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User Interface
The user interface is the most crucial part of the Expert System Software. This component takes the user's query in a readable form and passes it to the inference engine. After that, it displays the results to the user. In other words, it's an interface that helps the user communicate with the expert system.

Inference Engine
The inference engine is the brain of the expert system. Inference engine contains rules to solve a specific problem. It refers the knowledge from the Knowledge Base. It selects facts and rules to apply when trying to answer the user's query. It provides reasoning about the information in the knowledge base. It also helps in deducting the problem to find the solution. This component is also helpful for formulating conclusions.

Knowledge Base
The knowledge base is a repository of facts. It stores all the knowledge about the problem domain. It is like a large container of knowledge which is obtained from different experts of a specific field.

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Knowledge base:  The knowledge base is a database that allows the storage and retrieval of the knowledge pro vided by a collection of experts. It contains knowledge about the specific area for which the expert system can be used.

Infererence engine:  The inference engine is the part of the expert system that makes judgement s and reasoning using the knowledge base and user responses. It is designed to produce reasoning based on a set of rules.

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There are 3 types of components for expert systems

Knowledge Base: The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts.
Inference Engine: It will ask the user questions, based on their answers, it will follow a line of logic. This may lead to further questions and eventually a final result
User interface: The user interface is the way the user interacts with the expert system. They are often graphical in nature and have a range of selection processes or typing methods to allow the user to provide responses

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Forward chaining: Is an expert system, for which we don't have an answer, only the data and we need to go ahead to reach the solution.
Backward chaining:If the process starts with a known conclusion , but the path to it is unknown, the chaining will work in reverse.

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The table below shows how a car diagnosis expert system would be used:

1

A user interface screen presents user with a set of questions relating to different car problems.

You can try out an example of a car diagnosis expert system by clicking here

2

The user answers the questions by typing or selecting option buttons / check boxes

3

Inference engine uses the rules base to match user's answers to the data stored in the knowledge base

4

The system diagnoses the problem with the car and suggests how the user can fix it.

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Medical diagnosis (the knowledge base would contain medical information, the symptoms of the patient would be used as the query, and the advice would be a diagnose of the patient’s illness)

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1. The user interface is used to input the symptoms of the patient
2. Using the forward chaining method the inference engine uses the rule base to access the knowledge base to find the diagnosis of the illness.

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Forward chaining is known as data-driven technique because we reaches to the goal using the available data. Backward chaining is known as goal-driven technique because we start from the goal and reaches the initial state in order to extract the facts.
EG:
-MYCIN uses the backward chaining technique to diagnose bacterial infections.
-DENDRAL employs forward chaining to establish the structure of chemicals.

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Abdullah Sheikh

Forward chaining starts with the available data and uses inference rules to extract more data, until a goal is reached.An inference engine using forward chaining searches the inference rules until it finds one where the IF statement is true when such a rule is found the inference engine uses the 'THEN' part to cause the addition of new information

Backward chaining starts with a list of goals and works backwards. An inference engine using backward chaining would search the inference rules until it finds one which has a THEN part that matches the desired goal. if the IF part of that rule is not known to be true , then it is added to the list of goals.

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Example:
A DENDRAL expert system is a good example of how forward chaining is used in artificial intelligence. DENDRAL is used in the prediction of the molecular structure of substances.

Deducing the chemical structure starts by finding the number of atoms in every molecule. The mass spectrum of the sample is then used to establish the arrangement of the atoms. We can summarize these steps as follows.

The chemical formula is determined ( the number of atoms in every molecule).

The spectrum machine is used to form mass spectrums of the sample.

The isomer and structure of the chemical are identified.

In this example, the identification of the chemical structure is the endpoint. In the DENDRAL expert system, a generate and test technique is employed.

To solve the above problem, first, we will convert all the above facts into first-order definite clauses, and then we will use a forward-chaining algorithm to reach the goal.

Forward chaining can be used in planning, monitoring, controlling, and interpreting applications.

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Backward Chaining includes MYCIN.
MYCIN uses backward chaining technique to diagnose bacterial infections. The knowledge base of MYCIN consists of many antecedents-consequent rules that enable the system to recognise various causes of bacterial infections. The system will gather information of patient relating to symptoms and history of the patient. It will then analyse this.
Forward Chaining includes DENDRAL
DENDRAL is used to predict the molecular structure of substances.

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They provide answers to questions that are outside a user's knowledge and experience
They can aid professionals in areas where knowledge and experience is weak
Expert system used logical process which means they are consistent in the answers they give.
As they are a computer and not a human they can be accessed on a 24/7 basis.
Using an expert system means a less skilled workforce is required as system will provide the knowledge and experience needed.
Reduces time taken to solve a problem, questioning and reasoning process is much quicker than a normal interaction between humans.

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(1) they cannot use intuition . a human can often quickly see a link between pieces of information without having to go through lots of logical stages .a computer cannot do this

(2) they have no empathy - a patient may be encouraged by the warmth and sympathy of a human doctor than a computer

(3) they cannot learn from their mistakes . if there is a programming error in its logic or knowledge then a system will continue to produce incorrect solutions every time without realising , whereas a human would notice if they had made a mistake and change their approach .

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Give eg Forward  chaining and Backward Chaining

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Give eg Forward  chaining and Backward Chaining

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An expert system has a user interface to allow users to enter information into the system. It collects this information and compares it to what is already available in its knowledge base. It uses IF-THEN rules to come up with a solution/diagnosis. The inference engine applies logical rules to the knowledge base to deduce new information.

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Medical Application:
Firstly, the user interface provides a set of different questions that could relate to the condition of the patient, and once the user condition is found, using forward chaining, the inference engine refers to the knowledge and rules bases to look through the different data in the system collected from human expertise, then once the solution has been found, the system diagnoses the problem/condition of the patient and sends the required diagnoses to the user to treat the patient.

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For chess games:

The computer has a knowledge base which contains different possible moves and strategies.

When the human player moves a piece, the computer would respond by checking its knowledge base for the best possible move to counter.

When the best possible move has been found, the computer would move its piece to the best possible position

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Expert System is a computerised system that attempts to reproduce decision making process of an expert human being.

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How does an expert system use its components to diagnosing or finding solutions

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How does an expert system use its components to diagnosing or finding solutions

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Explain with an example

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Data processing systems
File types

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How does an expert system use its components to diagnosing or finding solutions

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How does an expert system use its components to diagnosing or finding solutions

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Explain with an example

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Explain Forward Chaining and Backward Chaining 

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Explain Forward Chaining and Backward Chaining 

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What is Expert system and list its applications and list down advantages and disadvantages

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What is Expert system and list its applications and list down advantages and disadvantages

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Expert System

1. What is Expert system and list its applications and list down advantages and disadvantages

2. Components of expert system

3. Forward Chaining and Backward Chaining

4. How does an expert system use its components to diagnosing or finding solutions