

Simulated 'mass extinctions' benefit artificial intelligence

13 August 2015


At the start of the simulation, a biped robot controlled by a computationally evolved brain stands upright on a 16m by 16m surface. The simulation proceeds until the robot falls or until 15 seconds have elapsed (image: Joel Lehman)


Computer scientists Risto Miikkulainen and Joel Lehman co-authored the study published today in the journal, PLOS One, which describes how simulations of mass extinctions promote novel features and abilities in surviving lineages.

"Focused destruction can lead to surprising outcomes," says UT Austin's Professor Miikkulainen. "Sometimes you have to develop something that seems objectively worse in order to develop the tools you need to get better."

In biology, mass extinctions are known for being highly destructive, erasing a lot of genetic material from the tree of life. But some evolutionary biologists hypothesise that extinction events actually accelerate evolution by promoting those lineages that are the most evolvable, meaning ones that can quickly create useful new features and abilities.



After hundreds of generations, a wide range of robotic behaviours had evolved to fill these niches, many of which were not directly useful for walking. Then the researchers randomly killed off the robots in 90 percent of the niches, mimicking a mass extinction.

After several such cycles of evolution and extinction, they discovered that the lineages that survived were the most evolvable and, therefore, had the greatest potential to produce new behaviours. Not only that, but overall, better solutions to the task of walking were evolved in simulations with mass extinctions, compared with simulations without them.


"This is a good example of how evolution produces great things in indirect, meandering ways," says Lehman, a former post-doctoral researcher in Miikkulainen's lab, now at the IT University of Copenhagen.

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