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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02717

A ‘KQRRN vs krnp’ mate in 3 chess problem generated by Chesthetica using the ‘Digital Synaptic Neural Substrate’ computational creativity method. It does not use endgame tablebases, artificial neural networks, machine learning or any kind of typical AI. The chess board is a virtually limitless canvas for the expression of creative ideas (even by computer). You can learn more about the DSNS here. The position below contains 9 pieces which means it simply could not have been derived even from an existing endgame tablebase which is presently limited to 7 pieces.

image.png

3n4/8/2r5/Nk3K2/1p6/R7/2R5/4Q3 w – – 0 1
White to Play and Mate in 3
Chesthetica v11.32 (Selangor, Malaysia)
Generated on 6 Aug 2019 at 4:49:47 PM
Solvability Estimate = Difficult

Most changes to Chesthetica that result in a slightly higher ‘version number’ are simply to improve the interface, by the way. White has a decisive material advantage in this position but the winning sequence may not be immediately clear. Try to solve this as quickly as you can. If you like it, please share with others. Take some time to study the analysis and you might appreciate the puzzle a little more. Anyway, if standard chess isn’t your thing, you might instead like SSCC.

Main Line of the Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02716

Here is a ‘KQRP vs kpppp’ study construct generated by a computer using the computational creativity approach which doesn’t use any kind of traditional AI or even deep learning. Learn about how these problems are selected here. Any chess position with this many pieces could not possibly have been obtained from known endgame databases. Chesthetica is therefore the real McCoy. Any analysis shown for this study could be flawed as chess engines may change their recommendations given more time. The first or key move, at least, is probably right.

image.png

8/8/8/5Qp1/4K3/1p4PR/2pp4/1k6 w – – 0 1
White to Play and Win
Chesthetica v11.32 (Selangor, Malaysia)
Generated on 6 Aug 2019 at 6:23:48 AM

The chess problems are published chronologically based on the composition date and time. However, later compositions may have an earlier version of Chesthetica listed because more than one computer (not all running the same version of the program) is used. Chesthetica composes everything autonomously (no human intervention) and even chooses the main line of the solution to show you. Try to solve this puzzle. Do try some of the others in the series as well before you go. Solving chess puzzles like this is probably good for your health as it keeps your brain active. Nobody wants something like early-onset Alzheimer’s.

Main Line of the Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02715

Here is a ‘KQRBPP vs kqrbn’ mate in 5 chess problem generated by a computer program, Chesthetica, using the computational creativity approach which doesn’t use any kind of traditional AI or even deep learning. 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 been seen by human eyes. This problem with 11 pieces goes even beyond that and was therefore composed without any such help.

image.png

4B3/1P6/2q5/2k2K2/8/1P1Q2R1/1b1nr3/8 w – – 0 1
White to Play and Mate in 5
Chesthetica v11.32 (Selangor, Malaysia)
Generated on 3 Aug 2019 at 1:55:28 PM
Solvability Estimate = Moderate

Composing a chess puzzle or problem requires creativity and it’s not easy even for most humans. If this one is too easy or too difficult for you, try out some of the others. Some of these problems may be trivial for you, especially if you’re a club or master player but bear in mind that chess lovers can be found at all levels of play. So do check out some of the other problems. You can probably find something more to your taste. If you’re bored of standard chess, though, why not try this?

Main Line of the Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02714

Here is a new ‘KRRNNP vs krnp’ mate in 3 chess problem generated autonomously by Chesthetica 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. There is also no proven limit to the quantity or type of legal compositions that can be automatically generated.

image.png

3kn3/K1RpN3/2r2P2/6N1/8/8/6R1/8 w – – 0 1
White to Play and Mate in 3
Chesthetica v11.32 (Selangor, Malaysia)
Generated on 3 Aug 2019 at 5:23:46 AM
Solvability Estimate = Difficult

A seemingly earlier version of Chesthetica on a problem composed later (based on the date and time stamp) simply means that version may have been running on a different computer or operating system user account. Chesthetica composes only unique or new constructs. If you have seen it before, cite the source and comment below because it is purely coincidental. Why not time yourself how long it took you to solve this? Take some time to study the analysis and you might appreciate the puzzle a little more. If you’re bored of standard chess, though, why not try this?

Main Line of the Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02713

A newly published and original KRRBBN vs kqrbb mate in 3 chess puzzle or problem (whichever you wish to call it) composed by a computer using the approach known as the DSNS from the sub-field of AI, computational creativity. You can learn more about the DSNS here. Any chess position over 7 pieces could not possibly have been derived from an endgame tablebase which today is limited to 7 pieces.

image.png

8/6K1/4bR2/3N2kq/8/3B2b1/4r2R/B7 w – – 0 1
White to Play and Mate in 3
Chesthetica v11.32 (Selangor, Malaysia)
Generated on 2 Aug 2019 at 11:44:12 PM
Solvability Estimate = Difficult

Everything composed by Chesthetica is original. 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. Feel free to copy the position into a chess engine and discover even more variations of the solution.

Main Line of the Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02712

Now, here we have a ‘KRNPP vs krnnp’ study-like chess problem generated by the prototype computer program, Chesthetica, using the ‘DSNS’ computational creativity approach which does not use any kind of machine or deep learning. 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. The solution shown for this study may not be the best line possible because it depends on the engine that was used and how much time it had to analyze. Regardless, the first move and overall evaluation (e.g. win, draw) should be right.

image.png

8/4P3/4r1kp/R7/2N1n3/7n/1K3P2/8 w – – 0 1
White to Play and Win
Chesthetica v11.32 (Selangor, Malaysia)
Generated on 1 Aug 2019 at 8:37:05 PM

Composing a chess puzzle or problem requires creativity and it’s not easy even for most humans. Do share and try out some of the others too. Take some time to study the analysis and you might appreciate the puzzle a little more. If you’re bored of standard chess, though, why not try this?

Main Line of the Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02711

A new if not unique KQRBNN vs krr four-move chess problem generated by a computer program, Chesthetica, using the ‘Digital Synaptic Neural Substrate’ computational creativity approach which does not use any kind of deep learning. There is no known limit to the quantity or type of compositions that can be generated. 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 9 pieces goes even beyond that and was therefore composed without any such help whatsoever.

image.png

3r4/1N6/Q3N1K1/4k3/8/8/3r2R1/2B5 w – – 0 1
White to Play and Mate in 4
Chesthetica v11.32 (Selangor, Malaysia)
Generated on 31 Jul 2019 at 6:03:40 PM
Solvability Estimate = Moderate

Chess puzzles are ancient. Some are over a thousand years old but only in the 21st century have computers been able to compose original ones on their own like humans can. White has a decisive material advantage in this position but the winning sequence may not be immediately clear. If this one is too easy or too difficult for you, try out some of the others. Some of these problems may be trivial for you, especially if you’re a club or master player but bear in mind that chess lovers can be found at all levels of play. So do check out some of the other problems. You can probably find something more to your taste.

Main Line of the Solution

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