Learning and adaptation in physical heterogeneous teams of robots

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 agent’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.This work was supported by the Spanish MCYT project DPI2007-66872-C02-01, Arquitecturas Multiagente de Control e Interacción de Robots Móviles en su Aplicación al Rescate de Supervivientes de Catástrofes con Agua and by EU project No. 34744 ONE: Open Negotiation Environment, FP6-2005-IST-5, ICT-for Networked Businesses