Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Evolving mobile robots able to display collective behaviors
Artificial Life
Division of labor in a group of robots inspired by ants' foraging behavior
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Towards Energy Optimization: Emergent Task Allocation in a Swarm of Foraging Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Self-organizing sync in a robotic swarm: a dynamical system view
IEEE Transactions on Evolutionary Computation
Synchronization and gait adaptation in evolving hexapod robots
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
The emergence of communication by evolving dynamical systems
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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Studies on social insects have demonstrated that complex, adaptive and self-organized behavior can arise at the macroscopic level from relatively simple rules at the microscopic level. Several past studies in robotics and artificial life have focused on the evolution and understanding of the rules that give rise to a specific macroscopic behavior such as task allocation, communication or synchronization. In this study, we demonstrate how colonies of embodied agents can be evolved to display multiple complex macroscopic behaviors at the same time. In our evolutionary model, we incorporate key features present in many natural systems, namely energy consumption, birth, death and a long evaluation time. We use a generic foraging scenario in which agents spend energy while they move and they must periodically recharge in the nest to avoid death. New robots are added (born) when the colony has foraged a sufficient number of preys. We perform an analysis of the evolved behaviors and demonstrate that several colonies display multiple complex and scalable macroscopic behaviors.