What are some fun project ideas in machine learning? :) • Hacker News Board

Post by S4M » Thu, Feb 14, 2013, 9:27 AM UTC

keefe wrote: post #10

S4M wrote: post #5

I have an idea of a project involving Machine Learning, applied to the game of chess: You would need a large database of chess games and a program that can detect some features in a position (for example: "white are a pawn up", "black pawn structure is better", "black king is not safe", etc.). The idea would be to see if some features in a position are making one side more likely to win. For example, we might find th…

Have you checked out chess problems and go problems? They're usually presented in bulk in structured data. http://www.chessproblems.com/ I haven't look at any in a long time, but it at least should highlight some problems hard for humans, which implies something can be learned there, imho.

Thanks for the link, but that's not exactly what I wanted. The chess problem you are showing are some composition problem: someone creating a position where it is possible to checkmate in two moves, but the moves are very unintuitive and the position is completely unnatural, which is very hard for a human player to find the solution. A computer, on the other hand, will have no problem to find it by brute force.

What I want is something different: mining tons of games to learn new stuff about positional strength in chess. For example, assume that we don't know that a queen is stronger than a knight, and instead think that the two pieces have the same value. We would trade those two pieces more or less indifferently, resulting in lots of games where one side has a queen and the other side has a knight. Now, the program I would like to do will mine all those games and say "99% of the cases where one side had a queen and the other side had a knight were a win for the side with the queen". And we would be able to learn that a queen is stronger to a knight... Now obviously this example is too simple, but I would like to see if it is possible to learn some more subtle things.