Chess PhD topic in machine learning? I searched this online and couldn't find sufficient answers.
Is there a topic in chess that includes machine learning and would be sufficient for a PhD thesis? I was thinking of utilizing a CNN that tells you the correct move based on the current position (and previous couple of moves) without calculating all the tree branch moves ahead. Would this be feasible?
You have little chance in your dissertation of surpassing state of the art chess engines. Perhaps you could find a hook which hasn't been explored so much. One idea is to train your program to play amateur chess in a convincing way. Can a program pass a sort of chess Turing Test where a human-player couldn't tell if they were playing an AI or playing a player rated e.g. 1600? When I play computer chess programs with a level setting at a level that I can actually beat, there is something not quite right about it. The program is deliberately hobbled (so I can beat it) in such a way that it sometimes makes blunders that a human player of the target level is unlikely to do, but at other times it still makes moves which seem just too good for that level. Trying to imitate human chess-playing rather than targeting optimal chess-playing seems like an interesting AI problem.