Learning is the act of obtaining new or modifying existing knowledge,
behaviours, skills or preferences. The ability to learn is found in humans, other
organisms and some machines. Learning is always based on some sort of observations
or data such as examples, direct experience or instruction. This paper presents a
classification algorithm to learn the density of agents in an arena based on the
measurements of six proximity sensors of a combined actuator sensor units (CASUs).
Rules are presented that were induced by the learning algorithm that was trained
with data-sets based on the CASU’s sensor data streams collected during a number
of experiments with “Bristlebots (agents) in the arena (environment)”. It was found
that a set of rules generated by the learning algorithm is able to predict the number of
bristlebots in the arena based on the CASU’s sensor readings with satisfying accuracy.