Evolutionary Behaviour Transfer in RoboCup Soccer - School of Computing Seminar Series

Start date and time

Friday 29 September 2017


Core44, Room C44 ,Merchiston Campus

This presentation overviews a study that investigates the impact of behavioural diversity maintenance methods on controller evolution in collective behaviour tasks (RoboCup keep-away soccer). The focus is to examine the impact of these methods on the transfer learning of behaviours, first evolved in a source task before being transferred for further evolution in different but related target tasks. The goal is to ascertain an appropriate controller design method for facilitating improved effectiveness given policy transfer between source
and target tasks. The study comparatively tests and evaluates the efficacy of coupling policy transfer with several neuro-evolution variants. Results indicate a hybrid of behavioural diversity maintenance and objective-based search yields significantly improved effectiveness for evolved behaviours across increasingly complex target tasks. Results also highlight the efficacy of coupling policy transfer with the hybrid of behavioural diversity maintenance and objective based search in order to address bootstrapping and deception problems endemic to complex collective behaviour tasks.

Geoff Nitschke is a Senior Lecturer at the University of Cape Town. His prevailing research interests are situated within the fields of Evolutionary Robotics, Swarm-Robotics, Neuro-Evolution (combining the fields of Evolutionary Computation and Artificial Neural Networks) and statistical based machine learning techniques such as Deep Learning.
A current research goal is to formalize biologically inspired principles of selforganization, evolution and learning to control and direct the emergence of social phenomena such as cooperation and competition in simulated (agent-based) or physical (robotic) swarm systems (that is, systems comprising thousands of interacting components). Once design principles for encouraging and directing emergent phenomena in robot swarms (and more generally distributed systems) are formalized, then such emergent phenomena could be used as a problem solving tool to aid in solving a diverse range of complex tasks. For example, behavioral adaptation of agents in large scale multi-player computer games, nano-robots that adapt to accomplish various medical operations, automated design of energy efficient self-adapting power grids, and robotic swarm space exploration and cooperative construction of artificial habitats. This research falls under the purview of guided-self-organisation in simulated and physical swarm-based systems, which are types of adaptive artificial complex systems.