27 Facts About Computer chess

Computer chess includes both hardware and software capable of playing chess.

Computer chess provides opportunities for players to practice even in the absence of human opponents, and provides opportunities for analysis, entertainment and training.

Nevertheless, solving chess is not currently possible for modern computers due to the game's extremely large number of possible variations.

Computer chess was once considered the "Drosophila of AI", the edge of knowledge engineering.

Programs such as PlayComputer chess allow you to play games against other players over the internet.

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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.

At the 1982 North American Computer Chess Championship, Monroe Newborn predicted that a chess program could become world champion within five years; tournament director and International Master Michael Valvo predicted ten years; the Spracklens predicted 15; Ken Thompson predicted more than 20; and others predicted that it would never happen.

Human–computer chess matches showed the best computer systems overtaking human chess champions in the late 1990s.

In 2009, Computer chess engines running on slower hardware have reached the grandmaster level.

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 impossibility of representing an entire game of Computer chess by constructing a tree from first move to last was immediately apparent: there are an average of 36 moves per position in Computer chess and an average game lasts about 35 moves to resignation .

The early Computer chess programs suffered in both areas: searching the vast tree required computational resources far beyond those available, and what Computer chess knowledge was useful and how it was to be encoded would take decades to discover.

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.

Data structure used to represent each Computer chess position is key to the performance of move generation and position evaluation.

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.

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.

Computer chess predicted the two main possible search strategies which would be used, which he labeled "Type A" and "Type B", before anyone had programmed a computer to play chess.

Computer chess 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.

Computer chess 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.

In 1977 Thompson's Belle Computer 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 .

One reason for this is that if the rules of Computer chess were to be changed once more, giving more time to win such positions, it will not be necessary to regenerate all the tablebases.

Idea of creating a Computer chess-playing machine dates back to the eighteenth century.

Around 1769, the Computer chess playing automaton called The Turk, created by Hungarian inventor Farkas Kempelen, became famous before being exposed as a hoax.

The field of mechanical chess research languished until the advent of the digital computer in the 1950s.

Efficiently updatable neural networks were originally developed in computer shogi in 2018 by Yu Nasu, and had to be first ported to a derivative of Stockfish called Stockfish NNUE on 31 May 2020, and integrated into the official Stockfish engine on 6 August 2020, before other chess programmers began to adopt neural networks into their engines.

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However, it has not been proven that no computationally cheap way of determining the best move in a Computer chess position exists, nor even that a traditional alpha–beta searcher running on present-day computing hardware could not solve the initial position in an acceptable amount of time.

The difficulty in proving the latter lies in the fact that, while the number of board positions that could happen in the course of a Computer chess game is huge, it is hard to rule out with mathematical certainty the possibility that the initial position allows either side to force a mate or a threefold repetition after relatively few moves, in which case the search tree might encompass only a very small subset of the set of possible positions.