RE: [computer-go] Pattern matching - example play

["Mark Boon" <tesuji@xxxxxxxxx>]

> -----Original Message-----
> From: computer-go-bounces@xxxxxxxxxxxxxxxxx
> [mailto:computer-go-bounces@xxxxxxxxxxxxxxxxx]On Behalf Of Heikki Levanto
> Sent: Saturday, December 04, 2004 9:18
> To: computer-go
> Subject: Re: [computer-go] Pattern matching - example play
>
>
> On Fri, Dec 03, 2004 at 10:27:58PM +0100, Vincent Diepeveen wrote:
> >
> > >I would not completely write off higher-level planning and neural nets,
> > >and other fancy theories. Many of them have shown their values in
> >
> > We can have lengthy discussions, but the majority of ANN top researchers
> > agree with me here that for game playing ANN is completely useless.
>
> Well, as far as I know, neural nets work quite well for backgammon. I
> admit it s a very different game, but shows that ANNS are not
> *completely* useless for *game* *playing*.
>
> I agree with you, that if you just feed the board position into an ANN,
> you can at best train it to recognize similar games, and you will not
> anywhere. But there are other ways to use ANNs.
>
> For example, if you feed it the number of groups, the number of their
> liberites, and so on, it should be relatively easy for it to learn that
> a position where groups are connected is a better one than a situation
> with many small isolated groups. And if you feed it the results of some
> influence calculations, it should easily learn that the one that has
> most territory is often ahead. Both of these are easier to program in
> hand, I admit. But the same ANN could also learn to balance these
> separate considerations, and a few more, and come up witha  decent
> evaluation function.

Although I'm a beginner when it comes to neural-nets I can see lot of
interesting potential for neural nets for problems that so far have eluded
current Go programs. Recognising and evaluating aji is one. An important
concept that I don't think any program handles well, if at all (Goliath has
a half-hearted attempt in it.) Properly estimating the effect on the score
of having one or more weak groups is another. This goes in general for
weaknesses in a position and is closely related to aji. If you can't
recognise aji, then a program is always immediately going to play aji-keshi
if it results in a small short-term gain in the evaluation. For brute-force
approaches this will be another serious road-block.

"The worst move is a forcing-move that is always sente." a wise lesson by
Otake Hideo. Really understanding this makes almost any but the strongest
amateurs a stone stronger.

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