Here is a 'KRBBNP vs knn' #4 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. Chesthetica is able to generate mates in 3, mates in 4, mates in 5, study-like constructs and also compose problems using specific combinations of pieces fed into it (e.g. compose something original using a knight versus three pawns). Read more about it on ChessBase. 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.
White to Play and Mate in 4
Chesthetica v10.63 : Selangor, Malaysia
2018.4.15 4:10:18 PM
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 is over a rook's worth in material but the precise win in this position still needs to be found. Did you find this one interesting or have something else to say? Leave a comment below! Feel free to copy the position into a chess engine and discover even more variations of the solution. Anyway, if standard chess isn't your thing, you might instead like SSCC.
Solution (Skip to 0:35)
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