Agent-based brain modeling by means of hierarchical cooperative coevolution.
We address the development of brain-inspired models that will be embedded in robotic systems to support their cognitive abilities. We introduce a novel agent-based coevolutionary computational framework for modeling assemblies of brain areas. Specifically, self-organized agent structures are employed to represent brain areas. In order to support the design of agents, we introduce a hierarchical cooperative coevolutionary (HCCE) scheme that effectively specifies the structural details of autonomous, yet cooperating system components. The design process is facilitated by the capability of the HCCE-based design mechanism to investigate the performance of the model in lesion conditions. Interestingly, HCCE also provides a consistent mechanism to reconfigure (if necessary) the structure of agents, facilitating follow-up modeling efforts. Implemented models are embedded in a simulated robot to support its behavioral capabilities, also demonstrating the validity of the proposed computational framework.
Maniadakis
Michail
M
Foundation for Science and Technology, Hellas University of Crete, Greece. mmaniada@ics.forth.gr
Trahanias
Panos
P
D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain; D002875:Chromosomes / Q000648:ultrastructure; D003198:Computer Simulation; D019143:Evolution, Molecular; D006801:Humans; D008954:Models, Biological; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D009154:Mutation; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012371:Robotics