Computer chess
Today, chess engines may be installed as software on ordinary devices like smartphones and PCs,[3] either alone or alongside GUI programs such as Chessbase and the mobile apps for Chess.com and Lichess (both primarily websites).
Hardware requirements for chess engines are minimal, but performance will vary with processor speed, and memory, needed to hold large transposition tables.
Chess databases allow users to search through a large library of historical games, analyze them, check statistics, and formulate an opening repertoire.
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.
After discovering refutation screening—the application of alpha–beta pruning to optimizing move evaluation—in 1957, a team at Carnegie Mellon University predicted that a computer would defeat the world human champion by 1967.
Researchers worked to improve programs' ability to identify killer heuristics, unusually high-scoring moves to reexamine when evaluating other branches, but into the 1970s most top chess players believed that computers would not soon be able to play at a Master level.
Levy won his bet in 1978 by beating Chess 4.7, but it achieved the first computer victory against a Master-class player at the tournament level by winning one of the six games.
[20] While reviewing SPOC in 1984, BYTE wrote that "Computers—mainframes, minis, and micros—tend to play ugly, inelegant chess", but noted Robert Byrne's statement that "tactically they are freer from error than the average human player".
The magazine described SPOC as a "state-of-the-art chess program" for the IBM PC with a "surprisingly high" level of play, and estimated its USCF rating as 1700 (Class B).
With increasing processing power and improved evaluation functions, chess programs running on commercially available workstations began to rival top-flight players.
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.
"[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".
These include: Adriaan de Groot interviewed a number of chess players of varying strengths, and concluded that both masters and beginners look at around forty to fifty positions before deciding which move to play.
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.
Many chess engines use pondering, searching to deeper levels on the opponent's time, similar to human beings, to increase their playing strength.
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.
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.
Instead of wasting processing power examining bad or trivial moves, Shannon suggested that type B programs would use two improvements: This would enable them to look further ahead ('deeper') at the most significant lines in a reasonable time.
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).
[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.
[44] Chess engines, like human beings, may save processing time as well as select strong variations as expounded by the masters, by referencing an opening book stored in a disk database.
[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.
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.
The deep neural networks used in AlphaZero's evaluation function required expensive graphics processing units, which were not compatible with existing chess engines.
Engines communicate with the GUI by standardized protocols such as the nowadays ubiquitous Universal Chess Interface developed by Stefan Meyer-Kahlen and Franz Huber.
[103] New In Chess had initially tried to compete with Chessbase by releasing a NICBase program for Windows 3.x, but eventually, decided to give up on software, and instead focus on their online database starting in 2002.
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.
[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.
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.
