1. KNOWLEDGE REPRESENTATION
Class 3
INFSY 565

2. Knowledge Representation
INTRODUCTION
OVERVIEW
TYPES OF KNOWLEDGE
KNOWLEDGE REPRESENTATION TECHNIQUES
Object-Attribute-Value Triplets
Rules
Semantic Networks
Frames
Logic
SUMMARY ON KNOWLEDGE REPRESENTATION

3. Introduction Will look at techniques for encoding knowledge in
an expert system
An expert system must encode knowledge, not data
Knowledge is represented in some symbolic form
Knowledge must be able to be manipulated by the
expert system
No one technique is suited for every application

4. Overview Knowledge - Understanding of a subject area
Domain - A well-focused subject area
Knowledge Representation - The method used to encode
knowledge in an expert system’s knowledge base
KNOWLEDGE IS POWER

5. Knowledge in AI Cognitive psychologists have formed a number of
theories to explain how humans solve problems
Theories have uncovered:
Types of knowledge humans commonly use
How humans mentally organize this knowledge
How humans use it efficiently to solve a problem
Researchers in AI have used the results to develop
techniques to best represent these different
knowledge types in the computer
A knowledge engineers’ responsibility is to choose
the knowledge representation technique best suited
for the given application

6. Approaches to Knowledge Representation
A good system for the representation of knowledge should possess
The following properties:
1. Representational Adequacy - the ability to represent all of the
kinds of knowledge that are needed in that domain.
2. Inferential Adequacy - the ability to manipulate the representational
structures in such a way as to derive new structures corresponding
to new knowledge inferred from old
3. Inferential Efficiency - the ability to incorporate into the know-
ledge structure additional information
4. Acquisitional Efficiency - the ability to acquire new information
easily

7. Types of Knowledge
1. Procedural Knowledge - describes how a problem is solved
provides direction on how to do something
Ex: rules, strategies, agendas and procedures
2. Declarative Knowledge - describes what is known about a problem
includes simple statements that are asserted to be true or false
includes a list of statements that more fully describe some concept
3. Meta-Knowledge - describes knowledge about knowledge
used to pick other knowledge best suited for solving a problem
experts use to enhance the efficiency of problem solving by
directing their reasoning into the most promising areas

8. Types of Knowledge - cont.
4. Heuristic Knowledge - describes a rule-of-thumb that guides the
reasoning process.
represents the knowledge compiled by an expert through the
experience of solving past problems
experts will take fundamental knowledge and compile it into
simple heuristics to aid problem solving
5. Structural Knowledge - describes knowledge structures
describes the expert’s overall mental model of the problem
concepts, sub-concepts, and objects are typical examples

9. Summary of Knowledge Types
Types of Knowledge
Rules; Strategies
Agendas; Procedures
Concepts; Objects
Facts
Knowledge about the other types of
knowledge and how to use them
Rules of Thumb
Rule Sets; Concept Relationships
Concept to Object Relationships
Procedural Knowledge
Declarative Knowledge
Meta-Knowledge
Heuristic Knowledge
Structural Knowledge

10. Knowledge Representation Techniques
Five most common Techniques:
Object-attribute-value triplets
Rules
Semantic Networks
Frames
Logic
Each technique emphasizes certain information about
a problem while ignoring other information
Each technique has advantages and disadvantages for
capturing efficiently the different types of knowledge
Choosing the correct representation for a given
application supports effective problem solving

11. Facts All cognitive theories of human knowledge organization use
facts as basic building blocks
A fact is a form of declarative knowledge that provides some
understanding of an event or problem
In expert systems, facts are used to help describe parts of frames
semantic networks, or rules
DEFINITION: A proposition (i.e. fact) is a statement that is
either true or false

12. Object-Attribute-Value Triplets (O-A-V Triplets)
In an expert system we assert into the working memory the
Boolean truth statement (TRUE OR FALSE), and use this
assertion in processing other knowledge
Simple proposition: It is raining
Fact is also used to assert a particular property value of some object
“The ball is red” assigns the value “red” to the ball’s color
This is known as an object-attribute-value (O-A-V) triplet

13. O-A-V Triplets Cont.
O-A-V triplets divides a given statement into three distinct
parts:
Object
Attribute
Attribute Value
Consider: “The chair’s color is brown”
Can represent as an O-A-V triplet as follows:
Object = “chair”
Attribute = “color”
Value = “brown”

14. O-A-V Triplets Cont.
For most problems addressed by an expert system, objects
have more than one important feature:
1 Foot
Diameter
Ball
Color
Red
Weight
1 Pound
Object Attribute Value
Object with multiple attributes

15. Single-Valued and Multi-Valued Facts
If only one answer makes sense, the O-A-V is single-valued:
Expert System Question: Please tell me if the barometric (object)
pressure (attribute) is
Falling (attribute values)
Steady
Rising
A: Falling
If multiple values make sense then the O-A-V is multi-valued:
Q: Please select the level of your education (attribute)
High School (attribute values)
College
Graduate School
A: High School
note: object “person” is implied

16. MYCIN
An Example

17. Rules Facts allow the system to understand the state of the world
System must have additional knowledge for it to work
intelligently with the facts
Most expert systems use rules to provide additional
knowledge
DEFINITION: Rule - A knowledge structure that relates
some known information to other information that can be
concluded or inferred to be known

18. Rules Cont. A rule is a form of procedural knowledge
It associates given information to some action
Action may be the assertion of new information or
some procedure to perform
A rule describes how to solve a problem

19. Structure of a Rule
The rules structure logically connects one or more antecedents
also called premises contained in the IF part, to one or more
consequents also called conclusions contained in the THEN part.
IF The ball’s color is red
THEN I like the ball
If the balls color is red then we can infer that I like the ball

20. Structure of a Rule - Cont.
A rule can have multiple premises joined with AND statements
(conjunctions), OR statements (disjunctions), or a combination of both.
Its conclusion can contain a single statement or a combination joined
with an AND.
The rule can also contain an ELSE statement, that is inferred to be
TRUE if one or more of its premises are FALSE

21. Rule Example IF Today’s time is after 10am AND Today is a weekday
AND I am at home
OR My boss called and said that I am late for work
THEN I am late for work
ELSE I am not late for work
RECOMMENDATION: Avoid the use of the OR
operator and ELSE statements

22. Avoid ORs and ELSEs Rule 1: IF A = X Rule 2: IF A = X
OR B = Y THEN D = XX
THEN D = XX ELSE D = YY
should be rewritten as: should be rewritten as:
Rule 1a: IF A = X Rule 2a: IF A = X
THEN D = XX THEN D = XX
Rule 1b: IF B = Y Rule 2b: IF A NOT = X
THEN D = XX THEN D = YY
Why: Easier to follow the inference process.
Easier to debug.
Clearer presentation
Validation of such rules more difficult
Not all expert system shells allow ORs or ELSEs. .

23. Examples of Bad Rules IF (engine_type==”Jet”) IF(Wing_Position==”Low”
Is B747
IF(engine_type==”High”)
IF(Bulges==”None”)
IS C5A
IF(Bulges==”Aft Wing”)
IS C141
IF(engine_type==”Prop”)

24. Example 2 IF Item Type is Tool THEN put on Peg Board ELSE
IF Item Type is Toy THEN
IF Toy Type is Indoor THEN put in Box in Back
ELSE put Along Wall
IF Item Type is Supply THEN
IF Size is Small THEN put in Drawer
ELSE put Under Bench
IF Item Type is Miscellaneous THEN
IF Need Again is No THEN put in Trash
ELSE put in Box in Back

25. Example 3 If weight is less than 10 lbs and is not a tool
Then put on top shelf
Else if it is for yard work
Then put in shed
Else hang above tool bench

26. Working Memory Expert systems employing rules are called rule-based
System matches the IF portion of the rules with facts
contained in the working memory
When a match is confirmed, the rule fires and its THEN
statements are added to the working memory
The new statements added to the working memory can
also cause other rules to fire

27. The Inference Engine Knowledge Base IF ball’s color is Red
THEN I like the ball
If I like the ball
THEN I will buy the ball
Step 3
Working Memory
Q: Ball’s color?
A: Red
Step 1
Ball’s color is red
I like the ball
I will buy the ball
Step 2
Step 4
Step 5

28. Executing a Procedure
Simple calculation: IF the area of the square is needed
THEN AREA = LENGTH*WIDTH
Procedural Program to call an external routine:
IF The design requires a new box
AND NUMBER = Number of items to pack
AND SIZE = Size of item
THEN CALL COMPUTE_BOX_VOLUME
AND SEND NUMBER, SIZE
AND RETURN VOLUME

29. Other examples of Procedures
SPREADSHEET:
IF January sales needed
THEN OPEN SALES
AND JANUARY_SALES = B7
DATABASE:
IF There is a plant emergency
AND NAME = Smith
THEN OPEN TELEPHONE
AND FIND NAME, NAME-FIELD
AND TELEPHONE = TELEPHONE-FIELD

30. Variable Rules In some applications, you will need to perform the same
operation on a set of similar objects.
Ex: IF John Smith is an employee
AND John Smith’s age is greater than 65
THEN John Smith can retire
Rule checks if John Smith is an employee of the company and
is over 65 before concluding he can retire.
What if you wanted the system to perform the same check for
other individuals?

31. Pattern-Matching
Most shells offer pattern-matching that include variables
that you can use to match similar problem statements.
IF ?X is EMPLOYEE
AND ?X AGE > 65
THEN ?X can retire
Rule scans working memory looking for matches, if
matches are found, the rule concludes that the person
can retire

32. Advantages to Using Variables
Designer only has to write one rule for many employees
Designer only has to change one rule to make a variable
change instead of many
Offers an efficient way to process information
Can directly capture general directions obtained from
an expert

33. Meta-Rules DEFINITION: Meta-knowledge - Knowledge about the
use and control of domain knowledge
DEFINITION: Meta-rule - A rule that describes how
other rules should be used

34. Meta Rules Cont. A meta-rule establishes strategies for the use of
domain specific rules, instead of concluding new
information
Meta-rules provide direction to the expert system during
the session
Ex: IF The car will not start
AND The electrical system is operating normally
THEN Use rules concerning the fuel system

35. Semantic Networks DEFINITION: Semantic Network - A method of knowledge
representation using a graph made up of nodes and arcs where the nodes
represent objects and the arcs the relationships between the objects
Wings
IS-A (Canary, Bird)
HAS
Canary
IS-A
Bird
TRAVEL
Semantic network of a bird
Fly

36. Expanding the Network
You can add a new object node by 1) a similar object, 2) a more
specific object, or (3) a more general object.
Wings
Air
HAS
BREATHE
Canary
IS-A
IS-A
Bird
IS-A
Animal
Tweety
IS-A
TRAVEL
Penguin
Fly
TRAVEL
Walk

37. Inheritance Nodes added to a semantic network automatically
inherit information from the network
“Tweety” breathes air because it is an “Animal”
Inheritance eases the task of coding knowledge
To support, objects must be organized into classes;
classes must be arranged in a generalization hierarchy
BREATHE (Tweety, Air)
Air
BREATHE
Canary
IS-A
IS-A
Bird
IS-A
Animal
Tweety

38. Inheritable Knowledge
Fielder
.262
.106
Three-Finger-
Brown
Pee-Wee-
Reese
Brooklyn-
Dodgers
Chicago-
Cubs
Baseball
Player
Adult-
Male
Person
Right
5-10
6-1
.252
Pitcher
team
instance
Batting-average
isa
height
bats
handed
Equal
isa shows class inclusion; instance shows class membership

39. Fault Detection System:
An Expert System Example

40. Frames DEFINITION: Frame - A data structure for representing
stereotypical knowledge of some concept
Minsky (first proposed frames) describes a frame as follows:
“When one encounters a new situation (or makes a substantial change
in one’s view of a problem) one selects from memory a structure called
a “frame.” This is a remembered framework to be adapted to fit reality
by changing details as necessary.”

41. Structure of a Frame
The basic structure of a frame is similar to many forms such as a “report card”
ISA(OBJECT1, OBJECT2)
REPORT CARD
GENERAL FRAME STRUCTURE
Student Name:
Frame Name:
Object1
Address:
Class:
Object2
Course
Grades
Property1
Chemistry
Properties:
Value1
Property2
Value2
Math
English
Property3
Value3
...
...
...

42. Class Frame A class frame represents the general characteristics
of some set of common objects.
Frame Name:
Properties:
Bird
Color
Unknown
Eats
Worms
No._Wings 2
Flies
True
Hungry
Unknown
Activity
Unknown

43. Instance Frame Instance Frame describes a specific instance of a
class frame. The frame inherits both properties and
property values from the class.
Frame Name:
Class:
Properties:
Tweety
Bird
Color
Yellow
Eats
Worms
No._Wings 1
Flies
False
Hungry
Unknown
Activity
Unknown
Cage
Lives

44. Hierarchical Structure
You can create complex frame structures organizing
by different levels of abstraction. Each level inherits
from the previous level.
Birds
Sparrows
Robins
Canaries
Bird1
Bird2
Bird3
Bird5
Bird4

45. Facets Provide you with additional control over property values
Can define a constraint on a property value - i.e.
Limit a numeric property value to some set range
limit a string value to one of a few possible values
restrict the type of data that can be stored in a
property value, i.e. string, numeric, or boolean
Can instruct a property how to obtain its value or what to
do if its value changes
IF-NEEDED facets
IF-CHANGED facets

46. Methods A method is a procedure that executes whenever a
property value is needed.
Property value can be obtained through a series of
computations, or from an external database or spreadsheet
Method is typically written in the procedural language of the
chosen shell and attached to the property’s IF-NEEDED facet

47. Example
1. Assume property value “Flies” in the “Tweety” frame is “Unknown”.
2. You want the system to determine if tweety can fly.
Method: IF Tweety has less than two wings
THEN Tweety can’t fly
IF Tweety has two wings
THEN Tweety can fly
To implement method, we need the property value of “No._Wings”
from the “Tweety” frame.

48. Example Con’t Shells differ but one example is Frame:Property
Using the code on the previous “Tweety frame, we have
Tweety:No._Wings = 1
Now we need to assert a value of True or False to the “Flies”
property depending on Tweety’s wing count.

49. Example Con’t Frame:Property = Value IF Tweety:No._Wings < 2
THEN Tweety:Flies = False
IF Tweety:No._Wings = 2
THEN Tweety:Flies = True
This method is attached to the IF-NEEDED facet of property
“Flies” of the frame “Tweety”

50. Logic in a computer One most often linked to intelligent systems are
Logic is the oldest form of knowledge representation
in a computer
One most often linked to intelligent systems are
propositional logic and predicate calculus
Techniques use symbols to represent knowledge
Operators applied to the symbols produce logical
reasoning

51. Predicate Logic Class Handout