The “artificial intelligence” found in most computer games isn’t very intelligent at all. Characters in the games tend to be controlled by algorithms that produce patterns of behaviors designed to seem natural and realistic, but the characters are actually rigid, with no capacity to learn or adapt.
One company hopes to come up with something a lot smarter by providing a platform that lets software learn how to behave within a game, whether in response to basic stimuli or to more complex situations. The hope is that this kind of learning will eventually allow complex behavior to emerge in game characters—and make for better AI in a range of applications.
Keen Software, based in the Czech Republic and the U.K., makes several “sandbox” games in which players can construct complex virtual structures and machines using realistic materials and physics. This July, the company spun out a business called GoodAI that aims to develop sophisticated AI using machine learning. Marek Rosa, Keen’s CEO, invested $10 million of his own money in the new company.
GoodAI has released open-source software called Brain Simulator that can be used to train a series of artificial neural networks in how to respond to stimuli from a game environment. Through trial and error, these networks can learn how to play a simple game. And several networks can be chained together to create more complex behavior, making it possible for software to learn how to achieve an objective that may require numerous steps.
The company’s researchers have shown that Brain Simulator can be used to train software to play some simple two-dimensional games. These include Breakout, in which a player bounces a ball off a wall of bricks (which disappear once hit), and a maze game that requires completing a series of different tasks.
The virtual character in the maze game “will start to do some random actions, and will be observing how he is changing the environment, or how it’s changing him,” Rosa says. “While he’s changing the environment, he’s learning all these associations and these patterns.”
Learning associations and patterns happens to be a key goal for AI in general, which is why Rosa hopes to eventually develop forms of artificial intelligence with broad utility beyond games. That’s reminiscent of the approach taken by an AI startup called DeepMind that Google bought last year (see “Google’s AI Masters Space Invaders”).DeepMind is using customized machine-learning approaches to teach software to play various simple games.
AI researchers have long used game play as a way to test artificial-intelligence software, says Roman Yampolskiy, an assistant professor at the University of Louisville. “From checkers to chess to poker and go, some of the greatest accomplishments in AI research have been demonstrated around the game board,” he says. What’s interesting about the approach GoodAI and DeepMind are taking is their computers are not given prior understanding of a game’s rules, he says.
However, it’s still not clear whether the strategy will be useful beyond games. Yampolskiy, who has looked at GoodAI’s software, says that while it is a worthwhile contribution to the field, it may be very hard to use as the basis for a more general-purpose AI.
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