2017-12-20 0:26 UTC+01:00, Dan <[email protected]>: > 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 _______________________________________________ Computer-go mailing list [email protected] http://computer-go.org/mailman/listinfo/computer-go
