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Abstract: To study the relevance of recurrent neural network structures for the behavior of autonomous agents a series of experiments with miniature robots is performed. A special evolutionary algorithm is used to generate networks of different sizes and architectures. Solutions for obstacle avoidance and phototropic behavior are presented. Networks are evolved with the help of simulated robots, and the results are validated with the use of physical robots. 1 1 (Update)
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BibTeX entry: (Update)
Pasemann, F., Steinmetz, U., Hlse, M., and Lara, B., "Evolving brain structures for robot control", Proceedings of IWANN'01, 13-15 June, Granada, Spain, LNCS, Springer Verlag, 2001 http://citeseer.ist.psu.edu/pasemann01evolving.html More
@article{ pasemann01evolving,
author = "Frank Pasemann and Uli Steinmetz and Martin H{\"u}lse and Bruno Lara",
title = "Evolving Brain Structures for Robot Control",
journal = "Lecture Notes in Computer Science",
volume = "2085",
pages = "410--??",
year = "2001",
url = "citeseer.ist.psu.edu/pasemann01evolving.html" }
Citations (may not include all citations):76 Understanding Intelligence (context) - Pfeifer, Scheier - 2000
26 Evolution versus design: Controlling autonomous robots (context) - Husbands, Harwey - 1992
12 Dynamics of a single model neuron (context) - Pasemann - 1993
12 Evolutionary Robotics: The Biology (context) - Nolfi, Floreano - 2000
7 Evolving neurocontrollers for balancing an inverted pendulum - Pasemann - 1998
5 and Ienne (context) - Mondala, Franzi
http://wwwi3s.unice.fr/om/khep-sim.html
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