Expert Systems: Everything You Want To Know About

Expert Systems: Everything You Want To Know About

Now a day there’s a huge buzz going around of Artificial Intelligence and expert systems. Everyone have a question that what an expert system is. Before we get started with Expert System lets get a brief knowledge about AI. In simple terms, Artificial Intelligence is a branch of computer science, which develops the system, software in such a way that they behave intelligently or just like a human, and expert system is one of the research areas of AI.

What are Expert Systems?

The expert systems are computer applications developed to solve complex problems in the required area. They try to make an intelligent computer in the similar manner as the intelligent human think, learn, decide while trying to solve a problem. Its various characteristics are high performance, understandable, reliable and highly responsive.
The expert systems can advise, instruct & assist the human in decision making, demonstrating, diagnosing, interpreting input and concluding advice. They predict the results and justify the conclusion as well as suggest alternative options to a problem.
However, they cannot produce accurate output from the inadequate knowledge base and cannot refine their own knowledge as they are not capable of substituting human decision-making ability.

What are the components of Expert Systems?

The components of Expert Systems include:
Knowledge Base:
It contains area specific high-quality knowledge. The information is organized as a collection of facts about the task area. The combination of information and the experience are termed as knowledge. The success of any Expert Systems depends mainly on the collection of highly accurate and precise knowledge.
Inference Engine:
Use of efficient procedures and rules by the Inference Engine are essential in coming to a correct and flawless solution. It sorts all the factual conditions, and rules & regulations applied, before coming to a solution.
•    It applies rules repeatedly to the facts obtained.
•    It adds new knowledge to the knowledge base if required.
•    It resolves any confusion when multiple rules are applicable to a case.
The Inference Engine uses the trial and error methods to arrive at the solution:
•    Forward Chaining – It is done to find out the next chain of happenings. To find out what can happen in future, for example, prediction of share market status as an effect of demonetization.
Fact 1 AND Fact 2 => Decision 1
Fact 3 OR Fact 4 => Decision 2
Decision 1 AND Decision 2 => Decision 3
•    Backward Chaining – It is done to find out the possibilities of past happenings. Here, the Inference Engine tries to find out what conditions could have happened in the past, for example, diagnosis of swine flu in humans.
Decision 3 => Decision 1 AND Decision 2
Decision 1 => Fact 1 AND Fact 2
Decision 2 => Fact 3 OR Fact 4User Interface
User Interface:
It provides interaction between the user of the expert area and the expert systems. It must help users to complete their goals in shortest possible way and time. It must be designed per the way user’s desired work practices. Its technology must be adaptable to user’s requirements. It should make efficient use of user input.


Expert systems are applied in various field like designing, Medical diagnosis, Data monitoring, Controlling a physical process, Faults in vehicles & computers and Fraud detection.


The process of Expert Systems development is as follows:
•    Identify the accurate problem area
•    Design the system as per requirement
•    Develop the model
•    Test and refine the model until finishing the job
•    Maintenance


•    Easily available due to mass production of software.
•    Affordable as production cost is reasonable.
•    Fast results as they reduce the amount of work.
•    Error rate is low as compared to human errors.
•    They can work in the environment dangerous to humans.
•    They work steadily without getting emotional, tensed or fatigued.


They require significant development time & computer resources. Large systems are costly. Several levels of Expert Systems technologies are needed, for example, computers, servers with the required software, powerful editors and debugging tools with multi-windows, but it is difficult to maintain them due to high development costs. The acquisition of expert knowledge is also difficult.


Expert systems have played a big role in many industries including financial services, telecommunications, healthcare, customer service, transportation, video games, manufacturing, aviation and written communication. The medical diagnoses like Dendral, has helped chemists identify organic molecules, and MYCIN has helped identify bacteria in the blood. It recommended antibiotics and dosages.
A recent development, ROSS, relies on self-learning systems. It uses data mining, pattern recognition, deep learning and natural language processing to imitate the way the human brain works.
Expert Systems and Artificial Intelligence systems have started a debate about the fate of humanity in the face of such intelligence. It looks as if computing power has surpassed our ability to control it.

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