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Serial no.

Particulars Pages no.

1. Acknowledgement 5

2. Executive Summary 8

3. Introduction of Report 8

4. Literature Review 9

5. Introduction of Expert Systems 10

6. Working of Expert Systems 11

7. Advantages and Examples of Expert Systems

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8. Conclusion and Recommendations 17

9. Summary 17

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First of all we would like to thanks to the mighty ALLAH that he courage us to complete this project. Secondly we would like to thanks to our dear teacher Mr. Manoj Kumar that he has assigned us to write this report. We have putted our best to complete this report. It is honor for us to

write a final report on the topic Expert Systems.

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Group Members;

Name : Syed Owais Ali SP07-BB-0135 Nabeel Hussain SP07-BB-0124

Project Report on Expert Systems

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Executive Summary:A computer program that uses artificial intelligence to solve problems within a specialized domain that ordinarily requires human expertise. The first expert system was developed in 1965 by Edward Feigenbaum and Joshua Ladenburg of Stanford University in California, U.S. Dendral, as their expert system was later known, was designed to analyze chemical compounds. Expert systems now have commercial applications in fields as diverse as medical diagnosis, petroleum engineering, and financial investing. In order to accomplish feats of apparent intelligence, an expert system relies on two components: a knowledge base and an inference engine. A knowledge base is an organized collection of facts about the system's domain. An inference engine interprets and evaluates the facts in the knowledge base in order to provide an answer. Typical tasks for expert systems involve classification, diagnosis, monitoring, design, scheduling, and planning for specialized endeavors. Facts for a knowledge base must be acquired from human experts through interviews and observations. This knowledge is then usually represented in the form of “if-then” rules (production rules): “If some condition is true, then the following inference can be made (or some action taken).” The knowledge base of a major expert system includes thousands of rules. A probability factor is often attached to the conclusion of each production rule, because the conclusion is not a certainty. For example, a system for the diagnosis of eye diseases might indicate, based on information supplied to it, a 90 percent probability that a person has glaucoma, and it might also list conclusions with lower probabilities. An expert system may display the sequence of rules through which it arrived at its conclusion; tracing this flow helps the user to appraise the credibility of its recommendation and is useful as a learning tool for students. Human experts frequently employ heuristic rules, or “rules of thumb,” in addition to simple production rules. For example, a credit manager might know that an applicant with a poor credit history, but a clean record since acquiring a new job, might actually be a good credit risk. Expert systems have incorporated such heuristic rules and increasingly have the ability to learn from experience. Nevertheless, expert systems remain supplements, rather than replacements, for human experts.

Introduction of Report:In this report we will give information of Expert Systems, in this we provide the working of an Expert System by covering different examples and also talk about its features and advantages. We also tell you about different expert systems and the way of solving Problems by Expert systems. Report also includes the rules on which Expert Systems work and the steps which are necessary for Making or for taking work from Expert System.

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Literature Review:An expert system is a computer program designed to simulate the problem-solving behavior of a human who is an expert in a narrow domain or discipline.  An expert system is normally composed of a knowledge base (information, heuristics, etc.), inference engine (analyzes the knowledge base), and the end user interface (accepting inputs, generating outputs).

Expert systems are capable of delivering quantitative information, much of which has been developed through basic and applied research (e.g. economic thresholds, crop development models, pest population models) as well as heuristics to interpret qualitatively derived values, or for use in lieu of quantitative information.  Another feature is that these systems can address imprecise and incomplete data through the assignment of confidence values to inputs and conclusions.

One of the most powerful attributes of expert systems is the ability to explain reasoning.  Since the system remembers its logical chain of reasoning, a user may ask for an explanation of a recommendation and the system will display the factors it considered in providing a particular recommendation.  This attribute enhances user confidence in the recommendation and acceptance of the expert system.

Basri (1999) noticed that an expert system attempts to emulate how a human expert solves a problem, mostly by the manipulation of symbols instead of numbers. Whereas conventional algorithmic programming replaced most of the sophisticated, analytical work of engineers, expert systems are especially suitable for the no-less important tasks of the ill-structured and less deterministic parts of planning and design.

Introduction:An expert system is a computer program conceived to simulate some forms of human reasoning (by the intermediary of an inference engine) and capable to manage an important quantity of specialized knowledge.

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A system that uses human knowledge captured in a computer to solve problems that ordinarily require human expertise (Turban & Aronson, 2001).

A computer program designed to model the problem solving ability of a human expert (Durkin, 1994).

An intelligent computer program that uses knowledge and inference procedures to solve problems that was difficult enough to acquire significant human expertise for their solutions (Feigenbaum).

An expert system is a computer application that solves complicated problems that would otherwise require extensive human expertise. To do so, it simulates the human reasoning process by applying specific knowledge and interfaces. This report explained on the expert system for decision making of giving the best solution to solve the PDA’s (Personal Digital Assistant) problems.

These capacities for reasoning and management allow the system to target a small number of relevant hypotheses in the mass of potential diagnoses and being able to find a satisfactory diagnostic conclusion. Two characteristics of the expert system are essential to accomplish this task:

the aptitude to process an important mass of specialized knowledge and the aptitude to simulate the human reasoning (in an imperfect manner).

The idea to develop this system has arises base on the capabilities and the potential of the expert system as described above.  It can be use mainly for giving appropriate countermeasures according to the accurate and consistent diagnosis of PDA’s (Personal Digital Assistant) troubleshoots.  It is called PDAMum.

PDAMum is essential since the emergence of handheld devices, and now it is rapidly owned by various levels of peoples, there is somehow a need of a system that able to help them to manage their devices whenever necessary.  PDAMum is making its decision during the interview, looking for the optimal way to reach their conclusions: to make a diagnosis.

Working of Expert System:Below are some diagrams that show the basis how an expert system works.

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Figure 1: Major parts of an expert system

Figure 2: Typical knowledge acquisition processes for building an expert system

Expert systems should be viewed as a particular type of information system. Expert systems are distinct in terms of their approach to problem representation, as information systems process

information, while expert systems attempt to process knowledge.  Knowledge in an expert system may originate from many sources, such as textbooks, reports, databases, case studies, empirical

data, and personal experience. The dominant source of knowledge in today's expert systems is the dominant expert. A knowledge engineer usually obtains knowledge through direct interaction

with the expert

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Figure 3: Expert System and User Interaction

In the expert system there are widely use in many application areas. There are few type of problem solving paradigm such as control, design, diagnosis, instruction, interpretation, monitoring, planning, prediction, prescription, selection and simulation.

Steps of Solving Problems:

Phase 1: Problem Assessment

Most organizations when considering any new technology will ask the very practical questions ‘Will it work?’ and ‘Why should we try it?’ Since PDAMum is relatively new, answers to these questions are at best educated guesses.

Then, this is a methodology for assessing the applicability of PDAMum to a given problem.

It’s structured according to the following tasks:

Task 1: Determine motivation of organization

Task 2: Identify candidate problems

Task 3: Performs feasibility study

Task 4: Perform cost/ benefit analysis

Task 5: Select the best project

Task 6: Write the project proposal

Phase 2: Knowledge Acquisition & Analysis

This task is the most difficult challenge in the development of an expert system. Knowledge acquisition is inherently a cyclical process. PDAMum used these tasks of knowledge collection,

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its interpretation and analysis, and the design of methods for collecting additional knowledge. An expert system gains its power from the knowledge it contains (Durkin, 1994).

Phase 3: Design and Implementation

This phase begin with the selection of the knowledge representation technique and control strategy. This is followed with the selection of a software tool that best meets the needs of the problem. A small prototype of PDAMum is then built to both validate the project and to provide guidance for future work. PDAMum is then further developed and refined to meet the project objectives.

This process is structured according to the following task:

Task 1: Select rules as Knowledge representation technique

Task 2: Select control technique

Task 3: Select Kawa as PDAMum development software

Task 4: Develop the prototype, interface, and product

A rule-based approach is suitable because PDAMum discusses the problem primarily using IF/ THEN type statements. This discussion will usually lack an in-depth description of the problem’s objects, which would justify the need for a frame-based approach. Classification problems are typical of this situation where the expert tries to classify the state of some issue according to available information.

The keys to effective interface design are consistency, clarify and control. Consistency is consistent on screen format. Various types of materials are presented on screen such as title, questions, answer, explanation and control functions. When presenting these materials, it supposes to be that the similar material is place in the same location. Clarify is to clarify of presented materials. PDAMum used screen to ask questions, provide explanation on the system’s reasoning and display intermediate or final results.

Phase 4: Testing

As the PDAMum will need to be periodically tested and evaluated to assure that its performance is converging toward established goals. It is important that these decisions be made early, at a time when the original project goals are established.

These are the evolution of testing or evaluation of testing for PDAMum:

Stage 1: Preliminary Testing

Study the complete knowledge base Uncover deficiencies in the knowledge and reasoning strategies Validate knowledge representation and inference approach

Stage 2: Demonstration Testing

Choose a problem of limited scope within the capabilities of PDAMum Use demonstration to validate the expert system approach Show off major PDAMum features Design interface to accommodate of the user

Stage 3: Informal Validation Testing

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Select typical past test cases Evaluate PDAMum’s ability in solving typical cases and Identify PDAMum deficiencies and obtain comments from user on the interface

Stage 4: Refinement Testing

Select unusual past test cases Evaluate PDAMum’s ability in solving unusual cases Uncover deficiencies in PDAMum’s knowledge and control Identify PDAMum deficiencies

Stage 5: Formal Testing

Select past test cases and define test criteria Run the PDAMum for each test case and ask evaluators to judge system’s performance

for each test case Obtain comments on the interface Identify PDAMum strength and deficiencies

Stage 6: Field Testing

Define test criteria for the field test Determine if PDAMum meets its original goals when applied to real problems

Phase 5: Documentation

This documentation serves as a personal diary of the project. It contains all the material collected during the project that needs to be reference for developing the system. If properly designed, it will also serve the later tasks of maintaining PDAMum and writing the PDAMum’s final report.

Phase 6: Maintenance

After finished all of the designing, implementing, testing and documentation, PDAMum may need to be refined or updated to meet current needs. It is extremely important to keep good records on any changes made to PDAMum. If this isn’t done, it is very easy to lose track of the PDAMum’s knowledge. And each time PDAMum is modified; the following critical pieces of information should be documented:

What was modified and who performed the modification When the modification was made Why the modification was made

Advantages of Expert System:

There are many well known advantages to using computerized tools and expert systems:

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reduction of missing data, better collection of data, no omission of questions, no data transcription, broader coverage of diagnoses

Examples and Applications in Real Life:

The few examples are as follow:

Agricultural Expert systems:

Rice-Crop Doctor National Institute of Agricultural Extension Management (MANAGE) has developed an expert system to diagnose pests and diseases for rice crop and suggest preventive/curative measures. The rice crop doctor illustrates the use of expert-systems.

Farm Advisory System Punjab Agricultural University, Ludhiana, has developed the Farm Advisory System to support agri-business management. The conversation between the system and the user is arranged in such a way that the system asks all the questions from user one by one which it needs to give recommendations on the topic of farm Management.

AGREX

Center for Informatics Research and Advancement, Kerala has prepared an Expert System called AGREX to help the Agricultural field personnel give timely and correct advice to the farmers. These Expert Systems find extensive use in the areas of fertilizer application, crop protection, irrigation scheduling, and diagnosis of diseases in paddy and post harvest technology of fruits and vegetables.

Medical Expert System:

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HELP The HELP (Health Evaluation through Logical Processes) System is a complete knowledge based hospital information system. It supports not only the routine application of an HIS including ADT, order entry/charge capture, pharmacy, radiology, nursing documentation, ICU monitoring, but also supports a robust decision support function.The HELP system is an example of this type of knowledge-based hospital information system.

PEIRS (Pathology Expert Interpretative Reporting System) appends interpretative comments to chemical pathology reports (Edwards et al., 1993). The knowledge acquisition strategy is the Ripple Down Rules method, which has allowed a pathologist to build over 2300 rules without knowledge engineering or programming support.. PEIRS commented on about 100 reports/day. Domains covered include thyroid function tests, arterial blood gases, glucose tolerance tests, hCG, catecholamines and a range of other hormones.

Puff

The Puff system diagnoses the results of pulmonary function tests. Puff went into production at Pacific Presbyterian Medical Center in San Francisco in 1977. Several hundred copies have been sold and are in use around the world. The PUFF system for automatic interpretation of pulmonary function tests has been sold in its commercial form to hundreds of sites world-wide (Snow et al., 1988). PUFF went into production at Pacific Presbyterian Medical Center in San Francisco in 1977, making it one of the very earliest medical expert systems in use. Many thousands of cases later, it is still in routine use.

SETH

The aim of SETH is to give specific advice concerning the treatment and monitoring of drug poisoning. Currently, the data base contains the 1153 most toxic or most frequently ingested French drugs from 78 different toxicological classes. The SETH expert system simulates expert reasoning, taking into account for each toxicological class, delay, clinical symptoms and ingested dose. It generates accurate monitoring and treatment advice, addressing also drug interactions and drug exceptions.

Vithoulkas Expert System

Now, there is an expert system that has been developed by Whole Health Now. The system called, Vithoulkas Expert System (VES). VES brings the case analysis of George Vithoulkas about the remedy decisions. George himself impressed with this software’s uncanny precision that he used own practice. And, his success rate has climbed from 80-85% astounding 90-95% as a result. Actually, VES is working with any RADAR package then will come out with the result. RADAR is Repertory software, based on the Synthesis Repertory first repertory to bridge the gap between repertories and clinic confirmed experience. RADAR allowed the user to create a report on own standards. "They wrote down what I said and then translated my thinking into mathematical formulas. This went on until we had created thousands of rules and sub-rules. Then we worked further to refine each rule until, it was as precise as it could possibly be."

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Conclusion and Recommendations:PDAMum like other expert system will go a tremendous phases from simple expert system to the complex multipurpose systems. Hybrid expert system and together with fuzzy expert system can be seen as a new techniques that be used by researchers lately.  Implementation of expert system in such fields is greatly influenced by techniques and methods from adaptive hypertext and hypermedia. Features of personalization, user modeling and ability of adaptive towards environment will become great challenges to settle. It can be used as a guideline to promote an expert system in various functions.

In future, PDAMum can be used together with artificial neural networks, fuzzy logic, genetic algorithms and other methods of Artificial Intelligence. These methods allow taking into account their advantages in the designed system and, therefore, new designed systems are more powerful instruments to facilitate various tasks that require instant, accurate and reliable results.

Summary: Expert systems have been used to solve a wide range of problems in domains such as medicine, mathematics, engineering, geology, computer science, business, law, defense and education. Within each domain, they have been used to solve problems of different types. Types of problem involve diagnosis (e.g., of a system fault, disease or student error); design (of a computer systems, hotel etc); and interpretation (of, for example, geological data). The appropriate problem solving technique tends to depend more on the problem type than on the domain.

PDAMum as an expert system might make mistakes, but it is less than a human did.  Furthermore it always performs consistently, never become tired or bored.  Other clear different is PDAMum can be use anywhere anytime compared to human.

User may clarify their PDA synchronization problem with immediate response and retrieve such reliable diagnosis through PDAMum.  This feature will assist them to recognize the causes that disallow their PDA synchronization.  They may ask PDAMum why they being ask such question during the interaction process. 

In future, PDAMum can be improve by convert it as web base application.  Instead of working stand alone, PDAMum can be reach through website which wider the access location.  User from various PDA owners may solely use single expert system to overcome their PDA’s problem.

PDAMum also should have better explanation facility.  This can be achieve if it has more rules to deals with other PDA problem such as sound and power management.  With additional knowledge, PDAMum can solve and refine the problem deeper, so that user may consider other possibility that blocked their PDA from perform well.

Other rational expansions are image viewer and uncertainty factor.  PDAMum can be utilize better if it can view meaningful image that will support it’s diagnose and decision.  Uncertainty also crucial since user sometime cannot express their feels.  With Certainty Factor (CF) capability, PDAMum will increase user confidence and convince them to make right choice.

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