The goal for Angelina is that it will dream up completely new elements of games—a form of computational creativity. On the surface, Angelina works with striking simplicity: Cook presses a button labeled “Play,” and it boots up. The AI then describes a new game in a unique description language that outlines both the game’s rules and its levels. It can make games from images that it pulls from license-free depositories such as Wikimedia Commons, and it can flesh out the premise and rules with characters and ideas lifted from online newspapers or social media (think U.S. presidents or Brexit, for example). This information is written to a text file that can then be run by a stand-alone application, in the same way that a game cartridge is read by a game console.

In its early years, Angelina was limited to developing platform games, in the style of Nintendo’s Super Mario Bros. But in the software’s current iteration, which has been designed for further expansion, its repertoire has expanded to other genres, such as puzzle games and adventures.

“Angelina doesn’t set out to make a game in a particular genre—instead, it tries to build games that match its notion of what a good game is,” says Cook. This notion of quality currently comes from Cook’s own ideas about game design (such as ensuring that the game is not impossible to win or lose, and offering players a number of interesting choices at each step), but in time, he says, the system will learn from the feedback of players, as well as the AI’s experience of playing games made by human designers.

In most cases today, semiautonomous game-making systems are supported by human designers. “There are two types of content in video games: ‘throwaway’ content, such as terrain, common enemies, quests that don’t add to the overall plot that make very little lasting impact on the player, and ‘memorable’ content, such as boss monsters, major plot points,” says Mark Riedl, a 42-year-old associate professor at Georgia Tech. “While the automated creation of ‘throwaway’ content has been used for decades using simple rule-based algorithms, generating ‘memorable’ content is a human-level AI problem.”

Despite the challenges, it’s an area that could, Cook believes, reap major rewards in unlocking new game concepts and mechanics. Recently, he fed Angelina a game outline about exploring a dungeon as an adventurer. Instead of designing basic levels for an adventure game, Angelina designed levels in which a player controls multiple adventurers simultaneously and must get some of them killed in order to rescue the remainder. “It frequently does things like this—looking beyond the assumptions I have, and finding interesting things I would not think to look for,” says Cook.

In the short term, Angelina’s experimentation could prompt human designers on commercial projects. “The AI can explore a space, identify things that are potentially interesting, and then present them to a designer to follow up on,” says Cook. “So your level designer receives a weekly digest of 10 level ideas every week. They’re unpolished, but they present a particular idea that might be developed into something bigger.”