Re: Is AlphaGo approach unsuitable to chess?
Posted: Wed May 31, 2017 11:59 pm
I am not so sure about that. The extension/reduction stuff seems to be non-standard as described in the Time assignment to children post.lucasart wrote:AFAIK Giraffe is an alpha/beta negascout search engine, like any other. Uses all the same techniques, such as null move, search reductions, quiescent search, etc. That's where almost all of Giraffe's elo is.melajara wrote:AFAIK, after Giraffe and the fact that the author is now a Deepmind employee, nobody followed this approach in chess, why so?
I created a modified version of Giraffe that uses the texel evaluation function. The source code is here.lucasart wrote:The only difference is that the author used neural networks for the evaluation. That is something entirely different from replacing the search with NN (which is totally hopeless for chess).
I'd say that NN have a negative elo contribution to Giraffe. Replace that with a normal eval, properly tuned, and Giraffe would likely be much stronger.
I then played some test games using the following programs:
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texel101 : Texel version 1.01 (rated 2788 on CCRL 40/40)
giraffe : Giraffe latest version from bitbucket.org (earlier version rated 2457 on CCRL 40/40)
giraffe_te : Same Giraffe version but using evaluation from latest texel development version
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prog1 tc1 prog2 tc2 elodiff draws depth1 depth2 nGames
texel101 6+0.06 giraffe 48+0.48 60 10% 11.7 15.4 1488
giraffe 12+0.12 giraffe 6+0.06 146 18% 13.1 11.7 5118
giraffe 24+0.24 giraffe 12+0.12 115 21% 14.4 13.1 3562
giraffe 48+0.48 giraffe 24+0.24 117 22% 15.7 14.3 1718
giraffe_te 24+0.24 giraffe 24+0.24 -3 13% 17.1 14.1 16172
The following observations can be made:
* Using the texel evaluation function in giraffe has a very small effect on the playing strength, even though it makes giraffe search about 3 ply deeper.
* The draw rate is extremely low. Manual inspection of some games suggests that quite a few games are decided by tactical blunders. This may make it harder to test the quality of the evaluation function.
* Self play with the original giraffe version using successive time control doublings shows that searching longer makes giraffe significantly stronger, so even though there are some tactical blunders the search must do some things right.
* I did not modify the texel evaluation function in any way, except by transforming it using the following formula to convert to giraffe's score range where -10000 means 0% expected score and +10000 means 100% expected score:
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giraffe_eval = 20000/(1+exp(-texel_eval*.00650480288770818))-10000
* It would probably be interesting to insert the giraffe evaluation function in texel to be able to compare the evaluation functions in an engine that has a more conventional search function.