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..

Acknowledgements

The greater mystery, after all, is not the answers the scientists contrive, but the questions they are driven to pose. Why? Why this question rather than another? Why this search, hope, despair, rather than another? Why this ill-lit, nil understood, hobo path? And why the outrageous confidence, born of no evidence, to tred it? I do not know. But I know that this sense is not rare. What a strange pleasure it is to seek