All rights res erved © L. Manevitz Lecture 7 1 Artificial Intelligence Representing Commonsense Knowledge L. Manevitz
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L. Manevitz Lecture 7 1
Artificial IntelligenceRepresenting 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.
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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
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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
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L. Manevitz Lecture 7 9
Some Important Distinctions cont.
Third try:
72
value
John
H1
height
Bill
H2
heightgreater-than
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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.
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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
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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
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L. Manevitz Lecture 7 14
Inheritance
• Is-a slot – appears between objects and classes.
• ako slot – appears between subsets.
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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
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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.
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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
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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 !
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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 !
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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
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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.
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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
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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.
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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.
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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.
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L. Manevitz Lecture 7 33
Scripts
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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
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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)
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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
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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)
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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)
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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)