1. Chapter 9. Rules and Expert Systems
Comp3710 Artificial Intelligence
Computing Science
Thompson Rivers University

2. Rules and Expert Systems
Course Outline
Part I – Introduction to Artificial Intelligence
Part II – Classical Artificial Intelligence, and Searching
Knowledge Representation
Searching
Search Methodologies
Advanced Search
Genetic Algorithms (relatively new study area)
Knowledge Represenation and Automated Reasoning
Propositinoal and Predicate Logic
Inference and Resolution for Problem Solving
Rules and Expert Systems
Part III – Machine Learning
Part IV – Advanced Topics
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Rules and Expert Systems

3. Rules and Expert Systems
Chapter Objectives
Rule-based expert systems
Do forward chaining with example rules and facts
Do backward chaining with example rules and facts
Explain what a rule-based expert system consists of
List the two parts in expert systems, which can not be easily solved
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Rules and Expert Systems

4. Rules and Expert Systems
Chapter Outline
Rules for Knowledge Representation
Rule-Based Systems
Forward Chaining
Backward Chaining
Rule-Based Expert Systems
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Rules and Expert Systems

5. Rules for Knowledge Representation
Here are some interesting quotes.
Each problem that I solved became a rule which served afterwards to solve other problems. - Rene Descartes
As a rule we disbelieve all the facts and theories for which we have no use.
- William James
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Rules and Expert Systems

6. Rules for Knowledge Representation
Hell, there are no rules here -- we're trying to accomplish something.
- Thomas A. Edison
Get your facts first, and then you can distort them as much as you please.
- Mark Twain
If the facts don't fit the theory, change the facts.
- Albert Einstein
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Rules and Expert Systems

7. Rules for Knowledge Representation
[Q] How do you want to represent our knowledge?
[Q] How do we do [logical] reasoning?
Facts and rules:
IF… THEN rules can be used to represent knowledge:
IF it rains, THEN you will get wet
Rules can also be recommendations or actions:
IF it rains, THEN you should wear a coat
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Rules and Expert Systems

8. Rules for Knowledge Representation
Topics
Rules for Knowledge Representation
A rule consists of an antecedent and a consequence (action, conclusion).
IF A THEN B, or A -> B
When there are more than one antecedent,
AND (&), OR (|), NOT (~), ... or
, , , ... (conjunction, disjunction, negation)
Truth table? (T  F)  (T  F) T -> F ???
In general, the antecedent of a rule compares an object [variable] with a possible value, using an operator. I.e., the antecedent of a rule is a Boolean expression.
IF x  3
IF name is “Bob”
IF weather is cold
IF name is “Bob”  weather is cold THEN tell “Bob” ‘Wear a coat’
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Rules and Expert Systems

9. Rules and Expert Systems
Topics
Rule-Based Systems
A rule-based system or production system is a system that uses knowledge in the form of rules to provide diagnoses or advice on the basis of input data (facts).
The system consists of
a database of rules (knowledge base),
a database of facts, and
an inference engine which reasons about the facts using the rules to draw conclusions.
Conclusions are often derived using deduction (deductive reasoning).
Forward chaining: using deduction to reach a conclusion from a set of antecedents (i.e., facts)
Backward chaining: starts from a conclusion (i.e., hypothesis) and tries to show it by following a logical path backward from the conclusion to a set of antecedents that are in the database of facts
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Rules and Expert Systems

10. Rules and Expert Systems
Forward Chaining
Forward chaining is a reasoning model that works from a set of facts and rules towards a set of conclusions, diagnoses or recommendations.
Also known as data-driven reasoning
When a fact matches the antecedent of a rule, the rule fires, and the conclusion of the rule is added to the database of facts.
Take the facts from the database.
See if any combination of these matches all the antecedents of one of the rules in the database. This step takes some time.
When all the antecedents of a rule are matched by facts, then this rule is triggered.
When a rule is triggered, it is then fired, which means its conclusion is added to the facts database.
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Rules and Expert Systems

11. Rules and Expert Systems
Forward Chaining
Example of elevator
Rule 1: IF floor = first & button = first
THEN open door
Rule 2: IF floor = first & button = second
THEN goto second floor
Rule 3: IF floor = first & button = third
THEN goto third floor
Rule 4: IF floor = second & button = first & going to third floor = T
THEN remember to goto first floor
Fact 1: floor = first
Fact 2: button = third
Fact 3: day = Tuesday
The facts match Rule ???
and the conclusion ??? is added to the database of facts
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Rules and Expert Systems

12. Rules and Expert Systems
Forward Chaining
Example of elevator – cont
Rule 1: IF floor = first & button = first
THEN open door
Rule 2: IF floor = first & button = second
THEN goto second floor
Rule 3: IF floor = first & button = third
THEN goto third floor
Rule 4: IF floor = second & button = first & going to third floor = T
THEN remember to goto first floor
Fact 1: floor = second (<- first)
Fact 2: button =
Fact 3: day = Tuesday
Fact 4: going to third floor = T
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Rules and Expert Systems

13. Rules and Expert Systems
Forward Chaining
Example of elevator – cont
Rule 1: IF floor = first & button = first
THEN open door
Rule 2: IF floor = first & button = second
THEN goto second floor
Rule 3: IF floor = first & button = third
THEN goto third floor
Rule 4: IF floor = second & button = first & going to third floor = T
THEN remember to goto first floor
Fact 1: floor = second (<- first)
Fact 2: button = first
Fact 3: day = Tuesday
Fact 4: going to third floor = T
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Rules and Expert Systems

14. Rules and Expert Systems
Forward Chaining
Example of elevator – cont
Rule 1: IF floor = first & button = first
THEN open door
Rule 2: IF floor = first & button = second
THEN goto second floor
Rule 3: IF floor = first & button = third
THEN goto third floor
Rule 4: IF floor = second & button = first & going to third floor = T
THEN remember to goto first floor
Fact 1: floor = second (<- first)
Fact 2: button = first
Fact 3: day = Tuesday
Fact 4: going to third floor = T
Rule 4 is fired.
[Q] What if someone pressed the button on the second floor?
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Rules and Expert Systems

15. Rules and Expert Systems
Forward Chaining
Example of elevator – cont
Fact 1: floor = third
Fact 2: button = second
Fact 3: button = first
There are more than one possible conclusion, i.e., there can be multiple rules matching with the facts.
Conflict resolution needs to be applied to decide which rule to fire.
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Rules and Expert Systems

16. Rules and Expert Systems
Conflict Resolution
Sometimes more than one rule will fire at once, and a conflict resolution strategy must be used to decide which conclusion[s] to use.
Some ideas:
Give rules priorities and to use the conclusion that has the highest priority.
The rule with the longest antecedent is applied.
The rule that matches with the most recently added facts is applied.
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Rules and Expert Systems

17. Rules and Expert Systems
Topics
Meta Rules
The rules that determine the conflict resolution strategy are called meta rules.
Meta rules define knowledge about how the system will work.
For example, meta rules might define that knowledge from Expert A is to be trusted more than knowledge from Expert B.
Meta rules are treated by the system like normal rules, but are given higher priority.
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Rules and Expert Systems

18. Rules and Expert Systems
Backward Chaining
In cases where a particular conclusion, i.e., hypothesis, is to be proved, backward chaining can be more appropriate.
Works back from a conclusion towards the original facts.
When a conclusion matches the conclusion of a rule in the database, the antecedents of the rule are compared with facts in the database.
The process looks like a recursion.
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Rules and Expert Systems

19. Rules and Expert Systems
Backward Chaining
Example:
Rule 1 A  B -> C
Rule 2 A -> D
Rule 3 C  D -> E
Rule 4 A  E  F -> G
Rule 5 A  E -> H
Rule 6 D  E  H -> I
Fact 1 A
Fact 2 B
Fact 3 F
[Q] What do we want to do with the above databases?
Goal to prove H
[Q] Do we humans use Forward Chaining or Backward Chaining?
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Rules and Expert Systems

20. Rules and Expert Systems
Topics
Backward Chaining
Rule 1 A  B -> C
Rule 2 A -> D
Rule 3 C  D -> E
Rule 4 A  E  F -> G
Rule 5 A  E -> H
Rule 6 D  E  H -> I
Fact 1 A
Fact 2 B
Fact 3 F
Goal to prove H
[Q] Which one is better???
[Q] Use both of them???
[Q] Prove or disprove G.
Using forward chaining:
Facts Rules triggered Rule 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
Using backward chaining:
Facts Goals Matching rules
A, B, F H 5
A, B, F E 3
A, B, F (C, D) 1
A, B, C, F (T, D) 2
A, B, C, D, F (T, T)  STOP
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Rules and Expert Systems

21. Types of Expert Systems
Rule-Based Systems
The 70s and 80s
Neural Networks
Belief (Bayesian) Networks
Blackboard Systems
Case-Based Reasoning
...
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Rules and Expert Systems

22. Architecture of Expert Systems
End users; domain experts; knowledge engineers
An expert system uses expert knowledge derived from human experts to diagnose illnesses, provide recommendations and solve other problems.
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Rules and Expert Systems

23. Architecture of Expert Systems
Knowledge base: database of rules (domain knowledge).
Explanation system: explains the decisions the system makes.
User Interface: the means by which the end user interacts with the expert system.
Knowledge base editor: allows domain experts or knowledge engineers to edit the information in the knowledge base.
Inference engine
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Rules and Expert Systems

24. Rules and Expert Systems
Expert System Shells
The part of an expert system that does not contain any domain specific or case specific knowledge is the expert system shell.
A single expert system shell can be used to build a number of different expert systems.
Examples of an expert system shell are CLIPS (C Language Interpreted Production System) and JESS (The Java Expert System Shell)
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Rules and Expert Systems

25. Rules and Expert Systems
Example: CLIPS
CLIPS is C Language Integrated Production System – an expert system shell.
CLIPS uses a LISP-like notation to enter rules.
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Rules and Expert Systems

26. Knowledge Engineering
A knowledge engineer takes knowledge from experts and inputs it into the expert system.
A knowledge engineer will usually choose which expert system shell to use.
The knowledge engineer is also responsible for entering meta-rules.
[Q] Is it the most difficult part in the development of an expert system?
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Rules and Expert Systems

27. Backward Chaining in Expert Systems
A common method for building expert systems is to use a rule-based system with backward chaining.
Typically a user enters a set of facts into the system, and
the system tries to see if it can prove any of the possible hypotheses using these facts.
In some cases it will need additional facts, in which case the system will often ask the user questions, to ascertain facts that could enable further rules to fire.
Backward chaining is often used in expert systems that are designed for medical diagnosis.
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Rules and Expert Systems

28. Backward Chaining in Expert Systems
Algorithm:
For each hypothesis, H:
If H is in the facts database, it is proved.
Otherwise, if H can be determined by asking a question, then enter the user’s answer in the facts database. Hence, it can be determined whether H is true or false, according to the user’s answer.
Otherwise, find a rule whose conclusion is H. Now apply this algorithm to try to prove this rule’s antecedents.  recursion
If none of the above applies, we have failed to prove H.
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Rules and Expert Systems

29. Backward Chaining in Expert Systems
Usually backward chaining is used in conjunction with forward chaining.
Forward chaining to derive new facts and asks questions in order to carry out its backward chaining analysis.
E.g.,
Rule 1: IF tired and headache THEN diagnose glandular fever
Rule 2: IF tired and sore throat THEN diagnose tonsillitis
Fact: headache
=> Forward chaining -> Hypothesis 1: G.F. -> backward chaining
=> ask if tired
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Rules and Expert Systems

30. Rules and Expert Systems
Example: CYC
A frame based production system.
Uses a database of over 1,000,000 facts and rules, encompassing all fields of human knowledge.
CYC can answer questions about all kinds of knowledge in its database, and can even understand analogies, and other complex relations.
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Rules and Expert Systems

31. Rules and Expert Systems
Topics
The Last Questions
[Q] Which part is difficult to implement?
[Q] Have expert systems been successful?
[Wikipedia] The expert system has a major flaw which explains its low success although the principle has existed for 70 years: knowledge collection and interpretation into rules, the knowledge engineering.
Most developers have no method to perform this task.
They work manually what opens to many possibilities for errors.
[Q] How to handle uncertainty and ambiguity?
E.g., IF name is “Bob”  weather is cold THEN tell “Bob” ‘Wear a coat’
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Rules and Expert Systems