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Grandmaster-level chess without search

(github.com)
365 points lawrenceyan | 2 comments | 17 Oct 24 19:13 UTC | HN request time: 0.016s | source
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tzs ◴[17 Oct 24 22:06 UTC] No.41874291[source]▶
OT: what's the state of the art in non-GM level computer chess?

Say I want to play chess with an opponent that is at about the same skill level as me, or perhaps I want to play with an opponent about 100 rating points above me for training.

Most engines let you dumb them down by cutting search depth, but that usually doesn't work well. Sure, you end up beating them about half the time if you cut the search down enough but it generally feels like they were still outplaying you for much of the game and you won because they made one or two blunders.

What I want is a computer opponent that plays at a level of my choosing but plays a game that feels like that of a typical human player of that level.

Are there such engines?

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ssijak ◴[18 Oct 24 09:26 UTC] No.41877679[source]▶
"Sure, you end up beating them about half the time if you cut the search down enough but it generally feels like they were still outplaying you for much of the game and you won because they made one or two blunders."

That is what winning in chess is. Minimising blunders.

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1. Sesse__ ◴[18 Oct 24 10:32 UTC] No.41878031[source]▶
“The winner of a game is the one who makes the second-to-last blunder.”

(Also this has come up in computer chess; it's more important to improve the quality of your worst moves than your average moves)

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2. salamo ◴[19 Oct 24 22:22 UTC] No.41891244[source]▶
I believe that observation is borne out in the statistics too, but traditional chess training usually centers around finding the best, hard-to-find move in a position rather than avoiding blunders. I think it would be great if there was more blunder-avoidance training. In other words, a normal position where a blunder looks attractive but the player needs to avoid it.