Development and usage of artificial intelligence in chess - Revision history

Mikiil: /* Monte Carlo tree search */

2021-05-03T18:32:35Z

Monte Carlo tree search

← Older revision		Revision as of 21:32, 3 May 2021
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	The Monte Carlo tree search is a heuristic algorithm which can be used for finding the best possible moves in a game, such as chess.		The Monte Carlo tree search is a heuristic algorithm which can be used for finding the best possible moves in a game, such as chess.
	The root is the current state of the game. The leaves are possible moves by either the opponent or the player from which no complete playout is completed. The first stage is selection, when a leaf with no complete playout has been selected. Second, child nodes are generated from that leaf in the expansion stage. Third is the simulation: one complete game is played out from a random node generated in the last stage. The last stage is backpropagation, where the result of the playout, either a loss or win, is propagated back to the original node.<ref>Sharma, S. (2018). ''Monte Carlo Tree Search''. https://towardsdatascience.com/monte-carlo-tree-search-158a917a8baa accessed on 2021--05-30</ref>		The root is the current state of the game. The leaves are possible moves by either the opponent or the player from which no complete playout is completed. The first stage is selection, when a leaf with no complete playout has been selected. Second, child nodes are generated from that leaf in the expansion stage. Third is the simulation: one complete game is played out from a random node generated in the last stage. The last stage is backpropagation, where the result of the playout, either a loss or win, is propagated back to the original node.<ref>Sharma, S. (2018). ''Monte Carlo Tree Search''. https://towardsdatascience.com/monte-carlo-tree-search-158a917a8baa accessed on 2021--05-30</ref>
	[[File:Mctspng.png\|~~frame~~\|Illustration of the Monte Carlo Tree Search. <ref>Rmoss92, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons. https://commons.wikimedia.org/wiki/File:MCTS-steps.svg accessed on 2021-05-03</ref>]]		[[File:Mctspng.png\|thumb\|Illustration of the Monte Carlo Tree Search. <ref>Rmoss92, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons. https://commons.wikimedia.org/wiki/File:MCTS-steps.svg accessed on 2021-05-03</ref>]]

	The schematic ~~above~~ describes one possible round of the Monte Carlo tree search. Each node shows the number of victories divided by the number of total playouts. For the playout in the image, white lost, so it was marked as 0/1. The total move count in all nodes was increased, but the win count was only increased for black nodes.		The schematic describes one possible round of the Monte Carlo tree search. Each node shows the number of victories divided by the number of total playouts. For the playout in the image, white lost, so it was marked as 0/1. The total move count in all nodes was increased, but the win count was only increased for black nodes.

	== Chess boom of 2020 ==		== Chess boom of 2020 ==

Mikiil: /* Monte Carlo tree search */

2021-05-03T18:32:16Z

Monte Carlo tree search

← Older revision		Revision as of 21:32, 3 May 2021
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	The Monte Carlo tree search is a heuristic algorithm which can be used for finding the best possible moves in a game, such as chess.		The Monte Carlo tree search is a heuristic algorithm which can be used for finding the best possible moves in a game, such as chess.
	The root is the current state of the game. The leaves are possible moves by either the opponent or the player from which no complete playout is completed. The first stage is selection, when a leaf with no complete playout has been selected. Second, child nodes are generated from that leaf in the expansion stage. Third is the simulation: one complete game is played out from a random node generated in the last stage. The last stage is backpropagation, where the result of the playout, either a loss or win, is propagated back to the original node.<ref>Sharma, S. (2018). ''Monte Carlo Tree Search''. https://towardsdatascience.com/monte-carlo-tree-search-158a917a8baa accessed on 2021--05-30</ref>		The root is the current state of the game. The leaves are possible moves by either the opponent or the player from which no complete playout is completed. The first stage is selection, when a leaf with no complete playout has been selected. Second, child nodes are generated from that leaf in the expansion stage. Third is the simulation: one complete game is played out from a random node generated in the last stage. The last stage is backpropagation, where the result of the playout, either a loss or win, is propagated back to the original node.<ref>Sharma, S. (2018). ''Monte Carlo Tree Search''. https://towardsdatascience.com/monte-carlo-tree-search-158a917a8baa accessed on 2021--05-30</ref>
	[[File:Mctspng.png\|\|Illustration of the Monte Carlo Tree Search. <ref>Rmoss92, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons. https://commons.wikimedia.org/wiki/File:MCTS-steps.svg accessed on 2021-05-03</ref>]]		[[File:Mctspng.png\|frame\|Illustration of the Monte Carlo Tree Search. <ref>Rmoss92, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons. https://commons.wikimedia.org/wiki/File:MCTS-steps.svg accessed on 2021-05-03</ref>]]

	The schematic above describes one possible round of the Monte Carlo tree search. Each node shows the number of victories divided by the number of total playouts. For the playout in the image, white lost, so it was marked as 0/1. The total move count in all nodes was increased, but the win count was only increased for black nodes.		The schematic above describes one possible round of the Monte Carlo tree search. Each node shows the number of victories divided by the number of total playouts. For the playout in the image, white lost, so it was marked as 0/1. The total move count in all nodes was increased, but the win count was only increased for black nodes.

Mikiil: /* Monte Carlo tree search */

2021-05-03T18:31:06Z

Monte Carlo tree search

← Older revision		Revision as of 21:31, 3 May 2021
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	The Monte Carlo tree search is a heuristic algorithm which can be used for finding the best possible moves in a game, such as chess.		The Monte Carlo tree search is a heuristic algorithm which can be used for finding the best possible moves in a game, such as chess.
	The root is the current state of the game. The leaves are possible moves by either the opponent or the player from which no complete playout is completed. The first stage is selection, when a leaf with no complete playout has been selected. Second, child nodes are generated from that leaf in the expansion stage. Third is the simulation: one complete game is played out from a random node generated in the last stage. The last stage is backpropagation, where the result of the playout, either a loss or win, is propagated back to the original node.<ref>Sharma, S. (2018). ''Monte Carlo Tree Search''. https://towardsdatascience.com/monte-carlo-tree-search-158a917a8baa accessed on 2021--05-30</ref>		The root is the current state of the game. The leaves are possible moves by either the opponent or the player from which no complete playout is completed. The first stage is selection, when a leaf with no complete playout has been selected. Second, child nodes are generated from that leaf in the expansion stage. Third is the simulation: one complete game is played out from a random node generated in the last stage. The last stage is backpropagation, where the result of the playout, either a loss or win, is propagated back to the original node.<ref>Sharma, S. (2018). ''Monte Carlo Tree Search''. https://towardsdatascience.com/monte-carlo-tree-search-158a917a8baa accessed on 2021--05-30</ref>
	[[File:Mctspng.png\|~~caption~~]]		[[File:Mctspng.png\|\|Illustration of the Monte Carlo Tree Search. <ref>Rmoss92, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, via Wikimedia Commons. https://commons.wikimedia.org/wiki/File:MCTS-steps.svg accessed on 2021-05-03</ref>]]

	The schematic above describes one possible round of the Monte Carlo tree search. Each node shows the number of victories divided by the number of total playouts. For the playout in the image, white lost, so it was marked as 0/1. The total move count in all nodes was increased, but the win count was only increased for black nodes.		The schematic above describes one possible round of the Monte Carlo tree search. Each node shows the number of victories divided by the number of total playouts. For the playout in the image, white lost, so it was marked as 0/1. The total move count in all nodes was increased, but the win count was only increased for black nodes.

Mikiil: /* Monte Carlo tree search */

2021-05-03T18:27:46Z

Monte Carlo tree search

← Older revision		Revision as of 21:27, 3 May 2021
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	=== Monte Carlo tree search ===		=== Monte Carlo tree search ===
	The Monte Carlo tree search is a heuristic algorithm which can be used for finding the best possible moves in a game, such as chess.		The Monte Carlo tree search is a heuristic algorithm which can be used for finding the best possible moves in a game, such as chess.
	The root is the current state of the game. The leaves are possible moves by either the opponent or the player from which no complete playout is completed. The first stage is selection, when a leaf with no complete playout has been selected. Second, child nodes are generated from that leaf in the expansion stage. Third is the simulation: one complete game is played out from a random node generated in the last stage. The last stage is backpropagation, where the result of the playout, either a loss or win, is propagated back to the original node.		The root is the current state of the game. The leaves are possible moves by either the opponent or the player from which no complete playout is completed. The first stage is selection, when a leaf with no complete playout has been selected. Second, child nodes are generated from that leaf in the expansion stage. Third is the simulation: one complete game is played out from a random node generated in the last stage. The last stage is backpropagation, where the result of the playout, either a loss or win, is propagated back to the original node.<ref>Sharma, S. (2018). ''Monte Carlo Tree Search''. https://towardsdatascience.com/monte-carlo-tree-search-158a917a8baa accessed on 2021--05-30</ref>
	[[File:Mctspng.png]]		[[File:Mctspng.png\|caption]]

	The schematic above describes one possible round of the Monte Carlo tree search. Each node shows the number of victories divided by the number of total playouts. For the playout in the image, white lost, so it was marked as 0/1. The total move count in all nodes was increased, but the win count was only increased for black nodes.		The schematic above describes one possible round of the Monte Carlo tree search. Each node shows the number of victories divided by the number of total playouts. For the playout in the image, white lost, so it was marked as 0/1. The total move count in all nodes was increased, but the win count was only increased for black nodes.

Mikiil at 10:14, 3 May 2021

2021-05-03T10:14:56Z

← Older revision		Revision as of 13:14, 3 May 2021
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	Attempts at using computers to play chess were made as early as in the 1950s, when Claude Shannon published a paper on the subject. Shannon’s paper outlines two possible approaches as to how computers would play chess. Type A being the brute force method, looking at possible moves ahead for a certain number of moves (lookahead), and then doing the best move. Shannon saw this method as inferior, as it required a lot of computing power, and exponentially more for every move added to lookahead. Type B programs would implement quiescence search, which attempts to simulate human “intuition”, where a human player would not play a move because it would look bad. Therefore type B would only look at a few good moves for each situation, rather than all possible moves. Shannon believed that Type B would be the more prevalent type, however, as computing power increased and became cheaper, it became apparent that Type A would lead the way with its full-width brute force search method. As computers became more advanced, type A was able to look at all possible moves and therefore find the best one, even if it didn’t quite match the “intuition” that type B tried to emulate, and with only a slight delay in time. <ref>Wheland, Norman D. (1978). A Computer Chess Tutorial. BYTE. p. 168. Available on https://archive.org/details/byte-magazine-1978-10/page/n167/mode/2up?view=theater accessed on 2021-04-30</ref><ref name="walker">Walker, L. (2020). ''The Anatomy of a Chess AI''. Available at https://medium.com/the-innovation/the-anatomy-of-a-chess-ai-2087d0d565 Accessed 24-04-2021</ref>		Attempts at using computers to play chess were made as early as in the 1950s, when Claude Shannon published a paper on the subject. Shannon’s paper outlines two possible approaches as to how computers would play chess. Type A being the brute force method, looking at possible moves ahead for a certain number of moves (lookahead), and then doing the best move. Shannon saw this method as inferior, as it required a lot of computing power, and exponentially more for every move added to lookahead. Type B programs would implement quiescence search, which attempts to simulate human “intuition”, where a human player would not play a move because it would look bad. Therefore type B would only look at a few good moves for each situation, rather than all possible moves. Shannon believed that Type B would be the more prevalent type, however, as computing power increased and became cheaper, it became apparent that Type A would lead the way with its full-width brute force search method. As computers became more advanced, type A was able to look at all possible moves and therefore find the best one, even if it didn’t quite match the “intuition” that type B tried to emulate, and with only a slight delay in time. <ref>Wheland, Norman D. (1978). A Computer Chess Tutorial. BYTE. p. 168. Available on https://archive.org/details/byte-magazine-1978-10/page/n167/mode/2up?view=theater accessed on 2021-04-30</ref><ref name="walker">Walker, L. (2020). ''The Anatomy of a Chess AI''. Available at https://medium.com/the-innovation/the-anatomy-of-a-chess-ai-2087d0d565 Accessed 24-04-2021</ref>

	To optimize the search for the best move, several optimizations are used. For example, pruning, which would remove moves that are obviously bad. Pruning had to be tuned correctly - too aggressive and a good move may be missed. Too little and the computer would waste time calculating bad moves, or even performing them if not searching deep enough. Transposition tables are used to record moves that have previously been calculated. For example, the IBM Deep Blue had 500 million entries in its transposition table.		To optimize the search for the best move, several optimizations are used. For example, pruning, which would remove moves that are obviously bad. Pruning had to be tuned correctly - too aggressive and a good move may be missed. Too little and the computer would waste time calculating bad moves, or even performing them if not searching deep enough. Transposition tables are used to record moves that have previously been calculated. For example, the IBM Deep Blue had 500 million entries in its transposition table.<ref name="walker" />

	One of the greatest problems for early chess computers was the endgame. It required a very long search depth to come out with a good endgame play. So instead, predefined endgame tablebases were used, where the endgame with for example a king and pawn is analyzed completely end to end. The endgame analysis has also provided more for the chess community itself as well. For example, several endings, which were first thought to be a loss, were able to result in a draw.		One of the greatest problems for early chess computers was the endgame. It required a very long search depth to come out with a good endgame play. So instead, predefined endgame tablebases were used, where the endgame with for example a king and pawn is analyzed completely end to end. The endgame analysis has also provided more for the chess community itself as well. For example, several endings, which were first thought to be a loss, were able to result in a draw. <ref>''Endgame Tablebase''. https://web.archive.org/web/20180823082639/https://chessprogramming.wikispaces.com/Endgame+Tablebases accessed on 2021-05-03</ref>

	Chess computers also use predefined openings that are also known to regular chess players. The openings are quite well defined, so the computer will usually follow them, where a human chess player would deviate and make their own strategy. During the early years, this posed a disadvantage to computers, but the openings in computers have become more in-depth now, so if a human player deviated from them, it would likely lose, as the computers now simply know more possible moves.		Chess computers also use predefined openings that are also known to regular chess players. The openings are quite well defined, so the computer will usually follow them, where a human chess player would deviate and make their own strategy. During the early years, this posed a disadvantage to computers, but the openings in computers have become more in-depth now, so if a human player deviated from them, it would likely lose, as the computers now simply know more possible moves. <ref>''Opening Book''. https://www.chessprogramming.org/Opening_Book accessed on 2021-05-03</ref>

	== Chess engines ==		== Chess engines ==
	=== Overview of chess engines ===		=== Overview of chess engines ===
	A chess engine is a piece of software that analyses chess and chooses the strongest possible moves. They usually have a command-line interface, and are paired with a front end to provide user interactivity and graphics. This is in contrast to what were known at the time as computer chess programs, which include graphics but are worse at playing chess. To communicate with the user, standardized protocols are used for developing the front-end, which sits between the user and the chess engine. These protocols include the Universal Chess Interface (UCI) or Chess Engine Communication Platform. (Knudsen, 2010)		A chess engine is a piece of software that analyses chess and chooses the strongest possible moves. They usually have a command-line interface, and are paired with a front end to provide user interactivity and graphics. This is in contrast to what were known at the time as computer chess programs, which include graphics but are worse at playing chess. To communicate with the user, standardized protocols are used for developing the front-end, which sits between the user and the chess engine. These protocols include the Universal Chess Interface (UCI) or Chess Engine Communication Platform. <ref>Knudsen, M (2010) ''Creating a chess engine from scratch (Part 1: Basics)''. https://www.chess.com/blog/zaifrun/creating-a-chess-engine-from-scratch-part-1 accessed on 2021-05-03</ref>

	Currently, the Stockfish engine tops the leaderboards, which is a free and open-source chess engine first released in 2008 and version 13 released in February 2021. Compared to other chess engines, Stockfish has a deeper search depth (Stockfish depth vs. others; challenge, 2013). It is the default chess engine in many freely available chess apps on Android and iOS. The source code is written in C++, but can also be compiled to JavaScript, which allows it to run in a browser. In 2013, Fishtest was released, which is a form of distributed computing where volunteers can donate some of their CPU resources to improve the development of Stockfish.		Currently, the Stockfish engine tops the leaderboards, which is a free and open-source chess engine first released in 2008 and version 13 released in February 2021. Compared to other chess engines, Stockfish has a deeper search depth <ref>''Stockfish depth vs. others; challenge'' (2013) http://www.talkchess.com/forum3/viewtopic.php?start=0&t=50220 accessed on 2021-05-03</ref>. It is the default chess engine in many freely available chess apps on Android and iOS. The source code is written in C++, but can also be compiled to JavaScript, which allows it to run in a browser. In 2013, Fishtest was released, which is a form of distributed computing where volunteers can donate some of their CPU resources to improve the development of Stockfish. <ref>''fishtest'' (2020) https://github.com/glinscott/fishtest/wiki accessed on 2021-05-03</ref>

	In 1999, Garry Kasparov played a game against the world - a team of over 50 000 people from 75 countries. Both sides used chess engines for assistance. Kasparov won the game after discovering a forced checkmate in 28 moves with the Deep Junior engine. The game lasted 4 months, and is often referred to as the greatest game of chess in all time.		In 1999, Garry Kasparov played a game against the world - a team of over 50 000 people from 75 countries. Both sides used chess engines for assistance. Kasparov won the game after discovering a forced checkmate in 28 moves with the Deep Junior engine. The game lasted 4 months, and is often referred to as the greatest game of chess in all time. <ref>''Garry Kasparov vs The World (1999) "Sitting on Top of the World''. https://www.chessgames.com/perl/chessgame?gid=1252350 accessed on 2021-05-03</ref>
	=== Chess engine Maia ===		=== Chess engine Maia ===
	The newcomer to the AI chess field is a customized version of Alpha-Zero called Maia.		The newcomer to the AI chess field is a customized version of Alpha-Zero called Maia.

Mikiil at 10:02, 3 May 2021

2021-05-03T10:02:56Z

← Older revision		Revision as of 13:02, 3 May 2021
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	== Development of AI in chess ==		== Development of AI in chess ==
	Attempts at using computers to play chess were made as early as in the 1950s, when Claude Shannon published a paper on the subject. Shannon’s paper outlines two possible approaches as to how computers would play chess. Type A being the brute force method, looking at possible moves ahead for a certain number of moves (lookahead), and then doing the best move. Shannon saw this method as inferior, as it required a lot of computing power, and exponentially more for every move added to lookahead. Type B programs would implement quiescence search, which attempts to simulate human “intuition”, where a human player would not play a move because it would look bad. Therefore type B would only look at a few good moves for each situation, rather than all possible moves. Shannon believed that Type B would be the more prevalent type, however, as computing power increased and became cheaper, it became apparent that Type A would lead the way with its full-width brute force search method. As computers became more advanced, type A was able to look at all possible moves and therefore find the best one, even if it didn’t quite match the “intuition” that type B tried to emulate, and with only a slight delay in time. <ref>Wheland, Norman D. (1978). A Computer Chess Tutorial. BYTE. p. 168. Available on https://archive.org/details/byte-magazine-1978-10/page/n167/mode/2up?view=theater accessed on 2021-04-30</ref>		Attempts at using computers to play chess were made as early as in the 1950s, when Claude Shannon published a paper on the subject. Shannon’s paper outlines two possible approaches as to how computers would play chess. Type A being the brute force method, looking at possible moves ahead for a certain number of moves (lookahead), and then doing the best move. Shannon saw this method as inferior, as it required a lot of computing power, and exponentially more for every move added to lookahead. Type B programs would implement quiescence search, which attempts to simulate human “intuition”, where a human player would not play a move because it would look bad. Therefore type B would only look at a few good moves for each situation, rather than all possible moves. Shannon believed that Type B would be the more prevalent type, however, as computing power increased and became cheaper, it became apparent that Type A would lead the way with its full-width brute force search method. As computers became more advanced, type A was able to look at all possible moves and therefore find the best one, even if it didn’t quite match the “intuition” that type B tried to emulate, and with only a slight delay in time. <ref>Wheland, Norman D. (1978). A Computer Chess Tutorial. BYTE. p. 168. Available on https://archive.org/details/byte-magazine-1978-10/page/n167/mode/2up?view=theater accessed on 2021-04-30</ref><ref name="walker">Walker, L. (2020). ''The Anatomy of a Chess AI''. Available at https://medium.com/the-innovation/the-anatomy-of-a-chess-ai-2087d0d565 Accessed 24-04-2021</ref>

	To optimize the search for the best move, several optimizations are used. For example, pruning, which would remove moves that are obviously bad. Pruning had to be tuned correctly - too aggressive and a good move may be missed. Too little and the computer would waste time calculating bad moves, or even performing them if not searching deep enough. Transposition tables are used to record moves that have previously been calculated. For example, the IBM Deep Blue had 500 million entries in its transposition table.		To optimize the search for the best move, several optimizations are used. For example, pruning, which would remove moves that are obviously bad. Pruning had to be tuned correctly - too aggressive and a good move may be missed. Too little and the computer would waste time calculating bad moves, or even performing them if not searching deep enough. Transposition tables are used to record moves that have previously been calculated. For example, the IBM Deep Blue had 500 million entries in its transposition table.
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	An algorithm using this function will understand the basics of the game such as what pieces are worth more and which less when to take a trade, and when not to. To further improve a basic evaluation function, it is required to add some positional intuition to it. Thus, scoring could be made more sophisticated and therefore accounting for more tactics available. Usually, evaluation functions and search algorithms are implemented independently of each other.		An algorithm using this function will understand the basics of the game such as what pieces are worth more and which less when to take a trade, and when not to. To further improve a basic evaluation function, it is required to add some positional intuition to it. Thus, scoring could be made more sophisticated and therefore accounting for more tactics available. Usually, evaluation functions and search algorithms are implemented independently of each other.
	[[File:pasted_image_0.png\|thumb\|Example of a weighing system <ref>Hartikka, L. (2017). ''A step-by-step guide to building a simple chess AI''. Available at https://medium.com/free-code-camp/simple-chess-ai-step-by-step-1d55a9266977 Accessed 24-04-2021</ref>]]		[[File:pasted_image_0.png\|thumb\|Example of a weighing system <ref>Hartikka, L. (2017). ''A step-by-step guide to building a simple chess AI''. Available at https://medium.com/free-code-camp/simple-chess-ai-step-by-step-1d55a9266977 Accessed 24-04-2021</ref>]]
	An algorithm using this function will understand the basics of the game such as what pieces are worth more and which less when to take a trade, and when not to. To further improve a basic evaluation function, it is required to add some positional intuition to it. Thus, scoring could be made more sophisticated and therefore accounting for more tactics available. Usually, evaluation functions and search algorithms are implemented independently of each other.<ref name="walker"~~>Walker, L. (2020). ''The Anatomy of a Chess AI''. Available at https:~~/~~/medium.com/the-innovation/the-anatomy-of-a-chess-ai-2087d0d565 Accessed 24-04-2021</ref~~>		An algorithm using this function will understand the basics of the game such as what pieces are worth more and which less when to take a trade, and when not to. To further improve a basic evaluation function, it is required to add some positional intuition to it. Thus, scoring could be made more sophisticated and therefore accounting for more tactics available. Usually, evaluation functions and search algorithms are implemented independently of each other.<ref name="walker" />

	=== The minimax search ===		=== The minimax search ===

Edvess at 18:37, 2 May 2021

2021-05-02T18:37:53Z

Show changes

Jheino at 17:46, 2 May 2021

2021-05-02T17:46:06Z

Krsama: /* Timeline */

2021-05-01T09:55:42Z

Timeline

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	* 2005 – Rybka takes first place in the IPCCC tournament and rockets to the top of the rankings.<ref>PADERBORN COMPUTER. Available at: http://www.rybkachess.com/docs/PADERBORNCOMPUTER.htm. [Accessed 22 April 2021].</ref>		* 2005 – Rybka takes first place in the IPCCC tournament and rockets to the top of the rankings.<ref>PADERBORN COMPUTER. Available at: http://www.rybkachess.com/docs/PADERBORNCOMPUTER.htm. [Accessed 22 April 2021].</ref>
	* 2006 – Deep Fritz defeats Vladimir Kramnik, the world champion, 4–2.<ref>Once Again, Machine Beats Human Champion at Chess - The New York Times. Available at: https://www.nytimes.com/2006/12/05/crosswords/chess/05cnd-chess.html#:~:text=A%20six%2Dgame%20chess%20match,match%2C%204%20games%20to%202. [Accessed 21 April 2021].</ref>		* 2006 – Deep Fritz defeats Vladimir Kramnik, the world champion, 4–2.<ref>Once Again, Machine Beats Human Champion at Chess - The New York Times. Available at: https://www.nytimes.com/2006/12/05/crosswords/chess/05cnd-chess.html#:~:text=A%20six%2Dgame%20chess%20match,match%2C%204%20games%20to%202. [Accessed 21 April 2021].</ref>
	* 2010 – Topalov trains for the 2010 World Chess Championship by sparring with the supercomputer Blue Gene, which has 8,192 processors and can perform 500 trillion (5 x 1014) floating-point operations per second. Vasik Rajlich, a Rybka creator, claims that Ippolit is a clone of Rybka.<ref></ref>		* 2010 – Topalov trains for the 2010 World Chess Championship by sparring with the supercomputer Blue Gene, which has 8,192 processors and can perform 500 trillion (5 x 1014) floating-point operations per second. Vasik Rajlich, a Rybka creator, claims that Ippolit is a clone of Rybka.<ref>Topalov training with super computer Blue Gene P \| Chessdom. Available at: http://players.chessdom.com/veselin-topalov/topalov-blue-gene-p [Accessed 19 April 2021].</ref>
	* 2011 – Rybka's WCCC titles were taken away by the ICGA.<ref></ref>		* 2011 – Rybka's WCCC titles were taken away by the ICGA.<ref>Rybka disqualified and banned from World Computer Chess Championships. Available at:https://web.archive.org/web/20140330145657/http://www.chessvibes.com/reports/rybka-disqualified-and-banned-from-world-computer-chess-championships/. Accessed [20 April 2021]</ref>
	* 2017 – In a 100-game match, AlphaZero, a neural net-based digital automaton, defeats Stockfish 28–0 with 72 draws.<ref></ref>		* 2017 – In a 100-game match, AlphaZero, a neural net-based digital automaton, defeats Stockfish 28–0 with 72 draws.<ref>AI In Chess: The Evolution of Artificial Intelligence In Chess Engines \| by Bharath K \| Apr, 2021 \| Towards Data Science. Available at: https://towardsdatascience.com/ai-in-chess-the-evolution-of-artificial-intelligence-in-chess-engines-a3a9e230ed50. [Accessed 20 April 2021].</ref>

	== Development of AI in chess ==		== Development of AI in chess ==

Krsama: /* Timeline */

2021-05-01T09:51:03Z

Timeline

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	* 1997 – Deep(er) Blue, a heavily updated version of the original, defeats Garry Kasparov in a six-game match, 3.5-2.5.<ref>Chess champion Garry Kasparov defeats IBM’s Deep Blue - HISTORY. Available at: https://www.history.com/this-day-in-history/kasparov-defeats-chess-playing-computer. [Accessed 23 April 2021].</ref>		* 1997 – Deep(er) Blue, a heavily updated version of the original, defeats Garry Kasparov in a six-game match, 3.5-2.5.<ref>Chess champion Garry Kasparov defeats IBM’s Deep Blue - HISTORY. Available at: https://www.history.com/this-day-in-history/kasparov-defeats-chess-playing-computer. [Accessed 23 April 2021].</ref>
	* 2000 – The Universal Chess Interface, drafted by Stefan Meyer-Kahlen and Rudolf Huber, is a protocol for GUIs to communicate with engines that would eventually become the standard method for new engines.<ref> UCI Protocol - Shredder Chess. Available at: https://www.shredderchess.com/chess-features/uci-universal-chess-interface.html. [Accessed 25 April 2021]</ref>		* 2000 – The Universal Chess Interface, drafted by Stefan Meyer-Kahlen and Rudolf Huber, is a protocol for GUIs to communicate with engines that would eventually become the standard method for new engines.<ref> UCI Protocol - Shredder Chess. Available at: https://www.shredderchess.com/chess-features/uci-universal-chess-interface.html. [Accessed 25 April 2021]</ref>
	* 2002 – Vladimir Kramnik and Deep Fritz draw an eight-game series.<ref></ref>		* 2002 – Vladimir Kramnik and Deep Fritz draw an eight-game series.<ref>Fritz Defends to Draw Game 8 and the Match! Final Score: 4-4. Available at: https://en.chessbase.com/post/fritz-defends-to-draw-game-8-and-the-match-final-score-4-4 [Accessed 21 April 2021].</ref>
	* 2003 – Kasparov drew six games in a row against Deep Junior and four games in a row against X3D Fritz.<ref></ref>		* 2003 – Kasparov drew six games in a row against Deep Junior and four games in a row against X3D Fritz.<ref>The chess games of Garry Kasparov. Available at: https://www.chessgames.com/player/Garry_Kasparov.html?kpage=352&archive=1 [Accessed 19 April 2021].</ref>
	* 2004 – A machine team (Hydra, Deep Junior, and Fritz) defeats a relatively strong human team (Veselin Topalov, Ruslan Ponomariov, and Sergey Karjakin, with an average Elo rating of 2681) 8½–3½. Fruit 2.1, a competitive closed source engine at the time, is released as source code by Fabien Letouzey. As a result, many writers revise their code to include the latest concepts.<ref></ref>		* 2004 – A machine team (Hydra, Deep Junior, and Fritz) defeats a relatively strong human team (Veselin Topalov, Ruslan Ponomariov, and Sergey Karjakin, with an average Elo rating of 2681) 8½–3½. Fruit 2.1, a competitive closed source engine at the time, is released as source code by Fabien Letouzey. As a result, many writers revise their code to include the latest concepts.<ref>Play Chess Vs Computer - playermultiprogram. Available at: https://playermultiprogram731.weebly.com/play-chess-vs-computer.html. [Accessed 19 April 2021].</ref>
	* 2005 – Rybka takes first place in the IPCCC tournament and rockets to the top of the rankings.<ref></ref>		* 2005 – Rybka takes first place in the IPCCC tournament and rockets to the top of the rankings.<ref>PADERBORN COMPUTER. Available at: http://www.rybkachess.com/docs/PADERBORNCOMPUTER.htm. [Accessed 22 April 2021].</ref>
	* 2006 – Deep Fritz defeats Vladimir Kramnik, the world champion, 4–2.<ref></ref>		* 2006 – Deep Fritz defeats Vladimir Kramnik, the world champion, 4–2.<ref>Once Again, Machine Beats Human Champion at Chess - The New York Times. Available at: https://www.nytimes.com/2006/12/05/crosswords/chess/05cnd-chess.html#:~:text=A%20six%2Dgame%20chess%20match,match%2C%204%20games%20to%202. [Accessed 21 April 2021].</ref>
	* 2010 – Topalov trains for the 2010 World Chess Championship by sparring with the supercomputer Blue Gene, which has 8,192 processors and can perform 500 trillion (5 x 1014) floating-point operations per second. Vasik Rajlich, a Rybka creator, claims that Ippolit is a clone of Rybka.<ref></ref>		* 2010 – Topalov trains for the 2010 World Chess Championship by sparring with the supercomputer Blue Gene, which has 8,192 processors and can perform 500 trillion (5 x 1014) floating-point operations per second. Vasik Rajlich, a Rybka creator, claims that Ippolit is a clone of Rybka.<ref></ref>
	* 2011 – Rybka's WCCC titles were taken away by the ICGA.<ref></ref>		* 2011 – Rybka's WCCC titles were taken away by the ICGA.<ref></ref>

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	The newcomer to the AI chess field is a customized version of Alpha-Zero called Maia.		The newcomer to the AI chess field is a customized version of Alpha-Zero called Maia.
	The difference of it compared to other chess engines in the field is that it focuses more on predicting real human moves, including all the mistakes they make while the other engines focus on winning the game. It can show the common mistakes people should practice more and which mistakes they should avoid when playing to improve their skills.		The difference of it compared to other chess engines in the field is that it focuses more on predicting real human moves, including all the mistakes they make while the other engines focus on winning the game. It can show the common mistakes people should practice more and which mistakes they should avoid when playing to improve their skills.
	According to Jon Kleinberg, a professor at Cornell University who led the development of Maia, says it is a first step toward developing AI that better understands human fallibility. He adds that the same kind of technology could be used also in the medical field.		According to Jon Kleinberg, a professor at Cornell University who led the development of Maia, says it is a first step toward developing AI that better understands human fallibility. He adds that the same kind of technology could be used also in the medical field. <ref> Maia chess engine(2021) ''Maia chess engine''. Available at https://maiachess.com Accessed 2021-03-22.</ref>

	What comes to the other chess engines in general, the Computer Chess Rating Lists website the best chess engines of the world in 2021, based on their rankings are Stockfish, Fat Fritz 2, Komodo and Houdini. Even though chess engines are great opponents and have shown new ways to improve the game in many ways, the ugly truth is that the development of chess engines has decreased the creativity of the players, now they are just focusing on learning different kinds of complex strategies straight from these machines.		What comes to the other chess engines in general, the Computer Chess Rating Lists website the best chess engines of the world in 2021, based on their rankings are Stockfish, Fat Fritz 2, Komodo and Houdini <ref>Kumar, V. (2021). 18 Best Chess Engines of 2021 \| Based On Their Ratings. Available on https://www.rankred.com/chess-engines/ Accessed on 2021-03-23</ref> . Even though chess engines are great opponents and have shown new ways to improve the game in many ways, the ugly truth is that the development of chess engines has decreased the creativity of the players, now they are just focusing on learning different kinds of complex strategies straight from these machines.

	== Functions and algorithms used by chess engines ==		== Functions and algorithms used by chess engines ==
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	== Chess as eSports ==		== Chess as eSports ==
	For decades there has been discussion whether chess is a sport or not and the International Olympic Committee recognized chess as a sport in 1999 and a year later it was seen at the Sydney Olympics too. One of the reasons that supports the argument above is the fact that playing chess puts a person through great amounts of mental pressure, and this exertion also manifests itself physically. Also, chess is a game that has to be practiced daily to develop skills and maintain them because of its competitive nature and rapidly evolving strategies. Looking at the reasons, it’s pretty clear that chess is a sport and it’s using the same objects as any other sport, what comes to eSports, it can be considered a subcategory within sports.		For decades there has been discussion whether chess is a sport or not and the International Olympic Committee recognized chess as a sport in 1999 and a year later it was seen at the Sydney Olympics too. One of the reasons that supports the argument above is the fact that playing chess puts a person through great amounts of mental pressure, and this exertion also manifests itself physically. Also, chess is a game that has to be practiced daily to develop skills and maintain them because of its competitive nature and rapidly evolving strategies. Looking at the reasons, it’s pretty clear that chess is a sport and it’s using the same objects as any other sport, what comes to eSports, it can be considered a subcategory within sports.<ref> Krishnaswamy, A. (2020). "Chess as an eSport? Theories and trends suggest there's a case to be made". Available at https://www.firstpost.com/sports/chess-as-an-esport-theories-and-trends-suggest-theres-a-case-to-be-made-8820501.html Accessed on 23-04-2021</ref>
	In August 2020 the chess grandmaster Hikaru Nakamura was signed with eSport gaming team Team SoloMid (TSM) which is one of the biggest eSports organizations in North America and became one of the first professional chess players that has been signed with an eSports organization, other has been WGM Qiyu Zhou with Counterlogic Gaming. At the same time the chess streaming grew enormously and made online chess even popular across the platforms with regular events like online amateur chess tournaments Pogchamps by chess.com.		In August 2020 the chess grandmaster Hikaru Nakamura was signed with eSport gaming team Team SoloMid (TSM) which is one of the biggest eSports organizations in North America and became one of the first professional chess players that has been signed with an eSports organization, other has been WGM Qiyu Zhou with Counterlogic Gaming.<ref> Doggers, P. (2020). "Nakamura Signs With TSM: Online Chess Is Esports". Available at Doggers, P. (2020). Nakamura Signs With TSM: Online Chess Is Esports Accessed on 24-04-2021</ref> At the same time the chess streaming grew enormously and made online chess even popular across the platforms with regular events like online amateur chess tournaments Pogchamps by chess.com.

	== Chess tournaments during the pandemic ==		== Chess tournaments during the pandemic ==
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	== Future of AI in chess ==		== Future of AI in chess ==
	The future of chess divides a lot of people’s opinions, others say that chess should remain as a board game and not be as popular as it is at the moment. However, chess big names like a chess world Champion Magnus Carlsen and a chess Grandmaster Gregory Seper estimate that the game keeps evolving and changes the world of board games more and more towards the online tournaments and games.		The future of chess divides a lot of people’s opinions, others say that chess should remain as a board game and not be as popular as it is at the moment. However, chess big names like a chess world Champion Magnus Carlsen and a chess Grandmaster Gregory Seper estimate that the game keeps evolving and changes the world of board games more and more towards the online tournaments and games.
	Gregory Seper stated in Chess.com webpage in 2017 that in 10 years or so, the opening monographs will completely switch to computer games and analysis. They will probably mention human games only to demonstrate typical mistakes to avoid. He also mentions that he believes that a major change happens in opening trends too.		Gregory Seper stated in Chess.com webpage in 2017 that in 10 years or so, the opening monographs will completely switch to computer games and analysis. They will probably mention human games only to demonstrate typical mistakes to avoid. He also mentions that he believes that a major change happens in opening trends too. <ref> Serper, G. (2017). "Is This The Future Of Chess?‎". Available at https://www.chess.com/article/view/is-this-the-future-of-chess Accessed on 06-04-2021</ref>
	Magnus Carlsen stated in 2020 when he was asked about the future of chess that he thinks that it’s just not realistic to expect people to play long games online and that it is not realistic to expect people to watch it with great interest. He also added that the rapid format is excellent for online play because you keep at least some semblance of high quality chess and it also doesn’t take too long. You get to play more games in a day and that way you get more excitement possibly.		Magnus Carlsen stated in 2020 when he was asked about the future of chess that he thinks that it’s just not realistic to expect people to play long games online and that it is not realistic to expect people to watch it with great interest. He also added that the rapid format is excellent for online play because you keep at least some semblance of high quality chess and it also doesn’t take too long. You get to play more games in a day and that way you get more excitement possibly. <ref> Chessbase (2020). Magnus Carlsen: “We’re trying to grow chess” Available at https://en.chessbase.com/post/magnus-carlsen-interview-us-chess-2020 Accessed on 23-04-2021</ref>
	Also Nick Barton, a director of business development at Chess.com stated in a interview of Dexerto in January 2021 that online chess is the future of chess itself because of its accessibility. In a matter of seconds you can play a chess game with anyone from around the world and you have a wealth of learning resources such as lessons, analysis and puzzles at your fingertips and the growth of online chess, especially among brand new players, has shifted the definition of what it means to play a game of chess.		Also Nick Barton, a director of business development at Chess.com stated in a interview of Dexerto in January 2021 that online chess is the future of chess itself because of its accessibility. In a matter of seconds you can play a chess game with anyone from around the world and you have a wealth of learning resources such as lessons, analysis and puzzles at your fingertips and the growth of online chess, especially among brand new players, has shifted the definition of what it means to play a game of chess. <ref> Fitch, A. (2021). "Is Chess an esport? Inside the game’s online rise on Twitch". Available at https://www.dexerto.com/esports/chess-esports-popularity-twitch-1491394/ Accessed on 24-04-2021</ref>

	== Conclusion ==		== Conclusion ==