Abstract
This paper investigates the relationship between spatially embedded neural network models and modularity. It is hypothesised that spatial constraints lead to a greater chance of evolving modular structures. Firstly, this is tested in a minimally modular task/controller scenario. Spatial networks were shown to possess the ability to generate modular controllers which were not found in standard, non-spatial forms of network connectivity. We then apply this insight to examine the effect of varying degrees of spatial constraint on the modularity of a controller operating in a more complex, situated and embodied simulated environment. We conclude that a bias towards modularity is perhaps not always a desirable property for a control system paradigm to possess.
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Fine, P., Di Paolo, E., Philippides, A. (2006). Spatially Constrained Networks and the Evolution of Modular Control Systems. In: Nolfi, S., et al. From Animals to Animats 9. SAB 2006. Lecture Notes in Computer Science(), vol 4095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840541_45
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DOI: https://doi.org/10.1007/11840541_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38608-7
Online ISBN: 978-3-540-38615-5
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