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 a voidance 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. © Springer-Verlag Berlin Heidelberg 2001.
Cite
CITATION STYLE
Pasemann, F., Steinmetz, U., Hülse, M., & Lara, B. (2001). Evolving brain structures for robot control. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2085 LNCS, pp. 410–417). Springer Verlag. https://doi.org/10.1007/3-540-45723-2_49
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