DeepMind’s AI studies game players to exploit weaknesses in their strategies

In a paper published on the preprint server Arxiv.org, scientists at Alphabet’s DeepMind propose a new framework that learns an approximate best response to players within games of many kinds. They claim that it achieves consistently high performance against “worst-case opponents” — that is, players who aren’t good, yet at least play by the rules and actually complete the game — in a number of games including chess, Go, and Texas Hold’em.

DeepMind CEO Demis Hassabis often asserts that games are a convenient proving ground to develop algorithms that can be translated into the real world to work on challenging problems. Innovations like this new framework, then, could lay the groundwork for artificial general intelligence (AGI), which is the holy grail of AI — a decision-making AI system that automatically completes not only mundane, repetitive enterprise tasks like data entry, but which reasons about its environment. That’s the long-term goal of other research institutions, like OpenAI.