Self-Supervised Inductive Logic Programming

Researchers developed a new self-supervised Inductive Logic Programming approach called Poker that can learn recursive logic programs without requiring expert-crafted negative examples or problem-specific background theories. The system automatically generates and labels new training examples during learning, showing improved performance over existing methods when negative examples are unavailable.