Living things communicate all the time. They bark, they glow, they make a stink, they thwack the ground. How their communication evolved is the sort of big question that keeps lots of biologists busy for entire careers. One of the reasons it’s so big is that there are many different things that organisms communicate. A frog may sing to attract mates. A plant may give off a chemical to attract parasitoid wasps to attack the bugs chewing its leaves. An ant may lay down pheromone trails to guide other ants to food. Bacteria emit chemical signals to each other so that they can build biofilms that line our lungs and guts.
Communication may work all very well in these cases, but scientists also want to know how they evolved in the first place. Roughly speaking, their question goes something like this. Say you’re an organism living a solitary life. Sending a signal to another member of your species may cost you more than it might bring back in benefits. If you come across some food and suddenly declare, “My, but those are some tasty grubs,” you may find yourself besieged by other members of your species all coming to have some for themselves. You might even attract the attention of a predator and become a meal yourself. So why not just shut up?
There are many ways to attack this question. You can go out and listen to birds. You can genetically engineer bacteria to tinker with their communication system and see what happens. Or you can build an army of robots.
Laurent Keller, an expert on social evolution at the University of Lausanne in Switzerland, chose the latter. Working with robotics experts at Lausanne, he constructed simple robots like the ones shown above. Each robot had a pair of wheeled tracks, a 360-degree light-sensing camera, and an infrared sensor underneath. The robots were controlled by a program with a neural network architecture. In neural networks, inputs come in through various channels and get combined in various combinations, and the combinations then produce outgoing signals. In the case of the Swiss robots, the inputs were the signals from the camera and the infrared sensor, and the output was the control of the tracks.
The scientists then put the robots in a little arena with two glowing red disks. One disk they called the food source. The other was the poison source. The only difference between them was that food source sat on top of a gray piece of paper, and the poison source sat on top of black paper. A robot could tell the difference between the two only once it was close enough to a source to use its infrared sensor to see the paper color.
Then the scientists allowed the robots to evolve. The robots—a thousand of them in each trial of the experiment—started out with neural networks that were wired at random. They were placed in groups of ten in arenas with poison and food, and they all wandered in a haze. If a robot happened to reach the food and detected the gray paper, the scientists awarded it a point. If it ended up by the poison source, it lost a point. The scientists observed each robot over the course of ten minutes and added up all their points during that time. (This part of the experiment was run on a computer simulation to save time and to be able to evolve lots of robots at once.)