On 20.12.2014 11:21, Detlef Schmicker wrote:
> it is not easy to get training data sets for an evaluation function?!
You seem be asking for abundant data sets, e.g., with triples Position,
Territory, Influence. Indeed, only dozens are available in the
literature and need a bit of extra work. Hundreds of available local
joseki positions do not fit your purpose, e.g., because also the Stone
Difference matters there. However, I suggest a different approach:
1) One strong player (strong enough to be accurate +-1 point of
territory when using his known judgement methods) creates a few
examples, e.g., by taking the existing examples for territory and adding
the influence stone difference. It should be only one player so that the
values are created consistently. (If several players are involved, they
should discuss and agree on their application of known methods.)
2) Code is implemented and produces sample data sets.
3) The same player judges how far off the sample data are from his own
judgement.
Thereby, training does not require many thousands of data sets. Instead
it requires much of a strong player's time to accurately judge dozens of
data sets. In theory, the player could be replaced by program judgement,
but I wish happy development of the then necessary additional theory and
algorithms! ;)
As you see, I suggest human/program collaboration to accelerate program
playing strength. Maybe 9p programs can be created without strong
players' help, but then we will not understand much in terms of go
theory why the programs will excel. For getting much understanding of go
theory from programs, human/program collaboration will be necessary anyway.
--
robert jasiek
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