Computer chess

This article is part of the series on
Chess programming
Board representations 0x88 Bitboards
Evaluation functions Deep neural networks (Transformers ) Attention Efficiently updatable neural networks Handcrafted evaluation functions Piece-square tables Reinforcement learning Stochastic gradient descent Supervised learning Texel tuning Unsupervised learning
Graph and tree search algorithms Minimax Alpha-beta pruning Principal variation search Quiescence search Monte Carlo tree search
Chess computers Belle ChessMachine ChipTest Cray Blitz Deep Blue Deep Thought HiTech Hydra Mephisto Saitek
Chess engines AlphaZero Chess Tiger Crafty CuckooChess Deep Fritz Dragon by Komodo Chess Fairy-Max Fritz Fruit GNU Chess HIARCS Houdini Ikarus Junior KnightCap Komodo Leela Chess Zero MChess Pro Mittens MuZero Naum REBEL Rybka Shredder Sjeng SmarThink Stockfish Torch Turochamp Zappa
v t e

Computer chess includes both hardware (dedicated computers) and software capable of playing chess. Computer chess provides opportunities for players to practice even in the absence of human opponents, and also provides opportunities for analysis, entertainment and training. Computer chess applications that play at the level of a chess grandmaster or higher are available on hardware from supercomputers to smart phones. Standalone chess-playing machines are also available. Stockfish, Leela Chess Zero, GNU Chess, Fruit, and other free open source applications are available for various platforms.

Computer chess applications, whether implemented in hardware or software, use different strategies than humans to choose their moves: they use

representing sequences of moves from the current position and attempt to execute the best such sequence during play. Such trees are typically quite large, thousands to millions of nodes. The computational speed of modern computers, capable of processing tens of thousands to hundreds of thousands of nodes or more per second, along with extension and reduction heuristics that narrow the tree to mostly relevant nodes, make such an approach effective.

The first chess machines capable of playing chess or reduced chess-like games were software programs running on digital computers early in the vacuum-tube computer age (1950s). The early programs played so poorly that even a beginner could defeat them. Within 40 years, in 1997, chess engines running on super-computers or specialized hardware were capable of defeating even the best human players. By 2006, programs running on desktop PCs had attained the same capability. In 2006, Monty Newborn, Professor of Computer Science at McGill University, declared: "the science has been done". Nevertheless, solving chess is not currently possible for modern computers due to the game's extremely large number of possible variations.^[1]

Computer chess was once considered the "Drosophila of AI", the edge of knowledge engineering. The field is now considered a scientifically completed paradigm, and playing chess is a mundane computing activity.^[2]

In the past, stand-alone chess machines (usually microprocessors running software chess programs; occasionally specialized hardware) were sold. Today,

Most chess programs and sites offer the ability to analyze positions and games using chess engines, and some offer the ability to play against engines (which can be set to play at custom levels of strength) as though they were normal opponents.

Hardware requirements for chess engines are minimal, but performance will vary with processor speed, and memory, needed to hold large transposition tables. Most modern chess engines, such as Stockfish, rely on

Types and features of chess software

Perhaps the most common type of chess software are programs that simply play chess. A human player makes a move on the board, the AI calculates and plays a subsequent move, and the human and AI alternate turns until the game ends. The

also has a Friend Mode where during the game it tries to match the level of the player.

Chess, a component of macOS

Chess databases allow users to search through a large library of historical games, analyze them, check statistics, and formulate an opening repertoire.

Programs such as

Playchess

allow players to play against one another over the internet.

Chess training programs teach chess.

for children. Convekta provides a large number of training apps such as CT-ART and its Chess King line based on tutorials by GM Alexander Kalinin and Maxim Blokh.

There is also software for handling chess problems.

After discovering refutation screening—the application of

In the late 1970s chess programs began defeating highly skilled human players.

The sudden improvement without a theoretical breakthrough was unexpected, as many did not expect that Belle's ability to examine 100,000 positions a second—about eight plies—would be sufficient. The Spracklens, creators of the successful microcomputer program Sargon, estimated that 90% of the improvement came from faster evaluation speed and only 10% from improved evaluations. New Scientist stated in 1982 that computers "play terrible chess ... clumsy, inefficient, diffuse, and just plain ugly", but humans lost to them by making "horrible blunders, astonishing lapses, incomprehensible oversights, gross miscalculations, and the like" much more often than they realized; "in short, computers win primarily through their ability to find and exploit miscalculations in human initiatives".^[20]

By 1982, microcomputer chess programs could evaluate up to 1,500 moves a second and were as strong as mainframe chess programs of five years earlier, able to defeat a majority of amateur players. While only able to look ahead one or two plies more than at their debut in the mid-1970s, doing so improved their play more than experts expected; seemingly minor improvements "appear to have allowed the crossing of a psychological threshold, after which a rich harvest of human error becomes accessible", New Scientist wrote.

At the 1982

two of the remaining five games of the match, for a convincing victory.

In May 1997, an updated version of Deep Blue defeated Kasparov 3½–2½ in a return match. A documentary mainly about the confrontation was made in 2003, titled Game Over: Kasparov and the Machine.

With increasing processing power and improved evaluation functions, chess programs running on commercially available workstations began to rival top-flight players. In 1998,

In fast games, computers played better than humans, but at classical time controls – at which a player's rating is determined – the advantage was not so clear.

In the early 2000s, commercially available programs such as Junior and Fritz were able to draw matches against former world champion Garry Kasparov and classical world champion Vladimir Kramnik.

In October 2002, Vladimir Kramnik and Deep Fritz competed in the eight-game Brains in Bahrain match, which ended in a draw. Kramnik won games 2 and 3 by "conventional" anti-computer tactics – play conservatively for a long-term advantage the computer is not able to see in its game tree search. Fritz, however, won game 5 after a severe blunder by Kramnik. Game 6 was described by the tournament commentators as "spectacular". Kramnik, in a better position in the early middlegame, tried a piece sacrifice to achieve a strong tactical attack, a strategy known to be highly risky against computers who are at their strongest defending against such attacks. True to form, Fritz found a watertight defense and Kramnik's attack petered out leaving him in a bad position. Kramnik resigned the game, believing the position lost. However, post-game human and computer analysis has shown that the Fritz program was unlikely to have been able to force a win and Kramnik effectively sacrificed a drawn position. The final two games were draws. Given the circumstances, most commentators still rate Kramnik the stronger player in the match.^{[citation needed]}

In January 2003, Kasparov played Junior, another chess computer program, in New York City. The match ended 3–3.

In November 2003, Kasparov played X3D Fritz. The match ended 2–2.

In 2005, Hydra, a dedicated chess computer with custom hardware and sixty-four processors and also winner of the 14th IPCCC in 2005, defeated seventh-ranked Michael Adams 5½–½ in a six-game match (though Adams' preparation was far less thorough than Kramnik's for the 2002 series).^[25]

In November–December 2006, World Champion Vladimir Kramnik played Deep Fritz. This time the computer won; the match ended 2–4. Kramnik was able to view the computer's opening book. In the first five games Kramnik steered the game into a typical "anti-computer" positional contest. He lost one game (

and was crushed.

There was speculation that interest in human–computer chess competition would plummet as a result of the 2006 Kramnik-Deep Fritz match.[26] According to Newborn, for example, "the science is done".^[27]

Human–computer chess matches showed the best computer systems overtaking human chess champions in the late 1990s. For the 40 years prior to that, the trend had been that the best machines gained about 40 points per year in the

at 3361.

This is in contrast to supercomputers such as Deep Blue that searched 200 million positions per second.

Advanced Chess

is a form of chess developed in 1998 by Kasparov where a human plays against another human, and both have access to computers to enhance their strength. The resulting "advanced" player was argued by Kasparov to be stronger than a human or computer alone. This has been proven in numerous occasions, such as at Freestyle Chess events.

Players today are inclined to treat chess engines as analysis tools rather than opponents.[32] Chess grandmaster Andrew Soltis stated in 2016 "The computers are just much too good" and that world champion Magnus Carlsen won't play computer chess because "he just loses all the time and there's nothing more depressing than losing without even being in the game."^[33]

Since the era of mechanical machines that played rook and king endings and electrical machines that played other games like hex in the early years of the 20th century, scientists and theoreticians have sought to develop a procedural representation of how humans learn, remember, think and apply knowledge, and the game of chess, because of its daunting complexity, became the "Drosophila of artificial intelligence (AI)".^{[Note 1]} The procedural resolution of complexity became synonymous with thinking, and early computers, even before the chess automaton era, were popularly referred to as "electronic brains". Several different schema were devised starting in the latter half of the 20th century to represent knowledge and thinking, as applied to playing the game of chess (and other games like checkers):

• Search based (brute force vs selective search)
  • Search in search based schema (minimax/alpha-beta, Monte Carlo tree search)
  • Evaluations in search based schema (
    neural networks, texel tuning, genetic algorithms, gradient descent, reinforcement learning
    )
• endgame tablebases
  )

Using "ends-and-means" heuristics a human chess player can intuitively determine optimal outcomes and how to achieve them regardless of the number of moves necessary, but a computer must be systematic in its analysis. Most players agree that looking at least five moves ahead (ten plies) when necessary is required to play well. Normal tournament rules give each player an average of three minutes per move. On average there are more than 30 legal moves per chess position, so a computer must examine a quadrillion possibilities to look ahead ten plies (five full moves); one that could examine a million positions a second would require more than 30 years.^[20]

The earliest attempts at procedural representations of playing chess predated the digital electronic age, but it was the stored program digital computer that gave scope to calculating such complexity. Claude Shannon, in 1949, laid out the principles of algorithmic solution of chess. In that paper, the game is represented by a "tree", or digital data structure of choices (branches) corresponding to moves. The nodes of the tree were positions on the board resulting from the choices of move. The impossibility of representing an entire game of chess by constructing a tree from first move to last was immediately apparent: there are an average of 36 moves per position in chess and an average game lasts about 35 moves to resignation (60-80 moves if played to checkmate, stalemate, or other draw). There are 400 positions possible after the first move by each player, about 200,000 after two moves each, and nearly 120 million after just 3 moves each.

So a limited lookahead (search) to some depth, followed by using domain-specific knowledge to evaluate the resulting terminal positions was proposed. A kind of middle-ground position, given good moves by both sides, would result, and its evaluation would inform the player about the goodness or badness of the moves chosen. Searching and comparing operations on the tree were well suited to computer calculation; the representation of subtle chess knowledge in the evaluation function was not. The early chess programs suffered in both areas: searching the vast tree required computational resources far beyond those available, and what chess knowledge was useful and how it was to be encoded would take decades to discover.

The developers of a chess-playing computer system must decide on a number of fundamental implementation issues. These include:

• Graphical user interface (GUI) – how moves are entered and communicated to the user, how the game is recorded, how the time controls are set, and other interface considerations
• Board representation
  – how a single position is represented in data structures;
• Search techniques – how to identify the possible moves and select the most promising ones for further examination;
• Leaf evaluation – how to evaluate the value of a board position, if no further search will be done from that position.

skills built from experience. This enables them to examine some lines in much greater depth than others by simply not considering moves they can assume to be poor. More evidence for this being the case is the way that good human players find it much easier to recall positions from genuine chess games, breaking them down into a small number of recognizable sub-positions, rather than completely random arrangements of the same pieces. In contrast, poor players have the same level of recall for both.

The equivalent of this in computer chess are evaluation functions for leaf evaluation, which correspond to the human players' pattern recognition skills, and the use of machine learning techniques in training them, such as Texel tuning, stochastic gradient descent, and reinforcement learning, which corresponds to building experience in human players. This allows modern programs to examine some lines in much greater depth than others by using forwards pruning and other selective heuristics to simply not consider moves the program assume to be poor through their evaluation function, in the same way that human players do. The only fundamental difference between a computer program and a human in this sense is that a computer program can search much deeper than a human player could, allowing it to search more nodes and bypass the horizon effect to a much greater extent than is possible with human players.

Computer chess programs usually support a number of common de facto standards. Nearly all of today's programs can read and write game moves as

.

Starting in the late 1990s, programmers began to develop separately engines (with a command-line interface which calculates which moves are strongest in a position) or a graphical user interface (GUI) which provides the player with a chessboard they can see, and pieces that can be moved. Engines communicate their moves to the GUI using a protocol such as the Chess Engine Communication Protocol (CECP) or Universal Chess Interface (UCI). By dividing chess programs into these two pieces, developers can write only the user interface, or only the engine, without needing to write both parts of the program. (See also chess engine.)

Developers have to decide whether to connect the engine to an opening book and/or endgame

or leave this to the GUI.

The

positions for compact long-term storage.

Search techniques

Computer chess programs consider chess moves as a game tree. In theory, they examine all moves, then all counter-moves to those moves, then all moves countering them, and so on, where each individual move by one player is called a "ply". This evaluation continues until a certain maximum search depth or the program determines that a final "leaf" position has been reached (e.g. checkmate).

One particular type of search algorithm used in computer chess are minimax search algorithms, where at each ply the "best" move by the player is selected; one player is trying to maximize the score, the other to minimize it. By this alternating process, one particular terminal node whose evaluation represents the searched value of the position will be arrived at. Its value is backed up to the root, and that evaluation becomes the valuation of the position on the board. This search process is called minimax.

A naive implementation of the minimax algorithm can only search to a small depth in a practical amount of time, so various methods have been devised to greatly speed the search for good moves. Alpha–beta pruning, a system of defining upper and lower bounds on possible search results and searching until the bounds coincided, is typically used to reduce the search space of the program.

In addition, various selective search heuristics, such as

, etc.). These selective search heuristics have to be used very carefully however. If the program overextends, it wastes too much time looking at uninteresting positions. If too much is pruned or reduced, there is a risk of cutting out interesting nodes.

Monte Carlo tree search (MCTS) is a heuristic search algorithm which expands the search tree based on random sampling of the search space. A version of Monte Carlo tree search commonly used in computer chess is PUCT, Predictor and Upper Confidence bounds applied to Trees.

DeepMind's

search algorithm as calculations on the GPU are inherently parallel. The minimax and alpha-beta pruning algorithms used in computer chess are inherently serial algorithms, so would not work well with batching on the GPU. On the other hand, MCTS is a good alternative, because the random sampling used in Monte Carlo tree search lends itself well to parallel computing, and is why nearly all engines which support calculations on the GPU use MCTS instead of alpha-beta.

Other optimizations

Many other optimizations can be used to make chess-playing programs stronger. For example,

pondering

, searching to deeper levels on the opponent's time, similar to human beings, to increase their playing strength.

Of course, faster hardware and additional memory can improve chess program playing strength. Hyperthreaded architectures can improve performance modestly if the program is running on a single core or a small number of cores. Most modern programs are designed to take advantage of multiple cores to do parallel search. Other programs are designed to run on a general purpose computer and allocate move generation, parallel search, or evaluation to dedicated processors or specialized co-processors.

History

The first paper on chess search was by Claude Shannon in 1950.^[34] He predicted the two main possible search strategies which would be used, which he labeled "Type A" and "Type B",^[35] before anyone had programmed a computer to play chess.

Type A programs would use a "brute force" approach, examining every possible position for a fixed number of moves using a pure naive minimax algorithm. Shannon believed this would be impractical for two reasons.

First, with approximately thirty moves possible in a typical real-life position, he expected that searching the approximately 10⁹ positions involved in looking three moves ahead for both sides (six plies) would take about sixteen minutes, even in the "very optimistic" case that the chess computer evaluated a million positions every second. (It took about forty years to achieve this speed.) A later search algorithm called alpha–beta pruning, a system of defining upper and lower bounds on possible search results and searching until the bounds coincided, reduced the branching factor of the game tree logarithmically, but it still was not feasible for chess programs at the time to exploit the exponential explosion of the tree.

Second, it ignored the problem of quiescence, trying to only evaluate a position that is at the end of an exchange of pieces or other important sequence of moves ('lines'). He expected that adapting minimax to cope with this would greatly increase the number of positions needing to be looked at and slow the program down still further. He expected that adapting type A to cope with this would greatly increase the number of positions needing to be looked at and slow the program down still further.

This led naturally to what is referred to as "selective search" or "type B search", using chess knowledge (heuristics) to select a few presumably good moves from each position to search, and prune away the others without searching. Instead of wasting processing power examining bad or trivial moves, Shannon suggested that type B programs would use two improvements:

1. Employ a quiescence search.
2. Employ forward pruning; i.e. only look at a few good moves for each position.

This would enable them to look further ahead ('deeper') at the most significant lines in a reasonable time. However, early attempts at selective search often resulted in the best move or moves being pruned away. As a result, little or no progress was made for the next 25 years dominated by this first iteration of the selective search paradigm. The best program produced in this early period was Mac Hack VI in 1967; it played at the about the same level as the average amateur (C class on the United States Chess Federation rating scale).

Meanwhile, hardware continued to improve, and in 1974, brute force searching was implemented for the first time in the Northwestern University Chess 4.0 program. In this approach, all alternative moves at a node are searched, and none are pruned away. They discovered that the time required to simply search all the moves was much less than the time required to apply knowledge-intensive heuristics to select just a few of them, and the benefit of not prematurely or inadvertently pruning away good moves resulted in substantially stronger performance.

In the 1980s and 1990s, progress was finally made in the selective search paradigm, with the development of

as a substrate for their search algorithm, these additional selective search heuristics used in modern programs means that the program no longer does a "brute force" search. Instead they heavily rely on these selective search heuristics to extend lines the program considers good and prune and reduce lines the program considers bad, to the point where most of the nodes on the search tree are pruned away, enabling modern programs to search very deep.

In 2006, Rémi Coulom created Monte Carlo tree search, another kind of type B selective search. In 2007, an adaption of Monte Carlo tree search called Upper Confidence bounds applied to Trees or UCT for short was created by Levente Kocsis and Csaba Szepesvári. In 2011, Chris Rosin developed a variation of UCT called Predictor + Upper Confidence bounds applied to Trees, or PUCT for short. PUCT was then used in AlphaZero in 2017, and later in Leela Chess Zero in 2018.

In the 1970s, most chess programs ran on super computers like Control Data Cyber 176s or Cray-1s, indicative that during that developmental period for computer chess, processing power was the limiting factor in performance. Most chess programs struggled to search to a depth greater than 3 ply. It was not until the hardware chess machines of the 1980s, that a relationship between processor speed and knowledge encoded in the evaluation function became apparent.

It has been estimated that doubling the computer speed gains approximately fifty to seventy Elo points in playing strength (Levy & Newborn 1991:192).

For most chess positions, computers cannot look ahead to all possible final positions. Instead, they must look ahead a few plies and compare the possible positions, known as leaves. The algorithm that evaluates leaves is termed the "evaluation function", and these algorithms are often vastly different between different chess programs. Evaluation functions typically evaluate positions in hundredths of a pawn (called a centipawn), where by convention, a positive evaluation favors White, and a negative evaluation favors Black. However, some evaluation function output win/draw/loss percentages instead of centipawns.

Historically, handcrafted evaluation functions consider material value along with other factors affecting the strength of each side. When counting up the material for each side, typical values for pieces are 1 point for a pawn, 3 points for a knight or bishop, 5 points for a rook, and 9 points for a queen. (See Chess piece relative value.) The king is sometimes given an arbitrarily high value such as 200 points (Shannon's paper) to ensure that a checkmate outweighs all other factors (Levy & Newborn 1991:45). In addition to points for pieces, most handcrafted evaluation functions take many factors into account, such as pawn structure, the fact that a pair of bishops are usually worth more, centralized pieces are worth more, and so on. The protection of kings is usually considered, as well as the phase of the game (opening, middle or endgame). Machine learning techniques such as Texel turning, stochastic gradient descent, or reinforcement learning are usually used to optimise handcrafted evaluation functions.

Most modern evaluation functions make use of

.

The output of the evaluation function is a single scalar, quantized in centipawns or other units, which is, in the case of handcrafted evaluation functions, a weighted summation of the various factors described, or in the case of neural network based evaluation functions, the output of the head of the neural network. The evaluation putatively represents or approximates the value of the subtree below the evaluated node as if it had been searched to termination, i.e. the end of the game. During the search, an evaluation is compared against evaluations of other leaves, eliminating nodes that represent bad or poor moves for either side, to yield a node which by convergence, represents the value of the position with best play by both sides.

Endgame tablebases

Endgame play had long been one of the great weaknesses of chess programs because of the depth of search needed. Some otherwise master-level programs were unable to win in positions where even intermediate human players could force a win.

To solve this problem, computers have been used to analyze some

was a pioneer in this area.

The results of the computer analysis sometimes surprised people. In 1977 Thompson's Belle chess machine used the endgame tablebase for a king and rook against king and queen and was able to draw that theoretically lost ending against several masters (see Philidor position#Queen versus rook). This was despite not following the usual strategy to delay defeat by keeping the defending king and rook close together for as long as possible. Asked to explain the reasons behind some of the program's moves, Thompson was unable to do so beyond saying the program's database simply returned the best moves.

Most grandmasters declined to play against the computer in the queen versus rook endgame, but Walter Browne accepted the challenge. A queen versus rook position was set up in which the queen can win in thirty moves, with perfect play. Browne was allowed 2½ hours to play fifty moves, otherwise a draw would be claimed under the fifty-move rule. After forty-five moves, Browne agreed to a draw, being unable to force checkmate or win the rook within the next five moves. In the final position, Browne was still seventeen moves away from checkmate, but not quite that far away from winning the rook. Browne studied the endgame, and played the computer again a week later in a different position in which the queen can win in thirty moves. This time, he captured the rook on the fiftieth move, giving him a winning position.^[36][37]

Other positions, long believed to be won, turned out to take more moves against perfect play to actually win than were allowed by chess's fifty-move rule. As a consequence, for some years the official FIDE rules of chess were changed to extend the number of moves allowed in these endings. After a while, the rule reverted to fifty moves in all positions – more such positions were discovered, complicating the rule still further, and it made no difference in human play, as they could not play the positions perfectly.

Over the years, other

In all of these endgame databases it is assumed that castling is no longer possible.

Many tablebases do not consider the fifty-move rule, under which a game where fifty moves pass without a capture or pawn move can be claimed to be a draw by either player. This results in the tablebase returning results such as "Forced mate in sixty-six moves" in some positions which would actually be drawn because of the fifty-move rule. One reason for this is that if the rules of chess were to be changed once more, giving more time to win such positions, it will not be necessary to regenerate all the tablebases. It is also very easy for the program using the tablebases to notice and take account of this 'feature' and in any case if using an endgame tablebase will choose the move that leads to the quickest win (even if it would fall foul of the fifty-move rule with perfect play). If playing an opponent not using a tablebase, such a choice will give good chances of winning within fifty moves.

The Nalimov tablebases, which use state-of-the-art

Endgame databases featured prominently in 1999, when Kasparov played an exhibition match on the Internet against the rest of the world. A seven piece Queen and pawn endgame was reached with the World Team fighting to salvage a draw. Eugene Nalimov helped by generating the six piece ending tablebase where both sides had two Queens which was used heavily to aid analysis by both sides.

The most popular endgame tablebase is syzygy which is used by most top computer programs like Stockfish, Leela Chess Zero, and Komodo. It is also significantly smaller in size than other formats, with 7-piece tablebases taking only 18.4 TB.^[43]

For a current state-of-the art chess engine like Stockfish, a table base only provides a very minor increase in playing strength (approximately 3 Elo points for syzygy 6men as of Stockfish 15).^[44]

Chess engines, like human beings, may save processing time as well as select variations known to be strong via referencing an opening book stored in a database. Opening books cover the opening moves of a game to variable depth, depending on opening and variation, but usually to the first 10-12 moves (20-24 ply). In the early eras of computer chess, trusting variations studied in-depth by human grandmasters for decades was superior to the weak performance of mid-20th-century engines. And even in the contemporary era, allowing computer engines to extensively analyze various openings at their leisure beforehand, and then simply consult the results when in a game, speeds up their play.

In the 1990s, some theorists believed that chess engines of the day had much of their strength in memorized opening books and knowledge dedicated to known positions, and thus believed a valid anti-computer tactic would be to intentionally play some out-of-book moves in order to force the chess program to think for itself. This seems to have been a dubious assumption even then; Garry Kasparov tried it via using the non-standard Mieses Opening at the 1997 Deep Blue versus Garry Kasparov Game 1 match, but lost. This tactic became even weaker as time passed; the opening books stored in computer databases can be far more extensive than even the best prepared humans, meaning computers will be well-prepared for even rare variations and know the correct play. More generally, the play of engines even in fully unknown situations (as comes up in variants such as Chess960) is still exceptionally strong, so the lack of an opening book isn't even a major disadvantage for tactically sharp chess engines, who can discover strong moves in unfamiliar board variations accurately.

In contemporary engine tournaments, engines are often told to play situations from a variety of openings, including unbalanced ones, to reduce the draw rate and to add more variety to the games.^[45]

CEGT,^[46] CSS,^[47] SSDF,^[48] WBEC,^[49] REBEL,^[50] FGRL,^[51] and IPON^[52] maintain rating lists allowing fans to compare the strength of engines. Various versions of Stockfish, Komodo, Leela Chess Zero, and Fat Fritz dominate the rating lists in the early 2020s.

CCRL (Computer Chess Rating Lists) is an organisation that tests computer chess engines' strength by playing the programs against each other. CCRL was founded in 2006 to promote computer-computer competition and tabulate results on a rating list.^[53]

The organisation runs three different lists: 40/40 (40 minutes for every 40 moves played), 40/4 (4 minutes for every 40 moves played), and 40/4

History

Pre-computer age

The idea of creating a chess-playing machine dates back to the eighteenth century. Around 1769, the chess playing

, which played a king and rook versus king ending, were too complex and limited to be useful for playing full games of chess. The field of mechanical chess research languished until the advent of the digital computer in the 1950s.

Early software age: selective search and Botvinnik

Since then, chess enthusiasts and

). Later he advised the team that created the chess program Kaissa at Moscow's Institute of Control Sciences. Botvinnik had his own ideas to model a Chess Master's Mind. After publishing and discussing his early ideas on attack maps and trajectories at Moscow Central Chess Clubin 1966, he found Vladimir Butenko as supporter and collaborator. Butenko first implemented the 15x15 vector attacks board representation on a M-20 computer, determining trajectories. After Botvinnik introduced the concept of Zones in 1970, Butenko refused further cooperation and began to write his own program, dubbed Eureka. In the 70s and 80s, leading a team around Boris Stilman, Alexander Yudin, Alexander Reznitskiy, Michael Tsfasman and Mikhail Chudakov, Botvinnik worked on his own project 'Pioneer' - which was an Artificial Intelligence based chess project. In the 90s, Botvinnik already in his 80s, he worked on the new project 'CC Sapiens'.

Later software age: full-width search

One developmental milestone occurred when the team from Northwestern University, which was responsible for the Chess series of programs and won the first three ACM Computer Chess Championships (1970–72), abandoned type B searching in 1973. The resulting program, Chess 4.0, won that year's championship and its successors went on to come in second in both the 1974 ACM Championship and that year's inaugural World Computer Chess Championship, before winning the ACM Championship again in 1975, 1976 and 1977. The type A implementation turned out to be just as fast: in the time it used to take to decide which moves were worthy of being searched, it was possible just to search all of them. In fact, Chess 4.0 set the paradigm that was and still is followed essentially by all modern Chess programs today, and that had been successfully started by the Russian ITEP in 1965.

In 1978, an early rendition of Ken Thompson's hardware chess machine Belle, entered and won the North American Computer Chess Championship over the dominant Northwestern University Chess 4.7.

Technological advances by orders of magnitude in processing power have made the brute force approach far more incisive than was the case in the early years. The result is that a very solid, tactical AI player aided by some limited positional knowledge built in by the evaluation function and pruning/extension rules began to match the best players in the world. It turned out to produce excellent results, at least in the field of chess, to let computers do what they do best (calculate) rather than coax them into imitating human thought processes and knowledge. In 1997

, a brute-force machine capable of examining 500 million nodes per second, defeated World Champion Garry Kasparov, marking the first time a computer has defeated a reigning world chess champion in standard time control.

In 2016, NPR asked experts to characterize the playing style of computer chess engines. Murray Campbell of IBM stated that "Computers don't have any sense of aesthetics... They play what they think is the objectively best move in any position, even if it looks absurd, and they can play any move no matter how ugly it is." Grandmasters Andrew Soltis and Susan Polgar stated that computers are more likely to retreat than humans are.^[33]

While

before other chess programmers began to adopt neural networks into their engines.

Some people, such as the

methods pioneered by AlphaZero are still extremely rare in computer chess.

Timeline

• 1769 – Wolfgang von Kempelen builds the Turk. Presented as a chess-playing automaton, it is secretly operated by a human player hidden inside the machine.
• 1868 – Charles Hooper presents the Ajeeb automaton – which also has a human chess player hidden inside.
• 1912 –
  Leonardo Torres y Quevedo builds El Ajedrecista, a machine that could play King and Rook versus King endgames
  .
• 1941 – Predating comparable work by at least a decade, Konrad Zuse develops computer chess algorithms in his Plankalkül programming formalism. Because of the circumstances of the Second World War, however, they were not published, and did not come to light, until the 1970s.
• 1948 – Norbert Wiener's book Cybernetics describes how a chess program could be developed using a depth-limited minimax search with an evaluation function.
• 1950 – Claude Shannon publishes "Programming a Computer for Playing Chess", one of the first papers on the algorithmic methods of computer chess.
• 1951 – Alan Turing is first to publish a program, developed on paper, that was capable of playing a full game of chess (dubbed Turochamp).^[67][68]
• 1952 – Dietrich Prinz develops a program that solves chess problems.

• 1956 – Los Alamos chess is the first program to play a chess-like game, developed by Paul Stein and Mark Wells for the MANIAC I computer.
• 1956 – John McCarthy invents the alpha–beta search algorithm.
• 1957 – The first programs that can play a full game of chess are developed, one by Alex Bernstein^[69] and one by Russian programmers using a BESM.
• 1958 – NSS becomes the first chess program to use the alpha–beta search algorithm.
• 1962 – The first program to play credibly, Kotok-McCarthy, is published at MIT.
• 1963 – Grandmaster David Bronstein defeats an M-20 running an early chess program.^[70]
• 1966–67 – The first chess match between computer programs is played. Moscow Institute for Theoretical and Experimental Physics (ITEP) defeats Kotok-McCarthy at Stanford University by telegraph over nine months.
• 1967 –
  Mac Hack VI, by Richard Greenblatt et al. introduces transposition tables
  and employs dozens of carefully tuned move selection heuristics; it becomes the first program to defeat a person in tournament play. Mac Hack VI played about C class level.
• 1968 – Scottish chess champion David Levy makes a 500 pound bet with AI pioneers John McCarthy and Donald Michie that no computer program would win a chess match against him within 10 years.
• 1970 – Monty Newborn and the Association for Computing Machinery organize the first North American Computer Chess Championships in New York.
• 1971 – Ken Thompson, an American Computer scientist at Bell Labs and creator of the Unix operating system, writes his first chess-playing program called "chess" for the earliest version of Unix.^[71]
• 1974 – David Levy, Ben Mittman and Monty Newborn organize the first World Computer Chess Championship which is won by the Russian program Kaissa.
• 1975 – After nearly a decade of only marginal progress since the high-water mark of Greenblatt's MacHack VI in 1967, Northwestern University Chess 4.5 is introduced featuring full-width search, and innovations of bitboards and iterative deepening. It also reinstated a transposition table as first seen in Greenblatt's program. It was thus the first program with an integrated modern structure and became the model for all future development. Chess 4.5 played strong B-class and won the 3rd World Computer Chess Championship the next year.^[72] Northwestern University Chess and its descendants dominated computer chess until the era of hardware chess machines in the early 1980s.
• 1976 – In December, Canadian programmer Peter R. Jennings releases Microchess, the first game for microcomputers to be sold.^[73]

• 1977 – In March, Fidelity Electronics releases Chess Challenger, the first dedicated chess computer to be sold. The International Computer Chess Association is founded by chess programmers to organize computer chess championships and report on research and advancements on computer chess in their journal. Also that year, Applied Concepts released Boris, a dedicated chess computer in a wooden box with plastic chess pieces and a folding board.
• 1978 – David Levy wins the bet made 10 years earlier, defeating Chess 4.7 in a six-game match by a score of 4½–1½. The computer's victory in game four is the first defeat of a human master in a tournament.^[21]
• 1979 – Frederic Friedel organizes a match between IM David Levy and Chess 4.8, which is broadcast on German television. Levy and Chess 4.8, running on a CDC Cyber 176, the most powerful computer in the world, fought a grueling 89 move draw.
• 1980 – Fidelity computers win the World Microcomputer Championships each year from 1980 through 1984. In Germany, Hegener & Glaser release their first Mephisto dedicated chess computer. The USCF prohibits computers from competing in human tournaments except when represented by the chess systems' creators.^[74] The Fredkin Prize, offering $100,000 to the creator of the first chess machine to defeat the world chess champion, is established.
• 1981 – Cray Blitz wins the Mississippi State Championship with a perfect 5–0 score and a performance rating of 2258. In round 4 it defeats Joe Sentef (2262) to become the first computer to beat a master in tournament play and the first computer to gain a master rating.
• 1984 – The German Company Hegener & Glaser's Mephisto line of dedicated chess computers begins a long streak of victories (1984–1990) in the World Microcomputer Championship using dedicated computers running programs ChessGenius and Rebel.
• 1986 – Software Country (see
  Software Toolworks) released Chessmaster
  2000 based on an engine by David Kittinger, the first edition of what was to become the world's best selling line of chess programs.
• 1987 –
  Chessbase, releasing the first chess database program. Stuart Cracraft releases GNU Chess, one of the first 'chess engines' to be bundled with a separate graphical user interface (GUI), chesstool.^[75]
• 1988 – HiTech, developed by Hans Berliner and Carl Ebeling, wins a match against grandmaster Arnold Denker 3½–½. Deep Thought shares first place with Tony Miles in the Software Toolworks Championship, ahead of former world champion Mikhail Tal and several grandmasters including Samuel Reshevsky, Walter Browne and Mikhail Gurevich. It also defeats grandmaster Bent Larsen, making it the first computer to beat a GM in a tournament. Its rating for performance in this tournament of 2745 (USCF scale) was the highest obtained by a computer player.^[76][77]
• 1989 – Deep Thought demolishes David Levy in a 4-game match 0–4, bringing to an end his famous series of wagers starting in 1968.
• 1990 – On April 25, former world champion Anatoly Karpov lost in a simul to Hegener & Glaser's Mephisto Portorose M68030 chess computer.^[78]
• 1991 – The ChessMachine based on Ed Schröder's Rebel wins the World Microcomputer Chess Championship
• 1992 – ChessMachine wins the 7th World Computer Chess Championship, the first time a microcomputer beat mainframes. GM John Nunn releases Secrets of Rook Endings, the first book based on endgame tablebases developed by Ken Thompson.
• 1993 – Deep Thought-2 loses a four-game match against Bent Larsen. Chess programs running on personal computers surpass Mephisto's dedicated chess computers to win the Microcomputer Championship, marking a shift from dedicated chess hardware to software on multipurpose personal computers.
• 1995 – Fritz 3, running on a 90 Mhz Pentium PC, beats Deep Thought-2 dedicated chess machine, and programs running on several super-computers, to win the 8th World Computer Chess Championships in Hong Kong. This marks the first time a chess program running on commodity hardware defeats specialized chess machines and massive super-computers, indicating a shift in emphasis from brute computational power to algorithmic improvements in the evolution of chess engines.
• 1996 – IBM's Deep Blue loses a six-game match against Garry Kasparov, 2–4.
• 1997 – Deep(er) Blue, a highly modified version of the original, wins a six-game match against Garry Kasparov, 3.5–2.5.
• 2000 – Stefan Meyer-Kahlen and Rudolf Huber draft the Universal Chess Interface, a protocol for GUIs to talk to engines that would gradually become the main form new engines would take.
• 2002 –
  Deep Fritz
  .
• 2003 – Kasparov draws a six-game match against Deep Junior and draws a four-game match against X3D Fritz.
• 2004 – a team of computers (Hydra, Deep Junior and Fritz) wins 8½–3½ against a strong human team formed by Veselin Topalov, Ruslan Ponomariov and Sergey Karjakin, who had an average Elo rating of 2681. Fabien Letouzey releases the source code for Fruit 2.1, an engine quite competitive with the top closed-source engines of the time. This leads many authors to revise their code, incorporating the new ideas.
• 2005 – Rybka wins the IPCCC tournament and very quickly afterwards becomes the strongest engine.^[79]
• 2006 – The world champion,
  Deep Fritz
  .
• 2009 – Pocket Fritz. 4 running on a smartphone, wins Copa Mercosur, an International Master level tournament, scoring 9½/10 and earning a performance rating of 2900.^[30] A group of pseudonymous Russian programmers release the source code of Ippolit, an engine seemingly stronger than Rybka. This becomes the basis for the engines Robbolito and Ivanhoe, and many engine authors adopt ideas from it.
• 2010 – Before the World Chess Championship 2010, Topalov prepares by sparring against the supercomputer Blue Gene with 8,192 processors capable of 500 trillion (5 × 10¹⁴) floating-point operations per second.^[80] Rybka developer, Vasik Rajlich, accuses Ippolit of being a clone of Rybka.
• 2011 – The ICGA strips Rybka of its WCCC titles.^[81][82]
• 2017 – AlphaZero, a neural net-based digital automaton, beats Stockfish 28–0, with 72 draws, in a 100-game match.
• 2018 – Efficiently updatable neural network (NNUE) evaluation is invented for computer shogi.^[83]
• 2019 – Leela Chess Zero (LCZero v0.21.1-nT40.T8.610), a chess engine based on AlphaZero, defeats Stockfish 19050918 in a 100-game match with the final score 53.5 to 46.5 to win TCEC season 15.^[84]
• 2020 – NNUE is added to Stockfish evaluation, noticeably increasing its strength.^[64][65]

These chess playing systems include custom hardware with approx. dates of introduction (excluding dedicated microcomputers):

• Belle 1976
• Bebe, a strong
  bit-slice processor
  1980
• HiTech 1985
• ChipTest 1985
• Deep Thought 1987
• Deep Thought 2 (Deep Blue prototype)~1994
• Deep Blue
  1996, 1997
• Hydra, predecessor was called Brutus 2002
• AlphaZero 2017 (used Google's Tensor Processing Units for neural networks, but the hardware is not specific to Chess or games)
  • MuZero 2019 (similar hardware to its predecessor AlphaZero, non-specific to Chess or e.g. Go), learns the rules of Chess

In the late 1970s to early 1990s, there was a competitive market for dedicated chess computers. This market changed in the mid-1990s when computers with dedicated processors could no longer compete with the fast processors in personal computers.

• Boris in 1977 and Boris Diplomat in 1979, chess computers including pieces and board, sold by Applied Concepts Inc.
• Chess Challenger, a line of chess computers sold by Fidelity Electronics from 1977 to 1992.
  World Microcomputer Chess Championships.^{[citation needed}
  ]
• ARM
  -based dedicated computer, which could run two engines:
  • "The King", which later became the Chessmaster engine, was also used in the TASC R30 dedicated computer.
  • Gideon, a version of Rebel, in 1992 became the first microcomputer to win the World Computer Chess Championship.^[86]
• Excalibur Electronics sells a line of beginner strength units.
• World Microcomputer Chess Championships.^{[citation needed}
  ]
• Novag sold a line of tactically strong computers, including the Constellation, Sapphire, and Star Diamond brands.
• Phoenix Chess Systems makes limited edition units based around StrongARM and XScale processors running modern engines and emulating classic engines.
• Saitek sells mid-range units of intermediate strength. They bought out Hegener & Glaser and its Mephisto brand in 1994.

Recently, some hobbyists have been using the Multi Emulator Super System to run the chess programs created for Fidelity or Hegener & Glaser's Mephisto computers on modern 64-bit operating systems such as Windows 10.^[87] The author of Rebel, Ed Schröder has also adapted three of the Hegener & Glaser Mephisto's he wrote to work as UCI engines.^[88]

These programs can be run on MS-DOS, and can be run on 64-bit Windows 10 via emulators such as

• Chessmaster 2000
• Colossus Chess
• Fritz 1–3
• Kasparov's Gambit
• Rebel
• Sargon
• Socrates II

Well-known computer chess theorists include:

• Georgy Adelson-Velsky, a Soviet and Israeli mathematician and computer scientist
• Hans Berliner, American computer scientist and world correspondence chess champion, design supervisor of HiTech (1988)
• Mikhail Botvinnik, Soviet electrical engineer and world chess champion, wrote Pioneer
• Alexander Brudno, Russian computer scientist, first elaborated the alphabeta pruning algorithm
• Feng-hsiung Hsu, the lead developer of Deep Blue (1986–97)
• Robert Hyatt developed Cray Blitz and Crafty^[90]
• Danny Kopec, American Professor or Computer Science and International Chess Master, developed Kopec-Bratko test
• Alexander Kronrod
  , Soviet computer scientist and mathematician
• Monroe Newborn
  , chairman of the computer chess committee for the Association for Computing Machinery
• Claude E. Shannon
  , American computer scientist and mathematician
• Alan Turing, English computer scientist and mathematician

The prospects of completely

meaning that determining the winning side in an arbitrary position of generalized chess provably takes exponential time in the worst case; however, this theoretical result gives no lower bound on the amount of work required to solve ordinary 8x8 chess.

Martin Gardner's Minichess, played on a 5×5 board with approximately 10¹⁸ possible board positions, has been solved; its game-theoretic value is 1/2 (i.e. a draw can be forced by either side), and the forcing strategy to achieve that result has been described.

Progress has also been made from the other side: as of 2012, all 7 and fewer pieces (2 kings and up to 5 other pieces) endgames have been solved.

A "chess engine" is software that calculates and orders which moves are the strongest to play in a given position. Engine authors focus on improving the play of their engines, often just importing the engine into a

.

Chess web apps

In 1997, the

Another popular web app is tactics training. The now defunct Chess Tactics Server opened its site in 2006,

One could play against the engine

Starting in 2007, Chess.com offered the content of the training program, Chess Mentor, to their customers online.^[108] Top GMs such as Sam Shankland and Walter Browne have contributed lessons.

The introduction of artificial intelligence transformed the game of chess, particularly at the elite levels. AI greatly influenced defensive strategies. It has the capacity to compute every potential move without concern, unlike human players who are bound to emotional and psychological impacts from factors such as stress or tiredness. As a result, many positions once considered not defensible are now recognized as defensible.

After studying millions of games, chess engines made new analysis and improved the existing theories of opening. These improvements led to the creation of new ideas and changed the way players think throughout all parts of the game.^[109] In classical chess, elite players commonly initiate games by making 10 to 15 opening moves that align with established analyses or leading engine recommendations.^[110]

Unlike traditional over-the-board tournaments where handheld metal detectors are employed in order to counter players attempts at using electronic assistance, fair-play monitoring in online chess is much more challenging.

During the 2020 European Online Chess Championship, which saw a record participation of nearly 4000 players over 80 participants were disqualified for cheating—most from beginner and youth categories.^[111] The event underscored the growing need for advanced detection methods in online competitions.

In response to these issues, chess platforms such as

Challenges in cheat detection

AI-based detection systems use a combination of machine learning to track suspicious player actions in different games. This is done by measuring discrepancies between the real moves and the predicted moves derived from the available statistics. Players of unusually high skill level or unusual strategies that can imitate moves characteristic of automated chess systems. Each case is examined by a human expert to ensure that the decision is correct before any actions are made to guarantee fairness and accuracy.[112]

The Maia Chess project was began in 2020 by the University of Toronto, Cornell University, and Microsoft Research. Maia Chess is a neural network constructed to impersonate a human’s manner of playing chess based on skill. Each Maia models was tested on 9 sets of 500,000 positions each, covering rating levels from 1100 to 1900. They perform best when predicting moves made by players at their targeted rating level, with lower Maias accurately predicting moves from lower-rated players (around 1100) and higher Maias doing the same for higher-rated players (around 1900). The primary goal of Maia is to develop an AI chess engine that imitates human decision-making rather than focusing on optimal moves. Through personalization across different skill levels, Maia is able to simulate game styles typical for each level more accurately.^[113][114]

While considered something done more for entertainment than for serious play, people have discovered that large language models (LLMs) of the type created in 2018 and beyond such as GPT-3 can be prompted into producing chess moves given proper language prompts. While inefficient compared to native chess engines, the fact that LLMs can track the board state at all beyond the opening rather than simply recite chess-like phrases in a dreamlike state was considered greatly surprising. LLM play has a number of quirks compared to engine play; for example, engines don't generally "care" how a board state was arrived at. However, LLMs seem to produce different quality moves for a chess position reached via strong play compared to the same board state produced via a set of strange preceding moves (which will generally produce weaker and more random moves).^[115]

• List of chess software
• History of chess engines
• Computer checkers
• Computer Go
• Computer Othello
• Computer shogi

 This article incorporates text by Chess Programming Wiki available under the CC BY-SA 3.0 license.

Sources

• ISBN 0-691-09065-3
• ISBN 0-7167-8121-2
• Newborn, Monty (1975), Computer Chess, Academic Press, New York
• Newborn, Monty (1997), Kasparov versus Deep Blue: Computer Chess Comes of Age, Springer,
  ISBN 0-387-94820-1
  (This book actually covers computer chess from the early days through the first match between Deep Blue and Garry Kasparov.)
• ISBN 1-901983-65-X
• Shannon, Claude E. (1950), "Programming a Computer for Playing Chess" (PDF), Philosophical Magazine, Ser.7, Vol. 41 (314), archived from the original
  (PDF) on 6 July 2010, retrieved 21 June 2009
• Mastering the Game: A History of Computer Chess at Computer History Museum
• Bill Wall's Computer Chess History Timeline

Further reading

• New Architectures in Computer Chess – Thesis on How to Build A Chess Engine
• Coles, L. Stephen (October 30, 2002), Computer Chess: The Drosophila of AI, Dr. Dobb's Journal
• Huberman (Liskov), Barbara Jane (1968), A program to play chess end games, Stanford University Department of Computer Science, Technical Report CS 106, Stanford Artificial Intelligence Project Memo AI-65
• Lasar, Matthew (2011). Brute force or intelligence? The slow rise of computer chess". Ars Technica.
• Newborn, Monty (1996). Outsearching Kasparov, American Mathematical Society's Proceeding of Symposia in Applied Mathematics: Mathematical Aspects of Artificial Intelligence, v. 55, pp 175–205, 1998. Based on paper presented at the 1996 Winter Meeting of the AMS, Orlando, Florida, Jan 9–11, 1996.
• Newborn, Monty (2000). Deep Blue's contribution to AI, Annals of Mathematics and Artificial Intelligence, v. 28, pp. 27–30, 2000.
• Newborn, Monty (2006). Theo and Octopus at the 2006 World Championship for Automated Reasoning Programs, Seattle, Washington, August 18, 2006
• Stiller, Lewis (1996), Multilinear Algebra and Chess Endgames (PDF), Berkeley, California:
  Mathematical Sciences Research Institute
  , Games of No Chance, MSRI Publications, Volume 29, retrieved 21 June 2009

External links

Look up computer chess in Wiktionary, the free dictionary.

Wikimedia Commons has media related to Computer chess.

• List of chess engine ratings and game files in PGN format
• Mastering the Game: A History of Computer Chess at the Computer History Museum
• ACM Computer Chess by Bill Wall
• "Computer Chess" by Edward Winter
• Computer Chess Information and Resources Archived 2019-01-18 at the Wayback Machine – blog following the creation of a computer chess engine
• Defending Humanity's Honor, an article by Tim Krabbé about "anti-computer style" chess
• A guide to Endgame Tablebases
• GameDev.net – Chess Programming by François-Dominic Laramée Part 1 Archived 2011-09-18 at the Wayback Machine 2 Archived 2011-09-27 at the Wayback Machine 3 Archived 2011-09-19 at the Wayback Machine 4 Archived 2011-09-19 at the Wayback Machine 5 Archived 2011-09-20 at the Wayback Machine 6 Archived 2011-08-07 at the Wayback Machine
• Colin Frayn's Computer Chess Theory Page
• ""How REBEL Plays Chess" by Ed Schröder" (PDF). (268 KB)
• "Play chess with God" Archived 2012-11-29 at archive.today – for playing chess against Ken Thompson's endgame database
• Chess programming wiki
• Computer Chess Club Forums
• The Strongest Computer Chess Engines Over Time

Media

• The History of Computer Chess: An AI Perspective Archived 2006-06-14 at the
  David Levy, John McCarthy, and Monty Newborn. at Computer History Museum