Abstract
The ability to obtain the minimal network that allows a robot to perform a given behavior without having to determine what sensors the behavior requires and to what extent each must be considered is one of the objectives of behavior based robotics. In this paper we propose Gaussian Synapse Networks as a very efficient structure for obtaining behavior based controllers that verify these conditions. We present some results on the evolution of controllers using Gaussian Synapse Networks and discuss the way in which they improve the evolution through their ability to smoothly select to what extent each signal and interval is considered within the internal processing of the network. In fact, the main result presented here is the way in which these networks provide a very efficient mechanism to prune the networks, allowing the construction of minimal networks that only make use of the signal intervals required.
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Becerra, J.A., Duro, R.J., Santos, J. (2002). Self Pruning Gaussian Synapse Networks for Behavior Based Robots. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_136
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DOI: https://doi.org/10.1007/3-540-46084-5_136
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