Abstract
This paper describes a procedure to provide a way for the Multilevel Darwinist Brain evolutionary cognitive architecture to be able to learn and preserve procedural knowledge while operating on-line. This procedural knowledge is acquired in the form of ANNs that implement behaviors in the sense of traditional evolutionary robotics. The behaviors are produced in real time as the robot is interacting with the world. It is interesting to see in the results presented that this approach of learning procedural representations instead of exhaustively selecting the appropriate action every instant of time provides better generalization results and more efficient action sequences.
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Funding for this work was related to the preparation of the DREAM project in the EU’s H2020 R&I programme under grant agreement No 640891.
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Salgado, R., Bellas, F., Duro, R.J. (2015). Autonomous Learning of Procedural Knowledge in an Evolutionary Cognitive Architecture for Robots. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_65
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DOI: https://doi.org/10.1007/978-3-319-16549-3_65
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