[Computer-go] mcts and tactics

Andy andy.olsen.tx at gmail.com
Tue Dec 19 16:57:54 PST 2017

How do you interpret this quote from the AGZ paper?
"Surprisingly, shicho (“ladder” capture sequences that may span the whole
board) – one of the first elements of Go knowledge learned by humans – were
only understood by AlphaGo Zero much later in training."

To me "understood" means the neural network itself can read at least some
simple whole board ladders, ladder breakers, and ladder makers. I would
find it a large oversell if they just mean the MCTS search reads the ladder
across the whole board.

2017-12-19 18:16 GMT-06:00 Stephan K <stephan.kunne at gmail.com>:

> 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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