Re: computer-go: Neural Nets: suggesting and evaluating

[Heikki Levanto <heikki@xxxxxx>]

On Mon, Jul 28, 2003 at 04:16:11PM -0700, Peter Drake wrote:
> It appears that there are two obvious ways to use a neural network in a 
> very simple Go program:
> 
> 1) Take the board as input and output a value for each potential move.  
> [...]

I have often been wondering about using much higher level inputs to the
NN. By doing a lot of tactical reading, it should be easy to get a good
picture of which stones are connected (and how well), which are alive to
some degree, and so on. Evaluating the position from such preprocessed
data ought to be much simpler than evaluating from a raw board position. 
In fact, I have a (totally untested) feeling that with enough analysis,
the network should not need to see the board position at all.

For example, if we discuss tactical stability only, it might be
sufficient to count how many strings have one liberty, how many have
two, and so on. Same for second-order liberties, and string sizes, and
strings with more liberties than their neighbours, and a few more
things. Out of these a network should be able to learn to distinguish
between two positions, for example to see that connecting two strings is
better than creating one more weak string. Similar thinking ought to
apply for eyes and territories and most of the game...

Is this a well-known approach, or have I stumbled upon a new idea? Any
experiences or even speculation on how it might work or fail?

-H


-- 
Heikki Levanto  LSD - Levanto Software Development   <heikki@xxxxxxxxxxxxxxxxx>