When the tree is expanded and we have no knowledge of go the
statistics for each move is initialized to 0 wins and 0 visits, as
well as 0 AMAF visits and 0 AMAF wins.
With prior knowledge such as that we know that the last move can be
captured in a ladder this move can be initialized with for example 10
Wins and 10 Visits. This makes sure this move will be searched until
is has been refuted or a better move found. The prior modification can
be added to the AMAF statistics, or to the "real" playout statistics
for the move.
Without priors the search will just play random moves until one seems
good. But for statistical reasons there will always be bad moves that
get a high win rate because of random variation and good moves get bad
win rates. With high quality priors the program will search moves that
must be searched locally at least making search sharper (without prior
values for moves tend to be the average of all moves searched before
AMAF kick in and select better moves) and robust against the
randomness of the playouts so there is less risk of missing to search
the best move.
When Valkyria expands the tree with a new node it will call its
tactical module for responses to the last move and add "prior wins and
visits" to all candidate moves of that node that has a tactical
response value. Other programs may call some slower pattern matching
module to get high quality priors for each move on the board. I think
this is what makes many faces strong.
Magnus
Quoting Fuming Wang <[email protected]>:
Erik,
When you say "priors in the tree", do you mean the tree data inherited from
calculations in the previous move?
Regards,
Fuming
On Thu, Sep 9, 2010 at 5:43 PM, Erik van der Werf
<[email protected]>wrote:
On Thu, Sep 9, 2010 at 9:33 AM, <[email protected]> wrote:
breaking even with gnugo on 9x9 at about 100 playouts), though I think
I get most of that from good priors in the tree. Maybe I should dust
off my old policy some time...
Erik
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