Logic abstracted as relationships and their derivates is the next evolution of programming. There is an assumption that, with the rise of artificial intelligence, the art of programming is dead, in part because it has the reputation of being expensive, with costs rising and production timelines expanding. But can anything be done? The answer is yes. However, complexity must be better understood.

This book introduces Network Logic Programming Theory, which tackles programming's challenges with a technique that separates complex programming algorithms into networks and computations (U.S. Patent 12,131,160 and Patent Pending). Networks store logic in the form of relationships to make decisions, while computations are reduced to simple algorithms. This network-based system utilizes the principles of network science to analyze complexity within software systems. AI and Network Logic Programming Theory are based on the concept of relationships and their derivates. The difference is AI extracts relationships from data using networks, while Network Logic Programming Theory programs networks using relationship-based abstraction. Such an approach can serve as the next evolution of programming and be integrated with AI to create deterministic systems.

Networks become a new version of assembly language and can store both static and dynamic information for data processing algorithms. When coupled with AI, there is an opportunity to use AI models that learn relationships from data to produce static graphs to define these relationships as input to Network Logic Programming Theory network programs where the generated complexity can be understood using Network Science technology. Also, Network Logic Programming Theory introduces the concept of the Network Processor. The Network Processor is designed to process network flow logic for static logic paths defined in network definitions. It can also generate new paths dynamically during run time based on stored relationships.