The Evolution of Reaction-diffusion Controllers for Minimally Cognitive Agents

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Data visualization with Python and JavaScript: scrape, clean, explore & transform your data

xxxiii, 553 p. ; 24 cm

Evolving Neural Network Controllers for Task Defined Robots Kyran Dale

Some recent attention in Artificial Intelligence (AI) research (specifically the subdiscipline known as Artificial Life) has been focussed on the possibility of using genetic algorithms to evolve neural network controllers for task-defined robots. Employing techniques formalised by Holland (1975), the hope is that by using various encoding methods for representing a neural network on a `genome' -commonly a binary stringand then manipulating a population of these genomes using, primarily, cross-over and mutation operators according to fitness-preferential dictates, one may efficiently search a large parametric state-space for useful networks. This paper deals with my attempt to evolve a neural network that, by mediating between a simulated robot's actions and its environmental input leads to a `guard-dog' behaviour. KEYWORDS: Genetic Algorithms, Neural Network, Task-defined Behaviour, Simulated Environment, Encoding Method. Acknowledgements Thanks to my supervisor Inman Harvey for som..

Using artificial evolution and selection to model insect navigation

AbstractBackground: An animal's behavioral strategies are often constrained by its evolutionary history and the resources available to it. Artificial evolution allows one to manipulate such constraints and explore how they influence evolved strategies. Here we compare the navigational strategies of flying insects with those of artificially evolved “animats” endowed with various motor architectures. Using evolutionary algorithms, we generated artificial neural networks that controlled a virtual animat's navigation within a 2D, simulated world. Like a flying insect, the animat possessed motors that generated thrust and torque, a compass, and visual sensors. Some animats were limited to forward motion, while others could also move sideways. Animats were selected for the precision with which they reached a target specified by a visual landmark.Results: Animats given sideways motors could alter flight direction without changing body orientation and evolved strategies similar to those of flying bees or wasps performing the same task. Both animats and insects first aimed at the landmark. In the last phase, both adopted a fixed body orientation and adjusted their position to keep the landmark at a fixed retinal location. Animats unable to uncouple flight direction and body orientation evolved subtly different strategies and performed less robustly.Conclusions: This convergence between the navigational strategies of animals and animats suggests that the insect's strategies are primarily an adaptation to the demands of using visual information and compass direction to reach a position in space and that they are not significantly compromised by the insect's evolutionary history