Why expert system is used

It was used in organic chemistry to detect unknown organic molecules with the help of their mass spectra and knowledge base of chemistry. MYCIN: It was one of the earliest backward chaining expert systems that was designed to find the bacteria causing infections like bacteraemia and meningitis. It was also used for the recommendation of antibiotics and the diagnosis of blood clotting diseases.

To determine the disease, it takes a picture from the upper body, which looks like the shadow. This shadow identifies the type and degree of harm. CaDeT: The CaDet expert system is a diagnostic support system that can detect cancer at early stages.

Characteristics of Expert System High Performance: The expert system provides high performance for solving any type of complex problem of a specific domain with high efficiency and accuracy.

Understandable: It responds in a way that can be easily understandable by the user. It can take input in human language and provides the output in the same way.

Reliable: It is much reliable for generating an efficient and accurate output. Highly responsive: ES provides the result for any complex query within a very short period of time. User Interface With the help of a user interface, the expert system interacts with the user, takes queries as an input in a readable format, and passes it to the inference engine.

Inference Engine Rules of Engine The inference engine is known as the brain of the expert system as it is the main processing unit of the system. It applies inference rules to the knowledge base to derive a conclusion or deduce new information. It helps in deriving an error-free solution of queries asked by the user. With the help of an inference engine, the system extracts the knowledge from the knowledge base.

There are two types of inference engine: Deterministic Inference engine: The conclusions drawn from this type of inference engine are assumed to be true. It is based on facts and rules. Probabilistic Inference engine: This type of inference engine contains uncertainty in conclusions, and based on the probability. Inference engine uses the below modes to derive the solutions: Forward Chaining: It starts from the known facts and rules, and applies the inference rules to add their conclusion to the known facts.

Backward Chaining: It is a backward reasoning method that starts from the goal and works backward to prove the known facts. Knowledge Base The knowledgebase is a type of storage that stores knowledge acquired from the different experts of the particular domain. It is considered as big storage of knowledge. The more the knowledge base, the more precise will be the Expert System.

It is similar to a database that contains information and rules of a particular domain or subject. One can also view the knowledge base as collections of objects and their attributes. Such as a Lion is an object and its attributes are it is a mammal, it is not a domestic animal, etc. Components of Knowledge Base Factual Knowledge: The knowledge which is based on facts and accepted by knowledge engineers comes under factual knowledge.

Heuristic Knowledge: This knowledge is based on practice, the ability to guess, evaluation, and experiences. In the case of MYCIN, human experts specialized in the medical field of bacterial infection, provide information about the causes, symptoms, and other knowledge in that domain.

In order to test it, the doctor provides a new problem to it. The problem is to identify the presence of the bacteria by inputting the details of a patient, including the symptoms, current condition, and medical history.

The ES will need a questionnaire to be filled by the patient to know the general information about the patient, such as gender, age, etc. Now the system has collected all the information, so it will find the solution for the problem by applying if-then rules using the inference engine and using the facts stored within the KB.

In the end, it will provide a response to the patient by using the user interface. Participants in the development of Expert System There are three primary participants in the building of Expert System: Expert: The success of an ES much depends on the knowledge provided by human experts.

These experts are those persons who are specialized in that specific domain. Knowledge Engineer: Knowledge engineer is the person who gathers the knowledge from the domain experts and then codifies that knowledge to the system according to the formalism.

End-User: This is a particular person or a group of people who may not be experts, and working on the expert system needs the solution or advice for his queries, which are complex.

Why Expert System? So below are the points that are describing the need of the ES: No memory Limitations: It can store as much data as required and can memorize it at the time of its application.

But for human experts, there are some limitations to memorize all things at every time. High Efficiency: If the knowledge base is updated with the correct knowledge, then it provides a highly efficient output, which may not be possible for a human.

Domain-specific shells are actually incomplete specific expert systems, which require much less effort in order to field an actual system. Expert system development environments. They run on engineering workstations, minicomputers, or mainframes; offer tight integration with large databases; and support the building of large expert systems. High-level programming languages. ESs are now rarely developed in a programming language. Three fundamental roles in building expert systems are:.

Expert - Successful ES systems depend on the experience and application of knowledge that the people can bring to it during its development. Large systems generally require multiple experts. Knowledge engineer - The knowledge engineer has a dual task.

This person should be able to elicit knowledge from the expert, gradually gaining an understanding of an area of expertise. Intelligence, tact, empathy, and proficiency in specific techniques of knowledge acquisition are all required of a knowledge engineer. Knowledge-acquisition techniques include conducting interviews with varying degrees of structure, protocol analysis, observation of experts at work, and analysis of cases. On the other hand, the knowledge engineer must also select a tool appropriate for the project and use it to represent the knowledge with the application of the knowledge acquisition facility.

User - A system developed by an end user with a simple shell, is built rather quickly an inexpensively. Larger systems are built in an organized development effort. A prototype-oriented iterative development strategy is commonly used.

ESs lends themselves particularly well to prototyping. Steps in the methodology for the iterative process of ES development and maintenance include:. Problem Identification and Feasibility Analysis:. The needed degree of integration with other subsystems and databases is established. Testing and Refinement of Prototype:. End users test the prototypes of the ES. Expert systems offer both tangible and important intangible benefits to owner companies.

These benefits should be weighted against the development and exploitation costs of an ES, which are high for large, organizationally important ESs. An ES is no substitute for a knowledge worker's overall performance of the problem-solving task. But these systems can dramatically reduce the amount of work the individual must do to solve a problem, and they do leave people with the creative and innovative aspects of problem solving.

Some of the possible organizational benefits of expert systems are:. An Es can complete its part of the tasks much faster than a human expert. The error rate of successful systems is low, sometimes much lower than the human error rate for the same task. ESs are a convenient vehicle for bringing to the point of application difficult-to-use sources of knowledge.

ESs can capture the scarce expertise of a uniquely qualified expert. ESs can become a vehicle for building up organizational knowledge, as opposed to the knowledge of individuals in the organization. When use as training vehicles, ESs result in a faster learning curve for novices. The company can operate an ES in environments hazardous for humans. No technology offers an easy and total solution. Large systems are costly and require significant development time and computer resources.

ESs also have their limitations which include:. Operational domains as the principal area of ES application. Maintaining human expertise in organizations. Expert systems are only one area of AI. Other areas include:. Being able to talk to computers in conversational human languages and have them A understand us in a goal of AI researchers. Natural language processing systems are becoming common. The main application for natural language systems at this time is as a user interface for expert and database systems.

AI, engineering, and physiology are the basic disciplines of robotics. This technology produces robot machines with computer intelligence and computer-controlled, human like physical capabilities, robotics applications.

The simulation of human senses is a principal objective of the AI field. The most advanced AI sensory system is compute vision, or visual scene recognition. By following a chain of conditions and derivations, the expert system deduces the outcome after considering all facts and rules. It then sorts them before arriving at a conclusion in terms of the suitable solution. This strategy is followed while working on conclusion, result, or effect.

For example, predicting how does the share market prediction of share market will react to the changes in the interest rates. Depending upon what has already occurred, the inference engine tries to identify the conditions that could have happened in the past to trigger the final result.

This strategy is used to find the cause or the reason behind something happening. For example, the diagnosis of different types of cancer in humans. Tools, as an ES technology, assists in reducing the effort and cost involved in developing an expert system to a large extent. A Shell an expert system that functions without a knowledge base. It provides developers with knowledge acquisition, inference engine, user interface, and explanation facility. There are numerous examples of expert systems.

Some of them are:. A key distinction between the traditional system as opposed to the expert system is the way in which the problem related expertise is coded.

Essentially, in conventional applications, the problem expertise is encoded in both program as well as data structures. On the other hand, in expert systems, the approach of the problem related expertise is encoded in data structures only. Moreover, the use of knowledge in expert systems is vital. However, traditional systems use data more efficiently than the expert system. One of the biggest limitations of conventional systems is that they are not capable of providing explanations for the conclusion of a problem.

That is because these systems try to solve problems in a straightforward manner.