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September 23, 2025Open Access

Self-Supervised Inductive Logic Programming

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Self-supervised ILP improves grammar learning in contexts with few examples, suggesting new methods for background theory use.

Poker learns effectively from positive examples and automatically generates negative examples, enhancing learning performance.
In experiments with grammar learning for context-free and L-System languages, Poker outperformed traditional methods without negative examples.
The new MIL algorithm integrates second-order background theory, eliminating the need for specialized task-specific background theories.
Poker's performance scales with the number of generated examples, contrasting with the over-generalization observed in existing systems.

Stassa Patsantzis (2025) studied this question.

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