Machine Learning parameters for human vs computer detection I would like to start a machine learning project that should recognize whether a player is a person or a computer.
I have a data set with labeled labels (computer or human).
In your opinion, what would be good input parameters?
I would like to also include use the chess position and the move. But which coding would be appropriate?
Other parameters would be some positional and material evaluation values and the centipawn loss
Coming from a machine learning background, although this sounds like a supervised learning task, do not expect any decent results.
Most players today use the computer to analyze positions and prepare quite deep with the engines. So the distinction there is lost in most medium to high level game play.
Also, simply knowing whether a game was played by a computer or not is not sufficient. To train any model, you need to have an intuitive understanding of your data. In this case, simply the sequence of moves played is not sufficient.
But if you're still bent on trying your hand at this, you could look into sequence classification using LSTM networks using an encoded matrix as inputs, but I don't see it working very well at all. If a human can't tell the difference, any model you develop won't be able to either.