Abstract
A capability for learning from uncertain data has been a major and perennial requirement for many real-life robotic applications. In that context, a new methodology for ultrafast learning using neural networks is presented. It requires only a single iteration to train a feed-forward network with near-optimal results. Uncertainty reduction algorithms are also incorporated in a natural and optimal fashion. As such, this methodology is intended to become an essential building block for future architectures of intelligent systems. Its application to multi-robot observation of multiple moving targets is illustrated.
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Barhen, J., Protopopescu, V. (2000). Ultrafast Neural Network Training for Robot Learning from Uncertain Data. In: Parker, L.E., Bekey, G., Barhen, J. (eds) Distributed Autonomous Robotic Systems 4. Springer, Tokyo. https://doi.org/10.1007/978-4-431-67919-6_38
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DOI: https://doi.org/10.1007/978-4-431-67919-6_38
Publisher Name: Springer, Tokyo
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