Uncategorized, chess, artificial intelligence, games, puzzles

Computer-Generated Chess Problem 02857

Contemplate this ‘KQQ vs krrbnn’ chess puzzle or problem (whichever you wish to call it) composed 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. 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.

image.png

1r1Q4/5K1k/5Q2/8/1n3r2/6b1/3n4/8 w – – 0 1
White to Play and Mate in 5
Chesthetica v11.62 (Selangor, Malaysia)
Generated on 3 Mar 2020 at 10:55:51 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. Everything composed by Chesthetica is original. Why not time yourself how long it took you to solve this? As a whole, these problems are intended to cater to players of all skill levels. If you’re bored of standard chess, though, why not try this?

Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02845

Now, this is a ‘KQRR vs krrnp’ chess puzzle created 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. You can learn more about the DSNS here. This position contains a total of 9 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.

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2rk4/5R2/2n1R3/1Qp2K2/8/1r6/8/8 w – – 0 1
White to Play and Mate in 5
Chesthetica v11.62 (Selangor, Malaysia)
Generated on 19 Feb 2020 at 5:05:52 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. Get a glimpse into the ‘mind’ of a computer composer. 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 also help improve your game.

Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02837

Take a look at this ‘KRBNNP vs kq’ mate in 5 chess construct composed autonomously by the program, Chesthetica, using the approach known as the DSNS from the sub-field of AI, computational creativity. The program can compose problems that may otherwise take decades, centuries or even longer for human composers to think of, or to arise in a real game. 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 8 pieces could have been taken from such a database.

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1k3q2/3B4/P2N4/8/1N6/7R/K7/8 w – – 0 1
White to Play and Mate in 5
Chesthetica v11.62 (Selangor, Malaysia)
Generated on 8 Feb 2020 at 3:29:23 AM
Solvability Estimate = Difficult

Composing a chess puzzle or problem requires creativity and it’s not easy even for most humans. Everything composed by Chesthetica is original. Try to solve this as quickly as you can. If you like it, please share with others. 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.

Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02829

Consider this ‘KQRP vs kqbn’ mate in 5 chess problem generated autonomously by a 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. 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.

image.png

4R1n1/1k1P4/5Q2/8/3b3K/8/8/q7 w – – 0 1
White to Play and Mate in 5
Chesthetica v11.59 (Selangor, Malaysia)
Generated on 10 Jan 2020 at 8:30:33 PM
Solvability Estimate = Moderate

Most changes to Chesthetica that result in a slightly higher ‘version number’ are simply to improve the interface, by the way. Chesthetica composes everything autonomously (no human intervention) and even chooses the main line of the solution to show you. Why not time yourself how long it took you to solve this? Over time, the tactics you see in these puzzles will help you improve your game.

Solution

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artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02821

Here is a ‘KQBBNNP vs kqp’ chess problem generated autonomously by the program, Chesthetica, 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. You can learn more about the DSNS here.

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1Q6/2B5/8/1B3p2/2P3N1/1q1N4/2k2K2/8 w – – 0 1
White to Play and Mate in 5
Chesthetica v11.59 (Selangor, Malaysia)
Generated on 31 Dec 2019 at 1:37:49 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 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. 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.

Solution

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