SugaR is a free UCI chess engine derived from Stockfish. It is not a complete chess program and requires some UCI-compatible GUI (e.g. XBoard with PolyGlot, eboard, Arena, Sigma Chess, Shredder, Chess Partner, Aquarium or Fritz) in order to be used comfortably. Read the documentation for your GUI of choice for information about how to use SugaR with it.
This version of SugaR supports up to 128 cores. The engine defaults to one search thread, so it is therefore recommended to inspect the value of the Threads UCI parameter, and to make sure it equals the number of CPU cores on your computer.
This version of SugaR has support for Syzygybases.
This distribution of SugaR consists of the following files:
Readme.md, the file you are currently reading.
Copying.txt, a text file containing the GNU General Public License.
source, a subdirectory containing the full source code, including a Makefile that can be used to compile SugaR on Unix-like systems.
Lower the Skill Level in order to make Stockfish play weaker (see also UCI_LimitStrength). Internally, MultiPV is enabled, and with a certain probability depending on the Skill Level a weaker move will be played.
Enable weaker play aiming for an Elo rating as set by UCI_Elo. This option overrides Skill Level.
If enabled by UCI_LimitStrength, aim for an engine strength of the given Elo. This Elo rating has been calibrated at a time control of 60s+0.6s and anchored to CCRL 40/4.
Default: 0, Min: 0, Max:8
0, no MultiPV.
For analysis (purpose)
- Value 1 corresponds to multiPV = 2
- Value 2 to multiPV = 4
- Value 3 to multiPV = 8
- Value 4 to multiPV =16
- Value 5 to multiPV = 32
- Value 6 to multiPV = 64
- Value 7 to multiPV = 128
- Value 8 to multiPV = 256
1-8 MultiPV: higher depths and longer time to reach them. So, fewer tactical shots missed, but loss of some ELO, increasingly until 8, corresponding to multiPV = 256.
Recommended values: from 2 to 5 ( > 5 too wide search width)
SugaR-NN can use two parallel books
original code by Thomas Zipproth: https://zipproth.de/Brainfish/brainfish/
NN section (Experimental Neural Networks inspired technics)
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
[https://github.com/Kellykinyama12/Stockfish] (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:
- 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
- [https://github.com/official-stockfish/Stockfish/compare/master...Stefano80:perceptron_new] ( Perceptron Sigmoid activation by Stefano Cardanobile) for Late Move Reductions search as training signal
NN MCTS Score
Boolean, Default: False
- [https://github.com/mcostalba/Stockfish/compare/master...joergoster:mcts_scores] ( (Montecarlo Tree Search Scores) by Jörg Oster) in main search function to an upper node.
Syzygybases are configured using the UCI options "SyzygyPath", "SyzygyProbeDepth", "Syzygy50MoveRule" and "SyzygyProbeLimit".
The option "SyzygyPath" should be set to the directory or directories that contain the .rtbw and .rtbz files. Multiple directories should be separated by ";" on Windows and by ":" on Unix-based operating systems. Do not use spaces around the ";" or ":".
It is recommended to store .rtbw files on an SSD. There is no loss in storing the .rtbz files on a regular HD.
Increasing the "SyzygyProbeDepth" option lets the engine probe less aggressively. Set this option to a higher value if you experience too much slowdown (in terms of nps) due to TB probing.
Set the "Syzygy50MoveRule" option to false if you want tablebase positions that are drawn by the 50-move rule to count as win or loss. This may be useful for correspondence games (because of tablebase adjudication).
The "SyzygyProbeLimit" option should normally be left at its default value.
What to expect If the engine is searching a position that is not in the tablebases (e.g. a position with 8 pieces), it will access the tablebases during the search. If the engine reports a very large score (typically 123.xx), this means that it has found a winning line into a tablebase position.
If the engine is given a position to search that is in the tablebases, it will use the tablebases at the beginning of the search to preselect all good moves, i.e. all moves that preserve the win or preserve the draw while taking into account the 50-move rule. It will then perform a search only on those moves. The engine will not move immediately, unless there is only a single good move. The engine likely will not report a mate score even if the position is known to be won.
It is therefore clear that behaviour is not identical to what one might be used to with Nalimov tablebases. There are technical reasons for this difference, the main technical reason being that Nalimov tablebases use the DTM metric (distance-to-mate), while Syzygybases use a variation of the DTZ metric (distance-to-zero, zero meaning any move that resets the 50-move counter). This special metric is one of the reasons that Syzygybases are more compact than Nalimov tablebases, while still storing all information needed for optimal play and in addition being able to take into account the 50-move rule.
Compiling it yourself
On Unix-like systems, it should be possible to compile SugaR directly from the source code with the included Makefile.
SugaR has support for 32 or 64-bit CPUs, the hardware POPCNT instruction, big-endian machines such as Power PC, and other platforms.
On Windows-like systems, it should be possible to compile SugaR directly from the source code with the included Sugar.sln with Visual Studio 15.3 Community from GUI or with command scenario using Visual Studio 15.3 Community Commands Shell.
In general it is recommended to run
make help to see a list of make
targets with corresponding descriptions. When not using the Makefile to
compile you need to manually
set/unset some switches in the compiler command line or use MSVC solution and project files provided; see file types.h
for a quick reference.
SugaR is free, and distributed under the GNU General Public License (GPL). Essentially, this means that you are free to do almost exactly what you want with the program, including distributing it among your friends, making it available for download from your web site, selling it (either by itself or as part of some bigger software package), or using it as the starting point for a software project of your own.
The only real limitation is that whenever you distribute SugaR in some way, you must always include the full source code, or a pointer to where the source code can be found. If you make any changes to the source code, these changes must also be made available under the GPL.
For full details, read the copy of the GPL found in the file named Copying.txt.