Expert System

An expert system is an artificial intelligence based system that converts the knowledge of an expert in a specific subject into a software code. This code can be merged with other such codes (based on the knowledge of other experts) and used for answering questions or queries submitted through a computer. In other words, it is a system that trys to copy the knowledge and decisions that human makes. In today's society there are many types of expert systems that attempt to solve problems.

There are three different parts that an expert systems typically consists of. First, a knowledge base which contains the information acquired by interviewing experts, and logic rules that govern how that information is applied. Second, an interference engine that interprets the submitted problem against the rules and logic of information stored in the knowledge base. Third, interface that allows the user to express the problem in a human language such as English.

The design of an expert system is built like human beings. The system must be a specialist and able to use heuristics, justify its conclusions, be able to learn, and estimate the reliability of its answer. Being a specialist means it must be able to know facts and know procedural rules. The system has to give an answer and be able to justify its answer with facts. A good expert system will be able to absorb tons of knowledge and the use the knowledge it absorbs. All of this knowledge to make this expert system be able to  function correctly is domain specific. In other words, it takes all of the knowledge of a human expert to create this advanced type of  expert system.

The expert system works off of a rule base or knowledge base. In expert system technology, the knowledge base is expressed with natural language rules IF ... THEN ... For examples 

"IF it is living THEN it is mortal"
"IF his age = known THEN his year of birth = date of today - his age in years"

IF is called the set of conditions and the set THEN is what happens as a result. Each result can be used as a new fact for the system or just as an action the system must take.

Expert systems can also use forward-chaining and backward-chaining to determine results. Forward-chaining is the questioning of an expert who has no idea of the solution and investigates progressively and is data driven. There is a supply of facts and it repeatedly applies rules to create new facts to get to a certain goal. Backward chaining is goal driven. The engine has an idea of the target. It starts from the goal in hopes of finding the solution as soon as possible by creating sub goals.

There are many benefits to having an expert system. Expert systems offer many advantages for users when compared to traditional programs because they operate like a human brain. Human expertise may not always be readily available when needed so an expert system would take over. Also, when traditional computer methods fail a company may want to implement an expert system. The expert system has a quick availability and it is able to program itself. As the rule base is in everyday language, expert systems can be written much faster than a conventional program, by users or experts, bypassing professional developers and avoiding the need to explain the subject. Expert systems have the ability to exploit a considerable amount of knowledge, the expert system uses a rule base unlike conventional programs which means that the volume of knowledge to program is not a major concern. Whether the rule base has 10 rules or 10 000, the engine operation is the same. The reliability of an expert system is the same as that of a database. For example, it is good or higher than that of a classical program. Also,  human experts can resign, retire, or even die which means valuable knowledge can disappear. But if it is recorded in an expert system, it becomes eternal.

There are also some challenges to designing an expert system.  The flaw is knowledge collection and its interpretation into rules. Most developers have no automated method to perform this task. Instead they work manually, increasing the likelihood of errors. Expert knowledge is generally not well understood either. For example, rules may not exist, be contradictory, or be poorly written and unusable. Also, most expert systems use an engine incapable of reasoning. As a result, an expert system will often work poorly, and the project abandoned.

An example of expert system application in business is in the financial field  which uses expert systems for mortgages. Loan departments are interested in expert systems for mortgages because of the growing cost of labor, which makes the handling and acceptance of relatively small loans less profitable. They also see a possibility for standardized efficient handling of mortgage loans by applying expert systems, appreciating that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans.