>>> For instance, if an intersection belongs to the same colour in all
>>> playouts, chances are
>>> that it is fairly secure (that doesn't mean one
>> shouldn't play there, sacrifices there may have an impact on other
>> intersections).
Ok, that one was well know (ownership maps, territory heuristic).
>> Or, if an intersection is black in all playouts won by black, and white
>> in all playouts won
>> by white, chances are that it is fairly important to play there (since
>> playouts are random,
>> there is no guarantee, but emphasizing such intersections, and their
>> ordering, in the top-level
>> tree search seems profitable).
That one was related to some existing work, and though perhaps not
exactly the same, already under research:
> We (the Orego team) have done some work along these lines this summer. We're
> working on a paper.
There is also this paper, right on topic:
Combining tactical search and Monte-Carlo in the game of Go
Tristan Cazenave, Bernard Helmstetter
http://www.citeulike.org/group/5884/article/2990531
That still seems to leave some other possibilities, though.
For instance, what about intersections that are 50/50, independent
of who plays them first? If they exist, it would seem that their fate is
determined by plays elsewhere, in particular, it doesn't seem like a
good idea to play there, and if one does already have a stone there,
one might have to do something about it. Also, in a balanced game,
one would like to see some balance of undecided intersections.
Or what about keeping the difference between statistical evaluations
for a few moves? Our own moves are driven by having positive effects
on those values, but if an opponent's move changes only one of the
values (without offering compensation in another value), one would
do well to answer it, and the particular values affected (does it unsettle
a stable group, or influence previously neutral territory, or ..) might give
an indication of the nature of the imbalance, and possible ways to
counter it (sufficiently to restore the balance).
Do these make sense? And are there other useful items one might be
able to extract from the playout statistics, online, during the game (as
opposed to offline learning, from self-play)?
Of course, some of this could be expected to emerge from playouts
without additional intervention, eg, if one always tries to optimize the
statistical results, then good answers to imbalances introduced by
opponent play should emerge. But given the size of the search space,
having more concrete handles on information to help directing the
search might still be useful.
Claus
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