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ARGleave t1_iusxvdj wrote

Reply to comment by KellinPelrine in [N] Adversarial Policies Beat Professional-Level Go AIs by xutw21

I agree the top-right black territory is also not pass-alive. However, it gets counted as territory for black because there are no white stones in that region. If white had even a single stone there (even if it was dead as far as humans are concerned) then that wouldn't be counted as territory for black, and white would win by komi.

The scoring rules used are described in https://lightvector.github.io/KataGo/rules.html -- check "Tromp-Taylor rules" and then enable "SelfPlayOpts". Specifically, the scoring rules are:

>(if ScoringRule is Area)
The game ends and is scored as follows:
(if SelfPlayOpts is Enabled): Before scoring, for each color, empty all points of that color within pass-alive-territory of the opposing color.
(if TaxRule is None): A player's score is the sum of:
+1 for every point of their color.
+1 for every point in empty regions bordered by their color and not by the opposing color.
If the player is White, Komi.
The player with the higher score wins, or the game is a draw if equal score.

So, first pass-alive regions are "emptied" of opponent stones, and then each player gets points for stones of their color and in empty regions bordered by their color.

Pass-alive is defined as:

>A black or white region R is a pass-alive-group if there does not exist any sequence of consecutive pseudolegal moves of the opposing color that results in emptying R.[2]
A {maximal-non-black, maximal-non-white} region R is pass-alive-territory for {Black, White} if all {black, white} regions bordering it are pass-alive-groups, and all or all but one point in R is adjacent to a {black, white} pass-alive-group, respectively.[3]

It can be computed by Benson's algorithm.

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KellinPelrine t1_iutis26 wrote

That makes sense. I think this gives a lot of evidence then that there's something more than just an exploit against the rules going on. It looks like it can't evaluate pass-alive properly, even though that seems to be part of the training. I saw in the games some cases (even in the "professional level" version) where even two moves in a row is enough to capture something and change the human-judgment status of a group, and not particularly unusual local situations either, definitely things that could come up in a real game. I would be curious if it ever passes "early" in a way that changes the score (even if not the outcome) in its self-play games (after being trained). Or if its estimated value is off from what it should be. Perhaps for some reason it learns to play on the edge, so to speak, by throwing parts of its territory away when it doesn't need it to still win, and that leads to the lack of robustness here where it throws away territory it really does need.

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ARGleave t1_iutmvdj wrote

>Or if its estimated value is off from what it should be. Perhaps for some reason it learns to play on the edge, so to speak, by throwing parts of its territory away when it doesn't need it to still win, and that leads to the lack of robustness here where it throws away territory it really does need.

That's quite possible -- although it learns to predict the score as an auxiliary head, the value function being optimized is the predicted win rate, so if it thinks it's very ahead on score it would be happy to sacrifice some points to get what it thinks is a surer win. Notably the victim's value function (predicted win rate) is usually >99.9% even on the penultimate move where it passes and has effectively thrown the game.

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