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outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Blunder cost in Go and Hex

Brausen Hayward M¨

uller Qadir Spies

computing

univ alberta

2011 november

(2)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

1

blunder analysis

2

Fuego blunder analysis

3

MoHex blunder analysis

4

conclusions

(3)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis simple game model

blunder

def’n: to stumble blindly

an ignorant move (bad when good available)

a random move

end game: usually fatal

early game: maybe not

(4)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis simple game model

blunder

def’n: to stumble blindly

an ignorant move (bad when good available)

a random move

end game: usually fatal

early game: maybe not

(5)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis simple game model

blunder

def’n: to stumble blindly

an ignorant move (bad when good available)

a random move

end game: usually fatal

early game: maybe not

(6)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis simple game model

blunder

def’n: to stumble blindly

an ignorant move (bad when good available)

a random move

end game: usually fatal

early game: maybe not

(7)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis simple game model

blunder

def’n: to stumble blindly

an ignorant move (bad when good available)

a random move

end game: usually fatal

early game: maybe not

(8)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis simple game model

blunder

def’n: to stumble blindly

an ignorant move (bad when good available)

a random move

end game: usually fatal

early game: maybe not

(9)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis

simple game model

blunder analysis

assume stochastic-player

blunder: random move

what if one player always blunders on move k?

Fuego vs Fuego-blunder

MoHex vs MoHex-blunder

(10)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis

simple game model

blunder analysis

assume stochastic-player

blunder: random move

what if one player always blunders on move k?

Fuego vs Fuego-blunder

MoHex vs MoHex-blunder

(11)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis

simple game model

blunder analysis

assume stochastic-player

blunder: random move

what if one player always blunders on move k?

Fuego vs Fuego-blunder

MoHex vs MoHex-blunder

(12)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis

simple game model

blunder analysis

assume stochastic-player

blunder: random move

what if one player always blunders on move k?

Fuego vs Fuego-blunder

MoHex vs MoHex-blunder

(13)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis

simple game model

blunder analysis

assume stochastic-player

blunder: random move

what if one player always blunders on move k?

Fuego vs Fuego-blunder

MoHex vs MoHex-blunder

(14)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder

blunder analysis

simple game model

blunder analysis

assume stochastic-player

blunder: random move

what if one player always blunders on move k?

Fuego vs Fuego-blunder

MoHex vs MoHex-blunder

(15)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{0, . . . , T}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(16)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{0, . . . , T}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(17)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{0, . . . , T}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(18)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{

0, . . . , T

}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(19)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{

0, . . . , T

}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(20)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{

0, . . . , T

}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(21)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{

0, . . . , T

}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(22)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{

0, . . . , T

}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(23)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{

0, . . . , T

}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

(24)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: parameters

parameters

2-player alternate-move no-draw

T: max moves per game

t

: move

in

{

0, . . . , T

}

e

t

: solving ease after

t

moves

0 (hard) . . . 1 (easy)

w

t

: fraction winning moves

-1 (all lose) . . . 1 (all win)

w

t

<

0 means

all moves lose

after best move,

w

t

+1

≈ −

w

T

m

t

: score of move

t

-1 (bad) . . . 1 (good)

r

t

: rank of move

t

1 (worst) . . . k (best)

s

p

: strength of player

p

-1 (anti-perfect) . . . 1 (perfect)

Brausen Hayward M¨uller Qadir Spies Blunder cost in Go and Hex

(25)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: game simulation

game simulation

assume games non-pathological

for each

t

sample

m

t

with

E

[

m

t

] =

s

p

×

e

t

compute

r

t

from

m

t

using

w

t

, make move with rank

r

t

sample

w

t

+1

using

w

t

and

r

t

(strongest move from winning

position leaves no opponent winning moves)

(26)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: game simulation

game simulation

assume games non-pathological

for each

t

sample

m

t

with

E

[

m

t

] =

s

p

×

e

t

compute

r

t

from

m

t

using

w

t

, make move with rank

r

t

sample

w

t

+1

using

w

t

and

r

t

(strongest move from winning

position leaves no opponent winning moves)

(27)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: game simulation

game simulation

assume games non-pathological

for each

t

sample

m

t

with

E

[

m

t

] =

s

p

×

e

t

compute

r

t

from

m

t

using

w

t

, make move with rank

r

t

sample

w

t

+1

using

w

t

and

r

t

(strongest move from winning

position leaves no opponent winning moves)

(28)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: game simulation

game simulation

assume games non-pathological

for each

t

sample

m

t

with

E

[

m

t

] =

s

p

×

e

t

compute

r

t

from

m

t

using

w

t

, make move with rank

r

t

sample

w

t

+1

using

w

t

and

r

t

(strongest move from winning

position leaves no opponent winning moves)

(29)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: game simulation

game simulation

assume games non-pathological

for each

t

sample

m

t

with

E

[

m

t

] =

s

p

×

e

t

compute

r

t

from

m

t

using

w

t

, make move with rank

r

t

sample

w

t

+1

using

w

t

and

r

t

(strongest move from winning

position leaves no opponent winning moves)

(30)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: game simulation

game simulation

assume games non-pathological

for each

t

sample

m

t

with

E

[

m

t

] =

s

p

×

e

t

compute

r

t

from

m

t

using

w

t

, make move with rank

r

t

sample

w

t

+1

using

w

t

and

r

t

(strongest move from winning

position leaves no opponent winning moves)

(31)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: game simulation

game simulation

assume games non-pathological

for each

t

sample

m

t

with

E

[

m

t

] =

s

p

×

e

t

compute

r

t

from

m

t

using

w

t

, make move with rank

r

t

sample

w

t

+1

using

w

t

and

r

t

(strongest move from winning

position leaves no opponent winning moves)

(32)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: blunder cost

E

[

m

t]

s

p

×

e

t

assume

s

p

>

0

blunder

E

[

m

t

] = 0

(

s

p

0)

blunder cost: resulting drop in expected win rate

indirectly measures

s

p

×

e

t

×

w

t

(33)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: blunder cost

E

[

m

t

]

s

p

×

e

t

assume

s

p

>

0

blunder

E

[

m

t

] = 0

(

s

p

0)

blunder cost: resulting drop in expected win rate

indirectly measures

s

p

×

e

t

×

w

t

(34)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: blunder cost

E

[

m

t

]

s

p

×

e

t

assume

s

p

>

0

blunder

E

[

m

t

] = 0

(

s

p

0)

blunder cost: resulting drop in expected win rate

indirectly measures

s

p

×

e

t

×

w

t

(35)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: blunder cost

E

[

m

t

]

s

p

×

e

t

assume

s

p

>

0

blunder

E

[

m

t

] = 0

(

s

p

0)

blunder cost: resulting drop in expected win rate

indirectly measures

s

p

×

e

t

×

w

t

(36)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: blunder cost

E

[

m

t

]

s

p

×

e

t

assume

s

p

>

0

blunder

E

[

m

t

] = 0

(

s

p

0)

blunder cost: resulting drop in expected win rate

indirectly measures

s

p

×

e

t

×

w

t

(37)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

blunder blunder analysis

simple game model

simple game model: blunder cost

E

[

m

t

]

s

p

×

e

t

assume

s

p

>

0

blunder

E

[

m

t

] = 0

(

s

p

0)

blunder cost: resulting drop in expected win rate

indirectly measures

s

p

×

e

t

×

w

t

(38)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments

settings

Fuego vs Fuego-blunder

board 7×7, 9×9, 13×13

for each move

t

, 500 games

for each game, 10K MCTS simulations / move

resign threshold 0.05

white komi 7.5

(39)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments

settings

Fuego vs Fuego-blunder

board 7

×

7, 9

×

9, 13

×

13

for each move

t

, 500 games

for each game, 10K MCTS simulations / move

resign threshold 0.05

white komi 7.5

(40)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments

settings

Fuego vs Fuego-blunder

board 7

×

7, 9

×

9, 13

×

13

for each move

t

, 500 games

for each game, 10K MCTS simulations / move

resign threshold 0.05

white komi 7.5

(41)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments

settings

Fuego vs Fuego-blunder

board 7

×

7, 9

×

9, 13

×

13

for each move

t

, 500 games

for each game, 10K MCTS simulations / move

resign threshold 0.05

white komi 7.5

(42)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments

settings

Fuego vs Fuego-blunder

board 7

×

7, 9

×

9, 13

×

13

for each move

t

, 500 games

for each game, 10K MCTS simulations / move

resign threshold 0.05

white komi 7.5

(43)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments

settings

Fuego vs Fuego-blunder

board 7

×

7, 9

×

9, 13

×

13

for each move

t

, 500 games

for each game, 10K MCTS simulations / move

resign threshold 0.05

white komi 7.5

(44)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 10 20 30 40 50

blunder-player win rate

blunder move # Fuego vs. Fuego-blunder (7x7) black blunder white blunder active games black baseline white baseline

(45)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 10 20 30 40 50 60 70 80

blunder-player win rate

blunder move # Fuego vs. Fuego-blunder (9x9) black blunder white blunder active games black baseline white baseline

(46)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions settings experiments 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 10 20 30 40 50 60 70 80 90 100

blunder-player win rate

blunder move # Fuego vs. Fuego-blunder (13x13) black blunder white blunder active games black baseline white baseline

(47)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves settings experiments

Hex

(48)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves

settings experiments

all winning moves

(49)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves

settings

experiments

settings

MoHex vs MoHex-blunder

board 7×7, 9×9, 11×11

for each move

t

, 500 games

for each game, 1K MCTS simulations / move

7×7: after, use solver to find winning moves

(50)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves

settings

experiments

settings

MoHex vs MoHex-blunder

board 7

×

7, 9

×

9, 11

×

11

for each move

t

, 500 games

for each game, 1K MCTS simulations / move

7×7: after, use solver to find winning moves

(51)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves

settings

experiments

settings

MoHex vs MoHex-blunder

board 7

×

7, 9

×

9, 11

×

11

for each move

t

, 500 games

for each game, 1K MCTS simulations / move

7×7: after, use solver to find winning moves

(52)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves

settings

experiments

settings

MoHex vs MoHex-blunder

board 7

×

7, 9

×

9, 11

×

11

for each move

t

, 500 games

for each game, 1K MCTS simulations / move

7×7: after, use solver to find winning moves

(53)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves

settings

experiments

settings

MoHex vs MoHex-blunder

board 7

×

7, 9

×

9, 11

×

11

for each move

t

, 500 games

for each game, 1K MCTS simulations / move

7

×

7: after, use solver to find winning moves

(54)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves settings experiments 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 5 10 15 20 25 30

blunder-player win rate

blunder move # Mohex vs. Mohex-blunder (7x7) black blunder white blunder active games black baseline white baseline

(55)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves settings experiments 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 5 10 15 20 25 30

blunder-player win rate

blunder move # Mohex vs. Mohex-blunder (7x7) black blunder white blunder active games black baseline white baseline available wins

(56)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves settings experiments 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 5 10 15 20 25 30 35 40 45 50

blunder-player win rate

blunder move # Mohex vs. Mohex-blunder (9x9) black blunder white blunder active games black baseline white baseline

(57)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves settings experiments 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 5 10 15 20 25 30 35 40 45 50

blunder-player win rate

blunder move #

Mohex vs. Mohex-blunder (9x9) 250 sim./move

black blunder white blunder active games black baseline white baseline

(58)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

Hex

all winning moves settings experiments 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70

blunder-player win rate

blunder move # Mohex vs. Mohex-blunder (11x11) black blunder white blunder active games black baseline white baseline

(59)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

conclusions

start: low cost

after few moves: more costly

after: cost smooth

time management ?

not yet . . .

(60)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

conclusions

start: low cost

after few moves: more costly

after: cost smooth

time management ?

not yet . . .

(61)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

conclusions

start: low cost

after few moves: more costly

after: cost smooth

time management ?

not yet . . .

(62)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

conclusions

start: low cost

after few moves: more costly

after: cost smooth

time management ?

not yet . . .

(63)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

conclusions

start: low cost

after few moves: more costly

after: cost smooth

time management ?

not yet . . .

(64)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

future work

smarter “blunders”

eg: sample only from reasonable moves ?

(Hex: stay in mustplay

Go: no eye fill-in)

eg: blunder player less time (ponder player more time) ?

(65)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

future work

smarter “blunders”

eg: sample only from reasonable moves ?

(Hex: stay in mustplay

Go: no eye fill-in)

eg: blunder player less time (ponder player more time) ?

(66)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

future work

smarter “blunders”

eg: sample only from reasonable moves ?

(Hex: stay in mustplay

Go: no eye fill-in)

eg: blunder player less time (ponder player more time) ?

(67)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

future work

smarter “blunders”

eg: sample only from reasonable moves ?

(Hex: stay in mustplay

Go: no eye fill-in)

eg: blunder player less time (ponder player more time) ?

(68)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

future work

smarter “blunders”

eg: sample only from reasonable moves ?

(Hex: stay in mustplay

Go: no eye fill-in)

eg: blunder player less time (ponder player more time) ?

(69)

outline blunder analysis Fuego blunder analysis MoHex blunder analysis conclusions

conclusions

future work

dank u

thank you

UofA GAMES, Schaeffer

Natural Sciences and Engineering Research Council of Canada

References

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