Expert Systems and Applied AI

- Expert Systems

The first expert systems (ESs) were created in the 1970s and then proliferated in the 1980s. ESs were among the first truly successful forms of AI software. 

In today's modern world with technological advances, we can process human minds, machines are designed to think like humans and imitate their behavior, so the overall process of designing machines that can act like humans is called AI. Some of the AI applications are expert systems, natural language processing, speech recognition, computer vision. 

AI is a software that simulates the behavior and judgment of humans or organizations with experts in a specific field, called an expert system. It does this by obtaining relevant knowledge from a knowledge base and interpreting it based on the user's questions. 

Data in the knowledge base is added by experts in a specific field, and the software is used by non-expert users to obtain some information. It is widely used in medical diagnosis, accounting, coding, gaming and other fields. 

Please refer to the following for more information:

• Wikipedia: Expert System

- Benefits of Expert Systems

Expert systems (ESs) are a type of artificial intelligence (AI) that can solve problems that would otherwise require human expertise. They can be used in a variety of programs, including human resources, medicine, supply chain, financial management, project management, and customer service.

An ES is AI software that uses knowledge stored in a knowledge base to solve problems that usually require human experts, thereby retaining the knowledge of human experts in its knowledge base. ​

ESs are computer programs that use AI to imitate the behavior and judgment of humans or organizations with expertise in a specific field. They are a form of AI that can handle unique situations thorough human training. 

ESs have several benefits, including:

• Cost savings: Expert systems can reduce the cost of consulting human experts.

• Fast solutions: Expert systems can provide fast and robust solutions to complex problems.
• Accuracy: Expert systems are accurate and can use logical deduction.
• Consistency: Expert systems make consistent recommendations.
• Low error rate: Expert systems have a low error rate.
• Capture expertise: Expert systems can capture the expertise of a uniquely qualified expert.
• Improve decision-making quality: Expert systems can improve decision-making quality.
• Gathers knowledge: Expert systems gather scarce knowledge and use it efficiently.

ESs are generally designed to complement rather than replace human experts. A few examples of an ES are DENDRAL, a molecular structure prediction tool for chemical analysis. Another example of an expert system that predicts the kind and extent of lung cancer is PXDES.

Some challenges of ESs include: 

• Linear thinking
• Lack of intuition
• Lack of emotion
• Points of failure

- Expert Systems: AI Applied

The most important applied area of AI is the field of expert systems (ESs). ESs are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise. ESs are assistants to decision makers and not substitutes for them. 

An ES is a knowledge-based system that employs knowledge about its application domain and uses an inferencing (reason) procedure to solve problems that would otherwise require human competence or expertise. The power of ESs stems primarily from the specific knowledge about a narrow domain stored in the ES's knowledge base. 

In AI, an ES is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. 

ESs do not have human capabilities. They use a knowledge base of a particular domain and bring that knowledge to bear on the facts of the particular situation at hand. The knowledge base of an ES also contains heuristic knowledge - rules of thumb used by human experts who work in the domain. 

An ES is divided into two core subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities. 

- The Five Components of the Expert System in AI

The expert system (ES) in AI has five components:

• Knowledge Base: The knowledge base contains the facts and rules in the expert system. It includes the specification of problem solving and the development of methods related to the domain and knowledge of a particular discipline.
• Inference engine: The most basic job of the inference engine is to collect relevant information from the knowledge base, analyze it, and determine solutions to user problems. The inference engine also has interpretation and troubleshooting capabilities.
• Explanation module: This module helps the expert system to give the user an explanation about how the expert system reached a particular conclusion.
• Knowledge acquisition and learning module: With the help of this component, the expert system can collect more information from many sources. Afterwards, the knowledge is stored in the knowledge base.
• User interface: This element allows non-expert users to communicate with the expert system and develop solutions.

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- Capabilities of Expert Systems

Expert Systems (ESs) are high performance, understandable, reliable, and highly responsive. 

The ESs 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

- Modern Expert Systems

The limitations of the previous approaches of expert systems (ESs) have urged researchers to develop new types of approaches. They have developed more efficient, flexible and powerful approaches in order to simulate the human decision-making process. 

Some of the approaches that researchers have developed are based on new methods of AI, and in particular in machine learning and data mining approaches with a feedback mechanism. 

Modern systems can incorporate new knowledge more easily and thus update themselves easily. Such systems can generalize from existing knowledge better and deal with vast amounts of complex data. Sometimes these type of ESs are called “intelligent systems.”

The strength of an ES derives from its knowledge base - an organized collection of facts and heuristics about the system's domain. An ES is built in a process known as knowledge engineering, during which knowledge about the domain is acquired from human experts and other sources by knowledge engineers. 

The accumulation of knowledge in knowledge bases, from which conclusions are to be drawn by the inference engine, is the hallmark of an expert system.