The Emergence of Communication by Evolving Dynamical Systems

  • Steffen Wischmann
  • Frank Pasemann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


In the context of minimally cognitive behavior, we used multi-robotic systems to investigate the emergence of communication and cooperation during the evolution of recurrent neural networks. The networks are systematically analyzed to identify their relevant dynamical properties. Evolution efficiently adapts these properties through small structural changes within the networks when specific environmental conditions are altered, such as the number of interacting robots. The findings signify the importance of reducing the predefined knowledge about resulting behaviors, dynamical properties of control, and the topology of neural networks in order to utilize the strength of the Evolutionary Robotics approach to Artificial Life.


Bifurcation Diagram Stochastic Resonance Recurrent Neural Network Synaptic Weight Sound Signal 
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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© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Steffen Wischmann
    • 1
  • Frank Pasemann
    • 2
  1. 1.Bernstein Center for Computational NeuroscienceGöttingenGermany
  2. 2.Fraunhofer Institute for Autonomous Intelligent SystemsSankt AugustinGermany

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