The robots — soccer ball-sized assemblages of wheels, sensors and flashing light signals, coordinated by a digital neural network — were placed by their designers in an arena, with paper discs signifying “food” and “poison” at opposite ends. Finding and staying beside the food earned the robots points.
At first, the robots moved and emitted light randomly. But their innocence didn’t last. After each iteration of the trial, researchers picked the most successful robots, copied their digital brains and used them to program a new robot generation, with a dash of random change thrown in for mutation.
Soon the robots learned to follow the signals of others who’d gathered at the food. But there wasn’t enough space for all of them to feed, and the robots bumped and jostled for position. As before, only a few made it through the bottleneck of selection. And before long, they’d evolved to mute their signals, thus concealing their location.
Signaling in the experiment never ceased completely. An equilibrium was reached in the evolution of robot communication, with light-flashing mostly subdued but still used, and different patterns still emerging. The researchers say their system’s dynamics are a simple analogue of those found in nature, where some species, such as moths, have evolved to use a biologist-baffling array of different signaling strategies.
“Evolutionary robotic systems implicitly encompass many behavioral components … thus allowing for an unbiased investigation of the factors driving signal evolution,” the researchers wrote Monday in the Proceedings of the National Academy of Sciences. “The great degree of realism provided by evolutionary robotic systems thus provides a powerful tool for studies that cannot readily be performed with real organisms.”
Of course, it might not be long before robots directed towards self-preservation and possessing brains modeled after — if not containing — biological components are considered real organisms.
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