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Challenges for implementing Monte Carlo Tree
Search in commercial games
Matthew Bedder
16/Sept/2016 1
16/Sept/2016 2
Disclaimer
This is pitched at people with little/no knowledge
of the area
(Sorry, AI people)
“A game is a series of
interesting choices”
16/Sept/2016 3
16/Sept/2016 4
Monte Carlo Tree Search
16/Sept/2016 5
16/Sept/2016 6
Monte Carlo Tree Search
Only generate the bits of the game tree we need
16/Sept/2016 7
MCTS – the basics
Iteratively builds a decision tree
16/Sept/2016 8
MCTS Selection
16/Sept/2016 9
MCTS Expansion
16/Sept/2016 10
MCTS Simulation
16/Sept/2016 11
Monte Carlo evaluations
Performing random actions gives us a (weak)
approximation of state values
X O X O
X
O
X
O
X
16/Sept/2016 12
MCTS Simulation
16/Sept/2016 13
MCTS Backpropagation
16/Sept/2016 14
MCTS – the basics
Applications of MCTS
16/Sept/2016 15
16/Sept/2016 16
MCTS applications
• Amazons
• Arimaa
• Connect 4
• Dou Di Zhu
• Go variants
• Havannah
• Hex
• Morpion Solitaire
• Othello
• Settlers of Catan
• Spades
• ...
Many board and card games, including:
16/Sept/2016 17
16/Sept/2016 18
MCTS applications
All of the strongest AI
players of Go use
variants of MCTS
16/Sept/2016 19
MCTS applications
Currently there are very limited applications to
commercial video games or other domains
16/Sept/2016 20
http://www.aifactory.co.uk/AIF_Games_Spades.htm
16/Sept/2016 21
MCTS Successes
http://aigamedev.com/open/coverage/mcts-rome-ii/
16/Sept/2016 22
http://gwaredd.github.io/nuclai_mcts
Challenges for MCTS
16/Sept/2016 23
16/Sept/2016 24
Games are hard
There are as many states of Civilization II as
there are atoms in the observable universe
10,000,000,000,000,000,000,000,000,000,000,000,000,
000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000
times over
16/Sept/2016 25
Computational cost
This is actually important
Game AIs (usually) are limited to time sliced CPU
AlphaGo used
• 1,920 CPUs and 280 GPUs
• ~1MW
16/Sept/2016 26
Monte Carlo Tree Search optimisations
Generally either:
Domain specific
or
Overly generic
16/Sept/2016 27
Monte Carlo Tree Search optimisations
Replacing random simulations helps
… usually
Adding prior knowledge helps
… usually
16/Sept/2016 28
Our approach
Domain-specific optimisations for arbitrary
domains
I’d love to talk about our work during/after coffee
www-users.cs.york.ac.uk/~bedder
matthew@bedder.co.uk
@bedder
Matthew Bedder

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Challenges for implementing Monte Carlo Tree Search in commercial games