Cultural evolution of probabilistic aggregation in synthetic swarms

• Published in: Applied soft computing Vol. 113; p. 108010
• Main Authors: Cambier, Nicolas, Albani, Dario, Frémont, Vincent, Trianni, Vito, Ferrante, Eliseo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2021; Elsevier
• Subjects: Artificial Intelligence; Computer Science; Computer Vision and Pattern Recognition; Cultural evolution; Language games; Robotics; Self-organized aggregation; Swarm robotics; Cultural evolution; Self-organized aggregation; Swarm robotics; Language games
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.108010

Local interactions and communication are key features in swarm robotics, but they are most often fixed at design time, limiting flexibility and causing a stiff and inefficient response to changing environments. Motivated by the need for higher adaptation abilities, we propose that information about emergent collective structures should percolate onto the individual behavior, modifying it in a way that determines suitable responses in the face of new working conditions and organizational challenges. Indeed, complex societies are driven by an evolving set of individual and social norms subject to cultural propagation, which contribute to determining the individual behaviors. We leverage ideas from the evolution of natural language – an undoubtedly efficient cultural trait – and exploit the resulting social dynamics to select and propagate microscopic behavioral parameters that adapt continuously to macroscopic conditions, which in turn affect the agents’ communication topography, and, therefore, feed back onto the social dynamics. This concept is demonstrated on a self-organized aggregation behavior, which is a building block for most swarm robotics behaviors and a striking example of how collective dynamics are sensitive to experimental parameters. By means of experiments with simulated and real robots, we show that the cultural evolution of aggregation rules outperforms conventional approaches in terms of adaptivity to multiple experimental settings. [Display omitted] •Cultural evolution provides feedback from the social environment into individual micro-controllers.•Scalable model of self-organized aggregation, both in simulation and on real robots.•Heterogeneous and dynamic controllers in the swarm outperform optimized, but static and homogeneous, controllers.