Contemplate this ‘KRRBBN vs kbn’ chess problem generated autonomously by the 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. Chesthetica is able to generate to generate various types of mates and study-like constructs and also compose problems using specific combinations of pieces fed into it (e.g. instructing it to compose something using a queen, rook and bishop vs. queen and two knights). Learn 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.
8/1K1RR3/8/N7/3B4/1B1b4/3k4/6n1 w – – 0 1
White to Play and Mate in 4
Chesthetica v10.77 : Selangor, Malaysia
2018.9.29 8:04:41 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. White has a decisive material advantage in this position but the winning sequence may not be immediately clear. Did you find this one interesting or have something else to say? Leave a comment below! 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)