My 1.5 cent: David Fotland has a nice score-estimator in his (old) ManyFaces bot. The score estimator is still from the days before the Monte Carlo version.
Perhaps, David can improve on this estimator with help of CNNs. Ingo. Gesendet: Dienstag, 23. Februar 2016 um 16:41 Uhr Von: "Justin .Gilmer" <jmgil...@gmail.com> An: computer-go@computer-go.org Betreff: 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 held out test set, although the accuracy greatly varies based on how far through the game you are. Can't say how well it would work in a go player. -Justin On Tue, Feb 23, 2016 at 7:00 AM, <computer-go-requ...@computer-go.org[computer-go-requ...@computer-go.org]> wrote:Send Computer-go mailing list submissions to computer-go@computer-go.org[computer-go@computer-go.org] To subscribe or unsubscribe via the World Wide Web, visit http://computer-go.org/mailman/listinfo/computer-go[http://computer-go.org/mailman/listinfo/computer-go] or, via email, send a message with subject or body 'help' to computer-go-requ...@computer-go.org[computer-go-requ...@computer-go.org] You can reach the person managing the list at computer-go-ow...@computer-go.org[computer-go-ow...@computer-go.org] When replying, please edit your Subject line so it is more specific than "Re: Contents of Computer-go digest..." Today's Topics: 1. Re: Congratulations to Zen! (Robert Jasiek) 2. Move evalution by expected value, as product of expected winrate and expected points? (Michael Markefka) 3. Re: Move evalution by expected value, as product of expected winrate and expected points? (Álvaro Begué) 4. Re: Move evalution by expected value, as product of expected winrate and expected points? (Robert Jasiek) ---------------------------------------------------------------------- Message: 1 Date: Mon, 22 Feb 2016 19:13:20 +0100 From: Robert Jasiek <jas...@snafu.de[jas...@snafu.de]> To: computer-go@computer-go.org[computer-go@computer-go.org] Subject: Re: [Computer-go] Congratulations to Zen! Message-ID: <56cb4fc0.4010...@snafu.de[56cb4fc0.4010...@snafu.de]> Content-Type: text/plain; charset=UTF-8; format=flowed Aja, sorry to bother you with trivialities, but how does Alphago avoid power or network failures and such incidents? -- robert jasiek ------------------------------ Message: 2 Date: Tue, 23 Feb 2016 11:36:57 +0100 From: Michael Markefka <michael.marke...@gmail.com[michael.marke...@gmail.com]> To: computer-go@computer-go.org[computer-go@computer-go.org] Subject: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points? Message-ID: <cajg7papu_gbhvny3cv+d-p238_hkqkv5pojxozjly4nsqas...@mail.gmail.com[cajg7papu_gbhvny3cv%2bd-p238_hkqkv5pojxozjly4nsqas...@mail.gmail.com]> Content-Type: text/plain; charset=UTF-8 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, whether winrate and expected territory (or points) always run in parallel or whether there are diverging moments. Computer Go programs play what are considered slack or slow moves when ahead, sometimes being too conservative and giving away too much of their potential advantage. If expected points and expected winrate diverge, this could be a way to make the programs play in a more natural way, even if there were no strength increase to be gained. Then again there might be a parameter configuration that might yield some advantage and perhaps this configuration would need to be dynamic, favoring winrate the further the game progresses. As a general example for the idea, let's assume we have the following potential moves generated by our program: #1: Winrate 55%, +5 expected final points #2: Winrate 53%, +15 expected final points Is the move with higher winrate always better? Or would there be some benefit to choosing #2? Would this differ depending on how far along the game is? If we knew the winrate prediction to be perfect, then going by that alone would probably result in the best overall performance. But given some uncertainty there, expected value could be interesting. Any takers for some experiments? -Michael ------------------------------ Message: 3 Date: Tue, 23 Feb 2016 06:44:04 -0500 From: Álvaro Begué <alvaro.be...@gmail.com[alvaro.be...@gmail.com]> To: computer-go <computer-go@computer-go.org[computer-go@computer-go.org]> Subject: Re: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points? Message-ID: <CAF8dVMWLPQBhD-Q07YeLZwqV9M9JCW+_VbSRVp=evj9cn6w...@mail.gmail.com[evj9cn6w...@mail.gmail.com]> Content-Type: text/plain; charset="utf-8" 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 from each game (which is known to be problematic); they used 30M positions from independent games. I expect you can learn a lot about ownership and expected number of points from a dataset like that. Unfortunately, generating such a dataset is infeasible with the resources most of us have. Here's an idea: Google could make the dataset publicly available for download, ideally with the final configurations of the board as well. There is a tradition of making interesting datasets for machine learning available, so I have some hope this may happen. The one experiment I would like to make along the lines of your post is to train a CNN to compute both the expected number of points and its standard deviation. If you assume the distribution of scores is well approximated by a normal distribution, maximizing winning probability can be achieved by maximizing (expected score) / (standard deviation of the score). I wonder if that results in stronger or more natural play than making a direct model for winning probability, because you get to learn more about each position. Álvaro. On Tue, Feb 23, 2016 at 5:36 AM, Michael Markefka < michael.marke...@gmail.com[michael.marke...@gmail.com]> wrote: > 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, > whether winrate and expected territory (or points) always run in > parallel or whether there are diverging moments. > > Computer Go programs play what are considered slack or slow moves when > ahead, sometimes being too conservative and giving away too much of > their potential advantage. If expected points and expected winrate > diverge, this could be a way to make the programs play in a more > natural way, even if there were no strength increase to be gained. > Then again there might be a parameter configuration that might yield > some advantage and perhaps this configuration would need to be > dynamic, favoring winrate the further the game progresses. > > > As a general example for the idea, let's assume we have the following > potential moves generated by our program: > > #1: Winrate 55%, +5 expected final points > #2: Winrate 53%, +15 expected final points > > Is the move with higher winrate always better? Or would there be some > benefit to choosing #2? Would this differ depending on how far along > the game is? > > If we knew the winrate prediction to be perfect, then going by that > alone would probably result in the best overall performance. But given > some uncertainty there, expected value could be interesting. > > > Any takers for some experiments? > > > -Michael > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org[Computer-go@computer-go.org] > http://computer-go.org/mailman/listinfo/computer-go[http://computer-go.org/mailman/listinfo/computer-go] -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://computer-go.org/pipermail/computer-go/attachments/20160223/700a08a3/attachment-0001.html[http://computer-go.org/pipermail/computer-go/attachments/20160223/700a08a3/attachment-0001.html]> ------------------------------ Message: 4 Date: Tue, 23 Feb 2016 12:54:22 +0100 From: Robert Jasiek <jas...@snafu.de[jas...@snafu.de]> To: computer-go@computer-go.org[computer-go@computer-go.org] Subject: Re: [Computer-go] Move evalution by expected value, as product of expected winrate and expected points? Message-ID: <56cc486e.1030...@snafu.de[56cc486e.1030...@snafu.de]> Content-Type: text/plain; charset=UTF-8; format=flowed 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 yes, do it. Note that there are very different definitions of territory. The most suitable definition for positional judgement (see Positional Judgement 1 - Territory) is sophisticated and requires a combination of expert rules (specifying for what to detemine, and how to read to determine it) and reading. A weak definition could predict whether a particular intersections will be territory in the game end's scoring position. Such can be fast for MC or NN, and maybe such is good enough as a very rough approximation for programs. For humans, such is very bad because it neglects different degrees of safety of (potential) territory and the strategic concepts of sacrifice and exchange. I have also suggested other definitions, but IMO they are less attractive for NN. -- robert jasiek ------------------------------ Subject: Digest Footer _______________________________________________ Computer-go mailing list Computer-go@computer-go.org[Computer-go@computer-go.org] http://computer-go.org/mailman/listinfo/computer-go ------------------------------ End of Computer-go Digest, Vol 73, Issue 42 *******************************************_______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go[http://computer-go.org/mailman/listinfo/computer-go] _______________________________________________ Computer-go mailing list Computer-go@computer-go.org http://computer-go.org/mailman/listinfo/computer-go
