Assignment 2 SUBMITTED BY Zainab Sajjad

Assignment 2

SUBMITTED BY
Zainab Sajjad (17240819-025)
Syeda Ayesha Gulzar (17240819-020)
Maryam Sadiqa (17240819-014)
Instructor
Ms. Fakhera Nazir
Department of Computer Science
Question 1:
Q1.1: What is an expert system?
An expert systems are the computer applications developed to solve complex problems in a particular domain. This system perform action on the decisions and conduct of human that have practiced and skilled knowledge as well as understanding in a particular field. Knowledge of many experts is required in Expert system. This system takes less time to solve a problem and any real life ambiguity. An Expert system can be a machine or a device. It contain immaterial data and facts. It enhanced the overall performance.

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Roseman said about the Expert system:
“An automated reasoning system that effort to mimic the performance of human expert.”
This is an efficient system as saves companies resources and time as it is capable of providing faster services. It appealing fact is that it can solve the current problems and also able to solve the future problems in the mean while. Nuerical calculation can also be done using this system.

Q1.2: Give 5 examples of expert system?
Application Description
Medical Domain Diagnosis Systems to deduce cause of disease from observed data, conduction medical operations on humans.

Monitoring Systems It keeps on comparing the data with the observed system or with the prescribed stuff. For example: It monitor the leakage of petroleum in pipelines
Procedure Control System It controls the physical process based on monitoring.

Information Domain It finds faults in vehicles, computers.

Finance/Commerce It detects for possible fraud, mistrustful transactions, stock market exchange, Airline scheduling, load scheduling.

Q1.3: What are benefits and downside of using expert system?
Benefits:
Helpful in providing the answers for conclusions, procedures and tasks that are repetitive
Can keep large amount of data and information as well
Employee training costs can be minimized
Decision making process can be centralized
Efficient as take least time to solve a problem
Intelligences of many human experts is utilized by combining them
Minimize the number of human errors and other halts and mistakes
Provide strategic and comparative advantages
Inspire the competitors to be in the market with more efficient solution of a particular problem
Concern for transactions that human experts may ignore
Disadvantage:
No common sense is used in decisions making
Least original comebacks that human experts are gifted of
Not accomplished of clarifying the logic and cognitive behind a decision
It is difficult to systematize complex processes
There is no flexibility and ability to adapt to change in surroundings
Not able to response when there is no answer
Q1.4: Difference b/w Knowledge based and Rule based expert system?
Rule based system Knowledge based system
It can process data rules. It can process data rules and knowledge.

It resulted as real-time decision Its output leads for information decisions.

Has a broad logic in domain scope Has a deep logic in domain scope
It usually used for initiative rules. It ordinarily used for departmental rules.

It is best for basic business rules. It is model for complex business rules
Rule represent in the system using IF-THEN structure It represent knowledge explicit via tools rather than the via code
Question No 2: Part A
Prove using forward chaining as Well as backward chaining:-
Sol:
Forward Chaining:
H

D

A

E

C

I

B

G

F

In forward chaining we will fire rules in the order they appear in the database, starting rule 1. In rule 1 which means we add C to our facts database. Next, rule 2 is fired, mean we add D. now, the facts have A, B, C, D, F, but we have not yet goal reach our goal.

Now, rule 3 is triggered and fired E in database. Rule 4 is fired and G be added in the database then, rule 5 is fired and H is added in the database. Now , we reach our goal and not necessary to move further.

Facts Rules Triggered Rules Fired
A, B, F 1,2 1
A, B, C, F 2 2
A, B, C, D, F 3 3
A, B, C, D, E, F 4,5 4
A, B, C, D, E, F, G 5 5
A, B, C, D, E, F, G, H 6 Stop
Backward Chaining:
I

H

E

G
C

D

A

F
B

In backward chaining the goal database start with conclusion H which we want to prove. We will see now rule 5 is the only one that has a H as a conclusion so, we must prove the antecedents of rule 5, which are A and E.

Facts A is already in the database so, only E is added to the goal database. Now we attempt rule 3 which has conclusion E. we must prove the antecedents of rule 3, which are C and D. where D is the conclusion of the rule 2 and C is the conclusion of rule 1 which are A and B and they are already in the facts database. So C and D also added in the facts database.so, we proved all the goals in the goal database and have therefor proved H and can stop.

Facts Goals Matching Rules
A, B, F H 5
A, B, F E 3
A, B, F C, D 1
A, B, C, F D 2
A, B, C, D, F Stop
Question No 2: Part A
Knowledge Base:
If X croaks and eats flies Then X is a frog
If X chirps and sings Then X is a canary
If X is a frog Then X is colored green
If X is a canary Then X is colored yellow
Fritz croaks and eats flies
Goal:
Fritz is colored Y?
Fritz croaks and eats flies
If X croaks and eats flies Then X is a frog

If X is a frog
Then X is colored green

Fritz is a frog

Fritz is colored Y?
Fritz is colored green

Y = green

Backward chaining is a goal driven method of deriving a particular goal from a given knowledge base and set of inference rules
Inference rules are applied by matching the goal of the search to the consequents of the relations stored in the knowledge bas
Backward chaining example
Fritz is colored Y

X = Fritz, Y = green
X croaks and eats flies
If X is a canary
Then X is colored yellow
Fritz croaks and eats flies
If X croaks and eats flies
Then X is a frog
X is a canary
X is a frog
If X is a frog
Then X is colored green

x

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