Evolutionary Conditions for the Emergence of Communication in Robots

Information transfer plays a central role in the biology of most organisms, particularly social species [1, 2]. Although the neurophysiological processes by which signals are produced, conducted, perceived, and interpreted are well understood, the conditions conducive to the evolution of communication and the paths by which reliable systems of communication become established remain largely unknown. This is a particularly challenging problem because efﬁcient communication requires tight coevolution between the signal emitted and the response elicited [3]. We conducted repeated trials of experimental evolution with robots that could produce visual signals to provide information on food location. We found that communication readily evolves when colonies consist of genetically similar individuals and when selection acts at the colony level. We identiﬁed several distinct communication systems that differed in their efﬁciency. Once a given system of communication was well established, it constrained the evolution of more efﬁcient communication systems. Under individual selection, the ability to produce visual signals resulted in the evolution of deceptive communication strategies in colonies of unrelated robots and a concomitant decrease in colony performance. This study generates predictions about the evolutionary conditions conducive to the emergence of communication and provides guidelines for designing artiﬁcial evolutionary systems displaying spontaneous 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 Complexity in Autonomous Robots

Joshua Evan Auerbach

Evolutionary robotics–the use of evolutionary algorithms to automate the production of autonomous robots–has been an active area of research for two decades. However, previous work in this domain has been limited by the simplicity of the evolved robots and the task environments within which they are able to succeed. This dissertation aims to address these challenges by developing techniques for evolving more complex robots. Particular focus is given to methods which evolve not only the control policies of manually-designed robots, but instead evolve both the control policy and physical form of the robot. These techniques are presented along with their application to investigating previously unexplored relationships between the complexity of evolving robots and the task environments within which they evolve.

University of Vermont2013

The evolution of communication in robot societies

Sara Mitri

EPFL2009