PHOENIX=CuckooChess with learning function in Falcon style !

[Sylwy]

Hey folks !

Take a look - please - to this paper:

http://rahular.com/phoenix/

The source-code is here:

https://github.com/rahular/phoenix/archive/master.zip

Interesting ?

SilvianR

[Ferdy]

Hey folks !

Take a look - please - to this paper:

http://rahular.com/phoenix/

The source-code is here:

https://github.com/rahular/phoenix/archive/master.zip

Interesting ?

SilvianR

Thanks for this info.

[supersharp77]

Interesting site......looks good!

http://rahular.com/phoenix/


https://github.com/rahular/phoenix/archive/master.zip

[fern]

Geeee....the kind of paper solely for NASA scientists...

fern

[petero2]

Take a look - please - to this paper:

http://rahular.com/phoenix/

The source-code is here:

https://github.com/rahular/phoenix/archive/master.zip

Interesting ?

I was recently made aware of this engine, and as the author of CuckooChess I got interested and decided to investigate it a bit. It initially seemed interesting, but I got the first clue that something was not right when I read the abstract.

However there are still many areas in which humans excel in comparison with the machines. One such area is chess. Even with great advances in the speed and computational power of modern machines, Grandmasters often beat the best chess programs in the world with relative ease.

Making such a claim in 2014 (the thesis is dated June 2014) is ridiculous.

The result section claims that CuckooChess is rated 2530 and the modified engine based on 1000 games got a rating of 2546, and therefor the modified engine is 19 elo stronger. How the author got 19 from 2546-2530 is a mystery. The report also contains output from elostat which says the rating difference is 2546-2514 = 32. However, if you look at the actual games it can be seen that the modified engine played white in all games. If the games are instead fed into bayeselo that takes the white advantage into account, the result is:

Code: Select all

Rank Name           Elo    +    - games score oppo. draws 
   1 mgGenes4         1   10   10  1000   55%    -1   25% 
   2 defaultGenes    -1   10   10  1000   45%     1   25% 

That is 2+-10 elo, so the strength increase seen by the author seems to be caused solely by the white advantage.

It does not end here though. In appendix A.2 of the report, the modified piece square tables that performed best are reported. I inserted these values into CuckooChess and played a match against the unmodified CuckooChess.

Code: Select all

Rank Name           Elo    +    - games score oppo. draws 
   1 Cuckoo113a9    213   23   21   525   93%  -213    7% 
   2 CuckooPhoenix -213   21   23   525    7%   213    7% 

As can be seen, the modified version is around 400 elo weaker than the original. This is not surprising after looking at the modified values, as they seem to be almost totally random.

From reading the paper and the source code, I believe what the author intended to do was the following:

1. Remove all evaluation features except material and piece square tables from CuckooChess.
2. Add a framework for deep learning and genetic algorithms to be able to train the piece square table values.
3. Perform the training.
4. Insert the modified values into the original engine and measure the change in playing strength.

However, based on my findings above, it seems like step 4 failed and the modified values were not actually used. Possibly this bug affected the training step too.

The patch containing the new piece square table values is available here.

It is not clear to me if the author intentionally made his method look good, or if he just made serious mistakes, but I suppose Hanlon's razor applies.

[IanO]

[extremely insightful comments]

Wow, both this student and the publisher really missed out by not contacting you beforehand for advice and peer review! I hope you took it upon yourself to write to both of them.