[Computer-go] Teaching Deep Convolutional Neural Networks to Play Go

René van de Veerdonk rene.vandeveerdonk at gmail.com
Mon Dec 15 20:46:43 PST 2014


Correct me if I am wrong, but I believe that the CrazyStone approach of
team-of-features can be cast in terms of a shallow neural network. The
inputs are matched patterns on the board and other local information on
atari, previous moves, ko situation, and such. Remi alluded as much on this
list sometime after his paper got published.

Without having studied the Deep Learning papers in detail, it seems that
these are the types of "smart features" that could be learned by a Deep
Neural Net in the first few layers if the input is restricted to just the
raw board, but could equally well be provided as domain specific features
in order to improve computational efficiency (and perhaps enforce
correctness).

These approaches may not be all that far apart, other than the depth of the
net and the domain specific knowledge used directly. Remi recently
mentioned that the number of patterns in more recent versions of CrazyStone
also number in the millions. I think the prediction rates for these two
approaches are also pretty close. Compare the Deep Learning result to the
other recent study of a German group quoted in the Deep Learning paper.

The bigger questions to me are related to engine architecture. Are you
going to use this as an input to a search? Or are you going to use this
directly to play? If the former, it had better be reasonably fast. The
latter approach can be far slower, but requires the predictions to be of
much higher quality. And the biggest question, how can you make these two
approaches interact efficiently?

René

On Mon, Dec 15, 2014 at 8:00 PM, Brian Sheppard <sheppardco at aol.com> wrote:
>
> >Is it really such a burden?
>
>
>
> Well, I have to place my bets on some things and not on others.
>
>
>
> It seems to me that the costs of a NN must be higher than a system based
> on decision trees. The convolution NN has a very large parameter space if
> my reading of the paper is correct. Specifically, it can represent all
> patterns translated and rotated and matched against all points in parallel.
>
>
>
> To me, that seems like a good way to mimic the visual cortex, but an
> inefficient way to match patterns on a Go board.
>
>
>
> So my bet is on decision trees. The published research on NN will help me
> to understand the opportunities much better, and I have every expectation
> that the performance of decision trees should be >= NN in every way. E.g.,
> faster, more accurate, easier and faster to tune.
>
>
>
> I recognize that my approach is full of challenges. E.g., a NN would
> automatically infer "soft" qualities such as "wall", "influence" that would
> have to be provided to a DT as inputs. No free lunch, but again, this is
> about betting that one technology is (overall) more suitable than another.
>
>
>
>
>
>
>
> *From:* Computer-go [mailto:computer-go-bounces at computer-go.org] *On
> Behalf Of *Stefan Kaitschick
> *Sent:* Monday, December 15, 2014 6:37 PM
> *To:* computer-go at computer-go.org
> *Subject:* Re: [Computer-go] Teaching Deep Convolutional Neural Networks
> to Play Go
>
>
>
>
>
>
> Finally, I am not a fan of NN in the MCTS architecture. The NN
> architecture imposes a high CPU burden (e.g., compared to decision trees),
> and this study didn't produce such a breakthrough in accuracy that I would
> give away performance.
>
>
>
>  Is it really such a burden? Supporting the move generator with the NN
> result high up in the decision tree can't be that expensive.
>
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