1. Knowledge Representation
Propositional Logic
Predicate Logic
Rules
Semantic Nets
Frame
Object 1

2. Propositional Logic It is raining It is sunny
We can deduce whether a certain proposition is true or false 2

3. Proposition Logic Socrates is a man Plato is a man
SOCRATESMAN
Plato is a man
PLATOMAN
we can not draw any conclusions about similarities between Socrates and Plato 3

4. Predicate Logic Socrates is a man Plato is a man
MAN (SOCRATES)
Plato is a man
MAN (PLATO)
Now the structure of representation reflects the structure of knowledge itself 4

5. Predicate Logic Marcus is a man Marcus is a Pompeian
POMPEIAN (Marcus)
All Pompeians were Romans
Vx POMPEIAN(x) -> ROMAN(x) 5

6. Predicate Logic All Romans were either loyal to Caesar or hated him
Vx ROMAN(x) -> loyalto (x, Caesar) v hate (x, Caesar)
It is difficult to represent knowledge in predicate logic 6

7. Rules If
(conditions)
Then
(actions)
Else 7

8. Semantic Nets
Semantic net is a knowledge presentation method based on a network structure
It consists of
points called nodes connected by
links called arcs
Nodes - object, concepts, events
Arcs - relationships between nodes 8

9. Semantic Nets
Common arcs used for representing hierarchies include isa and has-part. 9

10. Example: The queen mary is an ocean liner. Every ocean liner is a ship10

11. Has-part isa SHIP Ocean Liner Oil Tanker Engine Hull isa Swimming Pool
Liver Pool
Boiler
Queen Mary 11

12. Bill gives Judy a gift 12

13. Bill told Laura that he gave Judy a gift13

14. Frame 1 a data structure for representing a stereotyped situation
a network of nodes and relations organized in a hierarchy
the topmost nodes - general concepts
the lower nodes - more specific instances 14

15. Frame 2
The concepts at each node is described by a set of attributes and values of those attributes
Attributes are called slots
Each slot can have procedures (codes)
Typical procedures
if added procedure
if deleted procedure
if needed procedure 15

16. DSS Project Process Report
isa
isa
Progress Report
Technical Report
isa
DSS Project Process Report 16

17. A node in a frame system Procedure 1 Value 1 Slot 1 Procedure 217

18. Comparisons of KR Methods
Rules
Adv.
simple syntax, easy to understand, simple interpreter, high modular, flexible
Disadv.
Hard to follow hierarchies, inefficient for large systems, not all knowledge can be expressed as rules 18

19. Comparisons of KR Methods
Semantic Nets
Adv.
Easy to follow hierarchy, easy to trace association, flexible
Disadv.
Meaning attached to nodes might be ambiguous
exception handling is difficult
difficult to program

20. Comparisons of KR Methods
Frames
Adv.
Expressive power, easy to set up slots for new properties and relations
easy to create specialized procedures
easy to include default information and detect missing values
Disadv.
Difficult to program
difficult for inference