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But one game has captured his imagination since childhood: StarCraft, the popular online strategy franchise in which players accrue resources and construct armies of alien fighters to wage war across extraterrestrial landscapes. “We have an environment,” says Barbe.

Writing in Trends in Ecology and Evolution in 2020, Barbe, along with other ecologists from Université de Rennes and Brigham Young University, explain how AlphaStar’s abilities to manage the complex, multidimensional dynamics of StarCraft could be repurposed to test ideas about the dynamics of real-world ecosystems that have flummoxed traditional models.

To create an AI that could win against the best players at StarCraft II, DeepMind researchers used machine-learning techniques to train the AlphaStar algorithm.

Oriol Vinyals, who led the DeepMind team that created AlphaStar, compared the league itself to a sort of ecosystem subject to the process of natural selection.

Though he hasn’t formally tried it yet, Barbe thinks observing these interactions among AlphaStar agents in StarCraft could be a way to test hypotheses about ecological and evolutionary processes that regular statistical models are unable to capture — for example, predicting how a small change in available resources in one corner of the map in StarCraft will ripple across to impact Terran and Zerg units competing in the opposite corner.

“It could be like a sandbox” for scientists to play around with ecosystems, says Barbe.

Ten years ago, says Thessen, AI applications in ecology and environmental science were mostly limited to classification tasks, like rapidly identifying species in recordings of birdsong or types of landscapes in satellite images.

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