Emergence of Tool Construction in an Articulated Limb Controlled by Evolved Neural Circuits
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© 2017 IEEE. Tool construction requires sophisticated cognitive function and is only observed in higher mammals and a few avian species. In this paper, we will examine the spontaneous emergence of tool construction during the simulated evolution of a two-degree-of-freedom articulated limb controller in a reaching task environment. The limb controller is a recurrent neural network with a topology evolved using the NeuroEvolution of Augmenting Topologies (NEAT) algorithm. First, we show how broad fitness criteria such as distance to target, number of successful reaches, number of steps to reach the target, and number of instances holding the correct length tool are enough to give rise to tool construction. Second, we analyze how the number of tools and their location in the environment during evolution affect the evolved neural circuits' ability to detect tool affordances and employ the optimal decision strategy. We expect our results to help us understand the implications of tool use capability and the environmental conditions that may facilitate its development.
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