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.

本研究解决了神经符号人工智能领域中符号技术与统计方法融合的挑战，强调神经网络与符号推理的有效结合。通过首次将神经符号技术根据其架构进行分类，本研究揭示了框架的不同优势以及如何将符号方法作为黑箱来增强神经网络的实用性，为今后的研究者提供了相关框架的便利识别。 

通过架构绘制神经符号人工智能的全景图：增强深度学习与符号推理的手册