Abstract
This paper reports an experiment in which artificial foraging agents with dynamic, recurrent neural network architectures, are "evolved" within a simulated ecosystem. The resultant agents can compare different food values to "go for more," and display similar comparison performance to that found in biological subjects. We propose and apply a novel methodology for analysing these networks, seeking to recover their quantity representations within an Approximationist framework. We focus on Localist representation, seeking to interpret single units as conveying representative information through their average activities. One unit is identified that passes our "representation test", representing quantity by inverse accumulation.
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Hope, T., Stoianov, I., Zorzi, M. (2006). Searching for Emergent Representations in Evolved Dynamical 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_43
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DOI: https://doi.org/10.1007/11840541_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38608-7
Online ISBN: 978-3-540-38615-5
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