DeepMind’s AlphaStar, an artificial intelligence (AI) system, has reached the highest level in StarCraft II, an extremely popular and complex computer game. The AI outperformed 99.8% of all registered human players.
It took the AI system 44 days of training to be able to reach the level. It used recordings of some of the best human players, and it learned from them until eventually going up against itself.
“AlphaStar has become the first AI system to reach the top tier of human performance in any professionally played e-sport on the full unrestricted game under professionally approved conditions,” said David Silver, a researcher at DeepMind.
“Ever since computers cracked Go, chess and poker, the game of StarCraft has emerged, essentially by consensus from the community, as the next grand challenge for AI,” Silver said. “It’s considered to be the game which is most at the limit of human capabilities.”
The work was published in the scientific journal Nature.
What is StarCraft?
Put simply, the point of StarCraft is to build civilizations and fight against aliens.
It is a real-time strategy game where players control hundreds of units and have to make important economic decisions. Within the game, there are tens of thousands of time-steps and thousands of possible actions. These are selected in real-time throughout ten minutes of gameplay.
DeepMind developed AlphaStar “Agents,” and they created one for each of the different races in the game. The different races each have a unique set of strengths and weaknesses. In the “AlphaStar league,” the AI competed against itself and “exploiter” agents which targeted the weaknesses of AlphaStar.
One of the most impressive points of the AI was that it was not developed to perform actions at superhuman speed. Instead, it learned different winning strategies.
Just like the StarCraft game, real-world applications require artificial agents to interact, compete, and coordinate within a complex environment containing other agents. This is why StarCraft has become such an important aspect of artificial intelligence research.
Perhaps one of the more unexpected aspects of this work is that it’ll be of interest to the military.
“Military analysts will certainly be eyeing the successful AlphaStar real-time strategies as a clear example of the advantages of AI for battlefield planning. But this is an extremely dangerous idea with the potential for humanitarian disaster. AlphaStar learns strategy from big data in one particular environment. The data from conflicts such as Syria and Yemen would be too sparse to be of use,” said Noel Sharkey, a professor of AI and robotics at the University of Sheffield.
“And as DeepMind explained at a recent United Nations event, such methods would be highly dangerous for weapons control as the moves are unpredictable and can be creative in unexpected ways. This is against the laws that govern armed conflict.”
Coming a Long Way in Short Time
Back in January, professional StarCraft II player Grzegorz Komincz, defeated AlphaStar in the game. It was a huge set back for Google, who had invested millions of dollars into the technology. Since then, DeepMind’s AI has come a long way in a short amount of time, and these new developments have huge implications.
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