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March 2016
Mainstream Media
1997
Ing cup 1985 – 2000
(up to 1,400,000$)
(1985-2000)
5d win 1998
October 2015
This is the first time that a
computer program has defeated
a human professional player in the
full-sized game of Go, a feat
previously thought to be at
least a decade away.
Silver, D. et al., 2016. Mastering the game of Go with deep neural
networks and tree search. Nature, 529(7587), p.484-489.
January 2016
What did AlphaGo do to beat the
strongest human Go player?
Tobias Pfeiffer
@PragTob
pragtob.info
Go
Computational Challenge
Monte Carlo Method
Neural Networks
Revolution with Neural Networks
What did we learn?
Go
Computational Challenge
Go vs. Chess
Complex vs. Complicated
„While the Baroque rules of chess could only
have been created by humans, the rules of
go are so elegant, organic, and rigorously
logical that if intelligent life forms exist
elsewhere in the universe, they almost
certainly play go.“
Edward Lasker (chess grandmaster)
Larger board
19x19 vs. 8x8
Almost every move is legal
Average branching factor:
250 vs 35
State Space Complexity:
10171
vs 1047
1080
Global impact of moves
68957966354765
685766345
857635
563
6
MAX
MIN
MAX
MIN
MAX
Traditional Seach
68957966354765
685766345
857635
563
6
MAX
MIN
MAX
MIN
MAX
Evaluation Function
Monte Carlo Method
What is Pi?
How do you determine Pi?
2006
Browne, Cb, and Edward Powley. 2012. A survey of monte
carlo tree search methods. Intelligence and AI 4, no. 1: 1-49
2/4
1/1 0/1 1/1 0/1
A1
D5
F13C7
2/4
1/1 0/1 1/1 0/1
A1
D5
F13C7
Selection
2/4
1/1 0/1 1/1 0/1
A1
D5
F13C7
0/0
B5
Expansion
2/4
1/1 0/1 1/1 0/1
A1
D5
F13C7
0/0
B5
Simulation
Random
Not Human like?
3/5
2/2 0/1 1/1 0/1
A1
D5
F13C7
1/1
B5
Backpropagation
3/5
2/2 0/1 1/1 0/1
A1
D5
F13C7
1/1
B5
Perspective
2/5
1/2 0/1 1/1 0/1
A1
D5
F13C7
1/1
B5
Perspective
Multi Armed Bandit
Multi Armed Bandit
Exploitation vs Exploration
wins
visits
+explorationFactor
√ln(totalVisits)
visits
15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/23/3
15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/23/3
15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/23/3
Generate a valid random move
Who has won?
General Game Playing
Anytime
Lazy
Expert Knowledge
Neural Networks
2014
What does this even mean?
Neural Networks
Input
“Hidden” Layer
Output
Neural Networks
Weights
Bias/Threshold
4
2
-3
3.2
Activation
5.2 >= 4
2
-3
3.2
Activation
2.2 <= 4
2
-3
3.2
Activation
Activation
Training
Adjust parameters
Supervised Learning
Input
Expected
Output
Backpropagation
Data set
Training data + test data
Training
Verify
Overfitting
Deep Neural Networks
Convolutional Neural Networks
Local Receptive Field
Feature Map
Stride
Shared weights and biases
19 x 19
3 x 17 x 17
Multiple Feature maps/filters
Architecture
...
Input Features
12 layers with 64 – 192
filters
Output
Architecture
...
Input Features
12 layers with 64 – 192
filters
Output
Architecture
...
Input Features
12 layers with 64 – 192
filters
Output
2.3 million parameters
630 million connections
● Stone Colour x 3
● Liberties x 4
● Liberties after move played x 6
● Legal Move x 1
● Turns since x 5
● Capture Size x 7
● Ladder Move x 1
● KGS Rank x 9
Input Features
Training on game data predicting
the next move
55% Accuracy
Mostly beats GnuGo
Combined with MCTS in the
Selection
Asynchronous GPU Power
Revolution
Silver, D. et al., 2016. Mastering the game of Go with deep neural
networks and tree search. Nature, 529(7587), p.484-489.
Networks in Training
Silver, D. et al., 2016. Mastering the game of Go with deep neural
networks and tree search. Nature, 529(7587), p.484-489.
Networks in Training
Silver, D. et al., 2016. Mastering the game of Go with deep neural
networks and tree search. Nature, 529(7587), p.484-489.
AlphaGo Search
Selection
Action Value
Prior Probability
Visit Count
Selection
Action Value
Prior Probability
Visit Count
Action Value
Prior Probability
Visit Count
Selection
Action Value
Prior Probability
Visit Count
Selection
0.8
1.2 0.5 1.1 0.9
Action Value + Bonues
0.8
1.2 0.5 1.1 0.9
Expansion
0.8
1.2 0.5 1.1 0.9
Expansion
0.8
1.2 0.5 1.1 0.9
Prior Probability
0.8
1.2 0.5 1.1 0.9
Evalution
0.8
1.2 0.5 1.1 0.9
Evalution
0.8
1.2 0.5 1.1 0.9
Rollout
0.8
1.2 0.5 1.1 0.9
Value Network
0.81
1.3 0.5 1.1 0.9
Backup
1.6
1202 CPUs 176 GPUS
0.8
1.2 0.5 1.1 0.9
Tensor
Human Instinct Policy Network
Reading Capability Search
Positional Judgement Value Network
3 Strengths of AlphaGo
Human Instinct Policy Network
Reading Capability Search
Positional Judgement Value Network
Most Important Strength
More Natural
Lee Sedol match
Style
So when AlphaGo plays a slack looking move,
we may regard it as a mistake,
but perhaps it should more accurately be viewed
as a declaration of victory?
An Younggil 8p
Game 2
Game 4
Game 4
Game 4
What can we learn?
Making X faster
vs
Doing less of X
Benchmark everything
Solving problems the human way
vs
Solving problems the computer
way
Don't blindly dismiss approaches
as infeasible
One Approach
vs
Combination of Approaches
Joy of Creation
PragTob/Rubykon
pasky/michi
What did AlphaGo do to beat the
strongest human Go player?
Tobias Pfeiffer
@PragTob
pragtob.info
Sources
● Maddison, C.J. et al., 2014. Move Evaluation in Go Using Deep Convolutional Neural Networks.
● Silver, D. et al., 2016. Mastering the game of Go with deep neural networks and tree search.
Nature, 529(7587), p.484-489.
● Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press, 2015
http://neuralnetworksanddeeplearning.com
● Gelly, S. & Silver, D., 2011. Monte-Carlo tree search and rapid action value estimation in
computer Go. Artificial Intelligence, 175(11), p.1856-1876.
● I. Althöfer, “On the Laziness of Monte-Carlo Game Tree Search In Non-tight Situations,”
Friedrich-Schiller Univ., Jena, Tech. Rep., 2008.
● Browne, C. & Powley, E., 2012. A survey of monte carlo tree search methods. IEEE Transactions
on Intelligence and AI in Games, 4(1), p.1-49.
● Gelly, S. & Silver, D., 2007. Combining online and offline knowledge in UCT. Machine Learning,
p.273-280.
● https://www.youtube.com/watch?v=LX8Knl0g0LE&index=9&list=WL
Photo Credit
● http://www.computer-go.info/events/ing/2000/images/bigcup.jpg
● https://en.wikipedia.org/wiki/File:Kasparov-29.jpg
● http://www.geforce.com/hardware/desktop-gpus/geforce-gtx-titan-black/product-images
● http://giphy.com/gifs/dark-thread-after-lCP95tGSbMmWI
● https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chi
p.html
● https://gogameguru.com/i/2016/01/Fan-Hui-vs-AlphaGo-550x364.jpg
● http://makeitstranger.com/
● CC BY 2.0
– https://en.wikipedia.org/wiki/File:Deep_Blue.jpg
– https://www.flickr.com/photos/luisbg/2094497611/
● CC BY-SA 3.0
– https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#/media/File:AB_pruning.svg
● CC BY-SA 2.0
– https://flic.kr/p/cPUtny
– https://flic.kr/p/dLSKTQ
– https://www.flickr.com/photos/83633410@N07/7658272558/
Photo Credit
● CC BY-NC-ND 2.0
– https://flic.kr/p/q15pzb
– https://flic.kr/p/bHSj7D
– https://flic.kr/p/ixSsfM
– https://www.flickr.com/photos/waxorian/4228645447/
– https://www.flickr.com/photos/pennstatelive/8972110324/
– https://www.flickr.com/photos/dylanstraub/6428496139/
● https://en.wikipedia.org/wiki/Alphabet_Inc.#/media/File:Alphabet_Inc_Logo_2015.svg
● CC BY 3.0
– https://en.wikipedia.org/wiki/File:Pi_30K.gif

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