Expert Systems

Released: May 9, 2026 8:00 AM CST

Here is the thirty-second episode of Quantum Foam, Expert Systems. This subject deals with Artificial Intelligence that uses human decision-making for specific, complex problems by utilizing a knowledge base of facts and an inference engine to apply logical rules. The rise of personal computers is important when understanding the circumstances leading to the development of Expert Systems. It is a decision-making program that runs on automated software. It is a piece of hardware with software on board. It could be a device, system, network, or THE network. Essentially, it would be the brain and operating center of the system in purely digital form. We want to metamorphosize to create code that writes code and is capable of diagnosing itself. The idea is building these mainframes and then scaling them to whatever you need. We are attempting to emulate a human with Artificial Intelligence software. We are talking about Expert Systems as they deal with software. We want a computer program that can interpret and make real-time changes in its own codebase. In order to use this idea, you build your system from the ground up. There is engineering and design required to get up and running in the beginning. We are creating intelligible knowledge bases. We don't really want that much procedural code. It is likely to lead to a dead end within the program. We want to make a system that always knows what to do and not get hung up. We are using the idea of taking a snap shot of a frame and then applying physics equations to it. We are going for a semantic web. Expert humans are the ones who make these programs once, first, the first time. There is forward chaining and backward chaining. Backward chaining is more understandable for humans. This uses the consequent to the antecedent or the second concept in an if-then scenario first rather than second. In this podcast, we are supposed to be going over some of the most important questions ever asked. Forward chaining deals with depth and the consequent deals with depth. We went over some Quantum Computing. It is possibly related to Expert Systems.

The International Conference On Computing And Communication Networks is an event worth noting for the purposes of Expert Systems. This subject was likely included at some point at this event. We create knowledge bases and then something to run that knowledge base. It inquires to that database. We are more interested in backward chaining. We are dealing with inference engines here. We are planning, designing, diagnosing, predicting, monitoring, debugging, and repairing. How do you make a smart, polymorphic worm out of C code? We had better get to programming. The collaboration was with a Physicist, a Mathematician, and a Programmer in the past. These days we are one and the same. There is a difference between forward chaining and backward chaining. Forward chaining starts from known facts and applies rules to get more data. Backward chaining starts from the goal and moves backwards using various rules to determine facts that satisfy the goal. Are you data driven or goal driven? Are you down-up approach or top-down approach? Forward chaining is down-up. Backward chaining is top-down. The forward uses the breadth approach as a strategy. The backward would use the depth approach. They both use the inference rule. Forward chaining can create an infinite number of solutions. We only understand some of the math behind space-time. The math looked correct from some recent Large Hadron Collider particle smash reports. We are interested in software that can run itself with very little human interference. There are data systems that require certain data sets. There are Neural Networks and other types of Artificial Intelligence. There are different types of networks. Some are A. I. and some are not. There may be different levels of A. I. for all of them. We now have decent out-of-the-box Artificial Intelligence. This is the self-debugging and monitoring A. I. They have to split it off somewhere. How can you be polymorphically smart? At some point, programs lose their coherence and return a yes value when it really is not a yes. Does it make sense or not? An Expert System is able to numerically analyze itself. Programming algorithms is required.