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Robots 'Evolve' the Ability to Deceive

An experiment shows how “deceptive” behavior can emerge from simple rules.

  • August 18, 2009

Researchers at the Ecole Polytechnique Fédérale de Lausanne in Switzerland have found that robots equipped with artificial neural networks and programmed to find “food” eventually learned to conceal their visual signals from other robots to keep the food for themselves. The results are detailed in an upcoming PNAS study.

Courtesy of PNAS

The team programmed small, wheeled robots with the goal of finding food: each robot received more points the longer it stayed close to “food” (signified by a light colored ring on the floor) and lost points when it was close to “poison” (a dark-colored ring). Each robot could also flash a blue light that other robots could detect with their cameras.

“Over the first few generations, robots quickly evolved to successfully locate the food, while emitting light randomly. This resulted in a high intensity of light near food, which provided social information allowing other robots to more rapidly find the food,” write the authors.

The team “evolved” new generations of robots by copying and combining the artificial neural networksof the most successful robots. The scientists also added a few random changes to their code to mimic biological mutations.

Because space is limited around the food, the bots bumped and jostled each other after spotting the blue light. By the 50th generation, some eventually learned to not flash their blue light as much when they were near the food so as to not draw the attention of other robots, according to the researchers. After a few hundred generations, the majority of the robots never flashed light when they were near the food. The robots also evolved to become either highly attracted to, slightly attracted to, or repelled by the light.

Because robots were competing for food, they were quickly selected to conceal this information,” the authors add.

The researchers suggest that the study may help scientists better understand the evolution of biological communication systems.

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