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A survey paper analyzing over 50 neurosymbolic AI systems, comparing Datalog, answer set programming, and probabilistic logic programs across semantics, expressiveness, neural integration, and evaluation, with a decision matrix and open-problem roadmap for choosing rule-based languages.
Researchers at Chalmers University of Technology developed a closed-loop AI scientist that generates biological hypotheses, designs and runs experiments via lab robots, and learns from the results, testing it on yeast by producing 1,933 candidate hypotheses.
This paper introduces Inductive Logic Programming to extract symbolic representations of RL policies and proposes novel explainability metrics (activation rate, feature coverage, syntactic and semantic distance) for objective evaluation in single- and multi-agent settings.
This paper introduces a two-stage neuro-symbolic framework that uses weak supervision (as little as 1% labels) with a slot-based VAE to learn interpretable symbols for object-centric visual reasoning, outperforming foundation models in domain generalization.
This paper introduces gammaILP, a fully differentiable framework for learning first-order rules directly from image data without label leakage, addressing challenges in symbol grounding and predicate invention.
This paper introduces the Neural Rule Inducer (NRI), a foundation model for zero-shot logical rule induction that uses domain-agnostic statistical properties to generalize across tasks without retraining.