"Multi-player games present additional challenges not present in two-player zero-sum games".
Not only statistics but a lot of gut feeling goes into the game.
The implications of this achievement could extend well beyond poker.
In the five humans and one Pluribus matches (called 5H+1A1 in the published paper), the poker AI competed against a random selection of the following players: Jimmy Chou, Seth Davies, Michael Gagliano, Anthony Gregg, Dong Kim, Jason Les, Linus Loeliger, Daniel McAulay, Greg Merson, Nicholas Petrangelo, Sean Ruane, Trevor Savage, and Jacob Toole.
It also involves more players.
Programmers have developed a computerized poker champion that has successfully defeated Darren Elias (who holds the most World Poker Tour titles), Chris "Jesus" Ferguson (winner of six World Series of Poker events), and 13 pros who, between them, have won over $1 million from the game.
In each of those cases, the AI was successful because it attempted to estimate widely popular strategy- Nash equilibrium. This technique can dramatically change a poker game in one's favor but if used too much, it may make the player predictable.
In the likes of chess and Go, everything is laid out in the open. The above picture shows how the Monte Carlo Counterfactual Regret Minimization algorithm updates the traverser's strategy by assessing the value of real and hypothetical moves. Well, now Libratus's follow up - Pluribus - has taken away that crumb of comfort, something the researchers call a "recognised AI milestone". So the bot has to balance bluffing with betting on legitimately strong hands. The program was created using a 64-core server packed with less than 512GB of RAM, but it runs on two Haswell processors and uses 128GB during games.
The AI, called Pluribus, defeated poker professional Darren Elias, who holds the record for most World Poker Tour titles; and Chris "Jesus" Ferguson, victor of six World Series of Poker events. When Pluribus took on all five players, 10,000 were played. But in multiplayer games, especially with hidden information like in poker, finding a winning strategy is much harder for an algorithm. It uses self-play to teach itself how to win, with no examples or guidance on strategy. The Pittsburgh Supercomputing Center provided computing resources through a peer-reviewed XSEDE allocation.
In any game of poker, the goal is to win the "pot", the collection of bets players make throughout each deal.
Few applications in the real world scenario can be taking action on harmful content and dealing with cybersecurity challenges, as well as managing an online auction or navigating traffic.
In poker, however, you can't know all the information that your opponent knows, so it's more hard to anticipate what moves they may make - and it only gets more hard the more players you have.