ANDRE：一种基于注意力的神经符号可微规则提取器，用于归纳逻辑编程
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
AI总结：
提出ANDRE框架，通过注意力驱动的可微逻辑算子优化连续规则空间，实现从概率数据中学习一阶逻辑规则，在噪声环境下保持鲁棒性和可解释性。
摘要：
归纳逻辑编程（ILP）旨在从数据中学习可解释的一阶规则，但现有的符号和神经符号方法难以扩展到噪声和概率设置。经典ILP依赖于离散的组合规则搜索，在不确定性下脆弱，而可微ILP方法通常依赖预定义规则模板或不精确的模糊算子，这些算子在推理概率谓词估值时会遭受梯度消失或逻辑结构近似不佳的问题。本文提出基于注意力的神经符号可微规则提取器（ANDRE），一种新颖的ILP框架，通过基于注意力的逻辑算子优化连续规则空间来学习一阶逻辑程序。ANDRE用完全可微的、注意力驱动的合取和析取算子替代规则模板和逻辑算子，这些算子近似逻辑最小-最大语义，从而实现对概率数据的准确、稳定和可解释推理。通过在每条规则内软选择、否定或排除谓词，ANDRE在保持符号结构的同时支持灵活规则归纳。在经典ILP基准、大规模知识库以及带有概率谓词和噪声监督的合成数据集上的大量实验表明，ANDRE达到了有竞争力或更优的预测性能，同时在不确定性下可靠地恢复正确的符号规则。特别是，ANDRE对中等标签噪声保持鲁棒，在规则提取质量和稳定性上显著优于现有可微ILP方法。
英文摘要：
Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggle to scale to noisy and probabilistic settings. Classical ILP relies on discrete combinatorial rule search and is brittle under uncertainty, while differentiable ILP methods typically depend on predefined rule templates or inaccurate fuzzy operators that suffer from vanishing gradients or poor approximation of logical structure when reasoning over probabilistic predicate valuations. This paper proposes an Attention-based Neuro-symbolic Differentiable Rule Extractor (ANDRE), a novel ILP framework that learns first-order logic programs by optimizing over a continuous rule space with attention-based logical operators. ANDRE replaces both rule templates and logical operators with fully differentiable, attention-driven conjunction and disjunction operators that approximate logical min-max semantics, enabling accurate, stable, and interpretable reasoning over probabilistic data. By softly selecting, negating, or excluding predicates within each rule, ANDRE supports flexible rule induction while preserving symbolic structure. Extensive experiments on classical ILP benchmarks, large-scale knowledge bases, and synthetic datasets with probabilistic predicates and noisy supervision demonstrate that ANDRE achieves competitive or superior predictive performance while reliably recovering correct symbolic rules under uncertainty. In particular, ANDRE remains robust to moderate label noise, substantially outperforming existing differentiable ILP methods in both rule extraction quality and stability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
