Network Logic Programming Theory

Network Logic Programming Theory

A copyrighted data processing system for separating complex programming algorithms into networks and computations.

Programs are becoming Complex Systems

Intent of CPU

  • EThe original intent of CPU Programming was for computations.
  • EPrograms became more complex as programming evolved from computations.

Hard to Manage

  • EComplexity is hard to understand when represented as source code in traditional programming representation.
  • ETotally unrecognizable after translation into assembly or machine language.
  • EChallenge increases with program size and variability in styles of programmers.

Challenge to Maintain

  • EDuring maintenance, challenge to understand hidden dependencies or tribal knowledge after departure of original programmers.
  • EDocumentation typically becomes out of sync with source over time.
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).

When coupled with AI, the opportunities are to use Network Logic Programming Theory to support deterministic decision making, use AI models that learn relationships from data to produce static graphs that define these relationships as input to Network Logic Programming Theory network programs, and have AI generate systems using Network Logic Programming Theory so the generated complexity can be understood using Network Science technology. All in the name of supporting deterministic systems.

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. It can respond to queries during runtime about how items are related similar in concept to the query capability of a graph database such as Neo4j using cypher. The Network Processor and CPU work together to support the data processing model.

Complexity Theory

Complexity Theory is a set of theoretical frameworks used for modeling and analyzing complex systems. Derived from models developed from computer science, ecology, physics, mathmatics, and engineering. Common pattern found to establish generic framework to studying complex systems in the abstract. Complex systems may exhibit (1) self Organization, (2) Nonlinearity, (3) Phase Transitions, and (4) Networks.

System

A system is a set of parts and a set of connections between these parts called relations. Complexity is a property of the system. Systems that are complex have many parts that are interconnected and interdependent. Most Complex Systems are naturally represented as Networks.

Connectivity

Network Theory is the study of connectivity. A tool to help with the study and analysis of the structure of complexity. Allows for the understanding of the connections between parts and how that shapes the part and the overall system. Networks are the best mechanism to abstract, manage, and understand complexity.

Networks

Networks are very powerful mechanisms to support Complex Systems. Common Pattern within Nature to support Complex Systems. The Human brain is an example of a network. Proven with the rise of Artificial Intelligence using varius training techniques. If networks are good fror trained systems, they should be good for the evolution of programming. Why not harness the power of networks with both trained approaches and programming approaches.

Create Approach

Create Approach for programming that is composed of computations and network processing for complexity. All decisions are allocated to Networks. All computations are allocated to traditional CPUs. Complexity of traditionally CPU based programs reduced back to computations and complexity moved to abstraction via networks. Thus, Complexity for decisions stored as networks where they can be understood and analyzed using Network Theory. Traditional CPUs used for computations as originally intended.

Network Logic Programming Theory

Network Logic Programming Theory has been copyrighted. Book on Theory published. Patend Pending for IP: 9262RF-1NKM. A data processing system for separating complex programming algorithms into networks and computations is disclosed. The networks store logic in the form of relationships that is utilized to make decisions, and the computations are reduced to simple data processing algorithms. The data processing system includes a central processing unit; one or more relationship units; a layer unit; a layer router; a relationship path unit; a frame capture unit; and a frame controller.

Paradigm

Platform

  • ETheory to establish principles of programming
  • ETools
  • ENetwork Programming Support Tools
  • ENetwork Analysis Tools
  • ENetwork Compiler
  • ENetwork and CPU Integration Tools
  • ECPU Software Infrastructure
  • ESmall Software Infrastructure to incorporate Network Infrastructure into CPU Platforms
  • ENetwork Platform
  • ESimulator
  • EIP for Hardware (FPGA) first target
  • ESimulator
  • ERuns network processing on CPU
  • EIP
  • EDigital Design to run network processing on FPGA or ASIC
  • EWorks with CPU. CPU performs computations
Network Logic Programming Theory
Network Logic Programming Theory
Network Logic Programming Theory
Network Logic Programming Theory
Network Logic Programming Theory

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