AI Game Search
MENOUFIA UNIVERSITYFACULTY OF COMPUTERS AND INFORMATION
ALL DEPARTMENTSARTIFICIAL INTELLIGENCE
جامعة المنوفية
كلية الحاسبات والمعلومات
جميع األقسام
اإلصطناعيالذكاء
جامعة المنوفية
Ahmed Fawzy Gad
Minimax Game SearchTwo Players take turns:
Max and Min
Max : Maximizes Score.Min : Minimizes Score.
MAX
Minimax Game SearchTwo Players take turns:
Max and Min
Max : Maximizes Score.Min : Minimizes Score.
MAX
MIN
Minimax Game SearchTwo Players take turns:
Max and Min
Max : Maximizes Score.Min : Minimizes Score.
Special Case.Max is an expert.Min is a beginner.
MAX
MIN
Minimax Game Search
Which node to follow?No heuristic values.
A
B C
Hot to find heuristic values for other nodes?
Minimax Game Search
Which node to follow?No heuristic values.
A
B C
Hot to find heuristic values for other nodes?
Use children heuristics to calculate parent heuristic.
Minimax Game Search
Which node to follow?No heuristic values.
A
B C
Hot to find heuristic values for other nodes?
Use children heuristics to calculate parent heuristic.
Minimax Game Search Steps
Minimax Game Search
Which node to follow?No heuristic values.
A
B C
Hot to find heuristic values for other nodes?
Use children heuristics to calculate parent heuristic.
Minimax Game Search Steps
Calculate Heuristics
Minimax Game Search
Which node to follow?No heuristic values.
A
B C
Hot to find heuristic values for other nodes?
Use children heuristics to calculate parent heuristic.
Minimax Game Search Steps
Calculate Heuristics
Search
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
D
H I
C
D E
J
3 -2 5
B C
D E
H I J
Max
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
D
H I
C
D E
J
3 -2 5
B C
D E
H I J
Max
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
D
H I
C
D E
J
3 -2 5
B C
D E
H I J
Max
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
D
H I
C
D E
J
3 -2 5
B C
D E
H I J
Max
5
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
D
H I
C
D E
J
3 -2 5
B C
D E
H I J
Max
5
5
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
E
K L
C
D E
M
7 0 3
B C
D E
K L M
Max
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
E
K L
C
D E
M
7 0 3
B C
D E
K L M
Max
7
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
E
K L
C
D E
M
7 0 3
B C
D E
K L M
Max
7
7
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
E
K L
C
D E
M
7 0 3
B C
D E
K L M
Max
7
7
75
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
E
K L
C
D E
M
7 0 3
B C
D E
K L M
Max
7
7
7
Min
5
75
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
E
K L
C
D E
M
7 0 3
B C
D E
K L M
Max
7
7
7
Min5
55
75
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
E
K L
C
D E
M
7 0 3
B C
D E
K L M
Max
7
7
7
Min5
5
55
75
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
B
E
K L
C
D E
M
7 0 3
B C
D E
K L M
Max
7
7
7
Min5
5
55
7
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
F
N O
C
F G
P
0 -5 4
B C
N O P
5
F G
7
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
F
N O
C
F G
P
0 -5 4
B C
N O P
Max
5
F G
7
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
F
N O
C
F G
P
0 -5 4
B C
N O P
Max
4
5
F G
7
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
F
N O
C
F G
P
0 -5 4
B C
N O P
Max
4
5
4
F G
7
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
F
N O
C
F G
P
0 -5 4
B C
N O P
Max
4
5
4
F G4
7
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
F
N O
C
F G
P
0 -5 4
B C
N O P
Max
4
5
4
F G4
47
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
5
F G4
47
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
5
F G4
47
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
F G4
47
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4
47
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
47
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
Min
847
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
Min4
4
847
5
5
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
Min4
4
4
847
5
5
4
Max
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
Min4
4
4
847
5
5
4
Max
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
Min4
4
4
5
8
4
47
5
5
Max
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
Min4
4
4
5
5
8
4
47
5
5
Max
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
Min4
4
4
5
5
8
4
5
47
5
5
Max
Phase 1 : Heuristic Value CalculationDepth-First Search
A
B
C
G
Q R
C
F G
S
-6 8 2
B C
Q R S
Max
8
5
8
F G4 8
Min4
4
4
5
5
8
4
5
47
5
5
If both players play optimally then Max will win by a score 5.
Minimax Game Search Drawback• Expands all the tree
while not allexpanded nodes areuseful.
• In this example, justfew nodes of thewhole tree was usefulin reaching the goal.
Alpha-Beta Pruning = Minimax Except
• This game search strategy is a modification to Minimax game searchthat avoids exploring nodes that are not useful in the search.
• It gives the same results as Minimax but avoids exploring somenodes.
• In the previous example, the path explored using Minimax was A-B-D-J.
• Also the Alpha-Beta Pruning path will be A-B-D-J but withoutexploring all nodes as in Minimax.
Alpha-Beta Pruning Motivation
Never explore values that are not useful.
=Min(Max(1, 2, 5), Max(6, x, y), Max(1, 3, 4))
=Min(5, Max(6, x, y), 4)
=Min(Max(6, x, y), 4)
=4
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