Expert Systems in Artificial Intelligence (AI)

May 21, 2020

As companies become more comfortable with adopting artificial intelligence (AI) technology into their business models, a growing number of businesses are considering the value of expert systems and wondering how this new technology can optimize growth in the digital age. Expert systems are designed to help companies to solve complex challenges and accomplish everyday tasks with ease. In the realm of artificial intelligence, expert systems automate the decision-making abilities of a human expert.

Though expert systems have been applied to grand projects such as diagnosing spacecraft anomalies at NASA, they can also provide excellent opportunities for any company seeking a competitive edge over others. This tool for capturing and emulating knowledge and skill grants companies the ability to handle a high volume of work with accuracy and efficiency. At OmniLegion, we’re passionate about discovering innovative solutions that take your company to the next level of success. In today’s post, we’ll examine how expert systems work and how this technology can propel your business’s customer service standards to a consistently high level of performance.

What is an Expert System?


At its essence, an expert system is AI software that draws upon a knowledge base to solve specific problems. The knowledge base is supplied by one or more human experts, and the system preserves knowledge for automatically solving problems on a greater scale. These computer applications perform at high levels of intelligence, are reliable, and extremely responsive in addressing problems unique to each company. Applying this system to customer service software such as chatbot technology and call center programs allows your company to respond quickly to many customers at once through an integrated system.

Types of Expert Systems

There are five basic types of expert systems. These include a rule-based expert system, frame-based expert system, fuzzy expert system, neural expert system, and neuro-fuzzy expert system.  

A rule-based expert system is a straightforward one where knowledge is represented as a set of rules. Fuzzy logic expert systems are also called multi-valued logic and differentiate between members of the class from non-members in problem-solving. In a frame-based expert system, frames are used to capture and represent knowledge. A neural expert system replaces a traditional knowledge base with neural knowledge, storing it as weights in neurons. Finally, a neuro-fuzzy system combines parallel computation and learning abilities with knowledge representation and explanation abilities.

Examples of Expert Systems

Cape Canaveral U.S. Air Force Station

The intelligent capabilities of these systems are utilized across a broad range of industries. Here are a few expert system examples that are already in use:

  • SHINE: designed by NASA for monitoring, analyzing, and diagnosing real-time and non-real-time systems
  • MYCIN: was used for the medical diagnosis of a specific group of diseases
  • MUDMAN: for analyzing drilling fluids
  • XCON: for configuring computer systems and customizing network systems
  • PROSPECTOR: for evaluating geological sites in commercial development 
  • DENDRAL: for inferring a molecule’s structure based on its chemical composition its chemical formula and other data

Components of Expert Systems

An expert system is typically comprised of five components: 

  • a knowledge base that stores rules and knowledge in domains to form procedures for solving problems.
  • an inference engine that obtains data from the knowledge base and interprets the data to find a solution to the problem. This component also possesses debugging abilities to streamline the problem-solving process.
  • a knowledge acquisition and learning module that allows the application to acquire more data from sources and add it to the knowledge base;
  • a user interface which enables non-expert users to access the expert system and determine a solution; and
  • an explanation module which helps the non-expert user explain how the problem was solved.

Human System versus Expert System

man in suite and tie

An expert system provides many advantages over a traditional system of relying on human experts. While expert systems depend on knowledge from human experts, the fact remains that human intelligence is perishable. For example, if the human expert is an employee who has to go on leave, that source of knowledge is no longer available to those to need to solve a problem. An expert system is more consistent in nature because it relies on a permanent store of knowledge that can be accessed time and again, regardless of whether the user is an expert or non-expert. In the same example, even if a human expert is away from the workplace, any other agent or employee can access the expert system to solve a problem right away.

Human expert knowledge is also unpredictable in nature. On a day where the human expert isn’t feeling well, he or she may perform at a lower level than usual. In contrast, an expert system performs at the same high levels every time, ensuring consistent performance no matter who uses the system.

Human knowledge is also difficult to duplicate across multiple channels of communication. Consider the case of a human expert that must assist a high volume of callers who need technical support. The human can only help one customer at a time through one channel of communication at a time. Compare this to an automated expert system that can intelligently apply knowledge across all channels and assist many callers at the same time. For companies that experience a high volume of customer support calls, an expert system is an invaluable tool for ensuring accurate and efficient handling of those calls.

Human knowledge systems are difficult to document and disseminate across a complex network. Even when appropriately distributed, in the hands of non-experts, the information may not be communicated as intended or at the level of expertise required for the given situation. On the other hand, an expert system’s knowledge base is easily documented and simple for non-experts to deploy across an entire system of multiple channels.

Finally, human expert resources are expensive to maintain. Humans with expertise require a higher salary, and companies may have to hire more than one expert to provide adequate problem-solving resources at the ready. Meanwhile, an expert system is much more cost-effective, since it stores a human expert’s knowledge and disseminates it automatically for as long as the system is running correctly. Once the expert system is up and running, companies need not depend on the costly labor of human experts to keep their businesses running smoothly.

Benefits of an Expert System

Stacks of coins

Expert systems provide many benefits to companies that have adopted AI into their customer service models. Here are just some of the advantages presented by expert systems in artificial intelligence:

  • Availability. Expert systems are mass-produced and, therefore, readily available to any company willing to invest in an AI-powered system.
  • Efficiency. Expert systems are speedy and reduce the amount of labor expended to solve problems.
  • Lower Production Costs. Production costs of expert systems are economical, and thus the prices of expert systems are reasonable and accessible to many companies.
  • Ability to Diagnose and Monitor Problems. Expert systems can monitor patterns of issues that arise, pinpoint common problems, and address those problems over a relatively short amount of time.
  • Low Error Rate. Expert systems’ error rates are significantly lower than that of human expert systems.
  • Low-Risk Factor. Expert systems can be applied in environments that are unsafe for humans.
  • Consistent, Stable Response Rate. Expert systems are immune to tension and fatigue and can continue to run during off-work hours at a constant level of performance.

What are the capabilities of an expert system?

With its vast capabilities, expert systems can help your company in a multitude of ways. In the context of an AI-powered customer service system, expert systems can advise customers as to how to use your company’s product, how to re-route deliveries and make changes to their own accounts. They can provide demonstrations and instructions to clarify the proper use of a product or service. Expert systems can predict or diagnose problems your customers are experiencing and derive a solution for customers to follow. If that solution doesn’t work, the system can even provide a set of alternative solutions for the customer to try. This multifaceted system can make your customers’ experience an enjoyable one that addresses all of their needs and concerns.

In addition to addressing problems for your company’s customers, expert systems also obtain valuable insights to help your company make critical decisions. Expert systems are designed to interpret results and provide appropriate outputs relevant to those results. Over time, expert systems become adept at predicting outcomes, giving your company an opportunity to effectively monitor what problems are continually being addressed and determine how to improve the company moving forward. For instance, an expert system’s data can show whether customers are frequently calling in to solve a problem with a particular product. This data will inform your company of whether to discontinue or re-develop the product with those complaints in mind.

Steps to Develop and Maintain an Expert System

person working on two laptop

Though an expert system seems complicated to run, once it’s properly deployed, it is easy for end-users to access the benefits of the technology. There are six main steps to develop an expert system:

  1. Determining the problem domain. This step involves identifying whether the problem is appropriate for an expert system to solve, finding experts to contribute to the project, and establishing whether solving the problem is cost-effective for the business.
  2. Designing the system. This involves determining the degree of integration the system will have with other systems and databases.
  3. Developing a prototype. This step involves obtaining domain knowledge from the appropriate expert or experts and establishing rules for the expert system.
  4. Testing the prototype. This involves checking the prototype with sample cases. This step allows engineers to pinpoint any deficiencies in the system. This is also a time for end-users to test the system for usability, efficiency, and accuracy.
  5. Refining and completing the prototype. This step ensures the expert system integrates well with other systems and databases and works for all end-users as well. In addition, this is the time to document the expert system project and train all end users on how to use the expert system properly.
  6. Maintaining the expert system. This ongoing step involves regular reviewing and updating of the knowledge base. In addition, the system must be kept up to date as other integrated databases and information systems evolve over time.


books with pages

As your company develops its plans for future growth, consider the many advantages AI and expert systems can offer. From lower costs to higher efficiency and productivity rates, there is no end to the benefits of adopting a high tech modern solution for your business. Making an intelligent choice can be as simple as choosing an intelligent system to handle day-to-day tasks and address problems that keep your company from achieving its highest potential.

For busy companies that would like to deploy and maintain an efficient expert system, a team of experts like the ones at OmniLegion can help. We utilize the latest advancements in AI and machine learning to help support-focused companies offload everyday tasks to automation and enhance their customer service models with intelligent up-to-the-minute software solutions. Check out what we do here and put your business on the path toward success in the age of information technology.


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