Scaling-up behaviours in EvoTanks: Applying subsumption principles to artificial neural networks

摘要

Applying evolution to generate simple agent behaviours has become a successful and heavily used practice. However the notion of scaling up behaviour into something more noteworthy and complex is far from elementary. In this paper we propose a method of combining neuroevolution practices with the subsumption paradigm; in which we generate Artificial Neural Network (ANN) layers ordered in a hierarchy such that high-level controllers can override lower behaviours. To explore this proposal we apply our controllers to the dasiaEvoTankspsila domain; a small, dynamic, adversarial environment. Our results show that once layers are evolved we can generate competent and capable results that can deal with hierarchies of multiple layers. Further analysis of results provides interesting insights into design decisions for such controllers, particularly when compared to the original suggestions for the subsumption paradigm.