ShashChess is a free UCI chess engine derived from Stockfish family chess engines. For the evaluation function, we utilize the collaboration between Leela Chess Zero and Stockfish, for which we express our sincere gratitude. The goal is to apply Alexander Shashin theory exposed on the following book : https://www.amazon.com/Best-Play-Method-Discovering-Strongest/dp/1936277468 to improve
- base engine strength
- engine's behaviour on the different positions types (requiring the corresponding algorithm) :
- Tal
- Capablanca
- Petrosian
- the mixed ones
- Tal-Capablanca
- Capablanca-Petrosian
- Tal-Capablanca-Petrosian
Also during the search, to enhance it, we use both standard and Q/Self reinforcement learning.
Shashchess 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 ShashChess 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.
This distribution of ShashChessPro consists of the following files:
- Readme.md, the file you are currently reading.
- Copying.txt, a text file containing the GNU General Public License.
- src, a subdirectory containing the full source code, including a Makefile and the compilation scripts makeAll.bat (Windows) and makeAll.sh (Linux).
Integer, Default: 16, Min: 1, Max: 131072 MB (64-bit) : 2048 MB (32-bit)
The amount of memory to use for the hash during search, specified in MB (megabytes). This number should be smaller than the amount of physical memory for your system. A modern formula to determine it is the following:
(T x S / 100) MB where T = the average move time (in seconds) S = the average node speed of your hardware A traditional formula is the following: (N x F x T) / 512 where N = logical threads number F = clock single processor frequency (MB) T = the average move time (in seconds)
Button to clear the Hash Memory. If the Never Clear Hash option is enabled, this button doesn't do anything.
Integer, Default: 1, Min: 1, Max: 512 The number of threads to use during the search. This number should be set to the number of cores (physical+logical) in your CPU.
Boolean, Default: True Also called "Permanent Brain" : whether or not the engine should analyze when it is the opponent's turn.
Usually not on the configuration window.
Integer, Default: 1, Min: 1, Max: 500 The number of alternate lines of analysis to display. Specify 1 to just get the best line. Asking for more lines slows down the search. Usually not on the configuration window.
Whether or not ShashChess should play using Chess 960 mode. Usually not on the configuration window.
Default 30, min 0, max 5000 In ms, the default value seems to be the best on Linux systems, but must be increased for slow GUI like Fritz. In general, on Windows system it seems a good value to be 100.
Default 84, min 10, max 1000 "Time usage percent": how much the engine thinks on a move. Many engines seem to move faster and the engine is behind in time clock. With lower values it plays faster, with higher values slower - of course always within the time control.
Download at http://olympuschess.com/egtb/sbases (by Ronald De Man)
The path to the Syzygy endgame tablebases.this defines an absolute path on your computer to the tablebase files, also on multiple paths separated with a semicolon (;) character (Windows), the colon (:) character (OS X and Windows) character. The folder(s) containing the Syzygy EGTB files. If multiple folders are used, separate them by the ; (semicolon) character.
Integer, Default: 1, Min: 1, Max: 100 The probing tablebases depth (always the root position). If you don't have a SSD HD,you have to set it to maximize the depth and kn/s in infinite analysis and during a time equals to the double of that corresponding to half RAM size. Choice a test position with a few pieces on the board (from 7 to 12). For example:
- Fen: 8/5r2/R7/8/1p5k/p3P3/4K3/8 w -- 0 1 Solution : Ra4 (=)
- Fen: 1R6/7k/1P5p/5p2/3K2p1/1r3P1P/8 b - - 1 1 Solution: 1...h5 !! (=)
Disable to let fifty-move rule draws detected by Syzygy tablebase probes count as wins or losses. This is useful for ICCF correspondence games.
Integer, Default: 6, Min: 0, Max: 6 How many pieces need to be on the board before ShashChess begins probing (even at the root). Current default, obviously, is for 6-man.
Advanced analysis options, highly recommended for CC play
Integer, Default: 0, Min: 0, Max: 512 The number of settled threads to use for a full depth brute force search. If the number is greater than threads number, all threads are for full depth brute force search.
Boolean, Default: False If activated, thanks to Shashin theory, the engine will use the MonteCarlo Tree Search for Capablanca quiescent type positions and also for caos ones, in the manner specified by the following parameters. The idea is to exploit Lc0 best results in those positions types, because Lc0 uses mcts in the search.
Integer, Default: 0, Min: 0, Max: 512 The number of settled threads to use for MCTS search except the first (main) one always for alpha-beta search. In particular, if the number is greater than threads number, they will all do a montecarlo tree search, always except the first (main) for alpha-beta search.
Integer, Default: 20, Min: 0, Max: 100 Only in multi mcts mode, for tree policy.
Integer, Default: 5, Min: 0, Max: 1000 Only in multi mcts mode, for Upper Confidence Bound.
String, Default: "" (empty string)
The proxy URL to use for the live book. If empty, no proxy is used. The proxy should use the ChessDB REST API format.
Boolean, Default: False
If enabled, the engine will play a random (best) move by the proxy (query and not querybest action).
Boolean, Default: False
If enabled, the engine will use the Lichess live book by querying the Lichess API to access the game database available on the site. This option allows the engine to access a wide range of games played on Lichess to enhance its move choices.
Boolean, Default: False
If enabled, the engine will use the Lichess live book specifically for masters' games. This allows the engine to analyze games played at a high level and utilize the best moves made by master-level players.
String, Default: "" (empty string)
The Lichess player name to use for the live book. If left empty, the engine will not query for the specific player's game data. This option is useful for studying or adapting the engine to a particular player's style.
String, Default: "White"
Specifies the color the engine will play as in the Lichess live book for the specified player.
- "White": The engine considers the games played by the specified player as White. When it's Black's turn, the move that performed best against the player will be chosen.
- "Black": The engine considers the games played by the specified player as Black. When it's White's turn, the move that performed best against the player will be chosen.
- "Both": The engine will always pretend to be the player, regardless of color, and choose the best-performing moves for the specified player.
Boolean, Default: False
If enabled, the engine will use the ChessDB live book by querying the ChessDB API.
Integer, Default: 255, Min: 1, Max: 255
Specifies the depth to reach using the live book in plies. The depth determines how many half-moves the engine will consider from the current position.
Boolean, Default: False
If enabled, allows the engine to query the ChessDB API for Tablebase data, up to 7 pieces. This provides perfect endgame knowledge for positions with up to 7 pieces.
Boolean, Default: False
If enabled, allows the engine to query the Lichess API for Tablebase data, up to 7 pieces. This option also provides perfect endgame knowledge for positions with up to 7 pieces.
Boolean, Default: False
If enabled, allows the engine to store a move in the queue of ChessDb to be analyzed.
Default 0, min 0, max 512 The number of threads doing a full depth analysis (brute force). Useful in analysis of particular hard positions to limit the strong pruning's drawbacks.
Default is Off: no variety. The other values are "Standard" (no elo loss: randomicity in Capablanca zone) and Psychological (randomicity in Caos zones max).
Boolean, Default: False Set this option to true when running under CuteChess and you experiences problems with concurrency > 1 When this option is true, the saved experience file name will be modified to something like experience-64a4c665c57504a4.bin (64a4c665c57504a4 is random). Each concurrent instance of BrainLearn will have its own experience file name, however, all the concurrent instances will read "experience.bin" at start up.
Default is Off: no learning algorithm. The other values are "Standard" and "Self", this last to activate the Q-learning, optimized for self play. Some GUIs don't write the experience file in some game's modes because the uci protocol is differently implemented
The persisted learning is based on 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
- best move performance , a new parameter you can calculate with any learning application supporting this specification. An example is the private one, kernel of SaaS part of Alpha-Chess AI portal. The idea is to update it based on pattern recognition concept. In the portal, you can also exploit the reports of another NLG (virtual trainer) application and buy the products in the digishop based on all this. This open-source part has the performance default, based on score and depth. You can align the performance by uci token quickresetexp. Clearly, even if already strong, this private learning algorithm is a lot stronger as demostrate here: Graphical result The perfomance, in this case, is updated based on the latest Stockfish wdl model (score and material).
This file is loaded in an hashtable at the engine load and updated each time the engine receive quit or stop uci command. When BrainLearn starts a new game or when we have max 8 pieces on the chessboard, the learning is activated and the hash table updated each time the engine has a best score at a depth >= 4 PLIES, according to Stockfish aspiration window.
At the engine loading, there is an automatic merge to experience.exp files, if we put the other ones, based on the following convention:
<fileType><qualityIndex>.exp
where
- fileType=experience
- qualityIndex , an integer, incrementally from 0 on based on the file's quality assigned by the user (0 best quality and so on)
N.B.
Because of disk access, to be effective, the learning must be made at no bullet time controls (less than 5 minutes/game).
Boolean, Default: False If activated, the learning file is only read.
Boolean, Default: False If activated, the engine will use the experience file as the book. In choosing the move to play, the engine will be based first on maximum win probability, then, on the engine's internal score, and finally, on depth. The UCI token “showexp” allows the book to display moves on a given position.
Integer, Default: 100, Min: 1, Max: 100 The maximum number of moves the engine chooses from the experience book
Integer, Default: 4, Min: 1, Max: 255 The min depth for the experience book
Default: no option settled The engine will determine dynamically the position's type starting from a "Capablanca/default positions". If one or more (mixed algorithms/positions types at the boundaries) of the seven following options are settled, it will force the initial position/algorithm understanding If, in the wdl model, we define wdl_w=Win percentage, wdl_d=Drawn percentage and Win probability=(2*wdl_w+wdl_d)/10, we have the following mapping:
| Win probability range | Shashin position’s type | Informator symbols |
|---|---|---|
| [0, 6] | High Petrosian | -+ |
| [7, 11] | Middle-High Petrosian | -+ \ -/+ |
| [12,14] | Middle Petrosian | -/+ |
| [15,20] | Middle-Low Petrosian | -/+ \ =/+ |
| [21,24] | Low Petrosian | =/+ |
| [24,49] | Caos: Capablanca-Low Petrosian | =/+ \ = |
| [50] | Capablanca | = |
| [51,76] | Caos: Capablanca-Low Tal | = \ +/= |
| [77,79] | Low Tal | +/= |
| [80,85] | Low-Middle Tal | +/= |
| [86,88] | Middle Tal | +/- |
| [89,93] | Middle-High Tal | +/- \ +- |
| [94,100] | High Tal | +- |
N.B. The winProbability also take into account the depth at which a move has been calculated. So, it's more effective than the cp.
Attack position/algorithm
Strategical algorithm (for quiescent positions)
Defense position/algorithm (the "reversed colors" Tal)
- Kozlov Sergey Aleksandrovitsch for his very interesting patch and code on Sugar engine
- Omar Khalid for his great experience in microsoft c/cpp programming environment
- Alexei Chernakoff for his pretious suggestions about the android version and its contribution to it
- Dariusz Domagala for the Mac version
- The BrainFish, McBrain, CorChess, CiChess and Crystal authors for their very interesting derivative
- Obviously, the chess theorician Alexander Shashin, whithout whom I wouldn't had the idea of this engine
Stockfish community
- engine owner and main developer: ICCF IM Andrea Manzo (https://www.iccf.com/player?id=241224)
- IM Yohan Benitah for his professional chess understanding and help in testing against neural networks
- official tester: ICCF CCE and CCM Maurizio Platino (https://www.iccf.com/player?id=241094)
- official tester: Maurizio Colbacchini, FSI 1N
- official tester and concept analyst: ICCF GM Fabio Finocchiaro (https://www.iccf.com/player?id=240090), 2012 ICCF world champion
- official tester Dennis Marvin (NDL) (overall the online learning)
- tester and concept analyst: ICCF GM Matjas Pirs (https://www.iccf.com/player?id=480232), for his great experience and tests on positions analysis in different game's phases
Sorry If I forgot someone.
Stockfish is a free, powerful UCI chess engine derived from Glaurung 2.1. Stockfish is not a complete chess program and requires a UCI-compatible graphical user interface (GUI) (e.g. XBoard with PolyGlot, Scid, Cute Chess, eboard, Arena, Sigma Chess, Shredder, Chess Partner or Fritz) in order to be used comfortably. Read the documentation for your GUI of choice for information about how to use Stockfish with it.
The Stockfish engine features the NNUE evaluation based on efficiently updateable neural networks. The NNUE evaluation benefits from the vector intrinsics available on most CPUs (sse2, avx2, neon, or similar).
This distribution of Stockfish consists of the following files:
-
Readme.md, the file you are currently reading.
-
Copying.txt, a text file containing the GNU General Public License version 3.
-
src, a subdirectory containing the full source code, including a Makefile that can be used to compile Stockfish on Unix-like systems.
-
a file with the .nnue extension, storing the neural network for the NNUE evaluation. Binary distributions will have this file embedded.
Note: to use the NNUE evaluation, the additional data file with neural network parameters
needs to be available. Normally, this file is already embedded in the binary or it can be downloaded.
The filename for the default (recommended) net can be found as the default
value of the EvalFile UCI option, with the format nn-[SHA256 first 12 digits].nnue
(for instance, nn-c157e0a5755b.nnue). This file can be downloaded from
https://tests.stockfishchess.org/api/nn/[filename]
replacing [filename] as needed.
Currently, Stockfish has the following UCI options:
-
The number of CPU threads used for searching a position. For best performance, set this equal to the number of CPU cores available.
-
The size of the hash table in MB. It is recommended to set Hash after setting Threads.
-
Let Stockfish ponder its next move while the opponent is thinking.
-
Output the N best lines (principal variations, PVs) when searching. Leave at 1 for best performance.
-
The name of the file of the NNUE evaluation parameters. Depending on the GUI the filename might have to include the full path to the folder/directory that contains the file. Other locations, such as the directory that contains the binary and the working directory, are also searched.
-
An option handled by your GUI.
-
An option handled by your GUI. If true, Stockfish will play Chess960.
-
If enabled, show approximate WDL statistics as part of the engine output. These WDL numbers model expected game outcomes for a given evaluation and game ply for engine self-play at fishtest LTC conditions (60+0.6s per game).
-
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.
-
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.
-
Path to the folders/directories storing the Syzygy tablebase files. Multiple directories are to be separated by ";" on Windows and by ":" on Unix-based operating systems. Do not use spaces around the ";" or ":".
Example:
C:\tablebases\wdl345;C:\tablebases\wdl6;D:\tablebases\dtz345;D:\tablebases\dtz6It is recommended to store .rtbw files on an SSD. There is no loss in storing the .rtbz files on a regular HD. It is recommended to verify all md5 checksums of the downloaded tablebase files (
md5sum -c checksum.md5) as corruption will lead to engine crashes. -
Minimum remaining search depth for which a position is probed. Set this option to a higher value to probe less agressively if you experience too much slowdown (in terms of nps) due to TB probing.
-
Disable to let fifty-move rule draws detected by Syzygy tablebase probes count as wins or losses. This is useful for ICCF correspondence games.
-
Limit Syzygy tablebase probing to positions with at most this many pieces left (including kings and pawns).
-
Assume a time delay of x ms due to network and GUI overheads. This is useful to avoid losses on time in those cases.
-
Lower values will make Stockfish take less time in games, higher values will make it think longer.
-
Search for at least x ms per move.
-
Tells the engine to use nodes searched instead of wall time to account for elapsed time. Useful for engine testing.
-
Clear the hash table.
-
Write all communication to and from the engine into a text file.
The NNUE evaluation computes this value with a neural network based on basic inputs (e.g. piece positions only). The network is optimized and trained on the evaluations of millions of positions at moderate search depth.
The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward. It can be evaluated efficiently on CPUs, and exploits the fact that only parts of the neural network need to be updated after a typical chess move. The nodchip repository provides additional tools to train and develop the NNUE networks.
On CPUs supporting modern vector instructions (avx2 and similar), the NNUE evaluation results in stronger playing strength, even if the nodes per second computed by the engine is somewhat lower (roughly 60% of nps is typical).
Note that the NNUE evaluation depends on the Stockfish binary and the network parameter file (see EvalFile). Not every parameter file is compatible with a given Stockfish binary. The default value of the EvalFile UCI option is the name of a network that is guaranteed to be compatible with that binary.
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 153.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 this 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.
Stockfish supports large pages on Linux and Windows. Large pages make the hash access more efficient, improving the engine speed, especially on large hash sizes. Typical increases are 5..10% in terms of nodes per second, but speed increases up to 30% have been measured. The support is automatic. Stockfish attempts to use large pages when available and will fall back to regular memory allocation when this is not the case.
Large page support on Linux is obtained by the Linux kernel transparent huge pages functionality. Typically, transparent huge pages are already enabled and no configuration is needed.
The use of large pages requires "Lock Pages in Memory" privilege. See Enable the Lock Pages in Memory Option (Windows) on how to enable this privilege, then run RAMMap to double-check that large pages are used. We suggest that you reboot your computer after you have enabled large pages, because long Windows sessions suffer from memory fragmentation which may prevent Stockfish from getting large pages: a fresh session is better in this regard.
Stockfish has support for 32 or 64-bit CPUs, certain hardware instructions, big-endian machines such as Power PC, and other platforms.
On Unix-like systems, it should be easy to compile Stockfish
directly from the source code with the included Makefile in the folder
src. In general it is recommended to run make help to see a list of make
targets with corresponding descriptions.
cd src
make help
make net
make build ARCH=x86-64-modern
When not using the Makefile to compile (for instance with Microsoft MSVC) you need to manually set/unset some switches in the compiler command line; see file types.h for a quick reference.
When reporting an issue or a bug, please tell us which version and compiler you used to create your executable. These informations can be found by typing the following commands in a console:
./stockfish compiler
Stockfish's improvement over the last couple of years has been a great community effort. There are a few ways to help contribute to its growth.
Improving Stockfish requires a massive amount of testing. You can donate your hardware resources by installing the Fishtest Worker and view the current tests on Fishtest.
If you want to help improve the code, there are several valuable resources:
-
In this wiki, many techniques used in Stockfish are explained with a lot of background information.
-
The section on Stockfish describes many features and techniques used by Stockfish. However, it is generic rather than being focused on Stockfish's precise implementation. Nevertheless, a helpful resource.
-
The latest source can always be found on GitHub. Discussions about Stockfish take place in the FishCooking group and engine testing is done on Fishtest. If you want to help improve Stockfish, please read this guideline first, where the basics of Stockfish development are explained.
Stockfish is free, and distributed under the GNU General Public License version 3 (GPL v3). 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 Stockfish 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 v3 found in the file named Copying.txt.