Adaptive exploration of a frequently changingenvironment by a group of

Is it more eecient to use one or several robots? Will the performance of a group of robots working in a collaborative task be enhanced if the robots can communicate with one another? What learning abilities should the robot(s) be provided with for adapting to a continuously changing environment? We address these three issues in a speciic task, namely learning the topography of an environment whose features change frequently. We propose a learning algorithm based on an asso-ciative memory which allows a group of robots to keep an up-to-date account of the environmental state when this changes regularly. A prob-abilistic model is developed which gives an abstract representation of the system. It is used to determine boundaries for the system's variables (the number of robots, the frequency of environmental changes, and the environment's connguration) within which the learning is successful. The predictions of the probabilistic model are connrmed by simulations run in Webots, a 3-D simulator of Khepera robots.