A model to manage data reliability in behavior-based robotics

In behavior-based robotics, robots are controlled by behavioral modules that are triggered on data perceived by sensors or according to some model of the situation to be faced. However, data are acquired by imperfect sensors and, whenever a world modeler exists, are usually interpreted by modelling algorithms that abstract the relevant features inevitably loosing information. In this paper, we present our approach to data reliability and to its propagation through the path from data to actuation. We show how reliability is different from truth value or from approximation and how this impacts on the agent's performance