Neurosymbolic AI deals with models that combine symbolic processing, like
classic AI, and neural networks, as it's a very established area. These models
are emerging as an effort toward Artificial General Intelligence (AGI) by both
exploring an alternative to just increasing datasets' and models' sizes and
combining Learning over the data distribution, Reasoning on prior and learned
knowledge, and by symbiotically using them. This survey investigates research
papers in this area during recent years and brings classification and
comparison between the presented models as well as applications.

该论文调查了近年来神经符号 AI 领域的研究论文，对提出的模型及应用进行了分类和比较。这些模型结合了符号处理和神经网络，旨在通过学习数据分布，基于先前和学习的知识进行推理，并在其中进行共生使用，以实现人工智能通用。

神经符号 AI 及其分类：一项调查

Neurosymbolic AI and its Taxonomy: a survey

Jackendoff (2002) posed four challenges that linguistic combinatoriality and
rules of language present to theories of brain function. The essence of these
problems is the question of how to neurally instantiate the rapid construction
and transformation of the compositional structures that are typically taken to
be the domain of symbolic processing. He contended that typical connectionist
approaches fail to meet these challenges and that the dialogue between
linguistic theory and cognitive neuroscience will be relatively unproductive
until the importance of these problems is widely recognised and the challenges
answered by some technical innovation in connectionist modelling. This paper
claims that a little-known family of connectionist models (Vector Symbolic
Architectures) are able to meet Jackendoff's challenges.

该研究挑战性地审视了语言组合性和语言规则对脑功能理论的四个挑战，并探讨了联结主义建模中的技术创新如何解决这些挑战，认为一些鲜为人知的连接主义模型能够满足这些挑战。