FRAMES

By:

Abisekh Bahadur Niraula (071/MSCSKE/651)

Rasu Shrestha (071/MSCSKE/662)

Shikhar Basnet (071/MSCSKE/666)

Umesh Timalsina (071/MSCSKE/669)

Department of Electronics and Computer Engineering

IOE, Pulchowk Campus

1. FRAMES- INTRODUCTION:o Knowledge Representation Technique

o Similar to Semantic Nets.

o Proposed by: Marvin Minsky in 1974.

o Article: A Framework For Representing Knowledge.

o Common Sense System: Not Rule Based.

o AIs Primary Data Structures.

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1. FRAMES- INTRODUCTION-II:o Frame: Prototype of a Concept

o Denoting attributes of the concept

o The class of objects or concepts to which the concept inquestion belongs.

o And some other things

o An instance of a Frame.

o Representation of a specific object.

o Semantic Nets with Attributes.

o Description: Similar to Structure Data Type in C.

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1. FRAMES- INTRODUCTION-III:o A frame represents an entity as a set of slots

(attributes) and associated values

o Represents:

o General Concept (Frame)

o Specific Entry (Instance of the Frame)

o Implicit Relation with other Frames.

o Concept: Similar to OOS(Object Oriented Systems)

o Hierarchical Representations: Super and Sub Classes.

o Classes and Frames: Used interchangebly.

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2. EXAMPLES: 5

2. EXAMPLES-HIERARCHY: 6

2. EXAMPLES-HIERARCHY: 7

2. EXAMPLES- I:o FIDO is a Dog.

o Dog is a concept.

o Fido is a Particular dog.

How Can We Represent the Concept/ Notion of theCreature Dog?

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2. EXAMPLES:o FIDO is a Dog.

o Dog is a concept.

o Fido is a Particular dog.

How Can We Represent the Concept/ Notion of theCreature Dog?

A Way: ANIMALS>>>MAMMALS>>DOG

o FIDO: an instance of the Frame Dog.

o Notion of Frames: Felt by people out of CommonSense.

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2. EXAMPLES- FRAME: 10

SLOT NAME SLOT VALUES(Default)

IS_A Mammal

No_of_legs 4

Has_Tail Yes

Color_of_Skin

Owner

Type_of_Teeth Sharp

DOG:

2. EXAMPLES- FRAME: 11

SLOT NAME SLOT VALUES

IS A Mammal

No_of_legs 4

Has Tail Yes

Color of Skin White

Owner Shikhar

Type of Teeth Sharp

FIDO:

2. EXAMPLES- FRAME: 12A Book Frame:

Book Frame

Slot Filler

Title AI. A modern Approach

Author Russell & Norvig

Year 2003

2. EXAMPLES- FRAME: 13Inheritance: Similar to OOP Paradigm.

Hotel Room

what room

where hotel

contains

hotel chair

hotel phone

hotel bed

Hotel Chair

what chair

height 20-40cm

legs 4Hotel Phone

what phone

billing guest

Hotel Bed

what bed

size king

part mattressMattress

price 100$

3. SLOTS:o Slots Denote Attributes.

o Attributes are typed.

o Some Values: Already Instantiated.

o Some Values: Defined in the Instances only.

o Instantiated Slots: May be Used to AnswerQueries.

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3. SLOTS-II:Slots Can Contain:

o Values

o Types

o Constraints Over Possible Values

o Predicates:

o Daemons/ Functions.

o Slots Can be Structured- Frames Within the Slots-Frame Pointers.

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3. SLOTS-III:o Constraints over Possible Values:

BOY:

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Is_a Human_being Type Constraints

Name String

Age Float

3. SLOTS-IV:Predicates in a slot:

o Two Types of Predicates in General:

o If needed

o If added.

o If needed: State How to Find a Value.

o If added: Used to check constraints on the Values.

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3. SLOTS-IV:o Predicates in a slot- Example:

BOY:

If needed compute CGPA(BOY, results)

If added test(constraint).

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Is_a Human_being Type

Name String

Age Float

4. INHERITENCE: 19Question: Does Tom drink Milk?

TOM is an instance of BOY.

Also, Boy is a HUMAN BEING.

HUMAN BEINGS drink milk.

All Human Beings Drink Milk.

The answer can be inherited.

5. GENERIC and DEFAULT VALUES:o Generic Values:

o Hold True for all instances and sub-classes of the class.

o Inherited Down the Hierarchy

o Default Values:

o Filler Values

o Default Values may be over-written.

o Specified in the Parent-Class(Super Class).

o Conflicts: Multiple Inheritance.

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6. INFERENCE IN A FRAME:o Look for Candidate Frame or Instances.

o Look for Availability of Values.

o If Available- Solved.

o Else go to Preceding Level and so on.

o If Solved at a Higher Level, try to have MoreSpecific Answers Going Down the hierarchy.

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7. ANSWERING QUERIES- EXAMPLE: 22

9. ADVANTAGES: 23o Makes programming easier by grouping related

knowledge

o Easily understood by non-developers

o Expressive power

o Easy to set up slots for new properties and relations

o Easy to include default information and detect missing values.

9. DISADVANTAGES: 24o No standards (slot-filler values)

o More of a general methodology than a specific representation:o Frame for a class-room will be different for a professor and

for a maintenance worker

o No associated reasoning/inference mechanisms

10. REFERENCES: 251. Artificial Intelligence, Lecture-20, Prof. Dr.

Anupam Basu, Department of Computer Scienceand Engineering, IIT Kharagpur, NPTEL, MHRD,Government of India

11. QUERIES?? 26

THANK-YOU