用逻辑规则强化深度学习训练，让模型更懂专家知识。
Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach
- 用答案集编程构建可自动更新的损失函数
- 在医疗、电池制造等场景提升模型性能与可信度
- 无需重设计即可适配新领域，适合工业落地
本文提出一种混合方法，通过本体和答案集编程（ASP）将领域专家知识嵌入深度学习（DL）模型的训练过程。该方法将领域特定约束、规则与逻辑推理直接编码至学习流程中，从而提升模型性能与可信度。所提方法适用于回归与分类任务，在医疗、自动驾驶、工程及电池制造等领域展现通用性。与现有先进方法相比，其优势在于跨领域可扩展性强：仅需更新ASP规则即可自动化调整损失函数，实现系统高效扩展与用户友好。该设计使模型能无缝适配新领域，为电池制造等工业场景中专家知识的集成提供实用解决方案。
原文摘要 · Abstract (English)
This paper presents a hybrid methodology that enhances the training process of deep learning (DL) models by embedding domain expert knowledge using ontologies and answer set programming (ASP). By integrating these symbolic AI methods, we encode domain-specific constraints, rules, and logical reasoning directly into the model's learning process, thereby improving both performance and trustworthiness. The proposed approach is flexible and applicable to both regression and classification tasks, demonstrating generalizability across various fields such as healthcare, autonomous systems, engineering, and battery manufacturing applications. Unlike other state-of-the-art methods, the strength of our approach lies in its scalability across different domains. The design allows for the automation of the loss function by simply updating the ASP rules, making the system highly scalable and user-friendly. This facilitates seamless adaptation to new domains without significant redesign, offering a practical solution for integrating expert knowledge into DL models in industrial settings such as battery manufacturing.
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