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Dijk
stra
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h
m
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d
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c
a
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s
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we
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u
n
d
e
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d
i
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g
it
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c
h
a
ll
e
n
g
i
n
g
.
Va
rio
u
s
m
e
th
o
d
s
t
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tea
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h
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n
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re
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d
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with
m
ix
e
d
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su
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e
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r
o
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s
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-
led
a
p
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i
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a
m
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lea
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fe
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d
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k
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K
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s
:
Data
s
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Gam
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Qu
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C
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A
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:
R
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am
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Dep
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tm
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t o
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I
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m
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T
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Un
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Pan
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.
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1.
I
NT
RO
D
UCT
I
O
N
Netwo
r
k
Op
tim
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Pro
b
le
m
s
(
NOP)
ca
n
b
e
b
etter
an
al
y
ze
d
b
y
g
r
ap
h
[
1
]
.
Sh
o
r
test
Path
Pro
b
lem
(
SP
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is
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n
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o
f
NOP
wh
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s
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tio
n
m
o
s
tly
u
s
ed
Dijk
s
tr
a'
s
a
lg
o
r
ith
m
[
1
]
t
o
f
in
d
m
in
im
u
m
d
is
tan
ce
b
etwe
en
two
v
er
tices
[
2
]
.
T
h
e
alg
o
r
ith
m
co
m
m
o
n
l
y
u
s
ed
i
n
m
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u
f
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i
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g
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co
m
p
u
ter
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etwo
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k
s
,
tr
an
s
p
o
r
t,
an
d
telec
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m
m
u
n
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s
,
m
a
k
in
g
it a
cr
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to
p
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to
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u
n
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r
s
to
o
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b
y
s
tu
d
en
ts
.
Ho
wev
er
,
teac
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in
g
[
3
,
4
]
an
d
lear
n
in
g
[
5
]
th
e
alg
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m
c
an
b
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d
if
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icu
lt.
T
h
ese
wer
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x
p
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tu
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Fu
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m
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u
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ac
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th
an
co
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v
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tio
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al
lear
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[
6
]
.
T
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r
esear
ch
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s
ca
m
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u
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with
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tan
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in
g
o
f
th
e
alg
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r
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m
.
So
well
,
et
a
l.
[
7
]
i
m
p
lem
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t
ed
ac
tiv
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lear
n
in
g
w
h
er
e
s
tu
d
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d
id
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cises
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r
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th
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m
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in
s
tr
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r
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class
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ates.
C
h
en
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et
a
l.
[
8
]
ap
p
lied
k
in
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n
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ac
tiv
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KL
A)
b
y
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p
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wh
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r
ap
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tu
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H
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[
9
]
g
av
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alg
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r
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m
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(
AV)
co
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tr
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p
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tatio
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ass
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m
en
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wh
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s
tu
d
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ask
ed
t
o
ch
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an
ex
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p
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d
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to
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d
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r
esu
ltin
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an
im
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.
Ser
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an
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C
h
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i
[
6
]
d
ev
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p
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Flas
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b
ased
m
o
b
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lear
n
in
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p
r
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to
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R
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s
[
1
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4,
10
-
1
5
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cr
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AV
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wh
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all
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An
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d
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lear
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
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I
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N:
2252
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8
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2
2
Qu
esti
o
n
-
led
a
p
p
r
o
a
ch
i
n
d
esig
n
in
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Dijkst
r
a
a
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r
ith
m
g
a
m
e
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a
s
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lea
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:
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p
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R
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Un
f
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f
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ts
to
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p
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u
n
ab
le
to
o
p
tim
ally
en
g
ag
e
l
ea
r
n
e
r
s
[
1
6
]
.
W
ith
th
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cu
r
r
en
t
r
eso
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s
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u
ca
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if
f
er
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t
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p
p
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h
es
to
lear
n
in
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[
1
7
]
.
Sil
v
a
,
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t
a
l.
[
1
8
]
s
u
g
g
ested
g
am
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b
ased
lear
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(
GB
L
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t
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m
ak
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lea
r
n
in
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f
u
n
.
C
h
an
g
,
et
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l.
[
1
9
]
cr
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ted
a
b
o
ar
d
g
a
m
e
ca
lled
T
ick
et
to
R
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wh
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tu
d
en
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ch
o
s
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s
th
at
r
eq
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co
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city
to
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El
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Pra
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a
[
2
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]
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tu
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to
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ev
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ap
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Dijk
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d
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Gr
av
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et
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l.
[
2
1
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r
eq
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ested
s
tu
d
en
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to
p
la
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a
s
h
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r
in
th
.
Gr
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k
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to
p
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lo
u
,
et
a
l.
[
2
2
]
m
ad
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a
g
am
e
to
teac
h
s
ea
r
c
h
alg
o
r
ith
m
s
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ased
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Pacm
an
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No
tice
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es c
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e
r
b
e
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o
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a
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d
d
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v
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o
p
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b
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eith
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d
u
ca
to
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s
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s
tu
d
en
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.
Alth
o
u
g
h
v
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u
aliza
tio
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s
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p
lo
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ed
in
g
am
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ey
a
r
e
in
m
o
s
t
ca
s
es
n
o
t
in
ter
ac
tiv
e
[
1
8
]
.
T
h
is
p
ap
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p
r
o
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o
s
ed
co
m
b
in
i
n
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m
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s
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q
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lead
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tu
d
en
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g
h
th
e
p
r
o
ce
s
s
o
f
s
o
lv
in
g
p
r
o
b
le
m
s
[
2
3
]
,
to
d
esig
n
g
am
ep
lay
th
at
r
esu
lts
in
m
ea
n
in
g
f
u
l
lear
n
in
g
.
Use
o
f
q
u
esti
o
n
s
in
lear
n
in
g
p
r
o
ce
s
s
p
r
o
v
en
b
etter
th
an
co
n
v
en
ti
o
n
al
teac
h
in
g
[
2
4
]
as
it
m
o
tiv
ates
th
in
k
in
g
an
d
lear
n
in
g
[
2
5
]
,
e
n
h
an
ce
f
o
c
u
s
an
d
en
ab
le
r
ef
lectio
n
o
f
lear
n
i
n
g
p
r
o
ce
s
s
es
[
2
6
]
,
s
u
p
p
o
r
t
p
r
o
b
lem
-
s
o
lv
in
g
[
2
7
]
,
ef
f
ec
tiv
e
in
k
n
o
wled
g
e
in
te
g
r
atio
n
[
2
8
]
an
d
i
n
cr
ea
s
e
s
elf
-
r
e
g
u
lated
lear
n
in
g
co
m
p
eten
ce
[
2
9
]
.
T
h
e
ap
p
r
o
ac
h
u
tili
ze
d
in
d
if
f
er
en
t c
o
n
tex
ts
s
u
ch
as e
co
n
o
m
y
[
2
4
]
,
m
ath
em
atics [
2
3
]
an
d
p
h
y
s
ics [
3
0
]
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
Fig
u
r
e
1
s
h
o
ws
a
m
o
d
el
b
y
Ga
r
r
is
[
3
1
]
u
s
ed
to
d
esig
n
th
e
g
a
m
e.
I
n
s
tr
u
ctio
n
al
co
n
ten
t c
o
m
b
in
ed
with
g
am
e
ch
ar
ac
ter
is
tics
r
esu
lts
in
a
g
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e
cy
cle
o
f
u
s
er
j
u
d
g
em
en
t
a
n
d
b
e
h
av
io
u
r
an
d
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y
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tem
f
ee
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b
ac
k
.
Ach
iev
em
en
t o
f
lear
n
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g
o
u
tc
o
m
e
co
n
cl
u
d
ed
a
f
ter
d
e
b
r
ief
in
g
.
Fig
u
r
e
1
.
I
n
p
u
t
-
p
r
o
ce
s
s
-
o
u
tco
m
e
g
am
e
m
o
d
el
[
3
1
]
Ad
o
b
e
Flas
h
in
teg
r
ated
m
u
lti
m
ed
ia
elem
en
ts
.
Play
er
ca
n
s
tu
d
y
N
o
tes
b
ef
o
r
e
p
lay
.
Gam
e
co
n
tact,
g
r
ap
h
ics,
s
o
u
n
d
,
tim
e,
life
,
s
c
o
r
e
a
n
d
s
cr
ee
n
a
r
e
g
am
e
ch
ar
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ter
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tics
in
v
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u
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e
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ter
ac
t
with
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e.
2
D
g
r
ap
h
ics
c
r
ea
ted
/e
d
ited
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s
in
g
Ad
o
b
e
Ph
o
to
s
h
o
p
.
Z
o
m
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ie
ap
o
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ly
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s
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th
em
e
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ac
k
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r
o
u
n
d
au
d
io
co
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tin
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o
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s
ly
p
lay
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ce
r
tain
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en
ts
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ig
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ts
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g
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tto
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r
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ec
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wer
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tr
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e/f
alse)
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Play
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as
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tis
t)
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ee
d
s
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in
d
th
e
s
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ath
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s
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ith
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ie
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e
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-
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h
o
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lef
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liv
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an
d
s
co
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e
attain
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d
u
r
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a
m
e.
E
ac
h
w
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n
g
an
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wer
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ec
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ea
s
es
L
if
e
wh
er
ea
s
ea
ch
co
r
r
ec
t
an
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wer
in
cr
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es
Sco
r
e.
I
f
m
is
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io
n
f
in
is
h
es
with
in
g
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en
tim
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d
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es,
p
lay
er
win
s
.
Ga
m
e
s
cr
ee
n
s
ar
e
T
itle
to
d
is
p
lay
g
am
e
titl
e,
Me
n
u
th
at
p
r
o
v
id
e
o
p
t
io
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s
o
f
g
a
m
e
ap
p
licatio
n
,
Dem
o
to
g
iv
e
g
a
m
e
tu
to
r
ial,
Sto
r
y
o
v
er
v
iew
t
o
walk
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o
u
g
h
b
ac
k
g
r
o
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n
d
s
to
r
y
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f
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a
m
e,
Play
t
o
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lay
th
e
g
am
e,
W
o
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in
f
o
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m
g
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c
o
m
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letio
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,
L
o
s
t
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in
f
o
r
m
g
am
e
is
o
v
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,
E
x
it
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ask
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lay
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r
co
n
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ir
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atio
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to
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u
it
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e
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C
r
ed
its
to
d
is
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lay
in
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o
r
m
atio
n
o
f
g
am
e
d
esig
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er
s
an
d
d
ev
el
o
p
er
s
.
Giv
en
a
g
r
ap
h
an
d
a
s
o
u
r
ce
v
er
tex
in
th
e
g
r
ap
h
,
Dijk
s
tr
a
alg
o
r
ith
m
ca
n
f
in
d
s
h
o
r
test
p
ath
f
r
o
m
s
o
u
r
ce
to
all
v
er
tices in
th
e
g
iv
en
g
r
ap
h
.
Alg
o
r
ith
m
to
f
in
d
s
h
o
r
test
d
is
tan
ce
is
as f
o
llo
ws [
3
2
]
:
Set
d
is
tan
ce
o
f
s
tar
t v
er
tex
f
r
o
m
s
tar
t v
er
tex
=
0
Set d
is
tan
ce
o
f
all
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th
er
v
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tic
es f
r
o
m
s
tar
t =
∞
R
ep
ea
t u
n
til all
v
er
tices v
is
ite
d
Vis
it u
n
v
is
ited
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tex
with
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Fo
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ited
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en
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C
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late
d
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er
tex
I
f
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lated
d
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f
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v
e
r
t
ex
is
less
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an
k
n
o
wn
d
is
tan
ce
th
en
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
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8
8
2
2
I
n
t.
J
.
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v
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&
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2
6
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3
3
928
Up
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ate
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test
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Ad
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ited
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Up
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ated
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ith
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u
esti
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ar
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en
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er
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wer
s
q
u
esti
o
n
s
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ased
o
n
a
m
ap
a
n
d
a
tab
le
(
Fig
u
r
e
2
)
,
in
s
p
ir
e
d
b
y
[
3
3
]
.
Fig
u
r
e
2
.
Play
s
cr
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n
T
h
e
f
o
llo
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s
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ws
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f
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in
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r
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lay
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ased
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n
t
h
e
ab
o
v
e
alg
o
r
ith
m
:
Wha
t
is
t
he
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a
lue in c
o
lum
n
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o
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ce
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ve
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tex
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Up
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ate
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u
r
ce
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er
tex
co
l
u
m
n
i
n
r
o
w
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to
0
Wha
t
is
t
he
v
a
lue in t
he
re
m
a
ini
ng
co
lum
ns
(
un
ex
plo
re
d
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er
t
ices)
?
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d
ate
r
em
ain
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g
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lu
m
n
s
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o
w
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∞
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ep
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t u
n
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tices v
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m
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m
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o
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tices in
g
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Wha
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m
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rked
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he
r
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ro
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k
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lu
m
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at
h
as th
e
s
m
a
lles
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n
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ar
k
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alu
e
(
Fig
u
r
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2
)
C
o
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m
ar
k
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d
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e
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ew
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o
w
(
Fig
u
r
e
2
)
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d
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ited
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eig
h
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o
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r
(
s
)
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r
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tex
Fo
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ited
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I
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Wha
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r
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Va
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Wha
t
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r
M
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rk
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Wha
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Wha
t
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in v
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ted
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ve
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tex
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en
t m
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<
p
r
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e
Up
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ate
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n
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t
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r
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e
E
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illed
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le,
b
ac
k
tr
ac
k
f
r
o
m
th
e
f
in
al
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er
tex
.
T
h
e
alg
o
r
ith
m
is
as
f
o
llo
ws:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
v
al
&
R
es E
d
u
c
.
I
SS
N:
2252
-
8
8
2
2
Qu
esti
o
n
-
led
a
p
p
r
o
a
ch
i
n
d
esig
n
in
g
Dijkst
r
a
a
lg
o
r
ith
m
g
a
m
e
-
b
a
s
ed
lea
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n
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g
:
A
p
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t stu
d
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(
R
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s
n
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a
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)
929
T
ick
d
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ep
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til all
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is
ited
Mo
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ch
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v
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T
ick
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tex
t
h
at
was m
ar
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o
w
Mo
v
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p
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ter
to
p
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in
t to
m
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k
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s
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ased
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o
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ith
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tex
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ep
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ited
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H
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tex
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h
at
was m
ar
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eh
a
v
io
u
r
is
th
e
click
in
g
o
f
an
a
n
s
wer
.
T
h
e
co
r
r
ec
tn
ess
o
f
an
a
n
s
wer
t
r
ig
g
er
s
s
y
s
tem
f
ee
d
b
ac
k
in
th
e
f
o
r
m
o
f
a
u
d
i
o
(
n
e
g
ativ
e/p
o
s
itiv
e)
,
L
if
e
(
s
tay
/d
ec
r
ea
s
e)
,
Sco
r
e
(
s
tay
/in
cr
ea
s
e)
,
an
d
s
cr
ee
n
d
is
p
lay
e
d
(
Play
/W
o
n
/Lo
s
t)
.
T
h
e
cy
cl
e
r
ep
ea
ts
as
s
y
s
tem
f
ee
d
b
ac
k
will
th
en
af
f
ec
t
th
e
n
ex
t
u
s
er
ju
d
g
em
en
t.
Du
r
in
g
d
eb
r
ief
in
g
,
u
s
er
will
r
ef
lect
th
e
g
am
ep
lay
an
d
d
eter
m
in
e
wh
eth
e
r
th
e
lear
n
i
n
g
o
u
tco
m
es o
f
th
e
g
a
m
e
ac
h
ie
v
ed
,
wh
ich
is
to
u
n
d
er
s
tan
d
th
e
Dijk
s
tr
a
alg
o
r
ith
m
(
co
g
n
itiv
e)
,
s
o
lv
e
SP
P
u
s
in
g
Dijk
s
tr
a
alg
o
r
ith
m
o
n
p
ap
er
u
s
in
g
a
tab
le
(
p
s
y
c
h
o
m
o
t
o
r
)
an
d
ap
p
r
ec
iate
th
e
wo
r
th
o
f
th
e
Dijk
s
tr
a
alg
o
r
ith
m
in
s
o
lv
i
n
g
r
ea
l
life
p
r
o
b
lem
s
(
af
f
ec
tiv
e)
.
T
h
e
s
er
ies
o
f
q
u
esti
o
n
s
s
im
u
late
th
e
q
u
esti
o
n
th
in
k
in
g
p
r
o
ce
s
s
th
at
a
s
tu
d
e
n
t
s
h
o
u
l
d
u
n
d
er
g
o
to
s
o
lv
e
SP
P
u
s
in
g
Dijk
s
tr
a
alg
o
r
ith
m
.
As
g
am
e
ca
n
b
e
r
ep
etitiv
ely
p
lay
ed
,
s
tu
d
en
t
s
ab
le
to
r
em
em
b
er
an
d
u
n
d
e
r
s
tan
d
th
e
p
r
o
b
lem
-
s
o
lv
in
g
p
r
o
ce
s
s
an
d
f
o
r
m
u
late
th
e
r
ig
h
t seq
u
e
n
ce
o
f
q
u
esti
o
n
s
wh
en
s
o
lv
in
g
p
r
o
b
lem
s
o
f
s
im
ilar
n
atu
r
e.
T
h
e
r
esear
ch
em
p
lo
y
ed
a
q
u
a
s
i
-
ex
p
er
im
en
tal
d
esig
n
u
s
in
g
a
p
r
e
-
test
p
o
s
t
-
test
o
n
ly
d
esig
n
with
o
u
t
a
co
n
tr
o
l
g
r
o
u
p
.
T
h
e
g
a
m
e
was
p
ilo
t
-
test
ed
b
y
2
8
f
ir
s
t
-
y
ea
r
Dip
lo
m
a
in
I
n
f
o
r
m
atio
n
T
ec
h
n
o
lo
g
y
s
tu
d
en
ts
wh
o
lear
n
t
th
e
alg
o
r
ith
m
t
h
e
p
r
e
v
io
u
s
s
em
ester
,
d
u
r
in
g
class
tim
e,
clo
s
e
to
o
n
e
h
o
u
r
,
in
d
iv
id
u
ally
.
Play
er
s
an
s
wer
ed
a
p
r
e
-
g
am
e
test
,
p
la
y
ed
th
e
g
a
m
e,
an
s
wer
ed
a
p
o
s
t
-
g
am
e
test
an
d
f
illed
o
u
t a
g
a
m
e
f
ee
d
b
ac
k
s
u
r
v
ey
.
Pre
-
an
d
p
o
s
t
-
g
a
m
e
test
s
co
n
s
is
t
o
f
th
e
s
am
e
q
u
esti
o
n
s
o
n
Dijk
s
tr
a
alg
o
r
ith
m
wh
er
e
b
y
p
la
y
er
s
n
ee
d
to
co
m
p
lete
an
e
m
p
ty
tab
le
b
ased
o
n
a
g
iv
e
n
g
r
a
p
h
.
T
h
e
g
a
m
e
was
u
p
lo
ad
ed
i
n
th
e
class
’
s
W
h
atsap
p
g
r
o
u
p
with
.
s
wf
f
ile
f
o
r
m
at.
Stu
d
en
ts
d
o
wn
lo
ad
ed
th
e
g
am
e
an
d
o
p
en
ed
th
em
w
ith
Flas
h
Play
er
o
r
I
n
ter
n
et
E
x
p
lo
r
er
.
Gam
e
f
ee
d
b
ac
k
s
u
r
v
ey
m
o
d
i
f
ied
f
r
o
m
[
3
4
]
co
n
s
is
ts
o
f
n
in
e
L
ik
er
t
-
s
ca
le
q
u
esti
o
n
s
r
an
g
in
g
f
r
o
m
s
tr
o
n
g
ly
ag
r
ee
to
s
tr
o
n
g
ly
d
is
ag
r
ee
an
d
two
o
p
en
-
en
d
e
d
q
u
esti
o
n
s
o
n
f
a
v
o
u
r
ab
le
t
r
aits
o
f
g
am
e
an
d
s
u
g
g
esti
o
n
f
o
r
im
p
r
o
v
em
e
n
t
.
T
h
er
e
wer
e
m
an
y
r
ea
s
o
n
s
f
o
r
th
e
s
m
all
s
am
p
le
s
ize.
First,
o
n
ly
o
n
e
o
f
th
e
au
th
o
r
s
was
av
ailab
le
to
h
an
d
le
test
in
g
.
T
h
is
lack
o
f
m
an
p
o
wer
ca
u
s
ed
th
e
in
ab
ilit
y
t
o
co
n
d
u
ct
test
in
g
to
d
i
f
f
er
en
t
g
r
o
u
p
s
o
f
s
tu
d
en
ts
.
Seco
n
d
,
test
in
g
h
ad
to
b
e
d
o
n
e
d
u
r
i
n
g
wee
k
d
ay
as
class
r
o
o
m
u
s
ag
e
is
p
r
o
h
ib
ited
d
u
r
i
n
g
wee
k
en
d
.
B
ec
au
s
e
b
o
th
th
e
test
in
g
co
o
r
d
in
ato
r
a
n
d
th
e
s
tu
d
en
ts
h
ad
class
es,
th
e
tim
e
co
n
s
tr
ain
t
m
ad
e
it
d
if
f
icu
lt
to
f
in
d
a
tim
e
s
lo
t
wh
er
e
b
o
th
wer
e
f
r
ee
.
T
h
ir
d
,
f
in
d
in
g
a
v
en
u
e
f
o
r
t
h
e
tes
tin
g
d
u
r
in
g
wee
k
d
a
y
wa
s
a
ch
allen
g
e
s
in
ce
m
o
s
t
class
r
o
o
m
s
wer
e
o
cc
u
p
ied
m
o
s
t
o
f
t
h
e
tim
e.
L
o
ca
tio
n
co
n
s
tr
ain
t
m
ea
n
s
th
at
e
v
en
if
b
o
th
th
e
test
in
g
co
o
r
d
in
at
o
r
an
d
th
e
s
tu
d
e
n
ts
wer
e
f
r
ee
,
an
em
p
ty
class
r
o
o
m
is
n
o
t
n
ec
ess
ar
ily
av
ailab
le.
Fo
u
r
th
,
th
e
m
ax
im
u
m
ca
p
ac
ity
o
f
a
class
r
o
o
m
is
3
0
.
A
s
am
p
le
s
ize
o
f
m
o
r
e
th
an
th
at
r
eq
u
ir
es
th
e
te
s
tin
g
co
o
r
d
in
at
o
r
to
co
n
d
u
ct
test
in
g
to
m
o
r
e
th
an
o
n
e
g
r
o
u
p
o
f
s
tu
d
en
ts
,
wh
ich
was n
o
t p
o
s
s
ib
le
at
th
e
tim
e.
Giv
en
th
e
co
n
s
tr
ain
ts
m
en
tio
n
ed
ab
o
v
e
,
a
co
n
v
en
ien
ce
s
am
p
lin
g
was
d
o
n
e
wh
er
e
t
h
e
s
tu
d
en
ts
wh
o
p
ar
ticip
ated
in
th
e
test
in
g
w
as
a
s
ec
tio
n
th
at
th
e
test
in
g
co
o
r
d
in
at
o
r
was
teac
h
in
g
d
u
r
in
g
th
at
tim
e.
T
h
e
au
th
o
r
s
ar
e
awa
r
e
th
at
a
s
am
p
le
s
ize
in
s
u
f
f
icien
cy
m
ay
t
h
r
e
at
th
e
v
alid
ity
an
d
g
e
n
er
aliza
b
ilit
y
o
f
th
e
s
tu
d
y
’
s
r
esu
lts
.
Ho
wev
er
,
a
s
m
all
s
am
p
le
s
ize
is
co
m
m
o
n
in
e
d
u
ca
ti
o
n
r
esear
ch
’
s
p
ilo
t stu
d
ies.
C
h
en
an
d
Yao
[
3
5
]
d
id
an
em
p
ir
ical
ev
alu
atio
n
o
f
c
r
itical
f
ac
to
r
s
in
f
lu
en
cin
g
lea
r
n
in
g
s
atis
af
ac
tio
n
in
a
b
len
d
ed
lear
n
in
g
o
f
2
0
s
tu
d
en
ts
.
Flack
,
et
a
l.
[
3
6
]
lev
er
ag
ed
s
er
io
u
s
g
a
m
es
in
Air
Fo
r
ce
Mu
lti
-
Do
m
ain
Op
er
atio
n
s
ed
u
ca
tio
n
to
f
i
v
e
s
o
ld
ier
s
.
Daw
s
o
n
an
d
Su
th
er
l
an
d
-
Sm
ith
[
3
7
]
p
aid
s
ev
en
ex
p
er
ien
ce
d
m
ar
k
e
r
s
in
d
iv
id
u
ally
b
lin
d
m
ar
k
ed
th
e
s
am
e
b
u
n
d
le
o
f
2
0
s
ec
o
n
d
-
y
ea
r
p
s
y
ch
o
l
o
g
y
ass
ig
n
m
en
ts
to
estab
lis
h
th
eir
ac
cu
r
ac
y
at
d
etec
tin
g
co
n
tr
ac
t
ch
ea
tin
g
.
Asp
elin
an
d
J
o
n
s
s
o
n
[
3
8
]
s
tu
d
ied
r
elatio
n
al
co
m
p
eten
ce
in
teac
h
er
ed
u
ca
tio
n
wh
ich
f
o
cu
s
es
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
2
2
I
n
t.
J
.
E
v
al
.
&
R
es
.
E
d
u
c
.
Vo
l.
9
,
No
.
4
,
Dec
em
b
e
r
2
0
2
0
:
9
2
6
-
9
3
3
930
in
ter
p
er
s
o
n
al
asp
ec
ts
o
f
s
ix
p
r
eser
v
ice
teac
h
er
s
.
B
eltr
án
-
Vel
asco
,
et
a
l.
[
3
9
]
ex
p
l
o
r
ed
th
e
e
f
f
ec
t
o
f
d
i
f
f
er
en
ce
s
in
th
e
s
tr
ess
p
s
y
ch
o
p
h
y
s
io
lo
g
ical
r
esp
o
n
s
e
o
f
2
5
Ps
y
ch
o
lo
g
y
d
eg
r
ee
s
tu
d
en
ts
.
B
ak
er
,
et
a
l.
[
4
0
]
ass
ess
ed
p
atter
n
s
o
f
co
r
tical
ac
tiv
ity
th
a
t o
cc
u
r
wh
e
n
1
0
ch
ild
r
e
n
in
ter
ac
t w
ith
d
ig
ital m
ath
ap
p
s
.
Gen
d
er
,
ag
e,
an
d
k
n
o
wled
g
e
o
n
Dijk
s
tr
a
al
g
o
r
ith
m
m
ay
b
e
th
e
c
o
n
f
o
u
n
d
in
g
v
ar
ia
b
les
in
th
e
s
tu
d
y
.
R
estrictio
n
elim
in
ates
v
ar
iatio
n
in
t
h
e
co
n
f
o
u
n
d
er
[
4
1
]
.
T
h
u
s
,
wh
y
t
h
e
s
am
p
les
wer
e
t
h
o
s
e
o
f
s
am
e
ag
e.
Ho
wev
er
,
r
estrictio
n
lo
wer
s
g
en
er
aliza
b
ilit
y
[
4
2
]
.
Hen
ce
,
th
e
d
if
f
er
in
g
g
e
n
d
er
s
an
d
k
n
o
wled
g
e
o
f
Dijk
s
tr
a
alg
o
r
ith
m
o
f
th
e
s
am
p
les.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
ab
le
1
illu
s
tr
ates
s
u
m
m
ar
izatio
n
o
f
r
esu
lts
f
r
o
m
p
r
e
-
g
a
m
e
an
d
p
o
s
t
-
g
am
e
ex
p
er
im
e
n
t.
All
b
u
t
o
n
e
ce
ll
in
tab
le
ac
co
m
p
lis
h
ed
p
o
s
itiv
e
ch
an
g
e
i.e
.
p
o
s
t
-
test
m
a
r
k
is
h
ig
h
er
th
a
n
p
r
e
-
t
est
.
T
o
ex
tr
ac
t
f
ee
d
b
ac
k
o
n
g
am
e,
we
ask
ed
th
e
p
lay
er
s
to
an
s
wer
n
in
e
f
iv
e
-
p
o
in
t L
ik
er
t
s
ca
le
q
u
esti
o
n
s
r
an
g
in
g
f
r
o
m
s
tr
o
n
g
ly
d
is
ag
r
ee
to
s
tr
o
n
g
ly
ag
r
ee
.
T
h
e
r
esu
lts
ar
e
s
u
m
m
ar
ized
in
T
a
b
le
2
.
T
ab
le
1
.
C
o
m
p
a
r
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4
18
27
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2
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1
23
24
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R
2
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2
19
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R
2
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11
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3
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1
24
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3
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15
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Play
er
s
’
o
p
in
io
n
was
m
o
s
tly
p
o
s
itiv
e
f
o
r
all
q
u
esti
o
n
s
.
T
h
is
is
r
ef
lecte
d
in
m
an
y
o
f
th
e
p
lay
er
s
’
an
s
wer
wh
en
ask
ed
to
d
escr
ib
e
wh
at
th
ey
lik
e
a
b
o
u
t
t
h
e
g
am
e.
T
h
ey
s
tated
th
at
th
e
g
a
m
e
m
ad
e
it
“e
asy
to
u
n
d
er
s
tan
d
th
e
tech
n
iq
u
e
to
f
in
d
s
h
o
r
test
p
ath
,
”
th
er
ef
o
r
e
“im
p
r
o
v
e
k
n
o
wled
g
e”
a
n
d
“e
d
u
ca
te”
o
n
Dijk
s
tr
a
alg
o
r
ith
m
.
T
h
is
ag
r
ee
s
with
t
h
e
r
esu
lt
f
r
o
m
th
e
s
tu
d
y
b
y
[
4
3
]
,
wh
er
e
s
tu
d
e
n
ts
f
elt
th
at
ed
u
ca
tio
n
al
g
am
e
clar
if
ies d
ata
s
tr
u
ctu
r
e
co
n
ce
p
ts
.
T
h
is
m
ay
b
e
b
ec
au
s
e
th
e
n
o
t
es g
iv
e
n
ar
e
“d
etailed
”
b
u
t “
ea
s
y
to
u
n
d
e
r
s
tan
d
,
”
th
u
s
“h
elp
to
p
lay
t
h
e
g
am
e
.
”
Play
er
s
ca
n
also
u
s
e
th
e
g
am
e
to
“tr
ain
”
as
th
ey
ca
n
r
ep
etitiv
ely
p
lay
t
o
“im
p
r
o
v
e
th
in
k
in
g
s
k
ill
.
”
Feed
b
ac
k
in
th
e
f
o
r
m
o
f
tim
e
,
s
co
r
e
an
d
life
m
ad
e
th
e
GB
L
m
o
r
e
“f
u
n
”
,
“c
h
allen
g
in
g
”
an
d
“m
o
ti
v
ate”
p
lay
er
s
t
o
“n
o
t
ea
s
ily
g
iv
e
u
p
.
”
A
p
lay
er
“lik
e”
th
e
zo
m
b
i
e
ap
o
ca
ly
p
s
e
th
em
e
o
f
th
e
g
am
e
wh
ic
h
in
f
l
u
en
ce
s
th
e
h
o
r
r
o
r
/th
r
iller
b
ac
k
g
r
o
u
n
d
s
o
u
n
d
th
at
m
an
y
p
lay
e
r
s
lik
e
a
s
it
is
“lik
e
a
s
ca
r
y
f
ilm
”
an
d
d
ar
k
-
co
lo
u
r
ed
g
r
ap
h
ics,
wh
ich
a
p
lay
er
li
k
e.
T
wo
p
lay
er
s
co
n
clu
d
e
d
th
e
g
am
e
t
o
b
e
“v
er
y
s
p
ec
ial”
an
d
“p
er
f
ec
t
.
”
Play
er
s
also
s
u
g
g
ested
im
p
r
o
v
em
en
ts
in
v
ar
io
u
s
asp
ec
ts
o
f
th
e
g
am
e.
Stu
d
en
ts
r
e
q
u
est
ed
f
o
r
th
e
n
o
tes
to
b
e
s
im
p
le
r
as
it
“g
iv
e
s
m
e
an
x
iety
f
ee
ls
lik
e
I
’
m
r
ea
d
in
g
s
lid
es.
I
f
ee
l
lik
e
I
'
m
p
r
e
p
ar
in
g
f
o
r
test
wh
en
ac
tu
ally
I
am
g
ettin
g
r
ea
d
y
t
o
p
lay
th
e
g
a
m
e
.
”
B
ec
au
s
e
Dijk
s
tr
a
alg
o
r
ith
m
h
as
m
an
y
s
tep
s
,
we
wan
t
th
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
v
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&
R
es E
d
u
c
.
I
SS
N:
2252
-
8
8
2
2
Qu
esti
o
n
-
led
a
p
p
r
o
a
ch
i
n
d
esig
n
in
g
Dijkst
r
a
a
lg
o
r
ith
m
g
a
m
e
-
b
a
s
ed
lea
r
n
in
g
:
A
p
ilo
t stu
d
y
(
R
o
s
n
i R
a
mle
)
931
s
tu
d
en
ts
to
r
ea
lly
u
n
d
er
s
tan
d
ea
ch
o
n
e,
t
h
u
s
r
esu
lts
in
a
le
n
g
th
y
e
x
p
lan
atio
n
as
ea
ch
s
tep
is
v
is
u
alize
d
an
d
ex
p
lain
ed
.
As
th
e
g
am
e
r
eq
u
ir
es
th
e
p
lay
e
r
s
to
a
n
aly
s
e
a
m
a
p
an
d
a
ta
b
le
to
an
s
wer
q
u
esti
o
n
s
,
it
ca
n
b
e
a
b
it
o
v
er
wh
elm
in
g
an
d
co
n
f
u
s
in
g
.
T
o
in
cr
ea
s
e
u
s
ab
ilit
y
,
s
tu
d
en
ts
r
ec
o
m
m
en
d
ed
to
“
g
iv
e
m
o
r
e
in
s
tr
u
ctio
n
wh
en
p
lay
in
g
,
”
“
teac
h
m
o
r
e
s
p
ec
if
ically
,
”
an
d
“
m
ak
e
t
h
e
g
a
m
e
m
o
r
e
u
s
er
f
r
ien
d
ly
.
”
Pro
v
i
d
e
s
im
p
le
an
d
ea
s
y
in
s
tr
u
ctio
n
to
m
ak
e
th
e
b
eg
i
n
n
er
ea
s
y
t
o
p
la
y
.
An
i
n
s
tr
u
ctio
n
m
an
u
al
[
4
4
]
ca
n
b
e
m
ad
e
to
h
elp
p
la
y
er
s
n
av
ig
ate
an
d
u
n
d
er
s
tan
d
th
e
g
am
e.
T
o
in
cr
ea
s
e
th
e
ch
allen
g
e
,
th
e
y
p
r
o
p
o
s
ed
to
“
in
cr
e
ase
th
e
d
if
f
icu
lty
lev
el
o
f
th
e
g
am
e
,
”
t
o
im
p
r
o
v
e
lear
n
in
g
o
u
tco
m
es
[
4
5
]
.
T
h
is
ca
n
b
e
d
o
n
e
b
y
d
ec
r
ea
s
in
g
T
i
m
e
an
d
L
i
v
es,
in
cr
ea
s
in
g
n
u
m
b
er
o
f
v
er
tices,
an
d
p
u
t
m
o
r
e
th
a
n
o
n
e
s
m
allest
u
n
m
ar
k
e
d
v
al
u
e
in
a
r
o
w
i.e
.
m
o
r
e
th
an
o
n
e
s
h
o
r
test
p
at
h
.
Ho
wev
er
,
s
o
m
e
s
tu
d
en
ts
ask
ed
to
cr
ea
te
“
lev
e
ls
to
m
ak
e
th
e
b
eg
in
n
er
ea
s
y
to
p
lay
.
”
Stu
d
en
ts
wan
ted
t
h
e
g
am
e
to
“
k
ee
p
th
e
p
r
ev
io
u
s
s
co
r
e.
So
,
I
ca
n
tr
y
b
ea
t
th
e
s
co
r
e
in
th
e
n
e
x
t
g
am
e.
”
Stu
d
en
ts
p
r
o
p
o
s
ed
to
“
in
cr
ea
s
e
th
e
tim
e
,
”
ev
en
th
o
u
g
h
we
f
elt
th
at
th
e
ti
m
e
g
iv
en
is
ad
e
q
u
ate,
an
d
to
f
ix
tim
er
er
r
o
r
as
“
tim
e
s
p
ee
d
s
u
p
wh
en
r
e
p
lay
”.
Stu
d
en
ts
wis
h
ed
f
o
r
“
th
e
r
ea
s
o
n
f
o
r
e
v
er
y
r
ig
h
t
a
n
s
wer
.
”
C
u
r
r
en
tly
,
th
e
f
ee
d
b
ac
k
f
o
r
ea
ch
r
ig
h
t
an
s
wer
is
an
in
cr
ea
s
e
in
s
co
r
e
an
d
a
“p
o
s
itiv
e”
s
o
u
n
d
.
E
x
p
l
ai
n
in
g
ea
ch
r
ig
h
t
an
s
wer
,
in
o
u
r
o
p
in
io
n
,
wo
u
ld
clu
tter
th
e
s
cr
ee
n
an
d
le
n
g
th
e
n
th
e
p
lay
in
g
tim
e,
wh
ich
m
a
y
ca
u
s
e
d
is
en
g
ag
em
e
n
t
[
4
6
]
.
I
n
tem
s
o
f
g
r
a
p
h
ics,
s
tu
d
en
ts
ad
v
is
ed
f
o
r
it
to
b
e
m
o
r
e
“
r
ea
lis
tic
,
”
“f
u
n
,
”
an
d
“c
o
lo
r
f
u
l
.
”
W
e
th
in
k
a
d
ar
k
c
o
lo
r
th
em
e
f
o
r
th
e
g
am
e
is
s
u
itab
le
as
it
h
as
a
zo
m
b
ie
ap
o
ca
ly
p
s
e
th
e
m
e.
T
h
ey
also
d
esire
d
f
o
r
th
e
s
o
u
n
d
to
b
e
“
m
o
r
e
d
r
am
atic
.
”
T
o
k
ee
p
u
p
with
th
e
zo
m
b
ie
th
e
m
e,
we
u
s
e
a
lim
ited
co
m
b
in
atio
n
o
f
h
o
r
r
o
r
a
n
d
th
r
iller
au
d
io
s
,
to
n
o
t
d
is
tr
ac
t
p
lay
er
s
f
r
o
m
th
eir
task
.
No
n
e
o
f
t
h
e
p
lay
er
s
g
av
e
a
n
y
n
eg
ativ
e
co
m
m
e
n
t
o
r
s
u
g
g
esti
o
n
r
eg
ar
d
i
n
g
cu
r
io
s
ity
,
f
u
n
,
a
n
d
n
av
ig
atio
n
cr
iter
ia.
T
h
u
s
,
we
ar
e
u
n
ab
le
to
co
m
p
r
eh
e
n
d
wh
y
s
o
m
e
o
f
th
em
“d
is
ag
r
ee
”.
N
ev
er
th
eless
,
we
will
m
ak
e
ass
u
m
p
tio
n
s
r
eg
ar
d
in
g
th
e
r
esu
lts
o
f
th
ese
th
r
ee
cr
it
er
ia.
T
h
e
g
am
e
lack
m
y
s
ter
y
as
it
is
s
im
p
le
an
d
d
ir
ec
t,
th
u
s
m
ay
n
o
t
in
d
u
ce
m
u
ch
cu
r
io
s
ity
as
to
wh
at
h
a
p
p
en
s
n
ex
t.
B
u
t
a
p
lay
er
r
em
ar
k
ed
th
at
th
e
g
am
e
“to
tally
en
h
an
ce
s
m
e
cu
r
io
s
ity
.
”
B
alan
cin
g
b
etwe
en
f
u
n
an
d
lear
n
in
g
is
tr
ick
y
.
W
e
m
ig
h
t
h
av
e
f
o
c
u
s
m
o
r
e
o
n
th
e
le
ar
n
in
g
p
ar
t,
r
esu
ltin
g
in
th
e
g
a
m
e
b
ein
g
less
f
u
n
,
wh
ich
is
a
c
o
m
m
o
n
p
r
o
b
lem
in
GB
L
f
o
r
p
r
o
g
r
a
m
m
in
g
[
4
7
]
.
T
h
o
u
g
h
a
p
lay
er
co
m
m
en
ted
th
at
th
e
g
am
e
is
“f
u
n
en
o
u
g
h
”.
R
eg
ar
d
in
g
n
av
ig
atio
n
,
we
ass
u
m
e
th
at
p
lay
er
s
wh
o
“d
is
ag
r
ee
”
d
id
s
o
b
ec
au
s
e
o
f
th
e
tim
er
er
r
o
r
w
h
ich
ca
u
s
es
th
e
tim
er
to
b
e
f
aste
r
wh
en
r
e
p
lay
,
ev
en
t
h
o
u
g
h
th
is
is
n
o
t
r
elate
d
to
n
av
i
g
atio
n
.
W
e
th
in
k
th
at
th
e
“UI
is
ea
s
y
to
n
av
ig
ate
,
”
as
m
en
tio
n
ed
b
y
a
p
lay
er
.
4.
CO
NCLU
SI
O
N
T
h
is
r
esear
ch
in
teg
r
ated
two
ap
p
r
o
ac
h
th
at
ar
e
co
n
s
id
er
e
d
im
p
o
r
tan
t
in
2
1
s
t
ce
n
tu
r
y
ed
u
ca
tio
n
;
q
u
esti
o
n
-
led
lear
n
in
g
an
d
g
a
m
e
-
b
ased
lear
n
in
g
.
Ou
r
ap
p
r
o
ac
h
lead
s
s
tu
d
en
ts
th
r
o
u
g
h
th
e
p
r
o
ce
s
s
o
f
p
r
o
b
lem
s
o
lv
in
g
wh
er
e
q
u
esti
o
n
s
p
lay
s
an
im
p
o
r
tan
t
p
ar
t
in
th
e
lear
n
in
g
p
r
o
ce
s
s
.
Qu
esti
o
n
s
ar
e
f
o
r
m
u
lated
b
ased
o
n
ex
is
tin
g
alg
o
r
ith
m
.
T
h
e
r
esear
ch
also
p
r
esen
ts
f
in
d
in
g
s
o
n
th
e
im
p
lem
en
tatio
n
o
f
a
q
u
esti
o
n
-
led
ap
p
r
o
ac
h
in
d
esig
n
in
g
g
r
ap
h
d
ata
s
tr
u
ctu
r
e
Dijk
s
tr
a
alg
o
r
ith
m
GB
L
.
C
o
m
p
ar
is
o
n
b
etwe
en
p
r
e
-
g
am
e
a
n
d
p
o
s
t
-
g
a
m
e
test
s
as
well
a
s
an
aly
s
is
o
f
g
am
e
f
ee
d
b
ac
k
was
co
n
d
u
cte
d
.
T
h
e
f
i
n
d
in
g
s
s
h
o
w
ed
th
at
s
tu
d
en
ts
’
o
v
er
all
p
er
f
o
r
m
an
ce
af
ter
u
s
in
g
t
h
e
g
am
e
is
b
etter
th
an
b
ef
o
r
e
.
Gam
e
f
e
ed
b
ac
k
s
wer
e
m
o
s
tly
p
o
s
itiv
e.
H
o
wev
er
,
im
p
r
o
v
e
m
en
ts
n
ee
d
to
b
e
m
a
d
e
in
ter
m
s
o
f
s
im
p
licity
o
f
No
tes,
clar
ity
o
f
in
s
tr
u
ctio
n
s
,
lev
el
o
f
ch
allen
g
e,
s
co
r
e
-
k
ee
p
in
g
,
f
ee
d
b
a
ck
,
u
s
er
in
ter
f
ac
e
,
an
d
s
o
u
n
d
.
Stu
d
en
ts
ca
n
u
s
e
th
e
g
am
e
to
p
r
ac
tice
an
d
lear
n
u
n
til
th
e
y
c
an
s
elf
-
ask
w
h
ile
th
in
k
i
n
g
wh
en
s
o
lv
in
g
s
im
ilar
p
r
o
b
lem
s
.
Pro
f
icien
cy
o
f
th
e
alg
o
r
ith
m
will
p
o
s
itiv
ely
im
p
ac
t
s
tu
d
e
n
ts
’
ac
ad
e
m
ic
p
er
f
o
r
m
an
ce
an
d
co
n
s
eq
u
en
tly
,
ca
r
ee
r
p
r
o
s
p
ec
ts
.
E
d
u
ca
to
r
s
ca
n
u
s
e
th
e
p
r
o
p
o
s
ed
m
eth
o
d
t
o
d
esig
n
lear
n
in
g
to
o
l
th
at
ea
s
e
u
n
d
er
s
tan
d
i
n
g
o
f
an
alg
o
r
ith
m
ic
p
r
o
b
lem
-
s
o
lv
in
g
p
r
o
ce
s
s
.
Fu
tu
r
e
wo
r
k
r
e
q
u
ir
es
a
g
am
e
p
lay
d
esig
n
th
at
ca
n
b
alan
ce
f
u
n
an
d
lea
r
n
in
g
,
an
d
b
etter
r
esear
ch
d
esig
n
to
in
cr
e
ase
g
en
er
aliza
b
ilit
y
o
f
test
in
g
r
esu
lts
.
ACK
NO
WL
E
DG
E
M
E
NT
S
T
h
is
r
esear
ch
is
s
u
p
p
o
r
ted
b
y
T
ier
1
g
r
a
n
t H
1
1
7
f
r
o
m
UT
H
M’
s
R
esear
ch
Ma
n
ag
em
en
t Cen
tr
e.
RE
F
E
R
E
NC
E
S
[1
]
W.
d
a
S
il
v
a
Lo
u
re
n
ç
o
,
e
t
a
l.
,
"
T
ASNOP:
A
to
o
l
fo
r
tea
c
h
i
n
g
a
lg
o
rit
h
m
s
to
so
lv
e
n
e
two
r
k
o
p
ti
m
iza
ti
o
n
p
ro
b
lem
s,"
Co
mp
u
ter
A
p
p
li
c
a
ti
o
n
s i
n
E
n
g
in
e
e
rin
g
E
d
u
c
a
t
io
n
,
v
o
l
.
2
6
,
n
o
.
1
,
p
p
.
1
0
1
-
1
1
0
,
2
0
1
7.
[2
]
B.
Up
a
d
h
y
a
y
a
,
Da
t
a
S
tru
c
tu
re
s
a
n
d
Al
g
o
ri
th
ms
wit
h
S
c
a
l
a
:
A
Pra
c
ti
ti
o
n
e
r's
A
p
p
ro
a
c
h
w
it
h
Emp
h
a
sis
o
n
Fu
n
c
ti
o
n
a
l
Pr
o
g
ra
mm
i
n
g
.
Ch
a
m
:
S
p
ri
n
g
e
r
N
a
tu
re
S
witze
rlan
d
AG
,
2
0
1
9
.
[3
]
F
.
M
e
l
o
,
B.
Ba
rro
s
a
n
d
A.
M
o
ra
e
s,
"
Ed
u
c
a
ti
o
n
a
l
o
b
jec
ts
with
G
e
o
g
e
b
ra
fo
r
a
id
to
p
e
d
a
g
o
g
i
c
a
l
p
ra
c
ti
c
e
s
in
e
n
g
in
e
e
rin
g
,
"
in
1
5
th
Bra
zili
a
n
C
o
n
g
re
ss
o
n
E
n
g
i
n
e
e
rin
g
Ed
u
c
a
ti
o
n
,
Belé
m
,
p
p
.
1
-
12
,
1
9
9
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
2
2
I
n
t.
J
.
E
v
al
.
&
R
es
.
E
d
u
c
.
Vo
l.
9
,
No
.
4
,
Dec
em
b
e
r
2
0
2
0
:
9
2
6
-
9
3
3
932
[4
]
I.
M
a
k
o
h
o
n
,
D.
T.
Ng
u
y
e
n
,
M
.
S
o
so
n
k
i
n
a
,
Y.
S
h
e
n
a
n
d
M
.
N
g
,
"
Ja
v
a
Ba
se
d
Visu
laiz
a
ti
o
n
a
n
d
An
ima
ti
o
n
fo
r
Tea
c
h
in
g
t
h
e
Dijk
stra
S
h
o
rtes
t
P
a
th
Alg
o
ri
th
m
i
n
Tran
sp
o
rtatio
n
Ne
two
rk
s,"
I
n
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
S
o
ft
wa
r
e
En
g
i
n
e
e
rin
g
&
Ap
p
li
c
a
ti
o
n
s
,
v
o
l.
7
,
n
o
.
3
,
p
p
.
1
1
-
2
5
,
2
0
1
6
.
[5
]
A
.
Drig
a
s,
e
t
a
l.
,
"
A
we
b
b
a
se
d
e
lea
rn
in
g
a
n
d
e
-
p
sy
c
h
o
l
o
g
y
m
o
d
u
lar
e
n
v
ir
o
n
m
e
n
t
,
"
Pr
o
c
e
e
d
in
g
s
o
f
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Ne
x
t
Ge
n
e
ra
ti
o
n
W
e
b
S
e
rv
ice
s P
ra
c
ti
c
e
s
,
p
p
.
1
6
8
-
1
7
4
,
2
0
0
6
.
[6
]
M
.
S
e
ra
j
a
n
d
Y.
Ch
u
i,
"
A
S
tu
d
y
o
f
Us
e
r
In
terfa
c
e
De
sig
n
P
rin
c
i
p
les
a
n
d
Re
q
u
i
re
m
e
n
ts
fo
r
De
v
e
lo
p
i
n
g
a
M
o
b
il
e
Lea
rn
in
g
P
ro
t
o
ty
p
e
"
,
i
n
2
0
1
2
I
n
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Co
mp
u
ter
&
In
fo
rm
a
ti
o
n
S
c
ien
c
e
,
Ku
a
l
a
L
u
m
p
u
r
,
p
p
.
1
0
1
4
-
1
0
1
9
,
2
0
1
2
.
[7
]
R.
S
o
we
ll
,
Y.
Ch
e
n
,
J.
Bu
h
ler,
S
.
G
o
ld
m
a
n
,
C.
G
rimm
a
n
d
K.
G
o
ld
m
a
n
,
"
E
x
p
e
rien
c
e
s
with
Ac
ti
v
e
Lea
rn
in
g
in
CS
3,
"
J
o
u
rn
a
l
o
f
C
o
mp
u
ti
n
g
S
c
ien
c
e
s in
Co
ll
e
g
e
s
,
v
o
l.
2
5
,
n
o
.
5
,
p
p
.
1
7
3
-
1
7
9
,
2
0
1
0
.
[8
]
P
.
Ch
e
n
,
P
.
C
h
io
u
,
a
n
d
G
.
Yo
u
n
g
,
"
A
S
t
u
d
y
o
f
Lea
rn
in
g
Eff
e
c
ti
v
e
n
e
ss
o
n
t
h
e
Dijk
stra
’s
Alg
o
rit
h
m
M
o
d
e
led
in
a
n
In
tera
c
ti
v
e
KLA
Ap
p
r
o
a
c
h
,
"
in
T
h
e
1
3
th
I
n
t'l
C
o
n
f
o
n
Fro
n
ti
e
rs
in
E
d
u
c
a
ti
o
n
:
Co
m
p
u
ter
S
c
ie
n
c
e
a
n
d
Co
mp
u
te
r
En
g
i
n
e
e
rin
g
,
L
a
s
Veg
a
s
,
p
p
.
2
3
4
-
2
3
8
,
2
0
1
7
.
[9
]
C.
Hu
n
d
h
a
u
se
n
,
"
In
teg
ra
ti
n
g
a
lg
o
rit
h
m
v
is
u
a
li
z
a
ti
o
n
tec
h
n
o
lo
g
y
i
n
to
a
n
u
n
d
e
rg
ra
d
u
a
te
a
lg
o
rit
h
m
s
c
o
u
rse
:
e
th
n
o
g
ra
p
h
ic
stu
d
ies
o
f
a
so
c
ial
c
o
n
stru
c
ti
v
ist
a
p
p
ro
a
c
h
,
"
Co
mp
u
ter
s
&
Ed
u
c
a
ti
o
n
,
v
o
l
.
3
9
,
n
o
.
3
,
p
p
.
2
3
7
-
2
6
0
,
2
0
0
2
.
[1
0
]
P
.
Ka
ra
g
ian
n
is,
I
.
M
a
rk
e
li
s,
K.
P
a
p
a
rrizo
s,
N.
S
a
m
a
ra
s
a
n
d
A
.
S
ifale
ra
s,
"
E
-
lea
rn
i
n
g
tec
h
n
o
lo
g
i
e
s:
e
m
p
lo
y
i
n
g
M
a
tl
a
b
we
b
se
rv
e
r
to
fa
c
il
it
a
te
th
e
e
d
u
c
a
ti
o
n
o
f
m
a
th
e
m
a
ti
c
a
l
p
ro
g
ra
m
m
in
g
,
"
I
n
ter
n
a
t
io
n
a
l
J
o
u
r
n
a
l
o
f
M
a
t
h
e
ma
ti
c
a
l
Ed
u
c
a
ti
o
n
in
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
lo
g
y
,
v
o
l.
3
7
,
n
o
.
7
,
p
p
.
7
6
5
-
7
8
2
,
2
0
0
6
.
[1
1
]
M
.
S
á
n
c
h
e
z
-
To
rru
b
ia,
e
t
a
l.
,
"
P
a
th
F
i
n
d
e
r:
A
Vis
u
a
li
z
a
ti
o
n
e
M
a
th
Tea
c
h
e
r
fo
r
Ac
ti
v
e
ly
Lea
rn
in
g
Dij
k
stra
'
s
Alg
o
rit
h
m
,
"
El
e
c
tro
n
ic N
o
tes
in
T
h
e
o
re
ti
c
a
l
C
o
mp
u
ter
S
c
ien
c
e
,
v
o
l.
2
2
4
,
p
p
.
1
5
1
-
1
5
8
,
Ja
n
.
2
0
0
9
.
[1
2
]
A.
G
e
sc
h
k
e
,
U.
Ko
rten
k
a
m
p
,
B.
Lu
tz
-
Wes
tp
h
a
l
a
n
d
D.
M
a
terlik
,
"
Visa
g
e
—
Visu
a
li
z
a
ti
o
n
o
f
a
lg
o
r
it
h
m
s
in
d
isc
re
te
m
a
th
e
m
a
ti
c
s,"
Zen
tralb
latt
f
ü
r
Di
d
a
k
ti
k
d
e
r
M
a
th
e
m
a
ti
k
,
v
o
l
.
3
7
,
n
o
.
5
,
p
p
.
3
9
5
-
4
0
1
,
2
0
0
5
.
[1
3
]
T.
Ba
lo
u
k
a
s,
"
JA
VENG
A:
JA
v
a
-
b
a
se
d
Visu
a
li
z
a
ti
o
n
En
v
iro
n
m
e
n
t
fo
r
Ne
two
rk
a
n
d
G
ra
p
h
Al
g
o
ri
th
m
s,"
Co
mp
u
ter
Ap
p
li
c
a
ti
o
n
s i
n
E
n
g
i
n
e
e
rin
g
Ed
u
c
a
ti
o
n
,
v
o
l
.
2
0
,
n
o
.
2
,
p
p
.
2
5
5
-
2
6
8
,
2
0
0
9
.
[1
4
]
I.
Ka
ra
s
a
n
d
S
.
De
m
ir,
"
Dij
k
s
tra
a
lg
o
ri
th
m
i
n
tera
c
ti
v
e
trai
n
in
g
so
ftwa
re
d
e
v
e
l
o
p
m
e
n
t
f
o
r
n
e
two
r
k
a
n
a
ly
sis
a
p
p
li
c
a
ti
o
n
s
in
G
IS
,
"
En
e
rg
y
Ed
u
c
a
ti
o
n
S
c
ien
c
e
a
n
d
T
e
c
h
n
o
l
o
g
y
Pa
rt
A:
En
e
rg
y
S
c
ien
c
e
a
n
d
Res
e
a
rc
h
,
v
o
l.
2
8
,
n
o
.
1
,
p
p
.
4
4
5
-
4
5
2
,
2
0
1
1
.
[1
5
]
A.
Da
p
e
n
a
,
F
.
Vá
z
q
u
e
z
-
Ara
u
jo
,
P
.
Ca
stro
a
n
d
M
.
S
o
u
t
o
-
S
a
lo
r
io
,
"
A
fra
m
e
wo
rk
to
lea
rn
g
ra
p
h
th
e
o
ry
u
si
n
g
sim
p
l
e
wire
les
s n
e
two
rk
m
o
d
e
ls,"
C
o
mp
u
ter
Ap
p
li
c
a
ti
o
n
s
in
En
g
in
e
e
rin
g
Ed
u
c
a
ti
o
n
,
v
o
l
.
2
4
,
n
o
.
6
,
p
p
.
8
4
3
-
8
5
2
,
2
0
1
6.
[1
6
]
A.
Yo
h
a
n
n
is
a
n
d
Y.
P
ra
b
o
wo
,
"
S
o
rt
Attac
k
:
Visu
a
li
z
a
ti
o
n
a
n
d
G
a
m
ifi
c
a
ti
o
n
o
f
S
o
rti
n
g
Alg
o
rit
h
m
Lea
rn
in
g
,
"
in
7
t
h
In
ter
n
a
t
io
n
a
l
C
o
n
f
e
re
n
c
e
o
n
G
a
me
s a
n
d
Vi
rtu
a
l
W
o
rl
d
s fo
r
S
e
rio
u
s A
p
p
li
c
a
ti
o
n
s (
VS
-
Ga
me
s)
,
p
p
.
1
-
8
,
2
0
1
5
.
[1
7
]
A.
S
il
v
a
,
D.
M
a
rti
n
s
a
n
d
I.
Ni
g
ro
,
"
Co
m
p
u
ter
lab
s
in
t
h
e
tea
c
h
in
g
o
f
p
ro
d
u
c
ti
o
n
e
n
g
i
n
e
e
rin
g
,
"
INGE
PR
O
–
In
n
o
v
a
ti
o
n
,
M
a
n
a
g
e
me
n
t
a
n
d
Pro
d
u
c
ti
o
n
,
v
o
l.
2
,
n
o
.
1
2
,
p
p
.
23
-
2
9
,
2
0
1
0
.
[1
8
]
S
.
S
h
a
b
a
n
a
h
,
J.
C
h
e
n
,
H.
Wec
h
sl
e
r,
D.
Ca
rr
a
n
d
E.
Weg
m
a
n
,
"
De
sig
n
i
n
g
c
o
m
p
u
ter
g
a
m
e
s
to
tea
c
h
a
lg
o
rit
h
m
s,"
in
S
e
v
e
n
th
I
n
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
lo
g
y
,
L
a
s
Veg
a
s
,
p
p
.
1
1
1
9
-
1
1
2
6
,
2
0
1
0
.
[1
9
]
W.
Ch
a
n
g
,
T.
Wa
n
g
a
n
d
Y.
Ch
iu
,
"
Bo
a
rd
G
a
m
e
S
u
p
p
o
rt
in
g
Lea
rn
i
n
g
P
rim’s
Alg
o
rit
h
m
a
n
d
Dij
k
stra
’s
Alg
o
rit
h
m
,
"
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
M
u
l
ti
me
d
ia
D
a
ta
En
g
in
e
e
rin
g
a
n
d
M
a
n
a
g
e
me
n
t
,
v
o
l.
1
,
n
o
.
4
,
p
p
.
1
6
-
3
0
,
2
0
1
0
.
[2
0
]
E.
El
-
S
h
e
i
k
h
a
n
d
L.
P
ra
y
a
g
a
,
"
D
e
v
e
lo
p
m
e
n
t
a
n
d
Us
e
o
f
AI
a
n
d
G
a
m
e
Ap
p
li
c
a
ti
o
n
s
in
Un
d
e
rg
ra
d
u
a
te
C
o
m
p
u
ter
S
c
ien
c
e
Co
u
rse
s,"
J
o
u
rn
a
l
o
f
C
o
mp
u
ti
n
g
S
c
ien
c
e
s in
C
o
ll
e
g
e
s
,
v
o
l
.
2
7
,
n
o
.
2
,
p
p
.
1
1
4
-
1
2
2
,
2
0
1
1
.
[2
1
]
O.
G
ra
v
e
n
,
H.
Ha
n
se
n
a
n
d
L.
M
a
c
Kin
n
o
n
,
"
A
Blen
d
e
d
Lea
rn
i
n
g
E
x
e
rc
ise
u
sin
g
a
Co
m
p
u
ter
G
a
m
e
b
a
se
d
on
Ab
stra
c
t
Lea
rn
in
g
M
a
teria
ls,"
i
n
I
CL
2
0
0
9
In
ter
a
c
ti
v
e
Co
m
p
u
ter
Ai
d
e
d
L
e
a
r
n
in
g
,
Vi
ll
a
c
h
,
2
0
0
9
.
[2
2
]
F
.
G
riv
o
k
o
sto
p
o
u
l
o
u
,
I.
P
e
rik
o
s
a
n
d
I.
Ha
tzily
g
e
ro
u
d
is,
"
An
Ed
u
c
a
ti
o
n
a
l
G
a
m
e
fo
r
Tea
c
h
in
g
S
e
a
rc
h
Alg
o
rit
h
m
s,
"
in
8
th
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
m
p
u
ter
S
u
p
p
o
rte
d
E
d
u
c
a
t
io
n
,
Ro
me
,
p
p
.
1
2
9
-
1
3
6
,
2
0
1
6
.
[2
3
]
V.
S
o
a
n
c
a
t
l,
A.
Leó
n
,
C.
M
a
rtí
n
e
z
a
n
d
L.
T
o
rre
s,
"
Lea
d
in
g
S
tu
d
e
n
ts
to
S
o
lv
e
M
a
th
s
P
r
o
b
le
m
s
Us
in
g
Qu
e
sti
o
n
-
le
d
Lea
rn
in
g
,
"
i
n
4
th
Eu
ro
p
e
a
n
Co
n
f
e
re
n
c
e
o
n
Ga
me
s B
a
se
d
L
e
a
rn
i
n
g
,
Co
p
e
n
h
a
g
e
n
,
p
p
.
3
6
8
-
3
7
4
,
2
0
1
0
.
[2
4
]
R.
M
c
Ca
in
,
"
De
v
e
lo
p
i
n
g
a
n
O
n
-
L
in
e
Tex
tb
o
o
k
:
Qu
e
sti
o
n
-
Le
d
T
each
in
g
a
n
d
t
h
e
Wo
rl
d
Wi
d
e
Web
,
"
T
h
e
J
o
u
rn
a
l
o
f
Eco
n
o
mic
Ed
u
c
a
ti
o
n
,
v
o
l.
3
0
,
n
o
.
3
,
p
p
.
2
1
0
-
2
2
0
,
1
9
9
9
.
[2
5
]
M
.
Ad
a
m
s,
"
T
h
e
P
ra
c
ti
c
a
l
P
rima
c
y
o
f
Qu
e
stio
n
s
in
Ac
ti
o
n
Lea
rn
in
g
,
"
in
Acti
o
n
L
e
a
rn
i
n
g
a
n
d
It
s
Ap
p
li
c
a
ti
o
n
s
,
Pre
se
n
t
a
n
d
F
u
tu
re
,
1
st ed
.
Y.
Bo
sh
y
k
a
n
d
L.
Dilw
o
rth
,
E
d
.
P
a
l
g
ra
v
e
M
a
c
m
il
lan
UK
,
p
p
.
1
1
9
-
1
3
0
,
2
0
1
0
.
[2
6
]
X.
Ge
a
n
d
S
.
M
.
Lan
d
,
"
S
c
a
ffo
l
d
in
g
stu
d
e
n
ts’
p
ro
b
lem
-
so
lv
in
g
p
ro
c
e
ss
e
s
in
a
n
il
l
-
stru
c
tu
re
d
tas
k
u
sin
g
q
u
e
sti
o
n
p
ro
m
p
ts a
n
d
p
e
e
r
in
tera
c
ti
o
n
s,"
E
T
R
&
D
,
v
o
l.
5
1
,
n
o
.
1
,
p
p
.
2
1
-
3
8
,
2
0
0
3
.
[2
7
]
I.
Ch
o
i,
S
.
Lan
d
a
n
d
A.
Tu
r
g
e
o
n
,
"
In
stru
c
t
o
r
m
o
d
e
li
n
g
a
n
d
o
n
li
n
e
q
u
e
stio
n
p
r
o
m
p
ts
f
o
r
s
u
p
p
o
rti
n
g
p
e
e
r
-
q
u
e
sti
o
n
in
g
d
u
ri
n
g
o
n
li
n
e
d
isc
u
ss
io
n
,
"
J
o
u
r
n
a
l
o
f
E
d
u
c
a
ti
o
n
a
l
T
e
c
h
n
o
l
o
g
y
S
y
ste
ms
,
v
o
l.
3
6
,
n
o
.
3
,
p
p
.
2
5
5
-
2
7
5
,
2
0
0
8.
[2
8
]
E.
Da
v
is,
"
S
c
a
ffo
l
d
in
g
st
u
d
e
n
ts'
k
n
o
wle
d
g
e
in
teg
ra
ti
o
n
:
P
r
o
m
p
ts
fo
r
re
flec
ti
o
n
i
n
KIE,
"
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
S
c
ien
c
e
Ed
u
c
a
ti
o
n
,
v
o
l.
2
2
,
n
o
.
8
,
p
p
.
8
1
9
-
8
3
7
,
2
0
0
0
.
[2
9
]
G
.
v
a
n
d
e
n
B
o
o
m
,
e
t
a
l.
,
"
Re
flec
ti
o
n
p
r
o
m
p
ts
a
n
d
tu
to
r
fe
e
d
b
a
c
k
i
n
a
w
eb
-
b
a
se
d
lea
rn
i
n
g
e
n
v
ir
o
n
m
e
n
t:
E
ffe
c
ts
o
n
stu
d
e
n
ts'
se
lf
-
re
g
u
late
d
lea
rn
i
n
g
c
o
m
p
e
ten
c
e
,
"
Co
m
p
u
ter
s i
n
Hu
m
a
n
Beh
a
v
io
r
,
v
o
l
.
2
0
,
n
o
.
4
,
p
p
.
5
5
1
-
5
6
7
,
2
0
0
4
.
[3
0
]
V.
Law
a
n
d
C.
-
H.
Ch
e
n
,
"
P
r
o
m
o
t
in
g
sc
ien
c
e
lea
rn
in
g
i
n
g
a
m
e
-
b
a
se
d
lea
rn
in
g
with
q
u
e
stio
n
p
ro
m
p
ts an
d
fe
e
d
b
a
c
k
,
"
Co
mp
u
ter
s &
E
d
u
c
a
t
io
n
,
v
o
l
.
1
0
3
,
p
p
.
1
3
4
-
1
4
3
,
De
c
.
2
0
1
6
.
[3
1
]
R.
G
a
rris,
R.
A
h
lers
a
n
d
J
.
E
.
Drisk
e
ll
,
"
G
a
m
e
s,
m
o
ti
v
a
ti
o
n
,
a
n
d
lea
rn
in
g
:
A
re
se
a
rc
h
a
n
d
p
ra
c
ti
c
e
m
o
d
e
l,
"
S
imu
l
a
ti
o
n
&
Ga
mi
n
g
,
v
o
l.
3
3
,
n
o
.
4
,
p
p
.
4
4
1
-
4
6
7
,
2
0
0
2
.
[3
2
]
K.
Dru
m
m
,
G
ra
p
h
Da
ta S
tru
c
tu
re
4
.
Dij
k
stra
’s S
h
o
rtes
t
P
a
th
Alg
o
rit
h
m
,
2
0
1
6
.
[3
3
]
Y.
S
h
a
k
e
e
l,
Di
jk
stra
'
s Alg
o
ri
th
m
Lev
e
l
2
o
f
2
-
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I
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J.
Ha
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M
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I
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Co
mp
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ti
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(ICIC)
,
Lah
o
re
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p
p
.
1
-
8
,
2
0
1
9
.
Evaluation Warning : The document was created with Spire.PDF for Python.