On Sat, Dec 20, 2014 at 9:35 PM, Robert Jasiek <jas...@snafu.de> wrote: > 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. >
Sure, I tried lots of simple heuristics to ease the learning task for the networks. One might hope that 'deep' networks would be able to learn advanced concepts more easily, perhaps more on par with human understanding, but for the near future that might still just be wishful thinking. At the time I didn't really care much for a fundamental distinction between territory and influence; I just wanted to have a function to predict the outcome of the game for every intersection as well as possible (because it seemed useful as an evaluation function). Intersections colored with high probability for one side tend to coincide with what human players call territory, while mediocre probabilities tend to coincide more with influence. I know there are non-probabilistic ways to define the two, but I'm not sure it really matters. Perhaps the more effective approach is to just go directly for the probability of winning (like MC does). Best, Erik _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
