In this paper we present a novel approach to
assigning roles to robots in a team of physical heterogeneous
robots. Its members compete for these roles and get rewards for
them. The rewards are used to determine each agentâ™s preferences
and which agents are better adapted to the environment. These
aspects are included in the decision making process. Agent
interactions are modelled using the concept of an ecosystem in
which each robot is a species, resulting in emergent behaviour of
the whole set of agents. One of the most important features
of this approach is its high adaptability. Unlike some other
learning techniques, this approach does not need to start a
whole exploitation process when the environment changes. All
this is exemplified by means of experiments run on a simulator.
In addition, the algorithm developed was applied as applied
to several teams of robots in order to analyse the impact of
heterogeneity in these systems.