KR Using rulesIF . . THENECA (Event Condition Action)RULES . APLLICATIONSEXAMPLES1. If flammable liquid was spilled, call the fire

department.2. If the pH of the spill is less than 6, the spill

material is an acid.3. If the spill material is an acid, and the spill

smells like vinegar, the spill material is acetic acid.

( are used to represent rules)

FACTS

MATCH EXECUTE

[ ] [ ] [ ]

[ ] [ ] [ ]

Fig. 1 the rule Interpreted cycles through a Match- Execute sequence

FACTS

A flammable liquid was spilled

The pH of the spill is < 6

Spill smells like vinegar

The spill material is an acid

MATCH

EXECUTE

If the pH of the spill is less than 6,the spill material is acid

RULES

Fig.2 Rules execution can modify the facts in the knowledge base

New fact added to the KB

FACTS

A flammable liquid was spilled

The pH of the spill is < 6

Spill smells like vinegar

The spill material is an acid

ACETIC ACID

MATCH

EXECUTE

If the spill material is an acid and the spill smells like vinegar, the spill material is acetic acid

RULES

Fig.3 Facts added by rules can match rules

FACTS

A flammable liquid was spilled

The pH of the spill is < 6

Spill smells like vinegar

MATCHEXECUTE

If a flammable liquid was spilled, call the fire department

RULES

Fig.4 Rule execution can affect the real world

Fire dept is called

The pH of the spill is < 6

The spill material is an acid

Spill smells like vinegar

The spill material is an acetic acid

Fig.5 Inference chain for inferring the spill material

A

B

G

C

E

HD

A E

G C BH

B F

A E

G CH

D

Z

AG

FD

EH

B

C

MATCHMATCH MATCH EXECUTE

EXECUTEEXECUTE

F &B ZC &D F A D

F &B ZC &D F A D

F &B ZC &D F A D

RULES RULESRULES

Fig. 6 An example of forward chaining

A D

CF

BZ

Fig. 7 Inference chain produced by Fig. 6

FACTS FACTS FACTS FACTS FACTS FACTS FACTS FACTS FACTSStep 1 2 3 4 5 6 7 8

RULES RULES RULESRULESRULESRULESRULESRULESRULES

A E H

G CB

A EHG

B C

A EG HB C C C C C C

C

A A A A A AE E E E E E

G G G G G GH H H H H

H

B B B B B BD FD

FZ

F&B ZC&D FA D

F&B ZC&D FA D

F&B ZC&D FA D

F&B ZC&D FA D

F&B ZC&D FA D

F&B ZC&D FA D

F&B ZC&D FA D

F&B ZC&D FA D

F&B ZC&D FA D

Need to get

FB

Z not here

Want Z

Z

here

Get C D

F not here

Want F

F here

C here

Want C

Need toGet A

D not here

Want D Want A

A hereHave C & D

Have F & B

Have Z

Execute Execute Execute

D

here

Fig. 8 An example of Backward Chaining

Figure 1 ANTECEDENTS CONSEQUENTS

………………

…………Rn If if1

if2 : then then1 then2

: Z1 If ?x has hair

then ?x is a mammal

Z2 If ?x gives milkthen ?x is a mammal

Z3 If ?x has feathersthen ?x is a bird

Z4 If ?x flies?x lays eggs

then ?x is a bird

Z5 If ?x is a mammal?x eats meat

then ?x is carnivore

Z6 If ?x is a mammal?x has pointed teeth?x has claws?x has forward-pointing eyes

then ?x is carnivore

Z7 If ?x is a mammal?x has hoops

then ?x is an ungulate

Z8 If ?x is a mammal?x chews cud

then ?x is an ungulate

Z9 If ?x is a carnivore?x has tawny color?x has dark spots

then ?x is a cheetah

Z10 If ?x is a carnivore?x has tawny color

?x has dark spotsthen ?x is a tiger

Z11 If ?x is an ungulate?x has long legs?x has long neck?x has tawny color?x has dark spots

then ?x is a giraffe

Z12 If ?x is a ungulate?x has white color?x has black strips

then ?x is a zebra

Z13 If ?x is a bird?x does not fly?x has long legs?x has long neck?x is black and white

then ?x is a ostrich

Z14 If ?x is a bird?x does not fly?x has swim?x is black and white

then ?x is a penguin

Z15 If ?x is a bird?x is a good flyer

then ?x is an albatross

Stretch has hair.Stretch chews cud.Stretch has long legs.Stretch has long neck.Stretch has tawny color .Stretch has dark spots.

Z1

Z8

Z11

Fired firstHashair is a mammal

Fired second

is an ungulate

Fired third

is a giraffe

Has long legs

Has long neck

Has tawny color

Has dark sports

Chews cud

FIGURE: 2

Z6

Z1

Z9

Z5 First rule used

Second rule used

Third rule used

Fourth rule usedHas forward-pointing eyes

Has claws

Has pointed teethis a carnivore

is a carnivore

is a cheetah

is a mammal

Has hair

Eats meat

Has tawny color

Has dark sports

FIGURE: 3

ANIMAL

Bird Fish

Canary

Has Wings

Can fly

Has feathers

Has Skin

Can Move Around

Eats

Breathes

Can Sing

Is Yellow

Has Long Thin LegsIs all

Can’t fly

Can Bite

Is Dangerous

Shark

Is Pink

Is Edible

Swims Upstream to lay Eggs

Fig. 1 A Typical Semantic Network

.

.

. ... Salmon

Ostrich

Ross Quillian

PENGUIN

VICTOR

CHARLEY

POODLE

DOG

TERRIOR

LIKES

LIKES

INST SUBC SUBC

INST

Fig. 5 Semantic Network with Property Relations

PENGUIN

VICTOR

INST

Fig.2 Simple Semantic Network

1. Victor is a Penguin

2. All Penguins are birds

3. All Birds are animals

4. All Mammals are animals

5. Charles is a Poodle

6. All dogs are mammals

7. All Poodle are Dogs

8. All Terriors are Dogs

Fig. 3 Facts about the Animal Kingdom

ANIMAL

BIRD

PENGUIN

VICTOR

MAMMAL

DOG

POODLETERRIER

CHARLEY

SUBC SUBC

SUBC SUBC

INSTANCE SUBCSUBC

INST

Fig. 4 A larger Semantic Network

From fig. 2

ANIMAL

MAMMALBIRD

PENGUIN DOG

VICTOR POODLE

CHARLEY

RUN

CAN BARK

TERRIER

FRIENDLY

BLACK

HOSTILE

CAN FLYSUBC SUBC

SUBC SUBC

SUBCINST

INST

SUBC

PROP

PROP

PROP

PROP

PROP

PROP

LIKESLIKES

FIG.6 COMPLEX Semantic Network with properties

DOG

POODLE LABRADOR RETRIEVER

SUSIECHARLEY

BLACK

SUBC SUBC

INST INST

PROPPROP

Fig. 7 What can we do with this network ?

NAMESLOT – 1 FillerSLOT – 2 FillerSLOT – 3 Filler...

.

.

.SLOT - N Filler

INST

SUBS

INST

Slot / Filler

Pair INST

A Frame

Inheritance Link

Fig. 8 The Structure of a Frame System

HOME

EARTH

DOG

SUBC

ANIMAL

SLOT CHRIS

INST

INST

SLOT

CHARLEY

INST

SLOT

SLOT

COLOR

BLACK

INST

OWNER

LOGIC OF FRAMES

Fig- 9 Inheritance in a simple frame system

.

.

..

....

..

.

..

Conceptual Dependency

• Knowledge representation in natural language sentences

• The goal is to represent the knowledge in a way that:

– Facilitates drawing inference from the sentences

– Is independent of the language in which the sentences were originally stated.

p Oto

I ATRANS book

man

I<from

R

Symbols• Arrow – direction of dependency • Double arrows – two way link between actor and

action.• P indicates past tense• ATRANS is one of the primitive acts used by the

theory. It indicates transfer of possession.• O indicates object case relation.• R indicates the recipient case relation.

Fig.1 A sample conceptual dependency Representation.

PRIMITIVES

• ATRANS - Transfer of an abstract relationship• PTRANS - Trans of physical location of an object• PROPLE - Application of physical force to an object• MOVE - Movement of a body part by its owner (e.g.. Kick)• GRASP - Grasping of an object by an actor• INGEST - Ingestion of an object by an animal (e.g. Eat)• EXPEL - Expulsion of something from the body of an animal (e.g. Tell)• MTRANS - Transfer of mental information (e.g. Say)• SPEAK - Production of sounds (e.g. Say)• ATTEND – Focusing of a sense organ toward a stimulus (e.g.

Listen)

Dependencies among the ConceptualizationThere are four primitives conceptual categories from which dependency structures can be built. They are :

ACTS Actions PPs Objects (picture products)AAs Modifiers of action (action aiders)PAs Modifiers of PPs (picture aiders)

Rules Examples of their use English version of p the

example

1. PP ACT John PTRANS John ranRule 1 describes the relationship between an actor and the event he or she causes – Two way dependency – p past tense.

2. PP ⇚⇛ PA John (height > average) John is tallRule 2 describes the relationship between PP and a PA that is being asserted to describe it. Many state description such as height, are represented in CD as numeric scales.

3. PP PP Jhon doctor John is a doctor.Rule 3 describes the relationship between two PPs one of which belongs to the set defined by the other.

⇚⇛

⇚⇛ ⇚⇛

4. PP boy

A nice boy

PA nice

Rule 4 describes the relationship between a PP and an attribute that has already been predicted of it. Direction – toward PP

5. PP dog

Poss-by John’s dog

PP John

Rule 5 describes the relationship between two PPs, one of which provides a particular kind of information about the other. Three types of information are:

Possession – POSS-BY Location – LOC Physical containment – CONT The direction of arrow – towards the concept

6. ACT PP John PROPEL cart

John pushed the cart

Rule 6 describes the relationship between ac ACT and the PP that is the object of ACT. The direction of the arrow is toward the ACT since the context of the specific ACT determines the meaning of the object relation.

7. ACT

John

John ATRANS

book

John took the book from Mary

Rule 7 describes the relationship between an ACT and the source and the

recipient of the ACT

< Mary

P

PP

< PP

O O

O

R

R

8. ACT

P I John John INGEST

O do

O

spoonJohn ate ice cream with a spoon

Rule 8 describes the relationship between an ACT and the instrument with which it is performed. The instrument must always be a full conceptualization (i.e. it must contain an ACT) not just a single physical object.

ice cream

I

9. ACT John PTRANS

10. ⇚ Rule 10 represents the relationship between a PP and a state in which it started and another in which it ended.

>

<

PP

PP

Dfield

bag

>

<O

fertilizer

John fertilized the field

P D

Rule 9 describes the relationship between an ACT and its physical source and destination

>

<PP

PA

PA

⇚>

<

Size > x

Size = x

plants

The plants grow

⇛

11. (a) (b)

⇛

⇚>

<

Bill PEOPLE bullet>

<

Bob

gun

Bob ⇛

⇚>

<

health (-10)

Bill shot Bob

O R

p

Rule 11 describes the relationship between one conceptualization and another that causes it. Notice that the arrows indicate dependency of one conceptualization on another and so point in the opposite direction of the implication arrows. The two forms of the rule describe the cause of an action and the cause of a state change.

(12) John PTRANS

Yesterday

John ran yesterday

Rule 12 describes the relationship between a conceptualization and the time at which the event it describes occurred.

(13) PTRANS IO D

Home

IMTRANS

OFrog

R CP

Eyes

I

I

While going home, I saw a frog.

Rule 13 describes the relationship between one conceptualization and another that is the time of the first. The example for this rule also shows how CD exploits a model of the human information processing system; see is represented as the transfer of information between the eyes and the conscious processor.

P

<

PP Woods

I heard a frog in the woods.

Rule 14 describes the relationship between a conceptualization and the place at which it occurred.

14

MTRANS Frog O R

< Ears

CP

ScriptsScript-name: food marketTrack: super marketROLES: shopper

deli attendant seafood attendant checkout clerk sacking clerk other shoppers

Entry Conditions: shopper needs groceries food market openPROPS: shopping cart

display aisles market items checkout stands cashier money

Scene 1: Enter Market shopper PTRANS shopper into market shopper PTRANS shopping – cart to shopper Scene 2: Shop for Items shopper MOVE shopper through aisles. shopper ATTEND eyes to display items. shopper PTRANS items to shopping cart.Scene 3: Check out shopper MOVE shopper to checkout stand. shopper WAIT shopper turn.

shopper ATTEND eyes to charges. shopper ATRANS money to cashier. sacker ATRANS bags to shopper.Scene 4: Exit Market shopper PTRANS to exit market.Results: shopper has less money shopper has grocery items

market has less grocery items market has more money

Fig-1 A supermarket script structure

KNOWLEDGE ACQUISITION

Domain Expert Knowledge EngineerKnowledge Engineer

Knowledge Base

Knowledge Concepts, Solutions

Fig. 1 Typical Knowledge Acquisition Process.

Formal

Sources TEXTBOOKS

REPORTS

DATABASES

CASE STUDIES

EMPERICAL DATA

PERSONAL EXPERIENCE

DOMAINS EXPERTS ASSUME BASIC KNOWLEDGE - Competent (more) – less desirable.

KE Paradox

Don’t be your own expert

Don’t believe everything experts say.

Types of Expert problem Solving

Past Experience

A D

E G

D E

Match Match Match

Situation A

Situation D

Situation E

Situation G

a) Problem solving by an expert in a familiar situation

Types of Expert problem Solving

GENERAL PRINCIPLES

What Next ?

Situation 1

Situation 2

Situation 3

Situation 4

b) Problem solving by an expert in a novel situation

What Next ?

What Next ?

Techniques for Extracting Knowledge from a domain expert

• On-site observation (Watch)• Problem discussion (Explore the kind of data, knowledge & Procedures)• Problem description (Prototypical systems from expert)• Problem Analysis (Sample problems solved by expert given by KE)• System Refinement (Rules)• System Examination (Critics)• System Validation (Outside expert)