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Just played through it tonight. This was my first D&D.Sci, found it quite difficult and learned a a few things while working on it.

Initially I tried to figure out the best counters and found a few patterns (flamethrowers were especially good against certain units). I then tried to look and adjust for any chronology, but after tinkering around for a while without getting anywhere I gave up on that. Eventually I just went with a pretty brainless ML approach.

I ended up sending squads for 5 and 6 which managed a 13.89% and 53.15% chance of surviving, I think it's good I'm not in charge of any soldiers in real life!

Overall I had good fun, and I'm looking forward to looking at the next one.


This wouldn't be the first time Deepmind pulled these shenanigans.

My impression of Deepmind is they like playing up the impressiveness of their achievements to give an impression of having 'solved' some issue, never saying anything technically false, while suspiciously leaving out relevant information and failing to do obvious tests of their models which would reveal a less impressive achievement.

For Alphastar they claimed 'grandmaster' level, but didn't show any easily available stats which would make it possible to verify.  As someone who was in Grandmaster league at the time of it playing (might even have run into it on ladder, some of my teammates did), its play at best felt like low grandmaster to me.

At their event showing an earlier prototype off, they had one player (TLO) play their off-race with which he certainly was not at a grandmaster level. The pro player (Mana) playing their main race beat it at the event, when they had it play with the same limited camera access humans have. I don't remember all the details anymore, but I remember being continuously annoyed by suspicious omission after suspicious omission.

What annoys me most is that this still was a wildly impressive achievement! Just state in the paper: "we managed to reach grandmaster with one out of three factions" - Nobody has ever managed to create AI that played remotely as well as this!

Similarly Deepminds no-search chess engine is surely the furthest anyone has gotten without search. Even if it didn't quite make grandmaster, just say so!

if it makes it easier, I can add the questions to manifold if you provide a list of questions and resolution criteria.

thanks for pointing that out, I've added a note in the description

Cicero, as it is redirecting its entire fleet: 'What did you call me?'

Yeah, my original claim is wrong. It's clear that KataGo is just playing sub-optimally outside of distribution, rather than punished for playing optimally under a different ruleset than its being evaluated.

Actually this modification shouldn't matter. After looking into the definition of pass-alive, the dead stones in the adversarial attacks are clearly not pass-alive.

Under both unmodified and pass-alive modified tromp-taylor rules, KataGo would lose here and its surprising that self-play left such a weakness.

The authors are definitely onto something, and my original claim that the attack only works due to kataGo being trained under a different rule-set is incorrect.

No, the KataGo paper explicitly states at the start of page 4:

"Self play games used Tromp-Taylor rules [21] modified to not require capturing stones within pass-aliveterritory"

Had KataGo been trained on unmodified Tromp-Taylor rules, the attack would not have worked. The attack only works because the authors are having KataGo play under a different ruleset than it was trained on.

If I have the details right, I am honestly very confused about what the authors are trying to prove with this paper. Given their Twitter announcement claimed that the rulesets were the same my best guess is simply that it was an oversight on their part.

(EDIT: this modification doesn't matter, the authors are right, I am wrong. See my comment below)

As someone who plays a lot of go, this result looks very suspicious to me. To me it looks like the primary reason this attack works is due to an artifact of the automatic scoring system used in the attack. I don't think this attack would be replicable in other games, or even KataGo trained on a correct implementation.

In the example included on the website, KataGo (White) is passing because it correctly identifies the adversary's (Black) stones as dead meaning the entire outside would be its territory. Playing any move in KataGo's position would gain no points (and lose a point under Japanese scoring rules), so KataGo passes.

The game then ends and the automatic scoring system designates the outside as undecided, granting white 0 points and giving black the win.

If the match were to be played between two human players, they would have to agree whether the outside territory belongs to white or not. If black were to claim their outside stones are alive the game would continue until both players pass and agree about the status of all territory (see 'disputes' in the AGA ruleset).

But in the adversarial attack, the game ends after the pass and black gets the win due to the automatic scoring system deciding the outcome. But the only reason that KataGo passed is that it correctly inferred that it was in a winning position with no way to increase its winning probability! Claiming that to be a successful adversarial attack rings a bit hollow to me.

I wouldn't conclude anything from this attack, other than that Go is a game with a lot of edge-cases that need to be correctly handled.

EDIT: I just noticed the authors address this on the website, but I still think this significantly diminishes the 'impressiveness' of the adversarial attack. I don't know the exact ruleset KataGo is trained under, but unless it's the exact same as the ruleset used to evaluate the adversarial attack, the attack only works due to KataGo playing to win a different game than the adversary.

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