1. CCSB354 ARTIFICIAL INTELLIGENCE
Chapter 7
Introduction to Expert Systems
(Chapter 8, Textbook)
(Chapter 3 & Chapter 6, Ref. #1)
Instructor: Alicia Tang Y. C.

2. EXPERT SYSTEM (ES) Definition
ES is a set of computer programs that can advise, consult, diagnose, explain, forecast, interpret, justify, learn, plan and many more tasks that require ‘intelligence’ to perform.

3. From Oxford Science Publication
An expert system is defined as “a computerized clone of a human expert”

4. EXPERT SYSTEMS: CHARACTERISTICS
Perform at a level equivalent to that of a human expert.
Highly domain specific.
Adequate response time
Can explain its reasoning.
It can propagate uncertainties and provide alternate solutions through probabilistic reasoning or fuzzy rules .

5. AN EXPERT AND A SHELL ES SHELL EXPERT:
A special purpose tool designed for certain types of applications in which user supply only the knowledge base (e.g. EMYCIN)
It isolates knowledge-bases from reasoning engine
Hence software portability can be improved
EXPERT:
An expert in a particular field is a person who possess considerable knowledge of his area of expertise
Domain-specific

6. Shell Concept for Building Expert SystemsKB
e.g. rules
Consultation
Manager
KB Editors
& debugger
shell
Inference
Engine
Explanation
Program
KBMF

7. Comparison (I) Expert Systems Conventional Systems
knowledge base is separated from the processing (inference) mechanism
program may make mistake (we want it to make mistake!)
the system can operate with only a few rules (as the first prototype)
execution is done by using heuristic and logic
changes in the rules are easy to accomplish
Conventional Systems
information & its processing are combined in one sequential program
programs do not make mistake (but programmers do make it)
the system operates only when it is completed
execution is done on a step-by-step basis
changes in programs are tedious

8. Comparison (II) Conventional Systems
do not usually explain why or how conclusions were drawn
need complete information to operate
Efficiency is a major goal
easily deal with quantitative data
Expert Systems
explanation is part of most ES
can operate with incomplete or uncertain information
Effective is the major goal
easily deal with quantitative data

9. RIGHT TASKS FOR RIGHT SYSTEMS
Facts that are known
Expertise available but is expensive
Those involved
Analyzing large/diverse data
E.g. Production scheduling & planning, diagnosing and troubleshooting, etc.

10. Generic Categories of Expert Systems (1)
Diagnosis
inferring system malfunctions from observations
Interpretation
inferring situation descriptions from observation
Prediction
inferring likely consequences of given situations

11. Generic Categories of Expert Systems (2)
Design
configuring objects under constraints
Planning
developing plans to achieve goals
Repair
executing a plan to administer a prescribed remedy
Others
Debugging, Monitoring & control, Instruction

12. BENEFITS OF EXPERT SYSTEMS (I)
Expertise in a field is made available to many more people (even when human expert is not present).
Top experts’ knowledge gets saved rather than being lost, when they retire
“Systematic”; no factors forgotten.
Easy to keep on adding new knowledge
Allows human experts to handle more complex problems rapidly and reliably.

13. EXAMPLES of EXPERT SYSTEMS
MYCIN
USES RULE-BASED SYSTEM, GOAL-DRIVEN
EMPLOYED CF TO DERIVE CONCLUSION
PROSPECTOR
INCOPORATED BAYES THEOREM (PROBABILITY)
Interpret geologic data for minerals
XCON
RULE-BASED SYSTEM, DATA-DRIVEN
REVEAL
FUZZY LOGIC USED
CENTAUR
RULES AND FRAMES-BASED SYSTEM
DENTRAL – interpret molecular structure
HEARSAY I – for speech recognition

14. LIMITATIONS Systems are superficial Rapid degradation of performance
Interfaces are crude (not impressive)
No GUI
Inability to adapt to more than one type of reasoning (in most cases)

15. TYPICAL STRUCTURE OF AN EXPERT SYSTEM
Consultation Environment
(Use)
Development Environment
(Knowledge Acquisition)
User
Expert
Facts of
the Case
Recommendation,
Explanation
User Interface
Explanation
Facility
Knowledge
Engineer
Inference Engine
Facts of
the
Case
Knowledge
Acquisition
Facility
Working Memory
Knowledge
Base
Domain Knowledge
(Elements of Knowledge Base)

16. Key components of an Expert Systems
Another view:
Key components of an Expert Systems

17. Explanation Facility Why need it?
It provides sound reasoning besides quality result.
Common types question seeking for an explanation
“How” a conclusion was reached?
“Why” a particular question was asked?

18. Importance of Explanation
It can influence the ultimate acceptance of an Expert System.
It can serve as a debugging tool.
It is used as a tutoring system for new knowledge engineer in a project team.
Who needs explanation?
our clients:
in order to convince them to buy.
the knowledge engineer:
to check if all specifications are met?

19. Approaches Used (1) Canned Text Problem:
Prepared in advance all questions and answers as text strings
When a question is asked, the system will find explanation module and displays the corresponding answer
Problem:
Difficult to secure consistency when the KB size grows bigger
It is only suitable for slow changing system

20. Approaches Used (2) Paraphrase Tree Traversing is used
For examples, to answer “why..’ and ‘how..’ types of question
The explanation module will look up a search tree to answer “WHY this happened..”
The explanation module will look down the tree to see sub goals that were satisfied to achieve the goal state

21. Most expert systems are rule-based.
Rule-based Systems
Recall that:
Most expert systems are rule-based.

22. FACTS AND RULES (revisited)
A mammal is an animal
A bird is an animal
Adam is a man
Ben drives a car
RULES :
If a person has RM1,000,000 then he is a millionaire.
If an animal builds a nest and lays eggs then the animal is a bird.

23. Examples of rules: Rule 1: if you work hard and smart
then you will pass all examinations
Rule 2: if the food is good
then give tips to the waiter
Rule 3: if a person has US1,000,000
then he is a millionaire

24. Forward Chaining
and
Backward Chaining

25. Reasoning (Chaining) Systems
Forward chaining
it is used to predict the outcome from various facts
Facts are conditions in a rule
It is also used to find a suitable goal that all factors can be satisfied
Backward chaining
it is useful when trying to determine the reason once something has occurred
It is used when the result for a problem is already found, and we want to know how

26. Inference Strategies (I)
Conclusion
(Goals)
Many Possibilities
Input
Data
(a) Forward Chaining

27. Inference Strategies (II)
Conclusion
(Goals)
Input
Data
Few Possibilities
(b) Backward Chaining

28. Exercise #1
You have seen what tasks are “just right” for ES and now you are required to answer the following question:
List a “Too hard” task for computers and explain briefly why they are said too difficult. And, why?

29. For your information… supplementary topic

30. RULE-BASED VALIDATION
There are essentially 5 types of inconsistency that may be identified, these are:
Redundant rules
Conflicting rule
Subsumed
Unnecessary Premise(IF) Clauses
Circular rules

31. REDUNDANT RULES Rule 1 IFA = X AND B= Y THEN C = Z Rule 2
IF B=Y AND A=X THEN C=Z AND D=W
Rule 1 is made redundant by rule 2.

32. CONFLICTING RULES Rule 1 IF A = X AND B= Y THEN C = Z Rule 2
IF A=X AND B=Y THEN C=W
Rule 1 is subsumed by rule 2 thus becomes unnecessary.

33. SUBSUMED RULES Rule 1 if A = X AND B= Y THEN C = Z Rule 2
if A=X THEN C=Z
to be revised.

34. UNNECESSARY PREMISE (IF) CLAUSES
Rule 1
IF A = X AND B= Y THEN C = Z
Rule 2
IF A=X AND NOT B=Y THEN C=Z
Remove B=Y and NOT B=Y to have just one rule.

35. CIRCULAR RULES Rule 1 IF A = X THEN B = Y Rule 2
IF B=Y AND C=Z THEN DECISION=YES
Rule 3
IF DECISION=YES THEN A = X
Restructure these rules !

36. Example
Select Auto is an expert system designed to assist a user to make a right decision of buying a new car. It will review prospective cars that match with users’ need and preference

37. The car is made in Quality is 1. the United State 2. foreign countries
3. Don’t know 2
Quality is
1. the highest concern
2. of high concern
3. of moderate concern
4. Don’t know 1

38. Price of the car is RULE NUMBER: 5 1. important 2. unimportant
3. don’t know
WHY
RULE NUMBER: 5 IF
Price of a car is important
and
(2) The payment is in installments
THEN
The monthly payment is determined

39. Price of the car is 1. important 2. unimportant 3. don’t know
The monthly payment is no more than
1. $100
2. $150
3. $200
4. $250 4

40. The most considered factor in making a decision to buy a car is
1. Price
2. Fuel economy
3. quality
1, 3

41. Result Values based on -100 to +100 system VALUE Toyota Corolla 51
Proton Perdana 43