[Computer-go] mcts and tactics

Stephan K stephan.kunne at gmail.com
Tue Dec 19 16:16:57 PST 2017


2017-12-20 0:26 UTC+01:00, Dan <dshawul at gmail.com>:
> Hello all,
>
> It is known that MCTS's week point is tactics. How is AlphaZero able to
> resolve Go tactics such as ladders efficiently? If I recall correctly many
> people were asking the same question during the Lee Sedo match -- and it
> seemed it didn't have any problem with ladders and such.

Note that the input to the neural networks in the version that played
against Lee Sedol had a lot of handcrafted features, including
information about ladders. See "extended data table 2", page 11 of the
Nature article. You can imagine that as watching the go board through
goggles that put a flag on each intersection that would result in a
successful ladder capture, and another flag on each intersection that
would result in a successful ladder escape.

(It also means that you only need to read one move ahead to see
whether a move is a successful ladder breaker or not.)

Of course, your question still stands for the Zero versions.

Here is the table :

Feature			# of planes		Description

Stone colour		3				Player stone / opponent stone / empty
Ones			1				A constant plane filled with 1
Turns since		8				How many turns since a move was played
Liberties			8				Number of liberties (empty adjacent points)
Capture size		8				How many opponent stones would be captured
Self-atari size		8				How many of own stones would be captured
Liberties after move		8			Number of liberties after this move is played
Ladder capture	1				Whether a move at this point is a successful ladder capture
Ladder escape		1				Whether a move at this point is a successful ladder escape
Sensibleness		1				Whether a move is legal and does not fill its own eyes
Zeros			1				A constant plane filled with 0

Player color		1				Whether current player is black


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