SedatChess

[Homayoun]

Thank you very much. Interesting match and results.

[Sedat Canbaz]

Thank you very much. Interesting match and results.

Not at all.. it s my pleasure )

And once more we noticed that,
'PRACTICE' is more important than 'comments'

Btw, there is such useful saying over these issues:
He that travels far knows much

Greetings

[Anton101]

Great result.

Thank you Mr.Sedat.

Best regards.

[Sedat Canbaz]

Great result.

Thank you Mr.Sedat.

Best regards.

Mr. Anton,

You are welcome..

Btw, I often prefer to say please,
Do not take too seriously the played results etc.

But about latest Duel match,
Current results have to be taken 'too' seriously!
I do not know what kind of engine is that RaptoRa
In my 25 years Eng testings...for 1st time:
I've seen so well learning...and in same time
So stable, strong and besides compatible etc.

Keep up the good work!

Best,
Sedat

[Anton101]

Many thanks...Its a great engine...I really know.

[Sedat Canbaz]

Update

New test, but played under same conditions as before
Note: used same learned exp fies (dated on 25.07.2023)

In short,
The target is to see the influence via previous learned exp...

By the way,
Here are current played results, RapTora is leading again:

Score of Eman 8.91 vs RapTora 2.2: 3 - 7 - 27 [0.446]
... Eman 8.91 playing White: 3 - 2 - 13 [0.528] 18
... Eman 8.91 playing Black: 0 - 5 - 14 [0.368] 19
... White vs Black: 8 - 2 - 27 [0.581] 37
Elo difference: -37.7 +/- 58.1, LOS: 10.3 %, DrawRatio: 73.0 %
37 of 100 games finished.

Best,
Sedat

[Sedat Canbaz]

Update 2

Here are FINAL results, Winner is again RapTora!
My Congratulations to Anton, Eduard, SF team!

Code: Select all

Score of Eman 8.91 vs RapTora 2.2: 12 - 14 - 74 [0.490]
...      Eman 8.91 playing White: 12 - 3 - 35  [0.590] 50
...      Eman 8.91 playing Black: 0 - 11 - 39  [0.390] 50
...      White vs Black: 23 - 3 - 74  [0.600] 100
Elo difference: -6.9 +/- 34.8, LOS: 34.7 %, DrawRatio: 74.0 %
100 of 100 games finished.

GAMES:
https://mega.nz/file/ylxThLwQ#5hSYe1dPf ... VdEUE6CyMM

As Final Conclusion,
Even via latest re-run test.. it seems that RapTora is
Stronger..But according to other SCCT' competitions,
I can say that their performance is almost identical!

And what we noticed also that (via current Duel match),
With more learning games, Eman is performed better...

Plus it is clear 100% that,
The learning influence is playing as important role!
And RapTora 2.2 is doing not bad via Eman exp files!


Best,
Sedat

[Anton101]

Great news...

Thanks for the test.

Best regards.

A.Ces

[Sedat Canbaz]

Great news...

Thanks for the test.

Best regards.

A.Ces

Hello Mr. Anton,

Not at all...it is my pleasure )
And many thanks to you as well

Meanwhile,
Just I'd like to point out also that
1st of all, these kind of testings are
Quite useful..sure we need more of them
But anyhow, I think that these stats can
Be considered as not so bad data indeed!

On other hand,
Sure, if we re-run again...very likely
Then both Top engines will be more close
In points...but in my opinion is that
The most important: which engine is scored
Better in previous testings/tours ?

So according to SCCT's overall results,
We have to accept that
RapTora is RapTora..Reality is Reality!

And besides,
The superior RapTora's performance is
Archived also via Concurrent matches...
What does it mean ? in other words,
The picture is perfectly clear, I mean
It is not required to talk more, right ?!)

Greetings

[Anton101]

I am convinced in the use of experience files in the engines for the following:

1. Improvement in openings: Experience files allow the engine to choose proven and solid moves in openings, which can significantly improve its performance in the early stages of the game.

2. Saving compute time: Experience files can prevent the chess engine from having to calculate certain opening lines, which can save computation time and allow the engine to devote more resources to more critical moves or later stages of the game.

3. Variety of play styles: Depending on the set of games used to build the experience file, the engine can acquire a variety of playing styles and preferences in openings, which can make it more versatile in different situations.

It only remains to learn how to use the tools that Deeds has left in his repository on GitHub, work on the training of the engine, and have patience, this takes time. But, it's worth it.

Thank you Sedat, I hope that with your work reaching many users, these same users become trainers of their favorite engine. As more users are interested, innovative ideas about the learning tool may emerge.

Regards.

A. Ces