Rules and expert system

CS 484 – Artificial Intelligence 1
Announcements
• Choose Research Topic by today
• Project 1 is due Thursday, October 11
• Midterm is Thursday, October 18
• Book Review is due Thursday, October 25

Rules and Expert Systems
Lecture 8

Rules for Knowledge Representation
• IF… THEN Rules can be used to represent
knowledge:
• IF it rains, then you will get wet
• Rules can also be recommendations:
• IF it rains, then you should wear a coat

Rule Based Production Systems
• Production system - uses knowledge in the form of
rules to provide diagnoses or advice on the basis
of input data.
• Parts
• Database of rules (knowledge base)
• Database of facts
• Inference engine which reasons about the facts using
the rules

Forward Chaining
• Works from a set of facts and rules towards a set
of conclusions, diagnoses or recommendations.
• When a fact matches the antecedent of a rule
• Rule fires
• Conclusion added to facts

Conflict Resolution
• Problem: more than one rule fires at once
• Conflict resolution strategy decides which
conclusions to use.
• Give rules priorities and to use the conclusion that has
the highest priority.
• Apply the rule with the longest antecedent
• Apply the rule that was most recently added to the
database.

Examples
• IF patient has pain
THEN prescribe pain
killers (priority 10)
• IF patient has chest
pain
THEN treat for heart
disease (priority 100)
• IF patient has pain
AND patient is over
60
AND patient has a
history of heart
conditions
THEN take to
emergency room

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.

Backward Chaining
• Use backward chaining when proving a
particular conclusion
• 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.

Forward Chaining
• Rules
1. A Λ B → C
2. A → D
3. C Λ D → E
4. B Λ E Λ F → G
5. A Λ E → H
6. D Λ E Λ H → I
• Facts
1. A
2. B
3. F
• Goal
• H
Facts
Rules
triggered
Rule fired
A,B,F

Backward Chaining
• Rules
1. A Λ B → C
2. A → D
3. C Λ D → E
4. B Λ E Λ F → G
5. A Λ E → H
6. D Λ E Λ H → I
• Facts
1. A
2. B
3. F
• Goal
• H
Facts Goals
Matching
Rules
A,B,F
How do the methods compare?

The Architecture of Expert Systems (1)
• Expert knowledge
derived from human
experts
• Purpose:
• Diagnose illnesses
• Provide
recommendations
• Solve other problems

The Architecture of Expert Systems (2)
• Knowledge base: database of rules (domain
knowledge).
• Explanation system: explains the decisions the
system makes.
• User Interface: the means by which the user interacts
with the expert system.
• Knowledge base editor: allows the user to edit the
information in the knowledge base.

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.
• An example of an expert system shell is CLIPS.

The Rete Algorithm
• A rete is a directed, acyclic, rooted graph (a tree).
• A path from the root node to a leaf represents the
left hand side of a rule.
• Each node stores details of which facts have been
matched so far.
• As facts are changed, the changes are propagated
through the tree.
• This makes an efficient way for expert systems to
deal with environments which change often.

Knowledge Engineering
• Takes knowledge from experts and inputs it
into the expert system.
• Usually choose which expert system shell to
use.
• Responsible for entering meta-rules.

CLIPS
• CLIPS is C Language
Integrated Production
System – an expert
system shell.
• CLIPS uses a LISP-like
notation to enter rules.

Backward Chaining in Expert
Systems
• Backward chaining is often used in expert systems
that are designed for medical diagnosis:
• 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.
 If none of the above applies, we have failed to prove H.
• Usually backward chaining is used in conjunction with
forward chaining.

Simple Medical Expert System
• Rules
1. If headache then prescribe pain killer
2. If headache and sore throat and coughing than
diagnose flu
3. If tired and headache then diagnose glandular
fever
4. If tired and sore throat then diagnose
tonsillitis
5. If tired than diagnose stress

Simple Medical Expert System
• User interaction
• Patient: I have a headache
• System: I suggest you take a pain killer. Do you have a sore
throat?
• Patient: Yes, I do.
• System: Have you been coughing?
• Patient: No, why do you ask?
• System: If you had a head ache and a sore throat and had been
coughing, I would have to conclude you might have the flu. It
doesn't sound as though you have the flu? Have you been feeling
tired recently?
• Patient: Yes, I have.
• System: It sounds as though you may have glandular fever or
tonsillitis.

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.

Rules and expert system

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