Expert Systems and Applied AI

- Overview

An expert system is a type of artificial intelligence (AI) system that utilizes knowledge and reasoning techniques to solve complex problems in a specific domain. Expert systems mimic the decision-making ability of a human expert by using a knowledge base of facts and rules, along with an inference engine that applies logical reasoning to make decisions or provide recommendations.

The Expert systems (ESs) were first developed in the 1970s by Stanford University computer scientist Edward Feigenbaum, and became one of the first truely successful forms of AI software.

In today's modern world with technological advances, we can process human minds, machines are designed to think like humans and imitate their behavior, so the overall process of designing machines that can act like humans is called AI. Some of the AI applications are expert systems, natural language processing, speech recognition, computer vision. 

AI is a software that simulates the behavior and judgment of humans or organizations with experts in a specific field, called an expert system. It does this by obtaining relevant knowledge from a knowledge base and interpreting it based on the user's questions. 

Data in the knowledge base is added by experts in a specific field, and the software is used by non-expert users to obtain some information. It is widely used in medical diagnosis, accounting, coding, gaming and other fields. 

There are five main types of ESs: rule-based, frame-based, fuzzy, neural, and neuro-fuzzy.

Please refer to the following for more information:

• Wikipedia: Expert System

- Expert Systems: AI Applied

The most important applied area of AI is the field of expert systems (ESs). ESs are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise. ESs are assistants to decision makers and not substitutes for them. 

An ES is a knowledge-based system that employs knowledge about its application domain and uses an inferencing (reason) procedure to solve problems that would otherwise require human competence or expertise. The power of ESs stems primarily from the specific knowledge about a narrow domain stored in the ES's knowledge base. 

In AI, an ES is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. 

ESs do not have human capabilities. They use a knowledge base of a particular domain and bring that knowledge to bear on the facts of the particular situation at hand. The knowledge base of an ES also contains heuristic knowledge - rules of thumb used by human experts who work in the domain. 

An ES is divided into two core subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities. 

- Capabilities of AI Expert Systems

An AI expert system (ES) is a computer program designed to solve complex problems and provide decision-making capabilities like a human expert. ESs have been closely related to AI since the 1980s. 

AI ESs are high performance, understandable, reliable, and highly responsive. AI ESs allow you to capture and use human knowledge to help users in the same way as experts.

The AI ESs are capable of: 

• Advising 
• Instructing and assisting human in decision making
• Demonstrating
• Deriving a solution
• Diagnosing
• Explaining
• Interpreting input
• Predicting results
• Justifying the conclusion
• Suggesting alternative options to a problem

They are incapable of:

• Substituting human decision makers
• Possessing human capabilities
• Producing accurate output for inadequate knowledge base
• Refining their own knowledge

One of the most common applications of ESs in decision support is medical diagnosis. Expert systems help doctors and nurses diagnose diseases, recommend treatments, and monitor patients' conditions. For example, MYCIN is an expert system that can diagnose bacterial infections and recommend antibiotics.

- Modern Expert Systems

The limitations of the previous approaches of expert systems (ESs) have urged researchers to develop new types of approaches. They have developed more efficient, flexible and powerful approaches in order to simulate the human decision-making process. 

Some of the approaches that researchers have developed are based on new methods of AI, and in particular in machine learning and data mining approaches with a feedback mechanism. 

Modern systems (AI expert systems) can incorporate new knowledge more easily and thus update themselves easily. Such systems can generalize from existing knowledge better and deal with vast amounts of complex data. Sometimes these type of ESs are called “intelligent systems.”

The strength of an ES derives from its knowledge base - an organized collection of facts and heuristics about the system's domain. An ES is built in a process known as knowledge engineering, during which knowledge about the domain is acquired from human experts and other sources by knowledge engineers. 

The accumulation of knowledge in knowledge bases, from which conclusions are to be drawn by the inference engine, is the hallmark of an ES.