bob Kowalski for insightful comments that helped in significantly improving this paper. Authors acknowledge partial support from NSF grants IIS 1910131, IIP 1916206, and US DoD.
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Gupta, G. et al. (2023). Logic-Based Explainable and Incremental Machine Learning. In: Warren, D.S., Dahl, V., Eiter, T., Hermenegildo, M.V., Kowalski, R., Rossi, F. (eds) Prolog: The Next 50 Years. Lecture Notes in Computer Science(), vol 13900. Springer, Cham. https://doi.org/10.1007/978-3-031-35254-6_28
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