(a) Inference engine : The inference engine is the portion of the expert system that performs reasoning by using the contents of the knowledge base in a particular sequence. During the consultation, the inference engine examines the rules of the knowledge base one at a time, and when a rule’s condition is true, specified action is taken. In expert system terminology, the rule is fired when the action is taken.
Two main methods have been devised for the inference engine to use in examining the rules: forward reasoning and reverse reasoning.
In forward reasoning, also known as forward chaining, the rules are examined one after another in a certain order. The order might be the sequence in which the rules are entered into the rule set, or it might be some other sequence specified by the user. As each rule is examined, the expert system attempts to evaluate whether the condition is true or false.
In reverse reasoning, is faster than forward reasoning because it does not have to consider all the rules and does not make multiple passes through the rule set. Reverse reasoning is appropriate when
· There are multiple goal variables
· There are many rulesAll or most of the rules do not have to be examined in the process of reaching a solution.
Instant Life Insurance rates, Term Life Insurance, Sales Force Management
Tuesday, February 3, 2009
Saturday, January 31, 2009
Components of ES-1
Today we continue our talk on ES components.
(a) Knowledge based : The knowledge base contains both facts that describe the problem area and knowledge representation techniques that describe how the facts fits together in a logical manner. The term problem domain is used to describe the problem area. The popular knowledge representation techniques are rules and networks of rules.
The rules in the knowledge base are usually coded in the form. IF X THEN Y, where X is a condition, Y is an action to be taken if the condition is true. All the rules contained in an expert system are called the rule set.
The rules of a rule set are not physically linked, but their logical relationships can be established.
(b) Inference engine : The inference engine is the portion of the expert system that performs reasoning by using the contents of the knowledge base in a particular sequence. During the consultation, the inference engine examines the rules of the knowledge base one at a time, and when a rule’s condition is true, specified action is taken. In expert system terminology, the rule is fired when the action is taken.
(a) Knowledge based : The knowledge base contains both facts that describe the problem area and knowledge representation techniques that describe how the facts fits together in a logical manner. The term problem domain is used to describe the problem area. The popular knowledge representation techniques are rules and networks of rules.
The rules in the knowledge base are usually coded in the form. IF X THEN Y, where X is a condition, Y is an action to be taken if the condition is true. All the rules contained in an expert system are called the rule set.
The rules of a rule set are not physically linked, but their logical relationships can be established.
(b) Inference engine : The inference engine is the portion of the expert system that performs reasoning by using the contents of the knowledge base in a particular sequence. During the consultation, the inference engine examines the rules of the knowledge base one at a time, and when a rule’s condition is true, specified action is taken. In expert system terminology, the rule is fired when the action is taken.
Tuesday, January 27, 2009
Components of ES
Expert system consists of 4 major parts
1. user interface
2. knowledge base
3. inference engine
4. development engine
(a) Interface: The user interface enables the user to enter instructions & information into the expert system and also to receive information from it. The instructions specify the parameters that guide the expert system through its reasoning process. The information is in the form of value assigned to certain values. For example. For online blinds store, they ask which product they want- vertical blinds or roman shades, which size they want etc. Another example is Instant Life Insurance Rates company, who are providing Life Insurance Quotes online, they ask their customer to enter their certain details when they are applying for term life insurance.
1. Expert system inputs: The user can use 4 methods for input purposes : menus, commands, natural language and customized interfaces.
2. Expert system outputs : Expert systems are designed to recommend solutions. These solutions are supplemented by explanations. There are two types of explanations : Explanation of questions and Explanation of problem solution.
i) Explanation of questions: The manager may desire explanations while the expert system performs its reasoning. Perhaps the expert system will prompt the manager to enter some information. The manager asks why the information is needed, and the expert system provides an explanation.
ii) Explanation of problem solution: After the export system provides a problem solution, the manager can ask for an explanation of how it was reached. The expert will display each of the reasoning steps leading to the solution.
1. user interface
2. knowledge base
3. inference engine
4. development engine
(a) Interface: The user interface enables the user to enter instructions & information into the expert system and also to receive information from it. The instructions specify the parameters that guide the expert system through its reasoning process. The information is in the form of value assigned to certain values. For example. For online blinds store, they ask which product they want- vertical blinds or roman shades, which size they want etc. Another example is Instant Life Insurance Rates company, who are providing Life Insurance Quotes online, they ask their customer to enter their certain details when they are applying for term life insurance.
1. Expert system inputs: The user can use 4 methods for input purposes : menus, commands, natural language and customized interfaces.
2. Expert system outputs : Expert systems are designed to recommend solutions. These solutions are supplemented by explanations. There are two types of explanations : Explanation of questions and Explanation of problem solution.
i) Explanation of questions: The manager may desire explanations while the expert system performs its reasoning. Perhaps the expert system will prompt the manager to enter some information. The manager asks why the information is needed, and the expert system provides an explanation.
ii) Explanation of problem solution: After the export system provides a problem solution, the manager can ask for an explanation of how it was reached. The expert will display each of the reasoning steps leading to the solution.
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