This paper is about the use of roles in a multiagent adaptive context. We describe a simulation experiment in which several sheepdog agents have to coordinate their effort to drive a flock of ducks towards a goal area. We use the Classifier System formalism to control the agents. We show that using a notion of role is natural in such a context. We compare the performance of an expert controller with and without roles. Then we show how applying adaptive techniques to that "bootstrap" controller can improve the performance with respect to expert rules. From this empirical study, it appears that an emergent strategy gets better results than the conception of the roles we designed by hand. Thus we advocate the necessity of tackling the problem of evolving the roles of the agents.