The efficiency of the RULES-4 classification learning algorithm in predicting the density of agents

• Main Authors: Ziad Salem, Thomas Schmickl
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2014-12-01
• Series: Cogent Engineering
• Subjects: machine learning; data mining; classification; honeybees; robots
• Online Access: http://dx.doi.org/10.1080/23311916.2014.986262

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.