Artificial Intelligence Lecture Slide-03

Artificial Intelligence

1. June 24, 2024 Artificial Intelligence, Lecturer #3 1
Artificial Intelligence
Lecture #3

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Contents
Expert System: Introduction
Expert System:Basic Characteristics
Expert System:Basic Architecture
Structure of Rule-Based Expert System
Advantages & Disadvantages of Rule-based Expert
System
Recommended Text books

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Expert System:Introduction
A set of programs that manipulate encoded knowledge to solve
problems in a specialized manner.
Knowledge is a theoretical or practical understanding of a subjects or
domain.
Those who posses knowledge are called experts
Knowledge is obtained from expert source
Knowledge is Coded in a form that is suitable to use
Anyone can be considered a domain expert if he or she has deep knowle
dge and strong practical experience in a particular domain
An expert is a skilful person who can do things other people cannot.

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Expert System: Introduction (Cont.)
A computer Program Capable of performing at a
human-expert level in a narrow problem domain
area is called an expert system.
The most popular expert systems are rule-based
expert systems

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Expert System
Basic Characteristics
Use knowledge rather than data
Knowledge is encoded and maintained as an entity
separate from the control program
Capable of explaining how a particular conclusion
is reached and why requested information is needed
Use symbolic representation and perform inference
through symbolic computation
Often reason with knowledge about themselves

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Expert System:Basic Architecture
Explanation
module
I/O Interface
Editor
Inference
Engine
Knowledge
Base
Learning
module
Case history
Working
Memory

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The knowledge Base
Contains the domain knowledge useful for problem
solving. In a rule-based expert system, the knowledge is repres
ented as a set of rules.
Any rule consists of two parts: IF (Antecedent/Condition)--------
-------THEN (Consequent/Action).
IF –the ‘traffic light’ is green------THEN the action is ‘Go’
IF –the ‘traffic light’ is red------THEN the action is ‘Stop’
The Database includes a set of Facts used to match against
IF (condition) parts of rules stored in the knowledge base

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Inference Engine
The inference engine carries out the reasoning
whereby the expert system reaches a solution.
It links the rules given in the knowledge-base with the facts
provided in the database
Accepts user input query
Responses to questions through the I/O

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Inference Process
Done in three stages:
match select execute
Match : contents of the working memory are compared to
the facts and rules contained in the knowledge base
Select: When consistent match found the corresponding
rules are placed in the conflict set.
Execute: When all matched rules are placed in the conflict
set one of the rules is selected for execution

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The Explanation facilities enable the user to ask the expert
system how particular conclusion is reached and why a
specific fact is needed.
Explanation module trace the chain of rules fired during a
consultation with the user (the sequence of rules that led to
the conclusion).
Explanation module must be able to explain why certain
information is needed by the inference engine to complete
a step in the reasoning process
Explanation

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Editor: Building knowledge base
Special editor used by the developers:
to create new rules for addition to the knowledge base,
to delete outmoded rules, or
to modify existing rules in some way.
Editor of this type is designed to provide consistency test
for the newly created rules, to add missing condition to a
rule to reformat a newly created rule.

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The I/O interface
The external interface allows an expert system to
work with external data files and programs written
in conventional programming language.
User communicate with the system in a more natural
way.
The system must have special prompt and special
vocabulary which encompasses the terminology of
the given domain of expertise.

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Structure: Rule-Based Expert System
Knowledge base
Rule: IF…THEN
Database
Fact
Inference engine
Explanation facilities
User interface
User

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Thermostat:
A rule-based Expert System (1 of 3)
The system provides advices on how to select the thermost
at setting based on the season of the year, the day of the we
ek and time of the day.
It uses seven linguistic objects: month, day, time, today, o
peration, season and thermostat_setting.
Example values : month-January….; day-Monday…; time-a
fter 5 pm, before 9 am, between 9 am to 5 pm; today-workda
y, weekend; season-summer, autumn, winter, spring; Op
eration-during business hours, not during business hours;
Thermostat_setting-“14 degrees”, “18 degrees”, etc.

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Thermostat:
A rule-based Expert System (2 of 3)
Options: the final goal of a rule-based system is to
produce a solution to the problem based on input data
In THERMOSTAT, the solution is a temperature selected from
the list of 8 options: 20, 15, 24, 27, 20, 16, 18, 14
Dialogue: interaction with user
What month is it? August
Rule 9:
If month is ‘June’ or ‘July’ or ‘August’
Then the season is ‘winter’ (Australia)

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Thermostat:
A rule-based Expert System (3 of 3)
What day is it? Friday
Rule 1:
If the day is ‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday,
Then ‘Today’ is “Workday”
What time is it? Between 9 am and 5 pm
Rule 3:
If today is ‘Workday’, and time is ‘between 9 am and 5 pm’,
Then ‘operation’ is “During business hours”
Rule 17:
If season is ‘Winter’, and ‘operation’ is “During business hours”,
Then ‘thermostat_setting’ is “18 degrees”

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Rule-Based Expert Systems
Advantages & Disadvantages
Advantages:
naturalknowledgerepresentation,
uniformstructure,
separationofknowledgefromitsprocessing
Disadvantages:
especiallyopaquerelationsbetweenrules,
ineffectivesearchstrategyand
inabilitytolearn.

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Recommended Textbooks
[Negnevitsky,2001]M.Negnevitsky“ArtificialIntelligence:Aguideto
IntelligentSystems”,PearsonEducationLimited,England,2002.
[Russel,2003]S.RussellandP.NorvigArtificialIntelligence:AModern
ApproachPrenticeHall,2003,SecondEdition
[Patterson,1990]D.W.Patterson,“IntroductiontoArtificialIntelligence
andExpertSystems”,Prentice-HallInc.,EnglewoodCliffs,N.J,USA,19
90.
[Minsky,1974]M.Minsky“AFrameworkforRepresentingKnowledge”,
MIT-AILaboratoryMemo306,1974.
[Hubel,1995]DavidH.Hubel,“Eye,Brain,andVision”
[Horn,1986]B.K.P.Horn,RobotVision,MITPress,1986.
[Ballard,1982]D.H.BallardandC.M.Brown,“ComputerVision”,
PrenticeHall,1982.

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End of Presentation
Questions/Suggestions
Thanks to all !!!