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amchess / Alexander

A uci chess engine derived from classical Stockish
GNU General Public License v3.0
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Introduction

Alexander is a free UCI chess engine derived from Stockfish family chess engines. 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

Terms of use

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 Alexander 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.

Files

This distribution of AlexanderPro consists of the following files:

Uci options

Hash Memory

Hash

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)

Clear Hash

Button to clear the Hash Memory. If the Never Clear Hash option is enabled, this button doesn't do anything.

Threads

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.

Ponder (checkbox)

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.

MultiPV

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.

UCI_Chess960 (checkbox)

Whether or not Alexander should play using Chess 960 mode. Usually not on the configuration window.

Move overhead

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.

Slow mover

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.

Handicap mode

UCI_LimitStrength

Activate the handicap mode and the related following options: in this case, the evaluation function is always the classical one.

UCI_Elo (CB only for chessbase products users)

Default 2850, min 1350, max 2850 UCI-protocol compliant version of Strength parameter. A very refined handicap mode based on the four famous sovietic chess school levels: Internally the UCI_Elo value will be converted to a Strength value according to the following table:

Every school corresponds to a different evaluation function, more and more refined. The UCI_Elo feature is controlled by the chess GUI, and usually doesn't appear in the configuration window.

Handicapped Depth

The engine stop calculating when it joins the handicapped depth, based on the following table:

Elo range Handicapped Depth
[0,1999] [1,6]
[2000,2199] [7,9]
[2200,2399] [10,12]
[2400,3190] [13,20]

Handicapped avatar player

If enabled, the engine not only simulates the thought process of a player of a certain level but also the mistakes he can make. These mistakes become more frequent the lower the player's Elo rating. This is the handicap mode implemented by Stockfish, but when combined with the previous options that simulate a player's thinking system, it truly approximates a "human avatar".

Avatar File

A file in .avt format with the profile of a player. You can edit this file containing the weigths of Stockfish classical eval terms, from 0 to 100, but with a private tool, we can generate those values to simulate a real player.

Sygyzy End Game table bases

Download at http://olympuschess.com/egtb/sbases (by Ronald De Man)

SyzygyPath

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.

SygyzyProbeDepth

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:

Syzygy50MoveRule

Disable to let fifty-move rule draws detected by Syzygy tablebase probes count as wins or losses. This is useful for ICCF correspondence games.

SygyzyProbeLimit

Integer, Default: 6, Min: 0, Max: 6 How many pieces need to be on the board before Alexander begins probing (even at the root). Current default, obviously, is for 6-man.

Advanced Chess Analyzer

Advanced analysis options, highly recommended for CC play

Full depth threads

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.

MonteCarlo Tree Search section (experimental: thanks to original Stephan Nicolet work)

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.

MCTSThreads

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.

MCTS Multi Strategy

Integer, Default: 20, Min: 0, Max: 100 Only in multi mcts mode, for tree policy.

MCTS Multi MinVisits

Integer, Default: 5, Min: 0, Max: 1000 Only in multi mcts mode, for Upper Confidence Bound.

Live Book section (thanks to Eman's author Khalid Omar for windows builds)

Live Book (checkbox)

Default is Off: no livebook. The other values are "NoEgtbs" (no livebook for an endgame with at max 7 mens), "Egtbs" (livebook only for an endgame with at max 7 mens) and "Both" (the livebook whenever is possible).

Live Book URL

The default is the online chessdb https://www.chessdb.cn/queryc_en/, a wonderful project by noobpwnftw (thanks to him!)

https://github.com/noobpwnftw/chessdb http://talkchess.com/forum3/viewtopic.php?f=2&t=71764&hilit=chessdb

The private application can also learn from this live db.

Live Book Timeout

Default 5000, min 0, max 10000 Only for bullet games, use a lower value, for example, 1500.

Live Book Retry

Default 3, min 1, max 100 Max times the engine tries to contribute (if the corresponding option is activated: see below) to the live book. If 0, the engine doesn't use the livebook.

Live Book Diversity

Boolean, Default: False If activated, the engine varies its play, reducing conversely its strength because already the live chessdb is very large.

Live Book Contribute

Boolean, Default: False If activated, the engine sends a move, not in live chessdb, in its queue to be analysed. In this manner, we have a kind of learning cloud.

Live Book Depth

Default 255, min 1, max 255 Depth of live book moves.

Full depth threads

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.

Opening variety

Integer, Default: 0, Min: 0, Max: 40 To play different opening lines from default (0), if not from book (see below). Higher variety -> more probable loss of ELO

Concurrent Experience

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.

Persisted learning

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):

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

N.B.

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

Read only learning

Boolean, Default: False If activated, the learning file is only read.

Shashin section

Shashin section

Shashin section

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, 4] High Petrosian -+
[5, 9] Middle-High Petrosian -+ \ -/+
[10,12] Middle Petrosian -/+
[13,19] Middle-Low Petrosian -/+ \ =/+
[20,24] Low Petrosian =/+
[25,49] Caos: Capablanca-Low Petrosian =/+ \ =
[50] Capablanca =
[51,75] Caos: Capablanca-Low Tal = \ +/=
[76,80] Low Tal +/=
[81,87] Low-Middle Tal +/= +/-
[88,90] Middle Tal +/-
[91,95] Middle-High Tal +/- \ +-
[96,100] 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.

Tal

Attack position/algorithm

Capablanca

Strategical algorithm (for quiescent positions)

Petrosian

Defense position/algorithm (the "reversed colors" Tal)

Acknowledgments

Stockfish community

Alexander team

Sorry If I forgot someone.

Stockfish

Overview

Build Status Build Status

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 classical evaluation based on handcrafted terms. The classical evaluation runs efficiently on almost all CPU architectures.

Files

This distribution of Stockfish consists of the following files:

UCI options

Currently, Stockfish has the following UCI options:

A note on classical evaluation

It assigns a value to a position that is used in alpha-beta (PVS) search to find the best move. The classical evaluation computes this value as a function of various chess concepts, handcrafted by experts, tested and tuned using fishtest.

What to expect from Syzygybases?

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.

Large Pages

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.

Support on Linux

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.

Support on Windows

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.

Compiling Stockfish yourself from the sources

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 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

Understanding the code base and participating in the project

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.

Donating hardware

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.

Improving the code

If you want to help improve the code, there are several valuable resources:

Terms of use

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.