First-move advantage in chessIn chess, there is a general consensus among players and theorists that the player who makes the first move (White) has an inherent advantage. Since 1851, compiled statistics support this view; White consistently slightly more often than Black, usually scoring between 52 and 56 percent. White's is about the same for tournament games between humans and games between computers; however, White's advantage is less significant in blitz games and games between lower-level players.
Human–computer chess matchesThis article documents the progress of significant human–computer chess matches. Chess computers were first able to beat strong chess players in the late 1980s. Their most famous success was the victory of Deep Blue over then World Chess Champion Garry Kasparov in 1997, but there was some controversy over whether the match conditions favored the computer. In 2002–2003, three human–computer matches were drawn, but, whereas Deep Blue was a specialized machine, these were chess programs running on commercially available computers.
Computer shogiComputer shogi is a field of artificial intelligence concerned with the creation of computer programs which can play shogi. The research and development of shogi software has been carried out mainly by freelance programmers, university research groups and private companies. By 2017, the strongest programs were outperforming the strongest human players. Shogi has the distinctive feature of reusing captured pieces. Therefore, shogi has a higher branching factor than other chess variants.
Hydra (chess)Hydra was a chess machine, designed by a team with Dr. Christian "Chrilly" Donninger, Dr. Ulf Lorenz, GM Christopher Lutz and Muhammad Nasir Ali. Since 2006 the development team consisted only of Donninger and Lutz. Hydra was under the patronage of the PAL Group and Sheikh Tahnoon Bin Zayed Al Nahyan of Abu Dhabi. The goal of the Hydra Project was to dominate the computer chess world, and finally have an accepted victory over humans. Hydra represented a potentially significant leap in the strength of computer chess.
Game treeIn the context of Combinatorial game theory, which typically studies sequential games with perfect information, a game tree is a graph representing all possible game states within such a game. Such games include well-known ones such as chess, checkers, Go, and tic-tac-toe. This can be used to measure the complexity of a game, as it represents all the possible ways a game can pan out. Due to the large game trees of complex games such as chess, algorithms that are designed to play this class of games will use partial game trees, which makes computation feasible on modern computers.
Solving chessSolving chess consists of finding an optimal strategy for the game of chess; that is, one by which one of the players (White or Black) can always force a victory, or either can force a draw (see solved game). It is also related to more generally solving chess-like games (i.e. combinatorial games of perfect information) such as Capablanca chess and infinite chess. In a weaker sense, solving chess may refer to proving which one of the three possible outcomes (White wins; Black wins; draw) is the result of two perfect players, without necessarily revealing the optimal strategy itself (see indirect proof).
Ply (game theory)In two-or-more-player sequential games, a ply is one turn taken by one of the players. The word is used to clarify what is meant when one might otherwise say "turn". The word "turn" can be a problem since it means different things in different traditions. For example, in standard chess terminology, one move consists of a turn by each player; therefore a ply in chess is a half-move. Thus, after 20 moves in a chess game, 40 plies have been completed—20 by white and 20 by black.
Deep reinforcement learningDeep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.
General game playingGeneral game playing (GGP) is the design of artificial intelligence programs to be able to play more than one game successfully. For many games like chess, computers are programmed to play these games using a specially designed algorithm, which cannot be transferred to another context. For instance, a chess-playing computer program cannot play checkers. General game playing is considered as a necessary milestone on the way to artificial general intelligence.
KaissaKaissa (Каисса) was a chess program developed in the Soviet Union in the 1960s. It was named so after Caissa, the goddess of chess. Kaissa became the first world computer chess champion in 1974 in Stockholm. By 1967, a computer program by Georgy Adelson-Velsky, Vladimir Arlazarov, Alexander Bitman and Anatoly Uskov on the M-2 computer in Alexander Kronrod’s laboratory at the Institute for Theoretical and Experimental Physics had defeated Kotok-McCarthy running on the IBM 7090 at Stanford University.
