Announcements

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

2
Rules and Expert Systems

• Lecture 8

3
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

4
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

5
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

6
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.

7
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

8
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.

9
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.

10
Forward Chaining

• Rules
• A ? B ? C
• A ? D
• C ? D ? E
• B ? E ? F ? G
• A ? E ? H
• D ? E ? H ? I

• Facts
• A
• B
• F
• Goal
• H

11
Backward Chaining

• Rules
• A ? B ? C
• A ? D
• C ? D ? E
• B ? E ? F ? G
• A ? E ? H
• D ? E ? H ? I

• Facts
• A
• B
• F
• Goal
• H

How do the methods compare?
12
The Architecture of Expert Systems (1)

• Expert knowledge derived from human experts
• Purpose
• Diagnose illnesses
• Provide recommendations
• Solve other problems

13
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.

14
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.

15
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.

16
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.

17
CLIPS

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

18
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 users answer in the
  facts database. Hence, it can be determined
  whether H is true or false, according to the
  users answer.
• Otherwise, find a rule whose conclusion is H. Now
  apply this algorithm to try to prove this rules
  antecedents.
• If none of the above applies, we have failed to
  prove H.
• Usually backward chaining is used in conjunction
  with forward chaining.

19
Simple Medical Expert System

• Rules
• If headache then prescribe pain killer
• If headache and sore throat and coughing than
  diagnose flu
• If tired and headache then diagnose glandular
  fever
• If tired and sore throat then diagnose
  tonsillitis
• If tired than diagnose stress

20
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.

21
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.