[Computer-go] Building a database for training CNNs

Robert Jasiek jasiek at snafu.de
Sat Dec 20 12:35:41 PST 2014

On 20.12.2014 17:04, Erik van der Werf wrote:
> the critical part is in learning about life &
> death. Once you have that, estimating ownership is fairly easy
 > [...] See the following papers for more details: [...]
> http://erikvanderwerf.tengen.nl/pubdown/predicting_territory.pdf

Estimating ownership or evaluation functions to predict final scores of 
already played games are other things than estimating potential 
territory. Therefore I dislike the title of your paper. Apart from lots 
of simplistic heuristics without relation to human understanding of 
territorial positional judgement, one thing has become clear to me from 
your paper:

There are two fundamentally different ways of assessing potential territory:

1) So far mainly human go: count territory, do not equate influence as 
additional territory.

2) So far mainly computer go: count territory, equate influence as 
additional territory.

Human players might think as follows: "The player leads by T points. 
Therefore the opponent has to use his superior influence to make T more 
new points than the player." Computers think like this: "One value is 
simpler than two values, therefore I combine territory and influence in 
just one number, the predicted score."

Both methods have their advantages and disadvantages, but it does not 
mean that computers would always have to use (2); they can as well learn 
to use (1). (1) has the advantage that counting territory (or 
intersections that are almost territory) is "easy" for quiet positions.

Minor note on your paper: "influence" and "thickness" are defined now 
(see Joseki 2 - Strategy) and "influence stone difference" and 
"mobility" are related concepts if one wants simpler tools. "aji" has 
approached a mathematical definition a bit but still some definition 
work remains.

robert jasiek

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