The Software Architecture of AI Expert Systems

- Overview

An expert system is an example of a knowledge-based system. Expert systems were the first commercial systems to use a knowledge-based architecture. A knowledge-based system is essentially composed of two sub-systems: the knowledge base and the inference engine. 

The knowledge base represents facts about the world. In early expert systems such as Mycin and Dendral, these facts were represented mainly as flat assertions about variables. 

In later expert systems developed with commercial shells, the knowledge base took on more structure and used concepts from object-oriented programming. The world was represented as classes, subclasses, and instances and assertions were replaced by values of object instances. The rules worked by querying and asserting values of the objects. 

- The Knowledge Base

A knowledge base is a collection of facts about the domain of an expert system. It contains facts and rules. 

The inference engine interprets and evaluates the facts in the knowledge base to provide an answer. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities

- The Inference Engine

In the field of AI, inference engine is a component of the system that applies logical rules to the knowledge base to deduce new information. The first inference engines were components of expert systems. 

The typical expert system consisted of a knowledge base and an inference engine. The knowledge base stored facts about the world. The inference engine applies logical rules to the knowledge base and deduced new knowledge. This process would iterate as each new fact in the knowledge base could trigger additional rules in the inference engine. 

Inference engines work primarily in one of two modes either special rule or facts: forward chaining and backward chaining. Forward chaining starts with the known facts and asserts new facts. Backward chaining starts with goals, and works backward to determine what facts must be asserted so that the goals can be achieved

- Knowledge-based Systems

Knowledge-based systems are computer programs that use a centralized repository of data known as a knowledge base to provide a method for problem-solving. 

Knowledge-based systems are a form of AI designed to capture the knowledge of human experts to support decision-making. An expert system is an example of a knowledge-based system because it relies on human expertise. 

Here are some examples of knowledge-based systems: 

• Blackboard systems: These systems allow multiple sources to input new information into a system to help create solutions to potential problems. Blackboard systems rely heavily on updates from human experts.
• Content Management Systems (CMS): These systems can also be used as Knowledge Base Systems. Some examples include SharePoint, Intervowen TeamSite, Documentum, OpenText, and SiteCore.
• Mycin: This program is a famous example of a rule-based expert system. It's a program for medical diagnosis that can recognize different bacteria that might cause acute infections. It can also suggest medications based on the weight of the patient.