How to Implement the Expert System in Artificial Intelligence(AI)

How to Implement the Expert System in Artificial Intelligence(AI)

The expert system is one of the most discussed and prominent research domains of Artificial intelligence. The very first expert system was developed in 1965 by two scientists Edward Feigenbaumand Joshua Lederberg of stand fort university in California, US.

 In this article, we will explore this topic in depth.

The following important points will be covered in the article,

• What is Artificial intelligence?
• The relevance of Artificial Intelligence.
• What is the Expert System?
• Characteristics of Expert System.
• Main Components of the Expert System
• Three Stages of Designing ES.
• Main Areas of Application
• Advantages and Disadvantages

So, without further ado, let’s get started with this article,

• What is Artificial Intelligence?

 In today’s technology-driven era, the role of artificial intelligence in Industries and the business sector is at its peak. Companies have realized the disrupting power of artificial intelligence.

The term artificial intelligence was first coined by John McCarthy in 1956. He defined it as the science and engineering of making intelligent machines”.

Artificial intelligence or AI is a branch of computer science that focuses on making intelligent machines that adapt to their surrounding environment and act accordingly. In other words, they learn from their encountered environment and perform tasks similar to human beings.

• The relevance of artificial intelligence in the Real World:

The gadgets ranging from Alexa to smartphones to smartwatches and even our smart home appliances are all product of artificial intelligence. Another few examples include chatbots like SIRI, CORTANA which are very popular nowadays. Recently, The AI research department of HDFC bank has developed an application called EVA (Electronic Virtual Assistant), which deals in collecting knowledge from numerous resources and provide simple answers to a user within less than 0.4 seconds.

 There are countless examples of AI applications in different areas of our daily life.

Moving further, let’s, understand the Expert system in the context of Artificial Intelligence

• What is the Expert System?

The Expert system is an interactive, computer-based application or software, which uses a database of expert knowledge to offer advice or make decisions in such areas as, medical diagnosis, account, coding, or even games. The expert system is designed to solve complex problems for a specific domain, just like the level of human- intelligence or expertise. An expert system can also be defined as a computer-based decision-making system that can solve complex decision- making problems using both facts and heuristics

• Purposeof the Expert System

The main purpose of the expert system is to extract knowledge of a human- expert and replicate the same knowledge and skills in a particular area of human-expert. Then the system will use the same extracted knowledge and try to resolve the complex problems of that particular area with any participation of human-expert.

• Characteristics of Expert Systems

• High performance
• Understandable
• Reliable
• Highly responsive
• Cost-effective

• Main Components of an Expert System

     The main components are:

• Knowledgebase
• Working memory
• Inference engine
• Explanation system
• User interface
• Knowledgebase editor

• Three Stages of Designing ES

Knowledge Acquisition:

Knowledge acquisition is a process of gathering knowledge from different sources be it by interviewing or observing human experts, reading specific books.

Knowledge Base:

The knowledge base is like a databank of high-quality knowledge or skills. The accuracy of prediction and also the performance of the expert system is highly dependent on the collection of perfect, accurate, and precise knowledge contained with the knowledge base.

After collection data, it is important to represent data in an organized manner.

Knowledge Representation

Knowledge representation is a method of selecting and organizing the most appropriate structures to represent knowledge. It is a method of formalizing knowledge in the knowledge base. It is done in the form of IF-Then-Else rules.

 Knowledge Validation or Engineering

 The whole process of Testing and approving the knowledge of ES, whether it is correct or not, is

 Known as knowledge engineering.

Inference Engine:

The inference engine is like the mind of an expert system. 

The inference engine gathers and manipulates the knowledge from the knowledge base to arrive at a solution. Just like our human-mind.

In the case of rules-based ES,

• It acts as a resolver, whenever there is a rules conflict because of multiple rules applies to a particular case.
• It adds new knowledge to the knowledge base if required.

It applies rules repeatedly to every fact, which are obtained from earlier rule application.

The inference engine uses the following approaches:

• Forward chaining
• Backward chaining

In forward chaining, solves by following a chain of conditions and derivations. Whatever the knowledge is stored within a knowledge base it goes through all the knowledge and facts and sorts them, before arriving at a solution. By the forward chaining method, the expert system tries to answers, “what can happen next”?

Areas of application of forwarding chaining: real estate price prediction, stock market, or share market prediction.

 Backward chaining

 In the back chaining method, the expert system tries to answer, “Why this happened?”

When something happens in a particular domain, the inference engine tries to find similar instances in the past and the reason behind it. In other words, through this backward chaining method inference engine tries to find out cause and reason.

 Areas of application- diagnosis of skin-related issues in humans

• MainAreas of Application:

• Medical field

 The implementation of expert systems in the medical field has been a game-changer and goes well beyond the convenient storage and display of data. It includes analysis of x-rays, diagnosis of heart- or lung-related query, and even a computer-based robotic surgery.

• Business field

ROSS is a recently developed expert system, which is a self-learning system that uses data mining, pattern recognition, deep learning, and natural language processing similar to the functioning of the human brain.

• Finance/ commerce

Expert systems detect any possible fraud by looking over suspicious transactions. Sectors like the stock market, trading, Airline scheduling, cargo schedules.

• Monitoring systems and information

It also helps in comparing data continuously with the observed system, also helps in managing the stored information.

 There are other areas as well, like,

• Help desk management, 
•  Useful for repair and maintenance of reports, 
• Employee performance evaluation
• Warehouse optimization etc.

• Advantages of an Expert System.

1. Stores a huge amount of data
2. Reduction in employee training costs
3. Takes the lead in the decision-making process
4. Time-efficient by reducing the problem-solving time.
5. Combination of the various human expert intelligence
6. No scope of human errors.
7. Provides strategic and comparative advantages that may create problems for competitors.
8. Look over transactions that are beyond the imagination of human expert
9. Provide reliable and quick answers.

• Disadvantages of an Expert System

1. Lack of creativity in responses that the human mind is capable of.
2. Unable to explain logic and reasoning behind a response.
3. It is not easy to automate a complex process.
4. Zero adaptability and flexibility, required in a changing environment.
5. Lack of common sense in the decision-making process.

In the end, it can be concluded that the expert system in AI comes with a lot of scopes to offer. It depends on us, how we can utilize it in the best possible way.