Re: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points?

If you accumulate end scores of playout results, you can make a histogram by plotting the frequency of a score f(s) as a function of the score. The winrate is the sum(f(s)) where s > 0. The average score is sum(s * f(s)) / sum(s) summed over all s. When the distibution can be approximated by a

Re: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points?

On Tue, Feb 23, 2016 at 4:41 PM, Justin .Gilmer wrote: > I made a similar attempt as Alvaro to predict final ownership. You can > find the code here: https://github.com/jmgilmer/GoCNN/. It's trained to > predict final ownership for about 15000 professional games which were >

Re: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points?

I made a similar attempt as Alvaro to predict final ownership. You can find the code here: https://github.com/jmgilmer/GoCNN/. It's trained to predict final ownership for about 15000 professional games which were played until the end (didn't end in resignation). It gets about 80.5% accuracy on a

Re: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points?

On 23.02.2016 11:36, Michael Markefka wrote: whether one could train a DCNN for expected territory First, some definition of territory must be chosen or stated. Second, you must decide if territory according to this definition can be determined by a neural net meaningfully at all. Third, if

Re: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points?

I have experimented with a CNN that predicts ownership, but I found it to be too weak to be useful. The main difference between what Google did and what I did is in the dataset used for training: I had tens of thousands of games (I did several different experiments) and I used all the positions

[Computer-go] Move evalution by expected value, as product of expected winrate and expected points?

Hello everyone, in the wake of AlphaGo using a DCNN to predict expected winrate of a move, I've been wondering whether one could train a DCNN for expected territory or points successfully enough to be of some use (leaving the issue of win by resignation for a more in-depth discussion). And,