TECHNOLOGY
Researchers have built the first general-purpose artificial intelligence (AI) algorithm that can master a wide variety of games, dubbed ‘Student of Games’. Game algorithms are normally designed to master either information-perfect games like Go or chess, in which each player has all the information, or information-imperfect games like poker, in which some information is hidden from other players. This is because the process of training the algorithms has historically been different for the two types of games. The former uses search and learning, while the latter uses game-theoretic reasoning and learning. But the new Student of Games algorithm gets around this limitation by combining guided search, selfplay learning and game-theoretic reasoning.
When tested, Student of Games held its own in both the information-perfect chess and Go, as well as the information-imperfect Texas hold ’em and Scotland Yard. However, it couldn’t quite beat the best specialised AI algorithms in head-to-head matchups. “This is a step towards making even more general algorithms,” said Martin Schmid, CEO and cofounder of EquiLibre Technologies. “One takeaway is that you can indeed design a technique that can work for both perfect and imperfect information games, rather than having specialised algorithms. Another interesting observation was that one of the important steps was to come up with a new formalism, allowing for truly general design of search based algorithms.”
Games have long served as a benchmark for progress in the field of AI. In 2016, DeepMind’s AlphaGo beat a professional human Go player. The next year, the Libratus system beat the world’s best human poker players in a 20-day Texas hold ’em tournament. “Games are a well-defined benchmark, and there is a long history of AI progress being tied to milestones in AI for games,” Schmid explained. “Games are sometimes referred to as fruit flies of AI, allowing for quick development and gradual progress.”
But there has always been a divide between information-perfect and imperfect games. To get around this, the team trained its ge
