Analysis of tool use strategies in evolved neural circuits controlling an articulated limb

Tool use constitutes a range of complex behaviors that generally require a sophisticated level of cognition, and is only found in higher mammals and a number of avian species. In this paper, we will examine how different strategies for using a tool emerge during the simulated evolution of a two degree-of-freedom articulated limb in a reaching task environment. The limb is controlled by recurrent neural networks that are evolved using the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, which allows for evolution of the topology of the network. First, we evolve controllers using two different fitness functions. One of these involves only very broad fitness criteria, such as the distance to target, while the other relies on more task knowledge, such as the difference between the number of required and actual tool pickups. Second, we observe that these fitness functions favor evolution of two distinct tool use strategies, and that the more informed fitness function leads to superior performance. Third, we compare the topological structure of the evolved neural circuits in detail, and relate behavioral differences to significant topological differences. Our results allow us to determine when the correct network topology for a given behavior has been found during evolution using NEAT, after which further changes to topology do not substantially improve fitness.