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At present, a huge buzz is going around about expert systems and artificial intelligence (AI). As a result, everyone has a question in mind that what an expert system is and its benefits. If you are one among them, then you are at the right destination. Read the article completely to know everything about the Expert System in Artificial Intelligence.

Before getting started to obtain huge insight about AI and expert systems, understand those terms in simple words. Generally, AI is the branch of computer science that develops the system software in a way, which they behave just like a human or intelligently. On the other hand, the expert system is one of the most important research areas of artificial intelligence.

What are Expert System in Artificial Intelligence?

The expert systems are typically computer applications, which emulate the decision-making ability of the human. These systems were developed to fix complex issues in the required area.

Similar to intelligent human, they try to create an intelligent computer, which thinks, learn, and decide like a human when trying to fix the issue. A few characteristics of the machine to mention are,

  • Understandable
  • High performance
  • Highly responsive
  • Reliable

In fact, the expert’s system can able to advise, help, and instruct the human in taking a decision, diagnosing, demonstrating, interpreting input, and even concluding advice.

Not only this, they even predict the results and then justify the conclusion. In addition to, it also provides an alternative option to the issue.

Even though they do many wonders, they cannot able to deliver accurate output from the insufficient knowledge base. They cannot improve their own knowledge base because they are not capable of substituting the ability of human decision-making.

Overall, expert systems in artificial intelligence are computer applications, which can solve tough issues easily like humans. It is because they have the same level of human expertise and intelligence.

Usually, expert systems have a specific knowledge base for one problem domain, which includes engineering, medicine, science, and so on.

Expert knowledge is known as a knowledge base, which comprises accumulated experience that has been tested and loaded in the system.

Just like other AI systems, expert system knowledge may be improved using the add-ons to the knowledge base. The more experience offered to the expert system, the more the system can enhance its performance.

What are the characteristics of expert system?

In the aforementioned section, you may come to know some characteristics of the expert system but here you will explore all the features of the system.

  • It has the ability to display the intelligent behavior
  • It has capability to explain the reasoning
  • It is able to deal with the certainty
  • It is also able to draw the conclusions from the relationship, which are extremely complex
  • It cannot improve its own knowledge base
  • It is limited to the narrow issues
  • It is also to offer much required portable knowledge

Expert System Technology

Take a look at the technologies utilized in Expert systems in AI mentioned below

To develop an intelligent expert system, you will definitely need some hardware and tools. Bringing together all hardware and software results in the system, which performs in a similar way like a human expert. Here are the tools and hardware required to build the expert system environment.

  • Large databases
  • Minicomputer/mainframes/workstations
  • LISP (LISt programming) and PROLOG (PROgrammation en LOGique)

Expert Tools

Usually, tools are utilized to reduce the cost and effort of developing the system. For building an expert system, you must require certain tools for the following reasons.

  • Debugging tools and powerful editors with multi-windows
  • Have in-built definitions of the model, inference design, and knowledge representation
  • They render rapid prototyping

Benefits of Expert Systems

Are you thinking about the benefits of using expert systems in artificial intelligence? If so, then take a quick down on below section.

  • Ease of availability because of the mass production of software. This means anyone who wishes to access expert system can get it easily
  • As the cost of producing an expert system remains reasonable, it is made available at the affordable rate. It is one of the major benefits of using expert systems. As well, it is the biggest reason behind the popularity of the expert system
  • Since it is designed to have knowledge of several experts, it acts as the multiple expertise  systems and takes any decision quickly
  • Expert system usually render high speed when compared to others so that it reduces the amount of work required to be done by the individual
  • As compared to human errors, the error rate of the expert system is comparatively less
  • Expert systems are extremely beneficial to be accessed in a risky environment where humans are unable to work with. Therefore, it reduces the level of danger experienced by humans
  • Since their knowledge will last long, they offer outstanding performance. In fact, they work steadily without getting fatigued, emotional, and tensed
  • Apart from offering a solution to complex problems, it also hasability to explain the reason for providing this particular solution in detail.

Read More :

  1. Robotics and Artificial Intelligence – Outstanding Future
  2. What is Machine Learning and Machine Learning Algorithms

Capabilities of Expert Systems

At the time of data entry, the user is usually given continual assistance and guidance. If any given parameter is quite hard to estimate, the user may be advised to get help from various sources.

In such a case, working with an intelligent system that has expert system capabilities based on the knowledge of experienced users and software developers will solve the problem quickly and effortlessly. Below mentioned are some of the greatest capabilities of expert systems.

  • Capturing of expertise
  • Codifying of expertise
  • Transferring of expertise
  • Duplicating of expertise
  • Advising
  • Demonstrating
  • Explaining
  • Diagnosing
  • Interpreting input
  • Suggesting alternative options
  • Justifying the conclusion
  • Predicting results
  • Helping and instructing the human in decision making
  • Updating the knowledge base regularly
  • Save the human effort’s time and maintenance as well

Major components of expert systems

Knowledge base

In general, knowledge base contains area-specific superior quality knowledge. The information is usually organized as the collection of facts about the area of task.

The perfect combination of experience and information is coined as knowledge. Do you know that the collection of precise and accurate knowledge is the success of any expert system?

The knowledge base has two components such as factual knowledge (scholar and knowledge engineers utilized this in the task domain) and heuristic knowledge (it is all about practice, one’s ability of evaluation, and accurate judgment).

Do you know about knowledge representation? It is one of the types of methods used to organize and formalize knowledge in the knowledge base. However, this method is in form of If-Then-Else rules.

As mentioned already, accuracy and quality are key reasons for the expert system’s success in AI. On the other side, knowledge engineers obtain exact information though they collect this information directly from the subject expert. Using several ways, they collect the information including observation, interview, and recording.

With the help of If-Then-Else rules, they categorize and organize information in a meaningful way. The knowledge engineer even monitors the expert system development, which is called knowledge acquisition.

User interface

The user interface gives interaction between the expert system and the user of the expert area. It should assist users to finish their goals in a short time and route as well. It should design the way in which users desire work practices.

Additionally, its technology should be adaptable to any of the user requirements and should have the ability to use user input efficiently. Generally, the natural language including verbal narration displayed on the screen.

Along with this, the listing of rule numbers is also displayed on the screen. All these things make the user interface easy to trace the credibility of the deduction.

Inference engine

When it comes to correcting and offerings flawless solutions, the use of efficient rules and procedures by the inference engine becomes essential. It sorts all kinds of factual conditions as well as applied rules and regulations before coming to the final solution. It adds new knowledge whenever required to the knowledge base.

Additionally, it applies rules to the facts obtained repeatedly. It also solves all the confusion when multiple rules apply to the case. Out of all, it uses trial and error methods to reach the solution.

Forward chaining

This is actually done to determine the next chain of happenings. This means you can find out what can happen in the future of any area. Usually, it follows the chain of derivations and conditions.

It also minimizes the outcome but it requires considering all the rules and facts. In addition, it needs to sort them before concluding to the final solution. Out of all, this is normally followed by working on the result.

Backward chaining

It is performed to determine the possibilities of past happenings. In this method, the inference engine tried to discover the condition, which could have occurred in the past. Therefore, this strategy is followed by finding out the reason or cause.

Popularity of expert system today

Even though public opinion varies on when their jobs will replace by AI, expert systems come for white-collar and analytical jobs. They are highly proficient in classification, reasoning, pattern matching, configuration, planning, and diagnosis.

Expert systems are currently using in several industries such as customer service, healthcare, aviation, financial services, and much more.

The Pathfinder system is the first expert system to be approved by the American Medical Association. In the year 19080, this system was built at Stanford University.

For hematopathology diagnosis, this decision-theoretic expert system was developed. In simple words, Pathfinder is the expert system, which helps and diagnose lump-node diseases.

Finally, this system deals with more than sixty diseases and can find over a hundred symptoms. Its latest version outperforms its creators.

In terms of business, the expert system is being created and deployed across the world in myriad applications due to its symbolic reasoning and its explanation capabilities. ROSS, AI attorney is the recently created expert system.

It is supported by self-learning systems, which utilize pattern recognition, data mining, natural language processing, and deep learning. It actually works the way the human brain works.

Phases of expert system development

The process of developing the expert system is still a time-consuming task. In fact, developing the small expert system requires few months and needs one or two people to develop it.

However, the development of the sophisticated system may need a team of many people working together for over a year. The stages of the expert system development are as follows:

Identification – Before beginning the expert system development, it is vital to know the problem, which the system is going to solve. Along with this, you must determine the accurate nature of the issues and then state the precise goals, which indicate how you expect the system to contribute to the solution.

Conceptualization – After identifying the problem to solve, analyze the issue further to make sure its specifics and generalities. In this stage, the knowledge engineer develops the diagram of the issue to depict the relationship between the processes and objects in the issue domain. It helps to classify the issue into a series of sub-issues.

Formalization – During the identification and conceptualization stages, the main focus is on understanding the issue. Now, in the formalization state, the issue is connected to its original solution by analyzing the relationships crafted in the conceptualization stage.

During this stage, it is vital that the knowledge engineer be familiar with several techniques of knowledge representations, expert tools, and other expert systems to solve the same issue.

Implementation and testing–In the implementation stage, the formalized concept is programmed onto the computer, which has been selected for system development.

With the help of predetermined tools and techniques, the first pass prototype of the system will be implemented. The testing stage renders chances of finding a weakness in both the structure and implementation of the system in order to make appropriate corrections.

Conclusion

Therefore, you have learned everything about the expert system in artificial intelligence including components, characteristics, and benefits. This helps you understand the importance of an expert system in the 21st century.