What Is Expert System Information Technology Essay

The rubric for this study is Expert System. Statistically, the per centum of current organisations implementing adept systems for their use is really minimum. This is due to the keeping forces of implementing adept system outweigh its driving force. Yet, as the engineerings are often being upgraded, the restraints of implementing adept systems are acquiring easier to get the better of. Hence, the ground I chose this rubric for my study is due to my strong involvement in the hereafter of expert system where it may potentially be used domestically for supplying the best solutions for complex jobs. Besides, the cognition gained from this research will lend a batch for my concluding twelvemonth undertaking which will include in a simple expert system.

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This study will be explicating what an expert system is, the constituents of expert system, what a knowledge-based expert system is, the characteristics of expert system, the advantages of utilizing adept system, the drawbacks of utilizing adept system and eventually suggestions of implementing adept system into e-commerce systems.

In the appendix portion, the images which I have found from the Internet will be included in for supplying better understanding sing the inside informations of expert system.

What is Adept System?

Harmonizing to Wikipedia, an expert system is an advanced computing machine application that is implemented for the intent of supplying solutions to complex jobs, or to clear up uncertainnesss through the usage of non-algorithmic plans where usually human expertness will be needed. Adept systems are most common in complex job sphere and are considered as widely used options in seeking for solutions that requires the being of specific human expertness. The expert system is besides able to warrant its provided solutions based on the cognition and information from past users. Normally adept systems are used in doing concern selling strategic determinations, analysing the public presentation of existent clip systems, configuring computing machines and execute many other maps which usually would necessitate the being of human expertness.

The difference between an expert system with a normal problem-solving system is that the latter is a system where both plans and informations constructions are encoded, while for expert system merely the information constructions are hard-coded. Alternatively, the cognition of a human expertness is captured and codified in a procedure known as cognition technology. Hence, whenever a peculiar job requires the aid of a certain human expertness to supply a solution, the human expertness which has been codified will be used and processed in order to supply a rational and logical solution. This knowledge-based expert system enables the system to be often added with new cognition and adapt consequently to run into new demands from the ever-changing and unpredictable environment.

Components of Expert System

Figure 1.1 Expert system constituents and human interfaces

Beginning: ( hypertext transfer protocol: //www.amzi.com/ExpertSystemsInProlog/01introduction.htm )

Harmonizing to Brownston, Kant, Farrel and Martin ( 2000 ) from the web site of hypertext transfer protocol: //www.amzi.com/ExpertSystemsInProlog/01introduction.htm, an expert system has many nucleus system constituents to map and interfaces with persons of assorted functions. In the appendix country, there will be a diagram ( Figure 1.1 ) exposing adept system constituents and human interfaces. The major constituents are:

aˆ? Knowledge base – a set of regulations as representation of the expertness, largely in IF THEN statements.

aˆ? Inference engine – codifications in the system ‘s nucleus which retrieves recommendations from problem-domain informations in working storage and cognition base.

aˆ? Working storage – shops the solution informations for specific jobs.

aˆ? User interface – allows the user to interact with the system through comprehensible duologues

The expert system requires specific cardinal persons to interact with in order to to the full work its functionality and capableness. They are the:

aˆ? Domain expert – the person or persons whose expertnesss are used to get the better of the jobs and supplying solutions

aˆ? Knowledge applied scientist – the person who codifies the expert ‘s cognition into a format which expert system can use to pull decisions.

aˆ? User – the person who interacts with the system and acquiring advices and services from it

Majority of the adept systems are built with expert system shells which contains an illation engine and user interface. The shell will be used by a cognition applied scientist to construct a system catered for specific job sphere. Sometimes another extra person is required when adept systems are besides built with usage developed shells for certain applications.

aˆ? System engineer – is the single whose duty is to construct the user interface, specifying the formats and construction of the cognition base and designs the execution of the illation engine.

If the undertaking range and size is well little, the functions of cognition applied scientist and the system applied scientist can be played by one person. The format designing of the cognition base largely are closely related to the cryptography of the sphere cognition when custom-building a system. The cryptography of the cognition to a great extent depends on the construction and format of the cognition base.

The cognition technology procedure is one of the major hurdlings to get the better of when edifice expert systems. The procedure of codifying the expertness into a needed regulation format can be a boring and ambitious undertaking. A customized shell provides a important advantage since the planing format of the cognition base can be used to ease the cognition technology procedure.

Since the edifice of the cognition base is one of the major challenges in the development of expert system, minimising the difference between the regulations represented in the cognition base and the expert ‘s cognition is critically needed. When custom-making a system, the cognition base which formats and constructions are as closely related as possible to where sphere experts handle can be implemented.

Knowledge-based Expert Systems

Harmonizing to the diary from www.ajrhem.com/EXPERT.pdf, non all adept systems have larning constituents to accommodate in new environments or to run into new demands. But a common component each expert system possesses is that one time the system is to the full developed it will be tested and proven by necessitating it to work out existent universe jobs, by supplying the user aid in decision-making and trend-forecasting

Although mention books are able to supply a enormous sum of cognition, users have to read, grok and construe the cognition for it to be used. Conventional computing machine plans are built to execute maps utilizing conventional decision-making logic — holding merely small cognition along with the basic algorithm for executing the specific maps and carry through the necessary boundary conditions.

The alleged “ knowledgebase ” was created in intent of using some cognition representation formalism to gaining control and hive away the Subject Matter Expert ‘s cognition. The procedure includes capturing the SME ‘s cognition and converts the cognition into codifications harmonizing to a standardised format. Knowledge-based adept systems collect the little sections of human cognition and combined into a set of knowledge-base which is used to help in work outing a complex job. Any other job that is within the scope and sphere of the knowledge-base can besides be solved utilizing the same plan without reprogramming.

Knowledge-based adept systems solve jobs which usually require human intelligence. These said expert systems represent the expertness cognition as informations or regulations within a system. These regulations and informations can be used and called upon for mention when needed to work out complex jobs.

When compared to conventional scheduling, the system has the ability to ground the procedure with accounts by back-traces and cipher the degrees of assurance and trade with uncertainness. The cognition has to be codified into programming codification, therefore as the cognition alterations, the plan has to be changed consequently every bit good and so reconstruct.

Expert System Features

Harmonizing Hayes-Roth, Waterman, and Lenat ( 2000 ) , adept systems have a figure of characteristics to utilize to pull decisions. These characteristics allows the users to to the full use the expert system ‘s capableness handily in supplying the most logical and sensible determination in a debatable state of affairs.

Backward chaining – an illation technique which continuously interruptions and simplifies a end into smaller sub-goals which can be proven much easier via IF THEN regulations.

Covering with uncertainnesss – the system has the capableness to manage and ground with conditions that are unsure and informations which are non exactly known.

Forward chaining – an illation technique which deduce a job solution from initial informations via IF THEN regulations.

Data representation – the method where the system shops and accesses the particular job informations.

User interface – the codifications which are implemented for the intent of leting users to interact with the system.

Explanations – the capableness of the system to supply logical account behind the concluding procedure that it used to pull a conclusion..

Inference regulation

An illation regulation is a statement that has two parts, ancestor which is an if clause and consequent which is a so clause. This regulation is what the adept systems rely on and provides the capableness to happen solutions to name and work out troubles. An illation regulation illustration will be:

If the vocal pick is in Latin, and the vocalists are in a group,

Then the vocal pick is decidedly from Il Divo.

An expert system ‘s regulation base shops many illation regulations such as this. They are stored in as separate regulations and the illation engine will pull decisions by traveling through all of them. Rules can be removed and added without impacting others since they are non-connected, yet it will later impact which decisions are to be reached. Inference regulations has the better upper manus compared with traditional scheduling due to the fact that illation regulations are able to copy human logical thinking and warrant the solutions given.

Therefore, when a decision is drawn, the system is able to warrant its class of solution and convert the user. Furthermore, since the expert system uses cognition in a signifier indistinguishable to a certain expert, the solution provided will be non so different from an existent expert ‘s advice.


Forward chaining and backward chaining are the two chief methods used when utilizing illation regulations.

Forward Chaining

When informations is provided, frontward chaining Begins and illation regulations are used to reason more informations until the targeted end is achieved. Through frontward chaining, the illation engine will seek the illation regulations until it matches one of the if clause. It will so continue to reason the so clause and shops this information into its informations for future matching. The procedure will maintain on continue until a end is eventually reached. This method is besides called data driven as the being of informations is required for finding which illation regulations are used. A big figure of adept systems require the usage of forward chaining.

The information driven attack is practical when combinative detonation creates a apparently infinite figure of possible right replies where no definite reply is specified.

Forward chaining requires the input of informations before finding which illation regulations to be used while in the interim infusion more informations until a end is reached. Forward chaining illation engine matches the illation regulations until one of the if clause is found to be true. If matched, it can reason the consequent ( Then clause ) and add the new information into its informations.

Inferences engines will maintain continue on with this procedure until a end is reached. If a certain end is to find the colour of a pet named Fear, provided that it barks and wags tail, and the regulation base has the four regulations listed below:

If X barks and wits tail – Then X is a Canis familiaris

If X hushings and semivowels – Then X is a serpent

If X is a Canis familiaris – Then X is brown

If X is a snake – Then X is green

This regulation base would be searched and the first regulation would be selected, due to its ancestor ( If Fear barks and wits tail ) matches with the provided informations. Now the consequent ( Then X is a Canis familiaris ) is added in to the informations. After carry oning another hunt in the regulation base, the 3rd regulation will be selected as its ancestor this clip as it ( If Fear is a Canis familiaris ) matches the information that was merely confirmed. Now the new consequent ( Then Fear is brown ) is added into the information. Alas, the end of finding Fear ‘s colour has been achieved.

Due to the fact that the information determines which regulations are selected and used, this method is called data-driven, in contrast to goal-driven backward chaining illation.

One of the advantages of forward-chaining over backward-chaining is that the response of new informations can trip new illations, which makes the engine better suited to dynamic state of affairss in which conditions are likely to alter.

Backward Chaining

Backward chaining needs a list of ends or hypothesis to acquire started on and plants backwards from the consequent ( Then clause ) to the ancestor ( If clause ) to see if there is informations available that will back up any of these consequents. Backward chaining illation engine would seek the illation regulations until it matches a coveted end. If the ancestor of that regulation is unsure whether it is true or non, it will be added in to the list of ends. In order for one ‘s end to be confirmed one must besides supply informations that confirms this new regulation. An illustration of a system that uses rearward chaining will be Google hunt engine.

The system ‘s chief aim is to pick the best pick among other enumerated possibilities. Each chosen possibilities are described and explained by accessing the structured cognition which acts as a regulation. First, the regulation interruptions and simplifies the job into smaller sub-problems. For illustration, the top degree regulations in the undermentioned implemented in a system aid identifies the members of a household.


age is older than 50 and

hair is gray


the person is Father


age is 5 and

hair is black


the person is Brother.

All of the regulations in the system will be tried in order to fulfill the end of placing the person. Each regulation would trip more sub-goals. In these two regulations, the sub-goals of finding the person ‘s age and the colour of the person ‘s hair would be cross-checked and pursued. The undermentioned regulation is one that satisfies the person ‘s age sub-goal:


face furrows and

twelvemonth of birth is earlier than 1950


age is older than 50.

The sub-goals of look intoing whether the person ‘s twelvemonth of birth and the being of furrows in the face would be satisfied by reply provided from the user. Whenever the lowest degree sub-goal is either satisfied or denied by the user, the system will continue in transporting a duologue with the user. The user responds to the system ‘s inquiries by supplying replies which will let the system to happen the regulation which right identifies the member of the household.

Note that the ends ever match the affirmed versions of the consequents of deductions and even so, their ancestors are so considered as the new ends which finally must fit known facts which are normally defined as consequents whose ancestors are ever true.

Due to the ground that the list of ends determines which regulations are selected and used, this method is called goal-driven, in contrast to data-driven forward-chaining illation. The backward chaining attack is frequently employed by adept systems.

Figure 1.2 Difference between forward and backward chaining

Beginning: ( hypertext transfer protocol: //www.amzi.com/ExpertSystemsInProlog/01introduction.htm )

In contrast with the end goaded system of garnering informations as it requires, a information driven system must be ab initio provided with informations. Figure 1.2 shows the difference between backward and frontward chaining systems under two regulations. The forward chaining system is provided with the informations of a=1 and b=2 and uses the regulations to reason that d=4. The backward chaining system uses the two regulations in interrupting the end into two more sub-goals which is happening the values for a and B in order to fulfill the chief end of happening a value for vitamin D.


Occasionally, the concluding reply provided is non wholly certain. The expert ‘s regulations in the system may be excessively obscure and the user may be unsure with the reply for the inquiry. This state of affairs often occurs in medical diagnostic systems where the expert is unsure for the linkage between the diseases and symptoms and might offer multiple possible diagnosings.

Adept systems must besides take uncertainness into considerations in order to supply the most sensible reply in the existent universe. A simple method of tie ining a numeral value with each piece of information in the system which represents the certainty with which the information is known can be used to cipher uncertainnesss.

Data Representation

Depending on the complexness of the job, the informations representation can be either simple or sophisticated. The most basic method used will be the attribute-value braces, for illustration antique, and hair-grey.

It is required to supply the information of the object along with the attribute-value when a system is concluding about multiple objects. For illustration, the book arrangement system might be covering with multiple books with different properties, such as genre. In this instance, the object must be included in the information representation.

When objects are defined in the system, each of the objects might hold multiple properties. A record-based construction will be formed in the on the job storage and a individual information point will incorporate the object ‘s name and all of its associated attribute-value braces.

A more sophisticated manner of hive awaying objects and all the associated attribute-values will be the use of frames. Frames allow objects to inherit values from other objects.

User Interface

The quality-rating and acceptableness of an expert system lies on the interaction between users and the system through the user interface. In figure 1.4, it shows the simplest method to pass on with the system through a scrolling duologue. Users are able to come in bids and responses as replies while the system will go on ask inquiries until a decision is drawn.

In figure 1.5, it shows the more advanced interfaces which utilize pop-up bill of fare, mice, window buttons, and indistinguishable techniques. Artworks can besides be used as a powerful pass oning tool such as the development interface which is used by the cognition applied scientist in constructing the system.

Figure 1.4 Scrolling duologue user interface

Beginning: ( hypertext transfer protocol: //www.amzi.com/ExpertSystemsInProlog/01introduction.htm )

Figure 1.5 Window and menu user interface

Beginning: ( hypertext transfer protocol: //www.amzi.com/ExpertSystemsInProlog/01introduction.htm )

Supplying Explanations

One of the alone characteristics of expert system is the ability to explicate the suggestions provided by it. The system identifies which regulations were used during illation processing and it may deduce logical accounts from the regulations used.

The account provided could sometimes be dramatic as it is able to warrant the suggestion given like in the bird designation system. It can explicate why the suggestion was a laysan millstone due to its colour being white and is big in size.

However, sometimes the accounts given serve no intent to the user. Reason being the regulations set in the cognition base is non a comprehensive apprehension of the job sphere. Hence, even by mentioning to the regulations, the account will be nonmeaningful and obscure to the user.

However, the expert system ‘s ability to explicate is critical for cognition applied scientist to look into on the system ‘s behaviour every bit good as the interaction position of the regulations and informations. During development phase, this is a really valuable diagnostic tool.

Why Use Expert System?

In this subdivision, the advantages and disadvantages of implementing the adept systems are provided. Then, the pros and cons will be reviewed harmonizing to my point of view and I will reason as to why adept system SHOULD be implemented as a wiser option in obtaining the best solutions in get the better ofing complex jobs.

The Advantages of Using Expert System

Expert system has been faithfully used in the concern universe to derive tactical advantages and calculate the market ‘s status. In this globalisation epoch where every determination made in the concern universe is critical for success, the aid provided from an expert system is doubtless indispensable and extremely dependable for an organisation to win. Examples given below will be the advantages for the execution of an adept system:

Supplying consistent solutions – It is able to manage insistent determinations, procedures and undertakings while maintaining its consistence in the reply provided. Equally long as the regulation base in the system remains the same, irrespective of how many times similar jobs are being tested, the concluding decisions drawn will stay the same.

Provides sensible accounts – It has the ability to clear up the grounds why the decision was drawn and be why it is considered as the most logical pick among other options. If there are any uncertainties in reasoning a certain job, it will motivate some inquiries for users to reply in order to treat the logical decision.

Overcome human restrictions – It does non hold human restrictions and can work around the clock continuously. Users will be able to often utilize it in seeking solutions. The cognition of experts is an priceless plus for the company. It can hive away the cognition and utilize it every bit long as the organisation needs.

Easy to accommodate to new conditions – Unlike worlds who frequently have problems in accommodating in new environments, an expert system has high adaptability and can run into new demands in a short period of clip. It besides can capture new cognition from an expert and utilize it as illation regulations to work out new jobs.

The Disadvantages of Using Expert System

Although the adept system does supply many important advantages, it does hold its drawbacks as good. Examples given below will be the disadvantages for the execution of an adept system:

Lacks common sense – It does non posses the common sense required in some determination doing since all the determinations made are based on the illation regulations set in the system. It besides can non do originative and advanced responses as human experts would in unusual fortunes.

High execution and care cost – The execution of an expert system will be a fiscal load for smaller organisations since it has high development cost every bit good as the subsequent recurring costs to upgrade the system to accommodate in new environment.

Trouble in making illation regulations – Sphere experts will non be able to ever supply accounts for their logical thinking needed for the cognition technology procedure. Hence, the undertaking of codifying out the cognition is extremely complex and may necessitate high

May supply incorrect solutions – It is non error-free. There may be mistakes occurred in the processing due to some logic errors made in the cognition base, which it will so supply the incorrect solutions.


It is wholly subjective as to whether the advantages of expert system overweigh the disadvantages of implementing it. It depends on the organisations ‘ point of view as to which aims have the higher precedence, whether in cutting cost or in bring forthing a higher quality decision-making. However, in my sentiment, the execution of expert system is critical in supplying the better service towards clients every bit good as possessing the competitory advantages over strong rivals.

Cuting Cost VS Better Quality of Services

If an organisation is financially stable, the expert system is deserving passing money and resources on, based on its celebrity and history of presenting many positive consequences. Though some organisations may hold the cost-cutting aim as the top precedence, if a incorrect determination is made, it could take to heavier fiscal loss. Adding abuse to injury, the organisation ‘s repute will be tarnished and clients may lose assurance towards the services ‘ of the organisation.

Expert System VS Human Experts

Another chief restraint of implementing the expert system would be the procedure of capturing the cognition and codifying it into the system. However, an expert will non be available to supply his expertness around the clock. Hence, the importance of holding the cognition available all the clip for critical decision-making far overweighs the trouble that the organisation will confront in capturing the said cognition.

Worlds besides have restrictions as to how much knowledge a homo is able to digest and grok. As for expert system, it is able to hive away as much cognition as possible base on its storage infinite. Hence, in footings of public presentation, adept system is capable to execute every bit good if non better so human.

Implementing Expert System into e-commerce System

It has yet to be common for e-commerce systems to be implementing adept system to heighten its capableness and experience for web users. There are still non many web developers willing to implant an expert system into their e-commerce system, chiefly due to its trouble in the cognition technology procedure to codify the human expertness. Yet, it is plausible to hold a less complex expert system embedded in an e-commerce system to assistance clients make determinations. The appropriate illation technique to be used in an e-commerce system will be frontward chaining method, since clients will be supplying portion by portion of information which will so be compared with the regulation base to eventually pull a decision.

Supplying questionnaires

Through frontward chaining method, the decently organized questionnaires will be able to obtain parts of little information from clients who could n’t do their determination upon which point to be bought. Every individual inquiry will hold its intent in finding the status of the clients ‘ ideas and liking, and so the reply provided will be compared with the regulation base in the expert system to pull a concluding decision. This data-driven method is simple and productive since the procedure of codifying the human expertness of urging an point that suits the clients liking is n’t that complex.

Example of Questionnaire

The questionnaire below is used for the intent of achieving little parts of information from the client and the replies provided will be compared to the regulation base in order to bring forth a determination for him

Question 1

What is your budget scope? ( Determining the scope of public presentation from the desktop )

A. & lt ; RM 2000

B. & lt ; RM 3000

C. & gt ; RM 5000

Inference Rules

If X budget is less than RM2000 – Then X needs no NVIDIA in writing card

If X budget is less than RM3000 – Then X needs NVIDIA in writing card ( s )

If X budget is more than RM5000 – Then X needs NVIDIA in writing card ( s ) with better computing machine accoutrements

Note: If user chose & lt ; RM 2000 Question 2 will be skipped.

Question 2

What are the games you largely play? ( Determining the in writing card demands )

Massive Multiplayer Online Role Playing Games ( MMORPG )

First-Person-Shooting ( FPS )


Inference Rules

If X needs NVIDIA in writing card ( s ) AND X plays MMORPG – Then X needs NVIDIA GTX 260

If X needs NVIDIA in writing card ( s ) with better computing machine accoutrements AND X plays MMORPG – Then X needs NVIDIA GTX 260 and High Resolution Monitor

If X needs NVIDIA in writing card ( s ) AND X plays FPS – Then X needs NVIDIA GT 9600

If X needs NVIDIA in writing card ( s ) with better computing machine accoutrements AND X plays FPS – Then X needs NVIDIA GT 9600 and Gaming Laser Mouse

Question 3

How frequent do you download files such as vocals and films from the cyberspace? ( Determining the needed storage infinite )

A. Seldom

B. Often

Inference Rules

If X seldom download files – Then X needs 320GB storage infinite

If X frequently download files – Then X needs 500GB storage infinite

Supplying Explanations

Based on the questionnaire above, if a client selects C, B, and B, the account will be given:

The user selects NVIDIA GT 9600, Bet oning Laser Mouse and 500 GB storage infinite.

User plays First-Person-Shooting games which require middle-performance of in writing card and a gambling optical maser mouse to increase preciseness of mouse-controlling. User frequently downloads files and requires big storage infinite.

If a client selects C, A, and A, the account will be given:

The user selects NVIDIA GTX 260, High Resolution Monitor and 320GB storage infinite.

User plays MMORPG which require high-performance of in writing card and a high declaration proctor to heighten the gambling experience. User rarely downloads files and requires moderate storage infinite.


To reason this study, adept system is undeniably dependable in footings of supplying sensible and extremely valuable determinations. Knowledge and experiences from a human expert can take to the critical decision-making in accomplishing success. Yet, as worlds have restrictions in footings of how much of cognition is comprehensible by a individual and the possible weariness of covering with excessively much work, the expert system does n’t hold any.

As cognition is a valuable plus to an organisation, retaining the expert ‘s cognition is critical for the hereafter of the organisation. The adept system can play a critical function in hive awaying and retaining the cognition from a human expert. This saves the problem of holding the demand to engage experts within the same sphere for old ages.

The rapid alteration and betterment of engineerings will bit by bit diminish the cost for implementing an expert system. This will significantly cut down the fiscal load for little companies in make up one’s minding the execution of expert system. In the concern universe, organisations frequently faced problem in doing tough determinations and overcome complex jobs. Customers frequently require computerized systems to back up their decision-making. All these standards can be met with the execution of the expert system.