Network Logic Programming Theory

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.

The use of network logic makes it more natural to open the door for concepts such as runtime verification, true distributed logic processing systems (including network logic distributed across geographically dispersed locations), or more deterministic adaptive systems by providing generative logic paths. Generative logic paths allow for the runtime generation of logic using predefined relationships such as the capability of a GPS Application that calculates routes based on a predetermined set of streets and roads (relationships).