Rule Based Expert System - SKKUmodsim.skku.ac.kr/bbs/Course/Data/Simulation/LectureNote... · 2016....

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Rule Based Expert System


Rule-based Expert System

Rule

E.S

Interface EngineKnowledge Base

Facts, Assertions

(Work Memory)



Rule 1 : If Condition

then action

Rule 2 : If Condition

then action

.

.

.

Rule n : If Condition

then action



Example : Medical diagnosis expert system

Rules : medical knowledge

Facts : Status of patients

(blood pressure, running nose, weight, …)

Diagnosis is done by applying the rules to the facts.


A Toy Deduction System Identifies Animals

Z1 : If ?X has hair

Then ?X is a mammal

Z2 : If ?X gives milk

Then ?X is a mammal

Z3 : If ?X has feathers

Then ?X is a bird

Z4 : If ?X flies AND ?X lays eggs

Then ?X is a bird

Z5 : If ?X is a mammal

AND ?X eats meat

Then ?X is a carnivore

Z6 : If ?X is a mammal AND ?X eats meat AND ?X has claws

AND ?X’s eyes point forward

Then ?X is a carnivore

Z7 : If ?X is a mammal AND ?X has hoofs

Then ?X is an ungulate

Z8 : If ?X is a mammal AND ?X chews cud




Z9 : If ?X is a carnivore AND ?X has a tawny color

AND ?X has a dark spots

Then ?X is a cheetah

Z10 : If ?X is a carnivore AND ?X has a tawny color

AND ?X has black strips

Then ?X is a tiger

Z11 : If ?X is an ungulate AND ?X has long legs

AND ?X has a long neck

AND ?X has a tawny color AND ?X has dark spots

Then ?X is a giraffe

Z12 : If ?X is an ungulate AND ?X has a white color

AND ?X has black stripes

Then ?X is a zebra

Z13 : If ?X is a bird AND ?X does not fly AND ?X has long legs

AND ?X has a long neck AND ?X is black and white

Then ?X is an ostrich

Z14 : If ?X is a bird AND ?X does not fly AND swims AND ?X is black and white

Then ?X is a pengiun

Z15 : If ?X is a bird AND ?X is a good flyer

Then ?X is an albatross


Forward Chaining

Interface Engine

Forward Chaining Backward Chaining


Forward Chaining


Forward Chaining

1. Start with initial state

2. Determine the conflict set = rules where condition

is true (triggered rules)

3. Apply conflict Resolution to select a rule from

conflict set

4. Fire this rule – carry out its action to change state

5. Test for reaching goal

Repeat 2 to 5 until goal is reached or conflict set is

empty



Gives milk

chews cud

long legs

long neck

tawny color

dark spot

Z2 : If ?X gives milk

Then ?X is a mammal

* : inferred fact

** : goal fact

*mammal

Working Memory



Working Memory

Gives milk

chews cud

long legs

long neck

tawny color

dark spot

*mammal

Z8 : If ?X is a mammal AND ?X chews cud


* : inferred fact

** : goal fact

*ungulate

Z2



Working Memory

Gives milk

chews cud

long legs

long neck

tawny color

dark spot

*mammal

*ungulate* : inferred fact

** : goal fact** giraffe

Z11 : If ?X is an ungulate

AND ?X has long legs

AND ?X has a long neck

AND ?X has a tawny color

AND ?X has dark spots

Then ?X is a giraffe

Z2, Z8


A Toy Reaction System Bags Groceries

Item Container type Size Frozen?

Bread plastic bag medium No

Glop jar small No

Granola cardboard box large No

Ice cream cardboard carton medium Yes

Potato chips plastic bag medium No

Pepsi bottle large No

* List of Items to be bagged.

* Initial Data of Working MemoryStep is check-order

Bag1 is a bag

Bread is to be bagged

Glop is to be bagged

Granola is to be bagged

Ice cream is to be bagged

Potato chips are to be bagged



B1 : If step is check-order

potato chips are to be bagged

there is no Pepsi to be bagged

Then ask the customer whether he would like a bottle of Pepsi

B2 : If step is check-order B2: If step is check-order

Then step is no longer check-order delete step is check-order

step is bag-large-items add step is bag-large-items

B3 : If step is bag-large-items

a large item is to be bagged

the large item is a bottle

the current bag contains < 6 large items

delete the large item is to be bagged

add the large item is in the current bag



the current bag contains < 6 large items

delete the large item is to be bagged

add the large item is in the current bag





an empty bag is available

delete the current bag is the current bag

add the empty bag is the current bag


delete step is bag-large-items

add step is bag-medium-items

* A Inter mediate state of working memoryStep is bag-medium-items

Bag1 contains Pepsi

Bag1 contains granola

Bread is to be bagged


Ice cream is to be bagged

Potato chips are to be bagged



B7 : If step is bag-medium-items

a medium item is frozen, but not in a freezer bag

delete the medium item is not in a freezer bag

add the medium item is in a freezer bag


a medium item is to be bagged

the current bag is empty or contains only medium items

the current bag contains no large items

the current bag contains < 12 medium items

delete the medium item is to be bagged

add the medium item is in the current bag




a medium item is to be bagged





delete step is bag-medium-items

add step is bag-small-items

* A Inter mediate state of working memoryStep is bag-small-items

Bag1 contains Pepsi


Bag2 contains Bread

Bag2 contains Ice cream(in freezer bag)

Bag2 contains Potato chips




B11 : If step is bag-small-items

a small item is to be bagged

the current bag contains no large items

the current bag contains no medium items

the current bag contains < 18 medium items

delete the small item is to be bagged

add the small item is in the current bag

B12 :If step is bag-small-items

a small item is to be bagged






B13 : If step is bag-small-items

delete step is bag-small-items

add step is done

* A Inter mediate state of working memoryStep is done

Bag1 contains pepsi


Bag2 contains bread

Bag2 contains ice cream(in freezer bag)

Bag2 contains potato chips

Bag3 contains glop


Conflict Resolution Strategies

1. Rule Ordering : Arrange all rules in one long prioritized

list. Use the triggered rule that has the highest priority.

Ignore the others.

2. Context Limiting : Reduce the likelihood of conflict by

separating the rules into groups, only some of which

are active at any time.

3. Specificity Ordering : Whenever the conditions of one

triggered rule are a superset of the conditions of

another triggered rule, use the superset rule on the

ground that it deals with more specific situations.


Conflict Resolution Strategies

4. Data Ordering : Arrange all possible assertions in one

long prioritized list. Use the triggered rule that has the

condition pattern that matches the highest priority

assertion in the list.

5. Size Ordering : Use the triggered rule with the

toughest requirements, where toughest means the

longest list of conditions.

6. Recency Ordering : Use the least recently used rule.


Design of Expert System

Entities

Knowledge-base Inference-Engine

Synthesis

Knowledge

Base

Classification

Knowledge

Base

Synthesis

Inference

Engine

Classification

Inference

Engine

~inference-engine

~object-classes

~rule-class

~kb-component

~it-ie

~local-memory-name

~lst

~name

~ : class or instance variable

* : method


Expert System

Goal : If action is known,

Then print action

And conclude consultation.

1

Rule 4:

If means = drive,

and location = downtown,

Then action = take cab.

2

Rule 5:

If means = drive,

and location = not downtown,

Then action = drive.

9Rule 6:

If means = walk,

and weather = bad,

Then action =

take coat and walk.

10

Rule 7:

If means = walk,

and weather = good,

Then action = walk.

11

Rule 1:

If distance > 5 miles,

Then means = drive.

3

Question:

What is distance ?

4

(User answer: 2)

Rule 2:


and time < 15 minutes,

Then means = drive.

5

Question:

How many minutes until showtime?

6

(User answer: 10 minutes) (User answer: downtown)

Question:

Is location or in suburbs?

8

Rule 3:


and time > 15 minutes,

Then means = walk.

7


Expert System

Rules about Getting to the Theater

Rule IF: THEN:

(Premise) (Conclusion)

1 Distance > 5 miles Means is “drive”

2 Distance > 1 miles

and Time < 15 minutes Means is “drive”

3 Distance > 1 miles

and Time > 15 minutes Means is “walk”

4 Means is “drive”

and Location is

“downtown” Action is “take a cab”


Expert System

Rule IF: THEN:

(Premise) (Conclusion)

5 Means is “drive”

and Location is

not “downtown” Action is “drive your car”

6 Means is “walk”

and Weather is

“bad” Action is “take a coat and walk”

7 Means is “walk”

and Weather is

“good” Action “walk”


Expert System

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Forward Chaining ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(define-structure rule

condition

action

text

)

(define rules ’())

(define (test-condition rule)

(when (eval (rule-condition rule)) rule)

)

(define (execute-action rule)

(eval (rule-action rule))

)

(define (conflict-resolve rules)

(car rules)

)


Expert System

(define (forward-chain)

(let loop ( (temp rules) )

(if (test-condition goal)

(execute-action goal)

(let*

( (conflict-set

(clean (map (lambda (r) (test-condition r)) temp))

)

(fire (conflict-resolve conflict-set))

)

(if fire

(begin

(writeln (rule-text fire))

(newline)

(execute-action fire)

(loop (delete! fire temp))

) ; begin

(writeln “No rules left: -> terminates”)

); if fire

) ; let*

) ; if (test-cond…)

) ; let

) ; define


Expert System

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; getting to the theatre example ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(define distance ’u)

(define time ’u)

(define weather ’u)

(define location ’u)

(define means ’u)

(define action ’u)

(define (initialize)

(set! time ’u)

(set! weather ’u)

(set! location ’u)

(set! means ’u)

(set! action ’u)

)

(define r1 (make-rule

’condition ’(and (equal? means ’u)

(> distance 5)

)

’action ’(set! means “drive”)

’text “R1: if distance > 5 then means is drive”

) )


Expert System



(> distance 1) (< time 15)

)

’action ’(set! means “drive”)

’text “R2: if distance > 1 and time < 15 then means is drive”

)

)



(> distance 1)

(> time 15)

)

’action ’(set! means “walk”)

’text “R3: if distance > 1 and time > 15 then means is walk”

)

)


Expert System


’condition ’(and (equal? means “drive”)

(equal? location “downtown”)

)

’action ’(set! means “take a cab”)

’text “R4: if means is drive and location is downtown then take a cab”

)

)


’condition ’(and (equal? means “drive”)

(not (equal? location “downtown”))

)

’action ’(set! action “drive your car”)

’text “R5: if means is drive and location is not downtown then drive your car”

)

)


Expert System


’condition ’(and (equal? means “walk”)

(equal? weather “bad”)

)

’action ’(set! action “take a coat and walk”)

’text “R6: if means is walk and weather is bad then take a coat and walk”

)

)


’condition ’(and (equal? means “walk”)

(equal? weather “good”))

)

’action ’(set! action “walk”)

’text “R7: if means is walk and weather is good then walk”

)

)


Expert System

(define rules (list r1 r2 r3 r4 r5 r6 r7))

(define goal (make-rule

’condition ’(not (equal? Action ‘u))

’action ’(writeln “The action to take is: ”

action)

)

)


Expert System

;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Initial condition ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(define distance 2)

(define time 20)

(define weather “bad”)

(define location “downtown”)

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

(define (clean lst)

(delete! ’() lst)

)

Rule Based Expert System - SKKUmodsim.skku.ac.kr/bbs/Course/Data/Simulation/LectureNote... · 2016....

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Transcript of Rule Based Expert System - SKKUmodsim.skku.ac.kr/bbs/Course/Data/Simulation/LectureNote... · 2016....