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March 2016
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Mainstream Media
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1997
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Ing cup 1985 – 2000
(up to 1,400,000$)
(1985-2000)
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5d win 1998
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October 2015
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“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
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What did AlphaGo do to beat the
strongest human Go player?
Tobias Pfeiffer
@PragTob
pragtob.info
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Go
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Computational Challenge
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Monte Carlo Method
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Neural Networks
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Revolution with Neural Networks
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What did we learn?
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Go
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Computational Challenge
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Go vs. Chess
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Complex vs. Complicated
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„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)
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Larger board
19x19 vs. 8x8
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Almost every move is legal
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Average branching factor:
250 vs 35
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State Space Complexity:
10171 vs 1047
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1080
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Global impact of moves
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MAX
MIN
MAX
MIN
MAX
Traditional Seach
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Evaluation Function
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Monte Carlo Method
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What is Pi?
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How do you determine Pi?
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No content
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2006
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Browne, Cb, and Edward Powley. 2012. A survey of monte
carlo tree search methods. Intelligence and AI 4, no. 1: 1-49
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2/4
1/1 0/1 1/1 0/1
A1
D5
F13
C7
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2/4
1/1 0/1 1/1 0/1
A1
D5
F13
C7
Selection
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2/4
1/1 0/1 1/1 0/1
A1
D5
F13
C7
0/0
B5
Expansion
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2/4
1/1 0/1 1/1 0/1
A1
D5
F13
C7
0/0
B5
Simulation
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Random
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Not Human like?
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3/5
2/2 0/1 1/1 0/1
A1
D5
F13
C7
1/1
B5
Backpropagation
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3/5
2/2 0/1 1/1 0/1
A1
D5
F13
C7
1/1
B5
Perspective
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2/5
1/2 0/1 1/1 0/1
A1
D5
F13
C7
1/1
B5
Perspective
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Multi Armed Bandit
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Multi Armed Bandit
Exploitation vs Exploration
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wins
visits
+explorationFactor
√ln(totalVisits)
visits
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15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/2
3/3
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15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/2
3/3
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15042
86/193
0/1 1/2 0/2
36/1116
2/2
58/151
1/2 0/2
3/3
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Generate a valid random move
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Who has won?
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General Game Playing
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Anytime
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Lazy
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Expert Knowledge
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Neural Networks
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2014
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What does this even mean?
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Neural Networks
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Input
“Hidden” Layer
Output
Neural Networks
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Weights
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Bias/Threshold
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4
2
-3
3.2
Activation
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5.2 >= 4
2
-3
3.2
Activation
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2.2 <= 4
2
-3
3.2
Activation
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Activation
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Training
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Adjust parameters
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Supervised Learning
Input
Expected
Output
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Backpropagation
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Data set
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Training data + test data
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Training
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Verify
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Overfitting
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Deep Neural Networks
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Convolutional Neural Networks
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Local Receptive Field
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Feature Map
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Stride
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Shared weights and biases
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19 x 19
3 x 17 x 17
Multiple Feature maps/filters
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Architecture
...
Input Features
12 layers with 64 – 192
filters
Output
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Architecture
...
Input Features
12 layers with 64 – 192
filters
Output
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Architecture
...
Input Features
12 layers with 64 – 192
filters
Output
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2.3 million parameters
630 million connections
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Stone Colour x 3
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Liberties x 4
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Liberties after move played x 6
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Legal Move x 1
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Turns since x 5
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Capture Size x 7
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Ladder Move x 1
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KGS Rank x 9
Input Features
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Training on game data predicting
the next move
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55% Accuracy
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Mostly beats GnuGo
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Combined with MCTS in the
Selection
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Asynchronous GPU Power
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Revolution
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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
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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
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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
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Selection
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Action Value
Prior Probability
Visit Count
Selection
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Action Value
Prior Probability
Visit Count
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Action Value
Prior Probability
Visit Count
Selection
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Action Value
Prior Probability
Visit Count
Selection
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0.8
1.2 0.5 1.1 0.9
Action Value + Bonuses
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0.8
1.2 0.5 1.1 0.9
Expansion
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0.8
1.2 0.5 1.1 0.9
Expansion
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0.8
1.2 0.5 1.1 0.9
Prior Probability
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0.8
1.2 0.5 1.1 0.9
Evalution
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0.8
1.2 0.5 1.1 0.9
Evalution
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0.8
1.2 0.5 1.1 0.9
Rollout
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0.8
1.2 0.5 1.1 0.9
Value Network
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0.81
1.3 0.5 1.1 0.9
Backup
1.6
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1202 CPUs 176 GPUS
0.8
1.2 0.5 1.1 0.9
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Tensor
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Human Instinct Policy Network
Reading Capability Search
Positional Judgement Value Network
3 Strengths of AlphaGo
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Human Instinct Policy Network
Reading Capability Search
Positional Judgement Value Network
Most Important Strength
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More Natural
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Lee Sedol match
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Style
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Game 2
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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
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Game 4
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Game 4
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Game 4
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What can we learn?
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Making X faster
vs
Doing less of X
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Benchmark everything
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Solving problems the human way
vs
Solving problems the computer
way
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Don't blindly dismiss approaches
as infeasible
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One Approach
vs
Combination of Approaches
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Joy of Creation
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PragTob/Rubykon
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pasky/michi
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What did AlphaGo do to beat the
strongest human Go player?
Tobias Pfeiffer
@PragTob
pragtob.info
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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
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Gelly, S. & Silver, D., 2011. Monte-Carlo tree search and rapid action value estimation in
computer Go. Artificial Intelligence, 175(11), p.1856-1876.
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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
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Photo Credit
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http://www.computer-go.info/events/ing/2000/images/bigcup.jpg
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https://en.wikipedia.org/wiki/File:Kasparov-29.jpg
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http://www.geforce.com/hardware/desktop-gpus/geforce-gtx-titan-black/product-images
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http://giphy.com/gifs/dark-thread-after-lCP95tGSbMmWI
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https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chi
p.html
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https://gogameguru.com/i/2016/01/Fan-Hui-vs-AlphaGo-550x364.jpg
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http://makeitstranger.com/
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CC BY 2.0
– https://en.wikipedia.org/wiki/File:Deep_Blue.jpg
– https://www.flickr.com/photos/luisbg/2094497611/
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CC BY-SA 3.0
– https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#/media/File:AB_pruning.svg
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CC BY-SA 2.0
– https://flic.kr/p/cPUtny
– https://flic.kr/p/dLSKTQ
– https://www.flickr.com/photos/83633410@N07/7658272558/
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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/
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https://en.wikipedia.org/wiki/Alphabet_Inc.#/media/File:Alphabet_Inc_Logo_2015.svg
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CC BY 3.0
– https://en.wikipedia.org/wiki/File:Pi_30K.gif