DeepMind Player of Games Would Perform Well at Imperfect Info Games Like Poker

In a continuation of its work, DeepMind has created a system called Player of Games, which the company first revealed in a research paper published on the preprint server Arxiv.org this week.  DeepMind is the Artificial Intelligence (AI) lab backed by Google parent company Alphabet.

The previously developed DeepMind AI already had the ability to play perfect information games like chess and now it has the ability through algorithms like DeepStack and Libratus to play imperfect games such as poker.

From Venture Beat:

Tasks like route planning around congestion, contract negotiations, and even interacting with customers all involve compromise and consideration of how people’s preferences coincide and conflict, as in games. Even when AI systems are self-interested, they might stand to gain by coordinating, cooperating, and interacting among groups of people or organizations. Systems like Player of Games, then, which can reason about others’ goals and motivations, could pave the way for AI that can successfully work with others — including handling questions that arise around maintaining trust.

“[Player of Games] learns to play [games] from scratch, simply by repeatedly playing the game in self-play,” DeepMind senior research scientist Martin Schmid, one of the co-creators of Player of Games, told VentureBeat via email. “This is a step towards generality — Player of Games is able to play both perfect and imperfect information games, while trading away some strength in performance. AlphaZero is stronger than Player of Games in perfect information games, but [it’s] not designed for imperfect information games.”

- Ace King, Gambling911.com