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Rule Based Architecture of an Expert System

The most common form of architecture used in expert and other types of knowledge based systems is the production system or it is called rule based systems.



The most common form of architecture used in expert and other types of knowledge based systems is the production system or it is called rule based systems. This type of system uses knowledge encoded in the form of production rules i.e. if-then rules. The rule has a conditional part on the left hand side and a conclusion or action part on the right hand side. For example if: condition1 and condition2 and condition3



Then: Take action4


Each rule represents a small chunk of knowledge to the given domain of expertise. When the known facts support the conditions in the rule’s left side, the conclusion or action part of the rule is then accepted as known. The rule based architecture of an expert system consists of the domain expert, knowledge engineer, inference engine, working memory, knowledge base, external interfaces, user interface, explanation module, database spreadsheets executable programs s mentioned in figure.

Integration of Expert systems Components


The components of the rule based architecture are as follows.


1.     User Interface: It is the mechanism by which the user and the expert system communicate with each other i.e. the use interacts with the system through a user interface. It acts as a bridge between user and expert system. This module accepts the user queries and submits those to the expert system. The user normally consults the expert system for following reasons.

a)     To get answer of his/her queries.


b)    To get explanation about the solution for psychological satisfaction.


The user interface module is designed in such a way that at user level it accepts the query in a language understandable by expert system. To make the expert system user friendly, the user interface interacts with the user in natural language. The user interface provides as much facilities as possible such as menus, graphical interfaces etc. to make the dialog user friendly and more attractive.


2.Explanation Module: The explanation module explains the reasoning of the system to a user. It provides the user with an explanation of the reasoning process when requested. The credibility of  expert system will be established only when it is able to explain “how and why” a particular conclusion is drawn. This explanation increases the belief of user in the expert system.

a)     Explanation(How): To respond to a how query, the explanation module traces the chain of rules fired during a consolation with the user. This explanation mode can be activated once the process is over. It explains how a fact was deduced by the system and similarly how a rule was/wasn’t used. The simplest way to specify this is to explain the rule which allows the deduction. For e.g.


If the system (S) will give information about the parent-child relationship to the user (U) then the followings can be possible.


S: My diagnosis is “A is the father of B”


U: How?


S: The result was obtained by the addition of following facts and rules.


Fact no 11: A is the parent of Hari.


Fact no 15: A is a male.


Fact no 110: X is father of Y:


X is parent of Y, X is male.


So A is the father of B.


b)    Explanation (Why)? To respond to a why query, the explanation module must be able to explain why certain information is needed by the inference engine to complete a step in the reasoning process. This mode of explanation is dynamic and is used when the system functions in backward chaining. If the user doesn’t understand the reason behind the question. The system is capable of


explaining why the question was asked. For example S: Is the following true?


A is the father of B.




S:I need the fact:


A is the father of B to establish the following fact “B is the son of A”.


By using the rule no. 4:


A is the father of B:


B is the son of A.


3.     Working Memory: It is a global database of facts used by the rules.


Knowledge Engineering: The primary people involved in building an expert system are the knowledge engineer, the domain expert and the end user. Once the knowledge engineer has obtained a general overview of the problem domain and gone through several problem solving sessions with the domain expert, he/she is ready to begin actually designing the system, selecting a way to represent the knowledge, determining the search strategy (backward or forward) and designing the user interface. After making complete designs, the knowledge engineer builds a prototype. The prototype should be able to solve problems in a small area of the domain. Once the prototype has been implemented, the knowledge engineer and domain expert test and refine its knowledge by giving it problems to solve and correcting its disadvantages.


5.     Knowledge Base: In rule based architecture of an expert system, the knowledge base is the set of production rules. The expertise concerning the problem area is represented by productions. In rule based architecture, the condition actions pairs are represented as rules, with the premises of the rules (if part) corresponding to the condition and the conclusion (then part) corresponding to the action. Case-specific data are kept in the working memory. The core part of an expert system is the knowledge base and for this reason an expert system is also called a knowledge based system. Expert system knowledge is usually structured in the form of a tree that consists of a root frame and a number of sub frames. A simple knowledge base can have only one frame, i.e. the root frame whereas a large and complex knowledge base may be structured on the basis of multiple frames.


Inference Engine: The inference engine accepts user input queries and responses to questions through the I/O interface. It uses the dynamic information together with the static knowledge stored in the knowledge base. The knowledge in the knowledge base is used to derive conclusions about the current case as presented by the user’s input. Inference engine is the module which finds an answer from the knowledge base. It applies the knowledge to find the solution of the problem. In general, inference engine makes inferences by deciding which rules are satisfied by facts, decides the priorities of the satisfied rules and executes the rule with the highest priority. Generally inferring process is carried out recursively in 3 stages like match, select and execute. During the match stage, the contents of working memory are compared to facts and rules contained in the knowledge base. When proper and consistent matches are found, the corresponding rules are placed in a conflict set.

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