提出可学习逻辑结构的神经符号决策树，融合图像与规则推理。
Neurosymbolic Decision Trees
- 用神经概率逻辑扩展决策树，支持符号与非符号数据联合学习。
- 在多个任务上优于纯数据驱动的神经网络方法，提升可解释性。
- 适合需要背景知识推理、追求模型透明性的研究者使用。
神经符号（NeSy）AI研究神经网络与基于逻辑的符号推理的融合。现有方法多聚焦于学习神经网络、概率或模糊参数，对符号结构的学习关注较少。本文提出神经符号决策树（NDTs），并设计新型结构学习算法NeuID3。NeuID3在传统决策树自顶向下归纳基础上，引入来自DeepProbLog模型的神经概率逻辑表示。其核心优势在于同时支持符号与非符号数据（如图像），并在树结构生成中融入背景知识。实验表明，相较于纯数据驱动的神经网络方法，神经符号结构学习显著提升性能与可解释性。
原文摘要 · Abstract (English)
Neurosymbolic (NeSy) AI studies the integration of neural networks (NNs) and symbolic reasoning based on logic. Usually, NeSy techniques focus on learning the neural, probabilistic and/or fuzzy parameters of NeSy models. Learning the symbolic or logical structure of such models has, so far, received less attention. We introduce neurosymbolic decision trees (NDTs), as an extension of decision trees together with a novel NeSy structure learning algorithm, which we dub NeuID3. NeuID3 adapts the standard top-down induction of decision tree algorithms and combines it with a neural probabilistic logic representation, inherited from the DeepProbLog family of models. The key advantage of learning NDTs with NeuID3 is the support of both symbolic and subsymbolic data (such as images), and that they can exploit background knowledge during the induction of the tree structure, In our experimental evaluation we demonstrate the benefits of NeSys structure learning over more traditonal approaches such as purely data-driven learning with neural networks.
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