
DeepChess: End-To-End Deep Neural Network for Automatic Learning in Chess [pdf] - lainon
http://www.cs.tau.ac.il/~wolf/papers/deepchess.pdf
======
slm_HN
This is an interesting idea, but the authors lose credibility by using Crafty
as an opponent. Crafty isn't even among the top 30 current programs. Depending
on which rating list you reference Crafty is 300 to 400 points worse than
Stockfish.

>Crafty has successfully participated in numerous WCCCs, and is a direct
descendant of Cray Blitz, the WCCC winner of 1983 and 1986.

That is some pretty weak sauce. "successfully participated"? Is that really
worth mentioning? Also Crafty isn't exactly the direct descendant of Cray
Blitz even though they were both written by the same person. And while Cray
Blitz did win a World Championship some 30 years ago, it did so on the power
of its hardware, not the strength of its software. It was running on a Cray
supercomputer at a time when other programs were running on vastly inferior
hardware. Rebel, who finished one point behind Cray Blitz, was running on a
6502.

Section 5.1, where they show diagrams on moves made by DeepChess is only
meaningful if other programs miss the moves.

>This property has been associated with human grandmasters, and has always
been considered an area in which computer chess programs were lacking.

But...

In the Aronian game Stockfish running in the browser (PNACL) finds the move
Re5 in less than a second.

In the Alekhine game Stockfish finds the move d5 in about 2.5 seconds.

In the Seirawan game Stockfish finds the move c5 in about 1 second.

So while I find the idea interesting I'd like to see more intellectual honesty
in this type of paper.

~~~
adyavanapalli
But..but.. Crafty still has an ELO of 3030, which is only about 300 pts below
Stockfish. It would beat most human chess players (most grandmasters too),
which is still quite an achievement, given that this neural net learned to
play chess by itself (using no such human derived evaluation function). Cool
right?

~~~
myle
To put things into context, the best grandmasters in the world have a rating
around 2800.

------
myle
I found this implementation using TensorFlow: [https://github.com/mr-
press/DeepChess](https://github.com/mr-press/DeepChess)

------
myle
It is interesting that they removed they didn't allow positions that resulted
from captures in the training dataset. I wonder why would that affect
negatively the results. The explanation they give (opponent will likely
capture back soon) does not sound enough to me. The tactics of the position
should capture that. Is this because it is hard for the network to learn from
positions with less material and tactical opportunities?

