The Secret Life of Seconds: The Minds Behind the World Chess Championship
wsj.com
wsj.com
You might think that engines make opening preparation trivial: just play what the engine recommends. However, that misses a critical point. To simplify a bit, engines (whether neural network or classical engines) always play with the implicit assumption that they play against the best response of an equally strong opponent. But in human chess, the strategy is not just to make objectively good moves, but also to give your opponent a chance to make mistakes.
For example, Stockfish's search doesn't distinguish between a move where the opponent has an obvious answer, and a move where you need very deep calculation to find the correct response. Mathematically, both moves are equally good. Strong human players would, of course, always prefer the line that's more "difficult" to play for the opponent.
Therefore, in opening preparation, the goal is to find moves that are both:
* objectively "good enough" (that is, they don't immediately lose if the opponent happens to know or find a good answer)
* difficult/confusing enough for a world championship contender to give you at least the chance to gain an advantage
Finding such lines with engine help is to my understanding an important part of the work of seconds. Not only does that take a lot of time, you also need to be very good yourself to make realistic judgements about what a top player might or might not find over the board.
This is a bit of a simplification! Neural net engines generally use Monte Carlo Tree Search over a pure minimax/alpha-beta search. MCTS rewards moves that lead to wins in all explored variations, not just the best play for each player.
Your point is still true though: computers still aren't able to reliably find "this will be the most testing line in a practical game against a human opponent". They're too far ahead of us: the MCTS leads to better results against computers of similar strength, but doesn't accurately predict where humans will go wrong. Humans (with computer help) are still better at that.
First of all, neural networks are typically trained from (millions of) self-play games. So you have the "equally strong opponent" assumption right there in the weights of the network.
Second, when actually playing, the variations that are explored with high probability in MCTS are (recursively) informed by the same neural networks that are applied at the root. This means that there's no extra reward for moves that lead to complications in lines that only a "stupid human" would play.
In effect, neither alpha-beta search nor Monte-Carlo tree search have any concept of "tricking" a weaker opponent, admittedly for very different reasons and more probalistically in the MCTS case.
That's what I meant with "the implicit assumption that they play against the best response of an equally strong opponent" -- yes it's a simplification, but directionally true I think.
That's not true in classical chess, making sup-par move in classical in order to throw off your opponent is a loosing strategy.
That's why rapid and bullet formats are much more interesting, because those moves are viable. In those formats aggressive play is considered advantageous. In classical you will bounce off a hard wall defence with disadvantage.
Current games are described as most accurate games in history of classical chess. Those guys are like machines in this time format.
The thing is they don't see the exal table, and their candidate moves don't eval positions in 0.1 increments. So they cannot be sure if their weaker move will result in 0.1 or 1.0 advantage down the line.
Maybe you are referring to playing off book continuations of known openings? Those kind of fit your comment.
Though, those moves are heavily prepared before the games and advantage there is results of pre-game strategy, rather than a tactical decision to throw off/confuse the opponent.
Anyway I think we are splitting hair at this stage
> They need to be such excellent chess players that they can still possibly teach something to someone like Carlsen, which makes finding one the chess equivalent of hiring a physics tutor for Albert Einstein.
https://old.reddit.com/r/chess/comments/r4cw22/was_the_prep_...
For a world championship match, players have so much time to prepare and put so much focus on it that preparing for your opponent's previous openings is not as important - he's not that likely to stick with the same repertoire for this match. There are one or two exceptions: one wonders if France's Maxime Vachier-Lagrave would have stuck with his rather narrow repertoire, especially with Black, if he'd managed to reach this match.
To emphasize this, Nepo got a draw with black yesterday playing the Petroff, an opening that, according to the commentators, he had never before played in a competitive match.
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Asked again whether he was surprised by Nepomniachtchi’s Petrov, Carlsen re-states that it was one of the openings he was well-prepared for. “It was one of the main openings that I expected seeing that he played it in the Candidates and also, in the first black game, he went for a more classical approach rather than a sharp one,” he says. “So it was very much expected. Couldn’t know obviously which exact Petrov line he was going to go for, but the Petrov in itself was very much expected.”
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