AI3 Search

8/17/2019 AI3 Search

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FringeFringe

• Set of search nodes that hae not $een

e%panded yet

• &mplemented as a 'ueue F(&)*+ – &)S+(-node,F(&)*+.

– (+/01+-F(&)*+.

• he ordering of the nodes in F(&)*+defines the search strategy


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Breadth-First StrategyBreadth-First Strategy

)e" nodes are inserted at the end of F(&)*+

2 3

4 5

1

6 7

FRINGE = (1)


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FRINGE = (2, 3)2 3

4 5

1

6 7

3


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FRINGE = (4, 5, 6, 7)2 3

4 5

1

6 7

5


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e!th-First Strategye!th-First Strategy

)e" nodes are inserted at the front of F(&)*+

1

2 3

4 5

FRINGE = (1)

6


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1

2 3

4 5

FRINGE = (2, 3)

7


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1

2 3

4 5

FRINGE = (4, 5, 3)

89


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1

2 3

4 5

88


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1

2 3

4 5

82


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1

2 3

4 5

8:


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1

2 3

4 5

8


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1

2 3

4 5

83


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1

2 3

4 5

84


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1

2 3

4 5

85


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1

2 3

4 5

86


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;ontoh


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"ni#orm-Cost Strategy"ni#orm-Cost Strategy

• Each step has some cost ε > 0.• he cost o! the path to each !"#$%e $o&e N #s

%(N) = Σ costs o! a'' steps.• he %oa' #s to %e$e"ate a so't#o$ path o! m#$#ma'• he ee FRINGE #s so"te& #$ #$c"eas#$% cost.

*0

1+

5

15-* G

+

-

5

1

15

10

5

5

G11

G10

29


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In#ormed $%e&ristic'In#ormed $%e&ristic' SearchSearch


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Search (ith omainSearch (ith omain

)no(ledge added)no(ledge added

• =ninformed -$lind. searches are normally

ery inefficient

• >dding domain #no"ledge can improe

the search process?


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2:

Conce!t o# in#ormedConce!t o# in#ormed

$he&ristic' search$he&ristic' search

• !euristic -informed. search @A e%plore the

node that is most “li#ely” to $e the nearest

to a goal state?

• here is no guarantee that the heuristic

proided most “li#ely” node "ill get you

closer to a goal state than any other?


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2

)no(ledge*in#o)no(ledge*in#o

• +%ample

– 1isit the doctor

• Symptoms feer, nausea, headache, C

– eading 'uestions ho" longE, traeledE, C

-/alaria, typhoid, meningitis, flu,??.

– % Blood test, y test,???


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23

)no(ledge*in#o)no(ledge*in#o

• +%ample

– ;lim$ing a hill in thic# Fog

• !euristic function chec# the change in altitude in

directions the strongest increase is the direction in"hich to moe ne%t?


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24

%e&ristic Search +ethods%e&ristic Search +ethods

• /ethods that use a heuristic function to

proide specific #no"ledge a$out the

pro$lem• !euristic Functions

• !ill clim$ing

• *reedy search

• >G search algorithm


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25

%ill clim,ing on a s&r#ace o#%ill clim,ing on a s&r#ace o#

statesstates

!eight Defined $y+aluation

Function


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26

!ill clim$ing!ill clim$ing

• Steepest descent -H greedy $est@first "ith

no search. may get stuc# into local

minimum


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27

Ro,ot NaigationRo,ot Naigation

f-). I h-). I straight distance to the goal

Local-minim&m !ro,lem


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:9

%ill clim,ing e.am!le%ill clim,ing e.am!le2 8 3

1 6 4

7 5

2 8 31 4

7 6 5

2 3

1 8 4

7 6 5

1 3

8 4

7 6 5

2

3

1 8 4

7 6 5

2

1 3

8 4

7 6 5

2

start goal

-5

h = -3

h = -3h = -2

h = -1

h = 0h = -4

-5

-4

-4

-3

-2

f(n) = -(number of tiles out of place)


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:8

E.am!le o# a local ma.im&mE.am!le o# a local ma.im&m

1 2 5

7 4

8 6 3

1 2 5

7 4

8 6 3

1 2 5

7 4

8 6 3

1 2 5

7 4

8 6 3

1 2 5

7 4

8 6 3

-3

-4

-4

-4

0

start goal


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:2

Greedy SearchGreedy Search

• f-). I h-). greedy $est@first


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::



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:


9 288

36 5

5

:

5

4

5

4 : 2

6

4

3

2: :

:4 3 2 : 3

3 4

3

4

3

f-). I h-)., "ith h-). I /anhattan distance to the goal


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:3


9 288

36 5

5

:

5

4

5

4 : 2

6

4

3

2: :

:4 3 2 : 3

3 4

3

4

3

f-). I h-)., "ith h-). I /anhattan distance to the goal

5

9

What ha!!ened///


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:4

%e&ristic Searches -%e&ristic Searches - GreedyGreedy

searchsearch

• So named as it ta#es the $iggest “$ite” it can out

of the pro$lem?

hat is, it see#s to minimise the estimated cost

to the goal $y e%panding the node estimated to$e closest to the goal state

in other "ords,• >l"ays e%pand the heuristically $est nodes first?


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:5

Greedy search algorithmGreedy search algorithm

1.1. QUEUEQUEUE


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:6

%e&ristic Searches - Greedy%e&ristic Searches - Greedy

SearchSearch

• &t is only concerned "ith short term aims

• &t is possi$le to get stuc# in an infinite loop, unless

you chec# for repeated states

• &t is not optimal

• &t is not complete

Time and s!ace com!le.ity is 0$Bm'1 (here m is thede!th o# the search tree


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:7

+ore in#ormed search+ore in#ormed search

• Je #ept loo#ing at nodes closer and closer to thegoal, $ut "ere accumulating costs as "e gotfurther from the initial state

• 0ur goal is not to minimize the distance from thecurrent head of our path to the goal, "e "ant tominimize the overall length of the path to the goalK

• et g-). $e the cost of the $est path found so far $et"een the initialnode and )

• f-). I g-). L h-).


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9


f-). I g-).Lh-)., "ith h-). I /anhattan distance to goal

9 288

36 5

5

:

5

4

5

4 : 2

6

4

3

2: :

:4 3 2 : 3

3 4

3

4

35L9

4L8

4L8

6L8

5L9

5L2

4L8

5L2

4L8

6L8

5L2

6L:

5L2 4L:4L: 3L3L L3L3 :L4:L4 2L5

6L: 5L5L 4L3

3L4

4L: 3L4

2L5 :L6

L5

3L4 L5

:L6

L5 :L6:L6 2L72L7 :L89

2L7

:L6

2L7 8L898L89 9L88


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8

A2 SearchA2 Search

• +aluation function

f-). I g-). L h-).

"here

– g-). is the cost of the $est path found so far to ) – h-). is an admissi$le heuristic

• hen, $est@first search "ith this ealuation function iscalled >G search

• &mportant >& algorithm deeloped $y Fi#es and )ilsson inearly 59s? 0riginally used in Sha#ey ro$ot?


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2

Ro,ot naigationRo,ot naigation

;ost of one horizontalMertical step I 8

;ost of one diagonal step I √2

f-). I g-). L h-)., "ith h-). I straight@line distance from ) to goal


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:

E.am!le road ma!E.am!le road ma!

• &magine the pro$lem of finding a route on a road mapand that the )+ $elo" is the road map

DD EE

GG

SS

AA BB CC

FF

))

))22

// //

DD EE

GGSS

AA BB CC

FF

0.0.1.1.

1111

3.43.40.40.4 ))

Define f-. I the straight@line distance from to *

+he estimate+he estimatecan 5e %rong6can 5e %rong6

R d lRoad ma! e am!le


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Road ma! e.am!leRoad ma! e.am!le(e@introduce the costs of paths in the )+

AA

77

EE

88

**

99

))

// //

))22

AA

77 EEAA

88 EE EE 77 77 **

** 77 ** 88 EE AA 88 99

99 88 99 **

99

))

)) ))

))

))

22

22

22

////

// //

////

A2 Algorithm E.am!leA2 Algorithm E.am!le


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3

A Algorithm E.am!leA Algorithm E.am!le

road ma!road ma!

• &magine the pro$lem of finding a route on a road map? hepaths distances $et"een nodes define g-n.

DD EE

GG

SS

AA BB CC

FF

))

))22

// //

DD EE

GGSS

AA BB CC

FF

0.0.1.1.

1111

3.43.40.40.4 ))

Define h-n. I the straight@line distance from node to *


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4

AA DD) : 1. 1).) : 1. 1). : 3.4 12.4 : 3.4 12.4

SS

SS

AA DD

AA EE

1).1).

4 : 1. 14.4 : 1. 14. 0 : 0.4 12.40 : 0.4 12.4

SS

AA DD

AA EE

BB FF

1).1).

11 : 0. 1.11 : 0. 1. 1 : ). 1).1 : ). 1).

SS

AA DD

AA EE

BB FF

1).1).

1.1.

GG 1) : . 1).1) : . 1).

+"=6+"=6


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Re#erenceRe#erence

Russel, Stuart J., Peter Norvig,

!rti"i#ial $%tellige%#e, a &o'er%

a((roa#h), Se#o%' *'itio%, Pre%ti#e

+all, Ne Jerse, 2010.

i%sto%, Patri#/ +e%r, !rti"i#ial

$%tellige%#e), !''iso% esle.


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ERIMAERIMA

KASIH

ASIH

Date post:	06-Jul-2018
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