梳理神经符号系统架构，帮人用符号推理增强深度学习。
Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning
- 按架构分类神经符号方法，揭示不同设计与能力的关联。
- 展示如何将符号模块作为黑箱接入神经网络提升性能。
- 为研究者提供清晰路线图，加速新系统设计与选型。
将符号技术与统计方法结合是人工智能领域的长期挑战。其动机在于两者优势互补，弱点相克——通过融合可弥补单一方法的不足。神经符号AI聚焦于此，尤其关注神经网络与符号推理的整合。近年来该领域进展显著，神经符号系统已超越单独的逻辑或神经模型。然而，相比主流机器学习方法，神经符号AI仍处于早期阶段，尚未被广泛采用。本文首次基于架构对神经符号技术进行系统性分类，构建框架家族地图。此举具有三重价值：一是将不同框架的优势与其架构特性对应；二是展示工程师如何将符号模块作为黑箱嵌入神经网络以增强能力；三是全面覆盖该领域，帮助未来研究者快速定位相关工作。
原文摘要 · Abstract (English)
Integrating symbolic techniques with statistical ones is a long-standing problem in artificial intelligence. The motivation is that the strengths of either area match the weaknesses of the other, and $\unicode{x2013}$ by combining the two $\unicode{x2013}$ the weaknesses of either method can be limited. Neuro-symbolic AI focuses on this integration where the statistical methods are in particular neural networks. In recent years, there has been significant progress in this research field, where neuro-symbolic systems outperformed logical or neural models alone. Yet, neuro-symbolic AI is, comparatively speaking, still in its infancy and has not been widely adopted by machine learning practitioners. In this survey, we present the first mapping of neuro-symbolic techniques into families of frameworks based on their architectures, with several benefits: Firstly, it allows us to link different strengths of frameworks to their respective architectures. Secondly, it allows us to illustrate how engineers can augment their neural networks while treating the symbolic methods as black-boxes. Thirdly, it allows us to map most of the field so that future researchers can identify closely related frameworks.
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