Transcript Problem Solving and Search - Institut Teknologi Bandung

Route/Path Planning
Materi Kuliah IF3051 – Strategi Algoritma
Teknik Informatika – ITB
Oleh: Masayu Leylia Khodra
Referensi
Materi kuliah IF3054 Inteligensi Buatan Teknik
Informatika ITB, Course Website:
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http://kuliah.itb.ac.id  STEI  Teknik Informatika  IF3054
Stuart J Russell & Peter Norvig, Artificial Intelligence:
A Modern Approach, 3rd Edition, Prentice-Hall
International, Inc, 2010, Textbook
Site: http://aima.cs.berkeley.edu/ (2nd edition)
Free online course materials | MIT OpenCourseWare
Website:
Site: http://ocw.mit.edu/courses/electrical-engineeringand-computer-science/
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Route
Planning
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Source: Russell’s book
Search
O
71
151
Z
S
99
F
211
75
A
140
80
R
120
118
T
111
L
70
M
75
B
P
97
101
D
146
138
C
S: set of cities
i.s: A (Arad)
g.s: B (Bucharest)
Goal test: s = B ?
Path cost: time ~ distance
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Uninformed Search
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Breadth-First Search (BFS)
Treat agenda as a queue (FIFO)
O
71
151 S
Z
A
F
99
211
75
140
80
R
120
118
T
111
L 70
M
75
B
101
Simpul-E
D
138
146
C
Path: A S  F  B,
Path-cost = 450
6
P
97
IF3051/MLK/071112
Simpul Hidup
A
ZA,SA,TA
ZA
SA,TA,OAZ
SA
TA,OAZ,OAS,FAS,RAS
TA
OAZ,OAS,FAS,RAS,LAT
OAZ
OAS,FAS,RAS,LAT
OAS
FAS,RAS,LAT
FAS
RAS,LAT,BASF
RAS
LAT,BASF,DASR,CASR,PASR
LAT
BASF,DASR,CASR,PASR,MATL
BASF
Solusi ketemu
Depth-First Search (DFS)
Treat agenda as a stack (LIFO)
O
71
151 S
Z
A
F
99
211
75
140
80
R
120
118
T
111
L
70
M
75
97
B
P
101
D
146
138
C
Simpul-E
Path: A Z  O  S  F  B
Path-cost = 607
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Simpul Hidup
A
ZA,SA,TA
ZA
OAZ, SA,TA
OAZ
SAZO,SA,TA
SAZO
FAZOS, RAZOS,SA,TA
FAZOS
BAZOSF, RAZOS,SA,TA
BAZOSF
Solusi ketemu
IDS
Z
A
O 151
71
S
F
99
211
75
140
80
R
120
118
T
111
L
70
M
75
B
P
97
101
D
146
138
C
Depth=0: A: cutoff
Depth=1: A  ZA,SA,TA  ZA: cutoff, SA: cutoff, TA: cutoff
Depth=2: A  ZA,SA,TA  OAZ, SA,TA  OAZ : cutoff  FAS, RAS,TA 
FAS : cutoff  RAS : cutoff  LAT  LAT : cutoff
Depth=3: A  ZA,SA,TA  OAZ, SA,TA  SAZO,SA,TA  SAZO: cutoff 
FAS, RAS,TA  BASF, RAS,TA  BASF
Stop: B=goal, path: A S  F  B, path-cost = 450
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Uniform Cost Search (UCS)
BFS & IDS find path with fewest steps
If steps ≠ cost, this is not relevant (to optimal
solution)
How can we find the shortest path (measured
by sum of distances along path)?
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Z
O
71
S
151
F
99
Simpul-E
Simpul Hidup
A
ZA-75, TA-118, SA-140
ZA-75
TA-118, SA-140,OAZ-146
TA-118
SA-140,OAZ-146,LAT-229
SA-140
OAZ-146,RAS-220, LAT-229,FAS239,OAS-291
OAZ-146
RAS-220, LAT-229, FAS-239,OAS-291
RAS-220
LAT-229, FAS-239,OAS-291, PASR317,DASR-340,CASR-366
LAT-229
FAS-239,OAS-291,MATL-299, PASR317,DASR-340,CASR-366
FAS-239
OAS-291,MATL-299, PASR-317,DASR340,C ASR-366, BASF-450
OAS-291
MATL-299, PASR-317,DASR-340,CASR366, BASF-450
MATL-299
PASR-317,DASR-340,DATLM-364,CASR366, BASF-450
PASR-317
DASR-340,DATLM-364,CASR-366,
BASRP-418, CASRP-455, BASF-450
DASR-340
DATLM-364,CASR-366, BASRP-418,
CASRP-455, BASF-450
DATLM-364
CASR-366, BASRP-418, CASRP-455,
BASF-450
CASR-366
BASRP-418, CASRP-455, BASF-450
211
75
A
140
80
R
120
118
T
111
L 70
M
75
97
B
P
101
D
146
138
C
Path: A S  R  P  B
Path-cost = 418
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Informed Search
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Best-first search
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Idea: use an evaluation function f(n) for each node
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g(n) = cost so far to reach n
h(n) = estimated cost from n to goal
f(n) = estimated total cost of path
through n to goal
greedy best-first search: f(n) = h(n)
A* search: f(n) = g(n) + h(n)
e.g., hSLD(n) = straight-line distance from n to Bucharest
Romania with step costs in km
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Greedy best-first search example
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Greedy best-first search example
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Greedy best-first search example
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Greedy best-first search example
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A* search example
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A* search example
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A* search example
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A* search example
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A* search example
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A* search example
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