[bwfan123]

>It seems like neural networks will never figure out ladders (!!!!!). And it's not clear why such a simple pattern is impossible for deep neural nets to figure out.

this is very interesting (i dont play go) can you elaborate - what is the characteristic of these formations that elude AIs - is it that they dont appear in the self-training or game databases.

[dragontamer]

AlphaGo was trained on many human positions, all of which contain numerous ladders.

I don't think anyone knows for sure, but ladders are very calculation heavy. Unlike a lot of positions where Go is played by so called instinct, a ladder switches modes into "If I do X opponent does Y so I do Z.....", almost chess like.

Except it's very easy because there are only 3 or 4 options per step and really only one of those options continues the ladder. So it's this position where a chess-like tree breaks out in the game of Go but far simpler.

You still need to play Go (determining the strength of the overall board and evaluate if the ladder is worth it or if ladder breaker moves are possible/reasonable). But for strictly the ladder it's a simple and somewhat tedious calculation lasting about 20 or so turns on the average.

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The thing about ladders is that no one actually plays out a ladder. They just sit there on the board because it's rare for it to play to both players advantages (ladders are sharp: they either favor white or black by significant margins).

So as, say Black, is losing the ladder, Black will NEVER play the ladder. But needs to remember that the ladder is there for the rest of the game.

A ladder breaker is when Black places a piece that maybe in 15 turns (or later) will win the ladder (often while accomplishing something else). So after a ladder breaker, Black is winning the ladder and White should never play the ladder.

So the threat of the ladder breaker changes the game and position severely in ways that can only be seen in the far far future, dozens or even a hundred turns from now. It's outside the realm of computer calculations but yet feasible for humans to understand the implications.

[tasuki]

I'd argue it's clear why it's hard for a neural net to figure out.

A ladder is a kind of a mechanical one-way sequence which is quite long to read out. This is easy for humans (it's a one-way street!) but hard for AI (the MCTS prefers to search wide rather than deep). It is easy to tell the neural net as one of its inputs eg "this ladder works" or "this ladder doesn't work" -- in fact that's exactly what KataGo does.

See the pictures for more details about ladders: https://senseis.xmp.net/?Ladder

[dragontamer]

Doesn't MCTS deeply AND broadly search though?

Traditional MCTS searches all the way to endgame and estimates how the current position leads to either win or loss. I'm not sure what the latest and greatest is but those % chance to win numbers are literally a search result over possible endgames IIRC.

I guess I'd assume that MCTS should see ladders and play at least some of them out.

[immibis]

I don't know that much about MCTS, but I'd think that since a ladder requires dozens of moves in a row before making any real difference to either player's position, they just don't get sampled if you are sampling randomly and don't know about ladders. You might find that all sampled positions lead to you losing the ladder, so you might as well spend the moves capturing some of your opponent's stones elsewhere?

[tasuki]

The short ones, sure. The long ones, it's hard for pure MCTS to... keep the ladder straight?

[earnestinger]

https://senseis.xmp.net/?Ladder

(Kind of like wikipedia for go players)