It is common. The Chinese commentator Gu Li mentioned pair game and team game. But I cannot find an English page about it. Here is a Chinese Wikipedia page. https://zh.m.wikipedia.org/zh-hans/团队围棋

FWIW, the five people team played with Ke Joe before the game and won (again, according to Gu Li.)

Thank you. This is interesting and I didn't expect it!

Playing go cooperatively is very different from chess or most other turn-based tactical games in that combinatorics make the gamespace astronomically large. Most pro players understand what impact this has on gameplay and how to adapt their risk management strategies to the game. Way back in the day, popular man v cpu strategies took into account the limitations of monte carlo simulations and would intentionally make moves that change the game state as much as possible to exploit how hard the computer would need to work in order to begin finding optimal moves.

I think alpha go has advanced to the point where its unlikely human players can reliably defeat it, but I think there are still opportunities for better algorithmic players to defeat it.

Anyways, certain moves can cut off game states by the quadrillions, so it's often pretty intuitive what an optimal response to a move is, given certain context of the board and sometimes the player. In that respect, I'm curious how pro level go is going to change after these alpha go games are studied, because it has a very peculiar, decidedly calculatorish style of play, but it's obviously very effective regardless. Go has prospered for so long because there's so much room to express yourself in a move, pro players really play with their whole soul, but computers are just taking advantage of the pure mathematical angles of the game

It's fascinating stuff. I really want to see the alpha go team write a starcraft ai or something like that.

If you consider that the team is just a group of experts discussing an ongoing game and variations that may be better/worse, it seems pretty common. I'd imagine that multiple experts could perform better given sufficient time. With only a few hours, most of the time seems like it'd be lost in inefficient human IPC.

Stealing a comment from https://www.reddit.com/r/baduk/comments/6deoqk/team_alphago_...

> Gu Li was talking about previous Team matches, and how once a team of Shi Yue, Zhou Ruiyang and Chen yaoye kept on arguing about what to do, and couldn't reach an agreement...

> Meanwhile, the other team had Kang Dongyun, Park Junghwan and Choi Cheolhan. Choi Cheolhan occasionally looked at the variations to make sure there are no silly mistakes, Kang Dongyun's job was to buy lunch for everyone, and Park Junghwan played the game.

> The Korean team ended up winning.

Yeah, that was a question I had while watching the stream too. Is a team of Go experts working together typically considered stronger than a single, highly skilled player playing alone?

Also worth noting that due to the time constraints of this particular match, the human players didn't really have a lot of time to discuss and debate every move they chose, which could have had a negative impact on the potential advantages of working as a team.

My thoughts were on this line also... the same reason the crowd was shocked when Lee Sedol made an unexpected move in game 4. [0]

Is there any research done to prove/disprove that groupthink is inherently worse compared to a single genius? (less risky behavior, so less risk/less reward??) Are we training AIs that will be safe, not bold independent thinkers?

0: https://www.wired.com/2016/03/two-moves-alphago-lee-sedol-re...

Yes and no.

I always said in Pair Go , that 1 bad idea is better than 2 good ones.

But provided discussion, you can be a lot more thorough in consensus. Professional analyze and study games in groups, and some thing come out of that.

I'd say overall the result is a game with no blunders , but no edge. Since you are sharing the blame of a loss, you are not as focused on winning as you are on not losing.

I think that this is actually an interesting insight into the human condition as a whole.

Michael Redmond and Andrew Jackson talked briefly about historical team games while the game was getting underway:

https://youtu.be/V-_Cu6Hwp5U?t=4h26m30s

There are other points in the commentary where they discuss some of the team's strategy, as well as their time management.

Pair Go matches are trendy these days from what I read. It's a good training process for stronger player teaching the relatively weaker player in the pair in real matches.

Team Go playing is probably a different story I agree.

At this point no one expect alphago would lose. So just have fun

I'd love to take this as far as possible.

What happens if you allow the team to roll back decisions as they see they're at a disadvantage? How far away are we from an effectively unbeatable machine?

Given what people can learn over time, can they learn to beat it?

There is already a system in Go which allows to measure the difference between unequal opponents - handicap stones.

And it's interesting to note that according to DeepMind, the Master version of AlphaGo was 3 stones stronger than the version that played Lee Sedol. I don't know if the latest one playing Ke Jie is even stronger than Master or by how much, but the difference in Ke Jie and Lee Sedol isn't 3 stones.