I don't think you've understood what I meant by "flattening". I wanted to imply something like an embedding of search paths into a high dimensional space within which a point in the space represents a whole path, and the proximity of paths implies similar realisations.
I'd imagine one could directly realise this by training a network with chess games and the distances between them -- by some useful path related measure -- as inputs. I think that the network would succeed in learning an embedding for chess games. I wouldn't be surprised if such an embedding could be fine tuned to output win probability instead for example.
I also wouldn't be surprised if you could train a network on an auto-completion task of one-move-ahead prediction, and then using that to assign win probabilities, much in the same way that LLMs are used for part of speech tagging.
I'd imagine one could directly realise this by training a network with chess games and the distances between them -- by some useful path related measure -- as inputs. I think that the network would succeed in learning an embedding for chess games. I wouldn't be surprised if such an embedding could be fine tuned to output win probability instead for example.
I also wouldn't be surprised if you could train a network on an auto-completion task of one-move-ahead prediction, and then using that to assign win probabilities, much in the same way that LLMs are used for part of speech tagging.