#inductive-logic-programming News & Analysis

4 articles tagged with #inductive-logic-programming. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · May 127/10

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Weakly Supervised Concept Learning for Object-centric Visual Reasoning

Researchers present a weakly supervised learning approach that combines neural networks with symbolic AI for object-centric reasoning tasks, requiring only 1% of typical labels while outperforming foundation models in domain generalization. The method bridges perception and logical reasoning by using slot-based architectures and VAEs to ground symbolic outputs for frameworks like Inductive Logic Programming.

AIBullisharXiv – CS AI · May 77/10

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A Foundation Model for Zero-Shot Logical Rule Induction

Researchers introduce Neural Rule Inducer (NRI), a pretrained foundation model enabling zero-shot logical rule induction without task-specific retraining. By encoding domain-agnostic statistical properties instead of literal identities, NRI generalizes across different predicates and demonstrates robustness to label noise and spurious correlations, advancing toward foundation models for symbolic reasoning.

AINeutralarXiv – CS AI · May 76/10

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ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor

ANDRE is a novel neuro-symbolic AI framework that combines deep learning with interpretable logic programming to extract first-order rules from data. The method addresses long-standing scalability and robustness issues in Inductive Logic Programming by using attention-based differentiable operators instead of rigid rule templates or fuzzy approximations.

AINeutralarXiv – CS AI · Mar 54/10

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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.