Abstract
The objective of this work is to study neural control architectures for autonomous robots that explicitly handle time in tasks that require reasoning with the temporal component. The controllers are generated and trained through the methodology of evolutionary robotics. In this study, the reasoning processes are circumscribed to data provided by light sensors, as a first step in the process of evaluating the requirements of control structures that can be extended to the processing of visual information provided by cameras.
We are very grateful to Henrik H. Lund and John Hallam from the Department of Artificial Intelligence at the University of Edinburgh for their help in the development of this work. The work was funded by the Universidade da Coruna and Xunta de Galicia under project XUGA16602A96.
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Santos, J., Duro, R.J. (1998). Evolving neural controllers for temporally dependent behaviors in autonomous robots. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_418
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DOI: https://doi.org/10.1007/3-540-64574-8_418
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