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SugaR: Updated August, 13 2019

 

For Windows only.

 

After the latest surprising great performances (a tester of mine tried SugaR against Raubfisch, with a surprising result of Sugar doubling the wins), I couldn't avoid to host this engine on my website, but before I had to put my hand on it since I don't like all those files created in the same place where the chess engine is. So now they are all into a folder called 'SugaR-NN Files', a much more orderly way.

 

- Changelog: this distribution is an adaptation of SugaR-NN dated Jul 31, 2019, I have set the program to put any output file into the created directory 'SugaR-NN Files'.

SugaR is a free UCI chess engine derived from Stockfish made by Marco Zerbinati under the GPL license.

This version of SugaR supports up to 128 cores; the engine can use two parallel BIN books (original code by Thomas Zipproth) and has a Self Learning function implemented, see below for details.

 

SugaR

 

 

Maximize its strength by using my opening book Goi.

 

NN section (Experimental Neural Networks inspired techniques)

Experimental, MonteCarloTreeSearch, if activated, the engine's behaviour is similar to AlphaZero concepts.

Idea are implemented, integrated on SugaR:

NN Persisted Self-Learning

Boolean, Default: True

(Montecarlo by Kelly Kinyama) only when true. This creates three files for machine learning purposes: SugaR-NN implements a persistent learning algorithm by Kelly kyniama and Andrea Manzo. Reads and creates the following file types: pawngame.bin with the learning when there are max a total of 2 pieces for white and black

experience.bin with the learning for
- opening variation of max 16 moves (8 half-moves) and a total of at least 7 pieces (no pawns) for white and black
- positions with max 6 pieces (no pawns) for white and black

One or many .bin files, each one associated to a single position biunivocally associated to the (technically, hashKey), in an opening variation of max 8 moves (16 half-moves) and a total of at least 7 pieces (no pawns) for white and black. This position is also in the experience.bin. So, these files are to speed the load in memory.

Every .bin file is so a collection of one or more positions stored with the following format (similar to in memory Stockfish Transposition Table):

- best move
- board signature (hash key)
- best move depth
- best move score

At the engine loading, there is an automatic merge to pawn.bin and experience.bin files, if we put the other ones, based on the following convention:

.bin

where
- fileType="experience"/"bin"
- qualityIndex , an integer, incrementally from 0 on based on the file's quality assigned by the user (0 best quality and so on)

The opening files can be simply copied and, in case of conflict/same name, the user must choice the one to use.

Because of disk access, to be effective, the learning must be made at no bullet time controls (less than 5 minutes/game).

NN Perceptron Algorithm

Boolean, Default: False
(Perceptron Sigmoid activation by Stefano Cardanobile) for Late Move Reductions search as training signal

NN MCTS Score

Boolean, Default: False
((Montecarlo Tree Search Scores) by Jörg Oster) in main search function to an upper node.

This edition includes:

Windows:

- SugaR-NN_2019-08-13_32bit_general.exe for general 32-bit CPUs
- SugaR-NN_2019-08-13_32bit_old.exe for old computers
- SugaR-NN_2019-08-13_32bit_ppc-32.exe for 32bit power pc
- SugaR-NN_2019-08-13_32bit.exe for standard 32bit CPUs
- SugaR-NN_2019-08-13_x64_bmi2.exe for Haswell CPUs
- SugaR-NN_2019-08-13_x64_general.exe for general 64bit CPUs
- SugaR-NN_2019-08-13_x64_modern.exe for modern computers
- SugaR-NN_2019-08-13_x64_ppc-64.exe for 64bit power pc
- SugaR-NN_2019-08-13_x64.exe for standard 64-bit CPUs

 

SugaR-NN_2019-08-13_x64 single processor benchmark:

 

 

SugaR-NN_2019-08-13_32bit single processor benchmark:

 

 

SugaR-NN GitHub page.