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

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

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

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

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

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

Rules If (conditions) Then (actions) Else 7

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

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

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

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

Bill gives Judy a gift 12

Bill told Laura that he gave Judy a gift 13

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

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

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

A node in a frame system Procedure 1 Value 1 Slot 1 Procedure 2 17

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

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

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