An Expert System is a computer program that mimics human decision-making in a specific area. Similar to a human, it resolves problems, provides suggestions, and makes decisions based on a knowledge base and rules of logic.
Expert systems analyze information and make decisions based on pre-established rules and logic they do not think like humans.
They are often utilized to assist professionals in resolving complex issues in manufacturing, finance, law, and medicine.
NASA uses expert systems on space missions to assist astronauts and control autonomous spacecraft. During the Deep Space 1 mission, they employed an expert system called Remote Agent. This system had an impact on diagnosing system problems making real-time decisions, and adjusting navigation without human involvement. This AI-based system reduced the need for constant human oversight by allowing the spacecraft to operate on its own in deep space.
Benefits of Expert Systems
Following are the advantages of Expert Systems −
Availability: They are easily available due to mass production of software.
Making Decisions Consistently: Expert systems remove human inconsistencies caused by emotions or fatigue by arriving at consistent, objective decisions.
Less Production Cost: Production cost is reasonable. This makes them affordable.
Knowledge Preservation− These methods make sure that crucial information is not lost as a result of employee retirements or turnover by keeping specialized knowledge.
Speed: They offer great speed. They reduce the amount of work an individual puts in.
Scalability: Once developed, expert systems can be easily expanded and deployed to support multiple users across various locations simultaneously.
Less Error Rate: Error rate is low as compared to human errors.
Education: They shorten the learning curve in specialized areas by offering high-quality coaching to newcomers.
Reducing Risk: They can work in the environment dangerous to humans.
Steady response: They work steadily without getting motional, tensed or fatigued.
Expert Systems Limitations
No technology can offer easy and complete solution. Large systems are costly, require significant development time, and computer resources. ESs have their limitations which include −
Lack of Common Sense: Expert systems lack human intuition and general knowledge they just follow pre-established rules and facts which are stored in knowledge base.
Incapacity to Learn on Their Own: In contrast to humans who learn from experience, they cannot develop or change unless specifically revised.
High Development Cost and Time: Developing an expert system requires a lot of programming, knowledge acquisition, and maintenance.
Limited to Specific Areas: They work well in certain areas and work badly when applied to more general or unrelated problems.
Relying on engineers: with expertise is essential for updating or enhancing an expert system, which can make implementing changes quite difficult.
Challenges with Missing or Uncertain Data: Expert systems often struggle to make conclusions when they encounter missing or ambiguous information.
Lack of emotional intelligence: limits their use in some situations because they are unable to understand empathy, human feelings, or human interaction such as in customer support. For example, expert systems may struggle to handle sensitive situations in customer service where a human touch, empathy, and comprehension are needed.
Expert systems are artificial intelligence (AI)-based computer programs created to simulate human decision-making in particular domains. They provide responses based on specific information, rational principles, and reasoning, which lowers human error and boosts output. Many industries, including finance, healthcare, and manufacturing, rely heavily on expert systems.
Following are few applications of Expert Systems in artificial intelligence −
Expert System in Medical Domain
Expert systems utilize test results, medical history, and symptoms to assist healthcare professionals in diagnosing illnesses. They are especially beneficial in complex cases and can provide treatment suggestions.
One of the first expert systems created to identify bacterial infections and recommend antibiotics was MYCIN. Nowadays, advanced systems such as IBM Watson Health analyze data from cancer patients to offer tailored treatment options.
Financial Analysis
Banks and other financial institutions apply expert systems to credit risk assessment, fraud detection, and investment advice. The systems analyze huge quantities of financial information in order to help make decisions.
For example, banks use FICO scores to determine the credit history and loan eligibility of a borrower. Systems for preventing fraud at Visa and PayPal look for trends in transactions in order to detect and prevent suspicious behavior.
Manufacturing and Process Control
By monitoring equipment, predicting failures, and enhancing production efficiency, expert systems significantly boost industrial performance. They support resource management and assist in making quality control decisions.
For instance, Digital Equipment Corporation benefited from XCON (Expert Configurer) in effectively configuring computer systems. To avoid failures, modern AI-driven expert systems identify mechanical faults in various industries.
Systems for Legal Advice
These systems offer contract analysis, legal guidance, and compliance verification. By examining extensive legal datasets, they support lawyers in conducting case research and ensuring regulatory compilance.
For instance, ROSS Intelligence utilized AI to perform case research and suggest arguments. Businesses can evaluate contracts for potential risks and compliance issues through automated contract analysis software.
Customer Support and Help
AI-driven expert systems at workstations address customer inquiries, resolve technical problems, and deliver automated assistance. They alleviate the workload on human agents and enhance response times.
For example, chatbots employed by companies like Google and Amazon provide rapid answers to customer questions. Telecom firms use virtual assistants to identify network problems and offer solutions.
Cybersecurity and Fraud Detection
Through analysis of trends and suspicious activity, anti-fraud and cybersecurity expert systems are critical in identifying security threats and preventing fraud. They significantly enhance cybersecurity efforts through real-time risk identification.
For instance,IBM QRadar closely monitors network traffic to identify potential threats. Similarly, fraud prevention systems at banks monitor transactions to prevent illegal behavior.
Agriculture domain
AI-powered expert systems assist farmers by giving them information on how to control pests, keep soil healthy, and optimize agricultural production. This approach maximizes output and minimizes losses.
A great example of this is the Decision Support System for Agrotechnology Transfer (DSSAT), which assists farmers in selecting the optimal crops based on weather and soil conditions. AI-based analysis is also employed by smart irrigation systems to optimize water usage.
Education and Training
Expert systems assist in tutoring, grade automatically, and provide customized learning suggestions. Depending on their performance, they give students individualized learning paths.
For example, Coursera and Khan Academy employ AI to suggest courses based on student performance. Automated tutoring software assists students by providing practice problems and explanations.
The following table shows where ES can be applied.
Application
Description
Design Domain
Camera lens design, automobile design.
Medical Domain
Diagnosis Systems to deduce cause of disease from observed data, conduction medical operations on humans.
Monitoring Systems
Comparing data continuously with observed system or with prescribed behavior such as leakage monitoring in long petroleum pipeline.
Process Control Systems
Controlling a physical process based on monitoring.
Knowledge Domain
Finding out faults in vehicles, computers.
Finance/Commerce
Detection of possible fraud, suspicious transactions, stock market trading, Airline scheduling, cargo scheduling.
Expert systems (ES) are one of the prominent research domains of AI. It is introduced by the researchers at Stanford University, Computer Science Department.
What are Expert Systems?
The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.
Characteristics of Expert Systems
High performance
Understandable
Reliable
Highly responsive
Capabilities of Expert Systems
The expert systems are capable of −
Advising
Instructing and assisting human in decision making
Demonstrating
Deriving a solution
Diagnosing
Explaining
Interpreting input
Predicting results
Justifying the conclusion
Suggesting alternative options to a problem
They are incapable of −
Substituting human decision makers
Possessing human capabilities
Producing accurate output for inadequate knowledge base
Refining their own knowledge
Components of Expert Systems
The components of ES include −
Knowledge Base
Inference Engine
User Interface
Let us see them one by one briefly −
Knowledge Base
It contains domain-specific and high-quality knowledge.
Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate and precise knowledge.
What is Knowledge?
The data is collection of facts. The information is organized as data and facts about the task domain. Data, information, and past experience combined together are termed as knowledge.
Components of Knowledge Base
The knowledge base of an ES is a store of both, factual and heuristic knowledge.
Factual Knowledge: It is the information widely accepted by the Knowledge Engineers and scholars in the task domain.
Heuristic Knowledge: It is about practice, accurate judgment, ones ability of evaluation, and guessing.
Knowledge representation
It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of IF-THEN-ELSE rules.
Knowledge Acquisition
The success of any expert system majorly depends on the quality, completeness, and accuracy of the information stored in the knowledge base.
The knowledge base is formed by readings from various experts, scholars, and the Knowledge Engineers. The knowledge engineer is a person with the qualities of empathy, quick learning, and case analyzing skills.
He acquires information from subject expert by recording, interviewing, and observing him at work, etc. He then categorizes and organizes the information in a meaningful way, in the form of IF-THEN-ELSE rules, to be used by interference machine. The knowledge engineer also monitors the development of the ES.
Inference Engine
Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution.
In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.
In case of rule based ES, it −
Applies rules repeatedly to the facts, which are obtained from earlier rule application.
Adds new knowledge into the knowledge base if required.
Resolves rules conflict when multiple rules are applicable to a particular case.
To recommend a solution, the Inference Engine uses the following strategies −
Forward Chaining
Backward Chaining
Forward Chaining
It is a strategy of an expert system to answer the question, What can happen next?
Here, the Inference Engine follows the chain of conditions and derivations and finally deduces the outcome. It considers all the facts and rules, and sorts them before concluding to a solution.
This strategy is followed for working on conclusion, result, or effect. For example, prediction of share market status as an effect of changes in interest rates.
Backward Chaining
With this strategy, an expert system finds out the answer to the question, Why this happened?
On the basis of what has already happened, the Inference Engine tries to find out which conditions could have happened in the past for this result. This strategy is followed for finding out cause or reason. For example, diagnosis of blood cancer in humans.
User Interface
User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence.
It explains how the ES has arrived at a particular recommendation. The explanation may appear in the following forms −
Natural language displayed on screen.
Verbal narrations in natural language.
Listing of rule numbers displayed on the screen.
The user interface makes it easy to trace the credibility of the deductions.
Requirements of Efficient ES User Interface
It should help users to accomplish their goals in shortest possible way.
It should be designed to work for users existing or desired work practices.
Its technology should be adaptable to users requirements; not the other way round.
It should make efficient use of user input.
Expert Systems Limitations
No technology can offer easy and complete solution. Large systems are costly, require significant development time, and computer resources. ESs have their limitations which include −
Limitations of the technology
Difficult knowledge acquisition
ES are difficult to maintain
High development costs
Applications of Expert System
The following table shows where ES can be applied.
Application
Description
Design Domain
Camera lens design, automobile design.
Medical Domain
Diagnosis Systems to deduce cause of disease from observed data, conduction medical operations on humans.
Monitoring Systems
Comparing data continuously with observed system or with prescribed behavior such as leakage monitoring in long petroleum pipeline.
Process Control Systems
Controlling a physical process based on monitoring.
Knowledge Domain
Finding out faults in vehicles, computers.
Finance/Commerce
Detection of possible fraud, suspicious transactions, stock market trading, Airline scheduling, cargo scheduling.
Expert System Technology
There are several levels of ES technologies available. Expert systems technologies include −
Expert System Development Environment
The ES development environment includes hardware and tools. They are −
Workstations, minicomputers, mainframes.
High level Symbolic Programming Languages such as LISt Programming (LISP) and PROgrammation en LOGique (PROLOG).
Large databases.
Tools
They reduce the effort and cost involved in developing an expert system to large extent.
Powerful editors and debugging tools with multi-windows.
They provide rapid prototyping
Have Inbuilt definitions of model, knowledge representation, and inference design.
Shells
A shell is nothing but an expert system without knowledge base. A shell provides the developers with knowledge acquisition, inference engine, user interface, and explanation facility. For example, few shells are given below −
Java Expert System Shell (JESS) that provides fully developed Java API for creating an expert system.
Vidwan, a shell developed at the National Centre for Software Technology, Mumbai in 1993. It enables knowledge encoding in the form of IF-THEN rules.
Development of Expert Systems: General Steps
The process of ES development is iterative. Steps in developing the ES include −
Identify Problem Domain
The problem must be suitable for an expert system to solve it.
Find the experts in task domain for the ES project.
Establish cost-effectiveness of the system.
Design the System
Identify the ES Technology
Know and establish the degree of integration with the other systems and databases.
Realize how the concepts can represent the domain knowledge best.
Develop the Prototype
From Knowledge Base: The knowledge engineer works to −
Acquire domain knowledge from the expert.
Represent it in the form of If-THEN-ELSE rules.
Test and Refine the Prototype
The knowledge engineer uses sample cases to test the prototype for any deficiencies in performance.
End users test the prototypes of the ES.
Develop and Complete the ES
Test and ensure the interaction of the ES with all elements of its environment, including end users, databases, and other information systems.
Document the ES project well.
Train the user to use ES.
Maintain the System
Keep the knowledge base up-to-date by regular review and update.
Cater for new interfaces with other information systems, as those systems evolve.
Benefits of Expert Systems
Availability: They are easily available due to mass production of software.
Less Production Cost: Production cost is reasonable. This makes them affordable.
Speed: They offer great speed. They reduce the amount of work an individual puts in.
Less Error Rate: Error rate is low as compared to human errors.
Reducing Risk: They can work in the environment dangerous to humans.
Steady response: They work steadily without getting motional, tensed or fatigued.
