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 Vincent
> Diepeveen
> Sent: Saturday, December 04, 2004 17:43
> To: computer-go; computer-go
> Subject: RE: [computer-go] Pattern matching - example play
>
> If you make a small neural net you could just as well write the pattern
> knowledge yourself.
>
> If you create a big neural net of many layers, then training it
> is going to
> take too long.
>
> That's the problem of neural nets.

That's a possibility, as I said I don't have a lot of experience with Neural
Nets. I don't know to what point they can do some 'magic' by achieving a
weighting system that would be hard to achieve by hard-coding. But I know
that the coding is definitely hard :) and error-prone.

If NNs work for statistical games like backgammon, then I wouldn't want to
rule out its use for Go. I know someone who calls Go (tongue-in-cheek) a
game of chance. Although it sounds laughable at first, there's more than a
grain of truth in it. Go is so complex that even for the strongest players
there are a lot of uncertainties how the game is going to develop. It's a
game of perfect information in theory only. 'Good shape' for example, is
often (not always) nothing more than saying: empirically, in the past, it
has shown making these shapes cause problems later in the game less
frequently than 'bad shape'. Often human players agonize over making an
empty triangle. Their evaluation says there's immediate gain in playing it,
but their experience has given them statistical data indicating it could
still be a bad choice. When does one precede over the other is fuzzy to say
the least. And I thought NNs would be good at coping with this kind of
fuzziness. But it's just a guess.

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