expert system presentation

7/21/2019 Expert System Presentation

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E

XPERTS

YSTEMS

PRESENTED BY :

Shiromani Gupta ( 49-MBA-15 )

Vishesh Kapoor ( 63-MBA-

15 )

Tikshan Langer ( 59-MBA-

15 )


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TOPICSCOVERED

Introduction

Components Of Expert Systems

Various Examples

Architectures

Need Of An Expert System

Properties & Building An Expert System

ApplicationsBenefits And Challenges

Expert System-Eliza

Future Scope


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WHO IS AN EXPERT?

An EXPERTis a person who is very knowledgeable about or

skilful in aPARTICULAR FIELD.

EXAMPLES: Doctor,

Chattered accountant,

Sportsperson,

Lawyer,

Scientists, etc


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DEFININGEXPERT SYSTEM

ES is a computer software that :

BEHAVESlike,

ADVICESyou like,

HELPSyou like a human expert.

ES is an information system that is capable of mimicking

human thinking and making considerations during the processof decision-making.

ES is a system that can be used to solve a problem that

usually requires an expert to solve.


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USER (NON EXPERT)

USER INTERFACE

WORKING STORAGE

INFERENCE ENGINE

SYSTEM ENGINEER

KNOWLEDGE BASE

DOMAIN EXPERT

KNOWLEDGE ENGINEER

COMPONENTS

OF

EXPERT SYSTEMCA

RESULT COMPUTER

TAX RULES AI

TAX RETURN


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USER INTERFACE-A software that provides for the communicationexchange between user and the system.

INFERENCE ENGINE- A software that performs the inference reasoning

tasks. It uses the knowledge in the knowledge base and informationprovided by the user to infer new knowledge.

This acts rather like a search engine, examining the knowledge base for

information that matches the user's query.

With rule based expert systems there are two main types of reasoning -

forward chainingandbackward chaining.


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KNOWLEDGE BASE-

Contains rules and facts from knowledge collected from experts.

While knowledge in humans is gained by learning, experience and experimentation, knowledge ina computer is often represented by rules.

The knowledge base contains the facts and rules or knowledge of the expert. Below is an

example of how IF THEN rules might be applied in our Animal-ID expert system.

EXAMPLE:

IFanimal has backbone

THENvertebrate

IFanimal is vertebrate

ANDhas hair

THENmammal

IFanimal is mammal

ANDhas pointed teeth

ANDhas claws

THENcarnivore


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!Forward chaining:Forward chaining is a 'data driven' method of reasoning. It begins with

the available data, compares it with the facts and rules held in the

knowledge base and then infers or draws the most likely conclusion.IF THEN. Forward chaining starts with the symptoms and works

forward to find a solution.

!Backward chaining:

Backward chaining is a 'goal driven' method of reasoning. It begins with agoal and then looks at the evidence (data and rules) to determine whether

or not it is correct. THEN IF. Backward chaining starts with a

hypothesis and works backwards to prove or disprove it.


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VARIOUS EXAMPLESOF EXPERT SYSTEMS

AROUND US

1. Tax return system

2. Game of chess

I play a move.

I am? -> USER

Move stored in -> WORKING

STORAGE

Chess rules -> KNOWLEDGE

BASE

INFERENCE ENGINE used by

game to play a countermove.


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USER (NON EXPERT)

USER INTERFACE

WORKING STORAGE

INFERENCE ENGINE

SYSTEM ENGINEER

KNOWLEDGE BASE

DOMAIN EXPERT

KNOWLEDGE ENGINEER

CHESS PLAYER

COUNTER MOVE CHESS GAME

CHESS RULES

MOVE


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3. Bank loan

AIM: to get loan from ICICI bank to buy some land

WHAT TO DO? -> Call CUSTOMER CARE

-> They ask you certain questions, you answer them and you get to know

how much loan you can get

BUTWere u taking to the BANK MANAGER?

NO!!

The person there maybe some B.COM. 2nd year student who uses a

computer(expert system here) in front to answer the questions.

B.COM STUDENT SALARY- RS. 10,000 BANK MANAGER- RS. 1,00,000


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4. Autopilot

5. Weather forecasting

6. Medical diagnosis

and many more


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VARIOUS ARCHITECTURES

1. SIMPLE ARCHITECTURE


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2. EXTENDED ARCHITECTURE


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3. COMPLEX ARCHITECTURE


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NEED OF EXPERTSYSTEMS

An expert system is built for the two factors:

-EITHER TO REPLACE AN EXPERT

OR

-TO HELP AN EXPERT


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TO REPLACE AN EXPERT

-To enable the use of expertise after working hours or at

different locations.

-To automate a routine task that requires human expertise

all the time unattended, thus.

-reducing operational costs.

-to replace a retiring or an leaving employee who is an

expert

-To hire an expert is costly


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TO HELP AN EXPERT

-Help experts in their routine to improve productivity

-Help experts in some of their more complex and difficult

tasks so that the problem can be managed effectively.

-Help an expert to obtain information needed by other

experts who have forgotten about it or who are too busy to

search for it.


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NEED OFEXPERT SYSTEM


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PROPERTIES REQUIRED TO MAKE ANEXPERTSYSTEM

EXPERT

AVAILABILITY

COMPLEXITY

STRUCTURE

DOMAIN

Expert should be available

Expert should be available + he should be able to communicateproperly

System should solve complex problems

Even if data is missing or conflicting still System should work

The system should have deep knowledge of a particular field and notgeneral knowledge of all field


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APPLICATION CATEGORY TABLECATEGORY PROBLEM ADDRESSED EXAMPLES

Interpretation Inferring situation descriptions from sensor dataHearsay (Speech Recognition),

PROSPECTOR

Prediction Inferring likely consequences of given situations Pretirm Birth Risk Assessmen

DiagnosisInferring system malfunctions from observables CADUCEUS, MYCIN, PUFF, Mistral

PlanningDesigning actions Mission Planning for Autonomous

Underwater Vehicle

MonitoringComparing observations to plan vulnerabilities

REACTOR

DebuggingProviding incremental solutions for complex problems SAINT, MATHLAB, MACSYMA

Repair Executing a plan to administer a prescribed remedy Toxic Spill Crisis Management

DesignConfiguring objects under constraints Dendral, Mortgage Loan Advisor, R1

(Dec Vax Configuration)

InstructionDiagnosing, assessing, and repairing student behaviourSMH.PAL, Intelligent Clinical Training,

STEAMER

ControlInterpreting, predicting, repairing, and monitoring

system behaviours

Real Time Process Control,Space

Shuttle Mission Control
http://en.wikipedia.org/wiki/Expert_systems_for_mortgageshttp://en.wikipedia.org/wiki/Dendralhttp://en.wikipedia.org/wiki/MYCIN


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BENEFITS OFEXPERT SYSTEM

Preserve Knowledge.

Acts As a Real life Expert.

Assists non-experts in such a way that they feel they are

experts themselves.

Expert Systems are not Emotional.

They can be used in any and every field.


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CHALLENGES WITHEXPERT SYSTEMS

An expert system must exhibit accuracyand reliability.

Expert systems should be accurate and this can be stated with example-

A fault diagnosis system which suggests incorrect solution may cause

inconvenience but such a medical system which suggests incorrect treatment

could cause much more serious health impact.

Expert systems depend on rules in the knowledge baseand are unable to

address problems outside of this domain. This makes them unstable for some

problems hence affecting reliability.

If something is wrong in expert system: Experts provided incorrect or incomplete knowledge. Inference engine suffers some problem User interface is not working properly

the reliability and accuracy of the system are challenged.


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EXPERT SYSTEM- ELIZA

Expert system ELIZA acts like a psychoanalystby holding adialog with a person-The dialog would consist of the doctor (Eliza) asking questions,the human responding, and the doctor using the response to askanother question.

ELIZA was implemented using simple pattern matching

techniques, but was taken seriously by several of its users, even

after Weizenbaum explained to them how it worked.

For ELIZA the program was written so that it would generate an

English response/questionbased on a group of patterns-

If the user sentence matched a pattern, this pattern would be

used to generate the next sentence/question.
http://en.wikipedia.org/wiki/Pattern_matching


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ELIZA EXPERT SYSTEM

DEMO


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Given a set of rules in the form of input/output patterns, Eliza will attempt to

recognise user input phrases and generate relevant responses.

Working of Eliza in steps:

!Repeat:

Input a sentence Find a rule in the Eliza knowledge-base that matches the pattern

Attempt to perform pattern matchAttempt to perform segment match

If rule found, select one of the responses randomly (each pattern will have atleast one response)

Fill in any variables Substitute values (you for I, I for you, me for you, am for are, etc) Respond

!Until user quits.

WORKINGOFELIZA SYSTEMS


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ELIZARULESEach rule for Eliza is specified by an

input pattern and a list of outputpatterns.

A pattern is a sentence consisting of

space-separated words and variables.

Input pattern variables come in two

forms: single variablesand segment

variables.

Single variables (which take the form ?x)

match a single word, while segment

variables (which take the form ?*x) can

match a phrase.

The conversation proceeds by reading asentence from the user, searching

through the rules to find an input

pattern that matches, replacing variables

in the output pattern, and printing the

results to the user.

For instance, if the input were I want to have a

cheeseburger, the second pattern would match

Eliza would respond with one of three outputs

using to have a cheeseburger in place of ?y

Such as Why do you want to have

cheeseburger?

An excerpt from the rules of Eliza:

(defparameter *eliza-rules*'((((?* ?x) hello (?* ?y))(How do you do. Please state your problem.))(((?* ?x) I want(?* ?y))(What would it mean if you got ?y)(Why do you want ?y) (Suppose you got ?y soon))

(((?* ?x) if (?* ?y))(Do you really think its likely that ?y)(Do you wish that ?y)(What do you think about ?y) (Really-- if ?y))(((?* ?x) no (?* ?y))


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FUTURE SCOPE OF EXPERT SYSTEMS

Although expert system are so useful there are currently some problems

which when tackled will lead to a new world of computer learning and

advancement:

On the technical side, there is the problem of the size of the

databaseand using it efficiently.

-If the system consists of several thousand rules, it takes a very

powerful control program to produce any conclusions in a reasonableamount of time.

-If the system also has a large quantity of information in the working

memory, this will also slow things down unless you have a very good

indexing and search system.


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A second problem that comes from a large database is that as the

number of rules increases the conflict set also becomes large so agood conflict resolving algorithm is needed if the system is to be

usable.

Another problem that appears is that of responsibility.

-Take, for example, a system used by a doctor that is designed to

administer drugs to patients according to their needs and that it must

first determine what is wrong with them. If the system causes

someone to take the wrong medicine and the person is harmed, who is

legally responsible?


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A more obvious problem is that of gathering the rules. Human experts

are expensive and are not extremely likely to want to sit down and

write out a large number of rules as to how they come to their

conclusions.

What may be a way round this problem is to enable Expert Systems

to learn as they go, starting off with a smaller number of rules but

given the ability to deduce new rules from what they know and what

they 'experience'. This leads us very nicely into the field of Computer

Learning.

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