1. Advanced Higher Computing Based on Heriot-Watt University Scholar Materials
Rule Based Systems
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2. Lesson Objectives Expert Systems If .. Then Rules
Inferencing in Rule Based Systems
Certainty Factors
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3. Expert Systems An expert system has 3 components:
Often built using an expert system shell
Expert system shell has no
knowledge base
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4. Stages of Creating an Expert System
1. knowledge acquisition
2. knowledge representation
3. system validation
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5. Knowledge acquisition
Domain expert:
personal experience of problems to be solved
personal expertise in how to solve the problems
personal knowledge of the reasons for selecting certain methods.
Knowledge engineer:
no specialist knowledge of the domain
need to learn from the domain experts, prior to the start of the project and during the acquisition of knowledge.
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6. Alford Academy Business Education and Computing
Difficulties
Sort the 9 difficulties on pages 92 and
93 of the Scholar notes in order of negative
impact on knowledge acquisition:
Let 1 = least impact and 9 = most impact
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7. Knowledge Representation
Use an expert system shell
Use a declarative language (Prolog)
Use a procedural language – need to program all parts of the system
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8. Alford Academy Business Education and Computing
System Validation
System tests that compare the advice of the expert system with that of
the domain expert. Possible errors include:
Incomplete knowledge or incorrect representation
Misunderstanding of knowledge engineer with information provided by domain expert
Knowledge not being processed correctly
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9. Alford Academy Business Education and Computing
If .. Then Rules
Consider the following knowledge base
Can use forward or backward rules
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10. Alford Academy Business Education and Computing
Forward Rules
Of the form IF condition(s) THEN conclusion
If colour = bronze AND
size (mm) = 18 AND
shape = round AND
symbol = fish
Then value = 1H.
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11. Alford Academy Business Education and Computing
Backward Rules
Of the form conclusion IF condition(s)
value = 1H If
colour = bronze AND
size (mm) = 18 AND
shape = round AND
symbol = fish.
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12. Alford Academy Business Education and Computing
Visual Comparison
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13. Alford Academy Business Education and Computing
Practical Activity
Do Task on page 98 of Scholar notes.
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14. Justification and Explanation
it helps the enquirer to understand the subject better;
it means that the enquirer can learn how the expert thinks;
it gives the enquirer more confidence in the advice given.
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15. Investigation Do Task Exploring Justification Features on
page 99 of Scholar notes.
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16. How an Expert System reaches its conclusion
Essentially, the inference engine uses a search algorithm to search through the
rules in a similar fashion to the evaluation of a query in Prolog (see Topic 4).
The rules can be represented as a decision tree:
The tree can be searched either breadth-first or depth-first.
The inference engine can also be classified as either forward chaining or backward
chaining.
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17. Alford Academy Business Education and Computing
Practical Activity
Read notes on page 100 – do activity
Examining the behaviour of an inference engine
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18. Advantages and Disadvantages of Forward Chaining
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19. Advantages and Disadvantages of Backward Chaining
Many Expert Systems use a combination of backward and forward chaining
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20. Rules, facts and working memory
Backward chaining systems tend to use depth-first search with backtracking, in a similar way to Prolog. This is completely
predictable in operation, following a clear strategy to reach a conclusion.
For example, suppose a medical expert systems had the following facts in working memory:
Body temperature is 39.5ÆC
Patient’s skin colour is red
Patient has pains in stomach and legs
Age of patient is 63
Gender of patient is female
Condition has occurred before
One of the rules in the system is:
IF body temperature 38
AND patient’s skin colour is red THEN patient has fever
This rule could be fired, and would add the fact:
Patient has fever
to the working memory.
As a result, another rule:
IF patient has fever
AND patient has pains in stomach and legs THEN patient may have influenza.
could be fired, leading to a diagnosis / conclusion.
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21. Alford Academy Business Education and Computing
Conflict resolution
At any time, there may be hundreds of different rules that could be fired by the known facts. The inference engine must decide which of the possible rules should be fired next
The set of possible rules which could be fired at any particular time is known as the conflict set
The inference engine must use some form of conflict resolution to decide which rule from the conflict set to fire next
Many different conflict resolution strategies can be used, either separately or in combination
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22. Conflict resolution strategies
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23. Alford Academy Business Education and Computing
Certainty factors
a consultation with a doctor may lead to a less clear-cut diagnosis. The doctor may conclude that your headache is probably just a hangover, but it might be the start of flu, and there is a very slim chance that it could be a brain tumour or some other serious disease.
domains can contain inexact conclusions like this, and expert systems must be able to deal with this kind of knowledge. This involves using certainty factors
The number is called a certainty factor. It is added to the end of a fact or rule using the abbreviation CF.
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24. Examples of using certainty factors
Practical Activity – Do weather forecast expert system task on page 104
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25. Calculating certainty factors - complex
Example 1
IF the sky was red this morning (CF 60)
AND it rained yesterday (CF 75)
THEN it will rain today (CF 50).
CF = MIN(60%,75%) x 50%
= 30%
CF = 30
Example 2
IF the sky is blue (CF 90)
AND people around are speaking French (CF 40)
AND there are lots of Renault cars (CF 60)
THEN you are in France (CF 80)
CF = MIN(90%,40%,60%) x 80%
= 32%
CF = 32
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