Artificial Intelligence Lecture Slide-03

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

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

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.

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

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

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

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

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

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

 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

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.

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.

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

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.

Thermostat:
 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)

Thermostat:
 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”

Rule-Based Expert Systems
Advantages & Disadvantages
Advantages:
 natural knowledge representation,
 uniform structure,
 separation of knowledge from its processing
Disadvantages:
 especially opaque relations between rules,
 ineffective search strategy and
 inability to learn.

Recommended Textbooks
 [Negnevitsky, 2001] M. Negnevitsky “ Artificial Intelligence: A guide to
Intelligent Systems”, Pearson Education Limited, England, 2002.
 [Russel, 2003] S. Russell and P. Norvig Artificial Intelligence: A Modern
Approach Prentice Hall, 2003, Second Edition
 [Patterson, 1990] D. W. Patterson, “Introduction to Artificial Intelligence
and Expert Systems”, Prentice-Hall Inc., Englewood Cliffs, N.J, USA, 19
90.
 [Minsky, 1974] M. Minsky “A Framework for Representing Knowledge”,
MIT-AI Laboratory Memo 306, 1974.
 [Hubel, 1995] David H. Hubel, “Eye, Brain, and Vision”
 [Horn, 1986] B. K. P. Horn, Robot Vision, MIT Press, 1986.
 [Ballard, 1982] D. H. Ballard and C. M. Brown, “Computer Vision”,
Prentice Hall, 1982.

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

Artificial Intelligence Lecture Slide-03

More Related Content

Similar to Artificial Intelligence Lecture Slide-03 (20)

More from asmshafi1 (11)

Recently uploaded (20)

Artificial Intelligence Lecture Slide-03