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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02627

Take a look at this ‘KQRBNN vs kqrnp’ chess construct composed autonomously by the prototype computer program, Chesthetica, using the Digital Synaptic Neural Substrate (DSNS) computational creativity approach. It doesn’t use endgame tablebases, deep learning or any kind of traditional AI. There is no known limit to the quantity or type of compositions that can be generated. Any chess position with this many pieces could not possibly have been obtained from known endgame databases. Chesthetica is therefore the real McCoy.

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8/4K3/1r3R2/p7/3n4/1N5N/4k3/1QB1q3 w – – 0 1
White to Play and Mate in 4
Chesthetica v11.16 (Selangor, Malaysia)
Generated on 23 Apr 2019 at 11:58:15 AM
Solvability Estimate = Moderate

Composing a chess puzzle or problem requires creativity and it’s not easy even for most humans. White is significantly ahead in material. Try to solve this as quickly as you can. If you like it, please share with your friends. Collectively, these puzzles are intended to cater to players of all levels. If you’re bored of standard chess, though, why not try this?

Main Line of the Solution (Skip to 0:35)

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02626

A ‘KRRNNN vs kqbp’ chess problem generated by a computer using the Digital Synaptic Neural Substrate (DSNS) computational creativity approach. It doesn’t use endgame tablebases, neural networks or any kind of machine learning found in traditional AI. There is no known limit to the quantity or type of compositions that can be generated. Noteworthy here is that a chess position with over 7 pieces could not have been derived or taken from an endgame tablebase because 7 pieces is the present limit.

image.png

8/2K5/8/4p3/1b3N2/8/R1Nk4/1q1NR3 w – – 0 1
White to Play and Mate in 4
Chesthetica v11.17 (Selangor, Malaysia)
Generated on 22 Apr 2019 at 10:57:42 AM
Solvability Estimate = Moderate

Chesthetica, especially if running on multiple computers or operating system user accounts, is capable of generating far too many compositions than can be published in a timely fashion here. The newer ones will therefore only be published some time later. This is why the composition date above does not match today’s date. White is over a rook’s worth in material but the precise win in this position still needs to be found. Try to solve this puzzle. Do try some of the others in the series as well before you go. As a whole, these problems are intended to cater to players of all skill levels.

Main Line of the Solution (Skip to 0:35)

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02625

Here is a new ‘KQRRNPP vs kqrbbnnp’ #4 chess problem generated autonomously by a computer using the Digital Synaptic Neural Substrate AI computational creativity method. Chesthetica can compose problems that might otherwise take centuries or longer for human composers to think of, so you may enjoy them right now. Learn about how these problems are selected here. Any chess position over 7 pieces could not possibly have been derived from an endgame tablebase which today is limited to 7 pieces.

4kr2/1R2Ppb1/n2P4/3N1bQ1/5K2/7q/8/2nR4 w – – 0 1
White to Play and Mate in 4
Chesthetica v11.16 (Selangor, Malaysia)
Generated on 22 Apr 2019 at 7:14:11 AM
Solvability Estimate = Moderate

Humans have been composing original chess problems for over a thousand years. Now a computer can do it too. Try to solve this puzzle. Do try some of the others in the series as well before you go. As a whole, these problems are intended to cater to players of all skill levels.

Main Line of the Solution (Skip to 0:35)

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02624

Here is a new ‘KBNPPP vs kpp’ chess construct composed autonomously by the prototype computer program, Chesthetica, using the DSNS computational creativity approach which doesn’t use any kind of traditional AI. Chesthetica has the creative ability to compose positions that may otherwise take centuries to arise in an actual game, if ever. Chesthetica is able to generate #3s, #4s, #5s and study-like constructs and also compose problems using specific combinations of pieces fed into it (such as using only two bishops versus knight). Read more about it on ChessBase. Any chess position over 7 pieces could not possibly have been derived from an endgame tablebase which today is limited to 7 pieces.

8/8/pB1K4/2P5/k1PN4/p7/1P6/8 w – – 0 1
White to Play and Mate in 3
Chesthetica v11.17 (Selangor, Malaysia)
Generated on 22 Apr 2019 at 6:30:25 AM
Solvability Estimate = Moderate

Chesthetica composes everything autonomously (no human intervention) and even chooses the main line of the solution to show you. Leave a comment below, if you like. Solving chess puzzles like this can also help improve your game.

Main Line of the Solution (Skip to 0:35)

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02623

A new if not unique KQRBBP vs krnnp three-move chess construct composed autonomously by the prototype computer program, Chesthetica, using the Digital Synaptic Neural Substrate (DSNS) computational creativity approach. It doesn’t use endgame tablebases, neural networks or any kind of machine learning found in traditional AI. Chesthetica is able to generate mates in 3, mates in 4, mates in 5, study-like constructs and also compose problems using specific combinations of pieces fed into it (e.g. instructing it to compose something using a queen, rook and bishop vs. queen and two knights). Read more about it on ChessBase.

8/Q2Rrp2/6n1/5k2/7P/5n2/1B3KB1/8 w – – 0 1
White to Play and Mate in 3
Chesthetica v11.17 (Selangor, Malaysia)
Generated on 21 Apr 2019 at 3:59:18 PM
Solvability Estimate = Difficult

If you notice an earlier version of Chesthetica listed with a newer problem, that simply means an earlier version may have been running on a different computer or OS user account. What was the machine ‘thinking’ when it came up with this? Why not time yourself how long it took you to solve this? Collectively, these puzzles are intended to cater to players of all levels.

Main Line of the Solution (Skip to 0:35)

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02622

Here is a ‘KQRNN vs krbpp’ chess puzzle or problem (whichever you wish to call it) composed by a computer using the computational creativity approach which doesn’t use any kind of traditional AI or even deep learning. Chesthetica has the creative ability to compose positions on an 8×8 canvas that may otherwise take centuries to arise in an actual game, if ever. Depending on the type and complexity of the problem desired, a single instance of Chesthetica running on a desktop computer can probably generate anywhere between one and ten problems per hour. The largest (Lomonosov) tablebase today is for 7 pieces which contains over 500 trillion positions. With each additional piece, the number of possible positions increases exponentially. It is therefore impossible that this problem with 10 pieces could have been taken from such a database.

K7/6N1/8/3Nb3/5p1k/5R2/1r4p1/3Q4 w – – 0 1
White to Play and Mate in 5
Chesthetica v11.17 (Selangor, Malaysia)
Generated on 21 Apr 2019 at 1:14:50 PM
Solvability Estimate = Easy

Most changes to Chesthetica that result in a slightly higher ‘version number’ are simply to improve the interface, by the way. Okay, let me think for a minute if there’s anything else to say here. Do you think you could have composed something better with these pieces? Share in the comments and let us know how long it took you. Solving chess puzzles like this can be good for your health as it keeps your brain active. It may even delay or prevent dementia.

Main Line of the Solution (Skip to 0:35)

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02621

Take a look at this ‘KRRB vs krpp’ four-move chess problem generated by a computer using the Digital Synaptic Neural Substrate (DSNS) computational creativity approach. It doesn’t use endgame tablebases, neural networks or any kind of machine learning found in traditional AI. Depending on the type and complexity of the problem desired, a single instance of Chesthetica running on a desktop computer can probably generate anywhere between one and ten problems per hour. The largest endgame tablebase in existence today is for 7 pieces (Lomonosov) which contains over 500 trillion positions, most of which have not and never will be seen by human eyes. This problem with 8 pieces goes even beyond that and was therefore composed without any such help whatsoever.

8/8/8/3p4/4RB2/3K1p1r/1k6/R7 w – – 0 1
White to Play and Mate in 4
Chesthetica v11.17 (Selangor, Malaysia)
Generated on 21 Apr 2019 at 5:05:06 AM
Solvability Estimate = Moderate

Most changes to Chesthetica that result in a slightly higher ‘version number’ are simply to improve the interface, by the way. White is over a rook’s worth in material but the precise win in this position still needs to be found. If this one is too easy or too difficult for you, try out some of the others. As a whole, these problems are intended to cater to players of all skill levels.

A Similar Problem by Chesthetica: 00276

Main Line of the Solution (Skip to 0:35)

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