An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics
Cabri, G. (2013). An immune network approach for self-adaptive ensembles of autonomic components: a case study in swarm robotics. In: (Ed.) Proceedings of ECAL 2013 - 12th European Conference on Artificial Life, , () ( ed.). (pp. ). : . MIT Press.
We describe an immune inspired approach to achieve self-expression within an ensemble, i.e. enabling an ensemble of autonomic components to dynamically change their coordination pattern during the runtime execution of a given task. Building on previous work using idiotypic networks, we consider robotic swarms in which each robot has a lymph node containing a set of antibodies describing conditions under which different coordination patterns can be applied. Antibodies are shared between robots that come into communication range facilitating collaboration. Tests in simulation in robotic arenas of varying complexity show that the swarm is able to learn suitable patterns and effectively achieve a foraging task, particularly in arenas of high complexity.
Director of CEC
+44 131 455 2783
Areas of Expertise
See all areas of expertise
The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.