  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.
