artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02376

Now, this is a ‘KRBBPP vs kbbpppp’ #5 chess puzzle or problem (whichever you wish to call it) composed by the 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. 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 13 pieces goes even beyond that and was therefore composed without any such help.

8/4bB2/1K1p4/1b6/7p/P1RP4/3Bp1p1/3k4 w – – 0 1
White to Play and Mate in 5
Chesthetica v10.82 : Selangor, Malaysia
2018.11.14 3:21:09 AM
Solvability Estimate = Moderate

Some of the earliest chess problems by humans are over 10 centuries old but original ones by computer are very recent. Chesthetica composes everything autonomously (no human intervention) and even chooses the main line of the solution to show you. Try to solve this as quickly as you can. If you like it, please share with others. Feel free to copy the position into a chess engine and discover even more variations of the solution.

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

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02375

A newly published and original KRRBN vs krrnnp chess problem generated autonomously by Chesthetica using the ‘Digital Synaptic Neural Substrate’ computational creativity approach which does not use any kind of deep learning. There is also no proven limit to the quantity or type of legal compositions that can be automatically 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.

3n1r2/6B1/3R4/3rn3/Kp6/8/k7/3R1N2 w – – 0 1
White to Play and Mate in 5
Chesthetica v10.82 : Selangor, Malaysia
2018.11.13 2:53:05 PM
Solvability Estimate = Difficult

Some of the earliest chess problems by humans are over 10 centuries old but original ones by computer are very recent. Chesthetica composes only unique or new constructs. If you have seen it before, cite the source and comment below because it is purely coincidental. If this one is too easy or too difficult for you, try out some of the others. Over time, the tactics you see in these puzzles will help you improve your game. 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 02374

Now, here we have a ‘KRN vs kpp’ chess construct composed autonomously 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.

8/8/8/8/5p2/5Kp1/7k/4R2N w – – 0 1
White to Play and Mate in 5
Chesthetica v10.82 : Selangor, Malaysia
2018.11.12 11:00:17 AM
Solvability Estimate = Easy

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. 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.

A Similar Problem by Chesthetica: 01987

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

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02373

Consider this ‘KRRBPP vs knp’ #4 chess puzzle created 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 been seen by human eyes. This problem with 9 pieces goes even beyond that and was therefore composed without any such help.

8/5B2/3n4/5R1K/2P1k3/3R3p/2P5/8 w – – 0 1
White to Play and Mate in 4
Chesthetica v10.82 : Selangor, Malaysia
2018.11.12 1:10:39 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 has a decisive material advantage in this position but the winning sequence may not be immediately clear. 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 02372

This is an original ‘KRBBBNP vs kqppppp’ #4 chess puzzle created 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. 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.

1KB5/p1P2N2/1pk4q/8/3pp3/8/3p1R1B/6B1 w – – 0 1
White to Play and Mate in 4
Chesthetica v10.82 : Selangor, Malaysia
2018.11.11 10:39:14 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. Now, let’s see what else there is to say. Give me a moment. Try to solve this puzzle. Do try some of the others in the series as well before you go. 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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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02371

What we have here is a ‘KQRRBN vs krbnpp’ chess puzzle created by a computer using the ‘Digital Synaptic Neural Substrate’ computational creativity approach which does not use any kind of deep learning. After years of development, Chesthetica is able to use the technology to express original creative thought in this domain. Note that it also never had millions of IBM or Google dollars behind it. There is no known limit to the quantity or type of compositions that can be generated.

8/5B2/4N2b/8/K2R4/4Q2p/1R4p1/4rk1n w – – 0 1
White to Play and Mate in 4
Chesthetica v10.82 : Selangor, Malaysia
2018.11.11 7:36:30 PM
Solvability Estimate = Difficult

Okay, let me think for a minute if there’s anything else to say here. Try to solve this as quickly as you can. If you like it, please share with others. Solving chess puzzles like this can also help improve your game. Anyway, if standard chess isn’t your thing, you might instead like SSCC.

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

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02370

What we have here is a ‘KRRNN vs krrn’ chess problem generated autonomously 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 also no proven limit to the quantity or type of legal compositions that can be automatically generated.

5r2/2R5/6nr/4N2N/5R2/8/8/2K1k3 w – – 0 1
White to Play and Mate in 5
Chesthetica v10.82 : Selangor, Malaysia
2018.11.11 2:10:13 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 is significantly ahead in material. Do share and try out some of the others too. Solving chess puzzles like this can be good for your health as it keeps your brain active. It may even delay or prevent dementia. 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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