In a Swiss laboratory, a group of ten robots is competing for food. Prowling around a small arena, the machines are part of an innovative study looking at the evolution of communication, from engineers Sara Mitri and Dario Floreano and evolutionary biologist Laurent Keller.
They programmed robots with the task of finding a “food source” indicated by a light-coloured ring at one end of the arena, which they could “see” at close range with downward-facing sensors. The other end of the arena, labelled with a darker ring was “poisoned”. The bots get points based on how much time they spend near food or poison, which indicates how successful they are at their artificial lives.
They can also talk to one another. Each can produce a blue light that others can detect with cameras and that can give away the position of the food because of the flashing robots congregating nearby. In short, the blue light carries information, and after a few generations, the robots quickly evolved the ability to conceal that information and deceive one another.
Their evolution was made possible because each one was powered by an artificial neural network controlled by a binary “genome”. The network consisted of 11 neurons that were connected to the robot’s sensors and 3 that controlled its two tracks and its blue light. The neurons were linked via 33 connections – synpases – and the strength of these connections was each controlled by a single 8-bit gene. In total, each robot’s 264-bit genome determines how it reacts to information gleaned from its senses.
In the experiment, each round consisted of 100 groups of 10 robots, each competing for food in a separate arena. The 200 robots with the highest scores – the fittest of the population – “survived” to the next round. Their 33 genes were randomly mutated (with a 1 in 100 chance that any bit with change) and the robots were “mated” with each other to shuffle their genomes. The result was a new generation of robots, whose behaviour was inherited from the most successful representatives of the previous cohort.
Autumn is a time of incredible beauty, when the world becomes painted in the red, orange and yelllow palette of falling leaves. But there may be a deeper purpose to these colours, and the red ones in particular. In the eyes of some scientists, they aren’t just decay made pretty – they are a tree’s way of communicating with aphids and other insects that would make a meal of it. The message is simple: “I am strong. Don’t try it.”
During winter, trees withdraw the green chlorophyll from their leaves, and textbooks typically say that autumn colours are produced by the pigments that are left behind. That’s certainly true of yellows and oranges, but reds and purples are a different story.
They are the result of pigments called anthocyanins, which trees have to actively make. That uses up energy, which is lost to the tree when the leaf falls. An investment like that implies a purpose, and that’s what scientists have been trying to uncover.
Shortly before he died in 2000, the great William Hamilton (he of kin selection fame) suggested that autumn colours are a warning to insects. Many species, such as aphids, lay eggs in trees during autumn and their larvae feed off their host when spring arrives. That’s bad news for the tree, which defends itself with insecticidal poisons. Those that are particularly well-defended would benefit from advertising themselves as inhospitable hosts, and Hamilton suggested that they do this through red leaves.
Hamilton found some support for the idea – for example, he showed that trees that have the strongest autumn colours are also those that are plagued by the widest array of aphid pests. But his former student, Mario Archetti from the University of Oxford, has truly championed the theory and his latest findings provide the strongest support for it yet. They show that aphids avoid red-leaved apple trees, that they fare better on trees without them and that wild trees have far redder leaves than domesticated ones, which are less troubled by the challenges of insects.