The study was funded through the EU-funded ECAgents (Embodied and Communicating Agents) project, which aims to develop a new generation of robots which are able to interact with each other and their environment without human intervention. It is published online by the journal Current Biology.
The researchers created virtual robots whose actions and sensory information processors were encoded in artificial genomes which were subjected to mutation and recombination, thereby creating the kind of genetic variation arising from sexual reproduction.
They then followed 100 colonies made up of 10 robots over 500 generations, and tracked their behaviour and performance. In all colonies, the virtual environment of the robots contained both food sources and poisons. The robots were able to communicate by emitting a blue light. In theory, the efficiency of food foraging could be increased if the robots transmitted information to one another about food and poison locations.
To study the effects of kinship and selection level on the outcomes of the colonies, the researchers created situations with high and low levels of relatedness, and selection at either individual or colony level.
The researchers found that communication evolves rapidly in colonies where the robots are closely related genetically, or where evolutionary pressures work mainly at the group level. The only colonies where communication did not lead to improved foraging efficiency were those with low levels of relatedness and strong selection pressures at the individual level. In some of these colonies, deceptive signalling arose, causing the overall performance of the colony to decrease further.
Interestingly, two different communication strategies arose in the colonies of closely related robots with colony-level selection, with some colonies evolving a system of producing signals near food, and other colonies evolving a system of signalling the presence of poison. In each case, the response of the robots to light reflected the prevailing system, with robots in the food-signalling colonies being attracted to light, and robots in the poison-signalling colonies being repelled by it.
The study revealed that once these systems were in place, they tended not to switch to the other system. "This is because a change in either the signalling or response strategy would completely destroy the communication system and result in a performance decrease," the researchers write.
Having established what happens to the robot colonies in the virtual world, the researchers successfully transferred the programme to real robots.
"This study demonstrates that sophisticated forms of communication including cooperative communication and deceptive signalling can evolve in groups of robots with simple neural networks," the researchers write.
"Our experiments demonstrate that the evolutionary principles governing the evolution of social life also operate in groups of artificial agents subjected to artificial selection, indicating that transfer of knowledge from evolutionary biology can be useful for designing efficient groups of cooperative robots," they conclude.
For further information about the ECAgents project, please visit:
http://ecagents.istc.cnr.it/
To read the study, please visit Current Biology:
http://www.current-biology.com/
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