[Computer-go] Move Evaluation in Go Using Deep Convolutional Neural Networks
davidstarsilver at gmail.com
Mon Dec 22 04:38:30 PST 2014
- Would you be willing to share some of the sgf game records played by your
> network with the community? I tried to replay the game record in your
> paper, but got stuck since it does not show any of the moves that got
Sorry about that, we will correct the figure and repost. In the meanwhile
Aja will post the .sgf for that game. Also, thanks for noticing that we
tested against a stronger version of Fuego than Clark and Storkey, we'll
evaluate against Fuego 1.1 and post the results. Unfortunately, we only
have approval to release the material in the paper, so we can't really give
any further data :-(
One more thing, Aja said he was tired when he measured his own performance
on KGS predictions (working too hard on this paper!) So it would be great
to get better statistics on how humans really do at predicting the next
move. Does anyone want to measure their own performance, say on 200
randomly chosen positions from the KGS data?
- Do you know how large is the effect from using the extra features that
> are not in the paper by Clarke and Storkey, i.e. the last move info and the
> extra tactics? As a related question, would you get an OK result if you
> just zeroed out some inputs in the existing net, or would you need to
> re-train a new network from fewer inputs.
We trained our networks before we knew about Clark and Storkey's results,
so we haven't had a chance to evaluate the differences between the
approaches. But it's well known that last move info makes a big difference
to predictive performance, so I'd guess they would already be close to 50%
predictive accuracy if they included those features.
> - Is there a way to simplify the final network so that it is faster to
> compute and/or easier to understand? Is there something computed, maybe on
> an intermediate layer, that would be usable as a new feature in itself?
This is an interesting idea, but so far we only focused on building a large
and deep enough network to represent Go knowledge at all.
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