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

This paper introduces ANDRE, a novel Attention-based Neuro-symbolic Differentiable Rule Extractor (ILP) framework for learning first-order logic programs. It optimizes over a continuous rule space with fully differentiable, attention-driven logical operators, addressing scalability challenges in noisy and probabilistic settings.