The comparable figure for Libratus, which played against itself on a supercomputer for two months, would be on the order of $1 million, he says. “You could develop something like this on a cloud computing service for about $150, which makes it really feasible to apply this to other domains,” Brown says. AI bots developed for complex videogames such as DOTA 2 have required weeks of training on hundreds of thousands of processors. The bot needed eight days of playing against itself on a single powerful server with 64 processor cores to master the game.

The WIRED Guide to Artificial Intelligenceīrown says that new approach also has the advantage of requiring less computing power, making Pluribus relatively cheap to run. This kind of search had not previously been adapted so well to a game like poker, where some information is hidden. To avoid nasty surprises, it also takes into account how the value of different actions would change if opponents shifted their strategies. Instead, Brown and Sandholm developed a search function that looks only a few moves ahead at a time. But it would take too much computing power to explore the almost endless possibilities of a six-player game. Brown and Sandholm’s earlier bot attempted to map out all the possible twists and turns of a game to the end. The new bot is able to play a much more complex game than its predecessor in large part because it’s better at fine-tuning that core strategy by projecting possible outcomes from a particular point in a game-known as the search function. After each hand, the system reviews what happened and what might have worked better-any improvements are added to its core strategy. Pluribus is similar to Libratus in that it built up its skills by playing trillions of hands against versions of itself.

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