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Page 1: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

All rights reserved ©

L. Manevitz Lecture 7 1

Artificial IntelligenceRepresenting Commonsense

Knowledge

L. Manevitz

Page 2: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 2

Definitions

• Representation – a set of syntactic and semantic conventions that make it possible to describe things.

• Syntax – specifies the symbols that may be used and the ways those symbols may be arranged.

• Semantics – specifies how meaning is embodied in the symbol arrangements allowed by the syntax.

Page 3: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 3

Semantic Network Approach

• Nodes and Slots:

Nodes are objects,

or classes,

or properties.

Slots are of different types.

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L. Manevitz Lecture 7 4

A Semantic Network

Mammal

Person Nose

Pee-Wee-ReeseBlue Brooklyn-Dodgers

Is-ahas-part

instanceteam

uniform-color

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L. Manevitz Lecture 7 5

Representing Nonbinary Predicates

• Unary Predicates can be rewritten as binary ones.

man(Marcus)

could be rewritten as

instance(Marcus,Man)

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L. Manevitz Lecture 7 6

Representing Nonbinary Predicates cont.

• N-Place Predicates

score(Cubs,Dodgers,5-3)

becomes Game

G23 5-3

Dodgers

Cubs

Is-ascore

home-team

visiting-team

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L. Manevitz Lecture 7 7

A Semantic Net Representing a Sentence

“John gave the book to Mary.”

Give

EV7 BK23

Mary

John object

beneficiary

agentinstance

Book

instance

Page 8: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 8

Some Important Distinctions

First try:

Second try:

John 72height

John

H1

height

Bill

H2

heightgreater-than

Page 9: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 9

Some Important Distinctions cont.

Third try:

72

value

John

H1

height

Bill

H2

heightgreater-than

Page 10: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 10

Partitioned Semantic Nets

Bite

b m

Dogs

d

Is-avictimassailant

Mail-carrier

Is-aIs-a

a) The dog bit the mail carrier.

Page 11: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 11

Partitioned Semantic Nets cont.

b) Every dog has bitten a mail carrier.

Bite

b m

Dogs

d

Is-avictimassailant

Mail-carrier

Is-aIs-a

g

GS

Is-aform

SA

S1

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L. Manevitz Lecture 7 12

Partitioned Semantic Nets cont.

c) Every dog in town has bitten the constable.

Bite

b c

Town-Dogs

d

Is-avictimassailant

Constables

Is-aIs-a

g

GS

Is-aform

DogsSA

S1

Page 13: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 13

Partitioned Semantic Nets cont.

d) Every dog has bitten every mail carrier.

Bite

b m d

Is-avictimassailant

Mail-carrier

Is-aIs-a

g GS Is-a

form

DogsSA

S1

Page 14: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 14

Inheritance

• Is-a slot – appears between objects and classes.

• ako slot – appears between subsets.

Page 15: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 15

Inheritance -ProcedureF the given node; S the given slot;1. Form a Queue of F and all class nodes connected to F

via Is-A node. F is at top of Queue.2. Until Queue is empty or default has been found

determine if the first element of Queue has a value in its S slot:

a. Yes – a value has been found.b. No – remove the first element from Queue and add the nodes

related to the first element by AKO slots to the end of Queue.

3. If a value has been found say that this is the default value of F’s S slot.Otherwise announce Failure.

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L. Manevitz Lecture 7 16

Inheritance - Example

Is-a

shape

ako

Block

Brick

Brick12

rectangular

Is-a

ako

Wedge

Wedge18

shapeTriangular

Page 17: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 17

If-needed Inheritance -ProcedureF the given node; S the given slot;1. Form a Queue of F and all class nodes connected to F

via Is-A node. F is at top of Queue.2. Until Queue is empty or successful if-needed procedure

has been found determine if the first element of Queue has a procedure in the If-Needed facet of its S slot:

a. Yes – if the procedure produces a value than a value has been found.

b. No – remove the first element from Queue and add the nodes related to the first element by AKO slots to the end of Queue.

3. If a value has been found say that the value found is the value of F’s S slot.Otherwise announce Failure.

Page 18: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 18

If-needed Inheritance - Example

Weight (if-needed)

Block

Brick

Brick12

Block-weight-procedure

400

11

Volume

Density

Page 19: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 19

Example cont.

Weight

Block

Brick

Brick12 400

11

Volume

Density

4400

Weight is activated by request for the

weight of Brick12 !

Page 20: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 20

Default Inheritance ProcedureF the given node; S the given slot;1. Form a Queue of F and all class nodes connected to F

via Is-A node. F is at top of Queue.2. Until Queue is empty or default has been found

determine if the first element of Queue has a value in the Default facet of its S slot:

a. Yes – if the first element has a value than a value has been found.

b. No – remove the first element from Queue and add the nodes related to the first element by AKO slots to the end of Queue.

3. If a value has been found say that the value found is the default value of F’s S slot.Otherwise announce Failure.

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L. Manevitz Lecture 7 21

Default Inheritance - Example

Is-a

Color (Default)

ako

Block

Brick

Brick12

Red

Is-a

ako

Wedge

Wedge18

Color (Default)Blue

Has no default color therefore probably Blue

because of Block’s default

color !

Page 22: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 22

Perspective -Example

Is-a

Purpose

Support

Brick Structure

Is-a

Play Commemorate

Toy

shape

rectangular

Gift perspective

Toy perspective

Structure perspective

Brick12

Purpose

Is-a

Gift

Purpose

Is-a

Page 23: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 23

Special Links - Summary

• IS-A and AKO links make class membership and subclass-class relations explicit, facilitating the movement of knowledge from one level to another.

• VALUE facets make values explicit.

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L. Manevitz Lecture 7 24

Special Links – Summary cont.

• IF-NEEDED facets make procedures purposes explicit, and they relate procedures to the classes those procedures are relevant to.

• DEFAULT facets make likely values explicit without implying certainty.

• Perspectives make context sensitivity explicit, preventing confusion and ambiguity.

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L. Manevitz Lecture 7 25

Frames

• Frames : A collection of nodes that describe a stereotyped object, act or event.

• Example : newspaper report.

Page 26: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 26

Earthquake ExampleDisaster-event

Earthquake

Flood

Hurricane

Event Killed

Injured

Homeless

Damage

Magnitude

Fault

Crest

River

Wind-speed

Name

Place

Day

Time

Social-event

Birthday-party

Number-of-guests

Host

Age

Birthday-person

Page 27: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 27

Earthquake Example cont.

Earthquake Hits Lower Slabovia

• Today an extremely serious earthquake of magnitude 8.5 hit Lower Slabovia killing 25 people and causing $500,000,000 in damage. The president of Lower Slabovia said the hard-hit area near the Sadie Hawkins fault has been a danger zone for years.

Page 28: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 28

Earthquake Example cont.Earthquake13

place Lower Slabovia

Today

25

500,000,000

8.5

day

fatalities

damage

magnitude

fault Sadie Hawkins

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L. Manevitz Lecture 7 29

Earthquake Summary Pattern

• An earthquake occurred in value in location slot value in day slot. There were value in fatalities slot fatalities and value in damage slot in property damage. The magnitude was value in magnitude slot on the Richter scale, and the fault involved was the value in fault slot.

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L. Manevitz Lecture 7 30

Instantiated Earthquake Summary Pattern

• An earthquake occurred in Lower Slabovia today . There were 25 fatalities and $500 million in property damage. The magnitude was 8.5 on the Richter scale, and the fault involved was the Sadie Hawkins.

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L. Manevitz Lecture 7 31

Earthquake Example cont.

Earthquake Study Stopped

Today, the President of Lower Slabovia killed 25 proposals totaling $500 million for research in earthquake prediction. Our Lower Slabovian correspondent calculates that 8.5 research proposals are rejected for every one approved. There are rumors that the President’s science advisor, Sadie Hawkins, is at fault.

Page 32: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 32

Earthquake Example cont.

• The Earthquake Study Stopped story could be summarized, naively, as though it were the story about an actual earthquake, producing the same frame as the Earthquake Hits Lower Slabovia story does.

Page 33: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 33

Scripts

Page 34: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 34

Scripts

• Example - Restaurant script.

Script: Restaurant Roles: S=Customer

Track: Coffee Shop W=Waiter

Props: Tables C=Cook Menu M=Cashier

F=Food O=Owner

Check

Money

Page 35: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 35

Restaurant Example cont.

Entry conditions : S is hungry

S has money

Results : S has less money

O has more money

S is not hungry

S is pleased (optional)

Page 36: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 36

Restaurant Example cont.

Scene 1: Entering

S PTRANS S into restaurant

S ATTEND eyes to tables

S MBUILD where to sit

S PTRANS S to table

S MOVE S to sitting position

Page 37: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 37

Restaurant Example cont.

Scene 2: Ordering(menu on table) (W brings menu) (S asks for menu)S PTRANS menu to S S MTRANS signal to W

S MTRANS ‘need menu’ to WW PTRANS W to table

W PTRANS W to menu

W PTRANS W to tableW ATRANS menu to S

S MTRANS W to table*S MBUILD choice of FS MTRANS signal to WW PTRANS W to tableS MTRANS ‘I want F’ to W

W PTRANS W to CW MTRANS (ATRANS) to C

C DO (prepare F script) to Scene 3

C MTRANS ‘no F’ to WW PTRANS W to SW MTRANS ‘no F’ to S (go back to *) or (go to Scene 4 at no pay path)

Page 38: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 38

Restaurant Example cont.

Scene 3 : Eating

C ATRANS F to W

W ATRANS F to S

S INGEST F(Option : Return to Scene 2 to order more; otherwise go to Scene 4)

Page 39: All rights reserved ©L. Manevitz Lecture 71 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz

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L. Manevitz Lecture 7 39

Restaurant Example cont.

Scene 4 : Exiting

S MTRANS to W

W PTRANS W to SW MOVE (write check)

(W ATRANS check to S)

W ATRANS check to SS ATRANS tip to WS PTRANS S to MS ATRANS money to MS PTRANS S to out of restaurant

(No pay path)