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Comment by marcodiego | Hacker News Reader

Why not use something similar to alphago zero to carefully analyze chess games of a deceased player until it is able to mimic its decisions?

It could bring many players "back to life". It would be even possible to watch "impossible matches" like Kasparov vs Capablanca!

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You’d be training purely on games with outdated theory, in which case the engine would lose to those trained from more modern repertoires. Or you’d let it learn through self play after initially showing it the human games, in which case it would probably quickly lose the identifiable stylistic aspects of its initial training.

The point isn't to make an unbeatable chess player, it's to 'bring them back to life'.

But what I mean is Kasparov would destroy Capablanca. Even outside of what one might consider ‘raw chess talent’, he was drawing on decades of better theory and would deploy that knowledge. It would be hard to simulate Kasparov as if he were taught chess in Capablanca’s time (maybe not impossible and a fascinating project, I just don’t see how you’d do it).

> It would be hard to simulate Kasparov as if he were taught chess in Capablanca’s time (maybe not impossible and a fascinating project, I just don’t see how you’d do it).

I don't think they were suggesting that's the result they wanted - if you could somehow magically reanimate Capablanca in real life and pit him against peak Kasparov, he might lose badly.

A neural net having the same outcome is essentially what's being asked for. Kasparov raised on Capablanca's era chess or vice versa would be unrecognizably different players, and I don't think anybody expects an AI to simulate their soul.

Fair enough. I don’t think this is as interesting an experiment as people think though. Nobody wants to see Morphy on zero points but that’s what would happen.

I already want to take this back because Morphy probably would pick up points if we ran a tournament on the basis of all official and unofficial world champions, plus people with openings named after them. But the correlation between date of peak and performance would be extremely high.

That seems like a complex problem. People don’t play that many chess games in their life to get a dataset on the scale required for neural networks. You would need to train a general chess engine and then to tweak it using few-shot learning. But I doubt it could capture high level ideas behind player styles unless someone comes up with a smart architecture for that.

Chess.com has the "personality bots" that supposedly play with the style of various well-known players, streamers, and GMs.

But I remember watching Hikaru Nakamura stream once playing through each of these bots (and beating them fairly easily). He commented that several of the bots were doing things the real players would never do, both in style and even the opening move (1.e4 for a player that almost always opens 1.d4)

It was fairly early after the personality bots came out, so maybe they've fixed it by now.

My guess on the personality bots is that they set the bot to play at the players' current rating, not training ml based on the games.

Chessmaster 3k had that feature. But I was never good enough in chess to evaluate how well it worked. Still, I thought about the simplest method:

  - get a chess playing algorithm (I think it will probably well with minimax or mcts) with many tunables,

  - use a genetic algorithm to adjust the tunables of the first algorithm; use how similar it plays (make it choose a move on positions from a database of games from said player) as a goal function.

Doesn't seem terribly complicated to do, but don't know how similar to a human it would play.

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