computer-go: abstract info and neural nets

["Julian Churchill" <jjc97c@xxxxxxxxxxx>]

 Hi,

 I have been puzzling over something for a few days now and wonder if anyone
has any ideas or opinions on it.
 I am using neural networks as part of a Go playing program I am writing and
so far have only fed the nets with basic information, i.e. the board
contents. I have noticed that other neural net programs (NeuroGo and Nicol
Schraudolph et al.) used much more complex approaches, inputting information
calculated about strings and groups, such as number of liberties and
connection possiblities.
 I was hoping, perhaps naively, that the neural network might somehow
'discover' the relevance of such things and the importance of them be
reflected in the weight adjustments and so it would be unnecessary to
calculate all that extra, possibly complicated, data. So do you think it's
better to let the network absorb these concepts, liberties, eyes etc.., (and
is it even possible) or would it be more useful to a Go playing program to
have some expert knowledge calculated previously and just fed straight into
the net?

 I would be delighted to hear any responses.

 Cheers,
 Jules


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