Facebook’s AI Takes on Hanabi Game

Facebook AI Research (FAIR) has developed a new AI that produced extremely impressive results when put up against Hanabi. The new development is a major step forward for Facebook’s AI. 

Hanabi is a card game similar to Solitaire. While most games that are used for this technology place AI against humans directly, specifically chess or Go, Hanabi requires players to work with each other towards a common goal. 

Facebook employed bots to work together in the game until they outperformed previously used AI systems. The most recent best AI system achieved a score of 23.92 out of 25 in the game, while the new one reached 24.61 out of 25. 

Back in February, A Hanabi benchmark was proposed by researchers from Google, DeepMind, Carnegie Mellon University, and Oxford. They also included the creation of additional AI capable of playing the game, and they called it “a new frontier for AI research.” 

Researchers are excited about the new development since the same AI used to help the bots could possibly be used in other areas. One possible use is to improve the way that virtual assistants interact with people. 

Noam Brown, a Facebook AI researcher, spoke about the new AI system. 

“One of the really exciting things about this is that the improvement we’re observing is really orthogonal to the improvements that are being observed with deep reinforcement learning: You can add this on top of any strategy, and it will make it much stronger,” Bown said in an interview he gave to VentureBeat. “We’re seeing that the results are far beyond what we or other researchers expected. In fact, the benefits that we get from search are stronger than the benefits that have been gained through all of the deep reinforcement learning algorithms that have been used in the past.”

The new development with Facebook’s AI comes at a time when researchers are continuing to create software capable of going up against some of the most complex games. In 2016, Google’s DeepMind’s AI system beat the best human players in the Chinese board game Go. 

Hanabi is now considered the best game for testing AI since it is built around teamwork and strategy, a major milestone for AI to reach. When used in this environment, AI can improve and become more sophisticated.

Adam Lerer is a Facebook researcher and contributor to the paper. 

“One of the reasons we’re moving to these cooperative games is that I think we’re kind of at the point where there’s no games left at least in terms of competitive games,” he said. 

Hanabi has teams of two to five players who are given random cards. The cards are different colors and contain different numbers, and the teams place them on a table, by color and in the correct numerical order. 

Players are not able to see their own cards, but their teammates can. Players are permitted to give hints to others. For example, a teammate can give a hint about colors, leading to the other to play or discard the card. 

One of the more complex aspects of the game is that a player has to figure out the clues and what they mean. This part of the game is difficult for a bot to figure out with the information that they have. 

The bots were able to build a strategy due to the techniques and reinforcement learning that Facebook used. Facebook believes that this technology could be used in other applications like robotics, self-driving vehicles, and other systems. 

“This is something that comes very naturally to humans, this idea of being able to put yourself in the shoes of another person and understand why they’re taking the actions they’re taking, what they’re thinking, and even if they don’t know certain things. But it’s something that AI has historically really struggled with,” he said. “There’s been this long debate about whether primates have theory of mind and at what age do humans babies develop theory of mind, and I think it’s really fascinating to finally be seeing this sort of behavior in AI. And I think that that’s going to be really important if we want to deploy AI in the real world to interact with humans because humans expect this behavior.”