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It's in the supplementary material of the 2019 paper: http://www.cs.cmu.edu/~noamb/papers/19-Science-Superhuman_Su... . Look at the "Variance reduction via AIVAT" section.

The four humans were getting $120,000 between them. Their share of that was dependent on how much better they did than the other humans. That means there was no incentive to collud…

That's not true in practice for poker. Pluribus showed that if you run CFR in multiplayer poker you get a solution that works great in practice. Multiple equilibria are certainly a…

Bots are superhuman in self-play Hanabi: https://ai.facebook.com/blog/building-ai-that-can-master-com... The remaining challenge is getting it to play well with human partners. Doi…

Normally 10,000 hands would be too small a sample size but we used variance-reduction techniques to reduce the luck factor. Think things like all-in EV but much more powerful. It's…

Bots are superhuman in no-limit Texas hold'em. Libratus beat top humans in two-player in 2017 and Pluribus beat top humans in six-player in 2019: https://www.science.org/doi/abs/10…

Bridge has a similar challenge, though from what I understand Bridge AIs are not superhuman yet. I suspect our techniques could be applied to Bridge, though they may need to be ada…

Thanks! We're looking in a few different directions, but one thing I'm excited about is mixed cooperative/competitive settings. In poker, there is no room for cooperation. In Hanab…

Open source it, learn from it, and build upon it to continue to push forward the frontier of AI.

Definitely!

In terms of Hanabi, this bot arrived at conventions that are pretty different from how humans play the game. We invited an advanced Hanabi player to play with the bot and he pointe…

The search algorithm shares a lot in common with our Pluribus poker AI ( https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-... ), but we added "retrospective belief updat…

Hi! I'm one of the authors on the paper. We'd be happy to answer any questions. Ask us anything!

The humans knew the whole time which player was the bot.

The hand logs from the 5 humans + 1 AI experiment are included in the supplementary material of the Science paper.

There was real money at stake in this experiment. The pros were guaranteed $0.40 per hand just for participating, but that could increase to $1.60 per hand depending on how well th…

a. There was this paper a couple years ago applying CFR to single-agent settings: https://arxiv.org/abs/1710.11424 b. It really depends on the game and the situation. It can be sev…

Unfortunately we don't have any plans to do that currently.

We played 10,000 hands of poker in the 5 humans + 1 AI experiment. The number of hands won isn't a useful metric in poker. If you win only 10% of your hands and make $1,000 on thos…

Our goal is to make the research as accessible as possible to the AI community, so we include descriptions of the algorithms and pseudocode in the supplementary material. However, …

From an AI and game theory standpoint, there isn't much difference between two-team zero-sum and two-player zero-sum if the teammates are trained together. That said, the Dota 2 wo…

The CFR algorithm is actually somewhat similar to Q-learning, but the connection is difficult to see because the algorithms came out of different communities, so the notation is al…

I think the bot would make a lot of money playing against average recreational players, but it's absolutely true that if you can exploit bad players' weaknesses, then you can make …

Honestly, probably debugging. Training this thing is very cheap, but the variance in poker is huge (even with the best variance-reduction techniques) so it takes a very long time t…

No, I don't have any plans to do that. This is really about advancing fundamental AI research.