Relatedness influences signal reliability in evolving robots

Communication is an indispensable component of animal societies, yet many open questions remain regarding the factors affecting the evolution and reliability of signalling systems. A potentially important factor is the level of genetic relatedness between signallers and receivers. To quantitatively explore the role of relatedness in the evolution of reliable signals, we conducted artificial evolution over 500 generations in a system of foraging robots that can emit and perceive light signals. By devising a quantitative measure of signal reliability, and comparing independently evolving populations differing in within-group relatedness, we show a strong positive correlation between relatedness and reliability. Unrelated robots produced unreliable signals, whereas highly related robots produced signals that reliably indicated the location of the food source and thereby increased performance. Comparisons across populations also revealed that the frequency for signal production—which is often used as a proxy of signal reliability in empirical studies on animal communication—is a poor predictor of signal reliability and, accordingly, is not consistently correlated with group performance. This has important implications for our understanding of signal evolution and the empirical tools that are used to investigate communication.

The evolution of communication in robot societies

Sara Mitri

Communication is fundamental to life on earth. All social organisms, from bacteria to humans, use communicative signals to coordinate their behaviors with members of their own and other species. Despite its key role in social organization, many questions regarding the evolution of communication are yet to be answered. This is in part due to the difficulty of conducting experimental evolution on social species, and the challenges in experimentally manipulating and measuring signaling and response strategies in communicating organisms. In this thesis, we circumvent these problems by using a system of experimental evolution with groups of foraging robots that could emit and perceive light to communicate. With this system, we have explored how communication can emerge and how different evolutionary conditions can determine the level of reliability of evolving signals. Our experiments revealed that foraging robots initially produced inadvertent cues providing information to other robots about the location of the food. This resulted in increased foraging efficiency, and consequently, in competition near the food, which drove the co-evolution of signaling and response strategies. The reliability of the resulting communication system was found to depend on the level of relatedness between robots in a group and the level at which they were selected. Robots that were highly related or selected at the group level evolved reliable signals. In contrast, when relatedness between robots in a group was low and selection was acting at the level of the individual, robots were selected to suppress the inadvertent cues produced while foraging. However, because of the effect of mutations, these cues were never completely suppressed and some variability in signaling was maintained. Because similar co-evolutionary processes should be common in natural systems, our findings explain why communicative strategies are so variable in many animal species when interests between them conflict. They also predict that relatedness will play an important role in the evolution of signal reliability in natural systems of communication. Additionally, our analyses have led us to devise a quantitative measure of signal reliability, which may be applied to measure reliability in natural systems of communication. The results of this study, together with an extensive review of the literature, illustrate how evolutionary robotic systems can be used to explore issues that cannot easily be studied experimentally with living organisms, and thus contribute to our understanding of biological systems.

EPFL2009

Evolution of Adaptive-Synapse Controllers

Dario Floreano, Joseba Urzelai

This paper is concerned with artificial evolution of neuro-controllers with adaptive synapses for autonomous mobile robots. The method consists of encoding on the genotype a set of local modification rules that synapses obey while the robot freely moves in the environment [2]. The synaptic weights are not encoded on the genotype. In the experiments presented here, a "behavior-based fitness" function gives reproductive advantage to robots that can solve a sequential task. The results show that evolutionary adaptive controllers solve the task much faster and better than evolutionary standard (non-adaptive) controllers, that the method scales up well to large architectures whereas standard controllers do not, and that evolved adaptive controllers are not trivial and cannot be reduced to a fixed-weight network.

1999

The evolution of communication in robot societies

Sara Mitri

EPFL2009