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For no specific reason, I trained a neural network to generate random chess positions that look similar to positions from actual games (from lichess db). I also made it so you can condition it on some fixed pieces, and adjust the number of pieces. It turned out to be quite effective and I find it surprisingly fun and instructive to generate e.g. endgame positions with a certain pawn structure (set low temperature, place some pawns and position the kings, adjust number of pieces to get an endgame), and then figure out how to win vs. the computer in those positions. I hooked it up so that you can easily play vs. a computer, either via lichess analysis → continue from here, or via my own project Noctie. I’m thinking about whether I could develop it further to create position variants on a certain theme that the AI thinks I need to practice, or maybe make a PvP feature where you play a random position vs. a human. Ideas or feedback? |
I assume you're using an accuracy correlation, but those fail in lots of situations. For instance if somebody is substantially better than somebody else, they're probably going to have near 0 pawn loss, but that's only because the opponent never posed any problems to them.
Style issue also tend to break these correlations. E.g. - Capablanca was much more accurate, by this metric, than Kasparov. But that's because Capablanca had an extremely solid style. In reality, he would probably not fare well against Kasparov or most modern super GMs, even though many of them are far less accurate on paper.