Abstract
The Multilevel Darwinist Brain (MDB) is a cognitive architecture aimed at providing autonomous and self-motivated life-long learning capabilities for robots. This paper deals with a new structure and implementation for the long term memory (LTM) in MDB based on Fuster’s concept of Network memory and on the introduction of a new type of node or cognit called Context Node (Cnode). The idea of Network memory as proposed here, provides a path to hierarchically and progressively relate LTM knowledge elements, allowing for a developmental approach to learning that permits very efficient experience based responses from the robot. We include a simple, albeit quite illustrative, example of the application of these ideas using a real Baxter robot.
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Acknowledgements
This work has been partially funded by the EU’s H2020 research programme grant No 640891 (DREAM) as well as by the Xunta de Galicia and the European Regional Development Funds grant redTEIC network (ED341D R2016/012).
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Duro, R.J., Becerra, J.A., Monroy, J., Calvo, L. (2017). Multilevel Darwinist Brain: Context Nodes in a Network Memory Inspired Long Term Memory. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_3
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DOI: https://doi.org/10.1007/978-3-319-59740-9_3
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