Two different approaches to a macroscopic model of a bio-inspired robotic swarm
Robotics and Autonomous Systems
Exploiting loose horizontal coupling in evolutionary swarm robotics
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics
Proceedings of the 13th annual conference on Genetic and evolutionary computation
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
MONEE: using parental investment to combine open-ended and task-driven evolution
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Right on the MONEE: combining task- and environment-driven evolution
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Self-adapting fitness evaluation times for on-line evolution of simulated robots
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
In this book the use of ER techniques for the design of self-organising group behaviours, for both simulated and real robots is introduced. This research has a twofold value. From an engineering perspective, an automatic methodology for synthesising complex behaviours in a robotic system is described. ER techniques should be used in order to obtain robust and efficient group behaviours based on self-organisation. From a more theoretical point of view, the second important contribution brought forth by the author's experiments concerns the understanding of the basic principles underlying self-organising behaviours and collective intelligence. In this experimental work, the evolved behaviours are analysed in order to uncover the mechanisms that have led to a certain organisation. In summary, this book tries to mediate between two apparently opposed perspectives: engineering and cognitive science. The experiments presented and the results obtained contribute to the assessment of ER not only as a design too l, but also as a methodology for modelling and understanding intelligent adaptive behaviours.