Introduction
Explore the world of expert system applications and discover the many ways they are being used to solve complex problems in industries like healthcare, finance, and more. Learn about the latest research and advancements in expert systems.
Expert systems, sometimes referred to as knowledge-based systems, are computer programs created to simulate a human expert’s problem-solving skills in a certain field. These systems are made up of an inference engine that uses the knowledge base, which represents the expert’s body of knowledge, to draw conclusions and offer explanations.
Expert systems have found several real-world or expert system applications in a variety of industries since their invention in the 1960s and 1970s, including medical, finance, engineering, and customer service.
How does a specialist system operate?
Artificial intelligence and machine learning are used by contemporary expert knowledge systems to mimic the actions and decisions of subject matter experts. Just like humans, these systems can get better over time as they gain more experience.
Expert systems compile knowledge and experience into a knowledge base and combine it with an inference or rules engine, which is a system of rules the software uses to apply the knowledge base to given scenarios.
The knowledge base is accessed by the inference engine via one of two methods:
1. Forward chaining:
In order to make a rational forecast about what will happen next, forward chaining reads and analyses a series of data. Making forecasts regarding stock market movement is an illustration of forward chaining.
2. Backward chaining:
In order to arrive at a logical explanation for why something occurred, backward chaining reads and analyses a set of facts. Examining a group of symptoms to arrive at a medical diagnosis is an illustration of backward chaining.
Having a solid knowledge base is necessary for an expert system. Experts contribute knowledge to the system’s knowledge base, while non-experts use it to solve complicated issues that would typically call for a human expert.
Knowledge engineering is the process of creating and sustaining an expert system. Expert systems, outfitted with all the knowledge they require to tackle an issue thanks to knowledge engineers. To do this, they employ a variety of knowledge representation approaches, including symbolic patterns. By growing the system’s knowledge base or developing new rules, its capabilities, be improved.
What elements make up an expert system?
An expert system, made up of three key parts:
1. The information base.
This is where the data that the expert system uses just kept. Facts contributed by human specialists regarding the expert system’s specific topic or subject area, grouped in the knowledge base. The system can obtain knowledge from outside sources and store it in the knowledge base thanks to a knowledge acquisition module that only frequently included in the knowledge base.
2. The inference engine
To address a user’s issue, this component of the system retrieves pertinent information from the knowledge base. It is a rules-based system that analyses inputs and maps known information from the knowledge base to a set of rules before making judgments.
A module that explains how the inference engine arrived at its result frequently included in inference engines. Customer interface Users engage with this area of the expert system to find a solution to their query or issue.
Applications of Expert Systems
1. Applications in the Medical Field
The field of medicine has seen some of the most successful use of expert systems. Expert systems, used in medical diagnosis to give clinicians information about potential diseases and ailments based on a patient’s symptoms, assisting them in making diagnoses that are more precise and effective.
One of the earliest expert systems utilized in medicine, for instance, the MYCIN system, which created in the 1970s. Its purpose was to identify infectious infections and recommend effective antibiotic therapies.
Many expert systems are available now to help doctors diagnose a variety of illnesses, such as cancer, cardiovascular disease, and infectious infections. These systems evaluate medical data using artificial intelligence algorithms and machine learning techniques, giving doctors knowledge of potential diagnoses, available treatments, and prognoses.
Expert system use in medicine has shown to be advantageous in a variety of ways, in expert system applications including increased diagnostic accuracy, decreased diagnostic error rates, and quicker and more effective diagnoses.
Applications in Finance
The financial industry has made extensive use of expert systems. By analyzing financial data and making forecasts about upcoming trends and market circumstances. Expert systems, employed in finance to assist investment managers in making wise judgments.
As an illustration, investment managers employed the FORTUNE method. It developed in the 1980s, to assist them to decide on stock investments.
Financial organizations now use expert systems to examine a variety of financial data. It includes stock prices, economic indicators, and business financial statements.
These systems forecast future market patterns using artificial intelligence algorithms. Machine learning techniques, assisting investment managers in making well-informed investment decisions.
Applications in Engineering
Another expert system applications are Expert system, utilized in engineering to assist designers and engineers in making decisions regarding challenging engineering issues. The AIDE system, for instance, created in the 1980s. It assist engineers in designing aircraft engines by giving them knowledge of the numerous design options. Available and the trade-offs connected with each option.
Similar to this, the CADEX system, created to assist engineers in the design and optimization of complicated mechanical systems. It includes hydraulic and power transfers.
In order to analyze engineering data and give designers and engineers information about design possibilities and trade-offs. Expert systems in engineering use artificial intelligence algorithms and machine learning techniques.
Applications in Customer Service
Expert systems just used in the customer service industry. By automating the handling of frequent customer issues, these technologies, utilized to assist businesses in providing better customer service.
For instance, the ES-1 system, created to assist a telecoms business in improving customer service by automatically resolving difficulties with billing and service interruptions that are frequently encountered by customers.
Natural language processing and other artificial intelligence methods just used by expert customer care systems to communicate with clients and offer them assistance and information.
Conclusion
In conclusion, expert systems, demonstrated to be an invaluable tool in a range of real-world applications, enhancing productivity and effectiveness in businesses in sectors including healthcare, finance, engineering, and customer service with expert system applications.
Even though these systems have improved significantly since they were first created in the 1960s and 1970s. Further research and development will probably result in even more powerful and advanced systems in the future.
