An Expert System for Diabetes Diagnosis by Smitha V

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AN EXPERT SYSTEM FOR DIABETES DIAGNOSIS

Submitted in partial fulfillment of the requirements for the award of the degree of

Master of Philosophy

By

SMITHA V

(Roll No: 0935014)

Supervisor

Mrs. Rohini V

(Asst. Professor)

Department of Computer Science

CHRIST UNIVERSITY BANGALORE

2010


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DECLARATION

I hereby declare that the dissertation entitled An Expert System for Diabetes

Diagnosissubmitted for the M.Phill Degree is my original work and the dissertation has not

formed the basis for the ward of any degree, associateship fellowship or any other similar

titles.

Place: Bangalore SMITHA V

Date: Roll No.: 0935014

Signature:


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This is to certify that the dissertation work entitled An Expert System for Diabetes

Diagnosis is a bonafide research work carried out by SMITHA V, student of M.Phill

(Computer Science) Christ university, Bangalore, during the year 2009-2010, in partial

fulfillment of the requirements for the award of the Degree of Master of philosophy and that

the dissertation has not formed the basis for the award previously of any degree, diploma,

associateship, fellowship or any other similar title.

CERTIFICATE

Place: Bangalore Mrs. Rohini. V

Date: (Asst. Professor)

Signature


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APPROVAL SHEET

Thesis entitledAn Expert System for Diabetes DiagnosisbySmitha V is approved for the

degree of Master of Philosophy in Computer Science.

Examiners:

1. ___________________ ___________________

2. ___________________ ___________________

3. ___________________ ___________________

Supervisor (s)

1. ___________________ ___________________

2. ___________________ ___________________

3. ___________________ ___________________

Chairman:

___________________(Seal)

Date: ___________

Place: __________


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Abstract

Expert system is a computer system that emulates the decision making ability of a human

expert. That is it acts in all respects like a human expert. It uses human knowledge to solve

problems that would require human intelligence. The expert system represents expertise

knowledge as data or rules within the computer. These rules and data can be called upon

when needed to solve problems.

Diabetes is a knotty disease and very common in the modern world. Diabetes is a serious

disease that affects almost every organ in the body like heart, eyes, kidney, skin, nerves, blood

vassals, foot etc. If left the disease unchecked it will make serious complications including

death. Though the disease can not possible to cure completely, it can be well managed or

control and can lead a very healthy life. Early diabetes diagnosis plays a crucial role in

diabetic control, and can prevent further medical complications.

This paper presents the design and development of medical expert system for Diabetes

disease and it support diagnosis, give information about complications and act as diabetes

trainer. It used rule based approach to collect data and forward chaining inference technique.

This system provides a user interactive, menu driven environment. Symptoms and risk factors

associated with diabetes are taken as the basis of this study. In case of diagnosis the system

will ask a bunch of questions about the symptoms and risk factors to the expert system user

and user should give yes or no answer. According to the answer the system will make

judgment about the possibility of illness, how much severe it is like slight chance, moderate

chance, high chance, very high chance, diabetic or not. If the user wants to know the details of

diabetes complications he can select the complication option from the menu. It can also used

in teaching practice.

The system is drawn up with CLIPS expert system building tool version 6.3 and in

Windows/Dos environment.


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List of Tables

Table.1: Relation between sugar level and HbA1C 10

Table.2: Diabetic Personal Detail 48

Table 3: Rules employed for ESDD 62

List of Figures

Figure 1: Main components of an expert system 15

Figure 2: Structure of a rule based expert system 16

Figure 3: Hierarchy of expert system development process 46

Figure 4: Decision Flow diagram for Diabetes Diagnosis Expert System. 56

Figure 5: Menu of an Expert System for Diabetes Diagnosis 63


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Table of Contents

Abstract .................................................................................................................................... IVList of Tables ..............................................................................................................................VList of Figures .............................................................................................................................V1 INTRODUCTION AND MOTIVATION OF RESEARCH ................................................. 1

1.1. INTRODUCTION ........................................................................................................ 11.2. MOTIVATION OF RESEARCH .................................................................................. 2

2 DIABETES MELLITUS ..................................................................................................... 32.1. DIABETES DISEASE- AN OVERVIEW ..................................................................... 32.2. DIFFERENT TYPES OF DIABETES .......................................................................... 42.3. THE RISK FACTORS OF GETTING DIABETES ....................................................... 52.4. MAIN SYMPTOMS OF DIABETES ........................................................................... 62.5. THE MAIN COMPLICATIONS OF DIABETES ......................................................... 82.6. DIABETES DIAGNOSIS ............................................................................................. 82.7. DIABETES TREATMENT ........................................................................................ 10

3. ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEM ................................................... 113.1. ARTIFICIAL INTELLIGENCE ................................................................................. 113.2. EXPERT SYSTEMS .................................................................................................. 113.3. IMPORTANCE OF EXPERT SYSTEM ..................................................................... 123.4. MAIN COMPONENTS OF AN EXPERT SYSTEM .................................................. 143.5. RULE BASED EXPERT SYSTEMS .......................................................................... 163.6. THE NEED FOR EXPERT SYSTEM ........................................................................ 183.7. LIMITATIONS OF EXPERT SYSTEMS ................................................................... 183.8. EXPERT SYSTEM AND MEDICAL FIELD ............................................................. 20

4. EXPERT SYSTEM BUILDING TOOLS ............................................................................. 214.1. PROGRAMMING LANGUAGES FOR EXPERT SYSTEM ..................................... 214.2. CLIPS- EXPERT SYSTEM TOOL ............................................................................. 22

4.2.1 HISTORY ........................................................................................................... 224.2.2 FEATURES OF CLIPS ....................................................................................... 224.2.3 WORKING WITH CLIPS ................................................................................... 234.2.4 CLIPS PROGRAMMING ELEMENTS .............................................................. 244.2.5 GARBAGE COLLECTION ................................................................................ 39

5. LITERATURE REVIEW ...................................................................................................... 40


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5.1. REVIEWED PAPERS ................................................................................................ 40

6. METHODOLOGY................................................................................................................ 456.1. DESCRIPTION OF THE DEVELOPED SYSTEM .................................................... 456.2. EXPERT SYSTEM DEVELOPMENT PROCESS...................................................... 46

7. SUMMARY AND CONCLUSION....................................................................................... 657.1 FUTURE WORK........................................................................................................ 65

APPENDIX (I) ......................................................................................................................... 66PROGRAMMING OF EXPERT SYSTEM FOR DIABETES DIAGNOSIS ......................... 66

LITERATURE CITED ............................................................................................................105ACKNOWLEDGEMENT........................................................................................................107


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INTRODUCTION AND MOTIVATION OF

RESEARCH


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1 INTRODUCTION AND MOTIVATION OF RESEARCH

1.1. INTRODUCTION

This paper deals with designing and building an Expert System for Diabetes Mellitus

diagnosis which is a common health disorders in many people. This expert system interacts

with user with plain English language where user needs to answer only yes or no. Internally

the system classifies user input as primary symptoms, hereditary symptoms, complication

symptoms, other symptoms to analysis. The expert system uses a score accumulation method

to decide the level of impact of diabetes in individual. The different levels are classified as

slight chance, moderate chance, high chance, very high chance, or already diabetic or not.

Diabetes Mellitus (technical name of diabetes) is a metabolic disorder that affects morethan 100 million people in the world. In India 19.4 million people are suffering with this

chronic condition. Diabetes is a condition which associates with very high glucose level in

the blood. A hormone called insulin, secreted by pancreas helps to move the glucose from

the blood into liver, muscles and fat cells where it is used for fuel. The diabetic person does

not produce enough insulin or the body could not use the insulin properly as it should. It

causes to increase the blood sugar level. This not only harms the cells that need the glucose

for fuel but also harm certain organs and tissues. Diabetes is a serious disease that affects

almost every organ in the body like heart, eyes, kidney, skin, nerves, blood vassals, foot etc.

If left the disease unchecked it will make serious complications including death. Some of

these long-term complications of diabetes could be the damage of blood vessels and nerves,

loss of functioning of the kidneys, blindness, heart disease and strokes, and loss of

sensation. Most of the people, who have diabetes, do not know that they have it and hence

do not treat it till it become very late. There are several symptoms of diabetes. Most of the

people will get some of these symptoms before they know that they are diabetic. But some

people not get any symptoms initially and they come to know that they are diabetic only

after they get some complications. Some of the classical symptoms are excessive thirst,

frequent urination, extreme hunger, feeling tired or fatigue, unexplained weight loss,

sudden vision changes, tingling or numbness in hands or feet, slow healing and infections

than usual, nausea, vomiting, abdominal pain etc. Though the disease can not possible to

cure completely, it can be well managed or control and can lead a very healthy life. Early


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Diabetes diagnosis plays a crucial role in diabetic control, and can prevent further medical

complications.

1.2. MOTIVATION OF RESEARCHThe main reason of this research was to introduce the design and development of an expert

system which is able to fully diagnose the very knotty disease diabetes. The system An

Expert system for Diabetes Diagnosis is a medical expert system for diagnosis of the disease

Diabetes using rule based approach and it support diagnosis, give information about

complications and act as diabetes trainer. This system, on the basis of particular knowledge or

rules basis, draws conclusions, takes decisions, and its activity is similar to the human activity

in that sphere. It uses the inference mechanism of forward chaining-reasoning from facts to

the conclusion, resulting from those facts using CLIPS as the programming language. The

expert system provides an interactive way to the user, ask questions about personnel details

and symptoms, to answer whether the diabetes symptom is experienced by the individual or

not. According to the answer given by the person, the system will make judgment about the

possibility of illness, how much severe it is moderate chance or higher chance etc.

This expert system is use to diagnosis for all types of diabetes because nowadays diabetes is a

very common disease in children, youngsters, adults and old people due to a lot of factors like

life style, obesity etc which may be type 1 diabetes or type 2 diabetes .The symptoms of both

types of diabetes are almost similar. If a patient diagnosed as diabetic even the doctors cant

predict from the very beginning that it is type 1 or type 2.They need to examine the patient for

2 to 3 months.(In some cases they can predict from the beginning stage whether type 1 or type

2). So this expert system is a general tool for diagnosis of all type of diabetes.

Since, diabetes diseases are widely spreads in India, the expert system is not meant to replace

the human endocrinologist but users those who are diabetic or under risk will get a thorough

knowledge about diabetes that will help them to take proper treatment against this disease.

This system can be used to help the physician in their work. This expert system initially

evaluated with existing classical test cases. The result of the evaluation was accurate and

promising.


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DIABETES MELLITUS


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2 DIABETES MELLITUS

2.1. DIABETES DISEASE- AN OVERVIEW

Definition of disease Diabetes Mellitus:

Diabetes Mellitus is a clinical syndrome characterizes by hyperglycemia due to absolute or

relative deficiency of insulin. The lack of insulin affects the metabolism of the body. It causes

to increase the blood sugar level. That is there is not enough insulin to reduce the percentage

of glucose to its normal level.

All the people with diabetes have one thing common. They have too much sugar or glucose in

their blood. This is because a person who intake food is converted into glucose and the body

is unable to remove glucose from the blood and deliver it to the cells as a source of energy in

order to stay alive. A hormone called Insulin, secreted by the pancreas helps to convert

glucose to energy. When a person has diabetes, the body either does not generate enough

insulin or cannot use its own insulin as it should. This will cause sugar to rise in the blood. So

insulin is a key regulator of the body's metabolism. It works in the following way:

During and immediately after a meal the process of digestion breaks down

carbohydrates into sugar molecules (including glucose) and proteins into amino

acids.

Right after the meal, glucose and amino acids are absorbed directly into the

bloodstream, and blood glucose levels rise sharply.

The rise in blood glucose levels signals important cells in the pancreas, called beta

cells, to secrete insulin, which pours into the bloodstream. Within 10 minutes after

a meal, insulin rises to its peak level.

Insulin enables glucose and amino acids to enter cells in the body, particularly

muscle and liver cells. Here, insulin and other hormones direct whether these

nutrients will be burned for energy or stored for future use. As blood glucose levels

reach their peak, the pancreas reduces the production of insulin.

About 2 to 4 hours after a meal, both blood glucose and insulin are at low levels,

with insulin being slightly higher. The blood glucose levels are then referred to as

fasting blood glucose concentrations.


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2.2. DIFFERENT TYPES OF DIABETES

There are three types of Diabetes.

Type-1diabetes (Juvenile Diabetes or Insulin dependent diabetes):-In Type I diabetes, the

immune system attacks insulin-producing beta cells in the pancreas and destroys them. With

little or no insulin, the cells are prevented from taking up sugar from the blood. Patients with

type 1 do not make enough insulin and they are treated with insulin to sustain life. Genetic

and environment factors are involved in the development of type 1 diabetes such as

unidentified virus, stimulating an immune attack against the beta cells of pancreas in some

genetically predisposed people. This occurs in youngsters (below 18 years) and older people.

This occurs in older people due to destruction of pancreas by alcohol, disease or removal by

surgery or progressive failure of pancreatic beta cells which produce insulin.

Type 2 diabetes: :( Adult onset Diabetes or Insulin independent diabetes):- Patients with

type 2 do make insulin, but for some reason, the cells in their bodies are resistant to insulins

action or they do not make enough insulin. 90% of all types of diabetes are type 2. It may

involve the following three stages in most patients.

The first stage in type 2 diabetes is the condition called insulin resistance. Certain

mechanisms prevent insulin from moving glucose (blood sugar) into the cells where it can be

used. Thus lowering the rate in which glucose is utilized. Most patients with type 2 diabetes

produce variable, even normal or high, amounts of insulin. In the beginning, this amount is

usually sufficient to overcome such resistance.

Over time, the pancreas becomes unable to produce enough insulin to overcome resistance

and in turn increasing the glucose in the blood leading topre diabetes.It is the condition in

which the blood sugar level is more than the normal level but not reach to the diabetic level.

In type 2 diabetes, the initial effect of this stage is usually an abnormal rise in blood sugar

right after a meal (called postprandial hyperglycemia). This effect is now believed to be

particularly damaging to the body.

Eventually, the cycle of elevated glucose further impairs and possibly destroys beta cells,

thereby stopping insulin production completely and causing full-blown diabetes. This is made

evident by fasting hyperglycemia, in which elevated glucose levels are present most of the

time.


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Gestational diabetes:This appears during pregnancy in some women with no previous type-1

or type-2 diabetes and usually goes away after pregnancy. But after pregnancy 5 to 10

percentage of women with gestational diabetes are diagnosed with type 2 diabetes within 10

years. Pregnant women have enough insulin, but the effect of insulin is partially blocked by

other hormones produced in the placentas during pregnancy period. Around 13% of pregnant

women were diagnosed with this form of diabetes, including many who did not have any risk

factors. All pregnant women are tested for gestational diabetes between their 24th and 28th

week.

2.3. THE RISK FACTORS OF GETTING DIABETES

The main causes of diabetes are-

Hereditary and genetics factors

Infections caused by viruses

Stress

Obesity(A weight that is 20% more than ideal body weight)

Increased cholesterol level (HDL (good) cholesterol less than 35 mg/dL or

triglyceride level 250 mg/dL or higher)

High carbohydrate diet

Nutritional deficiency

Excess intake of oil and sugar

No physical exercise

Overeating

Tension and worries

High blood pressure(140/90 mm/Hg or higher)

Insulin deficiency

Insulin resistance

Age 45 or older

African American, Hispanic/Latin American, American Indian and Alaska Native,

Asian American, or Pacific Islander ethnicity

Polycystic ovary syndrome (metabolic disorder that affects female reproductive

system

If you are on high levels of prednisone for asthma or allergies or other autoimmune

diseases, you are very much at risk for diabetes.

A pregnant woman's risk factors include-:


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Family history of diabetes

African American, Hispanic, Asian, or Pacific Islander ethnicity

Overweight

Older than 25 years

Gestational diabetes with past pregnancy

Having given birth to a child weighing over 9 pounds

Diagnosis of pre-diabetes

The Risk factors that cannot control

The risk factors that cannot control include:

Family history: If you have a parent, brother, or sister who has type 2 diabetes, you

have a greater chance of developing the disease.

Age. The risk for getting pre diabetes and type 2 diabetes increases with age and the

number of children being diagnosed with type 2 diabetes is increasing. Usually,

children who get type 2 diabetes have a family history of the disease, are overweight,

and are physically inactive.

Race and ethnicity. African Americans, Hispanics, Native Americans, Asian

Americans, and Pacific Islanders are at higher risk than whites for type 2 diabetes.

History of gestational diabetes or having a baby weighing more than 9 lb (4 kg).

Women who have had gestational diabetes or who have had a large baby are at higher

risk for developing type 2 diabetes later in life.

Low birth weight. People who weighed less than 5.5 lb (2.5 kg) at birth are more

likely to develop type 2 diabetes later in life.

Polycystic ovary syndrome (PCOS), a hormone imbalance that interferes with normal

ovulation.

Metabolic syndrome, a group of abnormal physical findings related to the body's

metabolism.

2.4. MAIN SYMPTOMS OF DIABETES

The common symptoms are-

Classic Symptoms

Frequent urination with large volume of urine (poly urea)

Excessive thirst (polydipsia)

Extreme Hunger (polyphagia)

Unusual sudden unexplained weight loss


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Increased fatigue

Feeling very tired

Feeling ill

Sudden vision changes or Blurry vision

Nausea

Vomiting

Stomach pain

Other Symptoms

Hair loss or thinning of hair

Leg pain

Sugar in urine

Dry mouth

Irritability

Sleep disorder

Dry or itchy skin

Gestational diabetes

Bladder, Skin, or vaginal infection (in female)

Impotence (in male)Swollen red gums and loosing teeth

Lethargy

Over eating and rapid weight gain

Taking steroid medicine for Asthma

Loss of consciousness

Complication Symptoms

Delayed wound healingBruises that wont go away

Sores that wont heal

Tingling or numbness in hands or legs

A feeling of pins and needles in feet

Unexplained aches and pains

Burning pain in legs, feet, arms

Fruity smell of breath and sweat


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2.5. THE MAIN COMPLICATIONS OF DIABETES

Uncontrolled diabetes can harm the nerves in your body causing diabetic neuropathy.

Heart disease, stroke, heart attack are all caused by blockage of blood vessels causing

lack of oxygen in extreme case breakage of blood vessels may happen.

Diabetic retinopathy caused due to blood vessel damage in retina causing lack of

blood circulation thus lack of oxygen to nourish retina and in extreme cause leakage of

blood in retina.

Diabetic Gastroparesis is disorder of stomach that takes too long to empty its contents

it is caused by vagus nerve damage.

Diabetes kidney disease are caused due to small blood vessel of kidney get damaged

due to long run of high blood glucose. Erectile dysfunction or impotence is due to over all nerve damage, and also may be

nerves in penis.

Urinary incontinence, over active bladder, bladder problems are all caused by nerve

damage due to prolonged high blood glucose.

Urinary tract infection is also caused by nerve damage, but it is not directly related

with nerve cause. Due to Diabetic Gastroparesis the food emptying is delayed, thus

bacteria start growing and on it way it causes urinary tract infection. Also due to longterm diabetes immune system loss it capacity thus not able to fight the infection.

Diabetic ketoacidosis is a serious condition in which uncontrolled hyperglycemia

overtime creates a buildup in the blood of acidic waste products called ketones. High

level of ketones is very harmful. This is particularly happens in people with type 1

diabetes who do not have good blood glucose control. Diabetic ketoacidosis is caused

by infections, stress, trauma, missing medications like insulin or medical emergencies

like stroke and heart attack.

2.6. DIABETES DIAGNOSIS

Some diabetic patients will not get any warning sign or symptoms. The only way to be sure is

to have blood test for glucose. The diabetics diagnosis tests include-

Fasting Plasma Glucose: The fasting plasma glucose (FPG) test is the standard test for

diabetes. It is a simple blood test taken after 8 hours of fasting. Results indicate:

FPG levels are considered normal up to 100 mg/dL


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Levels between 100 and 125 mg/dL are referred to as impaired fasting glucose or

pre-diabetes. These levels are considered to be risk factors for type 2 diabetes and

its complications.

Diabetes is diagnosed when FPG levels are 126 mg/dL or higher on two or more

tests on different days.

Postprandial blood glucose test (PPB):This test is followed by Fasting plasma glucose test.

Take good amount of food after FPG wait 2 hours, and do the blood test again. Postprandial

glucose level should be under 140 mg/dL. The value between 140 and 199mg/dL indicate pre-

diabetes.200 and above value may indicate diabetes.

Random blood glucose test: A random blood glucose test can also be used to diagnose

diabetes.

A blood glucose level of 200 mg/dl or higher indicates diabetes

The Oral glucose tolerance test:This test is used for diagnosis of type 2. It is still commonly

used for diagnosing gestational diabetes and in conditions of pre-diabetes. With an oral

glucose tolerance test, the person fasts overnight (at least eight but not more than 16 hours).

Then first, the fasting plasma glucose is tested. After this test, the person receives 75 grams of

glucose (100 grams for pregnant women). Blood samples are taken at specific intervals to

measure the blood glucose over a period of three hours. In a person without diabetes, theglucose levels rise and then fall quickly. In someone with diabetes, glucose levels rise higher

than normal and fail to come back down as fast. People with glucose levels between normal

and diabetic have impaired glucose tolerance (IGT). People with impaired glucose tolerance

do not have diabetes, but are at high risk for progressing to diabetes.

Glucose tolerance tests may lead to one of the following diagnoses:

Normal response:A person is said to have a normal response when the 2-hour glucose

level is less than 140 mg/dl, and all values between 0 and 2 hours are less than 200mg/dl.

Impaired glucose tolerance:A person is said to have impaired glucose tolerance when

the fasting plasma glucose is less than 126 mg/dl and the 2-hour glucose level is

between 140 and 199 mg/dl.

Diabetes: A person has diabetes when two diagnostic tests done on different days

show that the blood glucose level is high.

Gestational diabetes: A woman has gestational diabetes when she has any two of the

following: a 100g OGTT, a fasting plasma glucose of more than 95 mg/dl, a 1-hour


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glucose level of more than 180 mg/dl, a 2-hour glucose level of more than 155 mg/dl,

or a 3-hour glucose level of more than 140 mg/dl.

Hemoglobin A1C

In the body, sugar sticks with proteins. The red blood cells are a protein that circulates in the

body live for about three months before they die off. When sugar st icks to these cells, it gives

us an idea of how much sugar is around for the preceding three months. In most labs, the

normal range is 4%-5.9 %. In poorly controlled diabetes, its 8.0% or above, and in well

controlled patients it's less than 7.0% (optimal is


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ARTIFICIAL INTELLIGENCE AND

EXPERT SYSTEM


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3.ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEM

3.1. ARTIFICIAL INTELLIGENCE

Definition of Artificial Intelligence (AI):AI is concerned with the design of intelligence in an artificial device or it is a branch of

computer Science concerned with the automation of intelligent behavior. The term is coined

by Mc. Carthy in 1956.

Artificial intelligence has several areas of interest such as robotics, vision, natural language

understanding and semantic modeling, speech, artificial neural system and parallel

processing, automated reasoning and theorem proving, expert systems, game playing and

human performance modeling.

The term intelligence covers many cognitive skills, including the ability to solve problems,

learn, and understand language; AI addresses all of those. But most progress to date in AI has

been made in the area of problem solving -- concepts and methods for building programs that

reason about problems rather than calculate a solution.

The root of expert system lies in many disciplines. One of the major roots of expert systems is

the area of human informational processing called cognitive science. Cognition is the study of

how human process information or how people think especially when solving problems.

3.2. EXPERT SYSTEMS

Expert system is a branch of Artificial intelligence.

Definition of Expert System:

It is an intelligent computer program that uses knowledge and inference procedures to solve

problems at the level of a human expert. It emulates the decision making ability of a human

expert. That is the expert system is intended to act in all respects like a human expert in a

particular field.

Building an expert system is known as knowledge engineering and its practitioners are called

knowledge engineers. The knowledge engineer must make sure that the computer has all the

knowledge needed to solve a problem. The knowledge engineer must choose a knowledge

representation. He must also ensure that the computer can use the knowledge efficiently byselecting from a handful of reasoning methods.


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Typically such a system contains a knowledge base containing accumulated experience and a

set of rules for applying the knowledge base to each particular situation that is described to

the program. The knowledge in expert system may be either expertise or knowledge that is

available from books, journals conferences, magazines, knowledge persons, internet etc.

Expert systems can be classified from different points of view. It can be the advisory systems,

the ones that suggest the direction; systems taking decisions without the help and human

interference; criticizing systems the ones which on the basis of a particular problem and the

predicted solution by a man, analyze and comment the particular reasoning and action way.

A human experts knowledge is specific to one problem domain. A problem domain is the

special problem area such as medicine, finance, science, or engineering. The problem domain

consist many problems. The experts knowledge about solving specific problems is called the

knowledge domain of the expert. In the same way expert systems are designed to address

specific problem in the problem domain, called knowledge domain.

It is important that there is a close cooperation between a system engineer and experts on

those interest fields when constructing the appropriate expert system. Nothing can replace the

knowledge and experts experience.

3.3. IMPORTANCE OF EXPERT SYSTEM

Expert systems have experienced tremendous growth and popularity in all fields like business,

science, engineering, manufacturing, medicine and many other fields. Conventional computer

programs make use of well structured algorithms, data structures for problem solving and use

crisp reasoning strategies to find solutions. The implementation of algorithm in a program is

called procedural program. So the terms algorithmic programming, procedural programmingand conventional programming are often use interchangeably. Problems that cannot solve by

conventional methods, use non conventional or AI method. Expert systems are designed for

problems those have no efficient algorithmic solution and rely on inferences to achieve a

reasonable solution. Such cases are called ill-structured problems that are difficult or

impossible to do by using conventional or procedural or programming. The reasoning may

offer the only hope of a good solution because there may so many possibilities or several

possible inferences. Explanation facility is an integral part of an expert system. The expert

system reasons or makes inferences in the same way that a human expert would infer the


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solution of a problem. That is, given some facts, a conclusion that follows is inferred. It must

be able to explain its reasoning to the user so that its reasoning can be checked.

The process of developing an expert system has an indirect benefit also since the knowledge

of human experts must be put into an explicit form for entering into the computer. Because

the knowledge is then explicitly known instead of being implicit in the experts mind, it can

be examined for correctness, consistency and completeness. The knowledge may then have to

be adjusted or re-examined, which improve the quality of the knowledge.

Expert systems have appropriate and inappropriate applications. Most applications of expert

systems will fall into one of the following categories.

Interpreting and identifying

Predicting

Diagnosing

Designing

Planning

Monitoring

Debugging and testing

Instructing and training

Controlling

Conventional computer programs perform job using a decision making logic containing very

little knowledge other than the basic algorithm for solving that specific problem. The basic

knowledge is embedded as part of the programming code, so that as the knowledge changes

the program has to be rebuilt. Knowledge based expert system collect small fragments of

human knowledge into knowledge base, which is used to reason through the problem usingthe knowledge that is appropriate. An important advantage here is that within the domain of

knowledge base, a different problem can be solved using the same program without

reprogramming efforts. Moreover expert systems could explain the reasoning process and

handle level of confidence and uncertainty which conventional algorithms do not handle.

The expert system, having a written expert knowledge from a chosen field, can use many

times in an economic and effective way because it does not need the presence of the expert.

The special advantage of such systems is the possibility of solving the particular tasks without


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the direct expert's participation, and also the possibility of knowledge aggregation in one

system of the numerous experts team.

3.4. MAIN COMPONENTS OF AN EXPERT SYSTEM

Figure 1 shows the concept of a knowledge based expert system. The expert system receives

facts from the user and provides expertise in return. The two main components of an expert

system (invisible from outside) are the knowledge base and inference engine.

The knowledge base: It contains the domain knowledge coded in the form of knowledge. It

stores all relevant information, data, rules, cases and relationships used by the expert system.

The knowledge base can combine the knowledge of multiple human experts. A knowledge

base is the nucleus of an expert system structure. A knowledge base is not a data base. Theknowledge base is created by the knowledge engineers who translate the knowledge of real

human experts into rules. In a rule based expert system a knowledge base is usually stored in

terms of if-then rules.

The knowledge baseof expert systems contains both factual and heuristic knowledge.

Factual knowledge is that knowledge of the task domain that is widely shared,

typically found in textbooks or journals, and commonly agreed upon by those

knowledgeable in the particular field.

Heuristic knowledge is the less rigorous, more experiential, more judgmental

knowledge of performance. In contrast to factual knowledge, heuristic knowledge is

rarely discussed, and is largely individualistic. It is the knowledge of good practice,

good judgment, and plausible reasoning in the field. It is the knowledge that underlies

the "art of good guessing." For the difficult problems with which expert system are

concerned it uses heuristics: Strategies that often lead to correct solution, but that also

sometime fail.

The inference engine: The Inference engine may infer solutions or conclusions from the

knowledge base, based on the facts supplied by the user. The inference engine acts as an

interpreter or scheduler that interprets which rules matches with the facts. The word inference

or reasoning is very important in expert system; because reasoning is the standard technique

by which expert system solve problems. It makes inferences by deciding which rules are

satisfied by facts, prioritizes the satisfied rules, and execute the rule with the highest priority.

These conclusions are the expert systems response to the user queries, as the user supplies

facts or other information to the expert system for getting the expertise or expert advice or


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response. So the purpose of inference engine to seek information and relationship from the

knowledge base and to provide answers, predictions and suggestions in the way a human

expert would. The inference engine must find the right facts, interpretations and rules and

assemble them correctly. Two types of inference methods are commonly used. Forward

Chaining and backward chaining.

Figure 1: Main components of an expert system

The other components are-

User interface: The mechanism by which the user and the expert system communicate. There

is specialized user interface software available, that can be use to designing, updating and

using expert system. The purpose of user interface is to ease use of expert system for

developers, users and administrators.Explanation facility: The explanation facility allows the user to understand how the expert

system arrived at certain results. That is it explains the reasoning of the system to the user.

Working memory: It is a global database of facts used by the rules. This data is supplied by

the end user.

Agenda: A prioritized list of rules created by the inference engine, whose patterns are satisfied

by facts in working memory.

Knowledge acquisition facility: The overall purpose of knowledge acquisition facility is to

provide a convenient and efficient means for capturing and storing all components of a

knowledge base. It also provides an automatic way for the user to enter knowledge in the

system instead of having the knowledge engineer explicitly code the knowledge. The

knowledge acquisition facility is an optional feature on many systems. Some expert system

tools like KEE (Knowledge engineering Expert) can learn by rule induction through examples

and can automatically generate rules. The examples are generally from tabular or spreadsheet

type data better suited to decision trees. General rules constructed by knowledge engineer can

be much more complex than the simple rules from rule induction. Figure 2 shows the

structure of an expert system


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Figure 2 Structure of a rule based expert system

3.5. RULE BASED EXPERT SYSTEMS

The main differences between the expert systems are how they capture and store knowledge

in the knowledge base or knowledge representation in the knowledge base. Knowledge

representation is of major importance in expert system for two reasons. First expert system

shells are designed for certain type of knowledge representation. Second the way in which

expert system represents knowledge affects the development, efficiency, speed andmaintenance of the system.

One of the most popular types of expert system today is the rule based or production rule

system. A rule is conditional statement that links given conditions to actions or outcomes.

Expert systems making use of rules to store knowledge are called rule based expert systems. It

is based on an efficient algorithm called rete pattern matching algorithm for matching facts

against the patterns in rules to determine which rules have had their conditions satisfied. Rete

algorithm performance is theoretically independent of the number of rules in the system and

requires less memory.Rule based expert system use human expert knowledge to solve real

world problems that normally would require human intelligence. Experts knowledge is

represented in the form of rules or as data within the computer. Depending upon the problem

requirement these rules and data can be recalled to solve problems. Knowledge can easily

grow incrementally in a rule based system. That is the knowledge base can grow little by little

as rules are added so that the performance and correctness of the system can be continuously

checked. The incremental growth of knowledge facilitates rapid prototyping so that the

knowledge engineer can quickly show the expert a working prototype of the expert system


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This is an important feature because it maintains both the experts and managements interest

in the project rapid prototyping also quickly exposes any gaps, inconsistencies or errors in the

experts knowledge or the system so that corrections can be made immediately. Rule based

expert system have played an important role in modern intelligent system and their

applications in design, planning, scheduling, fault monitoring, diagnosis etc.

A rule based expert system consists of if-then rules, a bunch of facts and an interpreter

controlling the application of rules, given facts. A single if-then rule assumes the form if X is

A then Y is B; if part of the rule X is A is called antecedent or premise and then part of the

rule Y is B is called consequent or conclusion.

Rule based expert systems are popular for a number of reasons.

Modular nature: This makes it easy to encapsulate knowledge and expand the expert

system by incremental development

Explanation facilities: It is easy to build explanation facilities with rules because the

antecedents of a rule specify exactly what is necessary to activate the rule. By keeping

track of which rules have fired, an explanation facility can present the chain of

reasoning that led to a certain conclusion.

Similarity to the human cognitive process: Rules appear to be the natural way of

modeling how humans solve problems. The simple IF.THEN representation of rules

makes it easy to explain to experts the structure of knowledge the knowledge engineer

trying to elicit from them and the process is called knowledge engineering.

Inference Engines in Rule Based Systems

The two broad kinds of inference engines used in rule based expert systems are forward

chining and backward chaining system.

Forward chaining: It starts with the facts and works forward to reach the conclusion. It

involves checking the condition part of the rule to determine whether it is true or false.

If the condition is true then the action part of the rule is also true. This procedure

continues until the solution is found or a dead end is reached. Forward chaining is

commonly referred to as data driven reasoning.

Backward chaining: It is the process of starting with the conclusion and working back-

ward to the supporting facts. It is the reverse of forward chaining. Backward chaining

is very good when all the outcomes are known and the number of possible outcome is

not large. In this case a goal is specified, and the expert system tries to determine what


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conditions are needed to arrive at the specified goal. Thus backward chaining is also

called goal-driven reasoning.

3.6. THE NEED FOR EXPERT SYSTEMExpert systems are necessitated by the limitations associated with conventional human

decision-making processes, including:

1. Human expertise is very scarce.

2. Humans get tired from physical or mental workload.

3. Humans forget crucial details of a problem.

4. Humans are inconsistent in their day-to-day decisions.

5. Humans have limited working memory.

6. Humans are unable to comprehend large amounts of data quickly.

7. Humans are unable to retain large amounts of data in memory.

8. Humans are slow in recalling information stored in memory.

9. Humans are subject to deliberate or inadvertent bias in their actions.

10.Humans can deliberately avoid decision responsibilities.

11.Humans lie, hide, and die.

Coupled with these human limitations are the weaknesses inherent in conventional

programming and traditional decision-support tools. Despite the mechanistic power of

computers, they have certain limitations that impair their effectiveness in implementing

human-like decision processes.

Conventional programs:

1. Are algorithmic in nature and depend only on raw machine power

2. Depend on facts that may be difficult to obtain

3. Do not make use of the effective heuristic approaches used by human experts

4. Are not easily adaptable to changing problem environments

5. Seek explicit and factual solutions that may not be possible

3.7. LIMITATIONS OF EXPERT SYSTEMS

Lack of Causal Knowledge

A practical limitation of many expert systems today is lack of causal knowledge. That is, the

expert systems do not really have an understanding of the underlying causes and effects in a

system. The programming using causal knowledge would be an enormous task and even if


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recommendations with the same confidence even if the data they are dealing with are

inaccurate or incomplete.

3.8. EXPERT SYSTEM AND MEDICAL FIELDThere already some expert systems are in medical field. Some of them are-

MYCIN: It was the first well known medical expert system developed by Shortliffe at

Stanford University (Buchanan and Shortliffe, 1984) used for diagnosis and remedy bacterial

infections. It uses backward chaining inference procedure. It helps doctors, not expert in

antimicrobial drugs; prescribe such drugs for blood infections. The limitation of MYCIN was

its knowledge base is incomplete since, it does not cover anything like the full spectrum of

infectious diseases. Running it would have required more computing power than most

hospitals could afford at that time (1976). Doctors do not relish typing at the terminal and

require a much better user interface than that provided one.

PERFEX: It is a medical expert system that support solving problems clinicians currently

have in evaluating perfusion studies (Ezquerra et al., 1992). The heart of the PERFEX system

is the knowledge base, containing over 250 rules. They were formulated using the expertise of

clinicians and researchers at Emory University Hospital. PERFEX limitation resides in its

output. It is mostly numerical.

INTERNIST-I: It is a rule-based expert system designed at the University of Pittsburgh in

1974 (Kumar et al., 2009) for the diagnosis of complex problems in general internal

medicine.

ONCOCIN: It is a rule-based medical expert system for oncology protocol management

(Wiederhold et al., 2001) developed at Stanford University. ONCOCIN was designed to assist

physicians with the treatment of cancer patients receiving chemotherapy.

Dxplain: It is a decision support system which uses a set of clinical findings (signs,

symptoms, laboratory data) to produce a ranked list of diagnoses which might explain (or be

associated with) the clinical manifestations (Elhanan et al., 1996). The Dxplain provides

justification for why each of these diseases might be considered, suggests what further clinical

information would be useful to collect for each disease and lists what clinical manifestations,

if any, would be unusual or a typical for each of the specific diseases.

PUFF: It is an expert system for the interpretation of pulmonary function tests for patients

with lung disease. PUFF was probably the first AI system to have been used in clinical

practice.


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EXPERT SYSTEM BUILDING TOOLS


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4. EXPERT SYSTEM BUILDING TOOLS

4.1. PROGRAMMING LANGUAGES FOR EXPERT SYSTEMExpert systems are typically written in special programming languages. An expert system

language is higher order language than other languages like C or artificial languages like LISP

(List Processing), PROLOG (Program logic) because it is easier to do certain things. Artificial

intelligence languages are mainly used for symbolic processing, but also used for developing

expert system. The specialized nature of expert system languages make them suitable for

writing expert systems but not for general purpose programming.

The major advantage of these languages, as compared to conventional programming

languages, is the simplicity of the addition, elimination, or substitution of new rules and

memory management capabilities. The primary functional difference between expert system

language and procedural languages is the focus of representation. Procedural languages focus

on providing flexible and robust techniques to represent data such as arrays, records, linked

list, stack, queue etc that are easily create and manipulate. In contrast, expert system

languages are providing flexible and robust ways to represent data. It allows two levels of

abstraction-data abstraction and knowledge abstraction. Expert system languages specifically

separate the data from the methods of manipulating the data. An example of this separation is

that of facts (data abstraction) and rules (knowledge abstraction) in a rule based expert system

language. So the expert system language requires less rigid control of the execution sequence.

The inference engine is used to apply the knowledge to the data. But in procedural language

there is a tight interleaving of data and knowledge and programmers must carefully describe

the sequence of execution. In expert system the separation of knowledge and data allows a

higher degree of parallelism and modularity.

Some of the distinguishing characteristics of programming languages needed for expert

systems work are:

Efficient mix of integer and real variables

Good memory-management procedures

Extensive data-manipulation routines

Incremental compilation

Tagged memory architecture


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Optimization of the systems environment

Efficient search procedures.

4.2. CLIPS- EXPERT SYSTEM TOOL

4.2.1 HISTORY

CLIPS, an acronym for C Language Integrated Production System, are a computer language

developed by NASAs Johnson Space Center for developing application in expert system.

The origin of the CLIPS(C language integrated production system) was in 1984 and is

designed using C programming language. CLIPS is a productive development and delivery

expert system tool which provides a complete environment for the construction of rule and/or

object based expert systems which includes features such as an integrated editor and adebugging tool. Because of its portability, extensibility, capabilities and low cost it accepted

widely in all fields like government, academia, industry etc. The development of CLIPS has

helped to improve the ability to deliver expert system technology throughout the public and

private sections for a wide range of applications. CLIPS have being used by numerous users

both in public and private sections include military, numerous federal bureaus, government

contractors, universities and many private companies. Originally CLIPS support only rule

based programming.

4.2.2 FEATURES OF CLIPS

Knowledge Representation:CLIPS provides a cohesive tool for handling a wide variety of

knowledge with support for three different programming paradigms: rule-based, object-

oriented and procedural. Rule-based programming allows knowledge to be represented as

heuristics, or "rules of thumb," which specify a set of actions to be performed for a given

situation. Object-oriented programming allows complex systems to be modeled as modular

components (which can be easily reused to model other systems or to create newcomponents). The procedural programming capabilities provided by CLIPS are similar to

capabilities found in languages such as C, Java, Ada, and LISP. So the different ways to

represent in CLIPS are:

Rules: Primarily intended for heuristic knowledge based on experience.

Deffunctions and generic functions: for procedural knowledge.

Object-oriented programming: Primarily for procedural knowledge.

Portability:CLIPS is written in C for portability and speed and has been installed on many

different operating systems without code changes. Operating systems on which CLIPS has


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been tested include Windows 95/98/NT, MacOS X, and UNIX. CLIPS can be ported to any

system which has an ANSI compliant C or C++ compiler. CLIPS come with all source code

which can be modified or tailored to meet a users specific needs.

Integration/Extensibility: CLIPS can be embedded within procedural code, called as a

subroutine, and integrated with languages such as C, Java, FORTRAN and ADA. CLIPS can

be easily extended by a user through the use of several well-defined protocols.

Interactive Development:The standard version of CLIPS provides an interactive, text oriented

development environment, including debugging aids, on-line help, and an integrated editor.

Interfaces providing features such as pull down menus, integrated editors, and multiple

windows have been developed for the MacOS, Windows 95/98/NT, and X Window

environments.

Verification/Validation: CLIPS includes a number of features to support the verification and

validation of expert systems including support for modular design and partitioning of a

knowledge base, static and dynamic constraint checking of slot values and function

arguments, and semantic analysis of rule patterns to determine if inconsistencies could

prevent a rule from firing or generate an error.

Fully Documented: CLIPS comes with extensive documentation including a Reference

Manual and a User's Guide.Low Cost:CLIPS is maintained as public domain software.

4.2.3 WORKING WITH CLIPS

The CLIPS interface is a simple, interactive, text oriented, command prompt interface for

high portability. The standard usage is to create or edit a knowledge base using any standard

text editor, exit the editor and execute CLIPS, then load the knowledge base into the CLIPS.

The primitive way of interacting with CLIPS is through CLIPS command prompt. The

interface provides commands for viewing the current state of the system, tracing execution,

adding or removing information, and clearing CLIPS. Comments can be included in CLIPS

code using semicolon. Everything from the semicolon until the next return character will be

ignored by CLIPS.

To start CLIPS, double-click on the CLIPSWin.exe file. Then get a window with nothing in

it but the command prompt CLIPS> comes. This mode is called top level. This is where type

commands and programs. To exit CLIPS, type (exit) or shut down the program like any other


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windows application. CLIPS commands are always encased in brackets. Here is a list of some

important commands:

(exit) Shuts down CLIPS

(clear)Removes all rules and facts from memory or all constructs currently contained in

CLIPS. Equivalent to shutting down and restarting CLIPS.

(reset) Removes facts information from memory (but not rules) and resets the agenda.

(run) Starts executing a CLIPS program.

The above commands can also be executed from the CLIPS menu bar.

The primary method for interacting with CLIPS is through the CLIPS command prompt on

top level. When CLIPS> prompt comes, commands can be entered in brackets. Whenever

the user wants to exit from CLIPS type (exit) from the command prompt.

4.2.4 CLIPS PROGRAMMING ELEMENTS

The basic components of CLIPS programming languages are different data types, functions

for manipulating data, and constructs for adding to knowledge base.

Data Types

The different data types available in are float, integer, symbol, string, external-address, fact-

address, instance-name and instance-address.

Floats & Integer: A number consist only of digits (0-9), a decimal point (.), a sign (+ or -)

and an e for exponential notation A number having only digits and its sign (optional) is

called integer and all other numbers are called floats.

Example: integers 123 +98 -135

Floats 1.378 +1.09 33e7

Symbol:A symbol in CLIPS is a sequence of characters that starts with any printable ASCII

character and is followed by zero or more characters. The end of the symbol is reached when

a delimiter is encountered. That is non printable ASCII characters like space, line feed, tabs,

carriage return etc

Example: fire, a81Ad @-+= 2xyz

CLIPS are a case sensitive language. So the symbols fire, Fire, FIRE are considered different

symbols by CLIPS.

String:String is a set of characters that starts and ends with double quotes

Example: a and b number 8


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External Address: An external address is an address of an external data structure returned by

a user defined function. That is a function written in language such as C and linked with

CLIPS to add additional functionality.

Fact-address: The fact in CLIPS as referred by facts index; facts index are called fact-

address

Example: where xxx is called index

Instance-name:An object is an instance of a class; an instance-name refers to an object of

the Specified name which is an instance of a user-defined class, instance name is represented

by a symbol enclosed within left and right brackets

Example: [abc] [value-03]

Instance-address: An object is an instance of a class; the address of an instance of user

defined class is called instance-address. It is obtained as the return value from a function.

Example: The instance-address is represented by

Function:Function in CLIPS is a piece of executable codes with specific name which return

useful value or perform useful actions.

Different types of function are user defined functions and system defined functions. System

defined functions are functions internally defined in CLIPS and user defined functions are

functions externally defined to CLIPS environment in C, FORTRAN or Ada etc. and linkedwith CLIPS environment. Using deffunction constructs user can create new functions in

CLIPS; these user function acts like any other functions.

Example: CLIPS> (+3 4)

CLIPS> 7

Constructs: Constructs are defined in CLIPS to change CLIPS environment adding to

CLIPS language base. Constructs form the core of CLIPS program by adding the

programmers knowledge to the CLIPS environment and are different from functions andcommands. Function calls return value but commands never have a return value.

Different constructs available in CLIPS are defmodule, defrule, deffacts, deftemplate,

defglobal, deffunction, defclass, definstances, defmessage-handler, defgeneric, and

defmethod.

Facts: Facts are the first component of a CLIPS system and are made up of fields, which are

symbols, strings, integers or floats. Facts are one of the basic high-level forms for

representing information in CLIPS. The number of facts in the fact list and the amount of

information that can be included in the fact is limited by the memory of computer. CLIPS


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operate by maintaining a list of facts and a set of rules which operate on them. A fact is a

piece of information such as (risk_factor smoking) or (risk_factor alcoholic) Facts are

created by asserting them onto the fact database using the assert command .The assert

command return a value called fact index. Example:

CLIPS> (assert (risk_factor smoking))

CLIPS> (assert (risk_factor alcoholic))

The facts command can be used to display the facts in the fact list.

CLIPS> (facts)

f-0 risk_factor smoking

f-1 risk_factor alcoholic

For a total of 2 facts

The term f-0 and f-1 are called fact identifiers assigned to the facts by CLIPS and the

integers following the letter f is called fact index.

The facts command can be used to display the facts in the fact list.

Facts can be retracted from fact data base by using retract CLIPS command

Example:

CLIPS> (retract 0)

CLIPS>(facts)

f-1 (risk_factor alcoholic)

for a total of 1 facts

A single retract command can be used to retract multiple facts at once.

(retract 0 1)

Will retract facts 0 and 1.The symbol * with the retract command would remove all facts

CLIPS>(retract *)

Facts are used to store a chunk of information in CLIPS. In that case facts consist of a relation

name, the first field of fact, is normally used to indicate the type of information stored in the

fact. The relation name (symbolic field) followed by zero or more slots (symbolic fields) and

their associated values. The following is an example of a fact.

(Person (name George)

(age 45)


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(gender M)

(height 175)

(weight 80))

Deftemplate construct:It is analogous to the record structure in languages like C or Pascal.

Deftemplate construct is used to assign slot names to specific fields of a fact beginning with a

specified relation name. Before facts can be created, CLIPS must be informed of the list of

valid slots for a given relation name. Group of facts that share the same relation name and

contain common information can be described using the deftemplate construct. So the person

fact can be described with the following deftemplate.

(deftemplate person

(multislot name)

(slot age)

(slot gender)

(slot height)

(slot weight)

)

Slots of a fact that have been specified with the slot keyword in their corresponding

deftemplates are allowed to contain only one value. These are referred to as single field slots.Slots of a fact that have been specified with the multislot keyword in their corresponding

deftemplates are allowed to contain zero or more values and are referred to as multislot fields.

Ordered facts: Facts with a relation name that has a corresponding deftemplate are called

deftemplate facts. Facts with a relation name that does not have a corresponding deftemplate

are called ordered facts. Ordered facts have a single implied multifield slot that is used to

store all values following the relation name. That is whenever CLIPS encounters an ordered

fact it automatically creates an implied deftemplate for that fact. Since an ordered fact hasonly one slot, the slot name is not required when defining the fact.

(number-list 7 9 4 5 6)

It is equivalent in defining the following deftemplate

(deftemplate number-list (multislot values))

And then adding the fact as follos

(number-list (values 7 9 4 5 6))

Normally deftemplate facts use more because the slot name makes the facts more readable

and easier to work with. There are two cases in which ordered facts are useful. First facts


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consist of just relation name and used as flags, second for facts containing a single slot, the

slot name is usually synonymous with the relation name.

Slot values of a deftemplate facts can be modified using modify command.

(modify+)

Where is ( )

Eg: CLIPS>(modify 0 (age 24))

The modify commandworks by retracting the original fact and then asserting a new fact

with the specified slot values modified. A new fact index is generated for a modified fact. The

facts in the fact list would be deleted, inserted or modified frequently; some fact indexes

would be missing in the fact list. So the fact identifiers may not be strictly sequential as a

CLIPS program executes.

The duplicate commandworks in the same way as modify command with the exception that

it does not retract the original fact.

(duplicate +)

The deffacts construct:

It is used to assert a set of facts instead of typing in the same assertions from the top level.

This is true for facts that are known to be true before running the program that is initial

knowledge. Groups of facts that represent initial knowledge can be defined using the deffactsconstruct. The general format of deffacts construct is

(deffacts )

The deffacts name is any valid symbol. The facts in the deffacts statement are asserted using

the CLIPS reset command. The reset command removes all facts from the fact list and then

asserts the facts from existing deffacts statement. The syntax is

(reset)

It also create a new fact called initial-fact. Upon startup CLIPS automatically defines twoconstructs.

(deftemplate initial-fact)

(deffacts initial-fact

(initial-fact)

So if not define any deffacts statement, a reset will assert the fact initial-fact. The fact

identifier of initial-fact is always f-0. The utility of initial-fact is to start the execution of a

program.


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Components of a Rule: An expert system consists of rules as well as facts. Rules can be type

directly into CLIPS or loaded in from a file of rules created by an editor. Before rules created

the deftemplate for the type of facts referred to by the rules must be defined. Rules can be

entered by typing it after the CLIPS prompt.If the rule entered correctly, the CLIPS prompt

will reappears; otherwise an error message will come. Comments can be including with rules

but it should begin with a semicolon. The general format of the rule is

(defrule []

;LHS of the rule

=>

); RHS of the rule

A rule may have multiple patterns and actions. The entire rule must be surrounded by

parentheses and each of the pattern and actions also must be surrounded by parentheses.

The header of the rule consists of three parts. The rule must start with a defrule keyword,

followed by the name of the rule. If the rule name already exist the new rule replace the old

rule. The third part is an optional comment string. A comment is normally used to describe

the purpose of the rule of any other information.

After the rule header are zero or more conditional elements (CEs).One is the pattern CE orsimply pattern. Each pattern consists of one or more constraints intended to match the fields

of a deftemplate fact. CLIPS attempted to match the pattern of the rule against the facts in the

fact list. If all the pattern of the rule matches facts, the rule is activated and put on the agenda,

the collection of activated rules. There may be zero or more rule in the agenda. The pattern of

the rule follows an arrow indicating the THEN part of the IF. THEN rule.

The last part of rule actions will be executed when the rule fires. A program stops executionwhen there are no rules in agenda. When there are multiple rules on agenda CLIPS

automatically determines which is the appropriate rule to fire. CLIPS orders the rules in

agenda according to priority and execute the rule with highest priority. The priority of the rule

is an integer attribute called salience.

The list of rules on the agenda can be displayed with the agenda command. The syntax of the

agenda command is

CLIPS> (agenda)


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Salience:

CLIPS provide salience techniques for controlling the execution of the program. Salience

allows the priority of rules to be explicitly specified. Normally the agenda acts like a stack.

The most recent activation placed on the agenda is the first one to fire. Salience allows most

important rules to be placed on the top of agenda regardless of when the rules were added.

Lower salience rules pushed below the higher salience rules on the agenda. Salience is set

using a numerical value ranging from the smallest value of -10,000 to the highest of

10,000.One of the use of salience is to force rules to fire in sequential fashion. Overuse of

salience results poorly coded program. A well coded expert system needs not more than three

or four salience values.

Halt function:

The halt function can be used on the RHS of the rule to stop the execution of rules on the

agenda. It requires no arguments. When called control will return on the top level prompt. The

halt function is particularly useful for halting execution when the user intends to restart

execution later using the run command.

BIND function:

For storing value in temporary variable to avoid recalculation, the bind function can be used

to bind the value of a variable to the value of an expression. The syntax is(bind )

The bind variable, , uses the syntax of a single field variable. The new value,

should be an expression that evaluates to either a single or a multi field value.

In addition to creating new variables for use on the RHS of the rule, the bind function can also

be used to rebind the value of a variable use in the LHS of the rule.

Printout command: The RHS of the rule can use to print out information using the printout

command. The syntax of printout command is(printout )

Where indicates the output destination of printout command and are zero or more items to be printed by this command.

The letter t after the printout command indicates the destination of the output. This destination

is also referred to as logical name. Normally the logical name t tells CLIPS to send the output

to the standard output device of the computer, usually the terminal. So the default standard

output device can be specified with the logical name t. The standard output device may


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something else such as a modem or a printer. In that case other logical names can be used

with the printout command to send the output to other destination other than screen.

Eg:

( defrule smoki ng( decl ar e ( salience 80))?p ( pri nt out t "Do you smoke? N=No Y=Yes" cr l f )( bi nd ?smoke ( r ead) )( whi l e ( and ( neq ?smoke Y) ( neq ?smoke y) ( neq ?smoke N) ( neq ?smoke n) ( neq?smoke Q) ( neq ?smoke q) )( pr i nt out t "You have ent er ed wr ong answer" cr l f

"Pl ease ent er yes or no ( y/ n) . . . " cr l f" i f you need t o qui t ent er q. . . . " cr l f )

(bind ?smoke ( r ead)))

( i f ( or ( eq ?smoke y) ( eq ?smoke Y) ( eq ?smoke N) ( eq ?smoke n) )t hen( - - - - - do requi red thi ng- - - - - )

)( i f ( or ( eq ?smoke y) ( eq ?smoke Y) )t hen ( - - - - - do requi red t hi ng- - - - - ) ) )( i f ( or ( eq ?smoke Q) ( eq ?smoke q) )t hen( halt) )(retract p))

Refraction:

Rules in CLIPS exhibit a property called refraction. It means CLIPS will not permit fire morethan once for a specific set of facts. Without refraction expert system would always caught in

trivial loops. That is as soon as the rule fired, it would keep on firing on that same fact over

and over again. So refraction prevents rules from being constantly activated by old facts. A

rule can be made to fire again by retracting the fact by using reset command and asserting it

again. Suppose two rules are there in agenda for ready to execute. If run command issued the

two rules would be executed and agenda would become empty. After enter agenda command

no rules or activations would be there in the agenda.

A refresh command can be used to make the rule fire again. The refresh command places all

activations that have already fired for a rule back on the agenda with the restrictions that the

facts that triggered the activations are still present in the fact list. The syntax is

(refresh )

Watch Command:

Watch command is useful for debugging programs. The syntax is

(watch )


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Where watch is one of the symbols facts, rules, activations, statistics, compilations, focus or

all. By default, when CLIPS is first started, compilations are watched and the remaining

watch items are not watched. The word all can be used to enable all of the watch features.

CLI PS>( f act s)

f - 0 ( person ( name Geor ge)

( age 45)

( gender M)

( hei ght 175)

( wei ght 80) )

For a t ot al of 1 f act.

CLI PS> ( watch f act s)

CLI PS> ( modi f y 0 ( age 35) )

f-1 ( person ( name George)

( age 35)

( gender M)

( hei ght 175)( wei ght 80) )

CLI PS>

The character sequences indicates that the fact is being retracted, the fact is

being created.

CLI PS>( r eset )

CLI PS>( watch act i vat i ons)

CLI PS>( asser t ( r i sk_f act or ( smoki ng) ) )

== >Act i vat i on 0 smoki ng: f - 1

CLI PS>( agenda)

0 smoki ng : f - 1

For a t ot al of 1 acti vat i on.

CLI PS> ( r et r act 1)

< ==Act i vat i on 0 smoki ng: f - 1

CLI PS> ( agenda)

CLI PS>

If rules are being watched, CLIPS will print a message FIRE whenever a rule is fired.CLI PS> ( r eset )CLI PS> ( watch r ul es)


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CLI PS> ( asser t ( r i sk_f act or ( smoki ng) ) )== > Act i vat i on 0 smoki ng : f - 1CLI PS> ( r un)FI RE 1 smoki ng: f - 1

CLI PS> ( agenda)CLI PS>

The effects of a watch command can be turned off by using corresponding unwatch command

( unwat ch )

Loading Constructs from a file:

A file of constructs made with a text editor can be loaded into CLIPS using the load

command. The syntax is

( l oad )

Where is a string or symbol containing the name of file to be loaded. While

loading the file the path name also should be specified. Normally the path is represented by

backslash character. But CLIPS consider backslash as an escape character, two backslash are

used to represent one backslash in a string. A file with name example.clp can be loaded as

( l oad c: \ \ user\ \ cl i ps\ \ exampl e. cl p)

If no error occurs during loading the file, the load command will return the symbol TRUE

otherwise return FALSE.

Watching Compilations:

When compilations are watched by default without using watch command, an informational

message including the construct name is printed for each construct loaded by the load

command.

Eg:

CLI PS> ( l oad t hesi s. cl p)Def i ni ng def gl obal : wei ghtDef i ni ng def gl obal : hei ghtDef i ni ng def r ul e: mai n_menu +j +jDef i ni ng def r ul e: per sonal _r ul e +j +jDef i ni ng def r ul e: smoki ng +j +jDef i ni ng def r ul e: al cohol i c +j +jDef i ni ng def r ul e: di agnosi sr ul e +j +jTRUECLI PS>

The message indicates that the above deftemplates and defrules were loaded. The +j string

at the end of the Defining defrule message is information from CLIPS about the internal

structure of the compiled rules.

If compilations are not being watched, then CLIPS prints a single character for each construct

loaded: * for defrule, % for deftemplates and $ for deffacts.

CLI PS> ( cl ear )CLI PS> ( unwatch compi l at i ons)CLI PS> ( l oad "dupl i cat e cl p")


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: : : : : : : : %$*****************************************TRUECLI PS>

Saving Constructs to a File:

The save command allows the set of constructs directly entered at the CLIPS prompt to besaved to a disk file. The syntax of the save command is

( save )

( save C: \ dupl i cat e. cl p)

The save command will save all the constructs in CLIPS to the specified file. It is not possible

to save specified files constructs to a file. But if an editor is used to create and modify the

constructs, no need to use the save command.

Commands for manipulating constructs:

Displaying the list of members of a specified construct:

(list-defrules): displays the current list of rules maintained by CLIPS.

(list-deftemplates): displays current list of deftemplates

(list-deffacts): displays the current list of deffacts

CLI PS> ( r eset )

CLI PS> ( l i st- def f acts)

i ni t i al - f act

For a t ot al of 1 def f acts.

CLI PS> ( l i st- def rul es)

CLI PS> ( l i st - def t empl at es)

i ni t i al - f act

For a t ot al of 1 def t empl at e.

CLIPS>

The ppdefrule (pretty print defrule), ppdeftemplate(pretty print deftemplate), ppdeffacts(pretty

print deffacts) commands can be used to display the text representation of a defrule,

deftemplate and deffacts respectively.

( ppdef r ul e )( ppdef t empl at e )( ppdef f act s )CLI PS> ( ppdef r ul e smoki ng)( def r ul e MAI N: : smoki ng

( decl ar e ( salience 80))?p ( pri nt out t "Do you smoke? N=No Y=Yes" cr l f )( bi nd ?smoke ( r ead) )( whi l e ( and ( neq ?smoke Y) ( neq ?smoke y) ( neq ?smoke N) ( neq ?smoke n) ( neq

?smoke Q) ( neq ?smoke q) )( pr i nt out t "You have ent er ed wr ong answer" cr l f"Pl ease ent er yes or no ( y/ n) . . . " cr l f


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" i f you need t o qui t ent er q. . . . " cr l f )(bind ?smoke ( r ead)))( i f ( or ( eq ?smoke y) ( eq ?smoke Y) ( eq ?smoke N) ( eq ?smoke n) )

t hen

( - - - - - do requi red thi ng- - - - - ))( i f ( or ( eq ?smoke y) ( eq ?smoke Y) )t hen ( - - - - - do requi red t hi ng- - - - - ) ) )( i f ( or ( eq ?smoke Q) ( eq ?smoke q) )t hen( halt) )(retract p))

The symbol MAIN:: preceeding each of the construct names indicate the module in which the

constructs have been placed. (ppdefacts ) has no text representation since it is

automatically created by CLIPS.Deleting a specified Construct member

The undefrule, undeftemplate and undeffacts commands are used to delete a defrule,

deftemplate and deffacts respectively. The syntaxes are

(undefrule )

(undeftemplate )

(undeffacts )

The initial-facts deffacts and initial-facts deftemplates can also be deleted using the above

commands. If a reset command performed after that, the (initial-fact) would not be added to

the fact list. If a symbol * is given as an argument to any of the construct deletion commands

then all constructs of the appropriate type are deleted.

(undefrule *) would delete all defrule constructs.

Variables:

CLIPS use variables to store values. Variables in CLIPs are written in the syntax of question

mark followed by a symbolic field name. There should be no space between the question

mark and symbolic field name. Variables are used on the LHS of the rule to contain slot

values that can later be compared to other values on the LHS of the rule or accessed on the

RHS of the rule. One common use of variable is to bind a variable on the LHS of the rule and

then use that value on the RHS of the rule. .

?speed

?sensor

?value

I/O functions:


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Functions are entered at the CLIPS top level command loop or are used on the LHS or RHS

of the rule. CLIPS provide several I/O functions. The open and close functions can be used to

open and closing files. Opened files are associated with logical names. Logical names can be

used in most functions that perform input and output to more than one type of physical

devices. The printout and read function use logical names. The printout function can output to

the terminal and files. The read function can input from the keyboard and files. The format

function allows more control over the appearance of the output. The readline function can be

used to read an entire line of data.

Read function: This function is used to read information from the keyboard. The read

function is used to input a single field at a time. To read more than one field as input it should

enter in double quotes and treated as strings.

CLI PS>( r ead)

Sout h Af r i ca

Sout h Af r i ca

CLI PS>

Open Function: CLIPS can read from and write to files. Before a file can be accessed for

reading or writing, it must be opened using the open function.

( open [ ] )

is a string representing the name of the file on the computer, is the

logical name CLIPS associates with the file. This is a global name by which CLIPS can

access the file from any rule or the top level prompt. Mostly the logical name would be the

same as file name. It is better to use another name to avoid confusion. argument

represent the access mode. It may be read access only (r),write access only(w), read and

write access(r+), append access only(a). If file access not included as an argument the

default value of r will be used. It return the symbol TRUE if a file successfully opened

otherwise FALSE is returned.Close function: If the file is no need to access for a long time it should be closed.

( cl ose [ ] )

The optional argument is the logical name of the file to be closed. If no

closed all open files will be closed. All open files will be closed when an exit command is

issued.

Reading and Writing to a file:For writing to a file, first the file opened with write access

using open function. Then the values are written to the file using the or logical

name as the first argument of the printout command, follows the values that need to write to


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the file. Once the value has been written to the file, the file is closed using the close

command.

An Expert System for Diabetes Diagnosis by Smitha V

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