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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