Hierarchical Cooperative CoEvolution Facilitates the Redesign of Agent-Based Systems

  • Michail Maniadakis
  • Panos Trahanias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


The current work addresses the problem of redesigning brain-inspired artificial cognitive systems in order to gradually enrich them with advanced cognitive skills. In the proposed approach, properly formulated neural agents are employed to represent brain areas. A cooperative coevolutionary method, with the inherent ability to co-adapt substructures, supports the design of agents. Interestingly enough, the same method provides a consistent mechanism to reconfigure (if necessary) the structure of agents, facilitating follow-up modelling efforts. In the present work we demonstrate partial redesign of a brain-inspired cognitive system, in order to furnish it with learning abilities. The implemented model is successfully embedded in a simulated robotic platform which supports environmental interaction, exhibiting the ability of the improved cognitive system to adopt, in real-time, two different operating strategies.


Ventral Tegmental Area Response Strategy Posterior Parietal Cortex Excitatory Neuron Coevolutionary Process 
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
    • 2
  • Panos Trahanias
    • 1
    • 2
  1. 1.Inst. of Comp. ScienceFoundation for Research and Technology-Hellas (FORTH)HeraklionGreece
  2. 2.Department of Computer ScienceUniversity of CreteHeraklionGreece

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