Expert System (artificial intelligence)

An expert system is a computer program that uses reasoning and knowledge to solve problems. Expert systems are usually interactive in that users input information and receive feedback or a solution based on this input. Well-designed expert systems are said to simulate human intelligence in a specific domain so closely that the results are similar to those that would come from a highly learned human being of the same domain—an expert. 

Expert systems are an important area in the field of artificial intelligence (AI). AI researchers aim to use technology to develop intelligent machines—that is, machines that can use deduction, reasoning, and knowledge to solve problems or produce answers. Expert systems are highly complex and utilize advanced technology as well as scientific ideas about thought and rationality.

Researchers are interested in creating intelligent machines in part because they can be helpful to humans. Expert systems are typically rule-based and do not learn automatically; however, some modern systems may incorporate learning components when combined with machine learning techniques.

Most expert systems include at least the following elements:

• Knowledge base: An expert system’s knowledge base is all the facts the system has about a subject. Expert systems are usually supplied with a huge amount of information on a particular topic. This information comes from experts, databases, electronic encyclopedias, etc.
• Rule set: The knowledge base also includes sets of rules, or algorithms. These rules tell the system how to evaluate and work with the information and how to answer or approach queries from users.
• Inference engine: An inference engine uses the knowledge base and set of rules to interpret and evaluate the facts to provide the user with a response or solution.
• User interface: The user interface is the part of the expert system that users interact with. It allows users to enter their input and shows them the results. User interfaces are designed to be intuitive, or easy to use.

Different types of expert systems exist and are used for a variety of purposes. For example, some can diagnose disease in humans, while others can identify malfunctions in machinery. Still other expert systems can classify objects based on their characteristics or monitor and control processes and schedules.

Brief History

Simple expert systems have existed for decades. In the 1970s, researchers at Stanford University created an expert system that could diagnose health problems, specifically identifying bacterial causes of infections and recommending antibiotics. The system was more effective than some junior doctors and performed almost as well as some medical experts. Throughout the 1970s and 1980s, different researchers continued to work on expert systems that diagnosed medical conditions. Eventually, the technology began to be applied in other areas. For example, new expert systems helped geologists to identify the best locations to drill for natural resources, while others helped financial advisers to invest funds wisely.

In modern times, expert systems continue to advance. For example, automotive companies have developed driverless vehicles; most of these vehicles rely on artificial intelligence systems. These AI systems must make decisions about accelerating, turning, and stopping, just as a human driver would. These tasks are much more complicated than the tasks of early expert systems, but they are based on some of the same principles.

Applications

Expert systems continue to affect many different aspects of society. Businesses can benefit from expert systems because they can save money by relying on a system rather than a human. Current technologies, including AI systems, handle large amounts of data, which can be beneficial for companies that crunch numbers, such as financial companies. For example, companies such as Morgan Stanley already benefit from the use of an AI-driven system that helps financial advisers in retrieving firm information effectively and quickly. Similarly, transportation companies can use automated and AI-based systems to operate complicated vehicles such as trains or airplanes. The autopilot that is installed on modern airplanes is an example of an expert system; it can assist with navigation tasks more quickly than human pilots.

Expert systems are also utilized in the medical world. PXDES is an expert system designed to assist in the diagnosis of lung cancer. DXplain is a medical expert system designed to suggest possible diseases based on a patient’s symptoms, offering doctors additional diagnostic tools. In addition, expert systems are widely used in industrial applications such as predictive maintenance, fault diagnosis, and automation, where they help improve efficiency and reduce operational costs.

Advantages and Disadvantages

Using expert systems rather than human experts can have some advantages. For example, an expert system’s knowledge is permanent. The system does not forget key details, as a human might. Another advantage is that expert systems are consistent; they make similar recommendations for similar situations without the burden of human bias, although some expert views indicate that they, too, can reflect biases present in their data, rules, or design.

Additionally, expert systems can sometimes solve problems in less time than humans, allowing them to react more quickly than people, which can be especially useful in situations where time is of the essence. They can also be replicated with relative ease, allowing for the availability and sharing of information in multiple places.

Although expert systems have many advantages, some experts have pointed out some disadvantages, too. Today’s expert systems do not have the same “common sense” as humans. That is, the system might produce answers that cannot or should not be applied in the real world. Additionally, expert systems may not recognize that some situations have no solution.

Finally, expert systems are only as good as the people who designed them, the accuracy of their data, and the precision of the rules. Thus, an expert system might make a bad choice because it is working from incorrect or incomplete information or because its rules are illogical. Traditional expert systems have increasingly been integrated with or replaced by artificial intelligence (AI) systems, including AI agents that can perform multi-step reasoning and make autonomous decisions. Many systems incorporate expert knowledge directly into artificial intelligence models through techniques such as fine-tuning, creating domain-specific AI systems in fields like finance, healthcare, and law.

Bibliography

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