Computer-Generated Chess Problem 02492

Now, this is a ‘KRRBNNP vs krrbnnp’ chess puzzle created by the prototype computer program, Chesthetica, 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. 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. This position contains a total of 14 pieces. The largest endgame tablebase in existence today is for 7 pieces (containing over 500 trillion positions anyway) which means the problem could not have been taken from it regardless.

K2k1Nn1/3r2b1/1r1R4/1N1nPp2/1R6/7B/8/8 w – f6 0 1
White to Play and Mate in 3
Chesthetica v10.98 (Selangor, Malaysia)
Generated on 3 Feb 2019 at 8:49:02 PM
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. Get a glimpse into the ‘mind’ of a computer composer. Did you find this one interesting or have something else to say? Leave a comment below! 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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Computer-Generated Chess Problem 02491

Contemplate this ‘KQRBNPPP vs kqrbnppppp’ chess problem generated autonomously by the prototype computer program, 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. There is no known limit to the quantity or type of compositions that can be generated. Any chess position over 7 pieces could not possibly have been derived from an endgame tablebase which today is limited to 7 pieces.

q3N1B1/1P3p1K/6Pp/2n1Q2p/2R5/4b3/kp2pr1P/8 w – – 0 1
White to Play and Mate in 5
Chesthetica v10.98 (Selangor, Malaysia)
Generated on 2 Feb 2019 at 6:32:13 PM
Solvability Estimate = Difficult

Humans have been composing original chess problems for over a thousand years. Now a computer can do it too. White actually has less material than Black. The white army is down by about 2 (Shannon) pawn units in value. There may also be a risk of Black promoting a pawn. 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.

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

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Computer-Generated Chess Problem 02490

A ‘KRNPP vs kqr’ chess puzzle created by Chesthetica 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. 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. This position contains a total of 8 pieces. The largest endgame tablebase in existence today is for 7 pieces (containing over 500 trillion positions anyway) which means the problem could not have been taken from it regardless.

3r4/2P3NK/5k2/4q2R/8/4P3/8/8 w – – 0 1
White to Play and Mate in 3
Chesthetica v10.98 (Selangor, Malaysia)
Generated on 2 Feb 2019 at 3:58:07 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. What was the machine ‘thinking’ when it came up with this? Did you find this one interesting or have something else to say? Leave a comment below! 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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Computer-Generated Chess Problem 02489

Published online for the first time, consider this KRBNP vs kpp #4 chess puzzle or problem (whichever you wish to call it) composed by a computer using the Digital Synaptic Neural Substrate (DSNS) computational creativity approach. The DSNS does not use endgame tablebases, neural networks or any kind of machine learning found in traditional artificial intelligence (AI). It also has nothing to do with deep learning. There is no known limit to the quantity or type of compositions that can be generated. The position below contains 8 pieces which means it simply could not have been derived even from an existing endgame tablebase which is presently limited to 7 pieces.

8/3K4/6N1/8/4k3/3p2P1/3p4/3R2B1 w – – 0 1
White to Play and Mate in 4
Chesthetica v10.98 (Selangor, Malaysia)
Generated on 1 Feb 2019 at 11:46:54 PM
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. Okay, let me think for a minute if there’s anything else to say here. Do share and try out some of the others too. Feel free to copy the position into a chess engine and discover even more variations of the solution.

A Similar Problem by Chesthetica: 00042

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

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Computer-Generated Chess Problem 02488

This is an original ‘KRBB vs krrn’ mate in 3 chess problem generated by 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 also no proven limit to the quantity or type of legal compositions that can be automatically generated. 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 8 pieces goes even beyond that and was therefore composed without any such help.

r6k/8/2n4K/r1B5/8/1B6/8/4R3 w – – 0 1
White to Play and Mate in 3
Chesthetica v10.98 (Selangor, Malaysia)
Generated on 31 Jan 2019 at 9:56:51 AM
Solvability Estimate = Difficult

These chess puzzles are published in order based on the composition date and time stamp above. Due to the sheer volume of compositions generated, the latest ones may therefore only be published later on. Okay, let me think for a minute if there’s anything else to say here. Why not time yourself how long it took you to solve this? 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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Computer-Generated Chess Problem 02487

Now, here we have a ‘KQRN vs kqbbnp’ chess problem generated by a computer using the Digital Synaptic Neural Substrate (DSNS) computational creativity approach. The DSNS does not use endgame tablebases, neural networks or any kind of machine learning found in traditional artificial intelligence (AI). It also has nothing to do with deep learning. You can learn more about the DSNS here. 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.

8/8/1R6/3q4/5K2/3k4/1QNp1n2/4bb2 w – – 0 1
White to Play and Mate in 3
Chesthetica v10.98 (Selangor, Malaysia)
Generated on 31 Jan 2019 at 3:30:46 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. White actually has less material than Black. The white army is down by about 2 (Shannon) pawn units in value. There may also be a risk of Black promoting a pawn. 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.

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

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Computer-Generated Chess Problem 02486

An original ‘KBNN vs kpp’ mate in 4 chess problem generated by the program, Chesthetica, using the Digital Synaptic Neural Substrate (DSNS) computational creativity approach. The DSNS does not use endgame tablebases, neural networks or any kind of machine learning found in traditional artificial intelligence (AI). It also has nothing to do with deep learning. There is also no proven limit to the quantity or type of legal compositions that can be automatically generated.

8/8/8/8/8/5pNK/p4k1N/2B5 w – – 0 1
White to Play and Mate in 4
Chesthetica v10.97 (Selangor, Malaysia)
Generated on 30 Jan 2019 at 12:57:13 PM
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. Everything composed by Chesthetica is original. Why not time yourself how long it took you to solve this? Solving chess puzzles like this can be good for your health as it keeps your brain active. It may even delay or prevent dementia.

Similar Problems by Chesthetica: 00529 01694

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

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