1. SISTEM INFORMASI MANAJEMEN 2
SISTEM INFORMASI MANAJEMEN 2* D3 MANAJEMEN INFORMATIKA ATA 05/06 Sistem Pakar
Ati Harmoni

2. Artificial Intelligence
Definition:
The activity of providing such machines as computers with the ability to display behavior that would be regarded as intelligent if it were observed in humans.

3. History
1956, Dartmouth College. John McCarthy coined term. Same year, Logic Theorist (first AI program. Herbert Simon played a part)
Past 20 or so years, DOD and NSF have funded AI research at top schools (Stanford, Carnegie Mellon, etc.)
Desert Storm opened up new funding (smart bombs, night vision)

4. Areas of Artificial Intelligence
Expert
systems AI
hardware
Robotics
Natural
language
Perceptive
systems
(vision,
hearing)
Neural
networks
Learning
Artificial Intelligence

5. The Appeal of Expert Systems
A computer program that attempts to code the knowledge of human experts in the form of heuristics (i.e. a rule of thumb)
Two distinctions from DSS
1. has the potential to extend the manager’s problem-solving ability beyond his or her normal capabilities
2. the ability to explain how the solution was reached

6. Expert and knowledge engineer
Instructions &
information
Solutions &
explanations
Knowledge
User
User
interface
Know-
ledge
base
Inference
engine
Problem
Domain
Development
engine
Expert
system
An Expert
System Model
Expert and knowledge engineer

7. Expert system model - main parts:
User interface
Knowledge base
Inference engine
Development engine

8. } User Interface User enters: Expert system provides: Instructions
Information
Expert system provides:
Solutions
Explanations of
Questions
Problem solutions }
Menus, commands, natural language, GUI
Menus, commands, natural language, GUI

9. Knowledge Base Description of problem domain
Rules: A knowledge representation technique
such as ‘IF:THEN’ logic
networks of rules
Lowest levels provide evidence
Top levels produce 1 or more conclusions
A conclusion is called a Goal variable.

10. A Rule Set That Produces One Final Conclusion Conclusion Conclusion
Evidence
Evidence
Evidence
Evidence
Evidence
Evidence
Evidence
Evidence

11. Ungulate Bird A Rule Set That Can Produce More Than One Final
Cheetah
Tiger
Giraffe
Zebra
Ostrich
Penguin
Albatross R9
R10
R11
R12
R13
R14
R15
And
And
And
And
And
And
And
And
Tawny
Dark
Long
Black
Long
Can’t
Black&
Flies
Swims
color
spots
legs
strips
neck
fly
White
Well
Ungulate
Bird Or Or
Mammal
Carnivore R7 R8 R3 R4 Or Or
And
And
Feathers
And R1 R2 R5 R6
Gives
milk
Chews
Lays
Hair
milk
Eats milk
Hoofs
Flies
cud
eggs
And
A Rule Set That
Can Produce More
Than One Final
Conclusion
LEGEND:
Rules
Pointed
Forward
Action
(conclusions)
Claws
teeth
Eyes
Conditions

12. Rule Selection Inference Engine
Selecting rules to efficiently solve a problem is difficult
Some goals can be reached with only a few rules; rules 3 and 4 identify bird
Inference Engine
Two basic approaches to using rules
1. Forward reasoning (data driven)
2. Reverse reasoning (goal driven)

13. Forward Reasoning (forward chaining)
Rule is evaluated as:
(1) true, (2) false, (3) unknown
Rule evaluation is an iterative process
When no more rules can fire, the reasoning process stops even if a goal has not been reached

14. The Forward Reasoning Process Rule 1 T F T T Rule 8 T T T T Rule 5
IF A
THEN B
Rule 7
IF B OR D
THEN K
Rule 10
Rule 2 F
IF K AND
L THEN N
IF C
THEN D T T
Rule 3
Rule 8
Rule 12 T T
IF N OR O
THEN P
IF M
THEN E
IF E
THEN L T
Rule 4 T
IF K
THEN F
Legend:
First pass
Rule 9
Rule 5
Rule 11 T T
IF (F AND H)
OR J
THEN M
IF G
THEN H
IF M
THEN O
Second pass T
Rule 6 F
Third pass
IF I
THEN J

15. Reverse Reasoning (backward chaining)
Divide problem into subproblems
Try to solve one subproblem
Then try another

16. A Problem and Its Subproblems
Rule 10
IF K AND L
THEN N
Rule 12
Legend:
IF N OR O
Rule 11
THEN P
Problem
IF M
THEN O
Subproblem

17. A Subproblem Becomes the New Problem
Rule 7
IF B OR D
THEN K
Rule 10
IF K AND
LTHEN N
Legend:
Rule 8
Rule 12
IF E
THEN L
Problem
IF N OR O
THEN P
Subproblem

18. The First Five Problems Are Identified
Step 4
Rule 1
Step 3
IF A THEN B
Rule 7
Step 2 T
IF B OR D
THEN K
Rule 10
Step 1 T
Rule 2
IF K AND L
THEN N
Rule 12
IF C
THEN D
IF N OR O
THEN P
Step 5
Rule 3
IF M
THEN E
IF E
THEN L
Rule 11
Legend:
Problems to
be solved
IF (F AND H)
OR J
THEN M
IF M
THEN O
IF M
THEN O
Rule 9

19. The Next Four Problems Are
Identified
Rule 12 T
If N Or O
Then P
Step 8
Rule 4
If K
Then F T
Step 7
Step 6
Step 9
Rule 5
If G
Then H
If M
Then O
IF (F And H)
Or J
Then M T T T
Rule 9
Rule 11
Legend:
Problems to
be solved
Rule 6
If I
Then J

20. Forward Versus Reverse Reasoning
Reverse reasoning is faster than forward reasoning
Reverse reasoning works best when
there are multiple goal variables
there are many rules
all or most rules do not have to be examined in the process of reaching a solution

21. Handling Uncertainty Development Engine Two types of uncertainty
Rules
Conditions
Certainty factors (CFs) range from 0.00 to 1.00
Development Engine
Programming languages Lisp, Prolog, and recently C++
Expert system shells

22. Role of the Systems Analyst
Knowledge engineers work with the expert in designing expert systems
Beyond traditional analyst skills, the following skills are needed
understand how the expert applies his or her knowledge
be able to extract the description of the knowledge (rules as well as facts)

23. System Development Process
Initiate the development process
Develop the expert system prototype
User participation
Expert system maintenance
Prototyping Approach
A new player: the expert
Delayed user involvement
Need for maintenance

24. Test the prototype system
Prototyping Is Incorporated in the Development of an Expert System
Systems analyst
Expert
User
Study the
problem
domain
step 1
Study the
Problem
domain
step 2
Define the problem
step 3
Specify the rule set
Need to redesign
Need to redesign
step 4
Test the prototype system
step 5
Construct the interface
step 6
Conduct
user tests
step 7
Use the
system
step 8
Maintain the system

25. Example: Financial Expert System
Credit approval
Knowledge base for the example consists of rules and a mathematical model
User interface
Five decision categories; credit amount influences weightings

26. Expert System Advantages
To managers
Consider more alternatives
Apply high level of logic
Have more time to evaluate decision rules
Consistent logic
To the firm
Better performance from management team
Retain firm’s knowledge resource

27. Expert System Disadvantages
Can’t handle inconsistent knowledge
Can’t apply judgment or intuition

28. Neural Networks The Human Brain
Expert systems should be able to learn, and improve their performance
Neural net design -- a bottom-up approach to modeling human intuition
The Human Brain
Neuron -- the information processor
Input -- dendrites
Processing -- soma
Output -- axon
Neurons are connected by the synapse

29. Simple Biological Neurons
Soma
(processor)
Axonal Paths
(output)
Synapse
Axon
Dendrites
(input)

30. Artificial Neural Systems (ANS)
McCulloch-Pitts mathematical neuron function (late 1930s)
Hebb’s learning law (early 1940s)
Neurocomputers
Marvin Minsky’s Snark (early 1950s)
Rosenblatt’s Perceptron (mid 1950s)

31. Current Methodology Mathematical models Complex networks
Repetitious training -- the ANS “learns” by example. An ANS can learn; an expert system cannot.

32. Single Artificial Neurony1 y2 y3
yn-1 y w1 w2 w3
wn-1

33. The Multi-Layer Perceptron
OUT1
OUTn
The Multi-Layer Perceptron
Input Layer Y1
Yn2
OutputLayer
IN1
INn

34. Prerequisite Activities for the EIS
Information
needs
Information
technology
standards
Analysis of
organization
Corporate
data model
Information
systems plan
Production and
performance systems
EIS