Uncategorized, chess, artificial intelligence, games, puzzles

Computer-Generated Chess Problem 02252

Now, this is a ‘KRNPPPP vs krbbppp’ study construct generated 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. 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 14 pieces goes even beyond that and was therefore composed without any such help. 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.

3k4/2b5/4P1p1/2R1N1K1/4rpp1/7P/b2PP3/8 w – – 0 1
Chesthetica v10.69 : Selangor, Malaysia
White to Play and Draw : 2018.8.6 7:31:35 PM

White actually has less material than Black yet manages to play for a draw. The white army is down by about 2 (Shannon) pawn units in value. Why not time yourself how long it took you to solve this? Note that not all the chess problems are like this. They cover quite the spectrum of solving ability and there are thousands published already.

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

Facebook | Twitter | Book | Website | Reddit

Advertisements
Uncategorized, chess, artificial intelligence, games, puzzles

Computer-Generated Chess Problem 02251

What we have here is a ‘KRRNPPP vs kqrp’ chess problem generated autonomously by 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. You can learn more about the DSNS here. This position contains a total of 11 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.

1q6/5pP1/5P2/7k/4KR1P/5r2/5NR1/8 w – – 0 1
White to Play and Mate in 4
Chesthetica v10.74 : Selangor, Malaysia
2018.8.5 7:44:57 PM
Solvability Estimate = Easy

Humans have been composing original chess problems for over a thousand years. Now a computer can do it too. White has a slight material advantage over Black. Leave a comment below if you like. Over time, the tactics you see in these puzzles will help you improve your game.

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

Amazon | BitChute | Minds | Steemit | YouTube

artificial intelligence, chess, games, puzzles, Uncategorized

Computer-Generated Chess Problem 02250

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

5K2/1bp3B1/8/1N1k4/1RR5/8/5p2/8 w – – 0 1
White to Play and Mate in 4
Chesthetica v10.69 : Selangor, Malaysia
2018.8.5 11:18:44 AM
Solvability Estimate = Moderate

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. Chesthetica composes only unique or new constructs. If you have seen it before, cite the source and comment below because it is purely coincidental. Try to solve this puzzle. Do try some of the others in the series as well before you go. Note that not all the chess problems are like this. They cover quite the spectrum of solving ability and there are thousands published already.

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

Facebook | Twitter | Books | Website | Reddit

Uncategorized, chess, artificial intelligence, games, puzzles

Computer-Generated Chess Problem 02249

Here is a new ‘KRRBP vs kqbpp’ study-like construct or or problem (whichever you wish to call it) composed by Chesthetica using the Digital Synaptic Neural Substrate AI computational creativity method. You can learn more about the DSNS here. 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.

k7/3p4/3R3b/K3p3/3R3B/3P4/8/2q5 w – – 0 1
Chesthetica v10.69 : Selangor, Malaysia
White to Play and Win : 2018.8.5 5:47:02 AM

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. The material value is about equal. Leave a comment below if you like. Feel free to copy the position into a chess engine and discover even more variations of the solution. If you’re bored of standard chess, though, why not try this?

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

Amazon | BitChute | Minds | Steemit | YouTube

Uncategorized, chess, artificial intelligence, games, puzzles

Computer-Generated Chess Problem 02248

Now, this is a ‘KRNNPP vs knn’ mate in 4 chess puzzle created by a computer using the Digital Synaptic Neural Substrate (DSNS) computational creativity approach. It doesn’t use endgame tablebases, deep learning or any kind of 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. Any chess position over 7 pieces could not possibly have been derived from an endgame tablebase which today is limited to 7 pieces.

2N5/1k1P4/3P4/4N3/Rn6/8/8/1K1n4 w – – 0 1
White to Play and Mate in 4
Chesthetica v10.74 : Selangor, Malaysia
2018.8.4 5:44:25 AM
Solvability Estimate = Easy

Now, let’s see what else there is to say. Give me a moment. Did you find this one interesting or have something else to say? Leave a comment below! 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)

Amazon | BitChute | Minds | Steemit | YouTube

Uncategorized, chess, artificial intelligence, games, puzzles

Computer-Generated Chess Problem 02247

Now, here we have a ‘KQRN vs kqp’ chess problem generated by Chesthetica using the ‘DSNS’ computational creativity approach which does not use any kind of machine or deep learning.

1K5R/8/8/3Q4/8/4k3/7p/1N3q2 w – – 0 1
White to Play and Mate in 5
Chesthetica v10.74 : Selangor, Malaysia
2018.8.4 1:07:09 AM
Solvability Estimate = Easy

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 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. 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 (Skip to 0:35)

Facebook | Twitter | Book | Website | Reddit

Uncategorized, chess, artificial intelligence, games, puzzles

Computer-Generated Chess Problem 02246

Here is a new ‘KBBBPPPPP vs kqnnn’ study construct generated autonomously 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. 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. The analysis presented for this study may not be perfect as it depends on the engine used and time allocated to it. However, the key move should be right.

n2k3n/6K1/P3P3/q1P5/8/P7/2BP2nB/1B6 w – – 0 1
Chesthetica v10.74 : Selangor, Malaysia
White to Play and Win : 2018.8.3 4:44:06 AM

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. 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. 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)

Facebook | Twitter | Books | Website | Reddit