MIT researchers have designed a computer system that learns how to play a text-based computer game with no prior assumptions about how language works. Although the system can’t complete the game as a whole, its ability to complete sections of it suggests that, in some sense, it discovers the meanings of words during its training.
In 2011, professor of computer science and engineering Regina Barzilay and her students reported a system that learned to play a computer game called “Civilization” by analyzing the game manual. But in the new work, on which Barzilay is again a co-author, the machine-learning system has no direct access to the underlying “state” of the game program—the data the program is tracking and how it’s being modified.
“When you play these games, every interaction is through text,” says Karthik Narasimhan, an MIT graduate student in computer science and engineering and one of the new paper’s two first authors. “For instance, you get the state of the game through text, and whatever you enter is also a command. It’s not like a console with buttons. So you really need to understand the text to play these games, and you also have more variability in the types of actions you can take.”
Narasimhan is joined on the paper by Barzilay, who’s his thesis advisor, and by fellow first author Tejas Kulkarni, a graduate student in the group of Josh Tenenbaum, a professor in the Department of Brain and Cognitive Sciences. They presented the paper last week at the Empirical Methods in Natural Language Processing conference. Read more