1. Dr. Bernard Chen Ph.D. University of Central Arkansas Spring 2011
Ch8Expert System
Dr. Bernard Chen Ph.D.
University of Central Arkansas
Spring 2011

2. Outline Expert System introduction Rule-Based Expert System
Goal Driven Approach
Data Driven Approach
Model-Based Expert System

3. Expert System Introduction
Human experts are able to perform at a successful level because they know a lot about their areas of expertise
An Expert System use knowledge specific to a problem domain to provide “expert quality” performance in that application area
As with skilled humans, expert systems tend to be specialists, focusing on a narrow set of problems

4. Expert System Introduction
Because of their heuristic, knowledge intensive nature, expert systems generally:
Support inspection of their reasoning processes
Allow easy modification in adding and deleting skills from knowledge base
Reason heuristically, using knowledge to get useful solutions

5. Expert System Introduction
Expert systems are built to solve a wide range of problems in domain such as medicine, math, engineering, chemistry, geology, computer science, business, low, defense and education
These programs address a variety of problems, the following list is a summary of general expert system problem categories:

6. Expert System Introduction
Interpretation --- forming high-level conclusions from collections of raw data
Prediction --- projecting probable consequences of given situations
Diagnosis --- determining the cause of malfunctions based on observable symptoms

7. Expert System Introduction
Design --- finding a configuration of system components that meets performance goals while satisfying a set of design constrains
Planning --- devising a sequence of actions that will achieve a set of goals given starting conditions and runtime constrains

8. The Design of Rule-Based Expert System
architecture of a typical expert system for a particular problem domain.

9. The Design of Rule-Based Expert System
The hear of the expert system is the knowledge base, which contains the knowledge of a particular application domain
In a rule-based expert system, this knowledge is most often represented in the form of if…then…
In the figure, the knowledge base contains both general and case-specific information

10. The Design of Rule-Based Expert System
The inference engine applies the knowledge to the solution of actual problems
It is important to maintain this separation of the knowledge and inference engine because:
Makes it possible to represent knowledge in a more natural fashion
Expert system builder can focus on capturing and organizing problem-solving knowledge than the details of code implementation
Allow change to be made easily
Allows the same control and interface software to be used in different systems

11. Selecting a problem
Expert System involve a considerable investment of money and human effort
Researchers have developed guidelines to determine whether a problem is appropriate for expert system solution:
The need for the solution justifies the cost and efforts of building an expert system
Human expertise is not available in all situation where it is needed

12. Selecting a problem
The problem domain is well structured and does not require common sense reasoning
The problem may not be solved using traditional computing methods
Cooperative and articulate experts exist
The problem is proper size and scope

13. NASA Example
NASA has supported its presence in space by developing a fleet of intelligent space probes that autonomously explore the solar system
To achieve success through years in the harsh conditions of space travel, a craft needs to be able to radically reconfigure its control regime in response to failures and then plan around these failures during it remaining flight

14. NASA Example
Finally, NASA expects that the set of potential failure scenarios and possible responses will be much too large to use software that supports preflight enumeration of all contingencies
Livingstone is an implemented kernel for a model-based reactive self-configuring autonomous system

15. NASA Example
A long-held vision of model-based reasoning has been to use a single centralized model to support a variety of engineering tasks
The tasks include
keeping-track of developing plans
Confirming hardware modes
Reconfiguring hardware
Detecting anomalies
Diagnosis
Fault recovery

16. NASA Example

17. NASA Example It consist of a helium tank Regulators Propellant tanks
A pair of main engine
Latch valves
Pyro valves

18. NASA Example
The helium tank pressurizes the two propellant tanks, with the regulators acting to reduce the high helium pressure
When propellant path to a main engine are open, the pressurized tank forces fuel and oxidizer into the main engine to produce thrust
The pyro valve is to isolate parts of the main engine subsystem until they are needed, or to permanently isolate failed components
The latch valve are controlled using valve drivers and the accelerometer

19. NASA Example
Thrust can be provided by either of the main engines and there are a number of ways of opening propellant paths to either main engine

20. NASA Example
Suppose the main engine subsystem has been configured to provide thrust from the left engine by opening the latch valves leading to it
And suppose this engine fails (overheating), so that is fails to provide the required thrust
To ensure that the desire thrust is provided, the spacecraft must be transitioned to a new configuration in which thrust is now provided by the main engine on the right side

21. Selecting a problem
The primary people involved in building an expert system are the knowledge engineer, domain expert, and end user
The domain expert is primarily responsible for spelling out skills to knowledge engineer
It is often useful for knowledge engineer to be a novice in the problem domain

22. Exploratory development cycle

23. Exploratory development cycle
It is also understood that the prototype may be thrown away if it becomes to cumbersome or if the designers decide to change their basic approach to the problem
Another major feature of expert system is that the program need never be considered “finished”

24. Outline Expert System introduction Rule-Based Expert System
Goal Driven Approach
Data Driven Approach
Model-Based Expert System

25. Strategies for state space search
In data driven search, also called forward chaining, the problem solver begins with the given facts of the problem and set of legal moves for changing state
This process continues until (we hope!!) it generates a path that satisfies the goal condition

26. “tic-tac-toe” state space graph

27. Strategies for state space search
An alternative approach (Goal Driven) is start with the goal that we want to solve
See what rules can generate this goal and determine what conditions must be true to use them
These conditions become the new goals
Working backward through successive subgoals until (we hope again!) it work back to

28. Rule-Based Expert System
Rule based expert system represent problem-solving knowledge as if…then…
It is one of the oldest techniques for representing domain knowledge in an expert system
It is also one of the most natural and widely used in practical and experimental expert system

29. Rule-Based Expert System
In a goal-driven expert system, the goal expression is initially placed in working memory
The system matches rule conclusions with the goal, selecting one rule and placing its premises in the working memory
This corresponds to a decomposition of the problems’ goal into simpler subgoals
The process continues in the next iteration of the production system, with these premises becoming the new goals to match

30. A unreal Expert System Example
Rule 1: if
the engine is getting gas, and
the engine will turn over,
then
the problem is spark plugs.
Rule 2: if
the engine does not turn over, and
the lights do not come on
the problem is battery or cables.
Rule 3: if
the lights do come on
the problem is the starter motor.
Rule 4: if
there is gas in the fuel tank, and
there is gas in the carburetor
the engine is getting gas.

31. The production system at the start of a consultation in the car
The production system at the start of a consultation in the car diagnostic example.

32. The production system at the start of a consultation in the car
The production system at the start of a consultation in the car diagnostic example.
Three rules match with this expression in working memory: rule 1, 2, and 3
If we resolve conflicts in favor of the lowest-numbered rule, then rule 1 will fire
This cause X to be bound to the value spark plugs and the premises of rule 1 to be placed in the working memory

33. The production system after Rule 1 has fired.

34. The production system after Rule 1 has fired.
Note that there are two premises to rule 1, both of which must be satisfied to prove the conclusion true
So now we need to find out whether
The engine is getting gas, and
The engine will turn over
We may then fire rule 4 for whether “The engine is getting gas”

35. The system after Rule 4 has fired
The system after Rule 4 has fired. Note the stack-based approach to goal reduction.

36. The and/or graph searched in the car diagnosis example, with the conclusion of Rule 4 matching the first premise of Rule 1.

37. Explanation and Transparency in Goal-Driven Reasoning
The following dialogue begins with the computer asking the user about the goals present in the working memory:
Gas in fuel tank?
YES
Gas in carburetor?
Engine will turn over?
WHY

38. Explanation and Transparency in Goal-Driven Reasoning
In general, the two questions answered by rule-based expert system are WHY? and HOW?
WHY means “why did you ask for that information”
The answer is the current rule that the production system is attempting to fire
HOW means “How did you get the result”
The answer is the sequence of rules that were used to conclude a goal

39. Explanation and Transparency in Goal-Driven Reasoning
The following dialogue begins with the computer asking the user about the goals present in the working memory:
Gas in fuel tank?
YES
Gas in carburetor?
Engine will turn over?
WHY
It has been established that:
1. The engine is getting gas,
2. The engine will turn over, (we need to know)
So that we can make the conclusion that
“Then the problem is the spark plugs.”

40. Explanation and Transparency in Goal-Driven Reasoning
Gas in fuel tank?
Yes
Gas in carburetor?
Engine will turn over?
Why
It has been established that:
1. The engine is getting gas,
2. The engine will turn over,
Then the problem is the spark plugs.
How the engine is getting gas
This follows from rule 4: if
gas in fuel tank, and
gas in carburetor
then
engine is getting gas.
gas in fuel tank was given by the user
gas in carburetor was given by the user

41. Outline Expert System introduction Rule-Based Expert System
Goal Driven Approach
Data Driven Approach
Model-Based Expert System

42. Data-Driven Reasoning
The previous example exhibits goal-driven search. The search was also depth-first search
Breadth-first search is more common in Data Driven reasoning
The algorithm for this category is simple: compare the contents of working memory with the conditions of each rule in the rule base according to the order of the rules

43. Data-Driven Reasoning
If a piece of information that makes up the premise of a rule is not the conclusion of some other rule,
then that fact will be deemed “askable”
For example: the engine is getting gas is not askable in the premise of rule 1

44. A unreal Expert System Example
Rule 1: if
(not askable) the engine is getting gas, and
the engine will turn over,
then
the problem is spark plugs.
Rule 2: if
the engine does not turn over, and
the lights do not come on
the problem is battery or cables.
Rule 3: if
the lights do come on
the problem is the starter motor.
Rule 4: if
there is gas in the fuel tank, and
there is gas in the carburetor
the engine is getting gas.

45. Data-Driven Reasoning

46. Data-Driven Reasoning
The premise, the engine is getting gas is NOT askable, so rule 1 fails and continue to rule 2
The engine does not turn over is askable
Suppose the answer to this query is false, so “the engine will turn over” is placed in working memory

47. The production system after evaluating the first premise of Rule 2, which then fails.

48. So finally, we move to rule 4
The production system after evaluating the first premise of Rule 2, which then fails.
Rule 2 fails, since the first of two AND premises is false, we move to rule 3
Where rule 3 also fails
So finally, we move to rule 4

49. The data-driven production system after considering Rule 4, beginning its second pass through the rules.

50. At this point, all the rules have been considered
The data-driven production system after considering Rule 4, beginning its second pass through the rules.
At this point, all the rules have been considered
With the new contents of working memory, we consider the rules in order for the second round

51. Outline Expert System introduction Rule-Based Expert System
Goal Driven Approach
Data Driven Approach
Model-Based Expert System

52. Model-Based Expert System
Human expertise is an extremely complex combination of:
Theoretical knowledge
Experienced based problem solving heuristics
Example of past problems and their solutions
Interpretive skills
Through years of experience, human expert develop very powerful rules for dealing with commonly encountered situations
These rules are often highly “complied”

53. Model-Based Expert System
In a rule-based expert system example for semiconductor failure analysis, a descriptive approach might base on:
Discoloration of components (burned-out)
History of faults in similar devices
Observation of component by electron microscope
However, approaches that use rules to link observations and diagnosis do not offer the benefits of a deeper analysis of device’s structure and function

54. Model-Based Expert System
A more robust, deeply explanatory approach would begin with a detailed model of the physical structure of the circuit and equations describing the expected behavior of each component and their interactions.
A knowledge based reasoner whose analysis is founded directly on the specification and functionality of a physical system is called a MODEL-BASED System

55. Model-Based Expert System
The model based system tells its user what to expect, and when observations differ from these expectations, it will lead to identification of faults
Qualitative model-based reasoning includes:
A description of each component in the device
A description of the devices’ internal structure
Observation of the devices’ actual performance

56. Model-Based Expert System Example
The expected output value are given in () and the actual outputs in [ ]

57. Model-Based Expert System Example
At F, we have a conflict
We check the dependencies at this point and determined ADD1, MULT1 and MULT2 are involved
One of these three devices must have a fault, so we have three hypotheses to consider:
Either the adder behavior is bad or
one of its two inputs was incorrect

58. Model-Based Expert System Example
Assuming ADD1 and one of its input X is correct (6)
Another input Y must be (4)
Continue this reasoning, Y can not be MULT2 since G is correct
We are left with the hypotheses that the fault lies in either MULT1 or ADD1

59. Model-Based Expert System Example
Finally, we should note that in the example, there was assumed to be a single faulty device.
The world is not always this perfect
Many other possible problems may occur:
Wire is broken
Faulty connection to the multiplier