From Supervised Ranking to Evolving Behaviours of A Robotic Team, GECCO’05

Using artificial evolution successfully to design behaviours of multiple robot systems has been reported in recent years. Most of such reports are focused on the design of low level controllers. Design of high level team coordination strategies is rarely covered perhaps because the design of an appropriate chromosome representation for a complex multi-agent system is not an easy task. In this paper we propose that by treating the action decisions of every team member as a supervised ranking problem, the chromosome design problem can be solved systematically. We have tested this approach by dynamically solving the problems in the Solomon’s benchmark of Vehicle Routing Problem with Time Windows [1]. Experiments show that our approach can create some simple behaviours which, whilst not optimal, are robust and above average in quality