Artificial Intelligence is software that simulates human behavior. An expert system performs like a human expert in a specific subject field. An effective expert system is easy to use and solves problems quickly and accurately.
Experts systems are usually used when the human experts are too costly.
Humans who are experts in a specific domain add the data to the knowledge base. The non-expert users use the expert system to acquire information.
Slow results, high error rate, unintentional human bias, and traveling are some disadvantages of hiring a human expert.
Why an expert system?
- It has a huge storage capacity and recalls the information whenever we require it without any limitations.
- Information in the knowledge bank is highly secure
- Generates 24×7 output.
- Gives the best possible output through collaborative knowledge of domain experts
How does an expert system work?
The expert system uses Artificial Intelligence and Machine Learning to simulate human behavior. Also, their performance can enhance over time with an experience similar to humans. The expert systems gather the knowledge in the knowledge base and integrate it with the rules engine or inference.
The rules engine uses one of the following methods for extracting information from the knowledge base:
- Forward chaining processes a set of facts and logically predicts the future. One best example of forwarding chaining is to predict the stock market movement.
- Backward chaining processes a set of facts to come to a logical conclusion about why something occurred. Conclude a medical diagnosis by observing the symptoms.
Developing and maintaining an expert system is called knowledge engineering. The Knowledge engineers ensure that required information exists in the knowledge base to solve a problem.
Expert systems components
The three major components of the expert system comprise the following :
- The knowledge base acts as a knowledge acquisition module that allows the system to collect information from various external sources and store them in the knowledge base.
- Inference Engine extracts relevant information from the knowledge base to solve user problems. The rule-based system maps the data from the knowledge base to specific rules and makes decisions depending on those inputs. Also, this component includes the explanation module that shows the users how the system could make the conclusions.
- The user interface is a vital element of the expert system that end users interact with to find responses to the questions. Here the master framework communicates with the user. It takes the client’s inquiry and passes it on to the inference engine.
Expert system characteristics
- Highly capable of giving the best performance 24×7
- Easy to comprehend
- Highly reliable with error-free results
- Highly proactive in responding to every detail of the problem
Knowledge base components
- Factual knowledge is dependent on the facts. Such knowledge is proven and accepted by all.
- Heuristic Knowledge is unorganized on depends on one’s evaluation.
Expert system tools
Expert system tools simplify the job of constructing an expert system. Expert system tools are categorized into four different groups, which will describe briefly.
The most popularly used programming language for expert system applications is LISP. PROLOG and CLIPS (C Language Integrated Production System)has even gained popularity in addition to this. These languages, relative to conventional programming languages, can ease adding, eliminating, or substituting new rules and managing memory capabilities. Few expert systems use FORTRAN and PASCAL.
Though LISP and CLIP give the required flexibility to the expert system, they fail to render guidance on how to represent knowledge for accessing the knowledge base. While knowledge engineering languages such as KAS gives less flexibility, however, provide readymade inference engines for controlling the usage of the knowledge base.
Knowledge Engineering Languages
Knowledge engineering language consists of an expert system-building language integrated into a supportive environment. It is used to develop and debug the expert system. The Knowledge Engineering Languages can be categorized as skeletal systems or general-purpose systems.
The skeletal system makes system development easy and quick. However, they lack flexibility and generality. The general-purpose knowledge engineering language can handle knowledge extraction and possess generality and flexibility.
The system building aids have programs that can represent the program and the domain expert knowledge. This program deals with challenging tasks. The system-building aids can be divided majorly into knowledge acquisition aids and design aids. AGE system demonstrates design aids. TIMM and SEEK are knowledge refinement aids. TEIRESIAS, MOLE, and SALT demonstrate knowledge acquisition.
Tool support environment facilities
The environment can be divided into the following instances:
- Those required during expert system development, such as knowledge base editors and debugging aids.
- Those required to enhance the developed programs, such as explanation mechanisms and input/output facilities.
Employing an expert systems in AI can be highly advantageous for business operations. The expert system tools can aid the expert system development and enhance the process. They are readily available with fewer production costs and greater speed. Moreover, they reduce risks and provide steady responses.