My impression is that the adversarial policy used in this work is much stranger than the strategies you talk about. It’s not a “new style”, it’s something bizarre that makes no game-sense but confuses the ANN. The linked article shows that even a Go novice can easily defeat the adversarial policy.
Yeah, but I think I registered that bizarreness as being from the ANN having a different architecture and abstractions of the game than we do. Which is to say, my confusion is from the idea that qualitatively this feels in the same vein as playing a move that doesn’t improve your position in a game-theoretic sense, but which confuses your opponent and results in you getting an advantage when they make mistakes. And that definitely isn’t trained adversarially against a human mind, so I would expect that the limit of strategies like this would allow for otherwise objectively far weaker players to defeat opponents they’ve customised their strategy to.
I’m not quite sure what you’re saying here, but the “confusion” the go-playing programs have here seems to be one that no human player beyond the beginner stage would have. They seem to be missing a fundamental aspect of the game.
Perhaps the issue is that go is a game where intuitive judgements plus some tree search get you a long way, but there are occasional positions in which it’s necessary to use (maybe even devise and prove) what one might call a “theorem”. One is that “a group is unconditionally alive if it has two eyes”, with the correct definition of “eye”. For capture races, another theorem is that the group with more liberties is going to win. So if you’ve got 21 liberties and the other player has 20, you know you’ll win, even though this involves looking 40 moves ahead in a tree search. It may be that current go-playing programs are not capable of finding such theorems, in their fully-correct forms.
These sort of large-scale capturing races do arise in real human-human games. More so in games between beginners, but possible between more advanced players as well. The capturing race itself is not a “bizarre” thing. Of course it is not normal in a human-human game for a player to give away lots of points elsewhere on the board in order to set up such a capture race, since a reasonably good human player will be able to easily defend the targeted group before it’s too late.
Qualifications: I’m somewhere around a 3 dan amateur Go player.
My impression is that the adversarial policy used in this work is much stranger than the strategies you talk about. It’s not a “new style”, it’s something bizarre that makes no game-sense but confuses the ANN. The linked article shows that even a Go novice can easily defeat the adversarial policy.
Yeah, but I think I registered that bizarreness as being from the ANN having a different architecture and abstractions of the game than we do. Which is to say, my confusion is from the idea that qualitatively this feels in the same vein as playing a move that doesn’t improve your position in a game-theoretic sense, but which confuses your opponent and results in you getting an advantage when they make mistakes. And that definitely isn’t trained adversarially against a human mind, so I would expect that the limit of strategies like this would allow for otherwise objectively far weaker players to defeat opponents they’ve customised their strategy to.
I’m not quite sure what you’re saying here, but the “confusion” the go-playing programs have here seems to be one that no human player beyond the beginner stage would have. They seem to be missing a fundamental aspect of the game.
Perhaps the issue is that go is a game where intuitive judgements plus some tree search get you a long way, but there are occasional positions in which it’s necessary to use (maybe even devise and prove) what one might call a “theorem”. One is that “a group is unconditionally alive if it has two eyes”, with the correct definition of “eye”. For capture races, another theorem is that the group with more liberties is going to win. So if you’ve got 21 liberties and the other player has 20, you know you’ll win, even though this involves looking 40 moves ahead in a tree search. It may be that current go-playing programs are not capable of finding such theorems, in their fully-correct forms.
These sort of large-scale capturing races do arise in real human-human games. More so in games between beginners, but possible between more advanced players as well. The capturing race itself is not a “bizarre” thing. Of course it is not normal in a human-human game for a player to give away lots of points elsewhere on the board in order to set up such a capture race, since a reasonably good human player will be able to easily defend the targeted group before it’s too late.
Qualifications: I’m somewhere around a 3 dan amateur Go player.