Expert Systems in AI: Your Partner to Transcend Problems

AI has spurred the development of smart technologies, enabling systems to make more informed decisions amidst fast-changing conditions. Revenues earned from AI software have almost tripled since 2018 and are projected to reach 126 billion USD by 2025, according to Statista [1]. Expert systems are one of the most effective applications of AI. Replicating human reasoning, this hardware and software make AI useful for organisations. They precisely evaluate data and draw intelligent conclusions like a human brain but on a much grander scale.

Discover what an expert system in AI is, its components, advantages, and other important details to leverage its true potential to your benefit. 

What is an expert system in AI?

An expert system in AI is a knowledge-based system that uses reasoning to resolve problems that typically require human expertise or competence. It uses knowledge about the application area. Expert systems' strength mainly comes from the detailed information about a particular subject in their knowledge base.

It's crucial to emphasise that expert systems support decision-makers instead of replacing them. Expert systems need to gain the capacities of humans. They use their knowledge in a particular field and apply it to the specific realities of the situation. Heuristic knowledge, or general principles employed by humans with domain expertise, is also included in the knowledge base of an expert system.

Expert systems are used in domains including manufacturing decisions, high-risk credit judgments, advertising decisions, chemistry, and medicine.

Popular examples of expert systems

Common examples of expert systems include:

• DENDRAL: AI-based system for chemical analysis that predicts molecular structure based on spectrographic data

• MYCIN: A bacteria identification system that helps identify infections and recommends drugs based on patient weight using backward chaining

• PXDES: Another clinical expert system that quickly determines the patient's lung cancer type and degree based on the input data

Characteristics of expert systems in AI

Key characteristics of expert systems include:

• High performance: They can solve complex problems of a particular domain with accuracy and efficiency.

• Understandable: The responses of an expert system are understandable to a user in that it receives input in a human language and produces output in the same.

• Highly reliable and responsive: It provides accurate results within short periods.  

• Advisory function: It can advice the human inquirer on any subject matter.

• Problem explanation: It can provide a detailed description of a given problem.

Components of an expert system

Three main components of an expert system are the user interface, inference engine, and knowledge base. Here's an overview of each. 

1. User interface

The expert system needs a way to interact with the user to understand and benefit from the output an expert system gives. This user interface accepts queries and converts them into a form that can be processed. These queries are then sent to the inference engine for processing, and the system displays the results to the user via the same user interface. In other words, the interface enables a user who needs more technical expertise to consult an expert system to solve a problem.

2. Inference engine 

Since it serves as the system's primary processing component, the inference engine can be called the expert system's "brain." It uses the knowledge base and inference rules to draw conclusions or infer new information. It aids in determining an error-free response to user requests.

The inference engine uses the information stored in the knowledge base to process queries and draw conclusions. Two different kinds of inference engines exist:

• Deterministic inference engine: This engine relies on rules and facets, and the user assumes the results are accurate.

• Probabilistic inference engine: This type draws conclusions based on probability, and its results contain uncertainty.

An inference engine employs the following modes to reach solutions:

• Forward chaining: This involves applying inference rules to the known facts and adding their conclusions to the existing knowledge. Based on available data, you use this mode to predict future events, such as marketing or investment decisions.

• Backward chaining: This is a technique for backward reasoning in which the aim is established first, and the known facts are then proven. This mode dedicates why an event, such as a medical illness or a software crash, occurred using available data.

3. Knowledge base

A knowledge base is an extensive data repository that houses knowledge gathered from subject-matter specialists in a particular field. A more comprehensive knowledge base will ensure a more accurate expert system.

It is comparable to a database with data and guidelines for a specific field or subject.

You can alternatively see the knowledge base as a collection of things and their attributes. It comprises the following two components: 

• Factual knowledge: Information based on facts and known to knowledge engineers 

• Heuristic knowledge: Information that relies on practice, guessing ability, experiences, and evaluation

Advantages of an expert system in AI

An expert system in AI can dramatically reduce an individual’s work involved in solving a problem. The advantages of using an expert system include:

• It efficiently provides quick solutions to queries.

• The error rate is considerably low. 

• It is consistent in making recommendations.

• It is suitable for bringing up difficult-to-use knowledge sources. 

• It can capture even the scarce expertise of a distinctively qualified expert.

• As training vehicles, an expert system produces a sharper learning curve.

• You can operate these systems in environments unfit for humans.

Limitations of expert systems

Like any other technology, expert systems come with some limitations, too. 

• Large systems are costly. 

• They require technical support. 

• Output can be erroneous if the knowledge base contains errors.

• You will still require human expertise in organisations to improvise on what these systems suggest.

Why use an expert system?

Why should we turn to expert systems for support when we have human experts in every field? The following points elaborate on why expert systems are a wise choice. 

• High-level security: These systems offer high-level security to address any question.

• High efficiency: Correct updates in the knowledge base produce a very efficient output, allowing people to devote their time to other tasks that require more creativity and human input.

• No memory restrictions: It can memorise the data at the moment of application and save as much of it as is necessary. However, some restrictions apply to human specialists' ability to remember everything simultaneously.

• No involvement of emotions: Human emotions, such as exhaustion, anger, depression, worry, and others, do not impact these systems. As a result, the performance is constant.

• Regular updates improve performance: If an expert system's output is incorrect, we can enhance the system's performance by upgrading the knowledge base.

• Domain expertise: Since numerous human experts are in each field, each with unique knowledge, experiences, and abilities, it might be challenging to produce a final result for a question from one source. However, integrating the information from human experts into the expert system delivers accurate results by combining all the information.

Next steps

AI has swiftly climbed the ladder of exponential popularity over the years. An expert system in AI has significantly contributed to various industries to help them resolve issues by providing much-needed expertise. 

If you are drawn to learning artificial intelligence, take the first step with the AI For Everyone course. This course offers an extensive introduction to artificial intelligence, making it perfect for beginners.