computer-go: Data Mining and Rulebase for GO

["Ran Xiao" <ranxiao@xxxxxxxxxxxx>]

Hi, there,

I would like to ask if some one already did the following and what are the
results:
1. Represent each point on GO board as 0 (blank), 1 (white), or 2 (black),
thus the board can be expressed as a large number (>=0, and <= 3^360).
2. Rotate, mirror, and flip the board to get other 8 or 16 large numbers
from  symmetric configurations.
3. Pick the board configuration that has the smallest number as
representative. This will eliminate duplicated board configuration due to
symmetric. Also, this will make
4. Category game end results as -1 (loss), 0 (tie), or 1 (win) for each
playable position. Treat each playable position as a sub-system, so there
are 361 sub-systems.
5. Rule- or ANN-learning for each sub-system from existing game records.
6. Apply learnt results by first mapping the board configuration to have the
smallest large number, then use the mapped board configuration to check the
361 sub-system to find where should be the position to play.

If ANN is used to learn and the results are good enough, then, we would have
had a static mathematical evaluate function. If Rule is used, then we would
have had a better understanding of playing GO.

The above approach should not require a large programming resources.

Weimin Xiao

----- Original Message -----
From: "Rong Zeng" <rodneyzeng@xxxxxxxxxxxxxxxxx>
To: <computer-go@xxxxxxxxxxxxxxxxx>
Sent: Wednesday, November 08, 2000 5:53 PM
Subject: Re: Re: computer-go: minimax and go


Hi, everyone:

Here I'd like to talk some of my idea about the mathematical modeling of Go
game.

I have read some articles in Chinese about this problem and have some
experience with the popular game programs. Most of them adopted the
force-field theory when considering the influence of stones. But this theory
has some basic shortcomings when two sides fight closely together and made
programs confused. This is not a perfect theory about Go modeling.

I think Go game is some geometry. Two sides play discretly but would build
up continuous boundaries; stones groups would be disconnected or connected
to each other in various ways; the shapes of any stones group would effcet
its surviving and its easiness to build up eyes. One must consider force
field and geometry together to make a good model.

There are bulk of questions in the dynamic precess of playing go. But
there's still basic problem of static modeling when we make a program. This
is relatively simple and we can make it precisely and close to exact way of
human's thinking when we play. The goal of static modeling is to hash the
trees greatly and make selective points as few as possible, like human do.

I think the OOP technique can be used here and build up an engine on which
the dynamic processes can be built. It's a geometry with force-field. And
this static model engine would not take much time as most of the CPU time is
spent on searching. According to my understanding of those excellent game
engines nowdays such as STARCRAFT of Blizzard and some of EA SPORTS,  I
think a such kind of engine is not a big problem to them.

I don't agree that advanced AI techniques are the most important factors in
Go game programming. As we all know AI techniques applied very well to
Chess. So it's the Go game itself that becomes the bottleneck. If we cannot
make a good model for it, little progress will we make.

Welcome any opinions to my idea above.

Regards,
 Rong Zeng
 rodneyzeng@xxxxxxxxxxxxxxxxx