Abstract
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.
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References
Port, R., van Gelder, T.: Mind as Motion. MIT Press, Cambridge (1995)
Thelen, E., Smith, L.: A Dynamic Systems Approach to the Development of Cognition and Action. MIT Press, Cambridge (1994)
Pasemann, F.: Neuromodules: A Dynamical Systems Approach to Brain Modellling. In: Supercomputing in Brain Research: From Tomography to Neural Networks, pp. 331–347. World Scientific, Singapore (1995)
Beer, R.D.: The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior 11(4), 209–243 (2003)
van Duijn, M., Keijzer, F., Franken, D.: Principles of minimal cognition. Adaptive Behavior 14(2) (in press, 2006)
Maturana, H.R., Varela, F.J.: The Tree of Knowledge: The Biological Roots of Human Understanding. rev. edn. Shambhala (1992)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Cambridge (2000)
Harvey, I., Di Paolo, E.A., Wood, R., Quinn, M., Tuci, E.: Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life 11(1-2), 79–98 (2005)
Langton, C.: Artificial Life: An Overview. MIT Press, Cambridge (1995)
Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (1999)
Pasemann, F.: Complex dynamics and the structure of small neural networks. Network: Computation in Neural Systems 13(2), 195–216 (2002)
MacLennan: Synthetic ecology: an approach to the study of communication. In: Langton, C., Taylor, C., Farmer, J., Rasmussen, S. (eds.) Proc. Artificial Life II. Addison-Wesley, Reading (1991)
Werner, G., Dyer, D.: Evolution of communication in artificial organisms. In: Langton, C., Taylor, C., Farmer, J., Rasmussen, S. (eds.) Proc. Artificial Life II. Addison Wesley, Reading (1991)
Quinn, M., Smith, L., Mayley, G., Husbands, P.: Evolving controllers for a homogeneous system of physical robots: Structured cooperation with minimal sensors. Philosophical Transactions of the Royal Society of London, Series A: Mathematical, Physical and Engineering Sciences 361(1811), 2321–2344 (2003)
Marocco, D., Nolfi, S.: Emergence of communication in teams of embodied and situated agents. In: Proc. of the 6th Int. Conference on the Evolution of Language (2006)
Wischmann, S., Hülse, M., Knabe, J., Pasemann, F.: Synchronization of internal neural rhythms in multi-robotic systems. Adaptive Behavior 14(2) (in press, 2006)
Pasemann, F.: Dynamics of a single model neuron. International Journal of Bifurcation and Chaos 2, 271–278 (1993)
Hülse, M., Wischmann, S., Pasemann, F.: Structure and function of evolved neuro-controllers for autonomous robots. Connection Science 16(4), 249–266 (2004)
Dieckmann, U.: Coevolution as an autonomous learning strategy for neuromodules. In: Supercomputing in Brain Research: From Tomography to Neural Networks, pp. 427–432. World Scientific, Singapore (1995)
Gammaitoni, L., Hänggi, P., Jung, P., Marchesoni, F.: Stochastic resonance. Reviews of Modern Physics 70(1), 223–287 (1998)
Di Paolo, E.A.: Organismically-Inspired Robotics: Homeostatic Adaptation and Teleology Beyond the Closed Sensorimotor Loop. In: Dynamical Systems Approach to Embodiment and Sociality. Advanced Knowledge International, pp. 19–42 (2003)
Beer, R.D.: Toward the evolution of dynamical neural networks for minimally cognitive behavior. In: From Animals to Animats 4: Proc. of the 4th Int. Conference on Simulation of Adaptive Behavior, pp. 421–429 (1996)
Beer, R.D.: Arches and stones in cognitive architecture. Adaptive Behavior 11(4), 299–305 (2003)
Camazine, S., Deneubourg, J.L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2001)
Strogatz, S.H., Stewart, I.: Coupled oscillators and biological synchronization. Scientific American 269(6), 102–109 (1993)
Bays, P.M., Flanagan, J.R., Wolpert, D.M.: Attenuation of self-generated tactile sensations is predictive, not postdictive. PLoS Biology 4(2) (2006)
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Wischmann, S., Pasemann, F. (2006). The Emergence of Communication by Evolving Dynamical Systems. In: Nolfi, S., et al. From Animals to Animats 9. SAB 2006. Lecture Notes in Computer Science(), vol 4095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840541_64
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DOI: https://doi.org/10.1007/11840541_64
Publisher Name: Springer, Berlin, Heidelberg
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