Ars Technica article about go

[luigi]

See this post a couple weeks ago:
https://forums.online-go.com/t/potentia ... hexahedron

The issue specifically is groups that wrap back to themselves, and not any other thing that you might think, as far as I know. For example, large capturing races can be hard for bots of course, and ko can also be hard, but neither of those is what's happening here.

To my best understanding all current AlphaZero-style neural nets learn one wrong algorithm or another for determining life and/or group strength. I also tested other independently trained nets such as ELF and LZ a couple years ago and they also had tons of trouble with cyclic groups. So as of a few years ago, two headed-dragon (https://senseis.xmp.net/?TwoHeadedDragon) situations were found to be a frequent misevaluation for AZ bots, just like ladders, this adversary is just the first time someone automated a bot to play for these kinds of positions.

To oversimplify a bit, I believe one algorithm a net can tend to learn is roughly equivalent to, "start anywhere on the group and walk along the group in every direction counting eyes and/or liberties until you reach the end of the group in every direction", and if the total of all the directions gives 2 eyes or lots of liberties (or at least, more eyes/liberties than neighboring groups), the group is deemed alive/strong. The net handles a small wraparound just fine (e.g. the stones that loop around a small eye), but when a group connects cyclically back to itself on a large scale, the problem is that such an algorithm never hits a dead end - it just keeps walking around the cycle over and over and therefore it double, triple, quadruple,.. counts all the liberties or eyes.

Such a naive algorithm works on 99.9%+ of the data in natural games, and among the times when it in theory doesn't work the group is often still alive anyways by chance, e.g. two headed dragons that genuinely do have enough liberties or eyes, given that many "false" eyes can now also work for life. So the net never has much pressure to learn a much more difficult algorithm that takes into account cycles.

They're also rare enough in pro games that it's hard to find enough "natural" examples to train on. But with the help of this adversary, it's much easier to generate lots of semi-random examples, so there's now an ongoing very early experiment to see if adding a tiny % of these positions to training will force the net to learn it.

Fascinating stuff, thanks.

[luigi]

two headed-dragon (https://senseis.xmp.net/?TwoHeadedDragon)

Someone mentioned there that these shapes are more frequent in Toroidal Go. Maybe KataGo should be trained on that Go variant as well?

[Javaness2]

The article is behind a paywall, but it seems there is a new chapter in this story
https://www.ft.com/content/175e5314-a7f ... 3219f433a1

One wonders why

[sorin]

The update is that they found a way to beat KataGo after giving it 9 stones handicap.

Article: https://goattack.far.ai/pdfs/go_attack_paper.pdf
KGS games: https://gokgs.com/gameArchives.jsp?user ... 23&month=1

Example game:

[kvasir]

Being curious I checked out the public peer reviews and I don't think the paper is really on track to be accepted for ICLR, then again it is early in the review process. Apparently the authors are eager and will update the paper with new experiments, promising more interesting results.

It is a bit odd how Ars Technica says the researchers "published a paper" when it is literally only submitted and is actually getting negative reviews. Anything (almost) could happen with the paper at this point. It could be rejected or if it is accepted it could be revised considerably first and occasionally there are very good papers that don't seem to be accepted anywhere until the arxiv preprint has been cited many many times.

Maybe it's best to check on it again only after the peer review is concluded.

The paper was rejected.

[Javaness2]

That's certainly more interesting than the first paper.