用分类网络统一神经与符号计算，实现高效互换。
Taxonomic Networks: A Representation for Neuro-Symbolic Pairing
- 提出分类网络作为神经与符号系统的共同表征
- 符号方法用更少数据和算力学得更好，神经方法资源多时精度更高
- 支持按需切换，适合需要灵活融合的AI系统
我们引入了神经符号对的概念——通过共同的知识表示将神经方法与符号方法相连接。接着，提出一种分类网络，其节点代表层级化的分类概念。利用该表征，构建了一个新型神经符号对并进行评估。结果表明，符号方法在数据和计算资源较少的情况下能更高效地学习分类网络；而神经方法在资源充足时可获得更高精度的分类网络。作为神经符号对，二者可根据实际需求无缝切换，必要时可相互转换。本工作为未来更深层次整合神经与符号计算的系统奠定了基础。
原文摘要 · Abstract (English)
We introduce the concept of a \textbf{neuro-symbolic pair} -- neural and symbolic approaches that are linked through a common knowledge representation. Next, we present \textbf{taxonomic networks}, a type of discrimination network in which nodes represent hierarchically organized taxonomic concepts. Using this representation, we construct a novel neuro-symbolic pair and evaluate its performance. We show that our symbolic method learns taxonomic nets more efficiently with less data and compute, while the neural method finds higher-accuracy taxonomic nets when provided with greater resources. As a neuro-symbolic pair, these approaches can be used interchangeably based on situational needs, with seamless translation between them when necessary. This work lays the foundation for future systems that more fundamentally integrate neural and symbolic computation.
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