Modelling Robotic Cognitive Mechanisms by Hierarchical Cooperative CoEvolution

  • Michail Maniadakis
  • Panos Trahanias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3955)


The current work addresses the development of cognitive abilities in artificial organisms. In the proposed approach, neural network-based agent structures are employed to represent distinct brain areas. We introduce a Hierarchical Cooperative CoEvolutionary (HCCE) approach to design autonomous, yet collaborating agents. Thus, partial brain models consisting of many substructures can be designed. Replication of lesion studies is used as a means to increase reliability of brain model, highlighting the distinct roles of agents. The proposed approach effectively designs cooperating agents by considering the desired pre- and post- lesion performance of the model. In order to verify and assess the implemented model, the latter is embedded in a robotic platform to facilitate its behavioral capabilities.


Work Memory Posterior Parietal Cortex Excitatory Neuron Robotic Platform Agent Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michail Maniadakis
    • 1
  • Panos Trahanias
    • 2
  1. 1.Inst. of Computer ScienceFoundation for Research and Technology-HellasHeraklion, CreteGreece
  2. 2.Department of Computer ScienceUniversity of CreteHeraklion, CreteGreece

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