This paper explores the integration of neural networks with logic programming, addressing the longstanding challenges of combining the generalization and learning capabilities of neural networks with the precision of symbolic logic. Traditional attempts at this integration have been hampered by difficulties in initial data acquisition, the reliability of undertrained networks, and the complexity of reusing and augmenting trained models. To overcome these issues, we introduce the COOL (Constraint Object-Oriented Logic) programming language, an innovative approach that seamlessly combines logical reasoning with neural network technologies. COOL is engineered to autonomously handle data collection, mitigating the need for user-supplied initial data. It incorporates user prompts into the coding process to reduce the risks of undertraining and enhances the interaction among models throughout their lifecycle to promote the reuse and augmentation of networks. Furthermore, the foundational principles and algorithms in COOL's design and its compilation system could provide valuable insights for future developments in programming languages and neural network architectures.

翻译：本文探讨了神经网络与逻辑编程的融合，旨在解决将神经网络的泛化与学习能力同符号逻辑的精确性相结合这一长期存在的挑战。传统融合方案长期受困于初始数据获取困难、欠训练网络可靠性不足，以及已训练模型的复用与扩展复杂性高等问题。为克服上述难题，我们提出了COOL（约束面向对象逻辑）编程语言，这是一种创新性方法，能够无缝融合逻辑推理与神经网络技术。COOL被设计为可自主处理数据收集，减少对用户提供初始数据的需求；其在编码过程中融入用户提示以降低欠训练风险，并通过增强模型在全生命周期中的交互性促进网络复用与扩展。此外，COOL设计及其编译系统中的基础原理与算法，可为未来编程语言与神经网络架构的发展提供重要启示。