Neuro-Symbolic (NeSy) AI could be regarded as an analogy to human dual-process cognition, modeling the intuitive System 1 with neural networks and the algorithmic System 2 with symbolic reasoning. However, for complex learning targets, NeSy systems often generate outputs inconsistent with domain knowledge and it is challenging to rectify them. Inspired by the human Cognitive Reflection, which promptly detects errors in our intuitive response and revises them by invoking the System 2 reasoning, we propose to improve NeSy systems by introducing Abductive Reflection (ABL-Refl) based on the Abductive Learning (ABL) framework. ABL-Refl leverages domain knowledge to abduce a reflection vector during training, which can then flag potential errors in the neural network outputs and invoke abduction to rectify them and generate consistent outputs during inference. ABL-Refl is highly efficient in contrast to previous ABL implementations. Experiments show that ABL-Refl outperforms state-of-the-art NeSy methods, achieving excellent accuracy with fewer training resources and enhanced efficiency.

翻译：神经符号人工智能可类比于人类双过程认知，其中神经网络建模直觉性的系统1，符号推理建模算法性的系统2。然而对于复杂学习目标，神经符号系统常产生与领域知识不一致的输出，且难以校正。受人类认知反思的启发——该机制能即时检测直觉反应中的错误并通过调用系统2推理进行修正，我们提出基于溯因学习框架引入溯因反思来改进神经符号系统。该方法在训练阶段利用领域知识溯因生成反思向量，该向量可在推理阶段标记神经网络输出的潜在错误，并通过溯因机制进行校正以生成一致输出。相较于先前的溯因学习实现，本方法具有显著高效性。实验表明，该方法在减少训练资源消耗并提升效率的同时，以优异准确率超越了当前最先进的神经符号方法。